Motor learning-induced changes in functional brain connectivity … · 2013-06-28 · training, there is very little information on how motor-task related brain areas function as

NeuroImage 61 (2012) 633–650

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Motor learning-induced changes in functional brain connectivity as revealed bymeans of graph-theoretical network analysis

M.H. Heitger a,⁎, R. Ronsse a,b, T. Dhollander c, P. Dupont c,d, K. Caeyenberghs a, S.P. Swinnen a

a Motor Control Laboratory, Research Center for Movement Control and Neuroplasticity, Department of Biomedical Kinesiology, Group Biomedical Sciences, K.U. Leuven, Belgiumb Centre for Research in Mechatronics, Institute of Mechanics, Materials, and Civil Engineering, Université Catholique de Louvain, Belgiumc Medical Imaging Research Center, University Hospital Gasthuisberg, Leuven, Belgiumd Laboratory for Cognitive Neurology, Department of Neurosciences, Group Biomedical Sciences, K.U. Leuven, Belgium

⁎ Corresponding author at: Laboratory of Motor ContControl and Neuroplasticity, Group Biomedical ScieLeuven, Tervuursevest 101, B-3001 Leuven-Heverlee, Be

E-mail address: [email protected] (

1053-8119/$ – see front matter © 2012 Elsevier Inc. Alldoi:10.1016/j.neuroimage.2012.03.067

a b s t r a c t
a r t i c l e i n f o
Article history:Accepted 21 March 2012Available online 30 March 2012

Keywords:Motor learningBimanual coordinationGraph-theoryFunctional connectivityFunctional networksfMRI

Complex bimanual motor learning causes specific changes in activation across brain regions. However, thereis little information on how motor learning changes the functional connectivity between these regions, andwhether this is influenced by different sensory feedback modalities. We applied graph-theoretical networkanalysis (GTNA) to examine functional networks based on motor-task-related fMRI activations. Two groupslearned a complex 90° out-of-phase bimanual coordination pattern, receiving either visual or auditory feedback.3 T fMRI scanning occurred before (day 0) and after (day 5) training. In both groups, improved motorperformance coincidedwith increased functional network connectivity (increased clustering coefficients, highernumber of network connections and increased connection strength, and shorter communication distances).Day×feedback interactions were absent but, when examining network metrics across all examined brainregions, the visual group had a marginally better connectivity, higher connection strength, and more directcommunication pathways. Removal of feedback had no acute effect on the functional connectivity of the trainednetworks. Hub analyses showed an importance of specific brain regions not apparent in the standard fMRIanalyses. These findings indicate that GTNA can make unique contributions to the examination of functionalbrain connectivity in motor learning.

© 2012 Elsevier Inc. All rights reserved.

Introduction

During motor learning, specific changes in the intensity of brainactivation in a range of brain regions are observed from pre- to post-training, and several previous studies have successfully used functionalmagnetic resonance imaging (fMRI) to demonstrate such training-induced changes in brain activation (Anguera et al., 2011; Coynel et al.,2010; Debaere et al., 2004b; Doyon and Benali, 2005; Graydon et al.,2005; Guillot et al., 2008; Kineses et al., 2008; Miall and Jenkinson,2005; Muller et al., 2002; Orban et al., 2010; Puttemans et al., 2005;Remy et al., 2008, 2010; Ronsse et al., 2011; Toni et al., 2002; Vogt et al.,2007). In addition, previous research has shown an association betweenmotor learning and changes in cortical excitability (Muellbacher et al.,2001; Perez et al., 2004). Diffusion tensor imaging (DTI) has providedevidence that visuomotor skill training indeed induces changes inwhitematter structure and subsequent structural connectivity (Scholz et al.,2009), and increased intra-regional functional connectivity in the primarymotor cortex has been reported while applying a 40-min training task

rol, Research Center for Motornces, Katholieke Universiteitlgium. Fax: +32 16 32919.M.H. Heitger).

rights reserved.

(McNamara et al., 2007). An important element in examining brainfunction is the consideration that efficient brain activity depends on thecollaboration and co-activation of many brain areas, which have tooperate as a coordinated network to achieve correct behavioral output(Womelsdorf et al., 2007). The examination of brain activation from anetwork perspective is crucial in understanding the factors that driveour motor behavior. However, despite multiple previous reports ofmotor-training being associated with changes in cerebral activations aswell as altered structural connectivity, there is only limited informationon training-induced changes in functional connectivity (i.e., changes inthe inter-regional statistical association in the time course of activation)within the cerebral networks activated during motor learning, inparticular in the context of complex training-dependent bimanual motorcoordination.

A number of previous studies have examined aspects of functionalconnectivity related to motor learning. A study by Coynel et al. (2010)reported training-related changes in functional connectivity in a fingertapping task by focusing on the dynamics of motor-related functionalintegration. However, their key measure of overall network integrationequated to a global quantification of changes in the network associationmatrix, summarizing changes across more than 100 correlation co-efficients in one number which then was used as indicator of whetherfunctional connectivity across the entire network changed as a result of

http://dx.doi.org/10.1016/j.neuroimage.2012.03.067mailto:[email protected]://dx.doi.org/10.1016/j.neuroimage.2012.03.067http://www.sciencedirect.com/science/journal/10538119

634 M.H. Heitger et al. / NeuroImage 61 (2012) 633–650

motor training. Consequently, their report contained no information onchanges in sub-domains of functional connectivity. Another recentstudy by Bassett et al. (2011) examined dynamic configuration ofhuman brain networks using functional connectivity measurementsof fMRI brain activity during motor learning using a visually-cued 4-finger motor task. However, the principal aim of the study was thedemonstration of a general statistical framework for the identifica-tion of modular architectures in evolving systems and it focusedsolely on the role of modular organization in human learning byidentifying dynamic changes in the allegiance of network nodes tofunctionally correlated sub-groups of regions (modules) within thenetwork without specifically integrating this into a motor learningcontext. The present study aimed at expanding the limited previouswork in the field of functional connectivity related to motor learningand examined changes in functional connectivity related to biman-ual motor learning using connectivity measures based on graph-theoretical network analysis (GTNA) (for review see Bullmore andSporns, 2009; Bullmore and Bassett, 2011; Rubinov and Sporns,2010).

An aspect that has not been sufficiently addressed with regard tothe training-related changes in functional network connectivity is theinfluence of different feedback conditions during motor learning.Motor control is guided by sensory feedback to (a) optimize motorperformance and (b) learn new motor actions or form internallyguided representations of new task dynamics. Especially during theacquisition of newmotor skills, the presence of augmented feedback is akey source of information for learning, and an important ingredient inmotor learning and optimizing the timely and precise assimilation ofpreviously unexperienced motor patterns (Debaere et al., 2003, 2004b;Kovacs and Shea, 2010, 2011; Kovacs et al., 2009, 2010; Lee et al., 1995;Puttemans et al., 2005; Ronsse et al., 2009, 2011; Swinnen et al., 1997,2002). However, despite the crucial role of feedback for effective motortraining, there is very little information on how motor-task relatedbrain areas function as a network with regard to motor learning underdifferent feedback modalities. The question remains whether thesefunctional networks indeed show differential changes in measures offunctional connectivity as a result of bimanual coordination trainingunder provision of visual or auditory augmented feedback.

In the present study, we built on the previous findings of Ronsseet al. (2011) and examined changes in the characteristics of task-related functional brain networks during bimanual coordination. Thelatter fMRI study focused specifically on the effects of providing eithervisual or auditory sensory feedback to guide the optimization of adifficult 90° out-of-phase bimanual coordination pattern. Both feedbackmodalities were shown to improve motor performance but the visualgroup became more dependent on this augmented feedback forperformance, whereas the auditory group performed equally well withor without augmented feedback by the end of the training. The fMRIfindings mirrored this pattern by emphasizing increased activity inmany sensory-specific areas in the visual groupwhile the activity changein the auditory group between pre-and post-training was marked by apredominant decrease in activity specifically in areas associated withcognitive/sensory monitoring of motor task performance. However,despite this differential nature of training-related activity changes acrossthe groups, the study indeed identified task-related brain areas showingsimilar activation changes in both groups aswell as areaswhere changesin activation developed differentially between both groups during the4-day training regime. As such, the study by Ronsse et al. (2011)provided an ideal setup to simultaneously examine changes in neuralnetwork functional connectivity as a result of bimanual motor-trainingas well as the influence of different types of augmented feedback onchanges in functional connectivity. We hypothesized that the motor-training regime would be associated with an increase in functionalconnectivity in the trained networks, and that this will manifest inmeasurable changes inmean graph-theoretical networkmeasureswhenassessing overall network connectivity. This hypothesis included the

consideration that motor learning will not only alter and strengthenexisting functional connections but also result in the formation of newfunctional relationships between brain areas. A priori, it was unclearwhether this effect might be more pronounced in the visual or theauditory group as both feedbackmodes promotemotor learning (Ronsseet al., 2011).We anticipated that an examination of changes in functionalneural network connectivity would enable us to demonstrate thatthe effects of training-induced motor learning manifest in significantchanges in functional brain connectivity and network cohesion whichare not easily accessible by the examination of voxel-based analyses ofregional changes in activation intensity.

Materials and methods

Participants, task description and procedure

The participant groups, task details and study procedures have beendescribed elsewhere (Ronsse et al., 2011). In summary, 34 adults (19females, 15 males), aged between 20 and 30 (mean=23) years, wererandomly assigned into two age- and gender-matched groups, whichdiffered only with respect to the mode of augmented sensory feedbackthey received during bimanual coordination training, i.e., visual (VIS)or auditory (AUD) feedback. The study was approved by the ethicscommittee of Biomedical Research at the K.U. Leuven, informed consentwas obtained, and participants were paid for participation. Both groupshad to learn a complex 90° out-of-phase bimanual coordination pattern(one hand led the other by a quarter-cycle) (see Ronsse et al., 2011).This complex bimanual pattern requires intensive practice to beperformed stably and consistently (Lee et al., 1995; Swinnen et al.,1997). Augmented feedback to the VIS groupwas provided bymeans ofa Lissajous figure, displaying the left and right angular wrist displace-ment on the ordinate and abscissa, respectively (Ronsse et al., 2011). Acursor on the screen in front of the participant showed the orthogonalplot of bothwrist displacements in real time, with the last 750 ms of thecursor trajectory remaining visible. When produced correctly, the 90°out-of phase pattern corresponded to a circular figure trajectory onthe screen. Augmented feedback to the AUD group was provided bydelivering a low tone when the left hand reached either maximumflexion or maximum extension, and a high tone when the right handreached either maximum flexion or extension. Perfect 90° out-of-phasecyclical movement corresponded to a regular alternation of both tones(Ronsse et al., 2011). All subjects practiced the 90° out-of-phase patternwith the help of augmented feedback (either AUD or VIS), performing8 blocks containing 10 trials of bimanual movement (duration=21 sper trial) each day across 4 training days. Moreover, subjects performedthe 90° out-of-phase pattern without augmented feedback during asingle block (10 trials, 21 s per trial) at the end of day 4. Short restperiods were offered to the subjects in the middle of and in betweeneach block. Participants were instructed to move at their preferredamplitude and frequency and to preserve these parameters across taskperformance. The training sessions (days 1, 2, 3, and 4) took place in adummy scanner, closely mimicking the experimental context of theactual scanner, including reproduction of the background noise.

MRI scanning and fMRI data analyses

The details on the scan procedures have been published previ-ously (Ronsse et al., 2011). Scanning occurred before (day 0, PRE) andafter (day 5, POST) training. Rest-conditions (without any informationpresented) were administered alongside the 90° out-of-phase conditionwith feedback (FB+) at the PRE scanning. In addition to the latter 2conditions, the POST scanning also included performance of the 90° out-of-phase task without augmented feedback (POST FB−) (Ronsse et al.,2011). Each scanning condition lasted 21 s and was symbolically cuedon the screen, remaining visible for 3 s. Subjects were instructedto switch conditions as soon as this template appeared. To avoid eye

635M.H. Heitger et al. / NeuroImage 61 (2012) 633–650

movements across conditions, subjects were instructed to keep theireyes open at all times and to fixate a cross projected in the middle oftheir visual field during each condition. To prevent carry over effectsbetween the different tasks, a short rest period (3 s) was addedwhenever 2 movement conditions succeeded one another immediately.A bite bar was used to minimize head movements, and a mirror wasutilized to allow vision of images projected onto a screenmounted abovethe shoulders. This setup prevented subjects from seeing their handsduring the movement task. Subjects wore headphones for communica-tionwith the experimenter and for hearing themovement-driven pacingtones (AUD group). Imaging data were processed and analyzed usingStatistical ParametricMapping (SPM) 5 software (WellcomeDepartmentof Imaging Neuroscience) implemented in MatLab 7.6 (R2008a) (Math-Works) (details as described in Ronsse et al., 2011).

Network definition for graph-theoretical network analyses

This study applied a data driven network definition, which wasbased on task-related cerebral activations in the subject groups (Ronsseet al., 2011). This primary data analysis of the imaging data identifiedthe brain areas active during performance of the learned 90° out-of-phase motor pattern, and training-related changes in brain activationfrom PRE to POST. fMRI changes PRE vs. POST manifested mainly indecreased activations in the auditory group and increased activations inthe visual group (Ronsse et al., 2011). Several sub-sets of brain regionssharing specific patterns of functional changes from PRE to POST wereidentified, which subsequently were defined as 3 types of networks tobe analyzed via GTNA: (1) brain regions characterized by the samefunctional change from PRE to POST in both groups (i.e., these regionsshowed either an increase or a decrease in activity in both groups) (Type1 network, network N1, Table 1 and Fig. 1), (2) regions exhibiting thepredominant functional behavior of each group (i.e., a set of regionsmanifesting a decrease in activity from PRE to POST in the AUD group,and a set of regionswhich all shared an increase in activity ‘typical’ for theVIS group) (Type 2 networks, networks N2 and N3, Tables 2 and 3 andFig. 1), (3) regions in which both groups showed differential activationchanges in terms of showing an increase or decrease in activity from PREto POST to an extent thatmanifested a significant group difference and/or

Table 1Regions of network N1 (Type 1) — areas with significant practice-related decrease (PRE>POFB+ 90° out-of-phase pattern.

x y

Primary sensory & (pre)motor cort.

Postcentral gyrus L (1) −50 −3

Supplementary motor area

Superior frontal gyrus2 R (2) 15

Frontal and prefrontal area

Middle orbital gyrus2 R (3) 30 5Middle frontal gyrus (DLPFC)2 R (4) 43 4

Occipital cortex

Superior occipital gyrus3 R (5) 20 −10

Temporal cortex

Middle temporal gyrus L (6) −58 −6Temporal pole2 5 R (7) 60 1

Parietal cortex

Superior parietal lobule R (8) 50 −3Supramarginal gyrus2 L (9) −65 −2

2, 3, 5 = Nodal sphere is partially colocated with a sphere in networks N2, N3 and N5 respe

Brain region Side (Fig. 1 label)

Peak activation

group×session interaction (Type 3 networks, networks N4 and N5,Tables 4 and 5 and Fig. 1). The network nodes for the GTNAwere definedby placing a sphere with radius 7.5 mm around the MNI coordinates ofthe respective cluster activation maximum. When extracting timecourses of activation from fMRI data using the present method of placingspheres around activationmaxima, sphere sizes of between6 and10 mmradius have become established as best practice (e.g., Bedenbender et al.,2011; Chen et al., 2008; Coste et al., 2011; Fox et al., 2009; He, 2011;Heuninckx et al., 2010; Newman et al., 2007; Sadaghiani et al., 2010;Sonty et al., 2007; Van de Winckel et al., 2005; Wenderoth et al., 2006;Wenger et al., 2004). Within this standard 6–10 mm range aroundactivationmaxima, the current sphere sizewas deemed the best solutionfor the present imaging data. In each individual subject, separate analyseswere conducted for each network. This also included the analysis of thePRE to POST changes in the functional connectivity for each group in bothType 2 networks answering the questions of (a) whether there is anycoordinated/synchronized activity for the VIS group within the type 2network as defined by the predominant fMRI changes of the AUD group(and vice versa), and (b) whether there are any significant changes infunctional connectivity from PRE to POST for the VIS group in the type 2network defined by the AUD group (and vice versa).

Several of the 5 networks contained activation foci falling withinbrain regions bearing the same or similar neuroanatomical labels.Further evaluation, however, showed that there was minimal overlapbetween the 5 different networks regarding the spheres defining thesignal-recruitment areas for the network nodes (i.e., overlap betweenspheres was present if their centre coordinates were separated by lessthan 15mm). Such overlap was present between N1 and N2 (5 nodes,Tables 1 and 2), N1 and N3 (1 node, Tables 1 and 3), N1 and N5 (1 node,Tables 1 and 5), N2 and N4 (1 node, Tables 2 and 4), and N2 and N5 (3nodes, Tables 2 and 5).

The choice of examining five networks in parallel was inspired bythe question of whether changes in functional connectivity may bedependent on the origin of the task-related network. In definingthese networks we took inspiration from the findings and emphasesof the primary imaging analyses. A priori, we hypothesized thatnot all of these networks would necessarily exhibit the same, or evensimilar, changes in functional connectivity as a result of motor training.

ST) or increase (POST>PRE) for the two groups combined, during the execution of the

z AUD VIS AUD VIS AUD VIS

3 63 BA 1/2 0 0 1 0 1 0

0 68 BA 6 1 3 2 3 1 3

0 −13 BA 11 0 1 0 0 0 03 25 BA 45 1 1 0 3 0 3

3 18 BA 17 1 0 0 0 0 0

8 3 BA 37 1 1 0 0 0 18 −8 BA 38 0 0 1 1 1 0

5 58 BA 1 0 3 3 1 3 28 23 BA 48 1 0 0 2 0 3

ctively.

BAHubscore (max. = 4)

PRE POST FB+ POST FB− coordinates

Fig. 1. Neuroanatomical location of network nodes in the 3 types of networks: N1— brain regions characterized by the same functional change from PRE to POST in both groups (i.e.,these regions showed either an increase or a decrease in activity), N2 and N3 — regions exhibiting the predominant functional behavior of each group (i.e., a decrease in activityfrom PRE to POST in the AUD group [N2], and an increase in activity in the VIS group [N3]), N4 and N5 — regions in which both groups showed differential activation changes interms of showing an increase or decrease in activity from PRE to POST to an extent that manifested a significant group difference and/or group×session interaction (N4: areasshowing significant group differences in activation when executing the FB+ 90° out-of-phase pattern; N5: areas showing significant interaction effects in activation between group(AUD/VIS) and time (PRE/POST) during the FB+ 90° out-of-phase pattern). Extent of network nodes in this figure is limited to 4 mm to visualize the precise node coordinates.Networks were visualized using BrainNet Viewer.


Importantly, when accounting for the applied node/sphere size, all thesenetworks represented neuro-anatomically largely distinct entitieswithminimal overlap. A priori, it was unclear whether only increasesor decreases in signal strength (Type 2 networks) may carry tangiblechanges in functional connectivity, or whether such changes may occurexclusively in sets of regionswherein both groups show the same patternwith regard to changes in signal intensity from PRE to POST (Type 1network). A further consideration for the inclusionof the Type 3networkswas that Type 1 and 2 represented sets of regions sharing the samepredominant pattern with regard to signal change from PRE to POST.However, such a type of functional network does not necessarilyresemble the ‘in vivo’ status or actual computational processes in thebrain, where interdependent excitatory and inhibitory processes, andthe accompanying increases and decreases in brain activation, occurconcomitantly. Subsequently, we considered each of the 5 networks asunique and non-redundant entities, whereby the outcome of thefunctional connectivity analysis was not a priori clear.

Signal extraction and connectivity thresholding for the GTNA

For each network node in each subject, the BOLD average timeseries (AVT) for the 90° out-of-phase epochs relative to rest was thenextracted for both the PRE and POST runs (105 image volumes wereavailable per condition at PRE and POST). At POST this included separateextractions for conditions (a)with and (b)without augmented feedback.The AVT was taken as the first eigenvariate from the singular valuedecomposition of a matrix composed of each time series of each voxelwithin the node-sphere, and the time series of all voxels of the spherewas then element-wise averaged, so as to obtain a single AVT for eachnode. The AVT extraction included whitening, filtering and removingnull space of contrast using SPM code. During extraction, the time serieswas adjusted using the contrast ‘Task of interest [either 90° out-of-phase

with feedback, at PRE and POST, or without feedback, at POST only] –Rest’. TheAVT datawere detrended before progressing to the calculationof the correlation matrices and the graph-theoretical analysis. Basedon the AVT data, the network connectivity was then determined bycalculating matrices of partial correlations between the network nodes,quantifying the unique relationship between each pair of nodes. Thecalculation of the partial correlations was based on the inverse ofthe covariance matrix. The partial correlation matrix is a symmetricmatrix in which each off-diagonal element is the correlation coefficientbetween a pair of variables after filtering out the contributions of allother variables included in the dataset. Therefore, the partial correlationbetween any pair of regions filters out the effects of the other brainregions. Consistent with previous studies, partial correlations werechosen to minimize the impact of indirect dependencies by other brainregions and to address the problem of complicating the interpretation ofthe GTNA by includingmultiple redundancies in quantifying inter-nodalfunctional dependencies when using simple correlation coefficients (Liuet al., 2008; Marrelec et al., 2006; Salvador et al., 2005). Among allmethods of evaluating functional interdependencies between fMRI timecourses in different regions of interest, partial correlations have beenfound to be amongst the most reliable approaches (Smith et al., 2011).

In the connectivity matrices, functional connections were definedas valid/existing between pairs of nodes based on the statistical levelof significance. We used a threshold of Pb0.001. Thresholding of theconnectivity matrices resulted in binary matrices where existing (valid)connections carried a value of 1 while the absence of a functionalconnection between network nodes was designated by a value of 0. Self-connections of nodes were not included in the analyses. The resultingthresholded adjacencymatrices of partial correlations served as principalinput for the GTNA. It has been shown that manipulating the connectiondensity in a network by varying the number of valid network connectionscan have a noticeable impact on GTNA metrics (Van Wijk et al., 2010).

Table 2Regions of network N2 (Type 2) — areas with significant practice-related decrease (PRE>POST) for the AUD group, during the execution of the FB+ 90° out-of-phase pattern.

x y z AUD VIS AUD VIS AUD VIS



PRE POST FB+ POST FB−Peak activation coordinates

Primary sensory & (pre)motor cort.

Postcentral gyrus R (1) 38 −45 73 BA 1/2 3 3 1 3 2 3L (2) −43 −43 68 BA 1/2 3 0 3 3 3 3

Supplementary motor area

Superior frontal gyrus1 R (3) 15 0 68 BA 6 0 1 1 1 1 1Superior frontal gyrus5 L (4) −10 −10 63 BA 6 1 3 0 0 0 0

Frontal and prefrontal areaMiddle orbital gyrus1 R (5) 28 53 −13 BA 11 0 1 1 0 1 0Middle frontal gyrus (DLPFC)1 R (6) 30 45 25 BA 46 0 1 1 3 2 3Inferior frontal gyrus (p. opercularis) R (7) 48 13 18 BA 44 0 0 0 0 1 0Inferior frontal gyrus (p. triangularis)4 R (8) 43 28 8 BA 45 2 2 2 1 2 2

Opercular areaRolandic operculum R (9) 53 −18 18 BA 48 0 0 3 0 2 2

L (10) −60 8 0 BA 44 1 1 2 1 0 1

Temporal cortexSuperior temporal gyrus R (11) 70 −33 20 BA 22 1 2 1 1 0 0

L (12) −43 3 −13 BA 48 1 1 0 1 0 0Temporal pole1 5 R (13) 63 15 -5 BA 38 3 0 0 0 1 0

Parietal cortexSupramarginal gyrus5 R (14) 60 −30 33 BA 2 2 4 3 1 2 1Supramarginal gyrus1 L (15) −63 −28 23 BA 48 1 1 0 1 0 0Superior parietal lobule R (16) 38 −58 68 BA 7 3 3 3 2 2 1

L (17) −18 −45 78 BA 1 0 0 0 0 0 0Inferior parietal lobule R (18) 33 −38 50 BA 2 0 0 1 2 1 3

L (19) −53 −28 43 BA 2 1 2 1 2 1 0

1, 4, 5 = Nodal sphere is partially colocated with a sphere in networks N1, N4 and N5 respectively.


Hence, we repeated the GTNA analyses across 5 threshold values (i.e.,for each subject, GTNA analyses were calculated based on connectivitymatrices thresholded at P=0.0001, 0.0005, 0.001, 0.005 and 0.01) toensure that the statistical results with regards to training-related GTNAeffects aswell as group effects between visual and auditory learnerswerestable and reliable at different connection densities. Related to the issueof repeating the analyses at different density thresholds is the issue ofcomparing aspects of network architecture using graph-theory. In orderto be able to compare topological features/aspects of network architec-ture within a network, it is deemed essential to keep the connectiondensity of the compared networks constant (Van Wijk et al., 2010).However, the underlying hypothesis to this study postulates that motorlearning is not only associatedwith alterations in brain activation butwillalsomodulate functional connectivity in the brain resulting in an increase

Table 3Regions of network N3 (Type 2) — areas with significant practice-related increase (POST>

Brain region Side(Fig. 1 label)

Peak activation coordinates

x y z

Occipital cortexSuperior occipital gyrus1 R (1) 18 −100 20Middle occipital gyrus L (2) −13 −98 3Calcarine gyrus R (3) 13 −95 3Lingual gyrus L (4) −13 −90 −10

CerebellumLobule IV–V R (5) 15 −48 −15Lobule VI R (6) 10 −65 −23Vermis IV−V − (7) 0 −55 −15Vermis VIII − (8) 5 −68 −40

1 = Nodal sphere is partially colocated with a sphere in network N1.

of significant/relevant functional relationships between task relevantareas, i.e., a better synchronization in BOLD activation across the brainareas in the networks will be expressed in a higher number of significantcorrelations/functional connections post-training. It follows that motorlearning should not only alter existing connections but foster newfunctional relationships between brain areas. In order to capture andquantify this aspect of motor learning, we did not enforce the sameconnection density in our networks for the pre- and post-trainingassessments. Consistent with this compromise, we report changes inthe mean graph-theoretical metrics across each entire network but didnot assess specific aspects of network architecture.

In addition to the binary matrices, we also calculated weightedmatrices, where for each valid functional connection between a pair ofnodes, the value of 1 in the binary matrix was replaced by the value of

PRE) for the VIS group, during the execution of the FB+ 90° out-of-phase pattern.

BA Hubscore (max.=4, min. hub criterion: 3)

PRE POST FB+ POST FB−

AUD VIS AUD VIS AUD VIS

BA 18 0 0 0 1 0 0BA 17 1 0 0 1 0 1BA 17 0 2 0 0 0 1BA 18 0 0 2 0 2 0

0 1 0 0 0 11 0 0 2 0 21 1 0 0 0 01 1 1 1 1 1

Unlabelled image

Table 4Regions of network N4 (Type 3) — areas showing significant group differences in activation when executing the FB+ 90° out-of-phase pattern.





Supplementary motor area R (1) 5 −15 68 BA 6 0 0 0 0 1 0

Frontal and prefrontal areaPrecentral gyrus L (2) −45 −5 43 BA 6 1 2 2 1 0 1Inferior frontal gyrus2 R (3) 48 25 0 BA 47 0 1 0 0 0 0

L (4) −40 15 18 BA 48 2 1 0 1 0 1

Opercular areaInferior operculum L (5) −33 3 28 BA 48 3 0 2 3 3 1

Temporal cortexSuperior temporal gyrus R (6) 63 −23 5 BA 22 0 3 0 3 0 1

L (7) −60 −23 3 BA 22 1 0 0 0 0 0

Parietal cortexSuperior parietal lobule R (8) 20 −60 60 BA 5 0 1 0 0 0 0

L (9) −20 −58 58 BA 5 2 1 2 2 2 0

Lingual gyrus L (10) -8 −58 0 BA 18 2 0 1 0 1 1

Occipital cortexMiddle occipital gyrus R (11) 33 −90 8 BA 18 1 1 3 0 1 0

L (12) −48 −78 0 BA 19 2 2 3 3 2 3

CerebellumLobule VIII R (13) 25 −65 −53 2 0 0 2 0 2

L (14) −25 −63 −50 0 2 2 2 2 2Vermis X −(15) 3 −43 −35 0 1 1 0 0 0

2 = Nodal sphere is partially colocated with a sphere in network N2.


the partial correlation for this node-pair, with the partial correlation thenrepresenting a proxy measure for the weight/strength in connectionbetween this pair of nodes. This approach allowed us to examineconnection strength between node-pairs and calculate a mean connec-tion strength for each node defined as the sum of its connection weights(i.e., the sum of partial correlations for valid connections) of a node withother nodes in the network. Subsequently, themean connection strengthof a network was then defined as the mean connection strength acrossall its network nodes.

Graph theoretical analysis

The area of graph-theory is an established mathematical fieldand this kind of brain connectivity analysis has proven a very effectiveand informative way to explore brain function and human behavior(Bullmore and Bassett, 2011; Bullmore and Sporns, 2009; Rubinovand Sporns, 2010). The main GTNA analyses were conducted usingthe Brain Connectivity Toolbox (Rubinov and Sporns, 2010) (https://sites.google.com/a/brain-connectivity-toolbox.net/bct/Home). At thePRE and then again at the POST time points, we calculated in eachnetwork and for each subject the regional (nodal) metrics of thefunctional network. For each node i of the network, we calculated(see below for measure definitions) the degree Ki, the local clusteringcoefficient Ci, the mean path length Li, the betweenness centrality biand the overall connection strength Si. For each network, the meanfor each of the above GTNA parameters was calculated as the averageof each measure across all nodes of a network.

Measures of functional connectivityConnectivity degree is one of the most basic and important

measures of network analysis. The degree Ki of a node i is defined asthe number of connections to that node (Sporns, 2002, 2011). Nodeswith a high degree are interacting with many other nodes in the

network. The degree K of a graph is the average of the degrees of all Nnodes in the graph G:

K ¼ 1N∑i�G

Ki

Connection strength is a measure quantifying how closely networknodes are connected in terms of showing a relationship in their timecourse of activation (Sporns, 2002, 2011). The overall connectionstrength Si is calculated as:

Si ¼ ∑i≠j�G

ri;j

where ri, j is the partial correlation between the average time series innode i and j.

Measures of functional segregationMeasures of segregation quantify the presence of functionally related,

densely interconnected groups of brain regions, known as clusterswithin the network. The local (nodal) clustering coefficient Ci (Watts andStrogatz, 1998) is defined as the number of existing connections amongthe node's neighbors divided by all their possible connections:

Ci ¼Ei

Ki Ki−1ð Þ=2

where Ei is the number of existing connections among the node'sneighbors. The clustering coefficient of a network is the average ofthe clustering coefficient of all nodes:

C ¼ 1N∑i�G

Ci

in which C quantifies the extent of local connectivity of the network.

https://sites.google.com/a/brain-connectivity-toolbox.net/bct/Homehttps://sites.google.com/a/brain-connectivity-toolbox.net/bct/Home

Table 5Regions of network N5 (Type 3)— areas showing significant interaction effects in activation between group (AUD/VIS) and time (PRE/POST) during the FB+ 90° out-of-phase pattern.





Primary sensory and (pre)motor cortexPostcentral gyrus L (1) −38 −25 43 BA 3a 3 0 2 0 0 1

Supplementary motor areaSuperior frontal gyrus2 L (2) −10 −10 60 BA 6 1 3 2 2 1 0

Frontal and prefrontal areaInferior frontal gyrus (p. orbitalis) R (3) 48 28 −15 BA 38 3 0 0 0 0 2

Temporal cortexSuperior temporal gyrus R (4) 45 −18 −3 BA 48 3 0 0 2 1 1

L (5) −58 −45 23 BA 42 1 1 0 1 1 0Temporal pole1 2 R (6) 65 10 −3 BA 38 0 1 1 0 0 1Transverse temporal gyrus (Heschl's) R (7) 53 −8 5 BA 48 3 0 0 0 0 0

Parietal cortexSuperior parietal lobule L (8) −33 −50 68 BA 7 3 0 0 0 3 1

Opercular areaRolandic operculum R (9) 50 0 8 BA 48 2 2 0 0 1 3

Parietal cortexSupramarginal gyrus2 R (10) 48 −23 28 BA 48 1 1 1 3 3 2

Cingulate cortexMiddle R (11) 15 15 38 BA 32 2 2 3 2 2 2

L (12) −8 3 43 BA 24 3 0 1 2 0 1Anterior R (13) 3 18 28 BA 24 3 0 0 1 0 0

CerebellumLobule IV-V R (14) 13 −50 −18 0 0 1 0 0 0Vermis IV-V −(15) 0 −55 −15 0 0 0 0 1 2Vermis VIII −(16) 3 −65 −40 1 1 0 2 1 0

1, 2 = Nodal sphere is partially colocated with a sphere in networks N1 and N2 respectively.


Measures of functional integrationMeasures of functional integration characterize the ability to rapidly

combine specialized information from distributed brain regions and arecommonly based on the concept of a path, with shorter paths implyingstronger potential for integration (Sporns, 2002, 2011). The mean pathlength Li of a node i is:

Li ¼1

N−1 ∑i≠j�GLi;j

in which Li,j is the smallest number of edges that must be traversed tomake a connection between node i and node j.

Measures of centralityAn important measure to assess whether a node has importance

for information flow and participates in many shortest paths within anetwork is betweenness centrality (Rubinov and Sporns, 2010; Sporns,2002, 2011). The betweenness centrality bi of a node i is defined as:

bi ¼1

N−1ð Þ N−2ð Þ ∑h; j�G;h≠j; h≠i; j≠iρhj ið Þρhj

in which ρhj is the number of shortest paths between nodes h and j andρhj(i) is the number of shortest paths between nodes h and j that passthrough node i. An important concept that is related to betweennesscentrality is that of network ‘hubs’. Commonly, somenodes in functionalnetworks will stand out in terms of having a more important role in thecommunity structure of a network as they are crucial centers (hubs) forthe information flow within the network and for connecting a highnumber different network nodes.

As part of our analysis, we classified the hub-status of networknodes PRE and POST. This classification was based on assessing 4parameters – clustering coefficient, path length, degree, and between-ness centrality – and was based on the consideration that hub nodesgenerally display a low clustering coefficient, a short average distancetowards the other nodes of the network, an above average high numberof connections, and a high level of betweenness centrality (Bullmoreand Sporns, 2009; Sporns et al., 2007). The hubs for each network PREand POSTwere identified by computing the hub-status for each node byexaminingwhether a node belonged to: (1) the top 25% nodes showingthe lowest clustering coefficient, (2) the top 25% of nodes showingthe shortest mean path length, (3) the top 25% of nodes showing thehighest level of connectivity degree; and/or (4) the top 25% of nodesshowing the highest betweenness centrality. Consistent with previousstudies (van den Heuvel et al., 2010), each node was assigned a scorebetween0 and 4, determined by the total number of hub criteria fulfilled.In order to be accepted as hub, a region needed to fulfill at least 3 ofthe 4 criteria, with one of these being a sufficiently high betweennesscentrality.

Small-world brain connectivityGlobal network architecture in terms of the small-worldness was

quantified via the normalized clustering coefficient and normalizedpath length. Previous research has shown that all networks foundin biological systems have non-random/non-regular or ‘small-world’architectures (Bullmore and Sporns, 2009; Sporns et al., 2007). Small-worldness is a relative measure for a network's level of functionaloptimization and deviation from randomness (Bullmore and Sporns,2009; Sporns et al., 2007), with alterations in this measure commonlyrepresenting a decrement in network functionality. Small-world


organization incorporates advantages of regular and random net-works, preserving optimal levels of connectivity within families offunctionally related node-clusters together with short overall commu-nication distances. Hence, small world network character is defined asbeingmore clustered than randomnetworks, yet having approximatelythe same characteristic path length as random networks (Watts andStrogatz, 1998), that is

Lnorm ¼ Lrealp =Lrandp ≈1Cnorm ¼ Crealp =Crandp > 1

where the Lreal and Crealare the mean path length and clusteringcoefficient of the real network, the Lrandand Crandare the equivalentvalues of matched random networks. In order to obtain the values forthe random network parameters in the above equations, we applied amethod estimating these random values while preserving the principalcharacteristics of the real network. If the mean vertex degree k of agraph G is defined as k=2E/N, where E is the number of edges and N isthe number of vertices of G, a random graph that is similar to G can bedefined with the same mean vertex degree k and number of vertices Nas G. Watts and Strogatz (1998) defined a reliable estimate of themeanclustering coefficient of a random graphwithmean vertex degree k andnumber of vertices N as Crand=k/N. This estimate can be used tonormalize the actual mean clustering coefficient of G for the calculationof ‘small world character’ of a network. Similarly, Watts and Strogatz(1998) found an estimate of the mean path length of a random graphLrand=ln(N)/ ln(k). Fronczak et al. (2004) however showed that this isactually an estimate of the diameter (i.e., the maximal shortest pathlength). They also derived an analytic solution for themean path lengthof a random graphwithmean vertex degree k and number of vertices Nas Lrand=((ln(N)−γ)/ln(k))+0.5 (where γ is Euler's constant). This

Table 6Change of graph-theoretical metrics for the 90° out-of-phase task from PRE to POST trainin

Auditory feedback group

Network PRE-trainingFB+

POST-trainingFB+

POST-traFB–

Mean SE Mean SE Mean

N1 (Table 1)Clustering coeff. 0.27 0.03 0.33 0.03 0.34Degree 2.95 0.14 3.37 0.09 3.41Path length 1.82 0.05 1.68 0.03 1.68Betweenness 6.43 0.45 5.29 0.25 5.42Connection strength 0.87 0.04 0.89 0.03 0.89





estimate can then be used to normalize the actual mean path length ofG with regard to calculation of the ‘small world character’ of a network.In order to ensure the reliability of this estimation method, we alsoapplied an actual randomization procedure taking advantage of randomnetwork generators (https://sites.google.com/a/brain-connectivity-toolbox.net/bct/Home) creating networks that have a random topologybut share the size, density and binary degree distribution of the originalnetwork. This procedure was repeated 100 times for each subject'snetwork matrices, then taking the mean random clustering coefficientsand mean random path lengths across these 100 iterations as valuesfor calculating the subject's new SWN coefficients for each network.The results of this analysis rendered the same findings with regard toSWN changes associated with motor learning and confirmed that theapplied estimation method provided reliable values for SWN clusteringcoefficients and path lengths.

Statistical analysis

For each of the kinematic parameters and the GTNA measures,training-related changes were first examined via 2×2 repeatedmeasures ANOVA with the factors GROUP (auditory vs. visual) andSESSION (PRE vs. POST). Significant effects were further explored bypost hoc tests using Tukey correction. Similarly, changes betweenperforming the 90° out-of-phase task with and without augmentedfeedback during the POST session were assessed with a 2×2 repeatedmeasures ANOVA with the factors GROUP (auditory vs. visual) andFEEDBACK (FB+ vs. FB−). This analysis was conducted for eachnetwork separately. Following this, we repeated the analysis combiningdata across all 5 networks. Normal probability plots (plotting theresiduals versus their expected normal value) calculated for the ANOVAanalyses confirmed that the residual values were normally

g.

Visual feedback group

ining PRE-trainingFB+

POST-trainingFB+

POST-trainingnon-FB+

SE Mean SE Mean SE Mean SE

0.03 0.25 0.04 0.39 0.03 0.41 0.030.11 3.03 0.12 3.66 0.16 3.70 0.150.03 1.80 0.05 1.64 0.04 1.62 0.040.24 5.99 0.46 5.08 0.33 4.98 0.310.03 0.91 0.04 1.00 0.04 1.02 0.04

0.02 0.25 0.02 0.29 0.02 0.29 0.020.11 4.10 0.15 5.15 0.12 5.13 0.140.03 2.16 0.06 1.89 0.03 1.90 0.030.63 20.90 1.06 16.06 0.47 16.13 0.520.03 1.12 0.04 1.29 0.03 1.29 0.03

0.05 0.41 0.04 0.46 0.03 0.43 0.030.14 2.81 0.12 3.09 0.14 3.07 0.150.07 1.89 0.07 1.80 0.07 1.78 0.070.55 6.25 0.51 5.62 0.52 5.49 0.460.04 1.00 0.04 1.07 0.03 1.07 0.03

0.02 0.22 0.03 0.27 0.02 0.25 0.030.17 3.27 0.12 3.85 0.18 3.79 0.160.05 2.24 0.05 1.90 0.02 1.94 0.030.66 16.27 0.91 12.74 0.54 13.30 0.500.04 0.96 0.03 1.03 0.04 1.03 0.04

0.02 0.22 0.03 0.29 0.02 0.29 0.010.11 3.50 0.12 4.16 0.11 4.18 0.130.06 2.24 0.05 2.01 0.05 2.01 0.050.91 18.13 0.89 15.14 0.73 15.09 0.800.03 1.05 0.03 1.14 0.03 1.15 0.03

https://sites.google.com/a/brain-connectivity-toolbox.net/bct/Homehttps://sites.google.com/a/brain-connectivity-toolbox.net/bct/Home

Table 7Changes in GTNA parameters from 90° out-of-phase PRE (FB+) to POST (FB+) — ANOVA results.

N1 N2 N3 N4 N5

df F P F P F P F P F P

Clustering coeff.GROUP 1, 16 0.64 0.435 0.24 0.634 0.07 0.791 0.57 0.460 0.01 0.932SESSION 1, 16 9.10 0.008 7.65 0.014 2.48 0.135 5.32 0.035 0.06 0.809GROUP×SESSION 1, 16 0.84 0.374 0.23 0.640 0.01 0.905 0.17 0.688 0.57 0.461

DegreeGROUP 1, 16 2.84 0.111 1.63 0.220 0.09 0.773 3.57 0.077 0.25 0.623SESSION 1, 16 12.0 0.003 55.3 b0.0001 2.66 0.123 15.0 0.001 0.97 0.340GROUP×SESSION 1, 16 0.90 0.356 3.07 0.099 0.52 0.481 0.08 0.776 0.01 0.918

Path lengthGROUP 1, 16 0.65 0.431 0.81 0.381 0.00 0.994 7.44 0.015 0.75 0.400SESSION 1, 16 13.0 0.002 31.7 b0.0001 2.20 0.157 27.0 b0.0001 8.40 0.010GROUP×SESSION 1, 16 0.10 0.753 1.88 0.189 0.03 0.868 0.20 0.657 2.19 0.158

BetweennessGROUP 1, 16 0.65 0.431 0.75 0.400 0.17 0.682 5.16 0.037 0.01 0.935SESSION 1, 16 6.83 0.019 35.0 b0.0001 2.59 0.127 12.1 0.003 1.83 0.195GROUP×SESSION 1, 16 0.12 0.732 0.99 0.334 0.01 0.905 0.03 0.866 0.01 0.910

Connection strengthGROUP 1, 16 4.34 0.054 0.49 0.492 0.06 0.811 0.91 0.355 0.78 0.390SESSION 1, 16 2.24 0.154 14.1 0.002 1.12 0.305 3.98 0.063 0.51 0.485GROUP×SESSION 1, 16 1.12 0.305 4.78 0.044 0.96 0.342 0.07 0.789 0.01 0.936


distributed in all models. This study examined changes in thefunctional connectivity associated with motor learning in 5 differentneural networks. Whilst each of the GTNA measures emphasizes adifferent facet of functional connectivity spectrum captured via graph-theory, these measures are inter-correlated (see also Table 11), and,thus, represent non-independent observations. In contrast, the 5networks are largely separate entities with minimal neuro-anatomicaloverlap between network nodes. Therefore, we applied a statisticalcorrection by reducing the level at which results were consideredsignificant in the ANOVAs to a P value ≤0.01 (i.e., 0.05/5).

Pearson R coefficients were used to examine correlations betweenmeasures. In order to examine correlations in kinematic changes andchanges in GTNA measures between the PRE and POST assessments,we calculated difference scores of POST–PRE in each subject for thekinematic key measures as well as all GTNA measures, followed bythe examination of correlations between these kinematic and GTNAdifference scores (correlations were considered significant at a P value≤0.05). All statistical analyses were performed with the Statisticasoftware (StatSoft, Inc).

Table 8SWN character of mean clustering coefficients and mean path lengths at PRE to POSTFB+.

Auditory feedback group Visual feedback group

Network PRE-training

POST-training

PRE-training

POST-training

Mean SE Mean SE Mean SE Mean SE

N1Clustering coeff. 0.99 0.122 0.93 0.093 0.78 0.112 0.98 0.071Path length 0.88 0.024 0.91 0.005 0.90 0.013 0.91 0.004





Results

Kinematic data

Motor performance of both groups improved similarlywith training.The kinematic data illustrated a clear improvement of the 90° out-of-phase pattern across practice for both the absolute error (AE) (i.e.,the absolute deviation of the relative phase between hands from thetarget relative phase; F1,16=36.5, P=0.0002), and standard deviation(SD) of relative phase (the SD estimates movement pattern stability,F1,16=62.7, Pb0.00001). Both groups started and ended at comparableperformance levels during the PRE- andPOST-test (Fig. 2), and significanteffects for GROUP or GROUP×SESSION interactions were absent for boththe AE and the SD measures. In contrast to the significant improve-ments in AE and SD of relative phase, neither the AUDnor the VIS groupshowed significant changes in speed of execution (cycling frequency) ormovement amplitude from PRE to POST. However, elimination of theaugmented FB strongly affected performance of the VIS group but notthe AUD group (Fig. 2). A 2×2 (group×augmented FB condition)ANOVA was conducted on the POST-training scanning runs, includingthe FB+and FB− 90° out-of-phase data. For AE there was amain effectof FB condition (F1,16=21.5, Pb0.0003), and there was a group×FBcondition interaction (F1,16=21.3, Pb0.003). Post hoc Tukey's testsfurther confirmed that both groups finally reached the same perfor-mance level when augmented FB was available but that the AE of theVIS group during the conditionwithout augmented FBwas significantlyhigher (P=0.0004). This suggests that the VIS group was highlydependent on augmented FB for successful performance, showingsignificant AE increases when this source of information was removed,whereas the AUD group largely preserved performance in the absenceof augmented FB. The same analysis on the SD reached significancefor the main effect of condition (F1,16=8.2, Pb0.02), with performancebeing less stable when the augmented FB was removed. Post hocpairwise comparisons did not reach significance.

Changes in functional connectivity between FB+ conditions from PREto POST

The repeated measures ANOVA of the GTNA measures across thePRE- and the POST-training fMRI data acquired under provision ofaugmented feedback (FB+) in both groups showed that the VIS aswell as the AUD feedback group showed similar changes in functional

Table 9Changes in GTNA parameters from 90° out-of-phase POST (FB+) to POST (FB-) - ANOVA results.

N1 N2 N3 N4 N5

df F P F P F P F P F P

Clustering coeff.GROUP 1, 16 2.11 0.166 0.48 0.497 0.01 0.922 0.10 0.755 0.06 0.809FB+/FB− 1, 16 0.44 0.518 0.06 0.817 2.85 0.111 1.28 0.275 0.01 0.932FB×GROUP 1, 16 0.29 0.596 0.16 0.696 1.62 0.221 0.36 0.556 0.57 0.461

DegreeGROUP 1, 16 2.68 0.121 5.55 0.032 0.54 0.473 0.63 0.440 0.97 0.340FB+/FB− 1, 16 0.71 0.413 0.01 0.926 0.16 0.696 0.08 0.787 0.25 0.623FB×GROUP 1, 16 b0.001 1.0 0.04 0.841 b0.001 1.000 2.03 0.174 0.01 0.918

Mean path lengthGROUP 1, 16 1.20 0.289 7.58 0.014 0.07 0.791 4.05 0.061 1.83 0.195FB+/FB− 1, 16 0.24 0.633 0.03 0.863 0.08 0.786 0.08 0.786 0.01 0.935FB×GROUP 1, 16 0.25 0.621 0.06 0.815 0.59 0.453 5.32 0.035 0.01 0.910

BetweennessGROUP 1, 16 0.87 0.364 4.92 0.041 0.01 0.915 2.46 0.136 1.83 0.195FB+/FB− 1, 16 0.01 0.927 0.02 0.894 0.08 0.780 0.00 0.959 0.01 0.935FB×GROUP 1, 16 0.81 0.382 0.27 0.613 0.58 0.458 1.38 0.258 0.01 0.910

Connection strengthGROUP 1, 16 4.50 0.05 4.22 0.057 0.83 0.377 0.13 0.720 0.51 0.485FB+/FB− 1, 16 1.50 0.239 0.01 0.919 0.10 0.757 0.32 0.579 0.78 0.390FB×GROUP 1, 16 0.39 0.542 0.00 0.952 0.01 0.942 1.70 0.211 0.01 0.936


connectivity at the end of their motor training regime. Withoutexception, both groups exhibited comparable increases in meanclustering coefficient, connectivity degree, and connection strength,together with decreases in path length between network nodesand a reduced betweenness centrality (Table 6). These findingsapplied unequivocally to each of the examined networks, manifest-ing significant main effects for SESSION in networks N1, N2, N4 andN5 (Table 7). Network N3 (the smallest of the examined networks) alsoshowednumerically comparable changes inGTNAmeasures as observedfor the other 4 networks, but here these changes coincided with aproportionally much higher intra-group variability (higher SEs), likelyfacilitated by the comparatively small number of network nodes, which,in turn, prevented the functional connectivity changes for the 90° out-of-phase task from PRE to POST FB+ reaching significance in this network.None of the networks showed any significant GROUP×SESSION in-teractions and significant main effects for GROUP were absent (Table 7).

All networks had the same small world character in both groups atall assessments (Table 8). However, a small world network (SWN)architecture is definedbyCnorm>1 andLnorm~1. Based on this definition,SWN character was preserved in most networks but the manifestationof this small world architecture was not strong, with Cnorm in mostnetworks falling into the range of 1.05–1.28 (N2–N5) and Lnorm beingbetween 0.92 and 0.98 (N2–N5). The parameters of network N1 fellslightly below this range. Numerically, the AUD group showed areduction in SWN clustering coefficients in 3 of the 5 networks fromPRE to POST FB+, whilst the VIS group had increases in 4 networks,the exception being the ‘auditory’ network N2. The SWN path lengthsaw little change from PRE to POST in the groups. Despite thesenumerically differential changes in values, there were no significanteffects of GROUP, SESSION, or GROUP×SESSION interactions for theSWN variables.

Changes in functional connectivity between POST FB+ and FB−conditions after training

In the trained subject groups, functional connectivity as quantifiedby the current GTNAmeasures remained unchanged between perform-ing the 90° out-of-phase task with and without augmented feedback atthepost training fMRI session.With exception of amarginally significantGROUP×FEEDBACK interaction for the path length in network N4(Table 9), a repeated measures ANOVA rendered no significant effectsfor GROUP, FEEDBACK (FB+/FB−) or GROUP×FEEDBACK interactionsfor the networks N1, N3, N4 and N5 (Table 9). In the N2 network (the

network defined by areas showing the increase in level of activationfromPRE to POST in the AUDgroup), amarginally significantmain effectof GROUPwas present for themeasures connectivity degree, path length,and betweenness centrality (Table 9). In all these measures, the VISgroup consistently showed a more favorable functionality than the AUDgroup in this network (i.e., higher connectivity degree, together withshorter mean path length and reduced mean betweenness centrality).

Due to the similar and consistent development of the GTNAparameters of both groups between the PRE and POST assessments inthe examined networks, we conducted an additional analysis combin-ing the measures of the five networks to quantify the mean changein network metrics across the entirety of all brain regions comprisedby the five networks. The principal purpose of this analysis was theconfirmation of the statistical findings of the individual network resultsin a statistically more sensitive analysis, and to potentially identifyeffects having fallen below the detection threshold of the individualnetworks. The combined analysis of all five networks showed similarresults as observed in the individual networks, albeit that the effectsemerged at a clearer level of significance (Table 10). From PRE to POST,both groups had – across thefivenetworks– increases inmean clusteringcoefficients (auditory and visual: 19% and 26%), connectivity degree(15% and 19%), and connection strength (4% and 10%), together withdecreases in mean path length (7% and 11%) and mean betweennesscentrality (15% and 19%) (Fig. 3). Significant effects of GROUP for PREto POST and POST FB+ vs. POST FB− (Table 10) emerged only forconnectivity degree. In addition, there were marginally significanteffects of GROUP for betweenness centrality, mean path length, andconnection strength (POST FB+ vs. POST FB− only), with the visualgroup having more and stronger connections, and shorter communica-tion distances (Table 10). The network functionality of both groupsmaintained the same normalized path length and clustering coefficientat PRE and POST (Fig. 3C).

Important network regions as defined by hub-status PRE and POST

In network N1 (Table 1, defined by regions showing activationchanges in the same intensity direction in both groups from PRE toPOST FB+), no area met the minimum criteria for hub-status beforethe training in the AUD group (hub criteria as defined in Section2.5.4). Only the right hemisphere (RH) superior parietal lobule gainedhub-status after the training in the AUD group at POST FB+. Removal ofthe augmented feedback during the 90° out-of phase task at the POSTassessment had no effect on this. Conversely, a more pronounced level

Table 1090° out-of-phase task — combined analysis of all networks PRE and POST-ANOVA results.

Measure PRE vs. POST (FB+) Measure POST (FB+) vs. POST (FB−)

df F P df F P

Clustering coeff. Clustering coeff.GROUP 1, 84 0.76 0.386 GROUP 1, 84 1.11 0.294SESSION 1, 84 29.04 b0.0001 FB+/FB− 1, 84 0.65 0.423GROUP×SESSION 1, 84 0.51 0.475 FB×GROUP 1, 84 0.78 0.381

Degree DegreeGROUP 1, 84 7.12 0.009 GROUP 1, 84 7.43 0.008SESSION 1, 84 77.44 b0.0001 FB+/FB− 1, 84 0.35 0.554GROUP×SESSION 1, 84 1.90 0.171 FB×GROUP 1, 84 0.77 0.382

Path length Path lengthGROUP 1, 84 4.31 0.041 GROUP 1, 84 6.56 0.012SESSION 1, 84 61.09 b0.0001 FB+/FB- 1, 84 0.05 0.820GROUP×SESSION 1, 84 2.01 0.160 FB×GROUP 1, 84 0.16 0.694

Betweenness BetweennessGROUP 1, 84 4.65 0.034 GROUP 1, 84 5.99 0.016SESSION 1, 84 43.72 b0.0001 FB+/FB− 1, 84 0.03 0.864GROUP×SESSION 1, 84 0.69 0.409 FB×GROUP 1, 84 0.49 0.485

Connection strength Connection strengthGROUP 1, 84 3.75 0.056 GROUP 1, 84 7.02 0.010SESSION 1, 84 22.14 b0.0001 FB+/FB− 1, 84 1.46 0.230GROUP×SESSION 1, 84 3.16 0.079 FB×GROUP 1, 84 0.19 0.668

Small World Network measuresSWN clustering coeff. SWN clustering coeff.

GROUP 1, 84 0.38 0.540 GROUP 1, 84 2.45 0.121SESSION 1, 84 0.94 0.335 FB+/FB− 1, 84 0.03 0.865GROUP×SESSION 1, 84 0.60 0.440 FB×GROUP 1, 84 0.59 0.443

SWN path length SWN path lengthGROUP 1, 84 1.13 0.291 GROUP 1, 84 0.55 0.460SESSION 1, 84 0.53 0.468 FB+/FB− 1, 84 0.57 0.452GROUP×SESSION 1, 84 2.78 0.099 FB×GROUP 1, 84 0.64 0.427


of change in hub-statuswas apparent for N1 areas in the VIS group, and,overall, a noticeably higher number of areas in the VIS group functionedas network hubs. In the VIS group, the RH superior parietal lobuleand the RH SMA had hub-status at the PRE FB+ assessment. The RHSMAmaintained this status post training in both POST FB+ and FB−conditions of the 90° out-of-phase task. In addition, the RH dorsolateralprefrontal cortex (DLPFC) had achievedhub-status in theVIS group, andmaintained this status throughout the POST FB+ and FB− conditions.The LH supramarginal gyrus had hub-status in the VIS group oncefeedback was removed in the POST FB− assessment.

Table 11Correlations between GTNA measures across the 5 networks.

Correlations between GTNA measures across the 5 networks

GTNAmeasures

Group Clusteringcoeff.

Degree Pathlength

Betweenness Connectionstrength

−−0.40***−0.37**−AUDClusteringcoeff. −−0.60***−0.59***−VIS

AUDVIS

AUDVIS

AUDVIS

AUD

VIS

0.83**0.36**−−Degree0.84**0.43***−

−− −

Path length −0.49*** − 0.91** −−0.40*** − 0.86** −

−0.89**0.25*−0.46***Betweenness0.26*0.91**0.27*−0.40***

Connectionstrength

0.29* 0.87** −0.29* −

0.32** 0.85** −0.32** −

Green fields = at PRE; blue fields = at POST FB+.⁎⁎⁎ Pb0.001.⁎⁎ Pb0.01.⁎ Pb0.05.

In the N2 network (Table 2), network nodes in the LH and RHpostcentral gyri were consistently important as hubs throughout PREand POST FB+ assessments in both groups. In the AUD group, the RHsuperior parietal lobule had hub-status at PRE and POST FB+, but notwhen auditory feedback was removed at the POST assessment. In theVIS group, the same area was designated as hub only in the untrained(PRE) network, as was the RH supramarginal gyrus. Two areas whichhad hub-status exclusively in the VIS group were the LH SMA (PREonly) and the RH DLPFC (POST FB+ and POST FB−). In the VIS group, athird parietal area, the RH inferior parietal lobule, achieved hub-statusonce the feedback was removed at the POST FB− assessment.

In theN3 network (Table 3), no areasmet the criteria for hubs. In theN4 network of the AUD group (Table 4), the LH inferior operculumserved as hub in the untrained network (PRE) and again once auditoryfeedback was removed in the trained network at the POST FB−assessment. Conversely, in the VIS group, this area had hub-status atthe POST FB+ assessment. Two other areas, the LH and RH occipitalgyri, achieved hub-status in the AUD group, namely at the POST FB+assessment. In the VIS group, the LH middle occipital gyrus similarlyserved as hub at the POST FB+ and FB− assessments. The activationfocus in the RH superior temporal gyruswas a hub in theVIS group only,both in the untrained network as well as at the POST FB+ assessment.

In the N5 network (Table 5), the AUD group showed the mostmarked discrepancy in hub-changes from PRE to POST FB+, wherebymany regions in the N5 network of the AUD group lost their hub-status. Such areas included the LH postcentral gyrus, RH inferiorfrontal gyrus, the LH middle cingulate and RH anterior cingulate gyri,RH transverse temporal gyrus, RH superior temporal gyrus, and LHsuperior parietal lobule. Only 3 areas gained hub-status in the AUDgroup thereafter (RH middle cingulate cortex at POST FB+, plus theLH superior parietal lobule and RH supramarginal gyrus at FB−). Inthe VIS group, only 3 areas reached hub-status at any stage: similar tonetworks N1 and N2, parts of the SMA in the untrained network,

Fig. 3. Improvement inGTNAmeasures fromPRE to POST (combined results across all fivenetworks)—Mean values for path length (A), clustering coefficient (A), betweenness (B),connection strength (B), normalized path length (C), normalized clustering coefficient(C). There were also changes significant changes between PRE and POST for connectivitydegree (AUD: PRE 3.25±0.09 vs. POST 3.72±0.09; VIS: 3.34±0.07 vs. 3.98±0.10,Pb0.0001). No change was seen for connectivity degree from POST FB+ to POST FB−(POST FB− AUD: 3.75±0.09; VIS: 3.98±0.10). Error bars show standard errors. Asteriskmarkers designate significant changes for measures between sessions (***Pb0.0001).

Fig. 2. Improvement in kinematic measures from PRE to POST — panels show theabsolute error [AE] of relative phase (A) and the standard deviation [SD] of relativephase (B) [measures as defined in Ronsse et al. (2011)]. Both groups improved theirmotor performance as a result of the 4-day motor training regime, resulting in reducedAEs and SDs. Removal of augmented feedback at the POST assessment did not affectkinematic measures of the AUD group but resulted in a marked increase of AE in theVIS group. Error bars show standard errors.


followed by the RH supramarginal gyrus at POST FB+ and the rolandicoperculum at POST FB−.

Correlations between changes in GTNA measures and improvedkinematics PRE to POST

The analysis of correlations between the improved network/GTNAmetrics from PRE to POST in the 5 networks and the concomitantimprovements in the kinematic parameters showed little directassociation between changes in functional network connectivity andthe improved quality of motor output produced by the neuralnetworks related to task performance in the present subject groups.For the auditory group, correlations were present in the N2(‘auditory’) network between changes in SD of relative phase andchanges in connectivity degree (R=0.57, P=0.017), and connectionstrength (R=0.58, P=0.015), pairing poorer motor performancewith higher network connectivity (over-connectivity) in the AUDgroup. In the VIS group, there were correlations between the changesin AE of relative phase and the changes in mean clustering coefficientin the N1 network (R=−0.5, P=0.039) and the N3 (‘visual’)network (R=−0.49, P=0.043), and between the change in SD ofrelative phase and the mean clustering coefficient in the N3 network(R=−0.61, P=0.009), pairing better motor performance withhigher mean clustering coefficients.

GTNA analyses at different connectivity thresholds

We calculated the connectivity matrices at different thresholds(P=0.0001; P=0.0005; P=0.001; P=0.005; P=0.01) to ensurethat the results were not critically dependent on the threshold used.The statistical results were equivalent across all network densities. For

all connectivity thresholds, the numbers of surviving edges betweenthe network nodes are shown in Fig. 4.

Discussion

This study applied graph-theoretical network analysis to quantifychanges in functional connectivity associated with the training-induced improvement of bimanual motor coordination supported bythe provision of augmented feedback in form of either auditory orvisual sensory stimuli. Our findings show that, after a motor trainingregime that extends over several days, improved motor performanceoccurs alongside increases in functional connectivity. This increasedfunctional connectivity in the trained task-related neural networks then

image of Fig.�2

Fig. 4. Number of edges between network nodes at different connectivity (networkdensity) thresholds — Shown are the number of valid functional connections (edges)based on significant partial correlations between node-pairs in networks N1 and N5(A) as well as networks N2, N3 and N4 (B). Consistent with the notion that fewerconnections will be considered as ‘existing’ at lower compared to higher P-levelthresholds, all networks showed a steady increase in the number of existing edgesacross the 5 different connectivity thresholds.


remains stable, at least in the short term, upon removal of augmentedfeedback. Importantly, these effects occurred in 4 out of 5 of the examinednetworks supporting the arguments that (a) these changes in functionalconnectivity are likely an unequivocal manifestation of the adjustmentof the neural networks to facilitate the learning of a new, complexmotor pattern, and (b) that this increase in network functionality isindependent of the network definition since these effects occurredsimilarly in Type 1, 2, and 3 networks. Interestingly, both sensoryfeedback modalities were associated with comparable increases infunctional connectivity, albeit that theVIS group had a higher functionalconnectivity on some GTNA measures. Importantly, the analysis of theconnectivity of individual functional network nodes revealed animportance of specific brain regions for motor learning not apparentin the standard fMRI analyses. We shall now discuss these findings inmore detail.

Connectivity changes associated with practice-induced plasticity anddifferent augmented feedback modes

All key GTNA measures showed consistent improvement in bothgroups between the PRE and POST assessments indicating that meannetwork connectivity increased significantly in the context of augmentedfeedback training. Increases in mean clustering coefficients, connectivitydegree, andmean connection strength suggest that each network in boththe auditory and the visual group became more closely connected at aregional and at a more macroscopic level following the 4-day trainingregime. Similarly, decreases in mean path length documented thattraining resulted in a more direct information transfer in the examined

networks. Thefinding of a decreased betweenness centrality is consistentwith this interpretation, asmore direct communication betweennetworkregions would likely be associated with the effect that – compared withthe pre-training status – a higher proportion of network nodes is gettingbypassed and does not participate anymore in the same number ofshortest pathswithin the network in order to promote faster informationexchange between network nodes. The combined analysis of all 5networks rendered the same results but at a stronger level of statisticalsignificance. This combined analysis also had sufficient power to indicate(albeit mostly marginal) group effects for connectivity degree, between-ness centrality, mean path length and connection strength, the VIS grouphaving the more favorable outcome on all these measures. An importantpoint that needs to be considered in the interpretation of this combinedanalysis is that this step combined GTNAparameters fromnetworkswithdifferent numbers of network nodes. In order to optimally compare andcombine parameters between networks, their size should be matchedperfectly (Van Wijk et al., 2010). However, combining the parametersfrom the 5 networks into one analysis was specifically aimed atquantifying the mean change in network metrics across the entirety ofall brain regions comprised by the five networks, andwas supported bythe important pre-requisite of having very similar changes in functionalconnectivity from PRE to POST as well as from POST FB+ to POST FB−when assessing the networks individually. Therefore, the results ofthis combined analysis should only be interpreted in combination withthe findings in the individual networks.

In order to appreciate the implications of these findings, it isimportant to re-consider the different nature of predominant fMRIactivation change in the auditory and the visual groups from PRE toPOST. The primary fMRI results showed that the visual group exhibited apredominant increase in neural activity, especially in vision/sensorimo-tor-specific areas (Ronsse et al., 2011). Conversely, neural activitydecrease was prevalent in the auditory group, particularly pronouncedin temporo-parieto-frontal regions associated with cognitive/sensorymonitoring of motor task performance, which in turn supported thedevelopment of a control mode that was less reliant on augmentedfeedback sources in the auditory group (Ronsse et al., 2011).

The combined interpretation of these findings together with thepresent result that network metrics improved similarly in bothgroups as a result of training indicates that similar optimization innetwork functionality can be associated with practice-induced in-creases in brain activation (VIS group) but also predominant reductionin brain activation (AUD group). This suggests that the brain employsrather different strategies to achieve (a) an improved phase synchro-nicity between regions, or (b) a better homogeneity of oscillations ofneuronal elements within a node sphere, which can equally contributeto improved inter-regional coupling (Zalesky et al., 2011). Studies onpsychological disorders such as schizophrenia support that both ofthese factors are crucial in determining good inter-regional functionalconnectivity (Bassett et al., 2012; Zalesky et al., 2011). While theunderlying factors for this disparity between the auditory and visualfeedback modalities in achieving an increased functional connectivityvia different changes in signal strength is beyond the scope of this study,it is an important consideration arising from our present findingsthat decreased brain activation is not synonymous with poorer inter-regional coupling. Only networkN3,which showed the smallest numberof network nodes, failed to exhibit the significant changes in networkmetrics between pre- and post-training. This is likely due to the limitedsize of the network in combination with a higher intra-group variabilityin all GTNA measures, and the fact that the nodes in this network wereconcentrated in only two brain regions, i.e., sections of the occipital lobeand the cerebellum. The subsequently smaller degrees of freedomregarding possibilities for change and variability between pre- and post-training likely contributed to the weaker performance of this networkwith regard to manifesting significant changes from PRE to POST FB+.

Considering that network N1 was defined by regions showing thesame behavior in terms of increases or decreases in activation between

image of Fig.�4


PRE and POST, it may not be surprising that both groups also showed asimilar development of network metrics. However, networks N2 andN3 were defined by the predominant ‘behavior’ in fMRI activationchange of one particular group only (i.e., AUD or VIS). Hence, it wouldhave been feasible to expect that only the ‘defining’ group shouldexhibit consistent changes/improvement in GTNAmeasures, also whenconsidering the only limited overlap between network N2 and N3 innetwork size and MNI coordinates of the network nodes. Yet, the GTNAparameters of both groups behaved similarly in N2 and N3, suggesting(a) that many of the areas in these networks showed coordinated andsynchronized task-related activity even in the ‘non-defining’ group, and(b) that this activity was susceptible to the same training effect inthe ‘defining’ and ‘non-defining’ group. NetworksN4andN5, defined byareas showing differential activation changes between the auditory andthe visual groups from PRE to POST, take this notion further, exhibitingsimilar improvements in GTNAmeasures and re-iterating the interpre-tation that (a) functional network optimization might be carried bydecreases in brain activation in the context of auditory feedback andincreases in activation during visual feedback conditions, and (b) thatboth increases and decreases in brain activation can be a manifestationof better neural network synchronization and improved task-relatedbrain functionality.While the GTNA findings in networks N2 andN3 arean important result of this study, it should also be considered that N4and N5, and with limitations N1, are the networks with the mostresemblance to general ‘real world’ neurological systems, whereby thenetwork nodes do not share the same kind of activation change butshow differential activation changes from PRE to POST. As such, onemight argue that these differential networks are more representative ofactual computational processes in the brain, where interdependentexcitatory and inhibitory processes, and the accompanying increasesand decreases in brain activation, occur concomitantly.

Importance of specific cerebral areas — expansion of the primary fMRIfindings

An important point that arises from the interpretation of thefunctional connectivity changes is that an apparent decreased activationfrom PRE to POST in a brain region is not necessarily indicative ofdiminishing importance of this area within the global functionality of atask-related neural network. The analysis of the hub-status of the areasin the networks supported this interpretation. The findings of theprimary fMRI analysis showed that the AUD group showed a globaldecrease in activity in temporo-parieto-frontal areas, including severalareas crucial for the processing of auditory information (superiortemporal areas, temporal pole, inferior frontal gyrus) (Bernal et al.,2004; Callan et al., 2003; Seung et al., 2005; Wang et al., 2003; Zarateand Zatorre, 2008), proprioceptive information and rhythmic coordina-tion (SMA) (Debaere et al., 2003, 2004b; Hagura et al., 2009; Immisch etal., 2001; Jancke et al., 2000; Naito et al., 2005; Rao et al., 1997; Stephanet al., 1999; Toyokura et al., 2002). Conversely, the VIS group exhibitedincreased activation in vision/sensorimotor-specific areas from PRE toPOST, in particular areas important for visuomotor transformations inthe occipital andparietal cortices (Buneo et al., 2002; Clower et al., 1996;Jeannerod et al., 1995; Kalaska et al., 1997; Rizzolatti et al., 1998;Rushworth et al., 1997) as well as cerebellar regions (O'Reilly et al.,2010). The importance of some of these regions apparent from themodulation of their activity between PRE to POST in the respectivegroups was also reflected in their importance for intra-networkinformation transfer (i.e., hub-status). In the AUD group, several regionsincluding areas in the left and right postcentral gyrus (Networks 2 and5), right superior parietal lobule (Networks 1, 2 and 5), areas in thetemporal cortex (Networks 2 and 5), inferior frontal and cingulatecortices (Network 5) were classified as hubs within the functionality oftheuntrained networks in the PRE condition.Manyof these regions thenwere no longer hubs within the functionality of the trained networkat the POST FB+ assessment. While one might consider this loss in

functional integration of particular areas consistentwith the decrease inactivation predominant in the AUD group from PRE to POST FB+, whichin turn highlighted the relevance of these areas for training-inducedtask mediation in the AUD group, the more intriguing interpretationwould be that, in addition to just a decrease in activation, the training-induced changes in the AUD group included a network re-organizationcomprising a de-centralization of network function, which was notapparent from the results of the primary fMRI analysis.

For the VIS group, the findings of the GTNA analysis also take theresults of the primary fMRI analysis further. Based on the changes infMRI activation from PRE to POST, a strong involvement of the rightDLPFC in the AUD group became evident at initiation of practice while,in the VIS group, comparable changes in intensity of activation wereabsent, suggesting that the role of the DLPFC as an internal controllermonitoring task performancewas crucial for theAUDgroup only duringthe initial training phase when attentional demands are high. TheDLPFC is crucially involved in different aspects of visual attention,processes relating toworking/short-termmemory, response control and(re)planning of actions, and also contributes to the formation of action-oriented representations derived from sensory information (Jueptneret al., 1997; Toni andPassingham, 1999), and itwould have been feasibleto expect this region to play a prominent role also for the VIS group. Thefindings of the GTNA demonstrated that the DLPFC indeed appears toplay an important role for facilitating training-induced task performancein the VIS group. In both networks including activation foci in the DLPFCas network nodes, theDLPFC consistently featured asnetwork hub in thetrained networks of the VIS group, both under FB+ and FB− conditions.

Similar to the DLPFC, the primary fMRI analysis indicated highrelevance of the activation foci within the SMA region for promotingmotor performance in the AUD group while, for the VIS group, thisstructure did not stand out in the fMRI analyses. The SMA is a crucialregion for interlimb coordination as well as eye–hand coordination (Foxet al., 1985; Gerloff et al., 1997; Ghez, 1991; Israel et al., 1995; Mushiakeet al., 1996; Pierrot-Deseilligny et al., 1993; Steyvers et al., 2003) andplays a vital role in the temporal organization and execution of bimanualtasks (Debaere et al., 2004a; Sadato et al., 1997; Stephan et al., 1999;Swinnen, 2002; Swinnen and Wenderoth, 2004; Toyokura et al., 1999;Ullen et al., 2003). SMA also shows increased activity duringmemorizedmovements (Mushiake et al., 1991; Tanji and Shima, 1994) and duringexecution of complex motor patterns in the absence of visual guidance(Hamzei et al., 2002). The present analysis emphasizes that, despitethe absence of drastic fluctuations in activity between PRE and POSTassessments, fociwithin either the left or right SMA served as hub-regionin 3 out of 4 networks that had SMA foci as nodes (networksN1, N2, N5),suggesting that the SMA also had an important role for facilitatingmotorlearning in the VIS group. This conclusion was apparent only as a resultof the graph-theoretical network analysis.

Relationship between changes in functional connectivity and motorperformance

An important point to consider is that network connectivity ofthe trained networks did not deteriorate upon removal of augmentedfeedback at the POST assessment. None of the networks showedany significant effects in the examined GTNA metrics between thePOST FB+ and FB− assessments. In case of the AUD group, thisfinding seems to be consistent with the behavioral result of preservedperformance of the newly learned 90° out-of-phase task. However, inthe VIS group the network metrics also remained stable despite aconsiderably poorer motor performance once feedback was removedat the POST FB− assessment.

It has been suggested that learning-induced performance of newcomplex motor patterns is firstly coded in a visual–spatial coordinatesystem, which develops more quickly (Kovacs et al., 2010), to befollowed by a representation based on a more stable motor coordinatesystem in later stages of practice. This might explain why there was no


drop inmotor performance in the POST FB− in the AUDgroup: becausethe nature of the AUD tasks presentation prevented coding in a visual–spatial coordinate system, it resulted in a quicker formation of a morestable motor coordinate reference frame in this group. Since feedbackfor the visual–spatial coordinate system was readily available in theVIS group, promoting coding in a visual–spatial reference frame (thisfaster developing visual–spatial representation being consistent withthe steeper learning curve of the VIS group), the transfer to a motorcoordinate frame, and the development of an internal model of therequired movement, may thus have been prevented or initiated at amuch later stage in the practice regime in the VIS group.

An alternative interpretation arises from the consideration thatboth groups behaved differentially in processing alternate sources ofsensory information in addition to the principal visual and auditoryfeedback modalities. The visual feedback may have been a much moresalient source of information in theVIS group, eclipsing all other availablesensory modalities, such, as, for example, proprioceptive information.This made the VIS group more vulnerable to the FB− condition, wherethe lack of visual feedback and the VIS-group's lack of trained ability toprocess alternative sensory information resulted in a considerable dropin motor performance. This view is further supported by our findingthat the DLPFC consistently achieved hub status in POST FB+ and FB−conditions in the VIS group, suggesting a less automatized movementbehavior in the VIS group compared to the AUD group, with the DLPFCmaintaining a role as internal controller in the VIS group. Conversely,the auditory feedback may have been comparatively less salient,promoting the parallel processing of alternative sensory information. Incase of the AUD group, the internal model was most likely based on aproprioceptive reference framework (Ronsse et al., 2011). The findingthat the AUD group did not show a drop in motor performance uponremoval of auditory feedback may be directly related to those subjectshaving successfully formed a proprioceptive movement model, withproprioceptive information remaining available for them as referenceresource after the removal of auditory feedback.

The finding that GTNAmetrics remained stable despite the drop inmotor performance in the VIS group suggests that, while duringmotor training better motor performance and increased functionalnetwork connectivity occur concomitantly as observed in our study,changes in neural network functional connectivity as a result of changesin environmental factors are subject to inertia. This indicates thatpermanent or recurrent influence of environmental factors or changes,such as, for example, the enduring provision or removal of sensoryfeedback, are necessary to ensure that training-induced changes in thequality of motor output are indeed associated with lasting changes inthe functional connectivity of the brain. This interpretation would beconsistent with our finding that there were no widespread associationsbetween changes in neural network functional connectivity acrossthe 4-day motor training regime and the change in the quality of themotor output as quantified by the kinematic measures (only isolatedGTNA measures showed some significant correlations with changes inkinematics, and this was not consistent across all networks with thecorrelations explaining a maximum of only 25–30% of the variance ineach case). The current findings indicate that any measurable changesin functional connectivity as a result of changes in feedback conditionsmay indeed be subject to a time course of days rather than minutes.

Study limitations and considerations for interpreting the current findings

In contrast to the previous motor-training studies by Bassett etal. (2011) and Coynel et al. (2010), the present study combined apartial-correlation approach to quantify functional connectivity witha data-driven network definition. This approach was chosen as fullcorrelations have been criticized for not filtering out the influenceof indirect connections between network nodes and template-baseda-priori parcellation of the brain to define the network nodes hasthe caveat of including brain regions with little or no task-related

activation. A recent study by Smith et al. (2011) provides strongindications that, in addition to using partial correlations to quantifyunique functional associations between nodes, a data-driven ap-proach by defining networks based only on areas showing clear task-related activation is preferable to template-based approaches in orderto minimize confounds and obtain a better picture on functionalconnectivity within neural networks. However, it has to be acknowl-edged that the best methodological approach to examine functionalconnectivity is an area of ongoing debate. It is important to notethat defining network nodes based on task-related activation has thepotential problem of being based on a group analysis rather thansubject-specific analyses, and, at least in our context, on incorpora-tion of general linear models (GLMs) (Friston et al., 1995) rather thanmodels that allow for nonlinear effects. However, the application ofthe GLMs, and the inference of the group results being representativeof brain activations in the individual, is established practice in fMRIanalysis and was successfully applied for the primary imaging analysis ofthe present data (Ronsse et al., 2011). Using the same GLMs to assessfunctional connectivity between time courses of activation in our subjectspresented itself as the natural progression to the primary imaging analysisand ensured that the examination of functional connectivity wasestablishedwithin, and

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Motor learning-induced changes in functional brain connectivity … · 2013-06-28 · training, there is very little information on how motor-task related brain areas function as