Transfer effects of abacus training on transient and sustained brain activation in the frontal–parietal network

NEUROSCIENCE RESEARCH ARTICLE Hui Zhou et al. / Neuroscience 408 (2019) 135–146

Transfer effects of abacus training on transient and sustained brain activation in the frontal–parietal network Hui Zhou, a Fengji Geng, b Yunqi Wang, c Chunjie Wang, a,d Yuzheng Hu e,* and Feiyan Chen a,* a

Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China

b

Department of Curriculum and Learning Sciences, College of Education, Zhejiang University, Hangzhou 310007, China

c

School of International Studies, Zhejiang University, Hangzhou 310058, China

d

State Key Lab of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China

e

Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310007, China

Abstract—Understanding the neural mechanisms of training-induced brain plasticity has significant implications for improving academic achievement. Previous studies suggest abacus-based mental calculation (AMC) training significantly improves individual's arithmetic capability, and the frontal–parietal network is suggested to be the key neural circuit underlying AMC. Yet, it remains unclear how AMC training shifts brain activation in this network and whether the training effect is transferable or not. The current study aimed to address these questions using a longitudinal design engaging an experimental group (20 days of AMC training) and a control group. The fMRI results indicated that AMC training increased sustained but reduced transient activation in the frontal–parietal network when the AMC group performed the training-related arithmetic task. More interestingly, similar pre- to post-training changes in activation were observed in two training-unrelated tasks. The control group, on the other hand, did not exhibit any pre- to post-training differences in brain activation on any of the three tasks. These findings extend the previous cross-sectional studies of AMC and suggest that AMC training induces functional changes in brain activation and such plasticity may be transferable beyond the AMC. The training effects on sustained and transient neural activity may also provide a new perspective to understand training-induced neural plasticity and related transfer effect. © 2019 IBRO. Published by Elsevier Ltd. All rights reserved. Key words: abacus-based mental calculation, neural plasticity, sustained and transient activity, training, working memory.

INTRODUCTION

2011) as well as greater numerical processing efficiency (Wang et al., 2013; Yao et al., 2015). Studies by our group (Chen et al., 2006; Du et al., 2014; Huan et al., 2015; Wang et al., 2015) and others (Hatano et al., 1977; Stigler, 1984; Hanakawa et al., 2003; Frank and Barner, 2012; Tanaka et al., 2012; Barner et al., 2016) have demonstrated that AMC training significantly affects arithmetic ability. In contrast to a linguistic strategy employed in conventional mental calculation (Dehaene et al., 1999), literature suggested that AMC experts utilize a non-verbal based visuospatial strategy for numerical operation (Hatano et al., 1977; Hatano and Osawa, 1983; Stigler, 1984; Hatta and Ikeda, 1988). Converging with behavioral data, neuroimaging studies demonstrated that, when participants performed mental calculation tasks, the bilateral frontal– parietal regions, which were considered as visuospatial working memory (WM) network (Smith and Jonides, 1998; Owen et al., 2005), were activated in AMC experts, whereas a language-related network including the left inferior frontal area (Broca's area) was activated in non-experts (Hanakawa et al., 2003; Chen et al., 2006). In addition,

Training induced neural plasticity and its potential transfer effects on non-trained tasks receive intensive attention recently. Abacus-based mental calculation (AMC), which is regarded as a high-efficiency calculation method, attracted researchers' attention because of its extraordinary calculation ability. Abacus, which represents numbers by arrangements of beads, is a device used to facilitate arithmetic calculation (Fig. 1). With long-term practice, AMC experts can mentally do calculations involving numbers of more than 10 digits rapidly and precisely (Stigler, 1984), presumably via an imaginary abacus in mind (Frank and Barner, 2012). In addition, they demonstrated greater digit memory span than non-experts (Tanaka et al., 2002; Hu et al., *Corresponding authors. E-mail address: [email protected] (Yuzheng Hu).chenfy@zju. edu.cn (Feiyan Chen). Abbreviations: AMC, abacus-based mental calculation; WM, working memory; AO, arithmetic operation; CVWM, complex visuospatial working memory; SVS, simple visuospatial stimulation; ANOVA, analysis of variance; ROI, regions of interest. https://doi.org/10.1016/j.neuroscience.2019.04.001 0306-4522/© 2019 IBRO. Published by Elsevier Ltd. All rights reserved. 135

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gyrus and superior parietal cortex showed transient activation in multiple tasks (Donaldson et al., 2001; Marklund et al., 2007). Moreover, it seems that the sustained and transient activities could be detected simultaneously within a given brain region (such as parietal cortex mentioned in above studies) (Donaldson et al., Fig. 1. A schematic illustration of abacus calculation. (A) The representation of 74. One bead in heaven 2001; Scheibe et al., 2006). deck (the row above the middle bar) represents the value of 5 when it is pushed down, and one bead in Training-induced changes in brain the earth deck (rows below the middle bar) represents the value of 1 when it is pushed up. (B) The process activation (Garavan et al., 2000; of adding 55 to 74. The process steps are labeled by lower cases. (C) Representation of results 129. Chen et al., 2006; Westerberg and Klingberg, 2007; Dong et al., 2016) may encourage a reconfiguration of neural resources to the greater activation in the frontal–parietal network was sustained/transient processes to achieve a performanceobserved in AMC experts than in non-experts when they efficiency balance, and such a strategic change is potentially performed a digit WM task (Tanaka et al., 2002). Our pretransferable beyond the trained task. vious study, using diffusion tensor imaging, also showed In the present study, a mixed block/event-related design enhanced white matter integrity in fiber tracts associated was adopted to depict brain activation associated with with the frontal–parietal network (Hu et al., 2011). Studies state- and event-related processes, respectively. With this mentioned above provide strong evidence that a visuospaparadigm, participants underwent an fMRI experiment contial strategy is used in AMC with frontal–parietal network sisting of a training-related arithmetic operation (AO) task, as the primary neural substrate, and that the AMC training a non-trained complex visuospatial working memory may induce neural plasticity in corresponding brain regions. (CVWM) task, and a non-trained simple visuospatial stimuThese cross-sectional studies, however, are less able to lation (SVS) task respectively before and after a 20-day provide direct evidence for the AMC training-induced neural AMC training procedure. Based on literature mentioned plasticity and its potential transfer effect on WM. A longitudiabove, we hypothesized that a) AMC training would induce nal design may give more powerful evidence by controlling functional changes in sustained and transient activity in the potential confounds. frontal–parietal network in the trained arithmetic task, and b) WM is a multi-component system supporting short-term the effect of such functional changes may be transferred storage and online manipulation of information, and conbeyond the trained task. sists of four components, namely phonological loop, visuospatial sketch pad, central executive and episodic buffer (Baddeley, 2012). As AMC includes a heavy visuospatial WM component, there is also a very interesting question EXPERIMENT PROCEDURES to ask whether the AMC-induced functional changes have Subject impacts on common visuospatial tasks. In general, cognitive operations, especially WM, can be hypothetically decomForty-two undergraduate students were recruited from Zhejiang posed into two types of processes, namely the “eventUniversity, among whom 18 were assigned to the AMC group related process” and “state-related process” (Visscher et and the other 24 were assigned to the control group. Three conal., 2003; Brahmbhatt et al., 2010). The former underlies trols did not complete all of the tests; and one was excluded due transient activity responding to individual items within the to large head motion (>3 mm/degree in any one of the six task, and could be related to information update and manipmotion parameters), resulting in a total of 20 participants in ulation (Brahmbhatt et al., 2010), whereas the latter underthe control group (20 males, mean age = 21.30, SD = 1.24) lies sustained activity maintaining across the whole task and 18 in the AMC group (17 males, 1 female, mean age = condition (Courtney et al., 1997; Donaldson et al., 2001; 21.35, SD = 0.68). There was no difference between the two Sakai, 2008), which could be related to individual's level of groups in age (t (36) = 0.14, p = 0.89) and in Intelligence Quoarousal and attention focus throughout the task or the maintient estimated using Raven Test (t (36) = 0.99, p = 0.33). All tenance of relevant information (Marklund et al., 2007). participants had normal vision or corrected to normal vision. Looking into these two processes could fully utilize the This study was approved by the Research Ethics Review BOLD signal and allow deeper comprehending of how brain Board of Zhejiang University. All participants signed written regions function on multiple timescales (Petersen and informed consent and received compensation for their time. Dubis, 2012). However, it has not yet well recognized which brain regions (networks) consistently show more sustained Training procedure activation and which regions show more transient activation. Nevertheless, previous studies do show such brain The AMC group received AMC training given by an experiregions as pre-supplementary area, anterior cingulate corenced AMC teacher for 20 successive days (Fig. 2A) with tex, medial prefrontal cortex, and parietal lobule were actia 1-day (the 14th day) break in between. AMC participants vated in a sustained fashion. In contrast, middle frontal were trained for two classes (90 min in total) on each

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Fig. 2. Study and fMRI experiment designs. (A) Procedure of the study. (B) The fMRI experiment design. Three task conditions, namely arithmetic operation, complex visuospatial working memory and simple visuospatial stimulation, were interleaved in a block-wise way, and within each block, trials were presented using an event-related design.

training day. On the first 3 days, they studied the abacus rules for calculation and practiced on a physical abacus (Fig. 1) to solve simple mathematical problems (one and two-digit addition/subtraction). From the fourth day, besides practicing a physical abacus, they were also instructed to imagine an abacus in mind and do calculations on it with finger movements to assist the operations. With practice, they were gradually able to do AMC mentally without moving fingers. During the AMC training, a national standard assessment of AMC (the 9th level, Fig. 3A, left panel) was conducted on the AMC group every 4 days to monitor the training outcome. This test measured the ability to solve arithmetic problems with AMC method. Each problem consisted of seven operations to add or subtract eight one- or two-digits vertically aligned in columns (Fig. 3A, left panel), with a minus symbol before the digit meaning a subtract operation, otherwise, an addition operation. Participants were required to write down their answers below each problem and solve as many problems as possible within 5 min. Each correct answer was scored as one point. The total score was used as a measure of the training outcome. The control group did not receive any training. They were recruited retrospectively to control any potential confounds related to the repetition of task and fMRI scanning. Two sessions of MRI data (detailed below) were collected before and after training in the AMC group (Fig. 2A) and with a similar gap of about 20 days in the control group.

FMRI tasks The fMRI experiment (Fig. 2B) included three task conditions: AO condition, CVWM condition, and SVS condition. Each task condition was presented in a block-wise for three times, interleaved with other task conditions. Each block began with a 4-s cue, followed by a 40-s task operation period and then a 4-s response window. During each task operation period, five visual stimulus trials (each on the screen for 2 s) were presented with an interval randomly selected from a pool of 2 s, 4 s, 4 s, 6 s, and 8 s, and the last interval before the response cue was always 6 s. An asterisk fixation was presented on the center of the screen during the interval. After the operation period, a response cue was presented and participants were required to respond by pressing a button (see below). The response cue disappeared and screen turned to black after a button press with a reaction time less than 4 s. A 10-s resting period with a fixation was presented between blocks. The fMRI experiment was administrated before and after the AMC training. The three task conditions were designed in a very similar fashion and were interleaved in the same experiment to minimize any undesired systematic bias. The AO task was essentially a trained task and was designed to examine how neural underpins of arithmetic are changed by AMC training, whereas the other two were non-trained tasks and were used to investigate the transfer effects of AMC training.

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Fig. 3. The changes of performance and brain activation with AMC training. (A) Examples of arithmetic problems in the national standard assessment of AMC (the 9th level) (left panel); Arithmetical calculation ability was improved with AMC training duration (right panel). (B) Sustained brain activation and (C) transient brain activation changes after training on arithmetic operation task. 1 = L MFG, 2 = R MFG, 3 = L SPL, 4 = R SPL, 5, 10 = sMPFC, 6 = R IPL, 7 = R PCC/precuneus, 8 = R OFC, 9 = R LPFC, 11 = SMA, 12, 13 = L GPrC, 14 = L MTG, 15 = L cereb/fusiform, 16 = R IFG, 17 = R GPrC, 18 = R cereb/cuneus/R fusiform.Abbreviations: L, left; R, right; MFG, middle frontal gyrus; SPL, superior parietal lobule; IPL, inferior parietal lobule; OFC, orbitofrontal cortex; LPFC, lateral prefrontal cortex; SMA, supplementary premotor area; sMPFC, superior medial prefrontal cortex; GPrC, precentral gyrus; IFG, inferior frontal gyrus; PCC, posterior cingulate cortex; MTG, middle temporal gyrus; cereb, cerebellum.

For AO condition (Fig. 2B), two columns of abacus beads representing a two-digit number were presented at the beginning as a task cue. Then a plus (+) or minus (−) symbol was presented as an operation cue. Participants were instructed to add 39 to or subtract 27 from the abacusrepresented number in the cue following the plus or minus

symbol, respectively, with the result being used as the input to repeat the next operation. After five manipulations with variable intervals mentioned above, another image of the two columns of abacus beads was presented as a response cue. Participants were required to press a button to determine whether the presented abacus image was the right

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answer after five steps of calculation. Though the AO task was not administrated exactly in the same way as arithmetic tasks conducted during the daily AMC training, we considered it as a trained task because the only difference was the introduction of intervals that allow us to examine event-related brain activation. For CVWM condition (Fig. 2B), a CVWM block started with a task cue composed of a red circle and a green triangle in two cells of a nine-square grid. A red or green arrow was presented as an operation cue. Participants were instructed to remember the positions of the red circle and green triangle on the task cue screen, and then mentally and iteratively move the red circle to the corresponding direction by one grid when the red arrow was shown, or move the green triangle when the green arrow was shown. After five manipulations with the same intervals mentioned above, a response cue was presented. Participants were required to press a button to determine if both the circle and triangle were in the correct cells after five steps of movement. For SVS condition, the task cue was a nine-square grid with a red circle and a green triangle and a red arrow in three of the squares. A red arrow pointing to the left/right or up/down was presented as an operation cue. Participants were instructed to pay attention to the orientation of the present arrow. After five arrows with the same intervals mentioned above, a response cue similar to the task cue was presented. Participants needed to press a button to judge whether or not the orientation of the arrow was as same as that of the previous arrow shown.

Imaging acquisition As mentioned above, the control group was recruited to control practice effect on fMRI tasks and scan interval. Due to the unavailability of scanner used for the AMC group, controls were scanned with a different scanner. MRI data of the AMC group were acquired on a 3-T Siemens Trio scanner. The functional images were collected using a T2*-weighted single-shot echo-planner (EPI) imaging sequence (TR/TE = 2000 ms/30 ms, flip angle = 90 o, FOV = 192 × 192 mm 2, matrix = 64 × 64, slice thickness = 4 mm, slice number = 32, voxel size = 3.0 × 3.0 × 4.0 mm 3). A high-resolution anatomical scan was also acquired for each participant using a T1-weighted 3D magnetization prepared rapid gradient echo sequence (TR/TE = 2530 ms/2.40 ms, FOV = 224 × 256 mm 2, matrix = 224 × 256, voxel size = 1 mm 3, and 192 slices in the sagittal plane). MRI data of the control group were acquired on a 3-T Siemens Prisma scanner. The functional images were collected using a T2*-weighted EPI sequence (TR/TE = 2000 ms/30 ms, flip angle = 78 o, FOV = 220 × 220 mm 2, matrix = 64 × 64, slice thickness/gap = 4 mm/0.65 mm, slice number = 30, voxel size = 3.4 × 3.4 × 4.0 mm 3). A high-resolution anatomical scan was also acquired for each participant using a T1-weighted 3D magnetization prepared rapid gradient echo sequence (TR/TE = 2300 ms/2.98 ms, FOV = 256 × 256 mm 2, matrix = 256 × 256, voxel size = 1 mm 3, and 176 slices in the sagittal plane).

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Image preprocessing The fMRI data were preprocessed with the DPABI toolbox (Yan et al., 2016). The functional images were first corrected for interleaved slice acquisition after removing the first three volumes for magnetization stability. Then these data were corrected for head motion using a six-parameter rigid-body transformation. The individual high-resolution anatomic image was co-registered with the mean functional image after motion correction. Gray matter, white matter and cerebrospinal fluid were segmented using SPM12 New Segmentation (http://www.fil. ion.ucl.ac.uk/spm/). Then the resultant gray matter and white matter images were used to normalize individual anatomical image to MNI space using The Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra tool (DARTEL) (Ashburner, 2007). The normalization parameters were applied to functional data, which were then smoothed with a Gaussian kernel of 6 mm 3 FWHM and resampled to a 3 × 3 × 3 mm 3 resolution.

First level fMRI modeling Individual level brain activation was detected with general linear modeling using AFNI (Cox, 1996). As shown in Fig. 2B, a mixed block/event-related design was employed. We modeled each task condition with four regressors: namely, task cue (the first screen of each block, duration of 4 s); button response (the last screen of each block, duration of 2 s); sustained (block) response (the period between task cue and response screen, duration of 40s); and transient (event) response (onset of each operation cue within the 40s block). In order to control motion and non-neuronal related artifacts, the 24 motionrelated parameters (Friston et al., 1996) and 5 components of white matter and cerebrospinal fluid were included as nuisance regressors (Behzadi et al., 2007). A censoring/scrubbing strategy (Power et al., 2012) was also used to remove time points with frame-wise displacement (FD) > 0.75 mm. The head motion measured by mean FD is not different between sessions in any group, nor is it different between groups at each session (lowest p = 0.31).

Group level statistical analysis As we were interested in the mental processes of information retaining and manipulation, the regression coefficient (beta) maps of six regressors, namely, AO_event, AO_block, CVWM_event, CVWM_block, SVS_event, and SVS_block, were used in the group level analyses to test our hypotheses detailed below. For voxel-wise analysis, correction for multiple comparisons was determined with voxel-wise p-value <0.001 and a minimum cluster size of 43 voxels based on 3dClustSim (using the average spatial smoothness parameters of the group with the mixed-model autocorrelation function (ACF)) in AFNI (Cox et al., 2017). To test the hypothesis that the AMC training changes the pattern of brain activation associated with arithmetical operation, we carried out a paired t-test to compare brain activation (Beta maps of AO_event and AO_block resultant from the first level analysis) of the AO condition before and after training. The involvement of the frontal–parietal

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network (among other regions) in AMC after training was expected based upon previous findings (Hanakawa et al., 2003; Chen et al., 2006). To explore whether AMC training changes the way brain responding to stimulation from a state/event-related dualprocess perspective, a voxel-wise 2 × 2 × 2 repeated measures analysis of variance (ANOVA) with factors of Group (AMC and control), Session (pre-, and post-training) and Regressor (AO_event and AO_block) was carried out on the trained task (the AO condition) activation. We expected to detect the Group-by-Session-by-Regressor interaction in brain regions underlying AMC, especially the frontal–parietal network. To examine the transfer effect of AMC induced functional changes, brain regions of the frontal–parietal network showing three-way interactions under the AO condition were used as regions of interest (ROIs), the same 2 × 2 × 2 ANOVAs were performed on the brain activation (beta values) of non-trained tasks (CVWM and SVS) in these predefined ROIs.

RESULTS

three responses under each condition and the ACC distributions were largely skewed, we only applied statistical analyses to RT. A Group (AMC and control)-by-Session (preand post-training) ANOVA revealed a significant Group-bySession interaction (F (1, 35) = 11.51, p = 0.002) in RT under AO condition. Post-hoc analysis with Bonferroni correction indicated a significant faster response after training in both groups (AMC p < 0.0001, control group p = 0.0005). While the two groups had similar RT in pretraining session (p = 0.58), the AMC group performed significantly faster in post-training session than control group did (p = 0.01). Under the CVWM condition, a parallel ANOVA revealed no main effect of Group or Session or interaction (smallest p = 0.12). As transfer effect was expected, paired-test, conducted to test whether AMC group had better performance after training, showed marginally significant decrease in RT (p = 0.09). Under the SVS condition, a parallel ANOVA analysis revealed a main effect of Session in RT, indicating a faster response in posttraining than pre-training (F (1, 34) = 4.26, p = 0.05). Further analysis showed that such Session effect was primarily driven by the decrease of RT in the AMC group (p = 0.05).

Training outcome After 4 days of AMC training, participants could solve about four problems (with each consisting seven successive addition/subtraction operations) within 5 min, while after 20 days of training, they could solve more than 18 similar problems. A repeated one-way ANOVA indicated a significant improvement in the arithmetic ability in the AMC group (F (4, 68) = 48.16, p < 0.001, Fig. 3A, right panel).

Behavioral results Qualitatively, accuracy (ACC) increased and reaction time (RT) decreased after training in every task for the AMC group but not for the controls (Fig. 4). As there were only

BRAIN IMAGING RESULTS Effects of AMC training on trained AO task After AMC training, the frontal–parietal network (including bilateral middle frontal and superior parietal regions) showed greater sustained activation on the AO task (Fig. 3B, P_corrected<0.05). While four regions (right inferior parietal lobule, superior medial prefrontal cortex, right lateral prefrontal cortex and right orbitofrontal cortex) showed greater sustained deactivation after AMC training, the posterior cingulate cortex showed reduced sustained deactivation. In contrast to sustained activation, AMC training reduced transient activation in response to a single arithmetic operation in several brain

Fig. 4. Behavioral performance of the fMRI tasks. No difference in RT was found between the AMC and control groups before training in any task. While both AMC and control groups showed shorter RT after training in the AO task, the AMC group showed even shorter RT than the controls. The AMC group also showed a margin significant reduction in RT in the non-trained CVWM and SVS tasks.Abbreviations: RT, reaction time; ACC, accuracy; Pre., pretraining; Post., post-training; AO, arithmetic operation; CVWM, complex visuospatial working memory; SVS, simple visuospatial stimulation.

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regions including bilateral inferior frontal gyrus (including the left Broca's language area), supplementary premotor area, bilateral precentral gyrus, bilateral fusiform, cuneus, occipital gyrus, and bilateral cerebellum. The superior medial prefrontal cortex, which showed enhanced sustained deactivation, exhibited less transient deactivation (Fig. 3C, P_corrected<0.05). The three-way ANOVA analysis revealed that the bilateral superior parietal lobe and right middle frontal gyrus along with other brain regions including bilateral inferior parietal lobe, superior medial prefrontal cortex, right lateral orbital frontal cortex, middle temporal gyrus and right lateral prefrontal cortex showed significant Group-by-Session-by-Regressor interaction (Corrected P < 0.05, Fig. 5, Table 1). Post-hoc F-tests on Session-by-Regressor indicated that the brain activation remained stable between sessions in the control group but significantly changed in the AMC group (Table 2). More specifically, changes in brain activation were driven by enhanced sustained activation and reduced transient activation in the AMC group but not in controls (Table 2, Fig. 6A).

Transfer effects of AMC training on non-trained visuospatial tasks In this analysis, we focused on the frontal–parietal network (i.e. the bilateral superior parietal lobule and right middle frontal gyrus ROIs in Fig. 5) because it is considered as the neural structure underlying visuospatial operation in AMC. At the ROI level, the AMC group showed significant increase in sustained activation and significant decrease in transient activation in the frontal–parietal regions on AO condition after training (Table 2, the first section).

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Table 1. Brain regions showing 3-way Group-by-Session-by-Regressor interactions when participants performed an arithmetic operation (AO) task.

Regions

R MFG R SPL L SPL R IPL L IPL R LPFC R OFC SMA/sMPFC R MTG

Cluster Size

MNI Coordinates (mm) x

Y

z

119 105 47 108 78 56 91 300 82

24 18 −21 57 −57 36 33 15 66

−12 −63 −66 −51 −45 18 63 24 −24

51 57 39 39 36 33 0 48 −21

Fvalue 40.31 20.56 19.54 23.26 23.34 23.05 22.35 36.05 30.22

Abbreviations: L, left; R, right; MFG, middle frontal gyrus; SPL, superior parietal lobule; IPL, inferior parietal lobule; LPFC, lateral prefrontal cortex; OFC, orbitofrontal cortex; SMA, supplementary premotor area; sMPFC, superior medial prefrontal cortex; MTG, middle temporal gyrus.

The three-way ANOVA analysis of the CVWM task at the ROI level revealed significant three-way interactions in left superior parietal lobule (F (1, 36) = 12.66, p = 0.001), right superior parietal lobule (F (1, 36) = 5.76, p = 0.02), and right middle frontal gyrus (F (1, 36) = 5.58, p = 0.02). Further 2 × 2 ANOVA analysis with factors of Session (pre-, and post-training) and Regressor (CVWM_event and CVWM_block) showed significant two-way interactions on all the three ROIs for the AMC group with the parietal regions showing larger effect size than the frontal region (Table 2). Post-hoc analysis following the Session-byRegressor ANOVA revealed that the AMC group showed significant decrease in transient activation in all frontal–

Fig. 5. Brain regions showing 3-way Group-by-Session-by-Regressor interactions in the arithmetic operation (AO) task.

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Table 2. Post hoc analyses following the 3-way ANOVA on sustained and transient activation of the frontal–parietal network in the AMC group.

Frontal–parietal ROIs

AO task Regressor by Session Sustained (post-hoc) Transient (post-hoc) CVWM task Regressor by Session Sustained (post-hoc) Transient (post-hoc) SVS task Regressor by Session Sustained (post-hoc) Transient (post-hoc)

R SPL

L SPL

R MFG

F(1,17) = 18.56 p < 0.001 p < 0.001

F(1,17) = 19.53 p < 0.001 p < 0.001

F(1,17) = 37.99 p < 0.001 p < 0.001

p = 0.006

p = 0.007

p < 0.001

F(1,17) = 21.40 p < 0.001 p = 0.238

F(1,17) = 20.23 p < 0.001 p = 0.049

F(1,17) = 11.29 p = 0.004 p = 0.073

p < 0.001

p < 0.001

p < 0.001

F(1,17) = 13.52 p = 0.002 p = 0.036

F(1,17) = 14.35 p = 0.001 p = 0.052

F(1,17) = 0.80 p = 0.383 p = 0.709

p < 0.001

p < 0.001

p = 0.223

Abbreviations: L, left; R, right; MFG, middle frontal gyrus; SPL, superior parietal lobule. AO: arithmetic operation task; CVWM, complex visuospatial working memory task; SVS: simple visuospatial stimulation task.

parietal ROIs, and significant increase in sustained activation in left parietal lobule (Fig. 6B, left column, Table 2, the second section). In contrast, no significant Session by Regressor interaction was found in controls (largest F (1, 19) = 1.91, p = 0.18), and both transient and sustained activation remained the same between the two scan sessions (Fig. 6B, right column). On the SVS task, a significant three-way interaction was observed in the right superior parietal region (F (1, 36) = 6.37, p = 0.02) and left superior parietal lobule (F (1, 36) = 7.38, p = 0.01), but not in the right middle frontal ROI. Further analyses showed significant Session (preand post-training)-by-Regressor (SVS_event and SVS_block) interaction in both left and right superior parietal region in the AMC group (Fig. 6C, left column). Post hoc analyses to unpack the interaction indicated that the AMC group showed significant increase in sustained activation and decrease in transient activation in right parietal region after training (Table 2, the third section). For the left parietal region, a marginally significant increase in sustained activation and a significant decrease in transient activation were found. Both transient and sustained activation, as expected, remained the same in the control group in the same 2 × 2 ANOVAs (Fig. 6C, right column, largest F (1, 19) = 0.52, p = 0.48). To confirm the specificity of above functional changes in the frontal–parietal network, we also applied the same ROI-based three-way ANOVAs to the rest of ROIs in Fig. 5. No significant three-way interaction (i.e. Group-by-Session-by-Regressor) was detected in any of them under either CVWM or SVS conditions (largest F (1, 36) = 3.57, lowest p = 0.07).

DISCUSSION In the current study, we showed that the AMC training induced functional changes in the frontal–parietal network, an important neural substrate for cognition. Specifically, AMC training enhanced sustained activation and reduced transient activation within this network when participants performed a trained arithmetic task. The same patterns of activation change in both frontal and parietal regions were observed when participants performed a non-trained CVWM task requiring a great cognitive demanding for information maintenance and manipulation, whereas only the parietal changes were observed in another non-trained task requiring less cognitive demanding. In addition, these neural activation changes were accompanied by faster responses under all three task conditions for the AMC group, with the largest effect on the trained AO task. These results not only provide direct evidence for AMC-induced changes in behavior and related brain activation but also provide a new perspective on neural plasticity and the transfer effect induced by cognitive training.

Activation changes on AO task after training For experienced AMC users, abacus-based numbers are represented with columns of abacus beads in their minds to solve arithmetic problems (Stigler, 1984; Frank and Barner, 2012), with each number being mapped to a unique spatial pattern. Like a visuospatial WM task, AMC calculation is achieved by updating these spatial patterns based on the AMC operation rules. As previous studies have consistently showed the activation of frontal–parietal network during AMC task (Tanaka et al., 2002; Hanakawa et al., 2003; Chen et al., 2006; Ku et al., 2012; Tanaka et al., 2012), the frontal–parietal network is regarded as the core neural substrate underlying the visuospatial processing in AMC. While all the previous studies are cross sectional, the current study, to our best knowledge, is the first longitudinal study to demonstrate the changes of brain activation induced by AMC training. Our findings that the frontal–parietal network was engaged in AMC training are highly consistent with previous findings (Tanaka et al., 2002; Hanakawa et al., 2003; Chen et al., 2006; Ku et al., 2012; Tanaka et al., 2012). In addition, an increase in sustained brain activation and a decrease in transient brain activation were found in the frontal–parietal network after AMC training, which further indicated the important role of this network in AMC and extended the previous findings from a state/ event-related dual-process perspective.

Transfer effect of training induced changes in transient and sustained activation While training induced changes in sustained and transient activation were detected in a trained AO task, the activation changes in non-trained tasks may advance our understanding of training-induced transfer effects. In the current study, we designed a CVWM task and a SVS task to examine the transfer effect of AMC training. When the AO task is solved using AMC strategy, it shares very similar spatial-pattern maintenance and manipulation processes with CVWM. In contrast, the SVS involves minimum requirement of manipulation but

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Fig. 6. Sustained and transient brain responses to the trained and non-trained tasks within the frontal–parietal network. (A) Beta value of trained (AO) task. (B) Beta value of untrained (CVWM) task. (C) Beta value of untrained (SVS) task. The vertical axes represent the effect size of sustained and transient brain activation (estimated beta values of corresponding block and event regressors).Abbreviations: L, left; R, right; MFG, middle frontal gyrus; SPL, superior parietal lobule; Pre, pre-training; Post, post-training; Ses1, session 1; Ses2, session 2.

considerable demand for visuospatial information maintenance. Similar to the AO task, we found the AMC group showed increased sustained and decreased transient brain activation in frontal–parietal regions on the CVWM task. Such functional changes identified on a trained task (AO task) and observed on a non-trained task suggest a transfer effect of AMC training on brain activation. On the SVS task, however, the change of activation pattern was only seen in the parietal regions but not in the frontal region. Given that the parietal nodes are more related to spatial information maintenance (Koch et al., 2005), whereas the frontal nodes are engaged in updating (D'Esposito et al., 1999; Passingham and Sakai, 2004; Koch et al., 2005), the lack of updating component in SVS task may contribute to the activation changeless of frontal regions. This result suggests that the AMC training does not simply alter the basal activation of the frontal–parietal network. Instead, the training effect on brain activation can be transferred adaptively based on task demand.

Transient and sustained activation in WM tasks Sustained brain activation in the frontal–parietal network may facilitate WM performance. A recent study demonstrated that

stimulation of the frontal–parietal network with transcranial alternating current stimulation (tACS) when participants performed a WM task increased sustained brain activation in the frontal–parietal regions in the stimulated hemisphere, and the change of brain activation was related to task performance (Violante et al., 2017). Relatedly, a previous study showed increased frontal–parietal activation after WM training (Olesen et al., 2004). However, decreased sustained activity was also observed in previous studies (Hempel et al., 2004; Clark et al., 2017) and our own (Dong et al., 2016; Wang et al., 2017). Many facets can lead to these inconsistent results, among which are, for example, the types of training tasks, and the degree to which such tasks place demand for different cognitive components (Brahmbhatt et al., 2010). In addition, experimental design may also impact the detection of brain activation, as previous studies demonstrated that cognitive functions involved a combination of state-related and eventrelated processes (Brahmbhatt et al., 2010; Petersen and Dubis, 2012). The event-related process might be modulated by changes of the state-related process or the reverse was true (Donaldson et al., 2001). However, neither block nor event-related design only is sufficient enough to detect the neural activity consisting of these two processes (Petersen

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and Dubis, 2012), which may result in these inconsistent findings. A mixed design is regarded as an effective tool for separating transient, trial-related activity from sustained activity in fMRI experiments (Visscher et al., 2003; Dosenbach et al., 2006). In the current study, we found enhanced sustained activation and reduced transient activation in the frontal– parietal network after AMC training (Fig. 6, Table 2). It is plausible that, by AMC training, participants developed a stronger ability to maintain task-related information and, therefore, could deal with item-related manipulation with less neural resources. This more proactive strategy might transfer to non-trained tasks and hence, improved behavioral performance consequently. Duration and intensity have been proposed to characterize the training-induced neural plasticity (Klingberg, 2010). Here our data suggest that the distinct changes in sustained and transient activation are another key feature of training induced plasticity, which warrants more attention in future studies on training and transfer effect on brain activation.

AMC training and working memory WM is theoretically conceptualized as a limited capacity brain system underlying short-term information maintenance and manipulation (Baddeley, 1992), and thus provides a framework to support cognitive processes such as language comprehension, arithmetic operation, planning and problem solving. Within the educational settings for healthy young population, WM predicts academic achievements (Alloway and Alloway, 2010). Given the importance of WM, it is of intensive interests to improve WM by training (Olesen et al., 2004; Klingberg et al., 2005; Owen et al., 2005; Jaeggi et al., 2008; Klingberg, 2010; Bigorra et al., 2016). AMC operation requires the integration of multiple cognitive processes including visual perception, retrieval of abacus principles, math facts, number representation, maintaining and updating intermediate results via an imaginary abacus (Stigler, 1984; Frank and Barner, 2012). Though not originally designed as a WM training program, AMC training may have served as a variant of WM training targeting the visuospatial component. Whether AMC training has an effect on the other WM components is an intriguing question. Based on the multi-component model (Baddeley, 2012), WM is a construct consisting of a “Central Executive” system interacting with three subsystems, namely, “Visuospatial sketch-pad”, “Episodic Buffer”, and “Phonological loop”. Although the AMC training seems to be more related to the “Visuospatial sketch-pad”, our previous studies found enhanced switch ability (Wang et al., 2015, 2017) and larger memory span for alphabetical sequences (Hu et al., 2011) in AMC children, with the former being closely related to the “Central Executive” component and the latter related to the “Phonological loop” component. As the “Central Executive” component is required in all types of WM, any WM training may have an impact on this component. Consequently, a specific type of WM training, such as visuospatial WM training, may be beneficial to other WM types. In the present study, we found

the AMC training affects the sustained/transient activity on the CVWM tasks that require subjects to constantly update a set of fixed elements maintained throughout the operation period. While the updating process is associated with transient activation and the maintenance process is more related to sustained activation in our analysis, the changes in sustained/transient activity after training could also result, at least partially, from changes in the “Central Executive” component. As such, we expect similar functional changes of the sustained/transient activity in the AMC children in any CVWM paradigm that requires both visuospatial information manipulation and maintenance. For example, in an adapted measure of the Corsi block-tapping task (Berch et al., 1998), the maintenance of the visuospatial sequence might be associated with enhanced sustained activation, whereas the retrieving of the sequence would be associated with reduced transient activity. However, whether similar changes in sustained/transient activity can also be obtained in other WM tasks beyond non-visuospatial modality warrants further study. We speculate that the complex AMC rules used to manipulate/update the imagery abacus (a two-dimensional visuospatial pattern) may result in strong training effects on neural plasticity and high cross-modal transferability. One limitation of the present study is that the control group was recruited retrospectively and scanned with a different MRI scanner due to the unavailability of the same scanner used in the AMC group. We are aware that differences in scanner performance will induce inter-scanner variability in brain activation. However, as the primary question we aimed to address is the training effect on brain activation, and the same scanner was used in pre- and posttraining within each group, the training-induced changes in activation within the AMC group could stand alone without the use of the control group. The employment of control subjects was mainly to control the test–retest effect. Indeed, no difference was found on activation between two sessions in the control group, indicating that brain activation changes in the AMC group more likely resulted from AMC training other than a test–retest effect. In the later ANOVAs, we did combine the two groups and looked for the GroupSession-Regressor 3-way interactions. The following post hoc analyses revealed that the interaction was driven by the pre–post activation changes in the AMC group, and the transfer effect was only identified in the AMC group in the specific frontal–parietal regions with a task-dependent fashion (Fig. 6A). It seems unlikely that the inter-scanner variability may cause activation changes in such a specific way. Another limitation is the lack of an active control group. Some of the changes in brain activation may simply result from the engagement in a training program in general. However, as a number of studies have suggested that AMC involves frontal–parietal network (Hanakawa et al., 2003; Chen et al., 2006; Hu et al., 2011), the functional change of this network reported by current study is more likely to be related to AMC training. Taken together, the present study identified an increase in sustained activity and a decrease in transient activity in frontal–parietal regions following AMC training. This reallocation

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of neural resources between these two processes may reflect that a more effective strategy was used to perform the task after AMC training. Moreover, the change of training-induced activation pattern was also adaptively transferred to a visuospatial working memory task and a visuospatial stimulation task. These findings provide direct evidence for AMC-induced functional change and the transfer effect of AMC training. The present study also offers a new state/event-related perspective to investigate the training effects on brain activation.

ACKNOWLEDGMENTS The authors are very grateful to Dr. Yongdi Zhou, Yixuan Ku, Yi Hu and Xianchun Li for their kind advices in experiment design. This study was supported in part by grants from the National Social Science Foundation (No. 17ZDA323), National Natural Science Foundation of China (No. 31270026, 61427807, 61525106, 61701436), the Public Project of Zhejiang Province Science and Technology Department of China (No. 2016C33156), the National Key Technology Research and Development Program of China (No. 2017YFE0104000), Zhejiang Provincial Natural Science Foundation of China (No. LY14C090003) and Zhejiang Provincial Social Sciences Foundation (No. 12JCWW21YB).

DECLARATIONS OF INTEREST None.

REFERENCES Alloway TP, Alloway RG. (2010) Investigating the predictive roles of working memory and IQ in academic attainment. J Exp Child Psychol 106:20-29, https://doi.org/10.1016/j.jecp.2009.11.003. Ashburner J. (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38:95-113, https://doi.org/10.1016/j. neuroimage.2007.07.007. Baddeley A. (1992) Working memory. Science 255:556-559, https://doi. org/10.1126/science.1736359. Baddeley A. (2012) Working memory: theories, models, and controversies. Annu Rev Psychol 63:1-29, https://doi.org/10.1146/annurevpsych-120710-100422. Barner D, Alvarez G, Sullivan J, Brooks N, Srinivasan M, Frank MC. (2016) Learning mathematics in a visuospatial format: a randomized, controlled trial of mental abacus instruction. Child Dev 87:1146-1158, https://doi.org/10.1111/cdev.12515. Behzadi Y, Restom K, Liau J, Liu TT. (2007) A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage 37:90-101, https://doi.org/10.1016/j. neuroimage.2007.04.042. Berch DB, Krikorian R, Huha EM. (1998) The Corsi block-tapping task: methodological and theoretical considerations. Brain Cogn 38:317338. Bigorra A, Garolera M, Guijarro S, Hervas A. (2016) Long-term fartransfer effects of working memory training in children with ADHD: a randomized controlled trial. Eur Child Adolesc Psychiatry 25:853-867, https://doi.org/10.1007/s00787-015-0804-3. Brahmbhatt SB, White DA, Barch DM. (2010) Developmental differences in sustained and transient activity underlying working memory. Brain Res 1354:140-151, https://doi.org/10.1016/j. brainres.2010.07.055. Chen F, Hu Z, Zhao X, Wang R, Yang Z, Wang X, Tang X. (2006) Neural correlates of serial abacus mental calculation in children: a

145

functional MRI study. Neurosci Lett 403:46-51, https://doi.org/ 10.1016/j.neulet.2006.04.041. Clark CM, Lawlor-Savage L, Goghari VM. (2017) Functional brain activation associated with working memory training and transfer. Behav Brain Res 334:34-49, https://doi.org/10.1016/j.bbr.2017.07.030. Courtney SM, Ungerleider BG, Keil K, Haxby JV. (1997) Transient and sustained activity in a distributed neural system for human working memory. Nature 386:608-611, https://doi.org/10.1038/386608a0. Cox RW. (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162-173, https://doi.org/10.1006/cbmr.1996.0014. Cox RW, Chen G, Glen DR, Reynolds RC, Taylor PA. (2017) FMRI clustering in AFNI: false-positive rates redux. Brain Connect 7:152171, https://doi.org/10.1089/brain.2016.0475. Dehaene S, Spelke E, Pinel P, Stanescu R, Tsivkin S. (1999) Sources of mathematical thinking: behavioral and brain-imaging evidence. Science 284:970-974, https://doi.org/10.1126/science.284.5416.970. D'Esposito M, Postle BR, Ballard D, Lease J. (1999) Maintenance versus manipulation of information held in working memory: an eventrelated fMRI study. Brain Cogn 41:66-86, https://doi.org/10.1006/ brcg.1999.1096. Donaldson DI, Petersen SE, Ollinger JM, Buckner RL. (2001) Dissociating state and item components of recognition memory using fMRI. NeuroImage 13:129-142, https://doi.org/10.1006/nimg.2000.0664. Dong S, Wang C, Xie Y, Hu Y, Weng J, Chen F. (2016) The impact of abacus training on working memory and underlying neural correlates in young adults. Neuroscience 332:181-190, https://doi.org/ 10.1016/j.neuroscience.2016.06.051. Dosenbach NUF, Visscher KM, Palmer ED, Miezin FM, Wenger KK, Kang HSC, Burgund ED, Grimes AL, et al. (2006) A core system for the implementation of task sets. Neuron 50:799-812, https://doi. org/10.1016/j.neuron.2006.04.031. Du F, Yao Y, Zhang Q, Chen F. (2014) Long-term abacus training induces automatic processing of abacus numbers in children. Perception 43:694-704, https://doi.org/10.1068/p7625. Frank MC, Barner D. (2012) Representing exact number visually using mental abacus. J Exp Psychol Gen 141:134-149, https://doi.org/ 10.1037/a0024427. Friston KJ, Williams S, Howard R, Frackowiak RSJ, Turner R. (1996) Movement-related effects in fMRI time-series. Magn Reson Med 35:346-355, https://doi.org/10.1002/mrm.1910350312. Garavan H, Kelley D, Rosen A, Rao SM, Stein EA. (2000) Practicerelated functional activation changes in a working memory task. Microsc Res Tech 51:54-63, https://doi.org/10.1002/1097-0029 (20001001)51:1<54::aid-jemt6>3.0.co;2-j. Hanakawa T, Honda M, Okada T, Fukuyama H, Shibasaki H. (2003) Neural correlates underlying mental calculation in abacus experts: a functional magnetic resonance imaging study. NeuroImage 19:296-307, https://doi.org/10.1016/s1053-8119(03)00050-8. Hatano G, Miyake Y, Binks MG. (1977) Performance of expert abacus operators. Cognition 5:47-55, https://doi.org/10.1016/0010-0277(77)90016-6. Hatano G, Osawa K. (1983) Digit memory of grand experts in abacusderived mental calculation. Cognition 15:95-110, https://doi.org/ 10.1016/0010-0277(83)90035-5. Hatta T, Ikeda K. (1988) Hemispheric specialization of abacus experts in mental calculation: evidence from the results of time-sharing tasks. Neuropsychologia 26:877-893, https://doi.org/10.1016/0028-3932(88)90056-5. Hempel A, Giesel FL, Caraballo NMG, Amann M, Meyer H, Wustenberg T, Essig M, Schroder J. (2004) Plasticity of cortical activation related to working memory during training. Am J Psychiatry 161:745-747, https://doi.org/10.1176/appi.ajp.161.4.745. Hu Y, Geng F, Tao L, Hu N, Du F, Fu K, Chen F. (2011) Enhanced white matter tracts integrity in children with abacus training. Hum Brain Mapp 32:10-21, https://doi.org/10.1002/hbm.20996. Huan J, Du FL, Yao Y, Wan Q, Wang XS, Chen FY. (2015) Numerical magnitude processing in abacus-trained children with superior mathematical ability: an EEG study. J Zhejiang Univ Sci B 16:661671, https://doi.org/10.1631/jzus.B1400287. Jaeggi SM, Buschkuehl M, Jonides J, Perrig WJ. (2008) Improving fluid intelligence with training on working memory. Proc Natl Acad Sci U S A 105:6829-6833, https://doi.org/10.1073/pnas.0801268105.

146

Hui Zhou et al. / Neuroscience 408 (2019) 135–146

Klingberg T. (2010) Training and plasticity of working memory. Trends Cogn Sci 14:317-324, https://doi.org/10.1016/j.tics.2010.05.002. Klingberg T, Fernell E, Olesen PJ, Johnson M, Gustafsson P, Dahlstrom K, Gillberg CG, Forssberg H, et al. (2005) Computerized training of working memory in children with ADHD - a randomized, controlled trial. J Am Acad Child Adolesc Psychiatry 44:177-186, https://doi.org/10.1097/00004583-200502000-00010. Koch G, Oliveri M, Torriero S, Carlesimo GA, Turriziani P, Caltagirone C. (2005) rTMS evidence of different delay and decision processes in a fronto-parietal neuronal network activated during spatial working memory. NeuroImage 24:34-39, https://doi.org/10.1016/j. neuroimage.2004.09.042. Ku Y, Hong B, Zhou W, Bodner M, Zhou Y-D. (2012) Sequential neural processes in abacus mental addition: an EEG and fMRI case study. PLoS One 7e36410, https://doi.org/10.1371/journal.pone.0036410. Marklund P, Fransson P, Cabeza R, Petersson KM, Ingvar M, Nyberg L. (2007) Sustained and transient neural modulations in prefrontal cortex related to declarative long-term memory, working memory, and attention. Cortex 43:22-37, https://doi.org/10.1016/s0010-9452(08) 70443-x. Olesen PJ, Westerberg H, Klingberg T. (2004) Increased prefrontal and parietal activity after training of working memory. Nat Neurosci 7:7579, https://doi.org/10.1038/nn1165. Owen AM, McMillan KM, Laird AR, Bullmore E. (2005) N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp 25:46-59, https://doi.org/ 10.1002/hbm.20131. Passingham D, Sakai K. (2004) The prefrontal cortex and working memory: physiology and brain imaging. Curr Opin Neurobiol 14:163-168, https://doi.org/10.1016/j.conb.2004.03.003. Petersen SE, Dubis JW. (2012) The mixed block/event-related design. NeuroImage 62:1177-1184, https://doi.org/10.1016/j. neuroimage.2011.09.084. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59:2142-2154, https://doi.org/10.1016/j.neuroimage.2011.10.018. Sakai K. (2008) Task set and prefrontal cortex. Annu Rev Neurosci 31:219-245, https://doi.org/10.1146/annurev. neuro.31.060407.125642. Scheibe C, Wartenburger I, Wustenberg T, Kathmann N, Villringer A, Heekeren HR. (2006) Neural correlates of the interaction between transient and sustained processes: a mixed blocked/event-related fMRI study. Hum Brain Mapp 27:545-551, https://doi.org/10.1002/ hbm.20199.

Smith EE, Jonides J. (1998) Neuroimaging analyses of human working memory. Proc Natl Acad Sci U S A 95:12061, https://doi.org/ 10.1073/pnas.95.20.12061. Stigler JW. (1984) “Mental abacus”: the effect of abacus training on Chinese children's mental calculation. Cogn Psychol 16:145-176, https://doi.org/10.1016/0010-0285(84)90006-9. Tanaka S, Michimata C, Kaminaga T, Honda M, Sadato N. (2002) Superior digit memory of abacus experts: an event-related functional MRI study. Neuroreport 13:2187-2191, https://doi.org/ 10.1097/00001756-200212030-00005. Tanaka S, Seki K, Hanakawa T, Harada M, Sugawara SK, Sadato N, Watanabe K, Honda M. (2012) Abacus in the brain: a longitudinal functional MRI study of a skilled abacus user with a right hemispheric lesion. Front Psychol 3, https://doi.org/10.3389/ fpsyg.2012.00315. Violante IR, Li LM, Carmichael DW, Lorenz R, Leech R, Hampshire A, Rothwell JC, Sharp DJ. (2017) Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance. Elife 6e22001, https://doi.org/10.7554/ elife.22001.001. Visscher KM, Miezin FM, Kelly JE, Buckner RL, Donaldson DI, McAvoy MP, Bhalodia VM, Petersen SE. (2003) Mixed blocked/event-related designs separate transient and sustained activity in fMRI. NeuroImage 19:16941708, https://doi.org/10.1016/s1053-8119(03)00178-2. Wang C, Geng F, Yao Y, Weng J, Hu Y, Chen F. (2015) Abacus training affects math and task switching abilities and modulates their relationships in Chinese children. PLoS One 10e0139930, https://doi. org/10.1371/journal.pone.0139930. Wang C, Weng J, Yao Y, Dong S, Liu Y, Chen F. (2017) Effect of abacus training on executive function development and underlying neural correlates in Chinese children. Hum Brain Mapp 38:52345249, https://doi.org/10.1002/hbm.23728. Wang Y, Geng F, Hu Y, Du F, Chen F. (2013) Numerical processing efficiency improved in experienced mental abacus children. Cognition 127:149-158, https://doi.org/10.1016/j.cognition.2012.12.004. Westerberg H, Klingberg T. (2007) Changes in cortical activity after training of working memory - a single-subject analysis. Physiol Behav 92:186-192, https://doi.org/10.1016/j.physbeh.2007.05.041. Yan CG, Wang XD, Zuo XN, Zang YF. (2016) DPABI: Data Processing & Analysis for (resting-state) brain imaging. Neuroinformatics 14:339-351, https://doi.org/10.1007/s12021-016-9299-4. Yao Y, Du FL, Wang CJ, Liu YQ, Weng J, Chen FY. (2015) Numerical processing efficiency improved in children using mental abacus: ERP evidence utilizing a numerical Stroop task. Front Hum Neurosci 9:245, https://doi.org/10.3389/fnhum.2015.00245.

(Received 26 October 2018, Accepted 1 April 2019) (Available online 11 April 2019)