- Email: [email protected]
Contextual interference enhances motor learning through increased resting brain connectivity during memory consolidation
Accepted Manuscript Contextual interference enhances motor learning through increased resting brain connectivity during memory consolidation Chien-Ho (Janice) Lin, Ho-Ching Yang, Barbara J. Knowlton, Allan D. Wu, Marco Iacoboni, Yu-Ling Ye, Shin-Leh Huang, Ming-Chang Chiang PII:
S1053-8119(18)30588-3
DOI:
10.1016/j.neuroimage.2018.06.081
Reference:
YNIMG 15087
To appear in:
NeuroImage
Received Date: 18 April 2018 Revised Date:
11 June 2018
Accepted Date: 28 June 2018
Please cite this article as: Lin, C.-H.(J.), Yang, H.-C., Knowlton, B.J., Wu, A.D., Iacoboni, M., Ye, Y.-L., Huang, S.-L., Chiang, M.-C., Contextual interference enhances motor learning through increased resting brain connectivity during memory consolidation, NeuroImage (2018), doi: 10.1016/ j.neuroimage.2018.06.081. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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ACCEPTED MANUSCRIPT TITLE PAGE
Contextual interference enhances motor learning through increased resting brain connectivity during memory consolidation
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Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, 112, Taiwan
b c
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Chien-Ho (Janice) Lina,b, Ho-Ching Yangc , Barbara J. Knowltond, Allan D. Wue,f, Marco Iacobonif,g, Yu-Ling Yec,h, Shin-Leh Huangc, and Ming-Chang Chiangc
Yeong-An Orthopedic and Physical Therapy Clinic, Taipei, 112, Taiwan
Department of Biomedical Engineering, National Yang-Ming University, Taipei, 112, Taiwan
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Department of Psychology, University of California, Los Angeles, California 90095, USA
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Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA
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Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, California 90095, USA Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California 90095, USA
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Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, 613, Taiwan
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Submitted to NeuroImage: April 18, 2018 Revised Version submitted: June 11, 2018 Abbreviated title: Contextual interference enhances brain connectivity Figures: 6, Tables: 4
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Email: Chien-Ho Lin ([email protected]), Ho-Ching Yang ([email protected]), Barbara J. Knowlton ([email protected]), Allan D. Wu ([email protected]), Marco Iacoboni ([email protected]), Yu-Ling Ye ([email protected]), Shin-Leh Huang ([email protected]), Ming-Chang Chiang ([email protected])
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Address Correspondence: Ming-Chang Chiang, Department of Biomedical Engineering, National Yang-Ming University, No.155, Sec.2, Linong Street, Taipei, 112, Taiwan, Phone 886-2-2826-7110, Fax 886-2-2821-0847, Email: [email protected]
ACCEPTED MANUSCRIPT 2 ABSTRACT (250 words) Increasing contextual interference (CI) during practice benefits learning, making it a desirable difficulty. For example, interleaved practice (IP) of motor sequences is generally more difficult than
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repetitive practice (RP) during practice but leads to better learning. Here we investigated whether CI in practice modulated resting-state functional connectivity during consolidation. 26 healthy adults (11 men/15 women, age = 23.3±1.3 years) practiced two sets of three sequences in an IP or RP condition
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over 2 days, followed by a retention test on Day 5 to evaluate learning. On each practice day,
functional magnetic resonance imaging (fMRI) data were acquired during practice and also in a resting
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state immediately after practice. The resting-state fMRI data were processed using independent component analysis (ICA) followed by functional connectivity analysis, showing that IP on Day 1 led to greater resting connectivity than RP between the left premotor cortex and left dorsolateral prefrontal cortex (DLPFC), bilateral posterior cingulate cortices, and bilateral inferior parietal lobules. Moreover, greater resting connectivity after IP than RP on Day 1, between the left premotor cortex and the
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hippocampus, amygdala, putamen, and thalamus on the right, and the cerebellum, was associated with better learning following IP. Mediation analysis further showed that the association between enhanced resting premotor-hippocampal connectivity on Day 1 and better retention performance following IP
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was mediated by greater task-related functional activation during IP on Day 2. Our findings suggest
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that the benefit of CI to motor learning is likely through enhanced resting premotor connectivity during the early phase of consolidation.
Keywords: desirable difficulty, contextual interference, serial reaction time task, independent component analysis, resting-state networks, functional magnetic resonance imaging
ACCEPTED MANUSCRIPT 3 1. INTRODUCTION When multiple tasks are practiced, the overall difficulty of these tasks may be manipulated by presenting them in a repetitive order or in an interleaved (non-repetitive) order. Practicing tasks in an
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interleaved order (interleaved practice, or IP) is generally more difficult, leading to inferior practice performance but superior retention as compared to practicing the same tasks in a repetitive order (repetitive practice, or RP). This is known as the contextual interference (CI) effect (Shea and Morgan,
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1979), and is an example of desirable difficulty, where conditions of learning that initially impose challenges during practice eventually benefit memory consolidation and learning (Bjork, 1994). This
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CI effect on learning may be accounted for in that interleaved practice, a condition with higher CI, forces the learner to elaborate task-relevant information through inter-task comparisons and thereby establish more readily retrievable memory traces (Lin et al., 2008; Shea and Titzer, 1993). In addition to affecting task performance during practice and retention, the level of CI during practice also modulates offline memory consolidation and the pattern of neural recruitment. For
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example, immediately following practice under high CI, perturbation of the dorsolateral prefrontal cortex (DLPFC) by repetitive transcranial magnetic stimulation (rTMS) attenuated motor skill retention, while rTMS perturbation of the primary motor cortex (M1) attenuated retention following
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practice under low CI (Kantak et al., 2010). This rTMS-perturbation approach allows causal inference
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about specific cognitive functions of a brain region; however, it is unable to delineate the functional network between that region and other distant regions, particularly the deep subcortical structures in the brain (Bolognini and Ro, 2010). To address this issue, we acquired resting-state functional magnetic resonance imaging (rsfMRI) data after the participants practiced multiple motor sequences arranged in an interleaved (IP) or a repetitive (RP) order, and compared the resting-state data with task-based fMRI data acquired during practice. The resting state is defined as when a person is awake but not engaged in any external cognitive tasks (Biswal et al., 1995). The brain in the resting state still remains active and exhibits a
ACCEPTED MANUSCRIPT 4 highly coherent pattern of neuronal activity, where interacting brain regions are integrated as a number of resting-state networks (RSNs) (Albert et al., 2009a; Allen et al., 2014; Biswal et al., 1995; Di and Biswal, 2014; Fox and Raichle, 2007; Guerra-Carrillo et al., 2014). Resting-state connectivity of the
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brain increased after practice may reflect an ongoing process of memory consolidation. This was supported by the study of Albert et al., where resting-state functional connectivity in the fronto-parietal area increased after the participants learned a visuomotor tracking task by adapting their joystick
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movements to a novel relationship between the joystick and the cursor displayed on the screen (the ‘motor learning’ group), but remained unchanged in the control participants who performed similar
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tracking movements with a veridical cursor feedback of the joystick (the ‘motor performance’ group) (Albert et al., 2009b). Moreover, Gregory et al. found that greater increase in resting functional connectivity in bilateral motor cortices after motor sequence tasks was associated with better memory consolidation and retention (Gregory et al., 2014). In addition, memory consolidation may be enhanced by retrieval of the practiced tasks (Robertson et al., 2004; Roediger and Butler, 2011), suggesting that
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the influences of resting brain connectivity on learning may be modulated by task-dependent brain activity. Nevertheless, how the resting and active states of the brain interact to support motor learning is still largely unknown.
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In this paper we investigated whether introducing CI as a desirable difficulty during motor skill
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practice would enhance functional connectivity across RSNs during the consolidation phase. We applied independent component analysis (ICA) to resting-state fMRI data to identify RSNs after practice, compared differences in functional connectivity of the RSNs with respect to the IP versus the RP conditions, and examined the associations between such differences in RSN connectivity and benefits of CI to learning. We further investigated how the resting and active states of brain systems contribute to CI benefits to motor learning. Specifically, we applied mediation analysis to examine whether the association between RSN connectivity and CI effects on retention performance was mediated by task-related activity during practice.
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2. METHODS
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2.1 Participants Twenty-six right-handed adults (11 men and 15 women, age = 23.3±1.3 years, education = 17.0±1.1 years, mean±SD) gave written informed consent to participate the current study approved by the local
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Institutional Review Board. They were recruited from National Yang-Ming University and the adjacent community. Participants were excluded if they were a musician or a professional typist, scored less
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than 28 on the Mini-Mental State Exam (MMSE; (Folstein et al., 1975)), or had any contraindications to MRI, uncorrected vision loss, or significant medical conditions that prevented them from performing the task.
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2.2 Experimental protocols for the serial reaction time (SRT) task
The participants practiced the serial reaction time (SRT) task during fMRI scanning on two consecutive training days (Days 1 and 2), and were tested for their delayed retention performance on Day 5.
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Previous work has shown that the effects of CI are most pronounced after an extended delay (see (Lee and Simon, 2004) for a review). Moreover, in our previous study on a different sample of participants
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we found that interleaved practice enhanced task-based functional connectivity with respect to the premotor and dorsolateral prefrontal cortices on Day 2 but not on Day 1 (Lin et al., 2013). The finding suggests that some neural substrates of the CI effect require more extended practice and may not be identified in a single-day experiment. Practice and retention were conducted in the morning on each day between 9 and 11 am. The experimental design is explained in Fig. 1, as has been reported in our previous study on a different sample of participants (Lin et al., 2011; Lin et al., 2012a, b; Lin et al., 2013; Lin et al., 2016). On each trial of the SRT task the participants were instructed to respond to one
ACCEPTED MANUSCRIPT 6 of three different 4-element sequences using the non-dominant (left) hand as quickly and as accurately as possible, where each sequence was represented by a specific permutation of 4 colored circles (Fig. 1A). This experimental procedure was similar to that used in (Cross et al., 2007) and (Immink and
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Wright, 1998). The use of the non-dominant hand in our experiments facilitates comparisons with the study of Cross et al. (Cross et al., 2007), which first investigated the neural substrates of the CI effect on motor sequence learning using fMRI, and our previous studies (Lin et al., 2011; Lin et al., 2012a, b;
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Lin et al., 2013; Lin et al., 2016). A custom-designed program written using the Psychtoolbox (version 3.0.12, http://psychtoolbox.org; RRID:SCR_002881) for MATLAB (MATLAB 2011b, the Mathworks
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Inc., Natick, Massachusetts, USA; RRID:SCR_001622) controlled the appearance of the colored circles and recorded the participants’ responses. The response time, defined as the time between stimulus onset and key press, were recorded for each key press. The total response time for every 4element sequence trial was calculated by adding up the response time of each of the four key presses. During the practice phases (Days 1 and 2), each participant was assigned to either the Repetitive
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practice (RP) or Interleaved practice (IP) condition. In the RP condition, the three sequences were presented in a repetitive order, while in the IP condition, the sequences were presented in an interleaved order as illustrated in Fig. 1B. There were three task-based fMRI sessions followed by one resting-state
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fMRI session on Days 1 and 2 respectively. During each task-based fMRI session the participants
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practiced 54 trials of sequences, while during the resting-state session, they were instructed to remain relaxed but alert, with their eyes kept open. We applied a block design for the task-based fMRI, with 18-second task blocks interleaved with 18-second rest blocks. Each of the 6 sequences in any fMRI task block was presented for a fixed duration of 3 seconds, so that each task block contained 6 trials of the SRT task. If the participant completed a sequence before 3 seconds, 4 transparent circles would appear on the screen to keep the participants’ attention and reduce eye movements. During the rest blocks, the circles were replaced by a fixation cross in the center of the screen. The use of the block design here was to facilitate comparisons of brain activity between different levels of CI (IP versus RP)
ACCEPTED MANUSCRIPT 7 on each day, rather than to differentiate the hemodynamic responses associated with single neural events such as the effects of color or position of each element in a sequence. In our experiment, the participants in the RP condition practiced each of the three sequences in each fMRI session on Days 1
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and 2, while those in the IP condition practiced the three sequences in an interleaved order in each of the three sessions (Fig. 1B). On this time scale, differences in brain activation of interest were
relatively static, and therefore using a block design was more likely to provide sufficient power and
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efficiency to detect such differences in BOLD signal as compared to an event-related design (Friston et al., 1999; Lazar, 2008). Moreover, by using block-design fMRI in our previous studies, we had also
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successfully identified functional brain networks that supported the effects of CI on motor sequence learning (Lin et al., 2011; Lin et al., 2012b; Lin et al., 2013; Lin et al., 2016). On Day 5, there were also three fMRI sessions. In the first two sessions the participants were tested with the three sequences they had practiced on Days 1 and 2, presented either in a repetitive order (denoted by Repetitive testing condition, abbreviated as RTC) or in an interleaved order (denoted
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by Interleaved testing condition, abbreviated as ITC). Each session consisted of 36 trials of sequences. Compared to 54 trials per session on Days 1 and 2, the duration of the fMRI sessions on Day 5 was shortened in order to limit further learning (Cross et al., 2007). The third session consisted of a total of
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36 trials for three novel sequences to assess whether learning was specific to the training sequences.
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The arrangement of fMRI task and rest blocks was the same as that in Days 1 and 2. We applied a within-participant cross-over design to control for inter-participant variability in learning ability, with the order of practice (IP versus RP) counter-balanced. 13 participants (6 men and 7 women, age = 23.2±1.4 years, education = 17.0±1.4 years, mean±SD) started with the RP followed by the IP condition at least two weeks later, while the remaining 13 participants started with the IP followed by the RP condition (5 men and 8 women, age = 23.5±1.1 years, education = 17.1±1.0 years). There was no difference in age (P = 0.45) or years of education (P = 0.87) between these two participant groups.
ACCEPTED MANUSCRIPT 8 The retention conditions (ITC versus RTC) were also counter-balanced across the participants (13 participants started with either testing condition).
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2.3 Imaging parameters All participants received structural and functional brain scanning on a Siemens-Trio 3T scanner (Erlangen, Germany) using a 32-channel head coil. The structural image data were acquired using a 3D
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T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) sequence (TR/TI/TE =
2200/900/3.4 ms; flip angle = 10°; FoV = 256 × 192 mm; acquisition matrix = 256 × 192 × 176; voxel
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dimensions = 1 × 1 × 1 mm). Both the task-based and resting-state fMRI scans were acquired in a transverse orientation using the gradient-echo echo planar imaging (EPI) using the same acquisition parameters. These acquisition parameters were: TR/TE = 2000/30 ms; flip angle = 90°; FoV = 192 × 192 mm; acquisition matrix = 64 × 64 × 34; in-plane resolution = 3 mm × 3 mm; slice thickness = 4 mm with 1-mm gap. On Days 1 and 2, each task-based fMRI session consisted of 157 EPI volumes
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with a scan time of 314 seconds. During the resting-state fMRI session, 174 EPI volumes were acquired within a scan time of 348 seconds. On Day 5, each task-based fMRI session lasted for 206 seconds, yielding 103 EPI volumes. There was no resting-state session on Day 5. The first 4 EPI
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approach equilibrium.
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volumes of each task-based or resting-state fMRI session were discarded to allow for magnetization to
2.4 Pre-processing of functional magnetic resonance images Both task-based and resting-state fMRI data were pre-processed using the Statistical Parametric Mapping software (SPM8, Wellcome Department of Cognitive Neurology, London, UK; RRID:SCR_007037). To correct for motion artifacts, functional image data were realigned to the first volume in each functional run and then resliced with 4th-degree B-Spline interpolation (Friston et al.,
ACCEPTED MANUSCRIPT 9 1995). None of the participants had scans with head motions greater than 2 mm. After realignment, the resulting mean images of each participant were normalized to the standard Montreal Neurological Institute (MNI) EPI template (Evans et al., 1993). The normalization parameters were then applied to
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all the functional images of that subject. The normalized images were resampled to 3 x 3 x 3 mm3 per voxel, and subsequently spatially smoothed with an isotropic Gaussian filter with full width at half
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maximum (FWHM) = 8 mm.
2.5 Analysis of task-based fMRI data
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We applied the general linear model (GLM) in SPM8 (Friston et al., 1995) to the task-based fMRI data to derive a t-statistic that estimated regional hemodynamic changes induced by neuronal activation, the so-called blood-oxygen-level-dependent (BOLD) signal, in each participant. The GLM was constructed by convolving a canonical hemodynamic response function (HRF) with a series of boxcars for the SRT tasks with the baseline occurring during the rest blocks. Note that explicitly modeling the rest blocks
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with a separate predictor and a column of the constant offset will over-parameterize the model, making the GLM either unsolvable or highly sensitive to noise in the data (Pernet, 2014). The six parameters of head motion obtained from rigid-body registration were included in the GLM as nuisance variables. An
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additional parametric regressor with the mean response time for each task block was applied to ensure
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that any differences in brain activity during practice and retention was due to the influences of the practice conditions but not to differences in the response time. The first-level contrast map for differences in BOLD signal between the IP and RP conditions in individual participants was then entered into a group-level random-effects model to compare the group differences in BOLD signal between these two practice conditions on each day, and to test the association between the CI effects on brain activation and benefits to learning. We used the RP-minus-IP difference in response time of the first 18 trials (i.e., first 3 blocks) under the Interleaved testing condition (ITC) on Day 5, denoted by D5RTRP-IP, to represent the magnitude of the CI effect on motor learning in the correlation analyses in
ACCEPTED MANUSCRIPT 10 this and the subsequent sections. This was because the results of the current study showed that the effect size of CI was the largest during this period, and therefore this measure provided the best index of the benefit of practice under CI. As such, a group-level correlation map was obtained by computing
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the association between the IP-minus-RP BOLD contrast values during practice and D5RTRP-IP during retention. Multiple comparisons across the whole brain volume were corrected using permutation
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testing, detailed in Section 2.7.2 below.
2.6 Analysis of resting-state fMRI data
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2.6.1 Identification of resting-state networks (RSN) using independent component analysis (ICA) Decomposition of rsfMRI data was performed using a group-level spatial ICA implemented by the Group ICA for fMRI Toolbox (GIFT v4.0a; http://mialab.mrn.org/software/gift/; RRID:SCR_001953)(Calhoun et al., 2009), which transformed the fMRI data into a linear combination
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of source components that were maximally statistically independent in space. The ICA method is popular for analyzing resting-state brain connectivity because it is free of a priori assumptions about the spatio-temporal characteristics of fMRI signal and noise (Beckmann and Smith, 2004; Jacobs et al.,
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2015). The fMRI data of all the participants were concatenated prior to ICA, with the two practice
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conditions (RP and IP) and the two practice days (Days 1 and 2) of a participant treated as separate sessions for that participant. This approach ensured that the estimation of independent sources for the fMRI data would not be biased by different practice days or conditions. Moreover, differences in resting-state functional connectivity between IP and RP could be compared on the same spatial component. These concatenated group-level fMRI data were then entered into a two-step principal component analysis (PCA) to reduce the dimensionality of the data, giving 50 and then 25 principal components. Estimation of independent sources for the rsfMRI data was performed on these 25 principal components using the Infomax algorithm (Bell and Sejnowski, 1995), yielding 25 group-level
ACCEPTED MANUSCRIPT 11 spatially independent component (IC) maps and their corresponding time courses. Finally, the grouplevel ICA estimates were back-reconstructed into participant-level spatial IC maps and time courses respectively for each practice condition and each practice day using the GICA3 back-reconstruction
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method (Erhardt et al., 2011). This back-reconstruction step accounted for data variability across the participants, and also facilitated comparisons of resting-state connectivity between IP and RP
independently for each practice day. For subsequent analyses, each participant-level IC map or time-
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course data was normalized into z-scores based on the mean and standard deviation across the brainonly voxels of that IC map or time-course data. Each z-transformed IC map then represented a resting-
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state network (RSN), where the magnitude of the z-score of a voxel in the IC map gauged the strength of functional connectivity between that voxel and the RSN (Jacobs et al., 2015). We selected RSNs from the 25 group-level IC maps based on two criteria: (1) the IC maps had low spatial overlap with known physiological, vessels or sinuses, motion, and susceptibility artifacts as determined by visual inspection; (2) their time courses were driven by low-frequency fluctuations. For
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criterion (2), we adopted two metrics derived from the frequency power spectrum of the time course of an IC: one was the dynamic range of the power spectrum, defined as the difference in frequencies corresponding to the peak power and minimum power; the other was the ratio of low frequency to high
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frequency power, defined as the ratio of the integral of spectral power below 0.10 Hz to the integral of
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power between 0.15 and 0.25 Hz (Robinson et al., 2009). An IC map was selected as an RSN if the dynamic range in the power spectrum of the corresponding time course was greater than 0.035, and the ratio of low frequency to high frequency power greater than 3.5 (Allen et al., 2011). 9 ICs were selected as our RSNs of interest (see Section 3.3 for details). The significance of each selected RSN was determined by applying a group-level one-sample t-test to the corresponding participant-level ztransformed IC maps. Multiple comparisons across a selected RSN and across all the 9 RSNs were corrected using permutation testing, detailed in Sections 2.7.2 and 2.7.3 below. 2.6.2 Associations between RSN connectivity and learning
ACCEPTED MANUSCRIPT 12 To assess the functional significance of the IC maps, we compared the difference in the resting-state connectivity strength between the IP and RP conditions with respect to each IC, and investigated whether such difference in the connectivity strength was associated with the CI effect on learning. For
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each participant and each IC, a map of the voxelwise IP-minus-RP difference in the z-scores was created. The significance of the z-difference maps for each IC was then assessed using a group-level one-sample t-test. Voxels with a positive t-value indicate that the connectivity strength was greater in
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the IP than the RP condition, and vice versa for a negative t-value. We next applied linear correlation analysis at each voxel of the IP-minus-RP z-difference map for each IC to identify regions where the
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association between the IP-minus-RP z-difference and the CI effect on learning, represented by D5RTRP-IP, was significant.
Given that IP and RP led to different learning curves particularly during the initial trials on Day 1 (see Results and Fig. 1), we also correlated the voxelwise IP-minus-RP z-difference with the IPminus-RP difference in response time of the first 18 trials (i.e., first 3 blocks) on Day 1, denoted by
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D1RTIP-RP, to test whether the difference in the resting-state connectivity strength between IP and RP was driven by the difference in initial performance between these two practice conditions. In this paper we set the direction of subtraction to RP-minus-IP for the difference in response time on Day 5
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(D5RTRP-IP) but IP-minus-RP for that on Day 1 (D1RTIP-RP) to make the values of these two measures
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positive because IP led to inferior practice performance but better retention compared to RP (see Results). Multiple comparisons across a single RSN and across all the 9 RSNs were corrected using permutation testing, detailed in Sections 2.7.2 and 2.7.3 below. 2.6.3 Delineation of resting-state functional connectivity with respect to the left premotor cortex Following ICA described in the above sections, we performed a secondary exploratory analysis that assessed the whole-brain resting-state functional connectivity of the left premotor cortex using the REST toolbox (REST V1.8; http://www.restfmri.net) (RRID:SCR_009641). The left premotor cortex was selected as the seed region of interest (ROI) because the association between the IP-minus-RP
ACCEPTED MANUSCRIPT 13 difference in the RSN connectivity strength and the CI effect on learning was significant in this area (see Results). Moreover, our previous study showed that a greater functional connectivity strength with respect to the premotor cortex during the IP than the RP condition was found to be associated with a
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stronger CI effect on motor learning (Lin et al., 2013). The mean time-series data were extracted from a sphere of 4-mm radius centered on the MNI coordinates [-27, -4, 67] in the left premotor cortex, which was the peak voxel of the above significant association between the RSN connectivity strength and the
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CI effect on learning. The pre-processed (normalized and smoothed) resting-state fMRI data were detrended to remove baseline drifts. A general linear model was then applied to regress out the effects
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of the 6 head-motion parameters, and the white matter and the cerebrospinal fluid signals from the resting-state data. The whole-brain mean signal, although generally regarded as a nuisance signal, was not included in the above regression model because it might serve as an index of baseline brain metabolism (Thompson et al., 2016). Finally, the resting-state data were filtered using a bandpass Butterworth filter (0.01 – 0.08 Hz) to ensure that the data were dominated by low-frequency
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fluctuations. For each participant, a participant-level whole-brain correlation r-map was obtained by correlating the time-series data of the left premotor seed ROI and every voxel in the brain, and then the r-map was converted to a z-map using the Fisher transformation (Fisher, 1915). In the group-level
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analysis, the IP-minus-RP difference at each voxel of the above participant-level z-map was further
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correlated with D5RTRP-IP, yielding a group-level correlation map. Multiple comparisons across the whole brain volume were corrected using permutation testing, detailed in Section 2.7.2 below.
2.7 Experimental design and statistical analysis 2.7.1 Behavioral data Differences in response time between the IP and RP conditions, i.e., D5RTRP-IP and D1RTIP-RP, were compared using a paired t-test (degree of freedom = 25) implemented in the statistical software package SPSS 24 (IBM Corp, Armonk, NY; RRID:SCR_002865). In addition to D5RTRP-IP, we also
ACCEPTED MANUSCRIPT 14 compared the forgetting score between the IP and RP conditions, to infer the influence of CI on memory consolidation (Censor et al., 2010; Lin et al., 2010). The forgetting score was defined as the difference in response time of the first block of ITC or RTC during retention minus the last practice
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block on Day 2. Both the difference in retention response time and the forgetting score have been applied to demonstrate the CI effect on learning (Lin et al., 2010; Lin et al., 2012a; Shea and Morgan, 1979). Performance accuracy or error rate was not included as a behavioral measure to assess the CI
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effect on learning or as a nuisance covariate in the GLM to estimate fMRI BOLD signal because the 4element SRT task was relatively easy and participants made few errors. Previous work examining the
were infrequent (Fraser et al., 2009).
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effect of CI on SRT learning also found differences in response time rather than accuracy as errors
2.7.2 Correction for multiple comparisons at the whole-brain level
For the image data, all the group-level analyses, including t-tests and linear regressions, were implemented in the Statistical nonParametric Mapping toolbox (SnPM13; http://warwick.ac.uk/snpm)
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(RRID:SCR_002092) (Nichols and Holmes, 2001), where multiple comparisons across all the voxels in the image were corrected using permutation testing to control the family-wise error (FWE) at a cluster level of 0.05. This means that only 5% of the clusters that were declared to be significant tended, on
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average, to be false positive findings. This permutation approach has been shown to yield accurate
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multiple comparison correction (Eklund et al., 2016). Here a cluster was defined as a set of contiguous voxels that had the size of the tested effect surpassing a cluster-defining threshold, set to P < 0.001. A total of 5000 permutations were generated by randomly assigning the test contrast of IP-minus-RP or vice versa to each participant in the group-level analyses. For each permutation, significant clusters were generated from the new group-level t-map by applying the same cluster-defining threshold. The FWE, or corrected P-value, of a cluster in the original t-map was then defined as the proportion of the 5000 permuted t-maps with their maximal cluster size greater than the size of that cluster in the original t-map. Only clusters that passed the FWE < 0.05 threshold were displayed in the Figures.
ACCEPTED MANUSCRIPT 15 2.7.3 Correction for multiple comparisons across the RSNs Given that the group-level analyses in Sections 2.6.1 and 2.6.2, including t-tests that assessed the significance of the RSNs (IC maps) and linear regressions that tested the associations between RSN
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connectivity and learning, were performed on each of the 9 selected RSNs separately, for findings that were significant after correction for multiple comparisons at the single-RSN level as described in Section 2.7.2 above, we further controlled FWE at the inter-RSN level using the ‘y-concatenation
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correction’ method to correct for multiple comparisons across the 9 RSNs (Littow et al., 2015). For each participant, all the 9 z-transformed IC maps (for statistical tests in Section 2.6.1) or the IP-minus-
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RP z-difference maps (for statistical tests in Section 2.6.2) were concatenated along the y direction. The resulting concatenated z-map was then entered into permutation testing with the same cluster-defining threshold (P < 0.001) and the total number of permutations (set to 5000) as those in Section 2.7.2 above, yielding the permutation distribution of the maximal cluster size for the concatenated z-map. Because the z-map for each IC was separated with the others by voxels outside the brain with the value
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set to zero, the non-zero voxels in all the z-maps remained spatially disjointed and as such the maximal cluster size of the concatenated z-map for each permutation was the volume of the largest significant cluster obtained from all the component z-maps. The inter-RSN cluster threshold was then defined as
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the 95th percentile of maximum cluster sizes across the 5000 permutations for the concatenated z-map,
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which controlled the FWE across the RSNs at the level of 0.05.
2.8 Mediation analysis
Given that correlations between the CI effect on sequence learning (the RP-minus-IP contrast in response time, or D5RTRP-IP) and the IP-minus-RP difference in the resting-state premotor connectivity were found on Day 1, while correlations between the CI effect on learning and the IP-minus-RP difference in task-related BOLD signal were found on Day 2 (see Results), we hypothesized that the influence on retention performance from the post-practice memory consolidation process on Day 1 may
ACCEPTED MANUSCRIPT 16 be mediated by the task-practice process on Day 2. To test this hypothesis, we applied a three-variable X-M-Y mediation model where the IP-minus-RP difference in functional connectivity between the left premotor cortex and an ROI on Day 1 was treated as the source variable (X), the IP-minus-RP
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difference in task-related BOLD signal in another selected ROI on Day 2 as the mediator (M), and D5RTRP-IP as the outcome variable (Y) (see Fig. 6A). The influence of X on Y under the presence of the mediator M is represented by the linear regression of Y against X controlling for M, where the
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partial regression coefficients b and c′ represent the indirect paths for M to Y and X to Y respectively. The path coefficient a represents the direct effect of X on M, and is the regression coefficient of M
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against X. The unmediated path for X to Y is represented by the regression coefficient c. The ROI for the source variable X was selected from a 4-mm sphere centered at the right putamen [24, 2, 10] and right hippocampus [33, -19, -14] respectively, where the MNI coordinates indicate the peaks of significant correlations between the CI effect and the IP-minus-RP difference in resting-state premotor connectivity (see Table 4). The ROI for the mediator M was selected respectively from the left
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entorhinal cortex [-24, 5, -14] and the left putamen [-15, 17, -8], where the association between the CI effect and the IP-minus-RP difference in task-related BOLD signal was significant (see Table 1).
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These regions were selected as ROIs because (1) they are crucial for motor sequence learning, and (2) are involved in different memory systems. The putamen is specifically involved in motor sequence
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learning (Orban et al., 2010) and plays a key role in procedural memory consolidation (Packard and Knowlton, 2002), while the hippocampus and entorhinal cortex are active in both explicit and implicit motor sequence learning (Schendan et al., 2003) and are responsible for declarative memory (Squire and Zola, 1996). Including these ROIs in the mediation analysis then facilitated the investigation of how brain regions in different memory systems as well as during resting or active states interact to mediate the CI effect on motor sequence learning (Albouy et al., 2008). The mediation analysis was performed using the function library in the BRAVO Mediation toolbox (https://sites.google.com/site/bravotoolbox/documentation/bravo-mediation), where the significance
ACCEPTED MANUSCRIPT 17 and confidence interval of the above path coefficients was estimated using an accelerated biascorrected bootstrapping procedure with 10000 samples (DiCiccio and Efron, 1996). If the effect of X on Y is mediated by M, then (i) the strength of the direct influence of X on Y (represented by the value
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of c) decreases significantly after M is added (where the mediated effect of X on Y is represented by c′), which means that c − c′ is greater than zero, and (ii) the path coefficients for X to M and for M to Y, represented by a and b respectively, are both significant. Note that (c − c′) is mathematically equivalent
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to a × b (Baron and Kenny, 1986; Ide and Li, 2011; MacKinnon et al., 2007). If c′ is not significant (P > 0.05), it indicates that X no longer directly influences Y, and the effect of X on Y is completely
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mediated by M. Otherwise, it means that there is some residual effect of X on Y that cannot be accounted for by M, and M is then a partial mediator (Ide and Li, 2011). The significance threshold for path coefficients a, b, c, and a × b was set to P < 0.05/4 = 0.0125, the Bonferroni’s adjustment for the 4 possible X-versus-M pairs (the right putamen and right hippocampus for X, and the left entorhinal
3. RESULTS
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cortex and left putamen for M).
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3.1 CI effects on motor sequence learning
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Figures 1C and D show that the participants exhibited the pattern of the CI effect on SRT task learning, as reported in our previous study on another sample of participants (Lin et al., 2011). Performance during practice was better under the RP than the IP conditions, where the response time was faster during RP than during IP (mean±SEM: RP = 962.2 ± 41.4, IP = 1191.2 ± 43.8 ms, averaged across all trials on Days 1 and 2; t(25) = 5.86, P = 4 × 10-6, paired t-test; Fig. 1D). The difference was greater when the two practice conditions were compared during the first 18 trials (i.e., the first 3 blocks) on Day 1 (RP = 1194.1 ± 51.3, IP = 1466.9 ± 49.6 ms, averaged across the first 18 trials on Day 1; t(25) =
ACCEPTED MANUSCRIPT 18 3.82, P = 0.0008, paired t-test). However, this pattern was reversed during retention on Day 5 when the practiced sequences were presented in an interleaved order (Interleaved testing condition, abbreviated as ITC, with a total of 36 trials), where the response time of the sequences practiced in the IP condition
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became faster (RP = 1169.5 ± 46.2, IP = 1059.4 ± 40.9 ms; t(25) = 3.15, P = 0.004, paired t-test; Fig. 1D). The CI effect was not significant when the practiced sequences were presented in a repetitive order during retention (Repetitive testing condition, abbreviated as RTC, with a total of 36 trials; RP =
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969.8 ± 45.8, IP = 997.4 ± 42.5 ms; t(25) = 1.05, P = 0.3, paired t-test; Fig. 1D). Considering possible confounding effects due to re-learning during retention, which may reduce the difference in response
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time between the IP and RP conditions (Cross et al., 2007), we divided the retention trials into the early (the first half of the 36 trials) and late (the last half of the 36 trials) trials respectively in ITC and RTC (Phelps et al., 2004). For ITC, the CI effect was more significant in the early (RP = 1210.7 ± 48.5, IP = 1068.4 ± 42.5 ms; t(25) = 3.71, P = 0.001, paired t-test) than the late (RP = 1128.3 ± 42.5, IP = 1050.3 ± 39.5 ms; t(25) = 2.21, P = 0.04, paired t-test) trials (Fig. 1E, left), where the early trials had a greater
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RP-minus-IP difference in response time (early = 142.3 ± 38.3, late = 77.9 ± 35.3 ms; t(25) = 2.74, P = 0.01, paired t-test; Fig. 1E, right). For RTC, the response time following IP was not different from that following RP, for both the early (the first 18 trials; RP = 979.8 ± 46.2, IP = 1019.5 ± 43.8 ms; t(25) =
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1.40, P = 0.17, paired t-test) or late (the last 18 trials; RP = 959.9 ± 45.6, IP = 975.4 ± 40.9 ms; t(25) =
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0.51, P = 0.6, paired t-test) trials (Fig. 1F). Taken the above results together, we therefore used the RPminus-IP difference in response time with respect to the early trials of the ITC during retention, denoted by D5RTRP-IP, to represent the CI effect on motor learning, and we tested the correlations between this measure and imaging measures in the subsequent analyses. For the novel sequences, there was a significant increase in the response time from the retention to the novel-task session on Day 5 in both the RP (retention under ITC = 1169.5 ± 46.2 or under RTC = 969.8 ± 45.8 versus Novel = 1271.2 ± 46.2 ms, P = 0.004 and 4 × 10-9 respectively) and IP (retention under ITC = 1059.4 ± 40.9 or under
ACCEPTED MANUSCRIPT 19 RTC = 997.4 ± 42.5 versus Novel = 1259.1 ± 44.5 ms, P = 2 × 10-7 and 4 × 10-9 respectively) conditions. This indicated that motor sequence learning had occurred in both practice conditions. Nevertheless, there was no significant difference in response time between RP and IP (RP = 1271.2 ±
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46.1, IP = 1259.1 ± 44.5 ms; t(25) = 0.36, P = 0.7, paired t-test; figure not shown), consistent with our previous finding that the CI effect is sequence-specific (Lin et al., 2011).
Consistent with the above findings showing better retention following IP, IP also led to less
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forgetting of the practiced sequences, or a lower forgetting score, than RP, no matter whether the forgetting score was obtained by comparing the last practice block on Day 2 with the first task block
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under ITC or RTC during retention (ITC: RP = 386.5 ± 40.4, IP = 49.2 ± 37.9 ms; t(25) = 6.00, P = 3 × 10-6; RTC: RP = 131.1 ± 34.0, IP = −3.1 ± 32.3 ms; t(25) = 2.87, P = 0.008; paired t-test; Fig. 1G). The order of practice (practicing the SRT task first in the IP versus first in the RP condition) did not influence the CI effect on motor sequence learning. The IP-minus-RP or RP-minus-IP differences
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in response time did not differ significantly between the IP-first and the RP-first groups during practice (IP-minus-RP difference in the response time: IP-first = 206.2 ± 62.8, RP-first = 251.9 ± 48.4 ms, averaged across all trials on Days 1 and 2; t(24) = 0.58, P = 0.57, two-sample t-test), or under ITC (RP-
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minus-IP difference in the response time: IP-first = 141.9 ± 57.8, RP-first = 78.4 ± 39.6 ms; t(24) = 0.91, P = 0.37, two-sample t-test) or RTC (RP-minus-IP difference in the response time: IP-first = −3.9
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± 38.2, RP-first = −51.3 ± 36.6 ms; t(24) = 0.90, P = 0.38, two-sample t-test) during retention.
3.2 Effects of practice conditions on the associations between task-related BOLD signal and learning Figure 2 shows that on both Days 1 and 2, practicing sequences in the IP condition resulted in greater BOLD signal in bilateral premotor (BA 6) and precuneus (BA 7) cortices compared to practicing
ACCEPTED MANUSCRIPT 20 sequences in the RP condition. On Day 5, there was no significant difference in BOLD signal between IP and RP during retention of the practiced sequences or during test with the novel sequences (figure not shown). Figure 3 further shows that a greater IP-minus-RP difference in BOLD signal on Day 2
IP)
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was associated with a greater RP-minus-IP difference in response time during retention (i.e., D5RTRPin bilateral precuneus cortices and the left entorhinal cortex (BA 34), insula (BA 48), putamen, and
orbitofrontal gyrus (BA 11), with the peak voxels listed in Table 1. However, the association between
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the IP-minus-RP difference in BOLD signal on Day 1 and D5RTRP-IP was not significant.
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3.3 Effects of practice conditions on the associations between RSN connectivity and learning Nine independent components (ICs) were selected as our RSNs of interest, including the precuneus network, primary visual networks (two ICs, denoted by I and II respectively), sensorimotor networks (two ICs, denoted by I and II respectively), the right and the left frontoparietal networks, occipitotemporal network, and the frontal network (Fig. 4). Each RSN was significant after correction for
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multiple comparisons at the single-RSN and further at the inter-RSN level. The inter-RSN cluster threshold for these 9 RSNs was 98 voxels, or 2646 mm3. Brain regions included in the RSNs are listed in Table 2. We next investigated whether CI in practice modulated the association between the
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functional connectivity in the RSNs and the retention performance. For each RSN, we compared the
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post-practice resting-state functional connectivity between IP and RP on Day 1 or 2, i.e., the differences in the voxelwise z-scores, and then tested the association between the IP-minus-RP zdifference on Day 1 or 2 and D5RTRP-IP. None of the 9 RSNs had significant differences in the connectivity strength between IP and RP on either Day 1 or 2 (multiple comparisons corrected at the single-RSN level; figure not shown). The association between the IP-minus-RP z-difference on Day 1 and D5RTRP-IP was significant at the single-RSN level in the left premotor cortex (Brodmann area 6) belonging to the sensorimotor network I (Fig. 5A). This means that greater connectivity between the left premotor cortex and other regions in the sensorimotor network I after IP than RP was associated
ACCEPTED MANUSCRIPT 21 with stronger CI effects during retention. However, this association did not pass the more stringent inter-RSN cluster threshold (equal to 46 voxels, or 1242 mm3) for correction of multiple comparisons across the 9 RSNs. Even so, the left premotor area identified above may still be used as a seed ROI (a
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4-mm sphere centered around the peak voxel for the association at [-27, -4, 67] in MNI coordinates) for subsequent explorations of voxel-wise functional connectivity with respect to the left premotor cortex. There was no significant association, with multiple comparisons corrected at the single-RSN level,
8 RSNs on Day 1 or any of the 9 RSNs on Day 2.
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between the retention performance on Day 5 and the resting-state connectivity in any of the remaining
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We also tested the association between the IP-minus-RP z-difference on Day 1 and D1RTIP-RP, where the latter represents the difference in initial performance between the two practice conditions. None of the 9 RSNs had a significant association between the IP-minus-RP difference in RSN connectivity and D1RTIP-RP (multiple comparisons corrected at the single-RSN level; figure not shown),
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indicating that RSN connectivity was not driven by initial performance.
3.4 Resting-state functional connectivity with respect to the left premotor cortex Figure 5B shows the differences between IP and RP in resting-state functional connectivity with the
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left premotor ROI on Day 1. Compared to RP, IP led to greater connectivity between the left premotor
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cortex and the left dorsolateral prefrontal cortex (DLPFC, BA 9 and 46), bilateral posterior cingulate cortices (BA 23), and the bilateral angular gyri/inferior parietal lobules (BA 39 and 40) (Table 3). Correlation analysis further shows that a greater IP-minus-RP difference in functional connectivity on Day 1 was associated with a greater D5RTRP-IP, in the amygdala, hippocampus, putamen, and the thalamus on the right side, and the cerebellar vermis (Figs. 5C and D, and Table 4).
3.5 Mediation effects of the task-related BOLD signal on the association between resting-state premotor connectivity and CI learning benefits
ACCEPTED MANUSCRIPT 22 The mediation analysis shows that the association between the IP-minus-RP difference in Day-1 resting-state left premotor-right hippocampus connectivity and the CI benefit to retention was completely mediated by the IP-minus-RP difference in the BOLD signal in the left entorhinal cortex on
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Day 2 (Fig. 6B). This finding indicates that the linkage between CI benefits and increased resting premotor-hippocampus connectivity after IP was attributable to higher functional activity in the left entorhinal cortex during IP on the next day. In contrast, the mediation effect was non-significant for the
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right putamen (X) versus the left entorhinal cortex (M), for the right hippocampus versus the left
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putamen, and for the right putamen versus the left putamen (Fig. 6C).
4. DISCUSSION
In this paper we analyzed fMRI data acquired during a 5-day motor sequence learning paradigm, showing the contextual interference (CI) effect on motor sequence learning and the underlying offline brain networks. Our results demonstrated that the CI effect was a robust phenomenon that could be
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observed even when retention was tested two days after practice – the IP group had poorer performance during practice but outperformed the RP group during retention. This finding was consistent with what was observed in the classical study of the CI effect by Shea and Morgan (Shea and Morgan, 1979). By
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analyzing fMRI data using independent component analyses (ICA), we identified resting brain
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networks during the post-practice consolidation phase, and found that the difference in resting-state brain connectivity between the interleaved practice (IP) and the repetitive practice (RP) conditions was associated with the CI effect on retention (D5RTRP-IP) but not the initial practice performance (D1RTIPRP).
This suggests that the difference in resting brain connectivity was not driven by the residual effect
of different initial learning curves between IP and RP. Greater connectivity between the left premotor cortex and the hippocampus, putamen, amygdala, thalamus, and the cerebellum after IP compared to RP on Day 1 was associated with better learning during retention. Moreover, using mediation analysis, we found that the association of enhanced retention performance on Day 5 with the increased resting-
ACCEPTED MANUSCRIPT 23 state functional connectivity between the left premotor cortex and right hippocampus after IP on Day 1 was completely mediated by the increased task-related BOLD signal in the left entorhinal cortex during IP on Day 2. These findings suggest that practicing tasks with higher CI as a desirable difficulty
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triggers functional synchronization of the brain during the memory consolidation phase, particularly between the structures important for motor sequence learning (e.g., the premotor cortex and the putamen) and structures more typically associated with declarative memory (e.g., the hippocampus and
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the entorhinal cortex).
The resting-state fMRI scan was performed after the task-based ones since the purpose of the
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current study is to study how CI during practice affects memory consolidation. We can do so by comparing differences in resting brain connectivity after IP versus RP, where the lower-CI RP condition was treated as a baseline. This design is determined by the fact that a main question this study investigates is how task-related brain activity affects brain networks at rest (Albert et al., 2009b; Gregory et al., 2014; Sami et al., 2014). We hypothesized that how the brain processes the two task
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conditions during practice would affect the post-practice phase, which might be construed as a period of memory consolidation. Indeed, our results suggest this is the case: the difference in post-practice resting connectivity between IP and RP was associated with the CI benefits on motor learning, which
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indicates that such difference in resting connectivity between IP and RP did not simply reflect the
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different nature of the tasks but rather differences in learning. Moreover, even though repetitive practice (RP) of sequences seemed more ‘automatic’ compared to IP, this seemingly automatic process was also a consequence of learning. This was evidenced by the significant increase in the response time from the retention task after RP to the novel task on Day 5, which indicates that motor learning had occurred in the RP condition. The RSNs identified during the post-practice resting state contained regions crucial for motor sequence learning, such as the precuneus (Buckner et al., 2008; Utevsky et al., 2014), the fusiform gyrus (Zhang et al., 2014), the dorsolateral prefrontal cortices (DLPFC) (Mary et al., 2017; Yoshida et
ACCEPTED MANUSCRIPT 24 al., 2010), and the premotor cortex (Kantak et al., 2012; Steele and Penhune, 2010; Wade and Hammond, 2015). The premotor cortex was also most associated with the benefits of CI compared to other regions in the RSNs. Greater resting connectivity between the left premotor cortex (in
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sensorimotor network I) and the left DLPFC, bilateral posterior cingulate cortices (PCC), and bilateral angular gyri/inferior parietal lobule (IPL) was found after IP compared to RP on Day 1. The DLPFC, PCC, and IPL are connected as part of the frontal motor network, involved in motor planning and
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execution (Luppino and Rizzolatti, 2000). Enhanced connectivity between these regions and the premotor cortex after IP suggests that consolidation of interleaved sequences facilitates functional
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integration of the motor system. The results from the correlation analysis further support the role of the premotor cortex in linking motor memory consolidation to retention performance. Greater difference in resting connectivity between the premotor cortex and hippocampus, basal ganglia, and cerebellum after IP than RP on Day 1 was associated with better retention performance following IP. This indicates that IP activated both the premotor-basal ganglia and premotor-limbic circuits, where interactions between
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these two circuits consolidated motor memory traces to optimize retention performance (Albouy et al., 2008). It has been well established that motor sequence learning involves early recruitment of brain networks including the cerebellum, basal ganglia, supplementary motor area, and motor and premotor
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cortices (Doyon et al., 2003). The hippocampus and other limbic regions are traditionally associated
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with encoding of declarative memory (Squire and Zola, 1996). Nevertheless, the hippocampus has been found to engage in both explicit and implicit motor sequence learning (Schendan et al., 2003), which possibly reflects the role of the hippocampus in associating discontinuous stimuli and their context across time and space during sequence learning (Poldrack and Rodriguez, 2003). In addition to the enhancement of post-practice functional connectivity, IP was also associated with greater functional activation during practice, a finding consistent with theories of CI effects (for example, the ‘elaborative-processing’ and ‘forgetting-reconstruction’ hypotheses; see below) as well as our previous observation (Lin et al., 2011). Compared to RP, IP led to greater task-related BOLD signal
ACCEPTED MANUSCRIPT 25 in bilateral premotor and precuneus cortices on both Days 1 and 2. Given that the precuneus is involved in processing visuospatial information (Mahayana et al., 2014), the higher activity in the precuneus and premotor cortices during IP suggests that practice with higher CI requires greater integration of
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visuospatial operations to guide motor learning. The association between the relative increase in taskrelated BOLD signal and the retention benefit with respect to IP was significant only on Day 2. By contrast, the association between the increase in post-practice resting connectivity and the retention
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benefit of IP was significant only on Day 1. This indicates that the functional significance of resting and active states of the brain varied at different stages of motor learning. The need to recruit neural
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resources for offline memory consolidation with respect to IP might be greatest immediately after the first day of practice, while task-related functional activity that supported the learning benefits of IP might take longer to develop until a certain proficiency in motor skill had been acquired after two days of practice. In our previous study applying the same 5-day motor sequence-learning paradigm to another cohort of participants, we also found that only on Day 2, the increase in task-related functional
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connectivity between the premotor and primary motor cortices, and between DLPFC and supplementary motor area accounted for the retention benefits of IP (Lin et al., 2013). The mediation analysis shows that the association between the retention benefits with respect to
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IP and enhanced resting premotor connectivity with the right hippocampus on Day 1 was completely
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mediated by increased task-related BOLD signal in the left entorhinal cortex on Day 2. This finding suggests that contextual interference during motor practice enhanced motor learning over a 5-day span by recruiting both the resting and active brain networks that involved the premotor and medial temporal structures. Nevertheless, the association between the retention benefits of IP and the enhanced resting premotor connectivity with the right putamen was not mediated by task-related BOLD signal in the left putamen or entorhinal cortex. A possible explanation for the discrepancy in the mediation effect with respect to the hippocampus and putamen is that the declarative component of motor memory (encoded mostly by the hippocampal formation) decayed relatively quickly and needed to be boosted by practice
ACCEPTED MANUSCRIPT 26 on the next day (Keisler and Shadmehr, 2010). By contrast, the non-declarative component of motor memory (mediated mostly by the putamen) was relatively sustained and as such the influence of enhanced Day-1 premotor-putamen connectivity on retention performance did not depend on functional
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activation in the putamen or entorhinal cortex on Day 2. A straightforward explanation for why IP led to better learning than RP is that it was simply due to the similarity between the practice and test conditions (Hamilton, 1943). In other words, retention
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after IP was better than RP under ITC because the practiced sequences were arranged in an interleaved order in ITC so that ITC was a favorable retention testing condition for IP. However, our data did not
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support this argument but favored a true CI effect because (1) retention after RP tested under RTC, a favorable retention testing condition for RP, did not outperform retention after IP, where the difference in response times under RTC was not significant; (2) the RP group exhibited more forgetting of the practiced sequences, with a higher forgetting score than the IP group compared even under RTC. Another explanation is that IP induced more spacing between distinct tasks than RP (Dempster, 1988).
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While the increased spacing between interleaved tasks may contribute to the benefits of CI, the effects of CI and spacing may be additive, suggesting that additional cognitive processes are involved (Richland et al., 2004). Two major hypotheses have been proposed to explain the CI benefits to
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learning, the ‘elaborative-processing’ (Shea and Zimny, 1983) and ‘forgetting–reconstruction’ (Lee and
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Magill, 1985) hypotheses. The elaborative-processing hypothesis assumes that during IP the learners have to compare and distinguish different tasks appearing in an interleaved order to compete for memory resources, and this repeated process of comparing and distinguishing motor tasks benefits learning (Shea and Zimny, 1983). On the other hand, the forgetting-reconstruction hypothesis suggests that a previously constructed action plan is more readily to be available in working memory during RP because the same task is practiced repeatedly. IP, however, benefits learning by forcing the learners to update their working memory by temporarily ‘forgetting’ the just-practiced task so that the upcoming new one can be planned (Lee and Magill, 1983; Lee and Magill, 1985). The two hypotheses are not
ACCEPTED MANUSCRIPT 27 mutually exclusive to each other and are both supported by previous research. For example, Kim et al. showed that learning of visuomotor adaption tasks was modulated by the degree of between-trial forgetting (Kim et al., 2015). Nevertheless, given the correlative nature of fMRI studies, our imaging
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data could not distinguish between the above two hypotheses − the association between IP and higher task-related BOLD signal than RP during practice could be attributed to greater recruitment of neural resources either to compare and distinguish motor sequences during IP (the elaborative-processing
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hypothesis), or to update working memory due to forgetting and retrieval of the interleaved sequences (the forgetting-reconstruction hypothesis). Changes in RSNs after early IP practice could reflect the
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consolidation of more elaborate or more retrievable memory traces. Using disruptive techniques such as transcranial magnetic stimulation (TMS) may provide a better insight into the underlying mechanisms. For example, in a TMS study by Lin et al., the elaborative-processing hypothesis was favored because TMS disrupted learning following IP by interfering with the comparing-anddistinguishing processes of the interleaved tasks, rather than by inducing more forgetting between the
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repetitive tasks (Lin et al., 2008).
Three issues about the experimental settings deserve further discussion. First, the 4-element
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motor sequences used in the current study were shorter than 8-element or longer ones used in other studies applying SRT tasks (Doyon et al., 2002; Shimizu et al., 2017; Spencer et al., 2006), which may
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lead to potential ambiguity whether the experiment elicited explicit motor memory only. Previous studies had successfully demonstrated the CI effect on sequence learning using 4-element sequences (Cross et al., 2007; Immink and Wright, 1998). As such, the rationale behind choosing short 4-element sequences in the current study was to ensure that the CI effect could be replicated. It is very possible that the participants gained some explicit knowledge of the sequences during practice. Nevertheless, it remains to be tested whether interleaved practice would have the same benefit if much longer sequences were practiced so that participants were not aware of the sequence structure. In fact, only a few studies have assessed the CI effect specifically under implicit motor sequence learning conditions
ACCEPTED MANUSCRIPT 28 (Wright et al., 2016). One possible reason is that motor sequences have to be relatively long to ensure implicit learning where participants are unaware of the sequences. Learning of these long sequences may not benefit from CI to the same degree as learning simpler sequences (Guadagnoli and Lee, 2004).
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In the current study, the participants practiced three sequences in an interleaved (IP) or repetitive (RP) order. If these sequences were lengthened to include 8 or 12 elements, each sequence would be separated by a substantial number of elements (up to 24), which might not support sequence learning
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effectively. Nevertheless, future studies that systematically investigate the influence of sequence length on the CI effect of motor sequence learning are warranted, as it is unclear whether explicit retrieval of a
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sequence is important for the benefit of interleaved practice. A second issue is that we used spatially separated colored circles in the SRT task (Fig. 1A). While most work using the SRT task has used spatially separate response locations to prevent the need to learn stimulus-response mappings (Robertson, 2007), our use of colored circles may have engaged different encoding mechanisms than if no color was used. Moreover, presenting colors in the SRT sequences enhance attention and facilitate
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motor sequence learning. In the study by Willingham et al., participants learned motor sequences based on color in random locations better than those who learned sequences based on location in random colors (Willingham et al., 1989). Thus our procedure may have facilitated learning by using both
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location and color to define a sequence, and it is possible that different resting-state networks would be
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involved in the consolidation of learning after IP if sequences were only based on color or location. Nevertheless, the IP and RP conditions in our study differed only in the order of presentation for the three sequences, while the frequency of presentation was identical for each color or location. Thus, both locations and colors were counterbalanced when the two practice conditions were compared to assess the effects of CI on learning. The third issue is that in comparison of two cognitive conditions, apparent brain activation in one condition could be due to deactivation in the other condition. As such, adding a non-sequence control experiment (for example, a random sequence or repeated finger tapping) helps identify task-induced increases or decreases in activation. For example, Fletcher et al. found
ACCEPTED MANUSCRIPT 29 learning-related decreases in activation in the left medial temporal cortex and the caudate nucleus by comparing SRT sequences with random ones (Fletcher et al., 2005). In the current study we did not include such a non-sequence control experiment to assess whether IP or RP respectively increased or
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decreased brain activation because the goal of the current study was to identify the neural substrates of the CI effect on motor sequence learning by comparing differences in resting or task-based brain activation or connectivity between the IP and RP conditions. Nevertheless, the positive BOLD signal
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respectively for the IP and RP conditions on Days 1 and 2 displayed in the bar graphs in Figure 2B indicates that the differences in BOLD signal between IP and RP in brain regions underlying the CI
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effect were not due to deactivation in these regions under the RP condition.
In the current study we used an 8-mm smoothing kernel that had also been used in (Schendan et al., 2003), which first provided the evidence for the involvement of the hippocampus in both explicit and implicit motor sequence learning. There is no clear consensus about the ideal level of smoothing that should be applied to fMRI data. Smoothing may improve the signal-to-noise ratio and to
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compensate for imperfect normalization of the images (Lee et al., 2013; Lindquist, 2008). For example, Kokkonen et al. found that smoothing with an 8-mm kernel was the most robust to yield identifiable sensorimotor signal sources in ICA, compared to smoothing with a 5-mm kernel or no smoothing
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(Kokkonen et al., 2009). An even larger smoothing kernel of 10 mm had also been used in resting-
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fMRI study to compare functional network connectivity between patients with schizophrenia and healthy controls (Jafri et al., 2008).
Conclusion. The current study is the first to demonstrate that the beneficial effect of CI on motor sequence learning starts from the early phase of memory consolidation, likely through driving offline functional connectivity with respect to the premotor cortex immediately after practice. This increase in resting connectivity was found to be between the premotor cortex and other brain areas involved in motor memory consolidation, including the DLPFC, inferior parietal lobule, hippocampus, putamen,
ACCEPTED MANUSCRIPT 30 and cerebellum. Mediation analysis further showed that the enhanced resting connectivity in the limbic regions on the first day led to higher task-related functional activation on the second day, and in turn to better retention performance. Our study advanced previous work that investigated the CI effect on task-
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related brain activation and motor learning (Cross et al., 2007; Lin et al., 2011), demonstrating that the functional network underlying offline consolidation is also a neural substrate of the CI effect, and that
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its connectivity could be enhanced by desirable difficulty during motor practice.
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Conflict of interest. The authors declare no competing financial interests.
Acknowledgments. This study was supported in part by the Ministry of Science and Technology (MOST 105-2221-E-010-004-MY3), the National Health Research Institutes (NHRI-EX104-10219EC), and the Brain Research Center, National Yang-Ming University (a grant from Ministry of Education,
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Aim for the Top University Plan, 100AC-B12), Taiwan (to MC Chiang).
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ACCEPTED MANUSCRIPT 42 FIGURE LEGENDS
Figure 1. Experimental design and behavioral analysis. (A) During fMRI scanning, the participants
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practiced the SRT task by pressing the corresponding key with their left hand as soon as they saw the colored circles appearing one by one through the magnet-compatible goggles. There was an 18-second break after every 6 trials of the SRT task, during which the circles were replaced by a fixation cross.
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(B) Each participant was assigned to either the RP or the IP group during the 5-day regime of the SRT task (practice of the task on Days 1 and 2; retention tests on Day 5). The SRT task included two sets of
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three different four-element sequences, (1, 2, 3) and (4, 5, 6), where numbers 1 to 6 represent different permutations of blue, red, green, and yellow colored circles shown in (A). In the RP condition, a sequence was practiced repetitively for 54 consecutive trials before the next sequence appeared (e.g., sequences 111…222…333…), while in the IP condition, the three sequences (sequences 4, 5, and 6) were arranged in a non-repetitive order. Resting-state fMRI scans were acquired after the task-based
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fMRI sessions. During the retention tests on Day 5, the practiced sequences were presented in an interleaved order in one fMRI run (Interleaved testing condition, abbreviated as ITC; shown by 132…for the RP, and 465... for the IP group), but in a repetitive order in the other fMRI run (Repetitive
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testing condition, abbreviated as RTC; shown by 1…2…3… for the RP, and 4…5…6… for the IP
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group). The order of the retention conditions (ITC versus RTC) was counter-balanced across the participants. The participants were tested with three novel sequences (sequences 789…for the RP, and 10 11 12... for the IP group) in the third fMRI run, denoted by ‘Novel’. Performance of SRT was presented in the line graph in (C). For better illustration, each dot represents the mean response time of a task block, which contained 6 consecutive trials. Performance during practice (Days 1 and 2) was better under the RP than the IP conditions, with a faster response time. However, retention performance of the practiced sequences on Day 5 was better for the IP than RP group under ITC, although the retention performance between the two groups did not differ under RTC. This CI effect on sequence
ACCEPTED MANUSCRIPT 43 learning is further illustrated in the bar graphs in (D). (E) We further compared the retention performance between the IP and RP groups on the early (the first 18 trials) and late (the last 18 trials) trials respectively under ITC and RTC. For ITC, the CI effect was more significant in the early (Early
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ITC) than the late (Late ITC) trials, where the early trials had a greater RP-minus-IP difference in response time. (F) For RTC, the difference in response time between the two practice conditions was not significant either in the early (Early RTC) or late (Late RTC) trials. (G) In addition to a faster
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response time, the IP group also had a lower forgetting score, which was defined as the difference in response time of the first block of ITC or RTC during retention minus the last practice block on Day 2.
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The bar graph shows that IP led to less forgetting, or better retention of the practiced sequences than RP, no matter the first task block of retention was selected from ITC or RTC. Note that there was no forgetting following IP when the practiced sequences were tested under RTC (the forgetting score = −3.1 ± 32.3 ms so that the corresponding bar was not visible due to the scaling of the y-axis), indicating that IP still led to better retention even when the retention testing condition was different from the
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practice one, and therefore the CI effect shown here was not just a direct consequence of similarity in the practice and test conditions. Abbreviations: ITC, Interleaved testing condition; RTC, Repetitive
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testing condition. *P < 0.05, **P < 0.005, ***P < 0.001.
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Figure 2. Differences in BOLD signal between IP and RP. (A) The brain maps show areas with significant IP-minus-RP BOLD contrasts during the practice phase on Days 1 and 2, particularly the bilateral premotor and precuneus cortices. The MNI z-coordinate (mm) is shown at the bottom. A positive t-value means excess in BOLD signal during IP, which also suggests that IP is associated with greater demand in neural recruitment than RP in these areas. The difference in BOLD signal between IP and RP conditions was not significant during retention (Day 5; figure not shown). (B) The line graphs for the percent change (left) and the bar graphs for the beta value (right) of BOLD signal also demonstrate a greater BOLD signal during IP on Days 1 and 2. The percent BOLD signal change for IP
ACCEPTED MANUSCRIPT 44 or RP in the line graphs was calculated as the mean signal intensity of each task block (which contains 6 consecutive trials and is represented by a dot) normalized to a baseline set to a 6-second interval
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before the task onset. Abbreviations: Pcu, precuneus; PM, premotor cortex.
Figure 3. Associations between greater BOLD signal during IP on Day 2 and the retention benefit of IP. (A) Greater excess in BOLD signal during IP on Day 2 was associated with a stronger CI effect
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on sequence learning, as represented by a greater RP-minus-IP difference in response time. This association was significant in bilateral precuneus cortices, the left insula, entorhinal cortex, and
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putamen, and was further illustrated by the scatter plots in (B) where D5RTRP-IP (x-axis) means the RPminus-IP difference in response time on Day 5, and BOLDIP-RP (y-axis) indicates IP-minus-RP difference in BOLD signal. All brain maps are displayed in neurological orientation, where ‘R’ indicates the right hemisphere. The MNI y- or z-coordinate (mm) is shown at the bottom of each slice.
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Abbreviations: EC, entorhinal cortex; Ins, insula; Pcu, precuneus; Put, putamen.
Figure 4. The resting-state networks (RSN). Each RSN was an independent component (IC) map whose significance was determined by one-sample t-test (FWE-corrected P-value < 0.05 at both the
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for each RSN.
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single-RSN and the inter-RSN levels). The MNI coordinates indicate the location of the peak of t-value
Figure 5. Associations between stronger resting-state premotor connectivity after IP on Day 1 and the CI benefits to learning. (A) For each RSN, we used correlation analysis to test the association between the RP-minus-IP difference in retention response time (Day 5) and the IP-minus-RP difference in z-scores that quantified the resting-state connectivity strength of each voxel. Multiple comparisons across the image were corrected using permutation testing. The brain map shows that stronger restingstate connectivity of the left premotor cortex (BA 6; belonging to the sensorimotor network I) after IP
ACCEPTED MANUSCRIPT 45 on Day 1 was significantly associated with better retention performance with respect to the IP condition. This finding indicates that increase in CI in motor practice enhanced functional connectivity of the premotor cortex during the consolidation phase of motor sequence learning. Nevertheless, this
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association only passed the correction for multiple comparisons at the single-RSN but not the interRSN level. (B) To identify brain regions that functionally interacted with the premotor cortex to affect retention performance, we selected a 4-mm sphere centered at the peak voxel of the above significant
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area in the left premotor cortex found in (A) as the seed region to quantify functional connectivity between the premotor seed region and every point of the brain (excluding the seed region) during the
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post-practice resting state on Day 1. The brain map shows that IP resulted in stronger resting-state connectivity between the left premotor cortex and the left DLPFC, bilateral posterior cingulate cortices, and bilateral angular gyri/inferior parietal lobules. (C) The IP-minus-RP difference in the connectivity strength between the premotor seed and each voxel on Day 1 was further correlated with the RP-minusIP difference in the response time during retention, yielding significant clusters (FWE-corrected P-
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value < 0.05) in the amygdala, hippocampus, putamen, and the thalamus on the right side, and the cerebellar vermis. (D) The correlations in (C) are further illustrated in the scatter plots where the IPminus-RP difference in functional connectivity between the above area and the left premotor cortex
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(Premotor Connectivity IP-RP) was regressed against the RP-minus-IP difference in response time during
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retention (D5RTRP-IP). ‘R’ indicates the right hemisphere. Abbreviations: Amg, amygdala; Cv, cerebellar vermis; DLPFC, dorsolateral prefrontal cortex; Hippo, hippocampus; IPL, inferior parietal lobule; PCC, posterior cingulate cortex; PM, premotor cortex; Put, putamen; Tha, thalamus.
Figure 6. Mediation analysis of Day-1 resting-state premotor connectivity, Day-2 task-related BOLD signal, and Day-5 retention performance. (A) The three-variable mediation model includes the source (X), mediator (M), and outcome (Y) variables. The source X is the IP-minus-RP difference in the premotor connectivity on Day 1 (PMConnIP-RP) in one of the two candidate regions shown in Fig.
ACCEPTED MANUSCRIPT 46 5C, the right hippocampus and right putamen. The mediator M is the IP-minus-RP difference in taskrelated BOLD signal on Day 2 (BOLDIP-RP) in either of the two candidate regions shown in Fig. 3A, the left entorhinal cortex and left putamen. The outcome Y is the RP-minus-IP difference in response
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time on Day 5 (D5RTRP-IP). The path coefficient a indicates the direct effect of X to M, b and c′ represent the partial regression coefficients of Y on M and X respectively, and c indicates the direct effect of X to Y. (B) The behavioral effect of the increase in the functional connectivity between the
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left premotor cortex and the right hippocampus after IP on Day 1 was completely mediated by the relative increase in task-related BOLD signal for IP compared to RP in the left entorhinal cortex on
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Day 2, where the path coefficients a and b and their product a × b were all significant (bootstrap Pvalue < 0.0125, Bonferroni-corrected for the 4 possible source-mediator pairs that had been tested, displayed in boldface), and c′ was non-significant (P > 0.05). (C) The mediation effect was not significant in the following source versus mediator pairs due to non-significant path coefficient b: the
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right putamen versus the left entorhinal cortex, the right hippocampus versus the left putamen, and the right putamen versus the left putamen. This finding suggests that the benefits to retention of enhanced Day-1 premotor connectivity after IP did not depend on functional activation in the putamen on Day 2.
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Numbers in square brackets indicate the 95% confidence interval of the null hypothesis for the path
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coefficients. Abbreviations: EC, entorhinal cortex; Hippo, hippocampus; L, left; R, right.
ACCEPTED MANUSCRIPT 47 Table 1. Brain areas where greater BOLD signal during IP than during RP on Day 2 was associated with the retention benefits of IP Peak t-value
MNI coordinates (mm)
R precuneus
5
5.4
12, -49, 52
L precuneus
5
4.9
L entorhinal cortex
34
4.8
L insula
48
4.6
L orbitofrontal gyrus
11
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BA
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BA, Brodmann area.
-24, 5, -14
-27, 20, -14
4.4
-24, 23, -11
3.6
-15, 17, -8
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L putamen
-12, -49, 52
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BA
Peak t-value
MNI coordinates (mm)
R precuneus
7
29.5
9, -64, 55
L precuneus
7
29.5
L lingual gyrus
17, 18
53.0
R calcarine gyrus
17, 18
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Table 2. Brain regions included in the selected RSNs
Primary visual network I
18
L calcarine gyrus
-9, -67, 52
0, -82, 4
31.4
9, -79, 7
29.3
-3, -79, 22
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L cuneus
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Precuneus network
17, 18
29.0
-12, -70, 19
18
32.0
27, -94, 7
18
30.0
-21, -97, -2
18
29.1
24, -97, -5
4
62.7
-18, -37, 67
2
39.4
24, -40, 67
4
38.0
15, -37, 67
L postcentral gyrus
2
37.5
-36, -40, 61
R dorsal premotor/supplementary motor
6
31.9
9, -13, 67
6
26.0
-3, -13, 64
Primary visual network II R middle occipital gyrus
R inferior occipital gyrus Sensorimotor network I
R postcentral gyrus
area
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R precentral gyrus
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L precentral gyrus
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L middle occipital gyrus
L dorsal premotor/supplementary motor area Sensorimotor network II
ACCEPTED MANUSCRIPT R supramarginal gyrus
40
28.9
63, -22, 22
L supramarginal gyrus
40
27.6
-60, -37, 31
R ventral premotor area
6
20.1
48, 8, 13
R angular gyrus
39
32.1
R inferior parietal lobule
40
22.9
R middle frontal gyrus
47
25.3
R superior frontal gyrus
32
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49
45, -61, 43 45, -46, 55 36, 53, 1
9, 35, 40
40
26.5
-48, -55, 43
47
25.6
-42, 44, -11
47
22.9
-42, 47, -2
19
29.8
45, -79, 7
19
29.5
-30, -85, 16
37
27.8
-48, -70, 10
37
24.8
42, -52, -17
37
24.3
-42, -67, -14
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L inferior parietal lobule
20.5
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Left frontoparietal network
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Right frontoparietal network
L superior frontal gyrus
10
40.4
-9, 56, 31
L dorsolateral prefrontal cortex
9
28.9
6, 44, 49
R dorsolateral prefrontal cortex
9
23.4
-12, 41, 49
L premotor/supplementary motor area
6
21.9
-1, 20, 67
L inferior frontal gyrus, orbital part L middle frontal gyrus
R middle occipital gyrus L middle occipital gyrus
R fusiform gyrus L fusiform gyrus
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L middle temporal gyrus
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Occipito-temporal network
Frontal network
BA, Brodmann area.
ACCEPTED MANUSCRIPT 50 Table 3. Brain areas showing greater functional connectivity with the left premotor cortex after IP than RP on Day 1 Peak t-value
MNI coordinates (mm)
R angular gyrus
39
6.3
48, -64, 43
R angular gyrus
39
5.4
L inferior parietal lobule
39
6.0
L inferior parietal lobule
40
4.5
L DLPFC
46
L DLPFC
9
L PCC
23
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BA
42, -70, 49
-51, -61, 40 -45, -52, 55 -39, 20, 40
5.0
-48, 20, 46
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5.3
4.4
0, -28, 43
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ACCEPTED MANUSCRIPT 51 Table 4. Brain areas where greater connectivity with the left premotor cortex after IP than RP on Day 1 was associated with the retention benefits of IP
Cerebellum vermis R amygdala
34
Peak t-value
MNI coordinates (mm)
6.1
3, -55, -14
5.7 5.5
R putamen
5.0 20
R hippocampus
20
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24, -1, -14 21, -19, 7 24, 2, 10
4.4
30, -13, -5
3.6
33, -19, -14
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R inferior temporal gyrus
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R thalamus
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I hereby verify that all information disclosed here is true and correct to the best of my knowledge: 1. Financial disclosure:
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(a) None of the authors has any actual or potential conflicts of interest including any financial, personal or other relationships with other people or organizations within three years of beginning the work submitted that could inappropriately influence (bias) their work. (b) None of the authors' institution has contracts relating to this research through which it or any other organization may stand to gain financially now or in the future.
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(c) None of the authors has any other agreements with respect to themselves or their institutions that could be seen as involving a financial interest in this work.
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2. Sources of financial support: This study was supported in part by the Ministry of Science and Technology (MOST 105-2221-E-010-004-MY3), the National Health Research Institutes (NHRI-EX104-10219EC), and the Brain Research Center, National Yang-Ming University (a grant from Ministry of Education, Aim for the Top University Plan, 100AC-B12), Taiwan (to MC Chiang).
3. The data contained in the manuscript being submitted have not been previously published, have not been submitted elsewhere and will not be submitted elsewhere while under consideration at NeuroImage.
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4. This study has been approved by the Institutional Review Board of the National Yang-Ming University, Taiwan. Written informed consent was obtained from all participants.
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5. All authors have reviewed the contents of the manuscript being submitted, approved of its contents and validated the accuracy of the data.
Ming-Chang Chiang, M.D., Ph.D. Associate Professor Department of Biomedical Engineering National Yang-Ming University
S1053-8119(18)30588-3
DOI:
10.1016/j.neuroimage.2018.06.081
Reference:
YNIMG 15087
To appear in:
NeuroImage
Received Date: 18 April 2018 Revised Date:
11 June 2018
Accepted Date: 28 June 2018
Please cite this article as: Lin, C.-H.(J.), Yang, H.-C., Knowlton, B.J., Wu, A.D., Iacoboni, M., Ye, Y.-L., Huang, S.-L., Chiang, M.-C., Contextual interference enhances motor learning through increased resting brain connectivity during memory consolidation, NeuroImage (2018), doi: 10.1016/ j.neuroimage.2018.06.081. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Contextual interference enhances motor learning through increased resting brain connectivity during memory consolidation
a
Department of Physical Therapy and Assistive Technology, National Yang-Ming University, Taipei, 112, Taiwan
b c
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Chien-Ho (Janice) Lina,b, Ho-Ching Yangc , Barbara J. Knowltond, Allan D. Wue,f, Marco Iacobonif,g, Yu-Ling Yec,h, Shin-Leh Huangc, and Ming-Chang Chiangc
Yeong-An Orthopedic and Physical Therapy Clinic, Taipei, 112, Taiwan
Department of Biomedical Engineering, National Yang-Ming University, Taipei, 112, Taiwan
d
Department of Psychology, University of California, Los Angeles, California 90095, USA
e
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Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA
f
Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, California 90095, USA Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California 90095, USA
h
Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, 613, Taiwan
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g
Submitted to NeuroImage: April 18, 2018 Revised Version submitted: June 11, 2018 Abbreviated title: Contextual interference enhances brain connectivity Figures: 6, Tables: 4
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Email: Chien-Ho Lin ([email protected]), Ho-Ching Yang ([email protected]), Barbara J. Knowlton ([email protected]), Allan D. Wu ([email protected]), Marco Iacoboni ([email protected]), Yu-Ling Ye ([email protected]), Shin-Leh Huang ([email protected]), Ming-Chang Chiang ([email protected])
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Address Correspondence: Ming-Chang Chiang, Department of Biomedical Engineering, National Yang-Ming University, No.155, Sec.2, Linong Street, Taipei, 112, Taiwan, Phone 886-2-2826-7110, Fax 886-2-2821-0847, Email: [email protected]
ACCEPTED MANUSCRIPT 2 ABSTRACT (250 words) Increasing contextual interference (CI) during practice benefits learning, making it a desirable difficulty. For example, interleaved practice (IP) of motor sequences is generally more difficult than
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repetitive practice (RP) during practice but leads to better learning. Here we investigated whether CI in practice modulated resting-state functional connectivity during consolidation. 26 healthy adults (11 men/15 women, age = 23.3±1.3 years) practiced two sets of three sequences in an IP or RP condition
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over 2 days, followed by a retention test on Day 5 to evaluate learning. On each practice day,
functional magnetic resonance imaging (fMRI) data were acquired during practice and also in a resting
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state immediately after practice. The resting-state fMRI data were processed using independent component analysis (ICA) followed by functional connectivity analysis, showing that IP on Day 1 led to greater resting connectivity than RP between the left premotor cortex and left dorsolateral prefrontal cortex (DLPFC), bilateral posterior cingulate cortices, and bilateral inferior parietal lobules. Moreover, greater resting connectivity after IP than RP on Day 1, between the left premotor cortex and the
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hippocampus, amygdala, putamen, and thalamus on the right, and the cerebellum, was associated with better learning following IP. Mediation analysis further showed that the association between enhanced resting premotor-hippocampal connectivity on Day 1 and better retention performance following IP
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was mediated by greater task-related functional activation during IP on Day 2. Our findings suggest
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that the benefit of CI to motor learning is likely through enhanced resting premotor connectivity during the early phase of consolidation.
Keywords: desirable difficulty, contextual interference, serial reaction time task, independent component analysis, resting-state networks, functional magnetic resonance imaging
ACCEPTED MANUSCRIPT 3 1. INTRODUCTION When multiple tasks are practiced, the overall difficulty of these tasks may be manipulated by presenting them in a repetitive order or in an interleaved (non-repetitive) order. Practicing tasks in an
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interleaved order (interleaved practice, or IP) is generally more difficult, leading to inferior practice performance but superior retention as compared to practicing the same tasks in a repetitive order (repetitive practice, or RP). This is known as the contextual interference (CI) effect (Shea and Morgan,
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1979), and is an example of desirable difficulty, where conditions of learning that initially impose challenges during practice eventually benefit memory consolidation and learning (Bjork, 1994). This
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CI effect on learning may be accounted for in that interleaved practice, a condition with higher CI, forces the learner to elaborate task-relevant information through inter-task comparisons and thereby establish more readily retrievable memory traces (Lin et al., 2008; Shea and Titzer, 1993). In addition to affecting task performance during practice and retention, the level of CI during practice also modulates offline memory consolidation and the pattern of neural recruitment. For
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example, immediately following practice under high CI, perturbation of the dorsolateral prefrontal cortex (DLPFC) by repetitive transcranial magnetic stimulation (rTMS) attenuated motor skill retention, while rTMS perturbation of the primary motor cortex (M1) attenuated retention following
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practice under low CI (Kantak et al., 2010). This rTMS-perturbation approach allows causal inference
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about specific cognitive functions of a brain region; however, it is unable to delineate the functional network between that region and other distant regions, particularly the deep subcortical structures in the brain (Bolognini and Ro, 2010). To address this issue, we acquired resting-state functional magnetic resonance imaging (rsfMRI) data after the participants practiced multiple motor sequences arranged in an interleaved (IP) or a repetitive (RP) order, and compared the resting-state data with task-based fMRI data acquired during practice. The resting state is defined as when a person is awake but not engaged in any external cognitive tasks (Biswal et al., 1995). The brain in the resting state still remains active and exhibits a
ACCEPTED MANUSCRIPT 4 highly coherent pattern of neuronal activity, where interacting brain regions are integrated as a number of resting-state networks (RSNs) (Albert et al., 2009a; Allen et al., 2014; Biswal et al., 1995; Di and Biswal, 2014; Fox and Raichle, 2007; Guerra-Carrillo et al., 2014). Resting-state connectivity of the
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brain increased after practice may reflect an ongoing process of memory consolidation. This was supported by the study of Albert et al., where resting-state functional connectivity in the fronto-parietal area increased after the participants learned a visuomotor tracking task by adapting their joystick
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movements to a novel relationship between the joystick and the cursor displayed on the screen (the ‘motor learning’ group), but remained unchanged in the control participants who performed similar
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tracking movements with a veridical cursor feedback of the joystick (the ‘motor performance’ group) (Albert et al., 2009b). Moreover, Gregory et al. found that greater increase in resting functional connectivity in bilateral motor cortices after motor sequence tasks was associated with better memory consolidation and retention (Gregory et al., 2014). In addition, memory consolidation may be enhanced by retrieval of the practiced tasks (Robertson et al., 2004; Roediger and Butler, 2011), suggesting that
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the influences of resting brain connectivity on learning may be modulated by task-dependent brain activity. Nevertheless, how the resting and active states of the brain interact to support motor learning is still largely unknown.
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In this paper we investigated whether introducing CI as a desirable difficulty during motor skill
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practice would enhance functional connectivity across RSNs during the consolidation phase. We applied independent component analysis (ICA) to resting-state fMRI data to identify RSNs after practice, compared differences in functional connectivity of the RSNs with respect to the IP versus the RP conditions, and examined the associations between such differences in RSN connectivity and benefits of CI to learning. We further investigated how the resting and active states of brain systems contribute to CI benefits to motor learning. Specifically, we applied mediation analysis to examine whether the association between RSN connectivity and CI effects on retention performance was mediated by task-related activity during practice.
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2. METHODS
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2.1 Participants Twenty-six right-handed adults (11 men and 15 women, age = 23.3±1.3 years, education = 17.0±1.1 years, mean±SD) gave written informed consent to participate the current study approved by the local
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Institutional Review Board. They were recruited from National Yang-Ming University and the adjacent community. Participants were excluded if they were a musician or a professional typist, scored less
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than 28 on the Mini-Mental State Exam (MMSE; (Folstein et al., 1975)), or had any contraindications to MRI, uncorrected vision loss, or significant medical conditions that prevented them from performing the task.
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2.2 Experimental protocols for the serial reaction time (SRT) task
The participants practiced the serial reaction time (SRT) task during fMRI scanning on two consecutive training days (Days 1 and 2), and were tested for their delayed retention performance on Day 5.
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Previous work has shown that the effects of CI are most pronounced after an extended delay (see (Lee and Simon, 2004) for a review). Moreover, in our previous study on a different sample of participants
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we found that interleaved practice enhanced task-based functional connectivity with respect to the premotor and dorsolateral prefrontal cortices on Day 2 but not on Day 1 (Lin et al., 2013). The finding suggests that some neural substrates of the CI effect require more extended practice and may not be identified in a single-day experiment. Practice and retention were conducted in the morning on each day between 9 and 11 am. The experimental design is explained in Fig. 1, as has been reported in our previous study on a different sample of participants (Lin et al., 2011; Lin et al., 2012a, b; Lin et al., 2013; Lin et al., 2016). On each trial of the SRT task the participants were instructed to respond to one
ACCEPTED MANUSCRIPT 6 of three different 4-element sequences using the non-dominant (left) hand as quickly and as accurately as possible, where each sequence was represented by a specific permutation of 4 colored circles (Fig. 1A). This experimental procedure was similar to that used in (Cross et al., 2007) and (Immink and
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Wright, 1998). The use of the non-dominant hand in our experiments facilitates comparisons with the study of Cross et al. (Cross et al., 2007), which first investigated the neural substrates of the CI effect on motor sequence learning using fMRI, and our previous studies (Lin et al., 2011; Lin et al., 2012a, b;
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Lin et al., 2013; Lin et al., 2016). A custom-designed program written using the Psychtoolbox (version 3.0.12, http://psychtoolbox.org; RRID:SCR_002881) for MATLAB (MATLAB 2011b, the Mathworks
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Inc., Natick, Massachusetts, USA; RRID:SCR_001622) controlled the appearance of the colored circles and recorded the participants’ responses. The response time, defined as the time between stimulus onset and key press, were recorded for each key press. The total response time for every 4element sequence trial was calculated by adding up the response time of each of the four key presses. During the practice phases (Days 1 and 2), each participant was assigned to either the Repetitive
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practice (RP) or Interleaved practice (IP) condition. In the RP condition, the three sequences were presented in a repetitive order, while in the IP condition, the sequences were presented in an interleaved order as illustrated in Fig. 1B. There were three task-based fMRI sessions followed by one resting-state
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fMRI session on Days 1 and 2 respectively. During each task-based fMRI session the participants
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practiced 54 trials of sequences, while during the resting-state session, they were instructed to remain relaxed but alert, with their eyes kept open. We applied a block design for the task-based fMRI, with 18-second task blocks interleaved with 18-second rest blocks. Each of the 6 sequences in any fMRI task block was presented for a fixed duration of 3 seconds, so that each task block contained 6 trials of the SRT task. If the participant completed a sequence before 3 seconds, 4 transparent circles would appear on the screen to keep the participants’ attention and reduce eye movements. During the rest blocks, the circles were replaced by a fixation cross in the center of the screen. The use of the block design here was to facilitate comparisons of brain activity between different levels of CI (IP versus RP)
ACCEPTED MANUSCRIPT 7 on each day, rather than to differentiate the hemodynamic responses associated with single neural events such as the effects of color or position of each element in a sequence. In our experiment, the participants in the RP condition practiced each of the three sequences in each fMRI session on Days 1
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and 2, while those in the IP condition practiced the three sequences in an interleaved order in each of the three sessions (Fig. 1B). On this time scale, differences in brain activation of interest were
relatively static, and therefore using a block design was more likely to provide sufficient power and
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efficiency to detect such differences in BOLD signal as compared to an event-related design (Friston et al., 1999; Lazar, 2008). Moreover, by using block-design fMRI in our previous studies, we had also
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successfully identified functional brain networks that supported the effects of CI on motor sequence learning (Lin et al., 2011; Lin et al., 2012b; Lin et al., 2013; Lin et al., 2016). On Day 5, there were also three fMRI sessions. In the first two sessions the participants were tested with the three sequences they had practiced on Days 1 and 2, presented either in a repetitive order (denoted by Repetitive testing condition, abbreviated as RTC) or in an interleaved order (denoted
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by Interleaved testing condition, abbreviated as ITC). Each session consisted of 36 trials of sequences. Compared to 54 trials per session on Days 1 and 2, the duration of the fMRI sessions on Day 5 was shortened in order to limit further learning (Cross et al., 2007). The third session consisted of a total of
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36 trials for three novel sequences to assess whether learning was specific to the training sequences.
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The arrangement of fMRI task and rest blocks was the same as that in Days 1 and 2. We applied a within-participant cross-over design to control for inter-participant variability in learning ability, with the order of practice (IP versus RP) counter-balanced. 13 participants (6 men and 7 women, age = 23.2±1.4 years, education = 17.0±1.4 years, mean±SD) started with the RP followed by the IP condition at least two weeks later, while the remaining 13 participants started with the IP followed by the RP condition (5 men and 8 women, age = 23.5±1.1 years, education = 17.1±1.0 years). There was no difference in age (P = 0.45) or years of education (P = 0.87) between these two participant groups.
ACCEPTED MANUSCRIPT 8 The retention conditions (ITC versus RTC) were also counter-balanced across the participants (13 participants started with either testing condition).
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2.3 Imaging parameters All participants received structural and functional brain scanning on a Siemens-Trio 3T scanner (Erlangen, Germany) using a 32-channel head coil. The structural image data were acquired using a 3D
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T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) sequence (TR/TI/TE =
2200/900/3.4 ms; flip angle = 10°; FoV = 256 × 192 mm; acquisition matrix = 256 × 192 × 176; voxel
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dimensions = 1 × 1 × 1 mm). Both the task-based and resting-state fMRI scans were acquired in a transverse orientation using the gradient-echo echo planar imaging (EPI) using the same acquisition parameters. These acquisition parameters were: TR/TE = 2000/30 ms; flip angle = 90°; FoV = 192 × 192 mm; acquisition matrix = 64 × 64 × 34; in-plane resolution = 3 mm × 3 mm; slice thickness = 4 mm with 1-mm gap. On Days 1 and 2, each task-based fMRI session consisted of 157 EPI volumes
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with a scan time of 314 seconds. During the resting-state fMRI session, 174 EPI volumes were acquired within a scan time of 348 seconds. On Day 5, each task-based fMRI session lasted for 206 seconds, yielding 103 EPI volumes. There was no resting-state session on Day 5. The first 4 EPI
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approach equilibrium.
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volumes of each task-based or resting-state fMRI session were discarded to allow for magnetization to
2.4 Pre-processing of functional magnetic resonance images Both task-based and resting-state fMRI data were pre-processed using the Statistical Parametric Mapping software (SPM8, Wellcome Department of Cognitive Neurology, London, UK; RRID:SCR_007037). To correct for motion artifacts, functional image data were realigned to the first volume in each functional run and then resliced with 4th-degree B-Spline interpolation (Friston et al.,
ACCEPTED MANUSCRIPT 9 1995). None of the participants had scans with head motions greater than 2 mm. After realignment, the resulting mean images of each participant were normalized to the standard Montreal Neurological Institute (MNI) EPI template (Evans et al., 1993). The normalization parameters were then applied to
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all the functional images of that subject. The normalized images were resampled to 3 x 3 x 3 mm3 per voxel, and subsequently spatially smoothed with an isotropic Gaussian filter with full width at half
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2.5 Analysis of task-based fMRI data
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We applied the general linear model (GLM) in SPM8 (Friston et al., 1995) to the task-based fMRI data to derive a t-statistic that estimated regional hemodynamic changes induced by neuronal activation, the so-called blood-oxygen-level-dependent (BOLD) signal, in each participant. The GLM was constructed by convolving a canonical hemodynamic response function (HRF) with a series of boxcars for the SRT tasks with the baseline occurring during the rest blocks. Note that explicitly modeling the rest blocks
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with a separate predictor and a column of the constant offset will over-parameterize the model, making the GLM either unsolvable or highly sensitive to noise in the data (Pernet, 2014). The six parameters of head motion obtained from rigid-body registration were included in the GLM as nuisance variables. An
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additional parametric regressor with the mean response time for each task block was applied to ensure
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that any differences in brain activity during practice and retention was due to the influences of the practice conditions but not to differences in the response time. The first-level contrast map for differences in BOLD signal between the IP and RP conditions in individual participants was then entered into a group-level random-effects model to compare the group differences in BOLD signal between these two practice conditions on each day, and to test the association between the CI effects on brain activation and benefits to learning. We used the RP-minus-IP difference in response time of the first 18 trials (i.e., first 3 blocks) under the Interleaved testing condition (ITC) on Day 5, denoted by D5RTRP-IP, to represent the magnitude of the CI effect on motor learning in the correlation analyses in
ACCEPTED MANUSCRIPT 10 this and the subsequent sections. This was because the results of the current study showed that the effect size of CI was the largest during this period, and therefore this measure provided the best index of the benefit of practice under CI. As such, a group-level correlation map was obtained by computing
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the association between the IP-minus-RP BOLD contrast values during practice and D5RTRP-IP during retention. Multiple comparisons across the whole brain volume were corrected using permutation
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testing, detailed in Section 2.7.2 below.
2.6 Analysis of resting-state fMRI data
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2.6.1 Identification of resting-state networks (RSN) using independent component analysis (ICA) Decomposition of rsfMRI data was performed using a group-level spatial ICA implemented by the Group ICA for fMRI Toolbox (GIFT v4.0a; http://mialab.mrn.org/software/gift/; RRID:SCR_001953)(Calhoun et al., 2009), which transformed the fMRI data into a linear combination
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of source components that were maximally statistically independent in space. The ICA method is popular for analyzing resting-state brain connectivity because it is free of a priori assumptions about the spatio-temporal characteristics of fMRI signal and noise (Beckmann and Smith, 2004; Jacobs et al.,
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2015). The fMRI data of all the participants were concatenated prior to ICA, with the two practice
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conditions (RP and IP) and the two practice days (Days 1 and 2) of a participant treated as separate sessions for that participant. This approach ensured that the estimation of independent sources for the fMRI data would not be biased by different practice days or conditions. Moreover, differences in resting-state functional connectivity between IP and RP could be compared on the same spatial component. These concatenated group-level fMRI data were then entered into a two-step principal component analysis (PCA) to reduce the dimensionality of the data, giving 50 and then 25 principal components. Estimation of independent sources for the rsfMRI data was performed on these 25 principal components using the Infomax algorithm (Bell and Sejnowski, 1995), yielding 25 group-level
ACCEPTED MANUSCRIPT 11 spatially independent component (IC) maps and their corresponding time courses. Finally, the grouplevel ICA estimates were back-reconstructed into participant-level spatial IC maps and time courses respectively for each practice condition and each practice day using the GICA3 back-reconstruction
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method (Erhardt et al., 2011). This back-reconstruction step accounted for data variability across the participants, and also facilitated comparisons of resting-state connectivity between IP and RP
independently for each practice day. For subsequent analyses, each participant-level IC map or time-
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course data was normalized into z-scores based on the mean and standard deviation across the brainonly voxels of that IC map or time-course data. Each z-transformed IC map then represented a resting-
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state network (RSN), where the magnitude of the z-score of a voxel in the IC map gauged the strength of functional connectivity between that voxel and the RSN (Jacobs et al., 2015). We selected RSNs from the 25 group-level IC maps based on two criteria: (1) the IC maps had low spatial overlap with known physiological, vessels or sinuses, motion, and susceptibility artifacts as determined by visual inspection; (2) their time courses were driven by low-frequency fluctuations. For
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criterion (2), we adopted two metrics derived from the frequency power spectrum of the time course of an IC: one was the dynamic range of the power spectrum, defined as the difference in frequencies corresponding to the peak power and minimum power; the other was the ratio of low frequency to high
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frequency power, defined as the ratio of the integral of spectral power below 0.10 Hz to the integral of
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power between 0.15 and 0.25 Hz (Robinson et al., 2009). An IC map was selected as an RSN if the dynamic range in the power spectrum of the corresponding time course was greater than 0.035, and the ratio of low frequency to high frequency power greater than 3.5 (Allen et al., 2011). 9 ICs were selected as our RSNs of interest (see Section 3.3 for details). The significance of each selected RSN was determined by applying a group-level one-sample t-test to the corresponding participant-level ztransformed IC maps. Multiple comparisons across a selected RSN and across all the 9 RSNs were corrected using permutation testing, detailed in Sections 2.7.2 and 2.7.3 below. 2.6.2 Associations between RSN connectivity and learning
ACCEPTED MANUSCRIPT 12 To assess the functional significance of the IC maps, we compared the difference in the resting-state connectivity strength between the IP and RP conditions with respect to each IC, and investigated whether such difference in the connectivity strength was associated with the CI effect on learning. For
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each participant and each IC, a map of the voxelwise IP-minus-RP difference in the z-scores was created. The significance of the z-difference maps for each IC was then assessed using a group-level one-sample t-test. Voxels with a positive t-value indicate that the connectivity strength was greater in
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the IP than the RP condition, and vice versa for a negative t-value. We next applied linear correlation analysis at each voxel of the IP-minus-RP z-difference map for each IC to identify regions where the
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association between the IP-minus-RP z-difference and the CI effect on learning, represented by D5RTRP-IP, was significant.
Given that IP and RP led to different learning curves particularly during the initial trials on Day 1 (see Results and Fig. 1), we also correlated the voxelwise IP-minus-RP z-difference with the IPminus-RP difference in response time of the first 18 trials (i.e., first 3 blocks) on Day 1, denoted by
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D1RTIP-RP, to test whether the difference in the resting-state connectivity strength between IP and RP was driven by the difference in initial performance between these two practice conditions. In this paper we set the direction of subtraction to RP-minus-IP for the difference in response time on Day 5
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(D5RTRP-IP) but IP-minus-RP for that on Day 1 (D1RTIP-RP) to make the values of these two measures
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positive because IP led to inferior practice performance but better retention compared to RP (see Results). Multiple comparisons across a single RSN and across all the 9 RSNs were corrected using permutation testing, detailed in Sections 2.7.2 and 2.7.3 below. 2.6.3 Delineation of resting-state functional connectivity with respect to the left premotor cortex Following ICA described in the above sections, we performed a secondary exploratory analysis that assessed the whole-brain resting-state functional connectivity of the left premotor cortex using the REST toolbox (REST V1.8; http://www.restfmri.net) (RRID:SCR_009641). The left premotor cortex was selected as the seed region of interest (ROI) because the association between the IP-minus-RP
ACCEPTED MANUSCRIPT 13 difference in the RSN connectivity strength and the CI effect on learning was significant in this area (see Results). Moreover, our previous study showed that a greater functional connectivity strength with respect to the premotor cortex during the IP than the RP condition was found to be associated with a
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stronger CI effect on motor learning (Lin et al., 2013). The mean time-series data were extracted from a sphere of 4-mm radius centered on the MNI coordinates [-27, -4, 67] in the left premotor cortex, which was the peak voxel of the above significant association between the RSN connectivity strength and the
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CI effect on learning. The pre-processed (normalized and smoothed) resting-state fMRI data were detrended to remove baseline drifts. A general linear model was then applied to regress out the effects
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of the 6 head-motion parameters, and the white matter and the cerebrospinal fluid signals from the resting-state data. The whole-brain mean signal, although generally regarded as a nuisance signal, was not included in the above regression model because it might serve as an index of baseline brain metabolism (Thompson et al., 2016). Finally, the resting-state data were filtered using a bandpass Butterworth filter (0.01 – 0.08 Hz) to ensure that the data were dominated by low-frequency
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fluctuations. For each participant, a participant-level whole-brain correlation r-map was obtained by correlating the time-series data of the left premotor seed ROI and every voxel in the brain, and then the r-map was converted to a z-map using the Fisher transformation (Fisher, 1915). In the group-level
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analysis, the IP-minus-RP difference at each voxel of the above participant-level z-map was further
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correlated with D5RTRP-IP, yielding a group-level correlation map. Multiple comparisons across the whole brain volume were corrected using permutation testing, detailed in Section 2.7.2 below.
2.7 Experimental design and statistical analysis 2.7.1 Behavioral data Differences in response time between the IP and RP conditions, i.e., D5RTRP-IP and D1RTIP-RP, were compared using a paired t-test (degree of freedom = 25) implemented in the statistical software package SPSS 24 (IBM Corp, Armonk, NY; RRID:SCR_002865). In addition to D5RTRP-IP, we also
ACCEPTED MANUSCRIPT 14 compared the forgetting score between the IP and RP conditions, to infer the influence of CI on memory consolidation (Censor et al., 2010; Lin et al., 2010). The forgetting score was defined as the difference in response time of the first block of ITC or RTC during retention minus the last practice
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block on Day 2. Both the difference in retention response time and the forgetting score have been applied to demonstrate the CI effect on learning (Lin et al., 2010; Lin et al., 2012a; Shea and Morgan, 1979). Performance accuracy or error rate was not included as a behavioral measure to assess the CI
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effect on learning or as a nuisance covariate in the GLM to estimate fMRI BOLD signal because the 4element SRT task was relatively easy and participants made few errors. Previous work examining the
were infrequent (Fraser et al., 2009).
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effect of CI on SRT learning also found differences in response time rather than accuracy as errors
2.7.2 Correction for multiple comparisons at the whole-brain level
For the image data, all the group-level analyses, including t-tests and linear regressions, were implemented in the Statistical nonParametric Mapping toolbox (SnPM13; http://warwick.ac.uk/snpm)
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(RRID:SCR_002092) (Nichols and Holmes, 2001), where multiple comparisons across all the voxels in the image were corrected using permutation testing to control the family-wise error (FWE) at a cluster level of 0.05. This means that only 5% of the clusters that were declared to be significant tended, on
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average, to be false positive findings. This permutation approach has been shown to yield accurate
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multiple comparison correction (Eklund et al., 2016). Here a cluster was defined as a set of contiguous voxels that had the size of the tested effect surpassing a cluster-defining threshold, set to P < 0.001. A total of 5000 permutations were generated by randomly assigning the test contrast of IP-minus-RP or vice versa to each participant in the group-level analyses. For each permutation, significant clusters were generated from the new group-level t-map by applying the same cluster-defining threshold. The FWE, or corrected P-value, of a cluster in the original t-map was then defined as the proportion of the 5000 permuted t-maps with their maximal cluster size greater than the size of that cluster in the original t-map. Only clusters that passed the FWE < 0.05 threshold were displayed in the Figures.
ACCEPTED MANUSCRIPT 15 2.7.3 Correction for multiple comparisons across the RSNs Given that the group-level analyses in Sections 2.6.1 and 2.6.2, including t-tests that assessed the significance of the RSNs (IC maps) and linear regressions that tested the associations between RSN
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connectivity and learning, were performed on each of the 9 selected RSNs separately, for findings that were significant after correction for multiple comparisons at the single-RSN level as described in Section 2.7.2 above, we further controlled FWE at the inter-RSN level using the ‘y-concatenation
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correction’ method to correct for multiple comparisons across the 9 RSNs (Littow et al., 2015). For each participant, all the 9 z-transformed IC maps (for statistical tests in Section 2.6.1) or the IP-minus-
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RP z-difference maps (for statistical tests in Section 2.6.2) were concatenated along the y direction. The resulting concatenated z-map was then entered into permutation testing with the same cluster-defining threshold (P < 0.001) and the total number of permutations (set to 5000) as those in Section 2.7.2 above, yielding the permutation distribution of the maximal cluster size for the concatenated z-map. Because the z-map for each IC was separated with the others by voxels outside the brain with the value
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set to zero, the non-zero voxels in all the z-maps remained spatially disjointed and as such the maximal cluster size of the concatenated z-map for each permutation was the volume of the largest significant cluster obtained from all the component z-maps. The inter-RSN cluster threshold was then defined as
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the 95th percentile of maximum cluster sizes across the 5000 permutations for the concatenated z-map,
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which controlled the FWE across the RSNs at the level of 0.05.
2.8 Mediation analysis
Given that correlations between the CI effect on sequence learning (the RP-minus-IP contrast in response time, or D5RTRP-IP) and the IP-minus-RP difference in the resting-state premotor connectivity were found on Day 1, while correlations between the CI effect on learning and the IP-minus-RP difference in task-related BOLD signal were found on Day 2 (see Results), we hypothesized that the influence on retention performance from the post-practice memory consolidation process on Day 1 may
ACCEPTED MANUSCRIPT 16 be mediated by the task-practice process on Day 2. To test this hypothesis, we applied a three-variable X-M-Y mediation model where the IP-minus-RP difference in functional connectivity between the left premotor cortex and an ROI on Day 1 was treated as the source variable (X), the IP-minus-RP
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difference in task-related BOLD signal in another selected ROI on Day 2 as the mediator (M), and D5RTRP-IP as the outcome variable (Y) (see Fig. 6A). The influence of X on Y under the presence of the mediator M is represented by the linear regression of Y against X controlling for M, where the
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partial regression coefficients b and c′ represent the indirect paths for M to Y and X to Y respectively. The path coefficient a represents the direct effect of X on M, and is the regression coefficient of M
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against X. The unmediated path for X to Y is represented by the regression coefficient c. The ROI for the source variable X was selected from a 4-mm sphere centered at the right putamen [24, 2, 10] and right hippocampus [33, -19, -14] respectively, where the MNI coordinates indicate the peaks of significant correlations between the CI effect and the IP-minus-RP difference in resting-state premotor connectivity (see Table 4). The ROI for the mediator M was selected respectively from the left
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entorhinal cortex [-24, 5, -14] and the left putamen [-15, 17, -8], where the association between the CI effect and the IP-minus-RP difference in task-related BOLD signal was significant (see Table 1).
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These regions were selected as ROIs because (1) they are crucial for motor sequence learning, and (2) are involved in different memory systems. The putamen is specifically involved in motor sequence
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learning (Orban et al., 2010) and plays a key role in procedural memory consolidation (Packard and Knowlton, 2002), while the hippocampus and entorhinal cortex are active in both explicit and implicit motor sequence learning (Schendan et al., 2003) and are responsible for declarative memory (Squire and Zola, 1996). Including these ROIs in the mediation analysis then facilitated the investigation of how brain regions in different memory systems as well as during resting or active states interact to mediate the CI effect on motor sequence learning (Albouy et al., 2008). The mediation analysis was performed using the function library in the BRAVO Mediation toolbox (https://sites.google.com/site/bravotoolbox/documentation/bravo-mediation), where the significance
ACCEPTED MANUSCRIPT 17 and confidence interval of the above path coefficients was estimated using an accelerated biascorrected bootstrapping procedure with 10000 samples (DiCiccio and Efron, 1996). If the effect of X on Y is mediated by M, then (i) the strength of the direct influence of X on Y (represented by the value
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of c) decreases significantly after M is added (where the mediated effect of X on Y is represented by c′), which means that c − c′ is greater than zero, and (ii) the path coefficients for X to M and for M to Y, represented by a and b respectively, are both significant. Note that (c − c′) is mathematically equivalent
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to a × b (Baron and Kenny, 1986; Ide and Li, 2011; MacKinnon et al., 2007). If c′ is not significant (P > 0.05), it indicates that X no longer directly influences Y, and the effect of X on Y is completely
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mediated by M. Otherwise, it means that there is some residual effect of X on Y that cannot be accounted for by M, and M is then a partial mediator (Ide and Li, 2011). The significance threshold for path coefficients a, b, c, and a × b was set to P < 0.05/4 = 0.0125, the Bonferroni’s adjustment for the 4 possible X-versus-M pairs (the right putamen and right hippocampus for X, and the left entorhinal
3. RESULTS
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cortex and left putamen for M).
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3.1 CI effects on motor sequence learning
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Figures 1C and D show that the participants exhibited the pattern of the CI effect on SRT task learning, as reported in our previous study on another sample of participants (Lin et al., 2011). Performance during practice was better under the RP than the IP conditions, where the response time was faster during RP than during IP (mean±SEM: RP = 962.2 ± 41.4, IP = 1191.2 ± 43.8 ms, averaged across all trials on Days 1 and 2; t(25) = 5.86, P = 4 × 10-6, paired t-test; Fig. 1D). The difference was greater when the two practice conditions were compared during the first 18 trials (i.e., the first 3 blocks) on Day 1 (RP = 1194.1 ± 51.3, IP = 1466.9 ± 49.6 ms, averaged across the first 18 trials on Day 1; t(25) =
ACCEPTED MANUSCRIPT 18 3.82, P = 0.0008, paired t-test). However, this pattern was reversed during retention on Day 5 when the practiced sequences were presented in an interleaved order (Interleaved testing condition, abbreviated as ITC, with a total of 36 trials), where the response time of the sequences practiced in the IP condition
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became faster (RP = 1169.5 ± 46.2, IP = 1059.4 ± 40.9 ms; t(25) = 3.15, P = 0.004, paired t-test; Fig. 1D). The CI effect was not significant when the practiced sequences were presented in a repetitive order during retention (Repetitive testing condition, abbreviated as RTC, with a total of 36 trials; RP =
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969.8 ± 45.8, IP = 997.4 ± 42.5 ms; t(25) = 1.05, P = 0.3, paired t-test; Fig. 1D). Considering possible confounding effects due to re-learning during retention, which may reduce the difference in response
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time between the IP and RP conditions (Cross et al., 2007), we divided the retention trials into the early (the first half of the 36 trials) and late (the last half of the 36 trials) trials respectively in ITC and RTC (Phelps et al., 2004). For ITC, the CI effect was more significant in the early (RP = 1210.7 ± 48.5, IP = 1068.4 ± 42.5 ms; t(25) = 3.71, P = 0.001, paired t-test) than the late (RP = 1128.3 ± 42.5, IP = 1050.3 ± 39.5 ms; t(25) = 2.21, P = 0.04, paired t-test) trials (Fig. 1E, left), where the early trials had a greater
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RP-minus-IP difference in response time (early = 142.3 ± 38.3, late = 77.9 ± 35.3 ms; t(25) = 2.74, P = 0.01, paired t-test; Fig. 1E, right). For RTC, the response time following IP was not different from that following RP, for both the early (the first 18 trials; RP = 979.8 ± 46.2, IP = 1019.5 ± 43.8 ms; t(25) =
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1.40, P = 0.17, paired t-test) or late (the last 18 trials; RP = 959.9 ± 45.6, IP = 975.4 ± 40.9 ms; t(25) =
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0.51, P = 0.6, paired t-test) trials (Fig. 1F). Taken the above results together, we therefore used the RPminus-IP difference in response time with respect to the early trials of the ITC during retention, denoted by D5RTRP-IP, to represent the CI effect on motor learning, and we tested the correlations between this measure and imaging measures in the subsequent analyses. For the novel sequences, there was a significant increase in the response time from the retention to the novel-task session on Day 5 in both the RP (retention under ITC = 1169.5 ± 46.2 or under RTC = 969.8 ± 45.8 versus Novel = 1271.2 ± 46.2 ms, P = 0.004 and 4 × 10-9 respectively) and IP (retention under ITC = 1059.4 ± 40.9 or under
ACCEPTED MANUSCRIPT 19 RTC = 997.4 ± 42.5 versus Novel = 1259.1 ± 44.5 ms, P = 2 × 10-7 and 4 × 10-9 respectively) conditions. This indicated that motor sequence learning had occurred in both practice conditions. Nevertheless, there was no significant difference in response time between RP and IP (RP = 1271.2 ±
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46.1, IP = 1259.1 ± 44.5 ms; t(25) = 0.36, P = 0.7, paired t-test; figure not shown), consistent with our previous finding that the CI effect is sequence-specific (Lin et al., 2011).
Consistent with the above findings showing better retention following IP, IP also led to less
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forgetting of the practiced sequences, or a lower forgetting score, than RP, no matter whether the forgetting score was obtained by comparing the last practice block on Day 2 with the first task block
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under ITC or RTC during retention (ITC: RP = 386.5 ± 40.4, IP = 49.2 ± 37.9 ms; t(25) = 6.00, P = 3 × 10-6; RTC: RP = 131.1 ± 34.0, IP = −3.1 ± 32.3 ms; t(25) = 2.87, P = 0.008; paired t-test; Fig. 1G). The order of practice (practicing the SRT task first in the IP versus first in the RP condition) did not influence the CI effect on motor sequence learning. The IP-minus-RP or RP-minus-IP differences
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in response time did not differ significantly between the IP-first and the RP-first groups during practice (IP-minus-RP difference in the response time: IP-first = 206.2 ± 62.8, RP-first = 251.9 ± 48.4 ms, averaged across all trials on Days 1 and 2; t(24) = 0.58, P = 0.57, two-sample t-test), or under ITC (RP-
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minus-IP difference in the response time: IP-first = 141.9 ± 57.8, RP-first = 78.4 ± 39.6 ms; t(24) = 0.91, P = 0.37, two-sample t-test) or RTC (RP-minus-IP difference in the response time: IP-first = −3.9
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± 38.2, RP-first = −51.3 ± 36.6 ms; t(24) = 0.90, P = 0.38, two-sample t-test) during retention.
3.2 Effects of practice conditions on the associations between task-related BOLD signal and learning Figure 2 shows that on both Days 1 and 2, practicing sequences in the IP condition resulted in greater BOLD signal in bilateral premotor (BA 6) and precuneus (BA 7) cortices compared to practicing
ACCEPTED MANUSCRIPT 20 sequences in the RP condition. On Day 5, there was no significant difference in BOLD signal between IP and RP during retention of the practiced sequences or during test with the novel sequences (figure not shown). Figure 3 further shows that a greater IP-minus-RP difference in BOLD signal on Day 2
IP)
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was associated with a greater RP-minus-IP difference in response time during retention (i.e., D5RTRPin bilateral precuneus cortices and the left entorhinal cortex (BA 34), insula (BA 48), putamen, and
orbitofrontal gyrus (BA 11), with the peak voxels listed in Table 1. However, the association between
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the IP-minus-RP difference in BOLD signal on Day 1 and D5RTRP-IP was not significant.
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3.3 Effects of practice conditions on the associations between RSN connectivity and learning Nine independent components (ICs) were selected as our RSNs of interest, including the precuneus network, primary visual networks (two ICs, denoted by I and II respectively), sensorimotor networks (two ICs, denoted by I and II respectively), the right and the left frontoparietal networks, occipitotemporal network, and the frontal network (Fig. 4). Each RSN was significant after correction for
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multiple comparisons at the single-RSN and further at the inter-RSN level. The inter-RSN cluster threshold for these 9 RSNs was 98 voxels, or 2646 mm3. Brain regions included in the RSNs are listed in Table 2. We next investigated whether CI in practice modulated the association between the
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functional connectivity in the RSNs and the retention performance. For each RSN, we compared the
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post-practice resting-state functional connectivity between IP and RP on Day 1 or 2, i.e., the differences in the voxelwise z-scores, and then tested the association between the IP-minus-RP zdifference on Day 1 or 2 and D5RTRP-IP. None of the 9 RSNs had significant differences in the connectivity strength between IP and RP on either Day 1 or 2 (multiple comparisons corrected at the single-RSN level; figure not shown). The association between the IP-minus-RP z-difference on Day 1 and D5RTRP-IP was significant at the single-RSN level in the left premotor cortex (Brodmann area 6) belonging to the sensorimotor network I (Fig. 5A). This means that greater connectivity between the left premotor cortex and other regions in the sensorimotor network I after IP than RP was associated
ACCEPTED MANUSCRIPT 21 with stronger CI effects during retention. However, this association did not pass the more stringent inter-RSN cluster threshold (equal to 46 voxels, or 1242 mm3) for correction of multiple comparisons across the 9 RSNs. Even so, the left premotor area identified above may still be used as a seed ROI (a
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4-mm sphere centered around the peak voxel for the association at [-27, -4, 67] in MNI coordinates) for subsequent explorations of voxel-wise functional connectivity with respect to the left premotor cortex. There was no significant association, with multiple comparisons corrected at the single-RSN level,
8 RSNs on Day 1 or any of the 9 RSNs on Day 2.
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between the retention performance on Day 5 and the resting-state connectivity in any of the remaining
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We also tested the association between the IP-minus-RP z-difference on Day 1 and D1RTIP-RP, where the latter represents the difference in initial performance between the two practice conditions. None of the 9 RSNs had a significant association between the IP-minus-RP difference in RSN connectivity and D1RTIP-RP (multiple comparisons corrected at the single-RSN level; figure not shown),
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indicating that RSN connectivity was not driven by initial performance.
3.4 Resting-state functional connectivity with respect to the left premotor cortex Figure 5B shows the differences between IP and RP in resting-state functional connectivity with the
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left premotor ROI on Day 1. Compared to RP, IP led to greater connectivity between the left premotor
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cortex and the left dorsolateral prefrontal cortex (DLPFC, BA 9 and 46), bilateral posterior cingulate cortices (BA 23), and the bilateral angular gyri/inferior parietal lobules (BA 39 and 40) (Table 3). Correlation analysis further shows that a greater IP-minus-RP difference in functional connectivity on Day 1 was associated with a greater D5RTRP-IP, in the amygdala, hippocampus, putamen, and the thalamus on the right side, and the cerebellar vermis (Figs. 5C and D, and Table 4).
3.5 Mediation effects of the task-related BOLD signal on the association between resting-state premotor connectivity and CI learning benefits
ACCEPTED MANUSCRIPT 22 The mediation analysis shows that the association between the IP-minus-RP difference in Day-1 resting-state left premotor-right hippocampus connectivity and the CI benefit to retention was completely mediated by the IP-minus-RP difference in the BOLD signal in the left entorhinal cortex on
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Day 2 (Fig. 6B). This finding indicates that the linkage between CI benefits and increased resting premotor-hippocampus connectivity after IP was attributable to higher functional activity in the left entorhinal cortex during IP on the next day. In contrast, the mediation effect was non-significant for the
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right putamen (X) versus the left entorhinal cortex (M), for the right hippocampus versus the left
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putamen, and for the right putamen versus the left putamen (Fig. 6C).
4. DISCUSSION
In this paper we analyzed fMRI data acquired during a 5-day motor sequence learning paradigm, showing the contextual interference (CI) effect on motor sequence learning and the underlying offline brain networks. Our results demonstrated that the CI effect was a robust phenomenon that could be
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observed even when retention was tested two days after practice – the IP group had poorer performance during practice but outperformed the RP group during retention. This finding was consistent with what was observed in the classical study of the CI effect by Shea and Morgan (Shea and Morgan, 1979). By
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analyzing fMRI data using independent component analyses (ICA), we identified resting brain
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networks during the post-practice consolidation phase, and found that the difference in resting-state brain connectivity between the interleaved practice (IP) and the repetitive practice (RP) conditions was associated with the CI effect on retention (D5RTRP-IP) but not the initial practice performance (D1RTIPRP).
This suggests that the difference in resting brain connectivity was not driven by the residual effect
of different initial learning curves between IP and RP. Greater connectivity between the left premotor cortex and the hippocampus, putamen, amygdala, thalamus, and the cerebellum after IP compared to RP on Day 1 was associated with better learning during retention. Moreover, using mediation analysis, we found that the association of enhanced retention performance on Day 5 with the increased resting-
ACCEPTED MANUSCRIPT 23 state functional connectivity between the left premotor cortex and right hippocampus after IP on Day 1 was completely mediated by the increased task-related BOLD signal in the left entorhinal cortex during IP on Day 2. These findings suggest that practicing tasks with higher CI as a desirable difficulty
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triggers functional synchronization of the brain during the memory consolidation phase, particularly between the structures important for motor sequence learning (e.g., the premotor cortex and the putamen) and structures more typically associated with declarative memory (e.g., the hippocampus and
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the entorhinal cortex).
The resting-state fMRI scan was performed after the task-based ones since the purpose of the
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current study is to study how CI during practice affects memory consolidation. We can do so by comparing differences in resting brain connectivity after IP versus RP, where the lower-CI RP condition was treated as a baseline. This design is determined by the fact that a main question this study investigates is how task-related brain activity affects brain networks at rest (Albert et al., 2009b; Gregory et al., 2014; Sami et al., 2014). We hypothesized that how the brain processes the two task
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conditions during practice would affect the post-practice phase, which might be construed as a period of memory consolidation. Indeed, our results suggest this is the case: the difference in post-practice resting connectivity between IP and RP was associated with the CI benefits on motor learning, which
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indicates that such difference in resting connectivity between IP and RP did not simply reflect the
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different nature of the tasks but rather differences in learning. Moreover, even though repetitive practice (RP) of sequences seemed more ‘automatic’ compared to IP, this seemingly automatic process was also a consequence of learning. This was evidenced by the significant increase in the response time from the retention task after RP to the novel task on Day 5, which indicates that motor learning had occurred in the RP condition. The RSNs identified during the post-practice resting state contained regions crucial for motor sequence learning, such as the precuneus (Buckner et al., 2008; Utevsky et al., 2014), the fusiform gyrus (Zhang et al., 2014), the dorsolateral prefrontal cortices (DLPFC) (Mary et al., 2017; Yoshida et
ACCEPTED MANUSCRIPT 24 al., 2010), and the premotor cortex (Kantak et al., 2012; Steele and Penhune, 2010; Wade and Hammond, 2015). The premotor cortex was also most associated with the benefits of CI compared to other regions in the RSNs. Greater resting connectivity between the left premotor cortex (in
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sensorimotor network I) and the left DLPFC, bilateral posterior cingulate cortices (PCC), and bilateral angular gyri/inferior parietal lobule (IPL) was found after IP compared to RP on Day 1. The DLPFC, PCC, and IPL are connected as part of the frontal motor network, involved in motor planning and
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execution (Luppino and Rizzolatti, 2000). Enhanced connectivity between these regions and the premotor cortex after IP suggests that consolidation of interleaved sequences facilitates functional
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integration of the motor system. The results from the correlation analysis further support the role of the premotor cortex in linking motor memory consolidation to retention performance. Greater difference in resting connectivity between the premotor cortex and hippocampus, basal ganglia, and cerebellum after IP than RP on Day 1 was associated with better retention performance following IP. This indicates that IP activated both the premotor-basal ganglia and premotor-limbic circuits, where interactions between
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these two circuits consolidated motor memory traces to optimize retention performance (Albouy et al., 2008). It has been well established that motor sequence learning involves early recruitment of brain networks including the cerebellum, basal ganglia, supplementary motor area, and motor and premotor
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cortices (Doyon et al., 2003). The hippocampus and other limbic regions are traditionally associated
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with encoding of declarative memory (Squire and Zola, 1996). Nevertheless, the hippocampus has been found to engage in both explicit and implicit motor sequence learning (Schendan et al., 2003), which possibly reflects the role of the hippocampus in associating discontinuous stimuli and their context across time and space during sequence learning (Poldrack and Rodriguez, 2003). In addition to the enhancement of post-practice functional connectivity, IP was also associated with greater functional activation during practice, a finding consistent with theories of CI effects (for example, the ‘elaborative-processing’ and ‘forgetting-reconstruction’ hypotheses; see below) as well as our previous observation (Lin et al., 2011). Compared to RP, IP led to greater task-related BOLD signal
ACCEPTED MANUSCRIPT 25 in bilateral premotor and precuneus cortices on both Days 1 and 2. Given that the precuneus is involved in processing visuospatial information (Mahayana et al., 2014), the higher activity in the precuneus and premotor cortices during IP suggests that practice with higher CI requires greater integration of
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visuospatial operations to guide motor learning. The association between the relative increase in taskrelated BOLD signal and the retention benefit with respect to IP was significant only on Day 2. By contrast, the association between the increase in post-practice resting connectivity and the retention
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benefit of IP was significant only on Day 1. This indicates that the functional significance of resting and active states of the brain varied at different stages of motor learning. The need to recruit neural
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resources for offline memory consolidation with respect to IP might be greatest immediately after the first day of practice, while task-related functional activity that supported the learning benefits of IP might take longer to develop until a certain proficiency in motor skill had been acquired after two days of practice. In our previous study applying the same 5-day motor sequence-learning paradigm to another cohort of participants, we also found that only on Day 2, the increase in task-related functional
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connectivity between the premotor and primary motor cortices, and between DLPFC and supplementary motor area accounted for the retention benefits of IP (Lin et al., 2013). The mediation analysis shows that the association between the retention benefits with respect to
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IP and enhanced resting premotor connectivity with the right hippocampus on Day 1 was completely
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mediated by increased task-related BOLD signal in the left entorhinal cortex on Day 2. This finding suggests that contextual interference during motor practice enhanced motor learning over a 5-day span by recruiting both the resting and active brain networks that involved the premotor and medial temporal structures. Nevertheless, the association between the retention benefits of IP and the enhanced resting premotor connectivity with the right putamen was not mediated by task-related BOLD signal in the left putamen or entorhinal cortex. A possible explanation for the discrepancy in the mediation effect with respect to the hippocampus and putamen is that the declarative component of motor memory (encoded mostly by the hippocampal formation) decayed relatively quickly and needed to be boosted by practice
ACCEPTED MANUSCRIPT 26 on the next day (Keisler and Shadmehr, 2010). By contrast, the non-declarative component of motor memory (mediated mostly by the putamen) was relatively sustained and as such the influence of enhanced Day-1 premotor-putamen connectivity on retention performance did not depend on functional
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activation in the putamen or entorhinal cortex on Day 2. A straightforward explanation for why IP led to better learning than RP is that it was simply due to the similarity between the practice and test conditions (Hamilton, 1943). In other words, retention
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after IP was better than RP under ITC because the practiced sequences were arranged in an interleaved order in ITC so that ITC was a favorable retention testing condition for IP. However, our data did not
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support this argument but favored a true CI effect because (1) retention after RP tested under RTC, a favorable retention testing condition for RP, did not outperform retention after IP, where the difference in response times under RTC was not significant; (2) the RP group exhibited more forgetting of the practiced sequences, with a higher forgetting score than the IP group compared even under RTC. Another explanation is that IP induced more spacing between distinct tasks than RP (Dempster, 1988).
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While the increased spacing between interleaved tasks may contribute to the benefits of CI, the effects of CI and spacing may be additive, suggesting that additional cognitive processes are involved (Richland et al., 2004). Two major hypotheses have been proposed to explain the CI benefits to
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learning, the ‘elaborative-processing’ (Shea and Zimny, 1983) and ‘forgetting–reconstruction’ (Lee and
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Magill, 1985) hypotheses. The elaborative-processing hypothesis assumes that during IP the learners have to compare and distinguish different tasks appearing in an interleaved order to compete for memory resources, and this repeated process of comparing and distinguishing motor tasks benefits learning (Shea and Zimny, 1983). On the other hand, the forgetting-reconstruction hypothesis suggests that a previously constructed action plan is more readily to be available in working memory during RP because the same task is practiced repeatedly. IP, however, benefits learning by forcing the learners to update their working memory by temporarily ‘forgetting’ the just-practiced task so that the upcoming new one can be planned (Lee and Magill, 1983; Lee and Magill, 1985). The two hypotheses are not
ACCEPTED MANUSCRIPT 27 mutually exclusive to each other and are both supported by previous research. For example, Kim et al. showed that learning of visuomotor adaption tasks was modulated by the degree of between-trial forgetting (Kim et al., 2015). Nevertheless, given the correlative nature of fMRI studies, our imaging
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data could not distinguish between the above two hypotheses − the association between IP and higher task-related BOLD signal than RP during practice could be attributed to greater recruitment of neural resources either to compare and distinguish motor sequences during IP (the elaborative-processing
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hypothesis), or to update working memory due to forgetting and retrieval of the interleaved sequences (the forgetting-reconstruction hypothesis). Changes in RSNs after early IP practice could reflect the
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consolidation of more elaborate or more retrievable memory traces. Using disruptive techniques such as transcranial magnetic stimulation (TMS) may provide a better insight into the underlying mechanisms. For example, in a TMS study by Lin et al., the elaborative-processing hypothesis was favored because TMS disrupted learning following IP by interfering with the comparing-anddistinguishing processes of the interleaved tasks, rather than by inducing more forgetting between the
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repetitive tasks (Lin et al., 2008).
Three issues about the experimental settings deserve further discussion. First, the 4-element
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motor sequences used in the current study were shorter than 8-element or longer ones used in other studies applying SRT tasks (Doyon et al., 2002; Shimizu et al., 2017; Spencer et al., 2006), which may
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lead to potential ambiguity whether the experiment elicited explicit motor memory only. Previous studies had successfully demonstrated the CI effect on sequence learning using 4-element sequences (Cross et al., 2007; Immink and Wright, 1998). As such, the rationale behind choosing short 4-element sequences in the current study was to ensure that the CI effect could be replicated. It is very possible that the participants gained some explicit knowledge of the sequences during practice. Nevertheless, it remains to be tested whether interleaved practice would have the same benefit if much longer sequences were practiced so that participants were not aware of the sequence structure. In fact, only a few studies have assessed the CI effect specifically under implicit motor sequence learning conditions
ACCEPTED MANUSCRIPT 28 (Wright et al., 2016). One possible reason is that motor sequences have to be relatively long to ensure implicit learning where participants are unaware of the sequences. Learning of these long sequences may not benefit from CI to the same degree as learning simpler sequences (Guadagnoli and Lee, 2004).
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In the current study, the participants practiced three sequences in an interleaved (IP) or repetitive (RP) order. If these sequences were lengthened to include 8 or 12 elements, each sequence would be separated by a substantial number of elements (up to 24), which might not support sequence learning
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effectively. Nevertheless, future studies that systematically investigate the influence of sequence length on the CI effect of motor sequence learning are warranted, as it is unclear whether explicit retrieval of a
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sequence is important for the benefit of interleaved practice. A second issue is that we used spatially separated colored circles in the SRT task (Fig. 1A). While most work using the SRT task has used spatially separate response locations to prevent the need to learn stimulus-response mappings (Robertson, 2007), our use of colored circles may have engaged different encoding mechanisms than if no color was used. Moreover, presenting colors in the SRT sequences enhance attention and facilitate
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motor sequence learning. In the study by Willingham et al., participants learned motor sequences based on color in random locations better than those who learned sequences based on location in random colors (Willingham et al., 1989). Thus our procedure may have facilitated learning by using both
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location and color to define a sequence, and it is possible that different resting-state networks would be
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involved in the consolidation of learning after IP if sequences were only based on color or location. Nevertheless, the IP and RP conditions in our study differed only in the order of presentation for the three sequences, while the frequency of presentation was identical for each color or location. Thus, both locations and colors were counterbalanced when the two practice conditions were compared to assess the effects of CI on learning. The third issue is that in comparison of two cognitive conditions, apparent brain activation in one condition could be due to deactivation in the other condition. As such, adding a non-sequence control experiment (for example, a random sequence or repeated finger tapping) helps identify task-induced increases or decreases in activation. For example, Fletcher et al. found
ACCEPTED MANUSCRIPT 29 learning-related decreases in activation in the left medial temporal cortex and the caudate nucleus by comparing SRT sequences with random ones (Fletcher et al., 2005). In the current study we did not include such a non-sequence control experiment to assess whether IP or RP respectively increased or
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decreased brain activation because the goal of the current study was to identify the neural substrates of the CI effect on motor sequence learning by comparing differences in resting or task-based brain activation or connectivity between the IP and RP conditions. Nevertheless, the positive BOLD signal
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respectively for the IP and RP conditions on Days 1 and 2 displayed in the bar graphs in Figure 2B indicates that the differences in BOLD signal between IP and RP in brain regions underlying the CI
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effect were not due to deactivation in these regions under the RP condition.
In the current study we used an 8-mm smoothing kernel that had also been used in (Schendan et al., 2003), which first provided the evidence for the involvement of the hippocampus in both explicit and implicit motor sequence learning. There is no clear consensus about the ideal level of smoothing that should be applied to fMRI data. Smoothing may improve the signal-to-noise ratio and to
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compensate for imperfect normalization of the images (Lee et al., 2013; Lindquist, 2008). For example, Kokkonen et al. found that smoothing with an 8-mm kernel was the most robust to yield identifiable sensorimotor signal sources in ICA, compared to smoothing with a 5-mm kernel or no smoothing
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(Kokkonen et al., 2009). An even larger smoothing kernel of 10 mm had also been used in resting-
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fMRI study to compare functional network connectivity between patients with schizophrenia and healthy controls (Jafri et al., 2008).
Conclusion. The current study is the first to demonstrate that the beneficial effect of CI on motor sequence learning starts from the early phase of memory consolidation, likely through driving offline functional connectivity with respect to the premotor cortex immediately after practice. This increase in resting connectivity was found to be between the premotor cortex and other brain areas involved in motor memory consolidation, including the DLPFC, inferior parietal lobule, hippocampus, putamen,
ACCEPTED MANUSCRIPT 30 and cerebellum. Mediation analysis further showed that the enhanced resting connectivity in the limbic regions on the first day led to higher task-related functional activation on the second day, and in turn to better retention performance. Our study advanced previous work that investigated the CI effect on task-
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related brain activation and motor learning (Cross et al., 2007; Lin et al., 2011), demonstrating that the functional network underlying offline consolidation is also a neural substrate of the CI effect, and that
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its connectivity could be enhanced by desirable difficulty during motor practice.
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Conflict of interest. The authors declare no competing financial interests.
Acknowledgments. This study was supported in part by the Ministry of Science and Technology (MOST 105-2221-E-010-004-MY3), the National Health Research Institutes (NHRI-EX104-10219EC), and the Brain Research Center, National Yang-Ming University (a grant from Ministry of Education,
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Aim for the Top University Plan, 100AC-B12), Taiwan (to MC Chiang).
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ACCEPTED MANUSCRIPT 42 FIGURE LEGENDS
Figure 1. Experimental design and behavioral analysis. (A) During fMRI scanning, the participants
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practiced the SRT task by pressing the corresponding key with their left hand as soon as they saw the colored circles appearing one by one through the magnet-compatible goggles. There was an 18-second break after every 6 trials of the SRT task, during which the circles were replaced by a fixation cross.
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(B) Each participant was assigned to either the RP or the IP group during the 5-day regime of the SRT task (practice of the task on Days 1 and 2; retention tests on Day 5). The SRT task included two sets of
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three different four-element sequences, (1, 2, 3) and (4, 5, 6), where numbers 1 to 6 represent different permutations of blue, red, green, and yellow colored circles shown in (A). In the RP condition, a sequence was practiced repetitively for 54 consecutive trials before the next sequence appeared (e.g., sequences 111…222…333…), while in the IP condition, the three sequences (sequences 4, 5, and 6) were arranged in a non-repetitive order. Resting-state fMRI scans were acquired after the task-based
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fMRI sessions. During the retention tests on Day 5, the practiced sequences were presented in an interleaved order in one fMRI run (Interleaved testing condition, abbreviated as ITC; shown by 132…for the RP, and 465... for the IP group), but in a repetitive order in the other fMRI run (Repetitive
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testing condition, abbreviated as RTC; shown by 1…2…3… for the RP, and 4…5…6… for the IP
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group). The order of the retention conditions (ITC versus RTC) was counter-balanced across the participants. The participants were tested with three novel sequences (sequences 789…for the RP, and 10 11 12... for the IP group) in the third fMRI run, denoted by ‘Novel’. Performance of SRT was presented in the line graph in (C). For better illustration, each dot represents the mean response time of a task block, which contained 6 consecutive trials. Performance during practice (Days 1 and 2) was better under the RP than the IP conditions, with a faster response time. However, retention performance of the practiced sequences on Day 5 was better for the IP than RP group under ITC, although the retention performance between the two groups did not differ under RTC. This CI effect on sequence
ACCEPTED MANUSCRIPT 43 learning is further illustrated in the bar graphs in (D). (E) We further compared the retention performance between the IP and RP groups on the early (the first 18 trials) and late (the last 18 trials) trials respectively under ITC and RTC. For ITC, the CI effect was more significant in the early (Early
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ITC) than the late (Late ITC) trials, where the early trials had a greater RP-minus-IP difference in response time. (F) For RTC, the difference in response time between the two practice conditions was not significant either in the early (Early RTC) or late (Late RTC) trials. (G) In addition to a faster
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response time, the IP group also had a lower forgetting score, which was defined as the difference in response time of the first block of ITC or RTC during retention minus the last practice block on Day 2.
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The bar graph shows that IP led to less forgetting, or better retention of the practiced sequences than RP, no matter the first task block of retention was selected from ITC or RTC. Note that there was no forgetting following IP when the practiced sequences were tested under RTC (the forgetting score = −3.1 ± 32.3 ms so that the corresponding bar was not visible due to the scaling of the y-axis), indicating that IP still led to better retention even when the retention testing condition was different from the
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practice one, and therefore the CI effect shown here was not just a direct consequence of similarity in the practice and test conditions. Abbreviations: ITC, Interleaved testing condition; RTC, Repetitive
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testing condition. *P < 0.05, **P < 0.005, ***P < 0.001.
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Figure 2. Differences in BOLD signal between IP and RP. (A) The brain maps show areas with significant IP-minus-RP BOLD contrasts during the practice phase on Days 1 and 2, particularly the bilateral premotor and precuneus cortices. The MNI z-coordinate (mm) is shown at the bottom. A positive t-value means excess in BOLD signal during IP, which also suggests that IP is associated with greater demand in neural recruitment than RP in these areas. The difference in BOLD signal between IP and RP conditions was not significant during retention (Day 5; figure not shown). (B) The line graphs for the percent change (left) and the bar graphs for the beta value (right) of BOLD signal also demonstrate a greater BOLD signal during IP on Days 1 and 2. The percent BOLD signal change for IP
ACCEPTED MANUSCRIPT 44 or RP in the line graphs was calculated as the mean signal intensity of each task block (which contains 6 consecutive trials and is represented by a dot) normalized to a baseline set to a 6-second interval
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before the task onset. Abbreviations: Pcu, precuneus; PM, premotor cortex.
Figure 3. Associations between greater BOLD signal during IP on Day 2 and the retention benefit of IP. (A) Greater excess in BOLD signal during IP on Day 2 was associated with a stronger CI effect
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on sequence learning, as represented by a greater RP-minus-IP difference in response time. This association was significant in bilateral precuneus cortices, the left insula, entorhinal cortex, and
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putamen, and was further illustrated by the scatter plots in (B) where D5RTRP-IP (x-axis) means the RPminus-IP difference in response time on Day 5, and BOLDIP-RP (y-axis) indicates IP-minus-RP difference in BOLD signal. All brain maps are displayed in neurological orientation, where ‘R’ indicates the right hemisphere. The MNI y- or z-coordinate (mm) is shown at the bottom of each slice.
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Abbreviations: EC, entorhinal cortex; Ins, insula; Pcu, precuneus; Put, putamen.
Figure 4. The resting-state networks (RSN). Each RSN was an independent component (IC) map whose significance was determined by one-sample t-test (FWE-corrected P-value < 0.05 at both the
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for each RSN.
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single-RSN and the inter-RSN levels). The MNI coordinates indicate the location of the peak of t-value
Figure 5. Associations between stronger resting-state premotor connectivity after IP on Day 1 and the CI benefits to learning. (A) For each RSN, we used correlation analysis to test the association between the RP-minus-IP difference in retention response time (Day 5) and the IP-minus-RP difference in z-scores that quantified the resting-state connectivity strength of each voxel. Multiple comparisons across the image were corrected using permutation testing. The brain map shows that stronger restingstate connectivity of the left premotor cortex (BA 6; belonging to the sensorimotor network I) after IP
ACCEPTED MANUSCRIPT 45 on Day 1 was significantly associated with better retention performance with respect to the IP condition. This finding indicates that increase in CI in motor practice enhanced functional connectivity of the premotor cortex during the consolidation phase of motor sequence learning. Nevertheless, this
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association only passed the correction for multiple comparisons at the single-RSN but not the interRSN level. (B) To identify brain regions that functionally interacted with the premotor cortex to affect retention performance, we selected a 4-mm sphere centered at the peak voxel of the above significant
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area in the left premotor cortex found in (A) as the seed region to quantify functional connectivity between the premotor seed region and every point of the brain (excluding the seed region) during the
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post-practice resting state on Day 1. The brain map shows that IP resulted in stronger resting-state connectivity between the left premotor cortex and the left DLPFC, bilateral posterior cingulate cortices, and bilateral angular gyri/inferior parietal lobules. (C) The IP-minus-RP difference in the connectivity strength between the premotor seed and each voxel on Day 1 was further correlated with the RP-minusIP difference in the response time during retention, yielding significant clusters (FWE-corrected P-
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value < 0.05) in the amygdala, hippocampus, putamen, and the thalamus on the right side, and the cerebellar vermis. (D) The correlations in (C) are further illustrated in the scatter plots where the IPminus-RP difference in functional connectivity between the above area and the left premotor cortex
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(Premotor Connectivity IP-RP) was regressed against the RP-minus-IP difference in response time during
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retention (D5RTRP-IP). ‘R’ indicates the right hemisphere. Abbreviations: Amg, amygdala; Cv, cerebellar vermis; DLPFC, dorsolateral prefrontal cortex; Hippo, hippocampus; IPL, inferior parietal lobule; PCC, posterior cingulate cortex; PM, premotor cortex; Put, putamen; Tha, thalamus.
Figure 6. Mediation analysis of Day-1 resting-state premotor connectivity, Day-2 task-related BOLD signal, and Day-5 retention performance. (A) The three-variable mediation model includes the source (X), mediator (M), and outcome (Y) variables. The source X is the IP-minus-RP difference in the premotor connectivity on Day 1 (PMConnIP-RP) in one of the two candidate regions shown in Fig.
ACCEPTED MANUSCRIPT 46 5C, the right hippocampus and right putamen. The mediator M is the IP-minus-RP difference in taskrelated BOLD signal on Day 2 (BOLDIP-RP) in either of the two candidate regions shown in Fig. 3A, the left entorhinal cortex and left putamen. The outcome Y is the RP-minus-IP difference in response
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time on Day 5 (D5RTRP-IP). The path coefficient a indicates the direct effect of X to M, b and c′ represent the partial regression coefficients of Y on M and X respectively, and c indicates the direct effect of X to Y. (B) The behavioral effect of the increase in the functional connectivity between the
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left premotor cortex and the right hippocampus after IP on Day 1 was completely mediated by the relative increase in task-related BOLD signal for IP compared to RP in the left entorhinal cortex on
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Day 2, where the path coefficients a and b and their product a × b were all significant (bootstrap Pvalue < 0.0125, Bonferroni-corrected for the 4 possible source-mediator pairs that had been tested, displayed in boldface), and c′ was non-significant (P > 0.05). (C) The mediation effect was not significant in the following source versus mediator pairs due to non-significant path coefficient b: the
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right putamen versus the left entorhinal cortex, the right hippocampus versus the left putamen, and the right putamen versus the left putamen. This finding suggests that the benefits to retention of enhanced Day-1 premotor connectivity after IP did not depend on functional activation in the putamen on Day 2.
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Numbers in square brackets indicate the 95% confidence interval of the null hypothesis for the path
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coefficients. Abbreviations: EC, entorhinal cortex; Hippo, hippocampus; L, left; R, right.
ACCEPTED MANUSCRIPT 47 Table 1. Brain areas where greater BOLD signal during IP than during RP on Day 2 was associated with the retention benefits of IP Peak t-value
MNI coordinates (mm)
R precuneus
5
5.4
12, -49, 52
L precuneus
5
4.9
L entorhinal cortex
34
4.8
L insula
48
4.6
L orbitofrontal gyrus
11
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BA
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BA, Brodmann area.
-24, 5, -14
-27, 20, -14
4.4
-24, 23, -11
3.6
-15, 17, -8
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L putamen
-12, -49, 52
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BA
Peak t-value
MNI coordinates (mm)
R precuneus
7
29.5
9, -64, 55
L precuneus
7
29.5
L lingual gyrus
17, 18
53.0
R calcarine gyrus
17, 18
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Table 2. Brain regions included in the selected RSNs
Primary visual network I
18
L calcarine gyrus
-9, -67, 52
0, -82, 4
31.4
9, -79, 7
29.3
-3, -79, 22
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L cuneus
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17, 18
29.0
-12, -70, 19
18
32.0
27, -94, 7
18
30.0
-21, -97, -2
18
29.1
24, -97, -5
4
62.7
-18, -37, 67
2
39.4
24, -40, 67
4
38.0
15, -37, 67
L postcentral gyrus
2
37.5
-36, -40, 61
R dorsal premotor/supplementary motor
6
31.9
9, -13, 67
6
26.0
-3, -13, 64
Primary visual network II R middle occipital gyrus
R inferior occipital gyrus Sensorimotor network I
R postcentral gyrus
area
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R precentral gyrus
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L precentral gyrus
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L middle occipital gyrus
L dorsal premotor/supplementary motor area Sensorimotor network II
ACCEPTED MANUSCRIPT R supramarginal gyrus
40
28.9
63, -22, 22
L supramarginal gyrus
40
27.6
-60, -37, 31
R ventral premotor area
6
20.1
48, 8, 13
R angular gyrus
39
32.1
R inferior parietal lobule
40
22.9
R middle frontal gyrus
47
25.3
R superior frontal gyrus
32
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45, -61, 43 45, -46, 55 36, 53, 1
9, 35, 40
40
26.5
-48, -55, 43
47
25.6
-42, 44, -11
47
22.9
-42, 47, -2
19
29.8
45, -79, 7
19
29.5
-30, -85, 16
37
27.8
-48, -70, 10
37
24.8
42, -52, -17
37
24.3
-42, -67, -14
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L inferior parietal lobule
20.5
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Left frontoparietal network
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Right frontoparietal network
L superior frontal gyrus
10
40.4
-9, 56, 31
L dorsolateral prefrontal cortex
9
28.9
6, 44, 49
R dorsolateral prefrontal cortex
9
23.4
-12, 41, 49
L premotor/supplementary motor area
6
21.9
-1, 20, 67
L inferior frontal gyrus, orbital part L middle frontal gyrus
R middle occipital gyrus L middle occipital gyrus
R fusiform gyrus L fusiform gyrus
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L middle temporal gyrus
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Occipito-temporal network
Frontal network
BA, Brodmann area.
ACCEPTED MANUSCRIPT 50 Table 3. Brain areas showing greater functional connectivity with the left premotor cortex after IP than RP on Day 1 Peak t-value
MNI coordinates (mm)
R angular gyrus
39
6.3
48, -64, 43
R angular gyrus
39
5.4
L inferior parietal lobule
39
6.0
L inferior parietal lobule
40
4.5
L DLPFC
46
L DLPFC
9
L PCC
23
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42, -70, 49
-51, -61, 40 -45, -52, 55 -39, 20, 40
5.0
-48, 20, 46
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4.4
0, -28, 43
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ACCEPTED MANUSCRIPT 51 Table 4. Brain areas where greater connectivity with the left premotor cortex after IP than RP on Day 1 was associated with the retention benefits of IP
Cerebellum vermis R amygdala
34
Peak t-value
MNI coordinates (mm)
6.1
3, -55, -14
5.7 5.5
R putamen
5.0 20
R hippocampus
20
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24, -1, -14 21, -19, 7 24, 2, 10
4.4
30, -13, -5
3.6
33, -19, -14
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R inferior temporal gyrus
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I hereby verify that all information disclosed here is true and correct to the best of my knowledge: 1. Financial disclosure:
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(a) None of the authors has any actual or potential conflicts of interest including any financial, personal or other relationships with other people or organizations within three years of beginning the work submitted that could inappropriately influence (bias) their work. (b) None of the authors' institution has contracts relating to this research through which it or any other organization may stand to gain financially now or in the future.
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(c) None of the authors has any other agreements with respect to themselves or their institutions that could be seen as involving a financial interest in this work.
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2. Sources of financial support: This study was supported in part by the Ministry of Science and Technology (MOST 105-2221-E-010-004-MY3), the National Health Research Institutes (NHRI-EX104-10219EC), and the Brain Research Center, National Yang-Ming University (a grant from Ministry of Education, Aim for the Top University Plan, 100AC-B12), Taiwan (to MC Chiang).
3. The data contained in the manuscript being submitted have not been previously published, have not been submitted elsewhere and will not be submitted elsewhere while under consideration at NeuroImage.
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4. This study has been approved by the Institutional Review Board of the National Yang-Ming University, Taiwan. Written informed consent was obtained from all participants.
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5. All authors have reviewed the contents of the manuscript being submitted, approved of its contents and validated the accuracy of the data.
Ming-Chang Chiang, M.D., Ph.D. Associate Professor Department of Biomedical Engineering National Yang-Ming University