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Shahabeddin Vahdat, PhD
Visiting Post Doctoral Fellow
M.Sc. Biomedical Engineering, Sherif University, Tehran
Ph.D. Kinesiology, McGill University, Montreal
Tel.: +1-514-398-6111
Email: sh_vahdat@yahoo.com
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Post Doctoral Fellow
Functional Neuroimaging Unit
Centre de recherché IUGM
University of Montreal
4545 Queen Mary Rd, Room 7824
Montreal (QC)
Canada, H3W 1W5
Tel: (514) 340-3540 #4121
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Journal
Articles
Sidarta A, Vahdat S, Bernardi NF and Ostry DJ (2016) Somatic and reinforcement-based plasticity in the initial
stages of human motor learning. J Neurosci. 36:11682-11692.
Abstract PDF
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.
Thiel A, Vahdat S (2014) Structural and resting-state brain connectivity after stroke, Stroke (in press).
Abstract Article in PDF format (-- MB)
Doyon J, Albouy G, Vahdat S, King B (2014) Neural correlates of motor skill acquisition and consolidation, Brain Mapping: An Encyclopedic Reference. Toga AW, Poldrack RA (Eds.) Amsterdam: Elsevier (in press).
Abstract Article in PDF format (-- MB)
Maneshi M, Vahdat S, Fahoum F, Grova C, Gotman J (2014) Specific resting-state brain networks in mesial temporal lobe epilepsy, Front. Neurol. 5:127.
Abstract Article in PDF format (1.10 MB)
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.
Vahdat S, Darainy M, Ostry DJ (2014) Structure of plasticity in human sensory and motor
networks due to perceptual learning. J Neurosci 34:2451-63.
Abstract Article in PDF format (2.25 MB)
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.
Darainy M, Vahdat S, Ostry DJ (2013) Perceptual learning in sensorimotor adaptation. J Neurophysiol 110: 2152-2162.
Abstract
Article in PDF format (935 KB)
Motor learning often involves situations in which the
somatosensory targets of movement are 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 paper, we test the idea that
perceptual learning alters somatosensory function and in
so doing produces changes to motor performance and sensorimotor
adaptation. Subjects in these experiments undergo perceptual
training in which a robotic device passively moves the 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 hours. The improvement in the
present studies 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 upon 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 component analysis for between-groups comparison, Neural Comput. 11:3052-90.
Abstract
Article in PDF format (2.45 MB)
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 after motor learning. J Neurosci. 31:16907–16915.
Abstract
Article in PDF format (603 KB)
Motor learning changes the activity of cortical motor and subcortical areas of the brain, but does learning affect sensory systems as well?
We examined inhumansthe 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.
Vahdat S, Maghsoudi A, Hajihasani M, Towhidkhah F, Gharibzadeh S, Jahed M (2006) Adjustable primitive pattern generator: a novel cerebellar model for reaching movements. Neurosci Lett. 406(3):232-4.
Letter PDF format (135 KB)
Salman B*,Vahdat S*, Lambercy O, Dovat L, Burdet E, Milner TE. Changes in muscle activation patterns following robot-assisted training of hand function after stroke. Proceedings of IEEE/RSJ International Conference on IROS, Taiwan, 2010. *Equal contribution with the first author.
Bayati H, Vahdat S, B. Vosoughi V. Investigating the properties of optimal sensory and motor synergies in a nonlinear model of arm dynamics. Proceding of International Joint Conference on Neural Networks, Atlanta GA, 2009.
Bayati H, Vahdat S, B. Vosoughi V. Shared and Specific Synchronous Muscle Synergies Arisen from Optimal Feedback Control Theory. The 4th International IEEE EMBS Conference on Neural Engineering, Antalia,Turkey, 2009.
Mehrtash A, Vahdat S, Soltanian-Zadeh H. Fuzzy edge preserving smoothing filter using robust region growing. Proceedings of IEEE World Congress on Computational Intelligence, Vancouver, BC, 2006. Received Best Session Presentation Award.
Oral / Poster Presentations
Vahdat S, Darainy M, A. Thiel A, Ostry DJ, "Plasticity in human motor system induced by somatosensory training in stroke patients", 44th Annual Meeting of the Society for Neuroscience, Nov. 2014, Washington.
Vahdat S, Fogel S, Benali H, Doyon J, "On-line, off-line, and sleep dependent consolidation of motor sequence learning revealed by fMRI", Oral presentation at Human Brain Mapping annual meeting, Hamburg Germany, 2014.
Vahdat S, Lungu O, Cohen-Adad J, Marchand-Pauvert V, Benali H, Doyon J, "Parsing out brain-spinal cord contributions to motor learning using fMRI", Oral presentation at society for the Neural Control of Movements, Amsterdam, April 2014.
Vahdat S, Lungu O, Doyon J, "Learning-dependent changes in spine-brain interaction revealed by functional magnetic resonance imaging", 43th Annual Meeting of the Society for Neuroscience, San Diego, Nov. 2013.
Vahdat S, Lungu O, Doyon J, "Spine-brain interaction during motor sequence learning: fMRI evidence from a simultaneous scanning paradigm" Platform presentation. 2013 Canadian Spinal Cord Conference, Halifax, April 2013.
Vahdat S, Ostry DJ, Darainy M, "Plasticity in motor system induced by somatosensory training", 42th Annual Meeting of the Society for Neuroscience, New Orleans, LA, 2012.
Ostry D, Vahdat S, Darainy M, "Somatosensory perceptual training changes sensorimotor networks in the resting brain", 22nd Annual Conference of Neural Control of Movement, Venice, Italy, 2012.
Vahdat S, Maneshi M, Grova C, Gotman J, Milner TE. Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparison. Submitted to Human Brain Mapping Conference, China, 2012.
Maneshi M, Vahdat S, Grova C, Gotman J. Validation of a new ICA-based method for between-group comparisons in fMRI. Submitted to Human Brain Mapping Conference, China, 2012.
Vahdat S, Darainy M, Milner TE, Ostry DJ (2011) Motor learning alters sensorimotor resting-state networks in the brain. Presented at the 41th Annual Meeting of the Society for Neuroscience, Washington, DC, 2011.
Vahdat S. Brain Extraction in MRI using a novel integrated method of edge detection and region growing., 2th annual symposium of ECE young researcher, Tehran, 2004.
Vahdat S. Fully automatic extraction of the brain in MRI. First annual symposium of ECE young researcher, 2003, Tehran. Awarded.