Publications
- Babadi S, Vahdat S, Milner T (2021) “Neural substrates of muscle co-contraction during dynamic motor adaptation”, Journal of Neuroscience, In press.
- Vahdat S, Khatibi A, Lungu O, Finsterbusch J, Christian B, Cohen-Adad J, Marchand-Pauvert V, Doyon J (2020) “Resting-state brain and spinal cord networks in humans are functionally integrated”, PLoS Biology, 18(7): e3000789. PDF
Abstract
In the absence of any task, both the brain and spinal cord exhibit spontaneous intrinsic activity organised in a set of functionally relevant neural networks. However, whether such resting-state networks (RSNs) are interconnected across the brain and spinal cord is unclear. Here, we used a unique scanning protocol to acquire functional images of both brain and cervical spinal cord (CSC) simultaneously and examined their spatiotemporal correspondence in humans. We show that the brain and spinal cord activities are strongly correlated during rest periods, and specific spinal cord regions are functionally linked to consistently reported brain sensorimotor RSNs. The functional organisation of these networks follows well-established anatomical principles, including the contralateral correspondence between the spinal hemicords and brain hemispheres as well as sensory versus motor segregation of neural pathways along the brain–spinal cord axis. Thus, our findings reveal a unified functional organisation of sensorimotor networks in the entire central nervous system (CNS) at rest.
- Cheng MY, Vahdat S, Pendharkar AV, Harvey S, Chiang T, Lee HJ, Lee JH, Steinberg G (2020) “Brain-wide circuit dynamics of post-stroke recovery after optogenetic stimulation”, Stroke, 51 (Suppl_1), A177-A177
- Vahdat S, Darainy M, Thiel A, Ostry DJ, (2019) “A single session of robot-controlled proprioceptive training modulates functional connectivity of sensory-motor networks and improves reaching accuracy in chronic stroke”; Neurorehabilitation & Neural Repair, Jan; 33(1):70-81. PDF
Abstract
BACKGROUND:Passive robot-generated arm movements in conjunction with proprioceptive decision making and feedback modulate functional connectivity (FC) in sensory motor networks and improve sensorimotor adaptation in normal individuals. This proof-of-principle study investigates whether these effects can be observed in stroke patients.
METHODS:A total of 10 chronic stroke patients with a range of stable motor and sensory deficits (Fugl-Meyer Arm score [FMA] 0-65, Nottingham Sensory Assessment [NSA] 10-40) underwent resting-state functional magnetic resonance imaging before and after a single session of robot-controlled proprioceptive training with feedback. Changes in FC were identified in each patient using independent component analysis as well as a seed region-based approach. FC changes were related to impairment and changes in task performance were assessed.
RESULTS:A single training session improved average arm reaching accuracy in 6 and proprioception in 8 patients. Two networks showing training-associated FC change were identified. Network C1 was present in all patients and network C2 only in patients with FM scores >7. Relatively larger C1 volume in the ipsilesional hemisphere was associated with less impairment ( r = 0.83 for NSA, r = 0.73 for FMA). This association was driven by specific regions in the contralesional hemisphere and their functional connections (supramarginal gyrus with FM scores r = 0.82, S1 with NSA scores r = 0.70, and cerebellum with NSA score r = -0.82).
CONCLUSION:A single session of robot-controlled proprioceptive training with feedback improved movement accuracy and induced FC changes in sensory motor networks of chronic stroke patients. FC changes are related to functional impairment and comprise bilateral sensory and motor network nodes.
METHODS:A total of 10 chronic stroke patients with a range of stable motor and sensory deficits (Fugl-Meyer Arm score [FMA] 0-65, Nottingham Sensory Assessment [NSA] 10-40) underwent resting-state functional magnetic resonance imaging before and after a single session of robot-controlled proprioceptive training with feedback. Changes in FC were identified in each patient using independent component analysis as well as a seed region-based approach. FC changes were related to impairment and changes in task performance were assessed.
RESULTS:A single training session improved average arm reaching accuracy in 6 and proprioception in 8 patients. Two networks showing training-associated FC change were identified. Network C1 was present in all patients and network C2 only in patients with FM scores >7. Relatively larger C1 volume in the ipsilesional hemisphere was associated with less impairment ( r = 0.83 for NSA, r = 0.73 for FMA). This association was driven by specific regions in the contralesional hemisphere and their functional connections (supramarginal gyrus with FM scores r = 0.82, S1 with NSA scores r = 0.70, and cerebellum with NSA score r = -0.82).
CONCLUSION:A single session of robot-controlled proprioceptive training with feedback improved movement accuracy and induced FC changes in sensory motor networks of chronic stroke patients. FC changes are related to functional impairment and comprise bilateral sensory and motor network nodes.
- Darainy M, Vahdat S, Ostry DJ. (2019) “Neural Basis of Sensorimotor Learning in Speech Motor Adaptation”, Cerebral Cortex, 29(7):2876-2889.
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.
- Bernardi NF, Van Vugt FT, Valle-Mena R, Vahdat S, Ostry DJ. (2018) “Error-related persistence of motor activity in resting state networks”. Journal of Cognitive Neuroscience, Aug 20:1-19.
- Doyon J, Gabitov E, Vahdat S, Lungu O, Boutin A, (2018) “Current issues related to motor sequence learning in humans”, Current Opinion in Behavioral Sciences, Volume 20, April 2018, Pages 89-97 PDF
- Vahdat S, Fogel S, Benali H, Doyon J. (2017) “Network-wide reorganization of procedural memory during non-REM sleep revealed by fMRI”. eLife, 2017; 6: e24987. PDF
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: the restoration and reorganization of newly-learned information during sleep, which underlie human motor memory consolidation.
- Vahdat S, Albouy G, King B, Lungu O, Doyon J, (2017) “Editorial: online and offline modulators of motor learning”. Frontiers in Human Neuroscience, 11:6, doi: 10.3389.
- Sidarta, A., Vahdat S, Bernardi N, Ostry DJ, (2016) “Somatic and reinforcement-based plasticity in the initial stages of human motor learning” Journal of Neuroscience, 16;36(46):11682-11692.
- Maneshi M, Vahdat S, Grova C, Gotman J, (2016) “Validation of shared and specific independent components analysis (SSICA) for between groups comparison in fMRI”. Brain Imaging Methods: Frontiers in Neuroscience, 10:417.
- Vahdat S, Lungu O, Cohen-Adad J, Marchand-Pauvert V, Benali H, Doyon J, (2015) “Simultaneous brain-cervical cord fMRI reveals intrinsic spinal cord plasticity during motor sequence learning”, PLoS Biology,13(6):e1002186. PDF
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.
- Thiel A, Vahdat S, (2015) “Structural and resting-state brain connectivity of motor networks after stroke”, Stroke, 46(1):296-301. PDF
ABSTRACT
Focal ischemic brain lesions primarily affect circumscribed brain regions and fiber tracts resulting in acute neurological deficits. Secondary damage because of apoptosis, inflammation, diaschisis, and neurodegeneration, however, can also affect remote brain areas and may result in a more widespread perturbation of entire functional networks even in the nonischemic hemisphere. Such network-wide effects may play a role in the development of poststroke cognitive impairment and may impose limits on functional recovery.
- Maneshi M, Vahdat S, Fahoum F, Grova C, Gotman J, (2014) “Specific resting-state brain networks in mesial temporal lobe epilepsy”, Frontiers in Neurology, 5:127.
- Vahdat S, Darainy M, Ostry DJ, (2014) “Structure of Plasticity in Human Sensory and Motor Networks Due to Perceptual Learning”, Journal of Neuroscience, 34(7):2451-2463. PDF
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.
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.
- Vahdat S, Maneshi M, Grova C, Gotman J, Milner TE, (2012) “Shared and Specific Independent Components Analysis for Between-Groups Comparison”, Neural Computation, 24(11):3052-90. PDF
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.
- Vahdat S, Darainy M, Milner TE, Ostry DJ, (2011) “Functionally specific changes in resting-state sensorimotor networks following motor learning”, Journal of Neuroscience, 31(47):16907-15. PDF
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.
- Salman B*, Vahdat S*, Lambercy O, Dovat L, Burdet E, Milner TE, (2010) “Changes in Muscle Activation Patterns Following Robot-assisted Training of Hand Function after Stroke”, Intelligent Robots and Systems, Proceedings of IEEE/RSJ, P.5145-5150, DOI:10.1109/IROS.2010.5650175. * Equal contribution. PDF
ABSTRACT
Robot-assisted rehabilitation has only recently begun to be applied to improvement of hand function after stroke. In a preliminary study, involving 4 post-stroke subjects, more than 2 years following the stroke, we have been able to show that 8 weeks of robot-assisted training leads to changes in patterns of arm and finger muscle activation. The patterns were quantified in terms of synchronous muscle synergies which allowed for comparison with muscle activation patterns of healthy age-matched subjects. We found that the muscle synergies of the post-stroke subjects became more similar to those of the healthy subject group following training.
- Bayati H, Vahdat S, VosoughiVahdat B, (2009) ”Shared and Specific Synchronous Muscle Synergies Arisen from Optimal Feedback Control Theory”, Neural Engineering, Proceeding of IEEE EMBS, P.155-158. DOI:10.1109/NER.2009.5109258.
- Bayati H, Vahdat S, VosoughiVahdat B, (2009) “Investigating the properties of optimal sensory and motor synergies in a nonlinear model of arm dynamics” Neural Networks, Proceeding of the IJCNN, P.272-279.
- Vahdat S, Maghsoudi A, Hajihasani M, Towhidkhah F, Gharibzadeh S, Jahed M, (2006) “Adjustable primitive pattern generator: a novel cerebellar model for reaching movements”, Neuroscience Letters; 406(3):232-4.
- Mehrtash A, Vahdat S, Soltanian-Zadeh H, (2006) “Fuzzy Edge Preserving Smoothing Filter Using Robust Region Growing” Fuzzy Systems, Proceedings of IEEE on Computational Intelligence, P.1748 – 1755, DOI: 10.1109/FUZZY.2006.1681942.