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  • Current issues related to motor sequence learning in humans Current Opinion in Behavioral Sciences Doyon, J., Gabitov, E., Vahdat, S., Lungu, O., Boutin, A. 2018; 20: 89-97
  • A single session of robot-controlled proprioceptive training modulates functional connectivity of sensory-motor networks and improves reaching accuracy in chronic stroke Neurorehabilitation and Neural Repair Vahdat, S., Darainy, M., Thiel, A., Ostry, D. 2018; In press
  • Neural Basis of Sensorimotor Plasticity in Speech Motor Adaptation. Cerebral cortex (New York, N.Y. : 1991) Darainy, M., Vahdat, S., Ostry, D. J. 2018

    Abstract

    When we speak, we get correlated sensory feedback from speech sounds and from the muscles and soft tissues of the vocal tract. Here we dissociate the contributions of auditory and somatosensory feedback to identify brain networks that underlie the somatic contribution to speech motor learning. The technique uses a robotic device that selectively alters somatosensory inputs in combination with resting-state fMRI scans that reveal learning-related changes in functional connectivity. A partial correlation analysis is used to identify connectivity changes that are not explained by the time course of activity in any other learning-related areas. This analysis revealed changes related to behavioral improvements in movement and separately, to changes in auditory perception: Speech motor adaptation itself was associated with connectivity changes that were primarily in non-motor areas of brain, specifically, to a strengthening of connectivity between auditory and somatosensory cortex and between presupplementary motor area and the inferior parietal lobule. In contrast, connectively changes associated with alterations to auditory perception were restricted to speech motor areas, specifically, primary motor cortex and inferior frontal gyrus. Overall, our findings show that during adaptation, somatosensory inputs result in a broad range of changes in connectivity in areas associated with speech motor control and learning.

    View details for DOI 10.1093/cercor/bhy153

    View details for PubMedID 29982495

  • Editorial: Online and Offline Modulators of Motor Learning FRONTIERS IN HUMAN NEUROSCIENCE Vahdat, S., Albouy, G., King, B., Lungu, O., Doyon, J. 2017; 11

    View details for DOI 10.3389/fnhum.2017.00069

    View details for Web of Science ID 000394544500001

    View details for PubMedID 28270758

    View details for PubMedCentralID PMC5318390

  • Network-wide reorganization of procedural memory during NREM sleep revealed by fMRI. eLife Vahdat, S., Fogel, S., Benali, H., Doyon, J. 2017; 6

    Abstract

    Sleep is necessary for the optimal consolidation of newly acquired procedural memories. However, the mechanisms by which motor memory traces develop during sleep remain controversial in humans, as this process has been mainly investigated indirectly by comparing pre- and post-sleep conditions. Here, we used functional magnetic resonance imaging and electroencephalography during sleep following motor sequence learning to investigate how newly-formed memory traces evolve dynamically over time. We provide direct evidence for transient reactivation followed by downscaling of functional connectivity in a cortically-dominant pattern formed during learning, as well as gradual reorganization of this representation toward a subcortically-dominant consolidated trace during non-rapid eye movement (NREM) sleep. Importantly, the putamen functional connectivity within the consolidated network during NREM sleep was related to overnight behavioral gains. Our results demonstrate that NREM sleep is necessary for two complementary processes: therestorationandreorganizationof newly-learned information during sleep, which underlie human motor memory consolidation.

    View details for DOI 10.7554/eLife.24987

    View details for PubMedID 28892464

    View details for PubMedCentralID PMC5593513

  • Somatic and Reinforcement-Based Plasticity in the Initial Stages of Human Motor Learning JOURNAL OF NEUROSCIENCE Sidarta, A., Vahdat, S., Bernardi, N. F., Ostry, D. J. 2016; 36 (46): 11682-11692

    Abstract

    As one learns to dance or play tennis, the desired somatosensory state is typically unknown. Trial and error is important as motor behavior is shaped by successful and unsuccessful movements. As an experimental model, we designed a task in which human participants make reaching movements to a hidden target and receive positive reinforcement when successful. We identified somatic and reinforcement-based sources of plasticity on the basis of changes in functional connectivity using resting-state fMRI before and after learning. The neuroimaging data revealed reinforcement-related changes in both motor and somatosensory brain areas in which a strengthening of connectivity was related to the amount of positive reinforcement during learning. Areas of prefrontal cortex were similarly altered in relation to reinforcement, with connectivity between sensorimotor areas of putamen and the reward-related ventromedial prefrontal cortex strengthened in relation to the amount of successful feedback received. In other analyses, we assessed connectivity related to changes in movement direction between trials, a type of variability that presumably reflects exploratory strategies during learning. We found that connectivity in a network linking motor and somatosensory cortices increased with trial-to-trial changes in direction. Connectivity varied as well with the change in movement direction following incorrect movements. Here the changes were observed in a somatic memory and decision making network involving ventrolateral prefrontal cortex and second somatosensory cortex. Our results point to the idea that the initial stages of motor learning are not wholly motor but rather involve plasticity in somatic and prefrontal networks related both to reward and exploration.In the initial stages of motor learning, the placement of the limbs is learned primarily through trial and error. In an experimental analog, participants make reaching movements to a hidden target and receive positive feedback when successful. We identified sources of plasticity based on changes in functional connectivity using resting-state fMRI. The main finding is that there is a strengthening of connectivity between reward-related prefrontal areas and sensorimotor areas in the basal ganglia and frontal cortex. There is also a strengthening of connectivity related to movement exploration in sensorimotor circuits involved in somatic memory and decision making. The results indicate that initial stages of motor learning depend on plasticity in somatic and prefrontal networks related to reward and exploration.

    View details for DOI 10.1523/JNEUROSCI.1767-16.2016

    View details for Web of Science ID 000391056600011

    View details for PubMedID 27852776

    View details for PubMedCentralID PMC5125226

  • Simultaneous Brain-Cervical Cord fMRI Reveals Intrinsic Spinal Cord Plasticity during Motor Sequence Learning PLOS BIOLOGY Vahdat, S., Lungu, O., Cohen-Adad, J., Marchand-Pauvert, V., Benali, H., Doyon, J. 2015; 13 (6)

    Abstract

    The spinal cord participates in the execution of skilled movements by translating high-level cerebral motor representations into musculotopic commands. Yet, the extent to which motor skill acquisition relies on intrinsic spinal cord processes remains unknown. To date, attempts to address this question were limited by difficulties in separating spinal local effects from supraspinal influences through traditional electrophysiological and neuroimaging methods. Here, for the first time, we provide evidence for local learning-induced plasticity in intact human spinal cord through simultaneous functional magnetic resonance imaging of the brain and spinal cord during motor sequence learning. Specifically, we show learning-related modulation of activity in the C6-C8 spinal region, which is independent from that of related supraspinal sensorimotor structures. Moreover, a brain-spinal cord functional connectivity analysis demonstrates that the initial linear relationship between the spinal cord and sensorimotor cortex gradually fades away over the course of motor sequence learning, while the connectivity between spinal activity and cerebellum gains strength. These data suggest that the spinal cord not only constitutes an active functional component of the human motor learning network but also contributes distinctively from the brain to the learning process. The present findings open new avenues for rehabilitation of patients with spinal cord injuries, as they demonstrate that this part of the central nervous system is much more plastic than assumed before. Yet, the neurophysiological mechanisms underlying this intrinsic functional plasticity in the spinal cord warrant further investigations.

    View details for DOI 10.1371/journal.pbio.1002186

    View details for Web of Science ID 000357339600021

    View details for PubMedID 26125597

    View details for PubMedCentralID PMC4488354

  • Structural and Resting-State Brain Connectivity of Motor Networks After Stroke STROKE Thiel, A., Vahdat, S. 2015; 46 (1): 296-301

    View details for DOI 10.1161/STROKEAHA.114.006307

    View details for Web of Science ID 000346735800065

    View details for PubMedID 25477218

  • Structure of Plasticity in Human Sensory and Motor Networks Due to Perceptual Learning JOURNAL OF NEUROSCIENCE Vahdat, S., Darainy, M., Ostry, D. J. 2014; 34 (7): 2451-2463

    Abstract

    As we begin to acquire a new motor skill, we face the dual challenge of determining and refining the somatosensory goals of our movements and establishing the best motor commands to achieve our ends. The two typically proceed in parallel, and accordingly it is unclear how much of skill acquisition is a reflection of changes in sensory systems and how much reflects changes in the brain's motor areas. Here we have intentionally separated perceptual and motor learning in time so that we can assess functional changes to human sensory and motor networks as a result of perceptual learning. Our subjects underwent fMRI scans of the resting brain before and after a somatosensory discrimination task. We identified changes in functional connectivity that were due to the effects of perceptual learning on movement. For this purpose, we used a neural model of the transmission of sensory signals from perceptual decision making through to motor action. We used this model in combination with a partial correlation technique to parcel out those changes in connectivity observed in motor systems that could be attributed to activity in sensory brain regions. We found that, after removing effects that are linearly correlated with somatosensory activity, perceptual learning results in changes to frontal motor areas that are related to the effects of this training on motor behavior and learning. This suggests that perceptual learning produces changes to frontal motor areas of the brain and may thus contribute directly to motor learning.

    View details for DOI 10.1523/JNEUROSCI.4291-13.2014

    View details for Web of Science ID 000331614700008

    View details for PubMedID 24523536

    View details for PubMedCentralID PMC3921420

  • Perceptual learning in sensorimotor adaptation JOURNAL OF NEUROPHYSIOLOGY Darainy, M., Vahdat, S., Ostry, D. J. 2013; 110 (9): 2152-2162

    Abstract

    Motor learning often involves situations in which the somatosensory targets of movement are, at least initially, poorly defined, as for example, in learning to speak or learning the feel of a proper tennis serve. Under these conditions, motor skill acquisition presumably requires perceptual as well as motor learning. That is, it engages both the progressive shaping of sensory targets and associated changes in motor performance. In the present study, we test the idea that perceptual learning alters somatosensory function and in so doing produces changes to human motor performance and sensorimotor adaptation. Subjects in these experiments undergo perceptual training in which a robotic device passively moves the subject's arm on one of a set of fan-shaped trajectories. Subjects are required to indicate whether the robot moved the limb to the right or the left and feedback is provided. Over the course of training both the perceptual boundary and acuity are altered. The perceptual learning is observed to improve both the rate and extent of learning in a subsequent sensorimotor adaptation task and the benefits persist for at least 24 h. The improvement in the present studies varies systematically with changes in perceptual acuity and is obtained regardless of whether the perceptual boundary shift serves to systematically increase or decrease error on subsequent movements. The beneficial effects of perceptual training are found to be substantially dependent on reinforced decision-making in the sensory domain. Passive-movement training on its own is less able to alter subsequent learning in the motor system. Overall, this study suggests perceptual learning plays an integral role in motor learning.

    View details for DOI 10.1152/jn.00439.2013

    View details for Web of Science ID 000326587400014

    View details for PubMedID 23966671

    View details for PubMedCentralID PMC4073967

  • Shared and Specific Independent Components Analysis for Between-Group Comparison NEURAL COMPUTATION Vahdat, S., Maneshi, M., Grova, C., Gotman, J., Milner, T. E. 2012; 24 (11): 3052-3090

    Abstract

    Independent component analysis (ICA) has been extensively used in individual and within-group data sets in real-world applications, but how can it be employed in a between-groups or conditions design? Here, we propose a new method to embed group membership information into the FastICA algorithm so as to extract components that are either shared between groups or specific to one or a subset of groups. The proposed algorithm is designed to automatically extract the pattern of differences between different experimental groups or conditions. A new constraint is added to the FastICA algorithm to simultaneously deal with the data of multiple groups in a single ICA run. This cost function restricts the specific components of one group to be orthogonal to the subspace spanned by the data of the other groups. As a result of performing a single ICA on the aggregate data of several experimental groups, the entire variability of data sets is used to extract the shared components. The results of simulations show that the proposed algorithm performs better than the regular method in both the reconstruction of the source signals and classification of shared and specific components. Also, the sensitivity to detect variations in the amplitude of shared components across groups is enhanced. A rigorous proof of convergence is provided for the proposed iterative algorithm. Thus, this algorithm is guaranteed to extract and classify shared and specific independent components across different experimental groups and conditions in a systematic way.

    View details for Web of Science ID 000309588700010

    View details for PubMedID 22920851

  • Functionally Specific Changes in Resting-State Sensorimotor Networks after Motor Learning JOURNAL OF NEUROSCIENCE Vahdat, S., Darainy, M., Milner, T. E., Ostry, D. J. 2011; 31 (47): 16907-16915

    Abstract

    Motor learning changes the activity of cortical motor and subcortical areas of the brain, but does learning affect sensory systems as well? We examined in humans the effects of motor learning using fMRI measures of functional connectivity under resting conditions and found persistent changes in networks involving both motor and somatosensory areas of the brain. We developed a technique that allows us to distinguish changes in functional connectivity that can be attributed to motor learning from those that are related to perceptual changes that occur in conjunction with learning. Using this technique, we identified a new network in motor learning involving second somatosensory cortex, ventral premotor cortex, and supplementary motor cortex whose activation is specifically related to perceptual changes that occur in conjunction with motor learning. We also found changes in a network comprising cerebellar cortex, primary motor cortex, and dorsal premotor cortex that were linked to the motor aspects of learning. In each network, we observed highly reliable linear relationships between neuroplastic changes and behavioral measures of either motor learning or perceptual function. Motor learning thus results in functionally specific changes to distinct resting-state networks in the brain.

    View details for DOI 10.1523/JNEUROSCI.2737-11.2011

    View details for Web of Science ID 000297586900003

    View details for PubMedID 22114261

    View details for PubMedCentralID PMC3260885

  • Error-related Persistence of Motor Activity in Resting-state Networks JOURNAL OF COGNITIVE NEUROSCIENCE Bernardi, N. F., Van Vugt, F. T., Valle-Mena, R., Vahdat, S., Ostry, D. J. 2018; 30 (12): 1883–1901

    Abstract

    The relationship between neural activation during movement training and the plastic changes that survive beyond movement execution is not well understood. Here we ask whether the changes in resting-state functional connectivity observed following motor learning overlap with the brain networks that track movement error during training. Human participants learned to trace an arched trajectory using a computer mouse in an MRI scanner. Motor performance was quantified on each trial as the maximum distance from the prescribed arc. During learning, two brain networks were observed, one showing increased activations for larger movement error, comprising the cerebellum, parietal, visual, somatosensory, and cortical motor areas, and the other being more activated for movements with lower error, comprising the ventral putamen and the OFC. After learning, changes in brain connectivity at rest were found predominantly in areas that had shown increased activation for larger error during task, specifically the cerebellum and its connections with motor, visual, and somatosensory cortex. The findings indicate that, although both errors and accurate movements are important during the active stage of motor learning, the changes in brain activity observed at rest primarily reflect networks that process errors. This suggests that error-related networks are represented in the initial stages of motor memory formation.

    View details for PubMedID 30125221

  • Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. NeuroImage Gros, C., De Leener, B., Badji, A., Maranzano, J., Eden, D., Dupont, S. M., Talbott, J., Zhuoquiong, R., Liu, Y., Granberg, T., Ouellette, R., Tachibana, Y., Hori, M., Kamiya, K., Chougar, L., Stawiarz, L., Hillert, J., Bannier, E., Kerbrat, A., Edan, G., Labauge, P., Callot, V., Pelletier, J., Audoin, B., Rasoanandrianina, H., Brisset, J., Valsasina, P., Rocca, M. A., Filippi, M., Bakshi, R., Tauhid, S., Prados, F., Yiannakas, M., Kearney, H., Ciccarelli, O., Smith, S., Treaba, C. A., Mainero, C., Lefeuvre, J., Reich, D. S., Nair, G., Auclair, V., McLaren, D. G., Martin, A. R., Fehlings, M. G., Vahdat, S., Khatibi, A., Doyon, J., Shepherd, T., Charlson, E., Narayanan, S., Cohen-Adad, J. 2018; 184: 901–15

    Abstract

    The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

    View details for PubMedID 30300751

  • Shared and specific synchronous muscle synergies arisen from optimal feedback control theory Neural Engineering Bayati, H., Vahdat, S., Vosoughi, B. : 155–58

    View details for DOI 10.1109/NER.2009.5109258

  • Changes in muscle activation patterns following robot-assisted training of hand function after stroke Intelligent Robots and Systems Vahdat, S., Salman, B., Lambercy, O., Dovat, L., Burdet, E., Milner, T. : 5145–50
  • Investigating the properties of optimal sensory and motor synergies in a nonlinear model of arm dynamics Neural Networks Bayati, H., Vahdat, S., Vosoughi, B. : 272–79
  • Online and Offline Modulators of Motor Learning Vahdat, S., Lungu, O., King, B., Albouy, G., Doyon, J. Frontiers Media SA. 2017
  • Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI FRONTIERS IN NEUROSCIENCE Maneshi, M., Vahdat, S., Gotman, J., Grova, C. 2016; 10

    Abstract

    Independent component analysis (ICA) has been widely used to study functional magnetic resonance imaging (fMRI) connectivity. However, the application of ICA in multi-group designs is not straightforward. We have recently developed a new method named "shared and specific independent component analysis" (SSICA) to perform between-group comparisons in the ICA framework. SSICA is sensitive to extract those components which represent a significant difference in functional connectivity between groups or conditions, i.e., components that could be considered "specific" for a group or condition. Here, we investigated the performance of SSICA on realistic simulations, and task fMRI data and compared the results with one of the state-of-the-art group ICA approaches to infer between-group differences. We examined SSICA robustness with respect to the number of allowable extracted specific components and between-group orthogonality assumptions. Furthermore, we proposed a modified formulation of the back-reconstruction method to generate group-levelt-statistics maps based on SSICA results. We also evaluated the consistency and specificity of the extracted specific components by SSICA. The results on realistic simulated and real fMRI data showed that SSICA outperforms the regular group ICA approach in terms of reconstruction and classification performance. We demonstrated that SSICA is a powerful data-driven approach to detect patterns of differences in functional connectivity across groups/conditions, particularly in model-free designs such as resting-state fMRI. Our findings in task fMRI show that SSICA confirms results of the general linear model (GLM) analysis and when combined with clustering analysis, it complements GLM findings by providing additional information regarding the reliability and specificity of networks.

    View details for DOI 10.3389/fnins.2016.00417

    View details for Web of Science ID 000384049900001

    View details for PubMedID 27729843

    View details for PubMedCentralID PMC5037228

  • Neural correlates of motor skill acquisition and consolidation Brain Mapping: An Encyclopedic Reference Doyon, J., Albouy, G., Vahdat, S., King, B. Academic Press: Elsevier. 2015; 1: 493–500
  • Specific resting-state brain networks in mesial temporal lobe epilepsy. Frontiers in neurology Maneshi, M., Vahdat, S., Fahoum, F., Grova, C., Gotman, J. 2014; 5: 127-?

    Abstract

    We studied with functional magnetic resonance imaging (fMRI) differences in resting-state networks between patients with mesial temporal lobe epilepsy (MTLE) and healthy subjects. To avoid any a priori hypothesis, we use a data-driven analysis assessing differences between groups independently of structures involved. Shared and specific independent component analysis (SSICA) is an exploratory method based on independent component analysis, which performs between-group network comparison. It extracts and classifies components (networks) in those common between groups and those specific to one group. Resting fMRI data were collected from 10 healthy subjects and 10 MTLE patients. SSICA was applied multiple times with altered initializations and different numbers of specific components. This resulted in many components specific to patients and to controls. Spatial clustering identified the reliable resting-state networks among all specific components in each group. For each reliable specific network, power spectrum analysis was performed on reconstructed time-series to estimate connectivity in each group and differences between groups. Two reliable networks, corresponding to statistically significant clusters robustly detected with clustering were labeled as specific to MTLE and one as specific to the control group. The most reliable MTLE network included hippocampus and amygdala bilaterally. The other MTLE network included the postcentral gyri and temporal poles. The control-specific network included bilateral precuneus, anterior cingulate, thalamus, and parahippocampal gyrus. Results indicated that the two MTLE networks show increased connectivity in patients, whereas the control-specific network shows decreased connectivity in patients. Our findings complement results from seed-based connectivity analysis (1). The pattern of changes in connectivity between mesial temporal lobe structures and other areas may help us understand the cognitive impairments often reported in patients with MTLE.

    View details for DOI 10.3389/fneur.2014.00127

    View details for PubMedID 25071712

    View details for PubMedCentralID PMC4095676

  • Adjustable primitive pattern generator: A novel cerebellar model for reaching movements NEUROSCIENCE LETTERS Vahdat, S., Maghsoudi, A., Hasani, M. H., Towhidkhah, F., Gharibzadeh, S., Jahed, M. 2006; 406 (3): 232-234

    Abstract

    Cerebellum has been assumed as an array of adjustable pattern generators (APGs). In recent years, electrophysiological researches have suggested the existence of modular structures in spinal cord called motor primitives. In our proposed model, each "adjustable primitive pattern generator" (APPG) module in the cerebellum is consisted of a large number of parallel APGs, the output of each module being the weighted sum of the outputs of these APGs. Each spinal field is tuned by a coefficient, representing a descending supraspinal command, which is modulated by ith APPG correspondingly. According to this model, motor control can be interpreted in terms of the modification of these coefficients. Vector summation of force fields implies that the complex nonlinearities in neuronal behavior are eliminated, causing our model to be simple and linear. The force field vectors, derived from motor primitives, depend on the state of movement and its derivative and the time that causes different repertoire of movement. This is physiologically plausible. Our model agrees with virtual trajectory hypothesis, stating that dynamics are not computed explicitly in central nervous system, but the desired trajectory, is fed into the spinal cord. We think that the dysmetria and the ataxia seen in some cerebellar diseases may be the result of local disruption of some APPGs. Accordingly, determining the exact location of related motor primitives in human spinal cord and stimulating them by functional neurostimulation may provide a good management for these clinical signs. Surely, experimental researches and clinical trials are needed to validate our hypothesis.

    View details for DOI 10.1016/j.neulet.2006.07.038

    View details for Web of Science ID 000240998700016

    View details for PubMedID 16930835