White matter microstructural properties correlate with sensorimotor synchronization abilities

    White matter microstructural properties correlate with sensorimotor synchronization abilities Tal Blecher, Idan Tal, Michal Ben-Shachar PII: DOI: Reference:

S1053-8119(16)30140-9 doi: 10.1016/j.neuroimage.2016.05.022 YNIMG 13179

To appear in:

NeuroImage

Received date: Revised date: Accepted date:

13 October 2015 3 May 2016 6 May 2016

Please cite this article as: Blecher, Tal, Tal, Idan, Ben-Shachar, Michal, White matter microstructural properties correlate with sensorimotor synchronization abilities, NeuroImage (2016), doi: 10.1016/j.neuroimage.2016.05.022

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.

ACCEPTED MANUSCRIPT White matter microstructural properties correlate with sensorimotor

PT

synchronization abilities

1

SC

RI

Tal Blecher1, Idan Tal1 and Michal Ben-Shachar1, 2*

The Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan,

2

NU

Israel.

Department of English Literature and Linguistics, Bar-Ilan University, Ramat-Gan,

AC CE P

TE

D

MA

Israel.

Abbreviated title: White matter and rhythmic synchronization Keywords: Diffusion MRI, Tractography, Arcuate fasciculus, Corpus callosum, Sensorimotor synchronization, Musical meter.

* Corresponding author: Michal Ben-Shachar, PhD The Gonda Brain Research Center Bar Ilan University Ramat Gan 5290002, Israel [email protected]

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization

Abstract Sensorimotor synchronization (SMS) to an external auditory rhythm is a

PT

developed ability in humans, particularly evident in dancing and singing. This ability is typically measured in the lab via a simple task of finger tapping to an auditory beat.

RI

While simplistic, there is some evidence that poor performance on this task could be

SC

related to impaired phonological and reading abilities in children. Auditory-motor synchronization is hypothesized to rely on a tight coupling between auditory and motor

NU

neural systems, but the specific pathways that mediate this communication have not been identified yet. In this study, we test this hypothesis and examine the contribution of

MA

fronto-temporal and callosal connections to specific measures of rhythmic synchronization. Twenty participants went through SMS and diffusion magnetic resonance imaging (dMRI) measurements. We quantified the mean asynchrony between

D

an auditory beat and participants’ finger taps, as well as the time to resynchronize (TTR)

TE

with an altered meter, and examined the correlations between these behavioral measures and diffusivity in a small set of predefined pathways. We found significant correlations

AC CE P

between asynchrony and fractional anisotropy (FA) in the left (but not right) arcuate fasciculus and in the temporal segment of the corpus callosum. On the other hand, TTR correlated with FA in the precentral segment of the callosum. To our knowledge this is the first demonstration that relates these particular white matter tracts with performance on an auditory-motor rhythmic synchronization task. We propose that left frontotemporal and temporal-callosal fibers are involved in prediction and constant comparison between auditory inputs and motor commands, while inter-hemispheric connections between the motor/premotor cortices contribute to successful resynchronization of motor responses with a new external rhythm, perhaps via inhibition of tapping to the previous rhythm. Our results indicate that auditory-motor synchronization skills are associated with anatomical pathways that have been previously related to phonological awareness, thus offering a possible anatomical basis for the behavioral covariance between these abilities.

2

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization

Introduction Sensorimotor synchronization involves fine temporal coordination of sequential

PT

motor actions to an external train of stimuli. We are engaged in sensorimotor synchronization (SMS) in our daily life, for example, when we move to the beat of our

RI

favorite song, or clap together with a large audience at the end of a concert. Other tasks may involve SMS implicitly, such as typing or writing (Pagliarini et al., 2015; Thomson

SC

and Goswami, 2008). Sensorimotor synchronization processes occur seamlessly and spontaneously for most people, but impairments in SMS have been reported in reading-

NU

and language- impaired individuals (Corriveau and Goswami, 2009; Huss et al., 2011; Leong and Goswami, 2014; Wolff, 2002; but see Seidenberg, 2011), as well as in

MA

individuals who stutter (Falk et al., 2015). Along the same lines, SMS abilities have been associated with pre-reading skills and with the precision of neural encoding of syllables in preschoolers (Woodruff Carr et al., 2014). These findings raise the hypothesis that

D

SMS may rely on similar pathways as those that serve phonology and reading acquisition.

TE

Electrophysiological (EEG) studies have shown that SMS involves fine temporal coupling between anterior (frontal) and posterior (temporal) brain regions (Nozaradan et

AC CE P

al., 2015; Repp and Su, 2013). Numerous fMRI studies revealed a wide network of cortical and subcortical regions involved in SMS tasks, including primary auditory and motor cortex, premotor and supplementary motor areas, inferior frontal and inferior parietal cortex, cerebellum and basal ganglia (for review see Repp and Su, 2013; Witt et al., 2008). In fact, it has been shown that rhythm perception, even without a motor component, involves interactions between auditory and motor cortices (Chen et al., 2006; Grahn et al., 2011; Grahn and Rowe, 2009; see Grahn, 2012; Large et al., 2015 for reviews). Based on such evidence, it has been hypothesized that synchronization between an auditory rhythm and a sequence of motor actions relies on long range white matter connections between and within the motor and auditory systems (e.g., Zatorre et al., 2007). However, to date, the specific white matter tracts that contribute to sensorimotor synchronization have not been identified experimentally. Our goal in this study is to examine the contribution of long range fronto-temporal and commissural connections of the temporal and frontal lobes to specific aspects of sensorimotor synchronization.

3

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization Behaviorally, the ability to synchronize between sensory cues and motor responses is commonly operationalized via a finger tapping paradigm, in which participants are requested to tap in synchrony with an external, typically auditory, rhythm

PT

(see Repp, 2005, Repp and Su, 2013 for comprehensive reviews). This task has been studied extensively since the late 19th century (e.g., Stevens, 1886; Miyake, 1902;

RI

Dunlap, 1910; Woodrow, 1932 and much later work), perhaps due to its minimalist setup,

SC

which still allows for rigorous quantification of different aspects of SMS. Using this task, a curious phenomenon was discovered: Participants’ finger taps tend to precede the

NU

auditory tones by about 20-80 milliseconds (Aschersleben and Prinz, 1997, 1995; Miyake, 1902; Woodrow, 1932), suggesting that they become entrained by the auditory

MA

rhythm and anticipate the occurrence of future sensory events (Aschersleben, 2002). This anticipatory tendency is captured by a parameter commonly named Negative Asynchrony (NA or NMA for Negative Mean Asynchrony), which is calculated as the mean temporal

D

shift between the taps and the auditory cues, averaged across the experimental session.

TE

For the sake of clarity, we refer to this parameter as Asynchrony (see Figure 1, in Methods), and its sign (negative or positive) can be observed in the reported values. The

AC CE P

standard deviation of these tap-cue temporal shifts is commonly used to quantify the stability of the prediction of each participant. There are many different accounts for NA, typically highlighting the interaction between central representation systems of different modalities (Aschersleben, 2002). NA is sometimes interpreted as evidence for anticipatory processes, which are necessary for synchronizing events in different sensory modalities that vary in their transfer speed (Aschersleben, 2002; Repp, 2005). Otherwise, if the system worked in a reactive, not anticipatory manner, the relatively slow feedback from the somatosensory modality (finger tap) would not coincide with the relatively fast feedback from the auditory modality. Such anticipatory processes play a critical role in a recent model of SMS, the ADaptation and Anticipation Model (ADAM; van der Steen and Keller, 2013). However, NA may also be explained as resulting from underestimating the temporal lag between the pacing sounds (Repp and Keller, 2008; Wohlschläger and Koch, 2000). Interestingly, the modality of the pacing stimulus affects the magnitude of the NA: auditory pacing stimuli evoke the largest NA, while visual and tactile pacing stimuli 4

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization result in reduced NA, sometimes even eliminating it altogether (Kolers and Brewster, 1985; Müller et al., 2008, 2000). fMRI studies have shown that synchronizing with an auditory stimulus generates activation in bilateral auditory cortex, bilateral premotor

PT

cortex (PMC, dorsal and ventral), SMA, bilateral basal ganglia and claustrum, right posterior IFG and right cerebellum (see, e.g., Chapin et al., 2010; Chen et al., 2006;

RI

Jäncke et al., 2000; Kung et al., 2013; Lewis et al., 2004; for review see Repp and Su,

SC

2013; Witt et al., 2008). Stimulation with repetitive transcranial magnetic stimulation (rTMS) increased the variability of asynchrony to an auditory pacing stimulus when

NU

applied to the ventral or dorsal PMC contralateral to the tapping hand (Del Olmo et al., 2007; Kornysheva and Schubotz, 2011). Based on these findings and on prior published

MA

hypotheses (Patel and Iversen, 2014; Zatorre et al., 2007), we hypothesized that measures of asynchrony to an auditory pacing stimulus would be related to the microstructural properties of the dorsal auditory pathways connecting the auditory cortices in left and

D

right STG with the lateral premotor cortices in the left and right posterior prefrontal

TE

cortex.

While NA is an intriguing and highly studied parameter, it fails to capture the

AC CE P

dynamic aspect of adapting to changes in an external pacing stimulus. The ability to adapt to a changing rhythm is an integral component of musical performance, dancing or singing. Adaptation to changes in the pacing stimulus plays an important role in current models of SMS. For example, in the ADAM (van der Steen and Keller, 2013), the abovementioned anticipatory module is complemented by an adaptive module, which implements reactive error correction of the phase and period of the motor. Adaptive processes are often manipulated in SMS literature through perturbations in phase and tempo of the pacing stimulus (see, e.g., Large et al., 2002). In the current study, we took a different approach. To introduce this properly, we first introduce the concepts of a ‘beat’, an underlying pulse that listeners experience when they listen to music (Drake, 1998; Grahn and Brett, 2007), and a ‘meter’, a regular grouping of the sounds which is typically marked by an accent (in pitch or amplitude) on the first sound of each group (‘bar’) (Large and Snyder, 2009). In adult humans, EEG-recorded brain responses show selective enhancement of the amplitude at beat- and meter- related frequencies, indicating spontaneous entrainment to the rhythm (Nozaradan et al., 2012). Furthermore, recent 5

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization findings demonstrated that the magnitude of the neural response at the pulse frequency was correlated with behavioral measures of rhythmic pulse perception (Nozaradan et al., 2016) . Interestingly, infants as young as 7 months of age may perceive a specific meter

PT

defined by accenting certain auditory beats with a bounce (Phillips-silver and Trainor, 2005). Adults may perceive a ‘beat’, a recurring pulse, even in an isochronous,

RI

monotonous train of auditory stimuli (Brochard et al., 2003), but this beat will be

SC

internally generated and thus hard to manipulate or control.

To examine resynchronization in a controlled situation, we used a variant of the

NU

SMS task in which participants are asked to tap together with an auditory beat using two fingers: One finger marks the accent (downbeat), while the other finger marks the weaker

MA

beats. Once in a few bars, the meter would change, say, from 2/4 to 3/4 and the participant would be expected to resynchronize their finger tapping to the new meter. The same experimental paradigm was used recently to assess the temporal accuracy of

D

cortico-cortical interactions in magnetoencephalography (MEG) signals (Tal and Abeles,

TE

2016) In the latter study, synchronized tapping and resynchronization to a new meter were associated with different spatio-temporal activation patterns, thus indicating the

AC CE P

importance of precise temporal interactions among different cortical regions. The time lag that it took a participant to resynchronize their tapping with the new meter (time to resynchronize, TTR, see Figure 1 in Methods) indicates their ability to disengage from the current entrainment process and to entrain to a new meter. Resynchronizing to a new meter relies on several processes: recognition of the change in meter, inhibition of the prior rhythmic tapping behavior, reprogramming and execution of a new tapping behavior. We hypothesized that inhibiting the motor actions related to the prior meter may be mediated by commissural inhibitory connections between the motor or premotor cortices (Ni et al., 2009), and therefore that larger TTRs may be associated with reduced connectivity through the motor segment of the corpus callosum. To our knowledge, this is the first study to use dMRI in order to examine the relation between white matter properties, Asynchrony and Time-to-Resynchronize to an external meter. In order to enhance statistical power and sensitivity, we adopted a hypothesis driven approach, focusing on a small number of tracts that are suspected to mediate auditory-motor synchronization based on existing models (Schubotz, 2007; 6

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization Zatorre et al., 2007). We targeted the left and right arcuate fasciculus and the temporal and motor segments of the corpus callosum in each participant’s native space, using tractography. We then analyzed the relation between diffusivity parameters extracted

PT

from specific segments of these tracts and behavioral measures that quantify performance on the SMS task. We tested the hypotheses that NA is associated with diffusivity in the

RI

bilateral arcuate fasciculus and that TTR is associated with diffusivity in the precentral

SC

segment of the corpus callosum. Precise characterization of the white matter pathways contributing to sensorimotor synchronization can lead to better understanding of the

NU

anatomical pathways that mediate SMS, and allow us to generate predictions about the effect of SMS training on these pathways and other functions which they may sustain.

MA

Methods Participants

D

Twenty healthy neurotypical participants were recruited for this study (age range:

TE

19-40 years, mean: 29.47±5.81 years, 9 males, 11 females). All participants were right handed, with a handedness quotient of over 80% (assessed using the Hebrew version of

AC CE P

the Edinburgh Handedness Inventory, Oldfield, 1971), without hearing deficits and without diagnosed ADHD or any other diagnosed neurological or psychiatric condition. Since this study involves rhythmic abilities which may be different in musicians and nonmusicians, we recruited only participants who have no more than 5 years of formal musical training and not actively practicing an instrument. Before participating, participants signed a written informed consent according to protocols approved by the ethics committee of the Tel-Aviv Sourasky Medical Center and by the ethics committee of Bar Ilan University.

Behavioral data acquisition Participants took part in an auditory-motor rhythm synchronization experiment. Throughout the experiment, participants listened to a sequence of short sounds presented at a fixed pace of 1 stimulus every 493 msec (i.e., the inter-onset interval, IOI, was always 493 msec). While listening, participants were asked to tap along with the sounds. This finger- tapping task is a popular quantitative paradigm for assessing motor 7

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization synchronization to an external auditory rhythm (Kadota et al., 2004; Konvalinka et al., 2010; Lewis et al., 2004). In the current experiment, we further introduced a meter, by highlighting the first sound in every bar. The highlighted sound (accent) in every bar was

PT

presented as a bass drum sound while the weaker sounds were presented as rim shots on a snare drum (Tal and Abeles, 2016). Participants were asked to use their right index finger

RI

to tap with the accent and their right middle finger to tap with the weaker sounds (Figure

SC

1). Stimulus presentation and response collection were timed using E-Prime (Psychology Software Tools, Pittsburgh, PA). Auditory stimuli were presented using a STAX SRS-

NU

005 amplifier and SR-003 electrostatic ear speakers. Tapping responses were recorded using the LUMItouchTM fiber optic response system (Photon Control Inc., Burnaby BC,

MA

Canada). The button press may create an acoustic artifact; therefore, a constant white noise was used as a mask. Given that the response box records a button press dichotomously (i.e., a button is either pressed or not pressed, no ‘strong’ or ‘weak’ taps

D

are coded), it is imperative to use two different fingers to indicate the meter (a pattern of

TE

strong and weak sounds) in this task. We asked participants to respond with two fingers of a single hand (and not, for example, with the index fingers of the left and right hands)

AC CE P

in order to avoid trans-callosal coordination of the motor response itself. Each participant was exposed to two experimental conditions, presented in separate blocks: In the Consistent Meter (CM) condition, stimuli were presented at a fixed meter, either 2/4 or 3/4, throughout the entire block (30 sec). Each of these meters was presented in two full blocks, for a total of 4 CM blocks in an experimental session. In the Alternating Meter (AM) condition, stimuli were presented at a given meter (2/4 or 3/4) for at least 5 bars, and then the meter switched, from 2/4 to 3/4 or vice versa. Participants were notified of potential meter changes ahead of the block and were requested to adjust their tapping to the changed meter whenever it happened throughout the block (110 sec). The experimental session included 8 AM blocks. Overall, there were 95 meter changes during the experiment. Participants were also presented with blocks of Listen only (AM and CM) and with blocks of Tap only (2/4, 3/4 and random tapping). These blocks are not used in the current analysis because it is impossible to calculate the Asynchrony and TTR parameters when either the auditory or the motor component is

8

ACCEPTED MANUSCRIPT

TE

D

MA

NU

SC

RI

PT

White Matter and Rhythmic Synchronization

AC CE P

Figure 1. Finger tapping task and parameters. This scheme demonstrates 5 seconds of the finger tapping task and the quantitative parameters mentioned in the text. In this excerpt, the meter changes from 3/4 (in the first 1.5 seconds) to 2/4 (in the rest of the excerpt). Each sound-tap pair is defined as a trial. The gray zone extends between 200 msec post stimulus N to 200 msec pre stimulus N+1. Taps issued in this temporal window are excluded from analysis because they may not be clearly classified as responses to stimulus N or to stimulus N+1. IOI- Inter-Onset Interval. ITI- Inter-Tap Interval. TTR- Time To Resynchronize.

missing, as is the case in these blocks. The experiment lasted a total of 30 minutes (17 min synchronization blocks, 11 min Tap only and Listen only blocks, and a total of 2 min breaks between blocks). Block order was pseudorandomized, such that one block of Listen only, Tap only, and Consistent Meter were presented in the beginning of a session, prior to any Alternating Meter blocks.

MRI Data acquisition Magnetic Resonance Imaging (MRI) data were collected using a 3T scanner (Signa Excite, General Electric Medical Systems, Milwaukee, WI) located at the Tel 9

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization Aviv Sourasky Medical Center. Scanning was conducted with an eight-channel head coil for parallel imaging. Head motion was minimized by padding the head with cushions, and participants were asked to lie still during the scan.

PT

A standard dMRI protocol was applied by means of a single-shot spin-echo diffusion-weighted echo-planar imaging sequence (~68 axial, 2mm thick slices, no gap;

RI

FOV = 240 mm, image matrix size= 128×128 providing a cubic resolution of ~2 × 2 × 2

SC

mm). 19 diffusion-weighted volumes (b = 1000 s/mm2) and one reference volume (b = 0 s/mm2) were acquired using a standard diffusion direction matrix (similar protocols were

NU

used successfully with tractography methods in Kronfeld-Duenias et al., 2016; Sasson et al., 2013; Tavor et al., 2014). The scan volume was adjusted to cover the entire cerebrum

MA

in each participant, so the exact number of slices varied slightly between participants. Total scan time for the dMRI sequence was 5:50 min. High resolution T1 anatomical images were also acquired for each participant using a 3D fast spoiled gradient-recalled

D

echo sequence (FSPGR; 150 ± 12 1-mm thick axial slices, covering the entire cerebrum;

AC CE P

Data analysis

TE

voxel size: 1 × 1 × 1 mm).

Behavioral data analysis

The following parameters were extracted from the behavioral responses of each subject to evaluate rhythmic performance: (a) Asynchrony, and (b) Time to resynchronize (TTR). Asynchrony is defined as the time interval between a sound and its corresponding tap (see Figure 1). Mean asynchrony is calculated, per subject, as the average time interval between each sound and its corresponding tap (the tap closest to it in time), averaged across all trials (i.e., across all sound-tap pairs, including taps by either finger). The standard deviation of asynchrony is similarly calculated across all trials. To minimize the chances of misclassifying a tap as relating to the wrong sound, we excluded taps that occurred in the “gray zone” between sounds (see Figure 1). Specifically, taps issued from 200 msec post stimulus N to 200 msec pre stimulus N+1 were excluded from analysis. Taps issued with the wrong finger were also excluded from the calculation of mean asynchrony. Participants usually tend to tap before the sound is heard, therefore the mean asynchrony value is usually negative (Aschersleben, 2002). Thus, this measure 10

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization provides information about the predictive tendency of a participant (mean asynchrony) and the stability of their timing (standard deviation (SD) of asynchrony). TTR measures how long it takes for the participant to resynchronize with an

PT

altered meter, across all meter alteration trials. Mean TTR was calculated as follows: for each meter-switch event, the first incorrect tap following the event was identified (for

RI

example, the 4th middle finger tap in Figure 1). Typically this would be the tap

SC

corresponding to the third sound following the meter switch, because this is the discriminating trial between a meter of 3/4 and a meter of 2/4. Mean TTR is measured as

NU

the average time lag between this first incorrect tap (issued with either the middle or the index finger), and the first correct tap that followed it (see Figure 1 for visualization).

MA

Taps that succeeded the beat that signaled a change in meter by more than 2.5 seconds were excluded from calculation of the mean TTR. All in all, 2-7% of all taps issued by each subject were excluded for either falling in the gray zone (asynchrony calculation) or

D

exceeding 2.5 seconds post meter change (TTR).

TE

A third parameter often extracted in SMS tasks, Inter-Tap Interval (ITI), measures the mean time interval between consecutive taps (Figure 1) (Caspi, 2002; Kolers and

AC CE P

Brewster, 1985; Stevens, 1886). In the current paradigm, this parameter is likely to be uninteresting, as it is largely determined by the IOI, which was fixed throughout the experiment. Based on previous literature (Repp and Su, 2013; Fling et al., 2012), and in order to enhance statistical power, we decided to exclude this parameter from our brainbehavior association analyses. We still examined the distribution of ITI-related measures, compared to prior literature. Mean ITI was calculated, per subject, by averaging the time lags between successive finger taps, whether issued with the same finger or different fingers. %ITI deviation was calculated, per subject, as the standard deviation of ITI across all trials, divided by the IOI (493 msec) (Chen et al., 2008; Kung et al., 2013). Imaging data analysis Data preprocessing: Data preprocessing was conducted using the open sourced ‘mrDiffusion’ package (http://web.stanford.edu/group/vista/cgibin/wiki/index.php/MrDiffusion) and Matlab 2012b (The Mathworks, Nattick, MA). Preprocessing followed the same standard steps as in our previous publications (Dougherty et al., 2007; Kronfeld-Duenias et al., 2016; Yeatman et al., 2011): T1 11

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization alignment to ac-pc, distortion and motion correction, alignment of the diffusion images to the T1 images and tensor fitting. First, we aligned the T1 images to an ac-pc orientation: the locations of the

PT

anterior and posterior commissures were identified manually on the T1 of each participant and these points were used to align the anatomical T1 volume to a canonical

RI

ac–pc orientation, using a rigid body transformation (no warping was applied). Second,

SC

distortions in the diffusion weighted images due to eddy currents and subject motion were corrected by a 14-parameter constrained non-linear co-registration algorithm based

NU

on the expected pattern of eddy-current distortions (Rohde et al., 2004). Third, diffusion images were registered to the ac-pc aligned T1 anatomical images. Alignment was

MA

achieved by registering the b0 images to the resampled T1 image using a rigid body mutual information maximization algorithm (implemented in SPM5; Friston and Ashburner, 2004). At this final alignment stage, the combined transform resulting from

D

motion correction, eddy current correction and anatomical alignment was applied to the

TE

raw diffusion data once, and the data was resampled at exactly 2 mm isotropic voxels. Next, the table of gradient directions was appropriately adjusted to fit the resampled

AC CE P

diffusion data (Leemans and Jones, 2009). Finally, we fitted a tensor model to the diffusion data in each voxel using a standard least-squares algorithm, and extracted the eigenvectors and eigenvalues (λ1, λ2, λ3) of the tensor. Given the limited number of directions (19) and the choice of b-value (b=1000), the tensor model is the most appropriate for the analysis of our data. Importantly, at this b-value, the tensor model provides high accuracy, similar to more complicated shapes (Rokem et al., 2015). Using the eigenvalues extracted from each tensor, we calculated the Fractional Anisotropy (FA) in each voxel as the weighted standard deviation of the three eigenvalues (Basser and Pierpaoli, 1996). Additional complementary measures were calculated, including Axial Diffusivity (AD, λ1) and Radial Diffusivity (RD, (λ2 + λ3)/2)). AD is defined as the diffusivity along the principal axis of diffusion, and RD as the average diffusivity along the two remaining minor axes. Tract identification and segmentation: Our analysis approach was tract based, conducted in the native space of each participant. We focused on a small set of preselected tracts, including the left and right arcuate fasciculus (AF) and two segments of the corpus 12

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization callosum (CC): callosal fibers connecting the precentral cortices (precentral-CC) and those connecting the temporal cortices (temporal-CC). These tracts connect within and between the auditory and motor/premotor cortices, which are necessary for rhythmic

PT

synchronization to an auditory stimulus.

In order to identify these tracts and quantify their diffusion parameters we used

RI

the Automatic Fiber Quantification (‘AFQ’) package, an automated segmentation tool

SC

(Yeatman et al., 2012) within mrDiffusion. AFQ consists of the following steps: (1) Whole brain fiber tractography, (2) Tract segmentation based on region-of-interest (ROI)

NU

and automatic cleaning of fiber outliers, and (3) Quantification of diffusion properties along the tracts. For whole brain tracking (step 1), we used deterministic Streamlines

MA

Tractography (STT), with a 4th Runge- Kutta path integration method and 1mm fixed step size (Basser et al., 2000; Mori et al., 1999; Press et al., 2002). Tract segmentation (step 2) was done in the native space of each participant, using ROIs defined on a T1 template

D

(ICBM 2009a Nonlinear Asymmetric template; Fonov et al., 2011), which were back-

TE

transformed into the participant’s native space (see Supplementary Figure S1 for the definition of the CC ROIs, and see Wakana et al., 2007, Fig. 6 for the position of the AF

AC CE P

ROIs). After tract segmentation, an automatic cleaning strategy was applied, removing fibers longer than 4 standard deviations from the mean fiber length and those that spatially deviated more than 5 standard deviations from the core of the tract (see Yeatman et al., 2012 for details regarding the automatic segmentation method). For the CC segments, our approach is motivated by (Huang et al., 2005), who proposed that the CC is best segmented by ROIs defined deep in white matter, in effect segmenting the CC according to the cortical projection zones of its fibers. The original scheme of 6 segments proposed by Huang was already refined by (Dougherty et al., 2007) into 7 segments. Here we further divided the superior frontal segment into a precentral segment and a superior prefrontal one (Supplementary Figure S1). The reason for this division is that the superior frontal segment is very large and we wanted to focus only on the precentral (motor and premotor) segment within it. The precentral CC segment was defined by ROIs which constitute a subdivision of Huang’s superior frontal ROIs, extending between the central sulcus (posterior border) and the precentral sulcus (anterior border). The temporal CC segment was defined according to the same 13

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization guidelines outlined in (Dougherty et al., 2007; Huang et al., 2005). Each set of ROIs were defined bilaterally on the template and warped back to each participant’s native space (by inverting the transformation calculated, but not applied, for registering the T1 anatomy to

PT

the MNI template). Whole brain fibers were restricted to only those that pass through 2 ROIs, the two homologs for the CC segments, and for the AF, a precentral ROI and a

RI

temporal ROI (as shown in Yeatman et al., 2012, following Wakana et al., 2007).

SC

Quantification of diffusion properties (step 3) was done a bit differently for the arcuate and for the callosal segments. Diffusion properties of the arcuate fasciculus (AF)

NU

were calculated at 100 equidistant nodes along the tract. The resulting FA-profiles, for the left and right AF of each participant, were subject to further statistical analysis. We

MA

have shown in previous studies that FA varies considerably along the arcuate fasciculus, therefore tract profiles provide much better sensitivity for detecting brain-behavior correlations within this tract (Yeatman et al., 2011).

D

In contrast, callosal segments were truncated to a 10mm mid-sagittal clip, 5mm to

TE

each side of the mid-sagittal plane. Then, we calculated the mean diffusion properties of the 10mm clip for each segment, the precentral-CC and the temporal-CC. This approach

AC CE P

is motivated by the high directional coherence of callosal fibers around the midsagittal plane, yielding reliable diffusivity measurements (Dougherty et al., 2007, 2005). Our clips were shorter than the ones used by Dougherty et al. (2007), because we found that 20mm clips (used originally) in fact start to bend in their lateral ends and are more susceptible to partial voluming with nearby tracts. For tract visualization we used ‘Quench’, an interactive 3D visualization tool (Akers, 2006; http://web.stanford.edu/group/vista/cgi-bin/wiki/index.php/QUENCH). Brain-behavior correlation analysis For each tract, we calculated Pearson correlation coefficients between the FA extracted from this tract and the behavioral synchronization measures, mean asynchrony, asynchrony-SD and mean TTR. For the CC segments, the mean FA values of the 10mm midsagittal clips were used, yielding 3 statistical tests per segment (FA ~ mean asynchrony, FA ~ asynchrony-SD and FA ~ mean TTR). For the left and right arcuate fasciculi, Pearson correlation coefficients were calculated at 100 nodes along the trajectory of the tracts (see Tract identification and segmentation). Significance was 14

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization corrected for 100 comparisons using a nonparametric permutation method, yielding a family-wise error (FWE) corrected alpha value of 0.05 (Nichols and Holmes, 2002). Significant correlations between FA and behavior were followed up by post-hoc

PT

correlation analyses with RD and AD, in order to further interrogate the basis for FA correlations. In addition, significant brain-behavior correlations were followed up with

RI

partial correlations while controlling for age. We report these findings but note that FA of

SC

the arcuate fasciculus and the corpus callosum is not expected to vary systematically with age within the age range of our sample (Lebel et al., 2012; Yeatman et al., 2014).

NU

To examine the pattern of covariation between FA and behavioral measures in the AF, we selected a window of 9 nodes centered on the most significant node of the AF,

MA

and extracted FA, RD and AD values from this window for each participant. We then plot this data against the relevant behavioral measure, in order to examine the distribution of individual points that gave rise to the significant correlation observed in the statistical

D

analysis (see previous paragraph). The window size of 9 nodes was selected a-priori as a

TE

reasonable size that balances generalizability and specificity, but very similar scatter plots were observed with cluster sizes of 6, 11 and 13 (not shown).

AC CE P

In order to further verify that the brain-behavior correlations we observed were not randomly generated, we applied two random resampling methods: bootstrapping and shuffling. Using the bootstrapping approach, we resampled the data 1000 times and calculated the standard error (SE) of the distribution of correlation values. Shuffling analysis was done by randomly shuffling FA (extracted from segments that gave rise to significant correlations) across participants 10,000 times, while the behavioral measurements remained fixed. After each shuffle, a new correlation was calculated between the behavioral measures and the shuffled FA values. Then we calculated the percentile (p(shuffle)) of the original correlation value with respect to the distribution of the correlations calculated over the 10,000 shuffles. This percentile indicates the likelihood of randomly acquiring the original correlation value.

15

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization

Results Both behavioral parameters, Asynchrony and Time to resynchronize,

PT

demonstrated considerable variability across participants (Figure 2). This variability is an

D

MA

NU

SC

RI

important pre-requirement in order to establish brain-behavior correlations.

AC CE P

TE

Figure 2. Individual variability in behavioral parameters. A) Asynchrony. B) Time to resynchronize. Error bars denote ±1SD across the trials. The solid gray lines represent the mean of each parameter across 20 subjects, and the dashed gray lines represent ±2SD from that mean. Notice the different scale on the y axes due to the different scale of the measures (as demonstrated in Figure 1).

No significant differences were found between the Asynchrony values calculated over the responses using the index finger and the middle finger (mean Asynchrony (±SD) for index finger: -63 msec (±28); middle finger: -66 msec (±25); t(19) = 0.79; p= 0.44). Therefore, Asynchrony was pooled across all responses from both fingers. Furthermore, no significant correlation was found between Asynchrony and TTR (r = -0.37, p > 0.1). We therefore consider Asynchrony-related measures and TTR-related measures separately in the analysis of brain-behavior correlations. As we anticipated, mean ITI values showed little variability in our sample (ranging from 487 to 498 msec), with values largely determined by the fixed IOI value (493 msec) used in this study. Measures of ITI variability showed a limited range as well (for example, % ITI deviation from the mean ITI per subject ranged from 5.19% to 11.49%, with a standard deviation of only 2%; similar values were attained for the

16

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization coefficient of variation in ITI values per subject). Clearly, measures of ITI variability may provide larger ranges and a richer basis for analyses of covariation, depending on the mean IOI used (see, e.g., Fling et al., 2012), or when the experimental paradigm

PT

introduces variations in metric complexity (Chen et al., 2008; Kung et al., 2013; Lewis et al., 2004). However, within the current paradigm, the limited variability in ITI-related

RI

measures provided further support for our decision to focus on the covariation of

SC

Asynchrony- and TTR- related measures with diffusivity parameters. The arcuate fasciculus was successfully identified bilaterally in 18 participants.

NU

We were unable to identify the left arcuate in 1 participant and the right arcuate in another participant. Therefore, those participants were excluded from the correlation

MA

analyses between the arcuate fasciculus and behavior. The corpus callosum (CC) was successfully identified and segmented automatically into 8 segments in all 20 participants (Figure 3). Specifically, the precentral and temporal segments were successfully

D

identified in all participants using our automatic methods (see Methods and

TE

Supplementary Figure S1 for more detail on the automatic segmentation of the callosum). Figure 3 shows individual callosal segmentations for each participant at the mid sagittal

AC CE P

plane. This figure demonstrates the individual variability in the sizes and locations of the segments, but also the consistent spatial organization of the segments across participants. The variability in the absolute position and size of each segment provides the motivation for our individual tract based segmentation, as opposed to the traditional mathematical division of the midsagittal CC area offered by Witelson (1989) and similar schemes. The consistency in the internal spatial organization of the segments establishes the reliability of our automatic segmentation methods. The main results of our analysis revealed several significant correlations between FA and behavioral measures of rhythmic synchronization. Below we present the findings within each of the analyzed tracts: Arcuate fasciculus (Figure 4), temporal CC (Figure 5) and precentral CC (Figure 6).

17

ACCEPTED MANUSCRIPT

TE

D

MA

NU

SC

RI

PT

White Matter and Rhythmic Synchronization

AC CE P

Figure 3. Individual corpus callosum segmentations. A mid-sagittal view of the corpus callosi of 20 subjects. Segmentations are overlaid on FA images, where the splenium is shown on the left and the genu is shown on the right of each image. The inset at the bottom right corner shows the segmented CC tracts of a 27 years old female. Color code for the CC segments in all figures: occipital (green), posterior parietal (yellow), superior parietal (blue), temporal (purple), precentral (magenta), superior prefrontal (red), anterior frontal (orange), orbital (light blue).

In the left arcuate fasciculus, an analysis of correlations along the tract revealed a significant positive association between mean asynchrony and FA in a frontal cluster of 3 nodes (Figure 4A, black arrow). This correlation was significant at p<0.05, corrected for multiple comparisons (Nichols and Holmes, 2002). In contrast, the right arcuate fasciculus did not show any significant correlation between FA and any of the behavioral measures.

18

ACCEPTED MANUSCRIPT

RI SC NU

AC CE P

TE

D

MA

Figure 4. Correlation between mean asynchrony and FA in the left arcuate fasciculus. A) Shown is the left arcuate fasciculus in a single participant. Colored overlay represents Pearson’s r values between mean asynchrony and FA along the core of the tract. Arrow indicates the significant cluster (p<0.05, FWE corrected, critical r = 0.671). B) FA profile along the left arcuate (N=18). The solid black line indicates the average FA profile. The shaded gray area represents ±2SD. The square represents a 9 node window around the significant nodes; C) FA values extracted from the 9 node window shown in B are plotted against the mean asynchrony values for each participant. Red line is the best linear fit for all the measurements.

PT

White Matter and Rhythmic Synchronization

In order to examine the pattern of individual variability that gave rise to the correlation in the left arcuate fasciculus, we defined a 9-node window around the most significant node and extracted FA from this window (marked by a rectangle in Figure 4B). Figure 4C shows a scatter plot of the mean FA values extracted from this window, 19

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization plotted against the mean asynchrony scores of the same participants. This plot shows that participants who are better synchronized with the beat (their asynchrony measures are closer to 0, which indicates perfect synchrony) also show higher FA values in the left

PT

arcuate (r=0.64, p<0.01; however notice that this r value is likely to be inflated by the selection of the window around the strongest correlated nodes). This effect remained

RI

significant after controlling for age (partial correlation coefficient: r = 0.593, p < 0.05).

SC

Similar results were found with window sizes of 6, 11, and 13 nodes (not shown). We followed up on the FA correlation in the left arcuate with post hoc correlations replacing

NU

FA with axial diffusivity (AD) and radial diffusivity (RD), extracted from the same 9node window. We found a significant correlation with AD (r = 0.56, p<0.05), and a trend towards a negative correlation with RD (r = -0.47, p=0.05124; see Supp. Fig. S2).

MA

In the temporal CC segment, we found significant negative associations between mean FA and both asynchrony measures (FA ~ mean asynchrony: r = -0.49 ± 0.1421,

D

p<0.05; FA ~ SD asynchrony: r = 0.55 ± 0.159, p<0.05; ± 1SE values of the correlations

TE

were derived using a bootstrap analysis, see Methods). These correlations are shown in Figure 5. We verified that these correlations were unlikely to be generated randomly

AC CE P

using a shuffling analysis (shuffling the FA values and recalculating the correlation

Figure 5. Correlations between mean FA of the temporal CC segment and behavior for 20 subjects. A) Correlation between mean FA of the temporal CC segment and mean asynchrony (r = -0.49 ± 0.1421, p<0.05). B) Correlation between mean FA of the temporal CC segment and asynchrony-SD (r = 0.55 ± 0.159, p<0.05, FDR corrected). Purple lines are the best linear fit for all the measurements.

20

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization 10,000 times, see Methods). The results of this non-parametric analysis confirmed that the r values were unlikely to be generated randomly (FA ~ mean asynchrony: p(shuffle)<0.05; FA ~ asynchrony-SD: p(shuffle)<0.01).To account for the 3 comparisons

PT

calculated within the temporal segment (correlations between FA and each of the 3 behavioral measures: mean asynchrony, SD asynchrony, mean TTR), we controlled the

RI

false discovery rate (FDR) across the 3 behavioral parameters at a 5% criterion. Only the

SC

association between FA in the temporal CC and the standard deviation of asynchrony survived this FDR correction. A partial correlation controlling for Age further replicated

NU

the association between FA in the temporal segment of the CC and asynchrony-SD (r=0.501, p<0.05). We followed up on this correlation with post hoc correlations replacing FA with AD and RD. We found significant correlations between asynchrony-

MA

SD and RD (r = -0.54, p<0.05), but not with AD (see Supplementary Figure S3). In the precentral CC segment we found a different pattern of correlations.

D

Specifically, we found a significant negative association between the mean FA of the

TE

precentral CC segment and the mean TTR (r = -0.45 ± 0.1241, p<0.05). This correlation is shown in Figure 6, where it is evident that participants with higher FA values in fact

AC CE P

resynchronize faster (shorter TTR). Thus, this is not a “negative” association in the traditional sense, the negativity stems from the nature of the TTR parameter, where higher values represent worse performance. We verified that this correlation was unlikely to be generated randomly using a shuffling analysis (shuffling the FA values and recalculating the correlation 10,000 times, as before). This non-parametric analysis confirmed that the r value was unlikely to be generated randomly (p(shuffle)<0.05). However, this correlation did not survive FDR correction for 3 comparisons (with 3 different behavioral parameters), and did not remain significant in a partial correlation analysis controlling for age. To better understand the source of this correlation, we followed up with post hoc correlations between the same behavioral parameter (mean TTR), AD and RD. We did not find a significant correlation with either of those, but there was a trend for a correlation with RD, not AD (RD: r = 0.39, p=0.088; AD: r=0.07, p>0.1; see Supplementary Figure S4).

21

ACCEPTED MANUSCRIPT

NU

SC

RI

PT

White Matter and Rhythmic Synchronization

AC CE P

Discussion

TE

D

MA

Figure 6. Correlations between mean FA of the precentral CC segment and behavior for 20 subjects. A) Segmentation of the CC, precentral segment is shown in magenta, indicated with a white arrow. B) The scatter plot shows the relation between the mean TTR and FA of the precentral CC segment (r = -0.45 ± 0.1241, p<0.05). ±1SE value was derived using a bootstrap analysis (1000 samples). A shuffling analysis (10,000 permutations) verified that the probability of achieving this correlation by random was less than 5%. Magenta line is the best linear fit for all the measurements.

In this study, we examined the relation between microstructural properties of white matter pathways and specific parameters of sensorimotor synchronization. Focusing on a small set of 4 candidate pathways, we discovered an interesting pattern of positive and negative associations with rhythmic synchronization parameters. The data support two of our main hypotheses. First, we found that negative asynchrony, which is assumed to reflect motor prediction of an auditory event, was significantly and positively correlated with FA of a frontal cluster in the left arcuate fasciculus. Within this cluster, higher FA values were associated with more synchronized tapping to the external meter (smaller absolute asynchrony). This finding supports the idea that efficient information transfer between premotor and auditory association regions is essential for auditorymotor rhythmic synchronization (Nozaradan, 2014). Second, we found that the time it took participants to resynchronize with a new meter (TTR), a measure of dynamic restructuring and inhibition of response, was significantly correlated with FA in the

22

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization midsagittal section of the precentral callosal segment, such that higher FA was associated with faster resynchronization to an altered meter. This finding is in line with the idea that prefrontal regions may exert inhibitory control over their cortical homologs through

PT

homotopic callosal connections (Bloom and Hynd, 2005; Ni et al., 2009; van der Knaap and van der Ham, 2011), which is essential for restructuring rhythmic tapping. The

RI

direction of this correlation is also in line with our predictions, such that participants who

SC

are faster to restructure their taps (shorter TTR) showed evidence for, roughly, stronger connectivity (higher FA; see below for some disclaimers relevant to this interpretation).

NU

This effect was weaker, and did not survive correction for three comparisons, considering the three behavioral parameters examined, Asynchrony – mean, Asynchrony – SD, and

MA

mean TTR. However, we still discuss the association between TTR and FA in the precentral CC, because it corroborated our a-priori hypothesis. A third finding, which was not predicted a-priori, concerns the temporal callosal

D

segment. In that pathway, we found correlations with measures of auditory-motor

TE

asynchrony, such that participants who were consistent in their synchronization (smaller asynchrony-SD) showed smaller FA values in the midsagittal section of the temporal CC

AC CE P

fibers. Finally, no significant associations were found within the right arcuate fasciculus. Below, we first discuss the results related to synchronization to a rhythmic auditory stimulus, and then discuss resynchronization when the meter of the stimulus is changed.

White matter pathways involved in auditory-motor rhythmic synchronization As anticipated, we found an association between asynchrony and FA in the left arcuate fasciculus, such that the more synchronous a participant’s tapping was with the auditory rhythm, the higher was FA in their left arcuate fasciculus. We propose that left fronto-temporal fibers are involved in ongoing comparison between motor programs and auditory input, in order to predict the auditory stimuli and optimize synchronization with them. There is evidence that the arcuate fasciculus contains fibers that connect auditory regions with premotor regions in the lateral precentral cortex (Brown et al., 2014; Dick and Tremblay, 2012; Friederici, 2012; Isenberg et al., 2012). Such bidirectional connections have been hypothesized to mediate auditory-motor interactions essential for music perception and production (Zatorre et al., 2007), beat perception (Iversen et al., 23

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization 2009; Patel and Iversen, 2014) and for the prediction of auditory events (Schubotz, 2007). Indeed, a recent EEG study (Nozaradan et al., 2015) demonstrated phase coupling of the cortical signals in sensory and motor cortices during rhythmic sensorimotor

PT

synchronization. Moreover, using TMS it has been shown that the left ventrolateral premotor cortex is critical for predicting auditory events (Kornysheva and Schubotz,

RI

2011, but see Malcolm et al., 2008). Within this framework of ideas and recent findings,

SC

we suggest that left dorsal auditory-motor connections through the arcuate fasciculus are involved in the ongoing comparisons that allow effective synchronization and monitoring

NU

of the taps to match the rhythm of the auditory stimuli.

An interesting question concerns the role of the right arcuate fasciculus in rhythm

MA

perception and reproduction. While our data does not indicate a correlation with asynchrony measures in this tract, ample fMRI evidence points to an important role of the right prefrontal cortex, including the right PMC and IFG, in rhythm perception and

D

reproduction (Chen et al., 2006; Grahn and Rowe, 2009; Grahn et al., 2011; Jäncke et al.,

TE

2000; Kung et al., 2013; Lewis et al., 2004 ; for reviews see Grahn, 2012; Large et al., 2015; Repp and Su, 2013; Witt et al., 2008). It is possible that significant correlations

AC CE P

between Asynchrony and diffusivity in the right fronto-temporal pathways may be found with larger samples and more sophisticated tractography and diffusion modelling approaches. We previously demonstrated that this tract, specifically, may be missed with deterministic tracking methods, and still be detected with probabilistic tracking (Yeatman et al., 2011). While we were able to reconstruct the right arcuate in 19/20 participants in the current study, the tract estimates by different tracking algorithms (and using different diffusion models) may vary, potentially leading to different findings. Alternatively, it is conceivable that different pathways, such as the fronto-parietal connections (Konoike et al., 2015, 2012), should be targeted in the right hemisphere, in order to detect correlations with asynchrony measures. A correlation with asynchrony was also found in the temporal segment of the corpus callosum, but this time in the opposite direction: more consistent synchronization was associated with lower FA values. The direction of this correlation may seem counterintuitive, because lower FA is often interpreted as indicative of reduced connectivity, which in turn is often related to worse performance on cognitive tasks. However, faster 24

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization communication in the brain is often achieved not through thicker myelin, but instead, through thicker axons, which have an inverse effect on FA, causing reduced FA and elevated RD values (Horowitz et al., 2014). In addition, some cognitive tasks may be

PT

performed more efficiently by a lateralized network, so enhanced callosal connectivity may in fact contribute negatively to performance. For either of these reasons, negative

RI

correlations between FA and performance, typically driven by positive correlations with

SC

RD, are often found in the corpus callosum (Frye et al., 2008; Odegard et al., 2009), and have been documented in the past specifically in the temporal segment (Dougherty et al.,

NU

2007). Consistent synchronizers may have larger temporal callosal axons that result in faster information transmission between the auditory cortices. Alternatively, auditory

MA

input processing may be more efficient when each hemisphere processes the auditory input separately and the information is integrated later. In future studies, these explanations can be differentiated empirically with new methods that allow quantitative

D

estimation of the mean axonal diameter (Assaf et al., 2008) and of the myelin content

TE

(Mezer et al., 2013; Stikov et al., 2011), separately. Interestingly, our findings offer a potential explanation for recent reports of

AC CE P

impaired sensorimotor synchronization in children and adults with developmental language and reading impairments (Corriveau and Goswami, 2009; Huss et al., 2011; Leong and Goswami, 2014; Olander et al., 2010; Thomson and Goswami, 2008; but see Seidenberg, 2011). While such findings may still turn out to be limited to a specific subpopulation of children with SLI or reading impairments (Heim et al., 2008; Zoccolotti and Friedmann, 2010), or to a specific task (e.g., Banai and Ahissar, 2006), they appear to be quite relevant in the context of the current findings. Both the left arcuate fasciculus and the temporal callosal pathways have been previously associated with language and reading related functions, particularly phonological awareness (Carreiras et al., 2009; Dougherty et al., 2007; Northam et al., 2012; Vandermosten et al., 2013; Yeatman et al., 2011). This overlap may be accidental, but it raises an exciting hypothesis that the association between SMS and language impairments may derive from the fact that these abilities rely on the same white matter pathways. One may further speculate that long term training on the sensorimotor synchronization task may build up myelin along the left arcuate fasciculus, which may in turn improve performance on phonological awareness 25

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization tasks and as a result provide better conditions for the acquisition of decoding skills for reading. While these ideas are still quite speculative (indeed, we are not aware of documented findings that support such generalized plasticity of a pathway), our study

PT

provides an adult baseline for future developmental and training studies that may shed

RI

more light on the plasticity and versatility of these pathways.

SC

White matter pathways supporting resynchronization with a changing meter The ability to resynchronize quickly with a new meter was associated in our

NU

sample with increased anisotropy in the precentral segment of the corpus callosum. This finding converges with an early finding showing enlarged anterior corpus callosum in

MA

musicians (Schlaug et al., 1995). To offer a more specific interpretation for this correlation, it is important to first analyze the cognitive processes involved in resynchronization. When a meter change occurs, a cascade of processes begins,

D

including: 1) Detection of the change in meter, 2a) Rhythmic analysis and recognition of

TE

the new rhythmic pattern, and, at the same time, 2b) Inhibition of tapping to the old meter; this is followed by 3) Planning and execution of tapping to the new meter.

AC CE P

In principle, the precentral CC fibers may contribute to any of these processes, however, we argue that phases 1 and 2a (auditory detection and recognition) are more likely to be implemented by primary and association auditory cortex, whereas phase 3 (particularly motor execution) is likely to rely on the left motor and premotor cortices. Thus, we suggest that a plausible interpretation of the reported correlation in the precentral CC segment concerns the inhibition of tapping to the old rhythm. We rely on previous studies showing that callosal connections are often inhibitory (Bloom and Hynd, 2005; Ni et al., 2009; van der Knaap and van der Ham, 2011), as well as on the well documented role of the frontal lobe in inhibition and switching (Aron et al., 2004; Mayr et al., 2006; Rubia et al., 2003; Tamm et al., 2004; Troyer et al., 1998). Specifically, Ni et al (2009) have demonstrated that motor regions in the right hemisphere exert interhemispheric inhibition at different time scales over the left primary motor cortex, presumably through the corpus callosum. Recently, Wahl et al. (2015) have shown that FA in the precentral CC segment was predictive of successful out-of-phase tapping, an ability that relies on interhemispheric inhibition. We propose that resynchronizing with a 26

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization new meter may engage frontal callosal connections involved in inhibiting the automatic tapping to the old meter and switching to a new perceived meter. While the sample size in this study was modest (N=20), the considerable

PT

variability in SMS behavior provided an appropriate setup for testing hypotheses about brain-behavior correlations. Many previous studies have established correlations between

RI

cognitive measures and diffusivity properties in similarly sized samples or smaller (e.g.,

SC

Gomez et al., 2015; Klingberg et al., 2000; Tavor et al., 2014). Admittedly, while this sample size may be sufficient for testing first order correlations, it does not provide

NU

sufficient power for testing the effects of additional covariates such as age or gender, which will be better addressed by future large scale studies. In order to assess

MA

developmental effects, the sample would have to be larger but also span a larger age range, because the examined tracts are not expected to vary systematically with age within the age range of our sample (Lebel et al., 2012; Yeatman et al., 2014). Another

D

covariate of particular interest is musical training (Zatorre et al., 2007). Several published

TE

studies demonstrated the effect of musical training on brain responses during rhythm perception and synchronization tasks (e.g., Bailey et al., 2014; Tierney et al., 2015).

AC CE P

White matter differences have been observed between musicians and non-musicians in both the arcuate fasciculus and the corpus callosum (Halwani et al., 2011; Vollmann et al., 2014; see Schlaug et al., 2015 for review). These findings provide a clear motivation for future studies that will examine the relation between white matter properties and synchronization measures as a function of musical training.

Hypothesis driven dMRI In this study, we focus on a small number of individually defined tracts, rather than search voxel-by-voxel for correlations across the entire brain. Our focus on auditorymotor connections was inspired by previous findings, showing auditory-motor coupling during SMS tasks (Nozaradan, 2014; Nozaradan et al., 2015, 2012), as well as by developmental evidence for impaired SMS in children and adults with developmental language and reading impairments (Corriveau and Goswami, 2009; Huss et al., 2011; Leong and Goswami, 2014; Olander et al., 2010; Thomson and Goswami, 2008). Admittedly, such an approach is limited in coverage. For example, in fMRI studies, SMS 27

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization activates many regions within the motor networks, including ventral and dorsal premotor cortex, SMA, basal ganglia, parietal cortex and the cerebellum (Chen et al., 2006; Ferrandez et al., 2003; Grahn, 2012; Konoike et al., 2012; Macar et al., 2004; Nenadic et

PT

al., 2003; Rao et al., 2001, 1997; Thaut et al., 2014). This suggests that SMS may involve motor pathways such as the cerebro-spinal tract and superior cerebellar peduncle, which

SC

of these pathways to different components of SMS.

RI

have not been examined here. Future studies will be necessary to examine the relevance

Nevertheless, there are several advantages to using dMRI in a hypothesis driven

NU

fashion. Most prominently, localized hypothesis testing reduces the probability of type I error (by reducing the number of comparisons), while enhancing the interpretability of

MA

the results (based on a-priori hypotheses). Limiting our analysis to a small number of predefined tracts also allows for individual definition of the tracts in native space, followed by visual inspection of every tract in each participant. Within this small number

D

of candidate tracts, we further limited our analysis to the midsagittal sections of the

TE

corpus callosum, in order to sample voxels with maximum directional coherence and improve the reliability and interpretability of the diffusion measures extracted from these

AC CE P

sections (Dougherty et al., 2005; Jeurissen et al., 2013; Rokem et al., 2015). In sum, we believe that, at this stage, a compact set of correlations within predefined tracts may in fact benefit our understanding of the white matter pathways involved in sensorimotor synchronization more than a long list of significant clusters distributed throughout the brain.

Summary and conclusions We tested the hypothesis that specific aspects of human performance in a sensorimotor synchronization task can be mapped to known white matter pathways connecting the premotor and auditory cortices (Zatorre et al., 2007). Focusing on a small set of long range and commissural white matter tracts identified in individual adult participants, we were able to corroborate this hypothesis. Specifically, we discovered that negative asynchrony, which is often interpreted as representing motor prediction of an auditory event, was significantly correlated with FA in the left arcuate fasciculus and in the temporal segment of the corpus callosum. Further, we found that TTR, a measure of 28

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization dynamic restructuring and inhibition of response, was significantly correlated with FA in the precentral segment of the corpus callosum. The results are important because they provide empirical support to the long hypothesized involvement of long range auditory-

PT

motor connections in sensorimotor synchronization, based on in-vivo structural measurements of white matter properties. The results also highlight the contribution of

RI

commissural fibers, possibly connecting homotopic cortices in the bilateral auditory and

SC

motor/premotor systems, in synchronization and re-synchronization of motor responses to an external auditory rhythm. These findings in adults will help focus future investigations

NU

into the development and response to intervention of white matter pathways underlying sensorimotor synchronization.

MA

Acknowledgements

This work was supported by the Israel Science Foundation (ISF grant 513/11) and

D

by the Israeli Center of Research Excellence in Cognition (I-CORE Program 51/11 of the

TE

Planning and Budgeting Committee). We thank the team at the Wohl institute for advanced imaging in Tel Aviv Sourasky Medical Center, for their assistance with

AC CE P

protocol setup and MRI scanning. We thank Jason Yeatman for his assistance with the AFQ code. We are grateful to Moshe Abeles for many discussions along the way and for taking part in conceptualizing the behavioral measurements. We thank Vered KronfeldDuenias, Oren Civier, Chen Gafni and Maya Yablonski for their help during the preparation of this manuscript.

References

Akers, D., 2006. CINCH : A cooperatively designed marking interface for 3D pathway selection, in: Proceedings of the 19th Annual ACM Symposium on User Interface Software and Technology. ACM, Montreux, Switzerland, pp. 33–42. Aron, A.R., Monsell, S., Sahakian, B.J., Robbins, T.W., 2004. A componential analysis of task-switching deficits associated with lesions of left and right frontal cortex. Brain 127, 1561–1573. Aschersleben, G., 2002. Temporal control of movements in sensorimotor synchronization. Brain Cogn. 48, 66–79. Aschersleben, G., Prinz, W., 1997. Delayed auditory feedback in synchronization. J. Mot. Behav. 29, 35–46. Aschersleben, G., Prinz, W., 1995. Synchronizing actions with events : Percept. Psychophys. 57, 305–317. 29

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization

AC CE P

TE

D

MA

NU

SC

RI

PT

Assaf, Y., Blumenfeld-Katzir, T., Yovel, Y., Basser, P.J., 2008. AxCaliber : A method for measuring axon diameter distribution from diffusion MRI. Magn. Reson. Med. 59, 1347–1354. Bailey, J.A., Zatorre, R.J., Penhune, V.B., 2014. Early musical training is linked to gray matter structure in the ventral premotor cortex and auditory–motor rhythm synchronization performance. J. Cogn. Neurosci. 26, 755–767. Banai, K., Ahissar, M., 2006. Auditory processing deficits in dyslexia: Task or stimulus related? Cereb. Cortex 16, 1718–1728. Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A., 2000. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625–632. Basser, P.J., Pierpaoli, C., 1996. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. 111, 209–219. Bloom, J.S., Hynd, G.W., 2005. The role of the corpus callosum in interhemispheric transfer of information: excitation or inhibition? Neuropsychol. Rev. 15, 59–71. Brochard, R., Abecasis, D., Potter, D., Ragot, R., Drake, C., 2003. The “ticktock” of our internal clock: Direct brain evidence of subjective accents in isochronous sequences. Psychol. Sci. 14, 362–366. Brown, E.C., Jeong, J.-W., Muzik, O., Rothermel, R., Matsuzaki, N., Juhász, C., Sood, S., Asano, E., 2014. Evaluating the arcuate fasciculus with combined diffusionweighted MRI tractography and electrocorticography. Hum. Brain Mapp. 35, 2333– 2347. Carreiras, M., Seghier, M.L., Baquero, S., Estévez, A., Lozano, A., Devlin, J.T., Price, C.J., 2009. An anatomical signature for literacy. Nature 461, 983–986. Caspi, A., 2002. The synchronization error: Attentional and timing aspects‫‏‬. Unpubl. Dr. Diss. Tel Aviv University, Israel. Chapin, H.L., Zanto, T., Jantzen, K.J., Kelso, S.J.A., Steinberg, F., Large, E.W., 2010. Neural responses to complex auditory rhythms: The role of attending. Front. Psychol. 1, 224. Chen, J.L., Penhune, V.B., Zatorre, R.J., 2008. Moving on time: brain network for auditory-motor synchronization is modulated by rhythm complexity and musical training. J. Cogn. Neurosci. 20, 226–239. Chen, J.L., Zatorre, R.J., Penhune, V.B., 2006. Interactions between auditory and dorsal premotor cortex during synchronization to musical rhythms. Neuroimage 32, 1771– 1781. Corriveau, K.H., Goswami, U., 2009. Rhythmic motor entrainment in children with speech and language impairments: tapping to the beat. Cortex 45, 119–130. Del Olmo, M.F., Cheeran, B., Koch, G., Rothwell, J.C., 2007. Role of the cerebellum in externally paced rhythmic finger movements. J. Neurophysiol. 98, 145–152. Dick, A.S., Tremblay, P., 2012. Beyond the arcuate fasciculus: consensus and controversy in the connectional anatomy of language. Brain 135, 3529–3550. Dougherty, R.F., Ben-shachar, M., Bammer, R., Brewer, A.A., Wandell, B.A., 2005. Functional organization of human occipital – callosal fiber tracts. Proc. Natl. Acad. Sci. U. S. A. 102, 7350–7355. Dougherty, R.F., Ben-Shachar, M., Deutsch, G.K., Hernandez, A., Fox, G.R., Wandell, B.A., 2007. Temporal-callosal pathway diffusivity predicts phonological skills in children. Proc. Natl. Acad. Sci. U. S. A. 104, 8556–61. 30

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization

AC CE P

TE

D

MA

NU

SC

RI

PT

Drake, C., 1998. Psychological processes involved in the temporal organization of complex auditory sequences : Universal and acquired processes. Music Percept. 16, 11–26. Dunlap, K., 1910. Reactions to rhythmic stimuli, with attempt to synchronize. Psychol. Rev. 17, 399–416. Falk, S., Müller, T., Dalla Bella, S., 2015. Non-verbal sensorimotor timing deficits in children and adolescents who stutter. Front. Psychol. 6, 847. Ferrandez, A.M., Hugueville, L., Lehéricy, S., Poline, J.B., Marsault, C., Pouthas, V., 2003. Basal ganglia and supplementary motor area subtend duration perception: an fMRI study. Neuroimage 19, 1532–1544. Fling, B.W., Kwak, Y., Peltier, S.J., Seidler, R.D., 2012. Differential relationships between transcallosal structural and functional connectivity in young and older adults. Neurobiol. Aging 33, 2521–2526. Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L., 2011. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54, 313– 327. Friederici, A.D., 2012. The cortical language circuit: from auditory perception to sentence comprehension. Trends Cogn. Sci. 16, 262–268. Friston, K.J., Ashburner, J., 2004. Generative and recognition models for neuroanatomy. Neuroimage 23, 21–24. Frye, R.E., Hasan, K., Xue, L., Strickland, D., Malmberg, B., Liederman, J., Papanicolaou, A., 2008. Splenium microstructure is related to two dimensions of reading skill. Neuroreport 19, 1627–1631. Gomez, J., Pestilli, F., Witthoft, N., Golarai, G., Liberman, A., Poltoratski, S., Yoon, J., Grill-Spector, K., 2015. Functionally defined white matter reveals segregated pathways in human ventral temporal cortex associated with category-specific processing. Neuron 85, 216–227. Grahn, J.A., 2012. Neural mechanisms of rhythm perception: current findings and future perspectives. Top. Cogn. Sci. 4, 585–606. Grahn, J.A., Brett, M., 2007. Rhythm and beat perception in motor areas of the brain. J. Cogn. Neurosci. 19, 893–906. Grahn, J.A., Henry, M.J., McAuley, J.D., 2011. FMRI investigation of cross-modal interactions in beat perception: Audition primes vision, but not vice versa. Neuroimage 54, 1231–1243. Grahn, J.A., Rowe, J.B., 2009. Feeling the beat: premotor and striatal interactions in musicians and nonmusicians during beat perception. J. Neurosci. 29, 7540–7548. Halwani, G.F., Loui, P., Rüber, T., Schlaug, G., 2011. Effects of practice and experience on the arcuate fasciculus: comparing singers, instrumentalists, and non-musicians. Front. Psychol. 2, 156. Heim, S., Tschierse, J., Amunts, K., Wilms, M., Vossel, S., Willmes, K., Grabowska, A., Huber, W., 2008. Cognitive subtypes of dyslexia. Acta Neurobiol. Exp. (Wars). 68, 73–82. Horowitz, A., Barazany, D., Tavor, I., Bernstein, M., Yovel, G., Assaf, Y., 2014. In vivo correlation between axon diameter and conduction velocity in the human brain. Brain Struct. Funct. 220, 1777–1788. Huang, H., Zhang, J., Jiang, H., Wakana, S., Poetscher, L., Miller, M.I., van Zijl, P.C.M., 31

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization

AC CE P

TE

D

MA

NU

SC

RI

PT

Hillis, A.E., Wytik, R., Mori, S., 2005. DTI tractography based parcellation of white matter: application to the mid-sagittal morphology of corpus callosum. Neuroimage 26, 195–205. Huss, M., Verney, J.P., Fosker, T., Mead, N., Goswami, U., 2011. Music, rhythm, rise time perception and developmental dyslexia: perception of musical meter predicts reading and phonology. Cortex 47, 674–689. Isenberg, A.L., Vaden, K.I., Saberi, K., Muftuler, L.T., Hickok, G., 2012. Functionally distinct regions for spatial processing and sensory motor integration in the planum temporale. Hum. Brain Mapp. 33, 2453–2463. Iversen, J.R., Repp, B.H., Patel, A.D., 2009. Top-down control of rhythm perception modulates early auditory responses. Ann. N. Y. Acad. Sci. 1169, 58–73. Jäncke, L., Loose, R., Lutz, K., Specht, K., Shah, N.J., 2000. Cortical activations during paced finger-tapping applying visual and auditory pacing stimuli. Cogn. Brain Res. 10, 51–66. Jeurissen, B., Leemans, A., Tournier, J.-D., Jones, D.K., Sijbers, J., 2013. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum. Brain Mapp. 34, 2747–2766. Kadota, H., Kudo, K., Ohtsuki, T., 2004. Time-series pattern changes related to movement rate in synchronized human tapping. Neurosci. Lett. 370, 97–101. Klingberg, T., Hedehus, M., Temple, E., Salz, T., Gabrieli, J.D.E., Moseley, M.E., Poldrack, R.A., 2000. Microstructure of Temporo-Parietal White Matter as a Basis for Reading Ability : Evidence from Diffusion Tensor Magnetic Resonance Imaging. Neuron 25, 493–500. Kolers, P. a., Brewster, J.M., 1985. Rhythms and responses. J. Exp. Psychol. Hum. Percept. Perform. 11, 150–167. Konoike, N., Kotozaki, Y., Jeong, H., Miyazaki, A., Sakaki, K., Shinada, T., Sugiura, M., Kawashima, R., Nakamura, K., 2015. Temporal and motor representation of rhythm in fronto-parietal cortical areas: An fMRI study. PLoS One 10, 1–19. Konoike, N., Kotozaki, Y., Miyachi, S., Miyauchi, C.M., Yomogida, Y., Akimoto, Y., Kuraoka, K., Sugiura, M., Kawashima, R., Nakamura, K., 2012. Rhythm information represented in the fronto-parieto-cerebellar motor system. Neuroimage 63, 328–338. Konvalinka, I., Vuust, P., Roepstorff, A., Frith, C.D., 2010. Follow you, follow me: continuous mutual prediction and adaptation in joint tapping. Q. J. Exp. Psychol. 63, 2220–30. Kornysheva, K., Schubotz, R.I., 2011. Impairment of auditory-motor timing and compensatory reorganization after ventral premotor cortex stimulation. PLoS One 6, e21421. Kronfeld-Duenias, V., Amir, O., Ezrati-Vinacour, R., Civier, O., Ben-Shachar, M., 2016. The frontal aslant tract underlies speech fluency in persistent developmental stuttering. Brain Struct. Funct. 221, 365–381. Kung, S.-J., Chen, J.L., Zatorre, R.J., Penhune, V.B., 2013. Interacting cortical and basal ganglia networks underlying finding and tapping to the musical beat. J. Cogn. Neurosci. 25, 401–420. Large, E.W., Fink, P., Kelso, J. a S., 2002. Tracking simple and complex sequences. Psychol. Res. 66, 3–17. 32

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization

AC CE P

TE

D

MA

NU

SC

RI

PT

Large, E.W., Herrera, J.A., Velasco, M.J., 2015. Neural Networks for Beat Perception in Musical Rhythm. Front. Syst. Neurosci. 9, 159. Large, E.W., Snyder, J.S., 2009. Pulse and meter as neural resonance. Ann. N. Y. Acad. Sci. 1169, 46–57. Lebel, C., Gee, M., Camicioli, R., Wieler, M., Martin, W., Beaulieu, C., 2012. Diffusion tensor imaging of white matter tract evolution over the lifespan. Neuroimage 60, 340–352. Leemans, A., Jones, D.K., 2009. The B-matrix must be rotated when correcting for subject motion in DTI data. Magn. Reson. Med. 61, 1336–1349. Leong, V., Goswami, U., 2014. Assessment of rhythmic entrainment at multiple timescales in dyslexia: evidence for disruption to syllable timing. Hear. Res. 308, 141–161. Lewis, P.A., Wing, A.M., Pope, P.A., Praamstra, P., Miall, R.C., 2004. Brain activity correlates differentially with increasing temporal complexity of rhythms during initialisation, synchronisation, and continuation phases of paced finger tapping. Neuropsychologia 42, 1301–1312. Macar, F., Anton, J.-L., Bonnet, M., Vidal, F., 2004. Timing functions of the supplementary motor area: an event-related fMRI study. Cogn. brain Res. 21, 206– 215. Malcolm, M.P., Lavine, A., Kenyon, G., Massie, C., Thaut, M., 2008. Repetitive transcranial magnetic stimulation interrupts phase synchronization during rhythmic motor entrainment. Neurosci. Lett. 435, 240–245. Mayr, U., Diedrichsen, J., Ivry, R., Keele, S.W., 2006. Dissociating Task-set Selection from Task-set Inhibition in the Prefrontal Cortex. J. Cogn. Neurosci. 18, 14–21. Mezer, A., Yeatman, J.D., Stikov, N., Kay, K.N., Cho, N.-J., Dougherty, R.F., Perry, M.L., Parvizi, J., Hua, L.H., Butts-Pauly, K., Wandell, B.A., 2013. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat. Med. 19, 1667–1672. Miyake, I., 1902. Researches on rhythmic activity. Stud. From Yale Psychol. Lab. 10, 1– 48. Mori, S., Crain, B.J., Chacko, V.P., Zijl, P.C.M. Van, 1999. Three-Dimensional Tracking of Axonal Projections in the Brain by Magnetic Resonance Imaging. Ann. Neurol. 45, 265–269. Müller, K., Aschersleben, G., Schmitz, F., Schnitzler, A., Freund, H.-J., Prinz, W., 2008. Inter- versus intramodal integration in sensorimotor synchronization: a combined behavioral and magnetoencephalographic study. Exp. brain Res. 185, 309–318. Müller, K., Schmitz, F., Schnitzler, A., Freund, H., Aschersleben, G., Prinz, W., 2000. Neuromagnetic Correlates of Sensorimotor Synchronization. J. Cogn. Neurosci. 12, 546–555. Nenadic, I., Gaser, C., Volz, H.-P., Rammsayer, T., Häger, F., Sauer, H., 2003. Processing of temporal information and the basal ganglia: new evidence from fMRI. Exp. brain Res. 148, 238–246. Ni, Z., Gunraj, C., Nelson, A.J., Yeh, I.-J., Castillo, G., Hoque, T., Chen, R., 2009. Two phases of interhemispheric inhibition between motor related cortical areas and the primary motor cortex in human. Cereb. cortex 19, 1654–1665. Nichols, T.E., Holmes, A.P., 2002. Nonparametric permutation tests for functional 33

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization

AC CE P

TE

D

MA

NU

SC

RI

PT

neuroimaging: a primer with examples. Hum. Brain Mapp. 15, 1–25. Northam, G.B., Liégeois, F., Tournier, J.-D., Croft, L.J., Johns, P.N., Chong, W.K., Wyatt, J.S., Baldeweg, T., 2012. Interhemispheric temporal lobe connectivity predicts language impairment in adolescents born preterm. Brain 135, 3781–3798. Nozaradan, S., 2014. Exploring how musical rhythm entrains brain activity with electroencephalogram frequency-tapping. Philos. Trans. R. Soc. London B Biol. Sci. 369, 20130393. Nozaradan, S., Peretz, I., Mouraux, A., 2012. Selective neuronal entrainment to the beat and meter embedded in a musical rhythm. J. Neurosci. 32, 17572–17581. Nozaradan, S., Peretz, I., Peter E. Keller, 2016. Individual Differences in Rhythmic Cortical Entrainment Correlate with Predictive Behavior in Sensorimotor Synchronization. Sci. Rep. Nozaradan, S., Zerouali, Y., Peretz, I., Mouraux, A., 2015. Capturing with EEG the Neural Entrainment and Coupling Underlying Sensorimotor Synchronization to the Beat. Cereb. cortex 25, 736–747. Odegard, T.N., Farris, E. a, Ring, J., McColl, R., Black, J., 2009. Brain connectivity in non-reading impaired children and children diagnosed with developmental dyslexia. Neuropsychologia 47, 1972–1977. Olander, L., Smith, A., Zelaznik, H.N., 2010. Evidence that a motor timing deficit is a factor in the development of stuttering. J. speech, Lang. Hear. Res. 53, 876–886. Oldfield, R.C., 1971. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9, 97–113. Pagliarini, E., Guasti, M.T., Toneatto, C., Granocchio, E., Riva, F., Sarti, D., Molteni, B., Stucchi, N., 2015. Dyslexic children fail to comply with the rhythmic constraints of handwriting. Hum. Mov. Sci. 42, 161–182. Patel, A.D., Iversen, J.R., 2014. The evolutionary neuroscience of musical beat perception: the Action Simulation for Auditory Prediction (ASAP) hypothesis. Front. Syst. Neurosci. 8, 57. Phillips-silver, J., Trainor, L.J., 2005. Feeling the Beat : Movement Influences Infant Rhythm Perception. Science (80-. ). 308, 1430. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P., 2002. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge, NY. Rao, S.M., Harrington, D.L., Haaland, K.Y., Bobholz, J.A., Cox, R.W., Binder, J.R., 1997. Distributed neural systems underlying the timing of movements. J. Neurosci. 17, 5528–5535. Rao, S.M., Mayer, A.R., Harrington, D.L., 2001. The evolution of brain activation during temporal processing. Nat. Neurosci. 4, 317–323. Repp, B.H., 2005. Sensorimotor synchronization: a review of the tapping literature. Psychon. Bull. Rev. 12, 969–992. Repp, B.H., Keller, P.E., 2008. Sensorimotor synchronization with adaptively timed sequences. Hum. Mov. Sci. 27, 423–456. Repp, B.H., Su, Y.-H., 2013. Sensorimotor synchronization: a review of recent research (2006-2012). Psychon. Bull. Rev. 20, 403–452. Rohde, G.K., Barnett, a S., Basser, P.J., Marenco, S., Pierpaoli, C., 2004. Comprehensive approach for correction of motion and distortion in diffusion34

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization

AC CE P

TE

D

MA

NU

SC

RI

PT

weighted MRI. Magn. Reson. Med. 51, 103–114. Rokem, A., Yeatman, J.D., Pestilli, F., Kay, K.N., Mezer, A., van der Walt, S., Wandell, B. a, 2015. Evaluating the accuracy of diffusion MRI models in white matter. PLoS One 10, e0123272. Rubia, K., Smith, A.B., Brammer, M.J., Taylor, E., 2003. Right inferior prefrontal cortex mediates response inhibition while mesial prefrontal cortex is responsible for error detection. Neuroimage 20, 351–358. Sasson, E., Doniger, G.M., Pasternak, O., Tarrasch, R., Assaf, Y., 2013. White matter correlates of cognitive domains in normal aging with diffusion tensor imaging. Front. Neurosci. 7, 32. Schlaug, G., Jäncke, L., Huang, Y., Staiger, J.F., Steinmetz, H., 1995. Increased corpus callosum size in musicians. Neuropsychologia 33, 1047–1055. Schlaug, G., Laboratories, R., Israel, B., Medical, D., 2015. Musicians and music making as a model for the study of brain plasticity. Prog. Brain Res. 217, 37–55. Schubotz, R.I., 2007. Prediction of external events with our motor system: towards a new framework. Trends Cogn. Sci. 11, 211–218. Seidenberg, M.S., 2011. What causes dyslexia?: comment on Goswami. Trends Cogn. Sci. 15, 2. Stevens, L.T., 1886. On the time-sense. Mind 11, 393–404. Stikov, N., Perry, L.M., Mezer, A., Rykhlevskaia, E., Wandell, B.A., Pauly, J.M., Dougherty, R.F., 2011. Bound pool fractions complement diffusion measures to describe white matter micro and macrostructure. Neuroimage 54, 1112–21. Tal, I., Abeles, M., 2016. Temporal Accuracy of Human Cortico-Cortical Interactions. J. Neurophysiol. 115, 181–1820. Tamm, L., Menon, V., Ringel, J., Reiss, A.L., 2004. Event-related FMRI evidence of frontotemporal involvement in aberrant response inhibition and task switching in attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 43, 1430–1440. Tavor, I., Yablonski, M., Mezer, A., Rom, S., Assaf, Y., Yovel, G., 2014. Separate parts of occipito-temporal white matter fibers are associated with recognition of faces and places. Neuroimage 86, 123–130. Thaut, M.H., Trimarchi, P.D., Parsons, L.M., 2014. Human brain basis of musical rhythm perception: common and distinct neural substrates for meter, tempo, and pattern. Brain Sci. 4, 428–452. Thomson, J.M., Goswami, U., 2008. Rhythmic processing in children with developmental dyslexia: Auditory and motor rhythms link to reading and spelling. J. Physiol. 102, 120–129. Tierney, A., Krizman, J., Kraus, N., 2015. Music training alters the course of adolescent auditory development. Proc. Natl. Acad. Sci. 112, 10062–10067. Troyer, A.K., Moscovitch, M., Winocur, G., Alexander, M.P., Stuss, D.O.N., 1998. Clustering and switching on verbal fluency: the effects of focal frontal- and temporal- lobe lesions. Neurophychologia 36, 499–504. van der Knaap, L.J., van der Ham, I.J.M., 2011. How does the corpus callosum mediate interhemispheric transfer? A review. Behav. Brain Res. 223, 211–221. van der Steen, M.C.M., Keller, P.E., 2013. The ADaptation and Anticipation Model (ADAM) of sensorimotor synchronization. Front. Hum. Neurosci. 7, 253. 35

ACCEPTED MANUSCRIPT White Matter and Rhythmic Synchronization

AC CE P

TE

D

MA

NU

SC

RI

PT

Vandermosten, M., Poelmans, H., Sunaert, S., Ghesquière, P., Wouters, J., 2013. White matter lateralization and interhemispheric coherence to auditory modulations in normal reading and dyslexic adults. Neuropsychologia 51, 2087–2099. Vollmann, H., Ragert, P., Conde, V., Villringer, A., Classen, J., Witte, O.W., Steele, C.J., 2014. Instrument specific use-dependent plasticity shapes the anatomical properties of the corpus callosum: a comparison between musicians and non-musicians. Front. Behav. Neurosci. 8, 245. Wahl, M., Lauterbach-Soon, B., Hattingen, E., Hübers, A., Ziemann, U., 2015. Callosal anatomical and effective connectivity between primary motor cortices predicts visually cued bimanual temporal coordination performance. Brain Struct. Funct. 1– 17. Wakana, S., Caprihan, A., Panzenboeck, M.M., Fallon, J.H., Perry, M., Gollub, R.L., Hua, K., Zhang, J., Jiang, H., Dubey, P., Blitz, A., van Zijl, P., Mori, S., 2007. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 36, 630–644. Witelson, S.F., 1989. Hand and Sex Differences in the Isthmus and Genu of the Human Corpus Callosum. Brain 112, 799–835. Witt, S.T., Laird, A.R., Meyerand, M.E., 2008. Functional neuroimaging correlates of finger-tapping task variations: An ALE meta-analysis. Neuroimage 42, 343–356. Wohlschläger, A., Koch, R., 2000. Synchronization error: an error in time perception, in: Rhythm Perception and Production. pp. 115–127. Wolff, P.H., 2002. Timing precision and rhythm in developmental dyslexia. Read. Writ. 15, 179–206. Woodrow, H., 1932. The effect of rate of sequence upon the accuracy of synchronization. J. Exp. Psychol. 15, 357–379. Woodruff Carr, K., White-Schwoch, T., Tierney, A.T., Strait, D.L., Kraus, N., 2014. Beat synchronization predicts neural speech encoding and reading readiness in preschoolers. Proc. Natl. Acad. Sci. U. S. A. 111, 14559–14564. Yeatman, J.D., Dougherty, R.F., Myall, N.J., Wandell, B.A., Feldman, H.M., 2012. Tract profiles of white matter properties: automating fiber-tract quantification. PLoS One 7, e49790. Yeatman, J.D., Dougherty, R.F., Rykhlevskaia, E., Sherbondy, A.J., Deutsch, G.K., Wandell, B.A., Ben-shachar, M., 2011. Anatomical Properties of the Arcuate Fasciculus Predict Phonological and Reading Skills in Children. J. Cogn. Neurosci. 23, 3304–3317. Yeatman, J.D., Wandell, B. a, Mezer, A., 2014. Lifespan maturation and degeneration of human brain white matter. Nat. Commun. 5, 4932. Zatorre, R.J., Chen, J.L., Penhune, V.B., 2007. When the brain plays music: auditorymotor interactions in music perception and production. Nat. Rev. Neurosci. 8, 547– 558. Zoccolotti, P., Friedmann, N., 2010. From dyslexia to dyslexias, from dysgraphia to dysgraphias, from a cause to causes: A look at current research on developmental dyslexia and dysgraphia. Cortex 46, 1211–1215.

36