motor learning-induced changes in functional brain connectivity as

Motor learning-induced changes in functional brain connectivity as revealed by means of graph-theoretical network analysisContents lists available at SciVerse ScienceDirect
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Motor learning-induced changes in functional brain connectivity as revealed by means 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, Belgium b Centre for Research in Mechatronics, Institute of Mechanics, Materials, and Civil Engineering, Université Catholique de Louvain, Belgium c Medical Imaging Research Center, University Hospital Gasthuisberg, Leuven, Belgium d Laboratory for Cognitive Neurology, Department of Neurosciences, Group Biomedical Sciences, K.U. Leuven, Belgium
Corresponding author at: Laboratory of Motor Cont Control and Neuroplasticity, Group Biomedical Scie Leuven, Tervuursevest 101, B-3001 Leuven-Heverlee, Be
E-mail address: [email protected] (
1053-8119/$ – see front matter © 2012 Elsevier Inc. All doi: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 2012 Available online 30 March 2012
Keywords: Motor learning Bimanual coordination Graph-theory Functional connectivity Functional networks fMRI
Complex bimanual motor learning causes specific changes in activation across brain regions. However, there is little information on how motor learning changes the functional connectivity between these regions, and whether this is influenced by different sensory feedback modalities. We applied graph-theoretical network analysis (GTNA) to examine functional networks based on motor-task-related fMRI activations. Two groups learned 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 motor performance coincidedwith increased functional network connectivity (increased clustering coefficients, higher number of network connections and increased connection strength, and shorter communication distances). Day×feedback interactions were absent but, when examining network metrics across all examined brain regions, the visual group had a marginally better connectivity, higher connection strength, and more direct communication pathways. Removal of feedback had no acute effect on the functional connectivity of the trained networks. Hub analyses showed an importance of specific brain regions not apparent in the standard fMRI analyses. These findings indicate that GTNA can make unique contributions to the examination of functional brain connectivity in motor learning.
© 2012 Elsevier Inc. All rights reserved.
Introduction
During motor learning, specific changes in the intensity of brain activation in a range of brain regions are observed from pre- to post- training, and several previous studies have successfully used functional magnetic 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 between motor learning and changes in cortical excitability (Muellbacher et al., 2001; Perez et al., 2004). Diffusion tensor imaging (DTI) has provided evidence that visuomotor skill training indeed induces changes inwhite matter structure and subsequent structural connectivity (Scholz et al., 2009), and increased intra-regional functional connectivity in the primary motor cortex has been reported while applying a 40-min training task
rol, Research Center for Motor nces, Katholieke Universiteit lgium. Fax: +32 16 32919. M.H. Heitger).
rights reserved.
(McNamara et al., 2007). An important element in examining brain function is the consideration that efficient brain activity depends on the collaboration and co-activation of many brain areas, which have to operate as a coordinated network to achieve correct behavioral output (Womelsdorf et al., 2007). The examination of brain activation from a network perspective is crucial in understanding the factors that drive our motor behavior. However, despite multiple previous reports of motor-training being associated with changes in cerebral activations as well as altered structural connectivity, there is only limited information on training-induced changes in functional connectivity (i.e., changes in the inter-regional statistical association in the time course of activation) within the cerebral networks activated during motor learning, in particular in the context of complex training-dependent bimanual motor coordination.
A number of previous studies have examined aspects of functional connectivity related to motor learning. A study by Coynel et al. (2010) reported training-related changes in functional connectivity in a finger tapping task by focusing on the dynamics of motor-related functional integration. However, their key measure of overall network integration equated to a global quantification of changes in the network association matrix, summarizing changes across more than 100 correlation coefficients in one number which then was used as indicator of whether functional connectivity across the entire network changed as a result of
motor training. Consequently, their report contained no information on changes in sub-domains of functional connectivity. Another recent study by Bassett et al. (2011) examined dynamic configuration of human brain networks using functional connectivity measurements of fMRI brain activity during motor learning using a visually-cued 4- finger motor task. However, the principal aim of the study was the demonstration of a general statistical framework for the identifica- tion of modular architectures in evolving systems and it focused solely on the role of modular organization in human learning by identifying dynamic changes in the allegiance of network nodes to functionally correlated sub-groups of regions (modules) within the network without specifically integrating this into a motor learning context. The present study aimed at expanding the limited previous work in the field of functional connectivity related to motor learning and examined changes in functional connectivity related to bimanual motor learning using connectivity measures based on graph- theoretical network analysis (GTNA) (for review see Bullmore and Sporns, 2009; Bullmore and Bassett, 2011; Rubinov and Sporns, 2010).
An aspect that has not been sufficiently addressed with regard to the training-related changes in functional network connectivity is the influence of different feedback conditions during motor learning. Motor control is guided by sensory feedback to (a) optimize motor performance and (b) learn new motor actions or form internally guided representations of new task dynamics. Especially during the acquisition of newmotor skills, the presence of augmented feedback is a key source of information for learning, and an important ingredient in motor learning and optimizing the timely and precise assimilation of previously 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 motor training, there is very little information on how motor-task related brain areas function as a network with regard to motor learning under different feedback modalities. The question remains whether these functional networks indeed show differential changes in measures of functional connectivity as a result of bimanual coordination training under provision of visual or auditory augmented feedback.
In the present study, we built on the previous findings of Ronsse et al. (2011) and examined changes in the characteristics of task- related functional brain networks during bimanual coordination. The latter fMRI study focused specifically on the effects of providing either visual or auditory sensory feedback to guide the optimization of a difficult 90° out-of-phase bimanual coordination pattern. Both feedback modalities were shown to improve motor performance but the visual group became more dependent on this augmented feedback for performance, whereas the auditory group performed equally well with or without augmented feedback by the end of the training. The fMRI findings mirrored this pattern by emphasizing increased activity in many sensory-specific areas in the visual groupwhile the activity change in the auditory group between pre-and post-training was marked by a predominant decrease in activity specifically in areas associated with cognitive/sensory monitoring of motor task performance. However, despite this differential nature of training-related activity changes across the groups, the study indeed identified task-related brain areas showing similar activation changes in both groups aswell as areaswhere changes in activation developed differentially between both groups during the 4-day training regime. As such, the study by Ronsse et al. (2011) provided an ideal setup to simultaneously examine changes in neural network functional connectivity as a result of bimanual motor-training as well as the influence of different types of augmented feedback on changes in functional connectivity. We hypothesized that the motor- training regime would be associated with an increase in functional connectivity in the trained networks, and that this will manifest in measurable changes inmean graph-theoretical networkmeasureswhen assessing overall network connectivity. This hypothesis included the
consideration that motor learning will not only alter and strengthen existing functional connections but also result in the formation of new functional relationships between brain areas. A priori, it was unclear whether this effect might be more pronounced in the visual or the auditory group as both feedbackmodes promotemotor learning (Ronsse et al., 2011).We anticipated that an examination of changes in functional neural network connectivity would enable us to demonstrate that the effects of training-induced motor learning manifest in significant changes in functional brain connectivity and network cohesion which are not easily accessible by the examination of voxel-based analyses of regional changes in activation intensity.
Materials and methods
Participants, task description and procedure
The participant groups, task details and study procedures have been described elsewhere (Ronsse et al., 2011). In summary, 34 adults (19 females, 15 males), aged between 20 and 30 (mean=23) years, were randomly assigned into two age- and gender-matched groups, which differed only with respect to the mode of augmented sensory feedback they received during bimanual coordination training, i.e., visual (VIS) or auditory (AUD) feedback. The study was approved by the ethics committee of Biomedical Research at the K.U. Leuven, informed consent was obtained, and participants were paid for participation. Both groups had 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 be performed stably and consistently (Lee et al., 1995; Swinnen et al., 1997). Augmented feedback to the VIS groupwas provided bymeans of a Lissajous figure, displaying the left and right angular wrist displace- ment on the ordinate and abscissa, respectively (Ronsse et al., 2011). A cursor on the screen in front of the participant showed the orthogonal plot of bothwrist displacements in real time, with the last 750 ms of the cursor trajectory remaining visible. When produced correctly, the 90° out-of phase pattern corresponded to a circular figure trajectory on the screen. Augmented feedback to the AUD group was provided by delivering a low tone when the left hand reached either maximum flexion or maximum extension, and a high tone when the right hand reached either maximum flexion or extension. Perfect 90° out-of-phase cyclical movement corresponded to a regular alternation of both tones (Ronsse et al., 2011). All subjects practiced the 90° out-of-phase pattern with the help of augmented feedback (either AUD or VIS), performing 8 blocks containing 10 trials of bimanual movement (duration=21 s per trial) each day across 4 training days. Moreover, subjects performed the 90° out-of-phase pattern without augmented feedback during a single block (10 trials, 21 s per trial) at the end of day 4. Short rest periods were offered to the subjects in the middle of and in between each block. Participants were instructed to move at their preferred amplitude and frequency and to preserve these parameters across task performance. The training sessions (days 1, 2, 3, and 4) took place in a dummy scanner, closely mimicking the experimental context of the actual scanner, including reproduction of the background noise.
MRI scanning and fMRI data analyses
The details on the scan procedures have been published previously (Ronsse et al., 2011). Scanning occurred before (day 0, PRE) and after (day 5, POST) training. Rest-conditions (without any information presented) were administered alongside the 90° out-of-phase condition with feedback (FB+) at the PRE scanning. In addition to the latter 2 conditions, 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 cued on the screen, remaining visible for 3 s. Subjects were instructed to switch conditions as soon as this template appeared. To avoid eye
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movements across conditions, subjects were instructed to keep their eyes open at all times and to fixate a cross projected in the middle of their visual field during each condition. To prevent carry over effects between the different tasks, a short rest period (3 s) was added whenever 2 movement conditions succeeded one another immediately. A bite bar was used to minimize head movements, and a mirror was utilized to allow vision of images projected onto a screenmounted above the shoulders. This setup prevented subjects from seeing their hands during the movement task. Subjects wore headphones for communica- tionwith the experimenter and for hearing themovement-driven pacing tones (AUD group). Imaging data were processed and analyzed using Statistical ParametricMapping (SPM) 5 software (WellcomeDepartment of 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 was based on task-related cerebral activations in the subject groups (Ronsse et al., 2011). This primary data analysis of the imaging data identified the brain areas active during performance of the learned 90° out-of- phase motor pattern, and training-related changes in brain activation from PRE to POST. fMRI changes PRE vs. POST manifested mainly in decreased activations in the auditory group and increased activations in the visual group (Ronsse et al., 2011). Several sub-sets of brain regions sharing specific patterns of functional changes from PRE to POST were identified, which subsequently were defined as 3 types of networks to be analyzed via GTNA: (1) 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 in both groups) (Type 1 network, network N1, Table 1 and Fig. 1), (2) regions exhibiting the predominant functional behavior of each group (i.e., a set of regions manifesting 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 the VIS group) (Type 2 networks, networks N2 and N3, Tables 2 and 3 and Fig. 1), (3) regions in which both groups showed differential activation changes in terms of showing an increase or decrease in activity from PRE to POST to an extent thatmanifested a significant group difference and/or
Table 1 Regions of network N1 (Type 1) — areas with significant practice-related decrease (PRE>PO FB+ 90° out-of-phase pattern.
x y
Supplementary motor area
Frontal and prefrontal area
Middle frontal gyrus (DLPFC)2 R (4) 43 4
Occipital cortex
Temporal cortex
Parietal cortex
Supramarginal 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 defined by placing a sphere with radius 7.5 mm around the MNI coordinates of the respective cluster activation maximum. When extracting time courses of activation from fMRI data using the present method of placing spheres around activationmaxima, sphere sizes of between6 and10 mm radius 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 around activationmaxima, the current sphere sizewas deemed the best solution for the present imaging data. In each individual subject, separate analyses were conducted for each network. This also included the analysis of the PRE to POST changes in the functional connectivity for each group in both Type 2 networks answering the questions of (a) whether there is any coordinated/synchronized activity for the VIS group within the type 2 network as defined by the predominant fMRI changes of the AUD group (and vice versa), and (b) whether there are any significant changes in functional connectivity from PRE to POST for the VIS group in the type 2 network defined by the AUD group (and vice versa).
Several of the 5 networks contained activation foci falling within brain regions bearing the same or similar neuroanatomical labels. Further evaluation, however, showed that there was minimal overlap between the 5 different networks regarding the spheres defining the signal-recruitment areas for the network nodes (i.e., overlap between spheres was present if their centre coordinates were separated by less than 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 (3 nodes, Tables 2 and 5).
The choice of examining five networks in parallel was inspired by the question of whether changes in functional connectivity may be dependent on the origin of the task-related network. In defining these networks we took inspiration from the findings and emphases of the primary imaging analyses. A priori, we hypothesized that not all of these networks would necessarily exhibit the same, or even similar, 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 0
3 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 1
8 −8 BA 38 0 0 1 1 1 0
5 58 BA 1 0 3 3 1 3 2
8 23 BA 48 1 0 0 2 0 3
ctively.
BA
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 activity from 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 in terms 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: areas showing 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.
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Importantly, when accounting for the applied node/sphere size, all these networks represented neuro-anatomically largely distinct entities withminimal overlap. A priori, it was unclear whether only increases or decreases in signal strength (Type 2 networks) may carry tangible changes in functional connectivity, or whether such changes may occur exclusively in sets of regionswherein both groups show the same pattern with regard to changes in signal intensity from PRE to POST (Type 1 network). A further consideration for the inclusionof the Type 3networks was that Type 1 and 2 represented sets of regions sharing the same predominant pattern with regard to signal change from PRE to POST. However, such a type of functional network does not necessarily resemble the ‘in vivo’ status or actual computational processes in the brain, where interdependent excitatory and inhibitory processes, and the accompanying increases and decreases in brain activation, occur concomitantly. Subsequently, we considered each of the 5 networks as unique and non-redundant entities, whereby the outcome of the functional 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 time series (AVT) for the 90° out-of-phase epochs relative to rest was then extracted for both the PRE and POST runs (105 image volumes were available per condition at PRE and POST). At POST this included separate extractions for conditions (a)with and (b)without augmented feedback. The AVT was taken as the first eigenvariate from the singular value decomposition of a matrix composed of each time series of each voxel within the node-sphere, and the time series of all voxels of the sphere was then element-wise averaged, so as to obtain a single AVT for each node. The AVT extraction included whitening, filtering and removing null space of contrast using SPM code. During extraction, the time series was 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 calculation of the correlation matrices and the graph-theoretical analysis. Based on the AVT data, the network connectivity was then determined by calculating matrices of partial correlations between the network nodes, quantifying the unique relationship between each pair of nodes. The calculation of the partial correlations was based on the inverse of the covariance matrix. The partial correlation matrix is a symmetric matrix in which each off-diagonal element is the correlation coefficient between a pair of variables after filtering out the contributions of all other variables included in the dataset. Therefore, the partial correlation between any pair of regions filters out the effects of the other brain regions. Consistent with previous studies, partial correlations were chosen to minimize the impact of indirect dependencies by other brain regions and to address the problem of complicating the interpretation of the GTNA by includingmultiple redundancies in quantifying inter-nodal functional dependencies when using simple correlation coefficients (Liu et al., 2008; Marrelec et al., 2006; Salvador et al., 2005). Among all methods of evaluating functional interdependencies between fMRI time courses in different regions of interest, partial correlations have been found to be amongst the most reliable approaches (Smith et al., 2011).
In the connectivity matrices, functional connections were defined as valid/existing between pairs of nodes based on the statistical level of significance. We used a threshold of Pb0.001. Thresholding of the connectivity matrices resulted in binary matrices where existing (valid) connections carried a value of 1 while the absence of a functional connection between network nodes was designated by a value of 0. Self- connections of nodes were not included in the analyses. The resulting thresholded adjacencymatrices of partial correlations served as principal input for the GTNA. It has been shown that manipulating the connection density in a network by varying the number of valid network connections can have a noticeable impact on GTNA metrics (Van Wijk et al., 2010).
Table 2 Regions 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
Brain region Side
Primary sensory & (pre)motor cort.
Postcentral gyrus R (1) 38 −45 73 BA 1/2 3 3 1 3 2 3
L (2) −43 −43 68 BA 1/2 3 0 3 3 3 3
Superior frontal gyrus1 R (3) 15 0 68 BA 6 0 1 1 1 1 1
Superior frontal gyrus5 L (4) −10 −10 63 BA 6 1 3 0 0 0 0
Middle orbital gyrus1 R (5) 28 53 −13 BA 11 0 1 1 0 1 0
Middle frontal gyrus (DLPFC)1 R (6) 30 45 25 BA 46 0 1 1 3 2 3
Inferior frontal gyrus (p. opercularis) R (7) 48 13 18 BA 44 0 0 0 0 1 0
Inferior frontal gyrus (p. triangularis)4 R (8) 43 28 8 BA 45 2 2 2 1 2 2
Opercular area
Rolandic 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 cortex
Superior 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 0
Temporal pole1 5 R (13) 63 15 -5 BA 38 3 0 0 0 1 0
Parietal cortex
Supramarginal gyrus5 R (14) 60 −30 33 BA 2 2 4 3 1 2 1
Supramarginal gyrus1 L (15) −63 −28 23 BA 48 1 1 0 1 0 0
Superior 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 0
Inferior 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 connectivity matrices thresholded at P=0.0001, 0.0005, 0.001, 0.005 and 0.01) to ensure that the statistical results with regards to training-related GTNA effects aswell as group effects between visual and auditory learnerswere stable and reliable at different connection densities. Related to the issue of repeating the analyses at different density thresholds is the issue of comparing aspects of network architecture using graph-theory. In order to be able to compare topological features/aspects of network architecture within a network, it is deemed essential to keep the connection density of the compared networks constant (Van Wijk et al., 2010). However, the underlying hypothesis to this study postulates that motor learning is not only associatedwith alterations in brain activation butwill alsomodulate functional connectivity in the brain resulting in an increase
Table 3 Regions 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 cortex Superior occipital gyrus1 R (1) 18 −100 20 Middle occipital gyrus L (2) −13 −98 3 Calcarine gyrus R (3) 13 −95 3 Lingual gyrus L (4) −13 −90 −10
Cerebellum Lobule IV–V R (5) 15 −48 −15 Lobule VI R (6) 10 −65 −23 Vermis IV−V − (7) 0 −55 −15 Vermis VIII − (8) 5 −68 −40
1 = Nodal sphere is partially colocated with a sphere in network N1.
of significant/relevant functional relationships between task relevant areas, i.e., a better synchronization in BOLD activation across the brain areas in the networks will be expressed in a higher number of significant correlations/functional connections post-training. It follows that motor learning should not only alter existing connections but foster new functional relationships between brain areas. In order to capture and quantify this aspect of motor learning, we did not enforce the same connection density in our networks for the pre- and post-training assessments. Consistent with this compromise, we report changes in the mean graph-theoretical metrics across each entire network but did not assess specific aspects of network architecture.
In addition to the binary matrices, we also calculated weighted matrices, where for each valid functional connection between a pair of nodes, 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 0 BA 17 1 0 0 1 0 1 BA 17 0 2 0 0 0 1 BA 18 0 0 2 0 2 0
0 1 0 0 0 1 1 0 0 2 0 2 1 1 0 0 0 0 1 1 1 1 1 1
Brain region Side
PRE POST FB+ POST FB− Peak activation coordinates
Supplementary motor area R (1) 5 −15 68 BA 6 0 0 0 0 1 0
Precentral gyrus L (2) −45 −5 43 BA 6 1 2 2 1 0 1
Inferior 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 area
Inferior operculum L (5) −33 3 28 BA 48 3 0 2 3 3 1
Temporal cortex Superior 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 cortex
Superior 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 cortex
Middle 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
Cerebellum
Lobule VIII R (13) 25 −65 −53 2 0 0 2 0 2
L (14) −25 −63 −50 0 2 2 2 2 2
Vermis 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 then representing a proxy measure for the weight/strength in connection between this pair of nodes. This approach allowed us to examine connection strength between node-pairs and calculate a mean connection 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 with other nodes in the network. Subsequently, themean connection strength of a network was then defined as the mean connection strength across all its network nodes.
Graph theoretical analysis
The area of graph-theory is an established mathematical field and this kind of brain connectivity analysis has proven a very effective and informative way to explore brain function and human behavior (Bullmore and Bassett, 2011; Bullmore and Sporns, 2009; Rubinov and Sporns, 2010). The main GTNA analyses were conducted using the Brain Connectivity Toolbox (Rubinov and Sporns, 2010) (https:// sites.google.com/a/brain-connectivity-toolbox.net/bct/Home). At the PRE and then again at the POST time points, we calculated in each network and for each subject the regional (nodal) metrics of the functional network. For each node i of the network, we calculated (see below for measure definitions) the degree Ki, the local clustering coefficient Ci, the mean path length Li, the betweenness centrality bi and the overall connection strength Si. For each network, the mean for each of the above GTNA parameters was calculated as the average of each measure across all nodes of a network.
Measures of functional connectivity Connectivity degree is one of the most basic and important
measures of network analysis. The degree Ki of a node i is defined as the number of connections to that node (Sporns, 2002, 2011). Nodes with 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 N nodes in the graph G:
K ¼ 1 N ∑ iG
Ki
Connection strength is a measure quantifying how closely network nodes are connected in terms of showing a relationship in their time course of activation (Sporns, 2002, 2011). The overall connection strength Si is calculated as:
Si ¼ ∑ i≠jG
ri;j
where ri, j is the partial correlation between the average time series in node i and j.
Measures of functional segregation Measures of segregation quantify the presence of functionally related,
densely interconnected groups of brain regions, known as clusters within the network. The local (nodal) clustering coefficient Ci (Watts and Strogatz, 1998) is defined as the number of existing connections among the node's neighbors divided by all their possible connections:
Ci ¼ Ei
Ki Ki−1ð Þ=2
where Ei is the number of existing connections among the node's neighbors. The clustering coefficient of a network is the average of the clustering coefficient of all nodes:
C ¼ 1 N ∑ iG
Ci
in which C quantifies the extent of local connectivity of the network.
Brain region Side
PRE POST FB+ POST FB− Peak activation coordinates
Primary sensory and (pre)motor cortex Postcentral gyrus L (1) −38 −25 43 BA 3a 3 0 2 0 0 1
Superior frontal gyrus2 L (2) −10 −10 60 BA 6 1 3 2 2 1 0
Inferior frontal gyrus (p. orbitalis) R (3) 48 28 −15 BA 38 3 0 0 0 0 2
Temporal cortex
Superior 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 0
Temporal pole1 2 R (6) 65 10 −3 BA 38 0 1 1 0 0 1
Transverse temporal gyrus (Heschl's) R (7) 53 −8 5 BA 48 3 0 0 0 0 0
Parietal cortex
Superior parietal lobule L (8) −33 −50 68 BA 7 3 0 0 0 3 1
Opercular area
Rolandic operculum R (9) 50 0 8 BA 48 2 2 0 0 1 3
Parietal cortex
Supramarginal gyrus2 R (10) 48 −23 28 BA 48 1 1 1 3 3 2
Cingulate cortex
Middle 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 1
Anterior R (13) 3 18 28 BA 24 3 0 0 1 0 0
Cerebellum
Lobule IV-V R (14) 13 −50 −18 0 0 1 0 0 0
Vermis IV-V −(15) 0 −55 −15 0 0 0 0 1 2
Vermis 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 integration Measures of functional integration characterize the ability to rapidly
combine specialized information from distributed brain regions and are commonly based on the concept of a path, with shorter paths implying stronger potential for integration (Sporns, 2002, 2011). The mean path length Li of a node i is:
Li ¼ 1
Li;j
in which Li,j is the smallest number of edges that must be traversed to make a connection between node i and node j.
Measures of centrality An important measure to assess whether a node has importance
for information flow and participates in many shortest paths within a network is betweenness centrality (Rubinov and Sporns, 2010; Sporns, 2002, 2011). The betweenness centrality bi of a node i is defined as:
bi ¼ 1
N−1ð Þ N−2ð Þ ∑ h; jG;h≠j; h≠i; j≠i
ρhj ið Þ ρhj
in which ρhj is the number of shortest paths between nodes h and j and ρhj(i) is the number of shortest paths between nodes h and j that pass through node i. An important concept that is related to betweenness centrality is that of network ‘hubs’. Commonly, somenodes in functional networks will stand out in terms of having a more important role in the community structure of a network as they are crucial centers (hubs) for the information flow within the network and for connecting a high number different network nodes.
As part of our analysis, we classified the hub-status of network nodes PRE and POST. This classification was based on assessing 4 parameters – clustering coefficient, path length, degree, and betweenness centrality – and was based on the consideration that hub nodes generally display a low clustering coefficient, a short average distance towards the other nodes of the network, an above average high number of connections, and a high level of betweenness centrality (Bullmore and Sporns, 2009; Sporns et al., 2007). The hubs for each network PRE and POSTwere identified by computing the hub-status for each node by examiningwhether a node belonged to: (1) the top 25% nodes showing the lowest clustering coefficient, (2) the top 25% of nodes showing the shortest mean path length, (3) the top 25% of nodes showing the highest level of connectivity degree; and/or (4) the top 25% of nodes showing the highest betweenness centrality. Consistent with previous studies (van den Heuvel et al., 2010), each node was assigned a score between0 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 of the 4 criteria, with one of these being a sufficiently high betweenness centrality.
Small-world brain connectivity Global network architecture in terms of the small-worldness was
quantified via the normalized clustering coefficient and normalized path length. Previous research has shown that all networks found in 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 functional optimization and deviation from randomness (Bullmore and Sporns, 2009; Sporns et al., 2007), with alterations in this measure commonly representing a decrement in network functionality. Small-world
organization incorporates advantages of regular and random networks, preserving optimal levels of connectivity within families of functionally related node-clusters together with short overall communication distances. Hence, small world network character is defined as beingmore clustered than randomnetworks, yet having approximately the same characteristic path length as random networks (Watts and Strogatz, 1998), that is
Lnorm ¼ Lrealp =Lrandp ≈1
Cnorm ¼ Creal p =Crand
p > 1
where the Lreal and Crealare the mean path length and clustering coefficient of the real network, the Lrandand Crandare the equivalent values of matched random networks. In order to obtain the values for the random network parameters in the above equations, we applied a method estimating these random values while preserving the principal characteristics of the real network. If the mean vertex degree k of a graph G is defined as k=2E/N, where E is the number of edges and N is the number of vertices of G, a random graph that is similar to G can be defined with the same mean vertex degree k and number of vertices N as G. Watts and Strogatz (1998) defined a reliable estimate of themean clustering coefficient of a random graphwithmean vertex degree k and number of vertices N as Crand=k/N. This estimate can be used to normalize the actual mean clustering coefficient of G for the calculation of ‘small world character’ of a network. Similarly, Watts and Strogatz (1998) found an estimate of the mean path length of a random graph Lrand=ln(N)/ ln(k). Fronczak et al. (2004) however showed that this is actually an estimate of the diameter (i.e., the maximal shortest path length). They also derived an analytic solution for themean path length of a random graphwithmean vertex degree k and number of vertices N as Lrand=((ln(N)−γ)/ln(k))+0.5 (where γ is Euler's constant). This
Table 6 Change of graph-theoretical metrics for the 90° out-of-phase task from PRE to POST trainin
Auditory feedback group
Network PRE-training FB+
Mean SE Mean SE Mean
N1 (Table 1) Clustering coeff. 0.27 0.03 0.33 0.03 0.34 Degree 2.95 0.14 3.37 0.09 3.41 Path length 1.82 0.05 1.68 0.03 1.68 Betweenness 6.43 0.45 5.29 0.25 5.42 Connection strength 0.87 0.04 0.89 0.03 0.89
estimate can then be used to normalize the actual mean path length of G with regard to calculation of the ‘small world character’ of a network. In order to ensure the reliability of this estimation method, we also applied an actual randomization procedure taking advantage of random network generators (https://sites.google.com/a/brain-connectivity-tool box.net/bct/Home) creating networks that have a random topology but share the size, density and binary degree distribution of the original network. This procedure was repeated 100 times for each subject's network matrices, then taking the mean random clustering coefficients and mean random path lengths across these 100 iterations as values for calculating the subject's new SWN coefficients for each network. The results of this analysis rendered the same findings with regard to SWN changes associated with motor learning and confirmed that the applied estimation method provided reliable values for SWN clustering coefficients and path lengths.
Statistical analysis
For each of the kinematic parameters and the GTNA measures, training-related changes were first examined via 2×2 repeated measures ANOVA with the factors GROUP (auditory vs. visual) and SESSION (PRE vs. POST). Significant effects were further explored by post hoc tests using Tukey correction. Similarly, changes between performing the 90° out-of-phase task with and without augmented feedback during the POST session were assessed with a 2×2 repeated measures ANOVA with the factors GROUP (auditory vs. visual) and FEEDBACK (FB+ vs. FB−). This analysis was conducted for each network separately. Following this, we repeated the analysis combining data across all 5 networks. Normal probability plots (plotting the residuals versus their expected normal value) calculated for the ANOVA analyses confirmed that the residual values were normally
g.
SE Mean SE Mean SE Mean SE
0.03 0.25 0.04 0.39 0.03 0.41 0.03 0.11 3.03 0.12 3.66 0.16 3.70 0.15 0.03 1.80 0.05 1.64 0.04 1.62 0.04 0.24 5.99 0.46 5.08 0.33 4.98 0.31 0.03 0.91 0.04 1.00 0.04 1.02 0.04
0.02 0.25 0.02 0.29 0.02 0.29 0.02 0.11 4.10 0.15 5.15 0.12 5.13 0.14 0.03 2.16 0.06 1.89 0.03 1.90 0.03 0.63 20.90 1.06 16.06 0.47 16.13 0.52 0.03 1.12 0.04 1.29 0.03 1.29 0.03
0.05 0.41 0.04 0.46 0.03 0.43 0.03 0.14 2.81 0.12 3.09 0.14 3.07 0.15 0.07 1.89 0.07 1.80 0.07 1.78 0.07 0.55 6.25 0.51 5.62 0.52 5.49 0.46 0.04 1.00 0.04 1.07 0.03 1.07 0.03
0.02 0.22 0.03 0.27 0.02 0.25 0.03 0.17 3.27 0.12 3.85 0.18 3.79 0.16 0.05 2.24 0.05 1.90 0.02 1.94 0.03 0.66 16.27 0.91 12.74 0.54 13.30 0.50 0.04 0.96 0.03 1.03 0.04 1.03 0.04
0.02 0.22 0.03 0.29 0.02 0.29 0.01 0.11 3.50 0.12 4.16 0.11 4.18 0.13 0.06 2.24 0.05 2.01 0.05 2.01 0.05 0.91 18.13 0.89 15.14 0.73 15.09 0.80 0.03 1.05 0.03 1.14 0.03 1.15 0.03
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 0.932 SESSION 1, 16 9.10 0.008 7.65 0.014 2.48 0.135 5.32 0.035 0.06 0.809 GROUP×SESSION 1, 16 0.84 0.374 0.23 0.640 0.01 0.905 0.17 0.688 0.57 0.461
Degree GROUP 1, 16 2.84 0.111 1.63 0.220 0.09 0.773 3.57 0.077 0.25 0.623 SESSION 1, 16 12.0 0.003 55.3 b0.0001 2.66 0.123 15.0 0.001 0.97 0.340 GROUP×SESSION 1, 16 0.90 0.356 3.07 0.099 0.52 0.481 0.08 0.776 0.01 0.918
Path length GROUP 1, 16 0.65 0.431 0.81 0.381 0.00 0.994 7.44 0.015 0.75 0.400 SESSION 1, 16 13.0 0.002 31.7 b0.0001 2.20 0.157 27.0 b0.0001 8.40 0.010 GROUP×SESSION 1, 16 0.10 0.753 1.88 0.189 0.03 0.868 0.20 0.657 2.19 0.158
Betweenness GROUP 1, 16 0.65 0.431 0.75 0.400 0.17 0.682 5.16 0.037 0.01 0.935 SESSION 1, 16 6.83 0.019 35.0 b0.0001 2.59 0.127 12.1 0.003 1.83 0.195 GROUP×SESSION 1, 16 0.12 0.732 0.99 0.334 0.01 0.905 0.03 0.866 0.01 0.910
Connection strength GROUP 1, 16 4.34 0.054 0.49 0.492 0.06 0.811 0.91 0.355 0.78 0.390 SESSION 1, 16 2.24 0.154 14.1 0.002 1.12 0.305 3.98 0.063 0.51 0.485 GROUP×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 the functional connectivity associated with motor learning in 5 different neural networks. Whilst each of the GTNA measures emphasizes a different 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 5 networks are largely separate entities with minimal neuro-anatomical overlap between network nodes. Therefore, we applied a statistical correction by reducing the level at which results were considered significant in the ANOVAs to a P value ≤0.01 (i.e., 0.05/5).
Pearson R coefficients were used to examine correlations between measures. In order to examine correlations in kinematic changes and changes in GTNA measures between the PRE and POST assessments, we calculated difference scores of POST–PRE in each subject for the kinematic key measures as well as all GTNA measures, followed by the examination of correlations between these kinematic and GTNA difference scores (correlations were considered significant at a P value ≤0.05). All statistical analyses were performed with the Statistica software (StatSoft, Inc).
Table 8 SWN character of mean clustering coefficients and mean path lengths at PRE to POST FB+.
Auditory feedback group Visual feedback group
Network PRE- training
Mean SE Mean SE Mean SE Mean SE
N1 Clustering coeff. 0.99 0.122 0.93 0.093 0.78 0.112 0.98 0.071 Path 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 the target 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 comparable performance levels during the PRE- andPOST-test (Fig. 2), and significant effects for GROUP or GROUP×SESSION interactions were absent for both the AE and the SD measures. In contrast to the significant improvements in AE and SD of relative phase, neither the AUDnor the VIS group showed significant changes in speed of execution (cycling frequency) or movement amplitude from PRE to POST. However, elimination of the augmented FB strongly affected performance of the VIS group but not the AUD group (Fig. 2). A 2×2 (group×augmented FB condition) ANOVA was conducted on the POST-training scanning runs, including the FB+and FB− 90° out-of-phase data. For AE there was amain effect of FB condition (F1,16=21.5, Pb0.0003), and there was a group×FB condition interaction (F1,16=21.3, Pb0.003). Post hoc Tukey's tests further confirmed that both groups finally reached the same performance level when augmented FB was available but that the AE of the VIS group during the conditionwithout augmented FBwas significantly higher (P=0.0004). This suggests that the VIS group was highly dependent on augmented FB for successful performance, showing significant AE increases when this source of information was removed, whereas the AUD group largely preserved performance in the absence of augmented FB. The same analysis on the SD reached significance for the main effect of condition (F1,16=8.2, Pb0.02), with performance being less stable when the augmented FB was removed. Post hoc pairwise comparisons did not reach significance.
Changes in functional connectivity between FB+ conditions from PRE to POST
The repeated measures ANOVA of the GTNA measures across the PRE- and the POST-training fMRI data acquired under provision of augmented feedback (FB+) in both groups showed that the VIS as well as the AUD feedback group showed similar changes in functional
Table 9 Changes 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 0.809 FB+/FB− 1, 16 0.44 0.518 0.06 0.817 2.85 0.111 1.28 0.275 0.01 0.932 FB×GROUP 1, 16 0.29 0.596 0.16 0.696 1.62 0.221 0.36 0.556 0.57 0.461
Degree GROUP 1, 16 2.68 0.121 5.55 0.032 0.54 0.473 0.63 0.440 0.97 0.340 FB+/FB− 1, 16 0.71 0.413 0.01 0.926 0.16 0.696 0.08 0.787 0.25 0.623 FB×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 length GROUP 1, 16 1.20 0.289 7.58 0.014 0.07 0.791 4.05 0.061 1.83 0.195 FB+/FB− 1, 16 0.24 0.633 0.03 0.863 0.08 0.786 0.08 0.786 0.01 0.935 FB×GROUP 1, 16 0.25 0.621 0.06 0.815 0.59 0.453 5.32 0.035 0.01 0.910
Betweenness GROUP 1, 16 0.87 0.364 4.92 0.041 0.01 0.915 2.46 0.136 1.83 0.195 FB+/FB− 1, 16 0.01 0.927 0.02 0.894 0.08 0.780 0.00 0.959 0.01 0.935 FB×GROUP 1, 16 0.81 0.382 0.27 0.613 0.58 0.458 1.38 0.258 0.01 0.910
Connection strength GROUP 1, 16 4.50 0.05 4.22 0.057 0.83 0.377 0.13 0.720 0.51 0.485 FB+/FB− 1, 16 1.50 0.239 0.01 0.919 0.10 0.757 0.32 0.579 0.78 0.390 FB×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. Without exception, both groups exhibited comparable increases in mean clustering coefficient, connectivity degree, and connection strength, together with decreases in path length between network nodes and a reduced betweenness centrality (Table 6). These findings applied unequivocally to each of the examined networks, manifesting significant main effects for SESSION in networks N1, N2, N4 and N5 (Table 7). Network N3 (the smallest of the examined networks) also showednumerically comparable changes inGTNAmeasures as observed for the other 4 networks, but here these changes coincided with a proportionally much higher intra-group variability (higher SEs), likely facilitated 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 interactions and significant main effects for GROUP were absent (Table 7).
All networks had the same small world character in both groups at all 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 manifestation of this small world architecture was not strong, with Cnorm in most networks falling into the range of 1.05–1.28 (N2–N5) and Lnorm being between 0.92 and 0.98 (N2–N5). The parameters of network N1 fell slightly below this range. Numerically, the AUD group showed a reduction in SWN clustering coefficients in 3 of the 5 networks from PRE to POST FB+, whilst the VIS group had increases in 4 networks, the exception being the ‘auditory’ network N2. The SWN path length saw little change from PRE to POST in the groups. Despite these numerically differential changes in values, there were no significant effects of GROUP, SESSION, or GROUP×SESSION interactions for the SWN variables.
Changes in functional connectivity between POST FB+ and FB− conditions after training
In the trained subject groups, functional connectivity as quantified by the current GTNAmeasures remained unchanged between performing the 90° out-of-phase task with and without augmented feedback at thepost training fMRI session.With exception of amarginally significant GROUP×FEEDBACK interaction for the path length in network N4 (Table 9), a repeated measures ANOVA rendered no significant effects for GROUP, FEEDBACK (FB+/FB−) or GROUP×FEEDBACK interactions for the networks N1, N3, N4 and N5 (Table 9). In the N2 network (the
network defined by areas showing the increase in level of activation fromPRE to POST in the AUDgroup), amarginally significantmain effect of GROUPwas present for themeasures connectivity degree, path length, and betweenness centrality (Table 9). In all these measures, the VIS group consistently showed a more favorable functionality than the AUD group in this network (i.e., higher connectivity degree, together with shorter mean path length and reduced mean betweenness centrality).
Due to the similar and consistent development of the GTNA parameters of both groups between the PRE and POST assessments in the examined networks, we conducted an additional analysis combining the measures of the five networks to quantify the mean change in network metrics across the entirety of all brain regions comprised by the five networks. The principal purpose of this analysis was the confirmation of the statistical findings of the individual network results in a statistically more sensitive analysis, and to potentially identify effects having fallen below the detection threshold of the individual networks. The combined analysis of all five networks showed similar results as observed in the individual networks, albeit that the effects emerged at a clearer level of significance (Table 10). From PRE to POST, both groups had – across thefivenetworks– increases inmean clustering coefficients (auditory and visual: 19% and 26%), connectivity degree (15% and 19%), and connection strength (4% and 10%), together with decreases in mean path length (7% and 11%) and mean betweenness centrality (15% and 19%) (Fig. 3). Significant effects of GROUP for PRE to POST and POST FB+ vs. POST FB− (Table 10) emerged only for connectivity degree. In addition, there were marginally significant effects of GROUP for betweenness centrality, mean path length, and connection strength (POST FB+ vs. POST FB− only), with the visual group having more and stronger connections, and shorter communication distances (Table 10). The network functionality of both groups maintained the same normalized path length and clustering coefficient at 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 activation changes in the same intensity direction in both groups from PRE to POST FB+), no area met the minimum criteria for hub-status before the training in the AUD group (hub criteria as defined in Section 2.5.4). Only the right hemisphere (RH) superior parietal lobule gained hub-status after the training in the AUD group at POST FB+. Removal of the augmented feedback during the 90° out-of phase task at the POST assessment had no effect on this. Conversely, a more pronounced level
Table 10 90° 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 0.294 SESSION 1, 84 29.04 b0.0001 FB+/FB− 1, 84 0.65 0.423 GROUP×SESSION 1, 84 0.51 0.475 FB×GROUP 1, 84 0.78 0.381
Degree Degree GROUP 1, 84 7.12 0.009 GROUP 1, 84 7.43 0.008 SESSION 1, 84 77.44 b0.0001 FB+/FB− 1, 84 0.35 0.554 GROUP×SESSION 1, 84 1.90 0.171 FB×GROUP 1, 84 0.77 0.382
Path length Path length GROUP 1, 84 4.31 0.041 GROUP 1, 84 6.56 0.012 SESSION 1, 84 61.09 b0.0001 FB+/FB- 1, 84 0.05 0.820 GROUP×SESSION 1, 84 2.01 0.160 FB×GROUP 1, 84 0.16 0.694
Betweenness Betweenness GROUP 1, 84 4.65 0.034 GROUP 1, 84 5.99 0.016 SESSION 1, 84 43.72 b0.0001 FB+/FB− 1, 84 0.03 0.864 GROUP×SESSION 1, 84 0.69 0.409 FB×GROUP 1, 84 0.49 0.485
Connection strength Connection strength GROUP 1, 84 3.75 0.056 GROUP 1, 84 7.02 0.010 SESSION 1, 84 22.14 b0.0001 FB+/FB− 1, 84 1.46 0.230 GROUP×SESSION 1, 84 3.16 0.079 FB×GROUP 1, 84 0.19 0.668
Small World Network measures SWN clustering coeff. SWN clustering coeff.
GROUP 1, 84 0.38 0.540 GROUP 1, 84 2.45 0.121 SESSION 1, 84 0.94 0.335 FB+/FB− 1, 84 0.03 0.865 GROUP×SESSION 1, 84 0.60 0.440 FB×GROUP 1, 84 0.59 0.443
SWN path length SWN path length GROUP 1, 84 1.13 0.291 GROUP 1, 84 0.55 0.460 SESSION 1, 84 0.53 0.468 FB+/FB− 1, 84 0.57 0.452 GROUP×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 functioned as network hubs. In the VIS group, the RH superior parietal lobule and the RH SMA had hub-status at the PRE FB+ assessment. The RH SMAmaintained this status post training in both POST FB+ and FB− conditions of the 90° out-of-phase task. In addition, the RH dorsolateral prefrontal cortex (DLPFC) had achievedhub-status in theVIS group, and maintained this status throughout the POST FB+ and FB− conditions. The LH supramarginal gyrus had hub-status in the VIS group once feedback was removed in the POST FB− assessment.
Table 11 Correlations between GTNA measures across the 5 networks.
Correlations between GTNA measures across the 5 networks
GTNA measures
AUD
VIS
AUD
VIS
AUD
VIS
AUD
VIS
−0.89**0.25*−0.46***Betweenness
0.26*0.91**0.27*−0.40***
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 RH postcentral gyri were consistently important as hubs throughout PRE and POST FB+ assessments in both groups. In the AUD group, the RH superior parietal lobule had hub-status at PRE and POST FB+, but not when auditory feedback was removed at the POST assessment. In the VIS group, the same area was designated as hub only in the untrained (PRE) network, as was the RH supramarginal gyrus. Two areas which had hub-status exclusively in the VIS group were the LH SMA (PRE only) and the RH DLPFC (POST FB+ and POST FB−). In the VIS group, a third parietal area, the RH inferior parietal lobule, achieved hub-status once the feedback was removed at the POST FB− assessment.
In theN3 network (Table 3), no areasmet the criteria for hubs. In the N4 network of the AUD group (Table 4), the LH inferior operculum served as hub in the untrained network (PRE) and again once auditory feedback was removed in the trained network at the POST FB− assessment. Conversely, in the VIS group, this area had hub-status at the POST FB+ assessment. Two other areas, the LH and RH occipital gyri, achieved hub-status in the AUD group, namely at the POST FB+ assessment. In the VIS group, the LH middle occipital gyrus similarly served as hub at the POST FB+ and FB− assessments. The activation focus 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 most marked discrepancy in hub-changes from PRE to POST FB+, whereby many regions in the N5 network of the AUD group lost their hub- status. Such areas included the LH postcentral gyrus, RH inferior frontal gyrus, the LH middle cingulate and RH anterior cingulate gyri, RH transverse temporal gyrus, RH superior temporal gyrus, and LH superior parietal lobule. Only 3 areas gained hub-status in the AUD group thereafter (RH middle cingulate cortex at POST FB+, plus the LH superior parietal lobule and RH supramarginal gyrus at FB−). In the VIS group, only 3 areas reached hub-status at any stage: similar to networks N1 and N2, parts of the SMA in the untrained network,
Fig. 3. Improvement inGTNAmeasures fromPRE to POST (combined results across all five networks)—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 connectivity degree (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. Asterisk markers designate significant changes for measures between sessions (***Pb0.0001).
Fig. 2. Improvement in kinematic measures from PRE to POST — panels show the absolute error [AE] of relative phase (A) and the standard deviation [SD] of relative phase (B) [measures as defined in Ronsse et al. (2011)]. Both groups improved their motor performance as a result of the 4-day motor training regime, resulting in reduced AEs and SDs. Removal of augmented feedback at the POST assessment did not affect kinematic measures of the AUD group but resulted in a marked increase of AE in the VIS group. Error bars show standard errors.
followed by the RH supramarginal gyrus at POST FB+ and the rolandic operculum at POST FB−.
Correlations between changes in GTNA measures and improved kinematics PRE to POST
The analysis of correlations between the improved network/GTNA metrics from PRE to POST in the 5 networks and the concomitant improvements in the kinematic parameters showed little direct association between changes in functional network connectivity and the improved quality of motor output produced by the neural networks 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 and changes in connectivity degree (R=0.57, P=0.017), and connection strength (R=0.58, P=0.015), pairing poorer motor performance with higher network connectivity (over-connectivity) in the AUD group. In the VIS group, there were correlations between the changes in AE of relative phase and the changes in mean clustering coefficient in 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 of relative phase and the mean clustering coefficient in the N3 network (R=−0.61, P=0.009), pairing better motor performance with higher 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 ensure that 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 between the network nodes are shown in Fig. 4.
Discussion
This study applied graph-theoretical network analysis to quantify changes in functional connectivity associated with the training- induced improvement of bimanual motor coordination supported by the provision of augmented feedback in form of either auditory or visual sensory stimuli. Our findings show that, after a motor training regime that extends over several days, improved motor performance occurs alongside increases in functional connectivity. This increased functional connectivity in the trained task-related neural networks then
remains stable, at least in the short term, upon removal of augmented feedback. Importantly, these effects occurred in 4 out of 5 of the examined networks supporting the arguments that (a) these changes in functional connectivity are likely an unequivocal manifestation of the adjustment of the neural networks to facilitate the learning of a new, complex motor pattern, and (b) that this increase in network functionality is independent of the network definition since these effects occurred similarly in Type 1, 2, and 3 networks. Interestingly, both sensory feedback modalities were associated with comparable increases in functional connectivity, albeit that theVIS group had a higher functional connectivity on some GTNA measures. Importantly, the analysis of the connectivity of individual functional network nodes revealed an importance of specific brain regions for motor learning not apparent in the standard fMRI analyses. We shall now discuss these findings in more detail.
Connectivity changes associated with practice-induced plasticity and different augmented feedback modes
All key GTNA measures showed consistent improvement in both groups between the PRE and POST assessments indicating that mean network connectivity increased significantly in the context of augmented feedback training. Increases in mean clustering coefficients, connectivity degree, andmean connection strength suggest that each network in both the auditory and the visual group became more closely connected at a regional and at a more macroscopic level following the 4-day training regime. Similarly, decreases in mean path length documented that training resulted in a more direct information transfer in the examined
networks. Thefinding of a decreased betweenness centrality is consistent with this interpretation, asmore direct communication betweennetwork regions would likely be associated with the effect that – compared with the pre-training status – a higher proportion of network nodes is getting bypassed and does not participate anymore in the same number of shortest pathswithin the network in order to promote faster information exchange between network nodes. The combined analysis of all 5 networks rendered the same results but at a stronger level of statistical significance. This combined analysis also had sufficient power to indicate (albeit mostly marginal) group effects for connectivity degree, betweenness centrality, mean path length and connection strength, the VIS group having the more favorable outcome on all these measures. An important point that needs to be considered in the interpretation of this combined analysis is that this step combined GTNAparameters fromnetworkswith different numbers of network nodes. In order to optimally compare and combine parameters between networks, their size should be matched perfectly (Van Wijk et al., 2010). However, combining the parameters from the 5 networks into one analysis was specifically aimed at quantifying the mean change in network metrics across the entirety of all brain regions comprised by the five networks, andwas supported by the important pre-requisite of having very similar changes in functional connectivity from PRE to POST as well as from POST FB+ to POST FB− when assessing the networks individually. Therefore, the results of this combined analysis should only be interpreted in combination with the findings in the individual networks.
In order to appreciate the implications of these findings, it is important to re-consider the different nature of predominant fMRI activation change in the auditory and the visual groups from PRE to POST. The primary fMRI results showed that the visual group exhibited a predominant increase in neural activity, especially in vision/sensorimotor-specific areas (Ronsse et al., 2011). Conversely, neural activity decrease was prevalent in the auditory group, particularly pronounced in temporo-parieto-frontal regions associated with cognitive/sensory monitoring of motor task performance, which in turn supported the development of a control mode that was less reliant on augmented feedback sources in the auditory group (Ronsse et al., 2011).
The combined interpretation of these findings together with the present result that network metrics improved similarly in both groups as a result of training indicates that similar optimization in network functionality can be associated with practice-induced increases in brain activation (VIS group) but also predominant reduction in brain activation (AUD group). This suggests that the brain employs rather different strategies to achieve (a) an improved phase synchro- nicity between regions, or (b) a better homogeneity of oscillations of neuronal elements within a node sphere, which can equally contribute to improved inter-regional coupling (Zalesky et al., 2011). Studies on psychological disorders such as schizophrenia support that both of these factors are crucial in determining good inter-regional functional connectivity (Bassett et al., 2012; Zalesky et al., 2011). While the underlying factors for this disparity between the auditory and visual feedback modalities in achieving an increased functional connectivity via different changes in signal strength is beyond the scope of this study, it is an important consideration arising from our present findings that decreased brain activation is not synonymous with poorer inter- regional coupling. Only networkN3,which showed the smallest number of network nodes, failed to exhibit the significant changes in network metrics between pre- and post-training. This is likely due to the limited size of the network in combination with a higher intra-group variability in all GTNA measures, and the fact that the nodes in this network were concentrated in only two brain regions, i.e., sections of the occipital lobe and the cerebellum. The subsequently smaller degrees of freedom regarding possibilities for change and variability between pre- and post- training likely contributed to the weaker performance of this network with regard to manifesting significant changes from PRE to POST FB+.
Considering that network N1 was defined by regions showing the same behavior in terms of increases or decreases in activation between
PRE and POST, it may not be surprising that both groups also showed a similar development of network metrics. However, networks N2 and N3 were defined by the predominant ‘behavior’ in fMRI activation change of one particular group only (i.e., AUD or VIS). Hence, it would have been feasible to expect that only the ‘defining’ group should exhibit consistent changes/improvement in GTNAmeasures, also when considering the only limited overlap between network N2 and N3 in network size and MNI coordinates of the network nodes. Yet, the GTNA parameters of both groups behaved similarly in N2 and N3, suggesting (a) that many of the areas in these networks showed coordinated and synchronized task-related activity even in the ‘non-defining’ group, and (b) that this activity was susceptible to the same training effect in the ‘defining’ and ‘non-defining’ group. NetworksN4andN5, defined by areas showing differential activation changes between the auditory and the visual groups from PRE to POST, take this notion further, exhibiting similar improvements in GTNAmeasures and re-iterating the interpretation that (a) functional network optimization might be carried by decreases in brain activation in the context of auditory feedback and increases in activation during visual feedback conditions, and (b) that both increases and decreases in brain activation can be a manifestation of better neural network synchronization and improved task-related brain functionality.While the GTNA findings in networks N2 andN3 are an important result of this study, it should also be considered that N4 and N5, and with limitations N1, are the networks with the most resemblance to general ‘real world’ neurological systems, whereby the network nodes do not share the same kind of activation change but show differential activation changes from PRE to POST. As such, one might argue that these differential networks are more representative of actual computational processes in the brain, where interdependent excitatory and inhibitory processes, and the accompanying increases and decreases in brain activation, occur concomitantly.
Importance of specific cerebral areas — expansion of the primary fMRI findings
An important point that arises from the interpretation of the functional connectivity changes is that an apparent decreased activation from PRE to POST in a brain region is not necessarily indicative of diminishing importance of this area within the global functionality of a task-related neural network. The analysis of the hub-status of the areas in the networks supported this interpretation. The findings of the primary fMRI analysis showed that the AUD group showed a global decrease in activity in temporo-parieto-frontal areas, including several areas crucial for the processing of auditory information (superior temporal areas, temporal pole, inferior frontal gyrus) (Bernal et al., 2004; Callan et al., 2003; Seung et al., 2005; Wang et al., 2003; Zarate and Zatorre, 2008), proprioceptive information and rhythmic coordination (SMA) (Debaere et al., 2003, 2004b; Hagura et al., 2009; Immisch et al., 2001; Jancke et al., 2000; Naito et al., 2005; Rao et al., 1997; Stephan et al., 1999; Toyokura et al., 2002). Conversely, the VIS group exhibited increased activation in vision/sensorimotor-specific areas from PRE to POST, in particular areas important for visuomotor transformations in the 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 the modulation of their activity between PRE to POST in the respective groups was also reflected in their importance for intra-network information transfer (i.e., hub-status). In the AUD group, several regions including areas in the left and right postcentral gyrus (Networks 2 and 5), right superior parietal lobule (Networks 1, 2 and 5), areas in the temporal cortex (Networks 2 and 5), inferior frontal and cingulate cortices (Network 5) were classified as hubs within the functionality of theuntrained networks in the PRE condition.Manyof these regions then were no longer hubs within the functionality of the trained network at the POST FB+ assessment. While one might consider this loss in
functional integration of particular areas consistentwith the decrease in activation predominant in the AUD group from PRE to POST FB+, which in turn highlighted the relevance of these areas for training-induced task mediation in the AUD group, the more intriguing interpretation would be that, in addition to just a decrease in activation, the training- induced changes in the AUD group included a network re-organization comprising a de-centralization of network function, which was not apparent from the results of the primary fMRI analysis.
For the VIS group, the findings of the GTNA analysis also take the results of the primary fMRI analysis further. Based on the changes in fMRI activation from PRE to POST, a strong involvement of the right DLPFC in the AUD group became evident at initiation of practice while, in the VIS group, comparable changes in intensity of activation were absent, suggesting that the role of the DLPFC as an internal controller monitoring task performancewas crucial for theAUDgroup only during the initial training phase when attentional demands are high. The DLPFC 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 (Jueptner et al., 1997; Toni andPassingham, 1999), and itwould have been feasible to expect this region to play a prominent role also for the VIS group. The findings of the GTNA demonstrated that the DLPFC indeed appears to play an important role for facilitating training-induced task performance in the VIS group. In both networks including activation foci in the DLPFC as network nodes, theDLPFC consistently featured asnetwork hub in the trained networks of the VIS group, both under FB+ and FB− conditions.
Similar to the DLPFC, the primary fMRI analysis indicated high relevance of the activation foci within the SMA region for promoting motor performance in the AUD group while, for the VIS group, this structure did not stand out in the fMRI analyses. The SMA is a crucial region for interlimb coordination as well as eye–hand coordination (Fox et al., 1985; Gerloff et al., 1997; Ghez, 1991; Israel et al., 1995; Mushiake et al., 1996; Pierrot-Deseilligny et al., 1993; Steyvers et al., 2003) and plays a vital role in the temporal organization and execution of bimanual tasks (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 duringmemorized movements (Mushiake et al., 1991; Tanji and Shima, 1994) and during execution of complex motor patterns in the absence of visual guidance (Hamzei et al., 2002). The present analysis emphasizes that, despite the absence of drastic fluctuations in activity between PRE and POST assessments, fociwithin either the left or right SMA served as hub-region in 3 out of 4 networks that had SMA foci as nodes (networksN1, N2, N5), suggesting that the SMA also had an important role for facilitatingmotor learning in the VIS group. This conclusion was apparent only as a result of the graph-theoretical network analysis.
Relationship between changes in functional connectivity and motor performance
An important point to consider is that network connectivity of the trained networks did not deteriorate upon removal of augmented feedback at the POST assessment. None of the networks showed any significant effects in the examined GTNA metrics between the POST FB+ and FB− assessments. In case of the AUD group, this finding seems to be consistent with the behavioral result of preserved performance of the newly learned 90° out-of-phase task. However, in the VIS group the network metrics also remained stable despite a considerably poorer motor performance once feedback was removed at the POST FB− assessment.
It has been suggested that learning-induced performance of new complex motor patterns is firstly coded in a visual–spatial coordinate system, which develops more quickly (Kovacs et al., 2010), to be followed by a representation based on a more stable motor coordinate system in later stages of practice. This might explain why there was no
drop inmotor performance in the POST FB− in the AUDgroup: because the nature of the AUD tasks presentation prevented coding in a visual– spatial coordinate system, it resulted in a quicker formation of a more stable motor coordinate reference frame in this group. Since feedback for the visual–spatial coordinate system was readily available in the VIS group, promoting coding in a visual–spatial reference frame (this faster developing visual–spatial representation being consistent with the steeper learning curve of the VIS group), the transfer to a motor coordinate frame, and the development of an internal model of the required movement, may thus have been prevented or initiated at a much later stage in the practice regime in the VIS group.
An alternative interpretation arises from the consideration that both groups behaved differentially in processing alternate sources of sensory information in addition to the principal visual and auditory feedback modalities. The visual feedback may have been a much more salient source of information in theVIS group, eclipsing all other available sensory modalities, such, as, for example, proprioceptive information. This made the VIS group more vulnerable to the FB− condition, where the lack of visual feedback and the VIS-group's lack of trained ability to process alternative sensory information resulted in a considerable drop in motor performance. This view is further supported by our finding that the DLPFC consistently achieved hub status in POST FB+ and FB− conditions in the VIS group, suggesting a less automatized movement behavior in the VIS group compared to the AUD group, with the DLPFC maintaining 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. In case of the AUD group, the internal model was most likely based on a proprioceptive reference framework (Ronsse et al., 2011). The finding that the AUD group did not show a drop in motor performance upon removal of auditory feedback may be directly related to those subjects having successfully formed a proprioceptive movement model, with proprioceptive information remaining available for them as reference resource after the removal of auditory feedback.
The finding that GTNAmetrics remained stable despite the drop in motor performance in the VIS group suggests that, while during motor training better motor performance and increased functional network connectivity occur concomitantly as observed in our study, changes in neural network functional connectivity as a result of changes in environmental factors are subject to inertia. This indicates that permanent or recurrent influence of environmental factors or changes, such as, for example, the enduring provision or removal of sensory feedback, are necessary to ensure that training-induced changes in the quality of motor output are indeed associated with lasting changes in the functional connectivity of the brain. This interpretation would be consistent with our finding that there were no widespread associations between changes in neural network functional connectivity across the 4-day motor training regime and the change in the quality of the motor output as quantified by the kinematic measures (only isolated GTNA measures showed some significant correlations with changes in kinematics, and this was not consistent across all networks with the correlations explaining a maximum of only 25–30% of the variance in each case). The current findings indicate that any measurable changes in functional connectivity as a result of changes in feedback conditions may 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 et al. (2011) and Coynel et al. (2010), the present study combined a partial-correlation approach to quantify functional connectivity with a data-driven network definition. This approach was chosen as full correlations have been criticized for not filtering out the influence of indirect connections between network nodes and template-based a-priori parcellation of the brain to define the network nodes has the caveat of including brain regions with little or no task-related
activation. A recent study by Smith et al. (2011) provides strong indications that, in addition to using partial correlations to quantify unique functional associations between nodes, a data-driven approach by defining networks based only on areas showing clear task- related activation is preferable to template-based approaches in order to minimize confounds and obtain a better picture on functional connectivity within neural networks. However, it has to be acknowl- edged that the best methodological approach to examine functional connectivity is an area of ongoing debate. It is important to note that defining network nodes based on task-related activation has the potential problem of being based on a group analysis rather than subject-specific analyses, and, at least in our context, on incorpora- tion of general linear models (GLMs) (Friston et al., 1995) rather than models that allow for nonlinear effects. However, the application of the GLMs, and the inference of the group results being representative of brain activations in the individual, is established practice in fMRI analysis and was successfully applied for the primary imaging analysis of the present data (Ronsse et al., 2011). Using the same GLMs to assess functional connectivity between time courses of activation in our subjects presented itself as the natural progression to the primary imaging analysis and ensured that the examination of functional connectivity was establishedwithin, and relates to, the framework of cerebral activations identified by the GLM method. Despite a good argument to focus only on areas with task-related activation for the purposes of task-related changes in functional connectivity, it has to be considered that a network definition based on a limited number of task-related ROIs focuses on partial brain rather than whole brain examination, and that this method may therefore o

motor learning-induced changes in functional brain connectivity as

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