Amir Shmuel, PhD
Amir Shmuel is the director of the Brain Imaging Signals Lab and a core faculty member of The Neuro’s McConnell Brain Imaging Centre. He is a Professor of McGill University's Departments of Neurology, Neurosurgery, Physiology, and Biomedical Engineering. Shmuel served as the chair of the organizing committee and chair of the sixth conference of the International Society for Brain Connectivity. He is the PI of an $18.7M grant funded by the Canada Foundation for Innovation to install the first large-bore 7 Tesla MRI scanner in Quebec. His research program focuses on understanding the mechanisms underlying functional brain imaging signals and resting-state functional connectivity, and on evaluating the extent to which they reflect the underlying neuronal activity, cortical lamina-resolved neurophysiology and neuroimaging, as well as computational modelling of these phenomena. In addition, he has been applying AI-based algorithms to develop analytical tools and differentiate MRI datasets obtained from healthy control subjects and patients with neurological diseases.
His lab employs an integrative approach that combines imaging and electrical recording techniques. These include structural and functional Magnetic Resonance Imaging (fMRI), optical imaging, multi-channel neurophysiological recordings, optogenetics, tissue processing, computational modelling, machine learning, and deep learning techniques. Together, these techniques encompass multiple levels of spatial and temporal resolution. Shmuel's research is currently funded by the Canadian Institute of Health Research, the Natural Sciences and Engineering Research Council of Canada, and the US Department of Defense.
Li Y, Bortel A, Ayad F, Shmuel A. (2025) Using Generative Adversarial Networks to eliminate RF and gradient interference in neurophysiology signals recorded simultaneously with functional MRI. Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Copenhagen, Denmark. IEEE Peer Reviewed Engineering Full Paper. doi: 10.1109/EMBC58623.2025.11254459
Kumar GMK, Mendola JD, Shmuel A. (2025). NestedMorph: Enhancing Deformable Image Registration with Nested Attention Mechanisms. IEEE International Conference on Applications of Computer Vision, pages 4683-4692, Tucson, AZ, USA. IEEE Peer Reviewed Engineering Full Paper. doi: 10.1109/WACV61041.2025.00459
Kumar GMK, Chadha A, Mendola J, Shmuel A. (2025). MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation. IEEE/CVF International Conference on Computer Vision, pages 1114-1124, Honolulu, HI, USA. IEEE Peer Reviewed Engineering Full Paper.
Pizarro R, Assemlal HE, Boopathy S, Jubault T, Antel S, Arnold D, Shmuel A (2023) Deep learning, data ramping, and uncertainty estimation for detecting artifacts in large, imbalanced databases of MRI images. Medical Image Analysis 90:102942.
Shmuel A, Park H, Rathi Y, Yang A (2022) Deep learning techniques and their applications to the healthy and disordered brain - during development through adulthood and beyond. Frontiers in Neuroscience 16:1118233. doi: 10.3389/fnins.2022.1118233
Shmuel A, Maier A (2022) Locally Measured Neuronal Correlates of functional MRI Signals. In: EEG-fMRI: Physiology, Technique and Applications, 2nd edition (editors, Mulert C, Lemieux L), Springer, Heidelberg, Germany
Chaimow D, Shmuel A (Neuroimage, under revision) A more accurate account of the effect of k-space sampling and signal decay on the effective spatial resolution in functional MRI. Available as pre-print in BioRxiv 097154. doi: https://doi.org/10.1101/097154
Mocanu MV, Shmuel A (2021) Optical Imaging-based guidance of viral microinjections and insertion of a laminar electrophysiology probe into a predetermined barrel in mouse area S1BF. Frontiers in Neural Circuits 15: 541676. doi: 10.3389/fncir.2021.541676
Bortel A, Pilgram R, Yao Z-S, Shmuel A (2020) Dexmedetomitine – commonly used in functional imaging studies – increases susceptibility to seizures in rats but not in wild type mice. Frontiers in Neuroscience 14:832. doi: 10.3389/fnins.2020.00832
Bortel A, Yao Z-S, Shmuel A (2019) A rat model of somatosensory-evoked reflex seizures induced by peripheral stimulation. Epilepsy Research, 157:106209. doi: https://doi.org/10.1106/j.eplepsres.2019.106209
Lahmiri S, Shmuel A (2019) Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer's disease. Biomedical Signal Processing and Control, 52:414-419, https://doi.org/10.1016/j.bspc.2018.08.009
Pizarro R, Assemlal HE, De Nigris D, Elliot C, Antel S, Arnold D, Shmuel A (2019) Using deep learning algorithms to automatically identify the brain MRI contrast: implications for managing large databases. NeuroInformatics, 17(1):115-130. https://doi.org/10.1007/s12021-018-9387-8
Lahmiri S, Shmuel A (2019) Detection of Parkinson’s Disease Based on Voice Pattern Ranking and Optimized Support Vector Machine. Biomedical Signal Processing and Control, 49 (2019) 427–433.
Lahmiri S, Shmuel A (2019) Accurate Classification of Seizure and Seizure-Free Intervals of Intra-Cranial EEG Signals from Epileptic Patients. IEEE Transactions on Instrumentation & Measurement. 68:791-796. https://Doi.org/10.1109/TIM.2018.2855518
Boopathy Jegathambal S, Mok K, Rudko DA, Shmuel A (2018) MRI based brain-specific 3D-printed model aligned to stereotactic space for registering histology to MRI. Proceedings of the 40th conference of IEEE International Engineering in Medicine and Biology Society, Honolulu, Hawaii: 802-805. IEEE conference peer-reviewed full paper. doi: 10.1109/EMBC.2018.8512346.
Milham MP et al. (2018) An open resource for non-human primate imaging. Neuron. Oct 10;100(1):61-74.e2. doi: 10.1016/j.neuron.2018.08.039. Epub 2018 Sep 27.
Lahmiri S, Dawson DA, Shmuel A (2018) Performance of machine learning methods in diagnosing Parkinson's disease based on dysphonia measures. Biomedical Engineering Letters (Springer), 8:29-39. https://link.springer.com/article/10.1007/s13534-017-0051-2
Mendola JD, Lam J, Rosenstein M, Lewis LB, Shmuel A (2018) Partial correlation analysis reveals abnormal retinotopically organized functional connectivity of visual areas in amblyopia. NeuroImage Clinical, 18:192-201. https://doi.org/10.1016/j.nicl.2018.01.022.
Chaimow D, Yacoub E, Uğurbil K, Shmuel A (2018) Spatial specificity of the functional MRI blood oxygenation response relative to neuronal activity. NeuroImage, 164:32-47. https://doi.org/10.1016/j.neuroimage.2017.08.077
Chaimow D, Uğurbil K, Shmuel A (2018) Optimization of functional MRI for detection, decoding and high-resolution imaging of the response patterns of cortical columns. NeuroImage, 164:67-99. https://doi.org/10.1016/j.neuroimage.2017.04.011
Lahmiri S, Shmuel A (2017) Variational mode decomposition based approach for accurate classification of color fundus images with hemorrhages. Optics and Laser Technology, 96:243-248.
Kropf P, Shmuel A (2016) 1-D current-source density (CSD) estimation in inverse theory: a unified framework for higher-order spectral regularization of quadrature and expansion type CSD methods. Neural Computation, 28:1305-1355.
Dawson DA, Lewis LB, Carbonell F, Mendola JD, Shmuel A (2016) Partial-correlation based retinotopically organized resting-state functional connectivity within and between areas of the visual cortex reflects more than cortical distance. Brain Connectivity, 6(1):57-75. doi: 10.1089/brain.2014.0331.
Sotero RC, Bortel A, Na’aman S, Mocanu MV, Kropf P, Villeneuve M, Shmuel A (2015) Laminar distribution of phase-amplitude coupling of spontaneous current sources and sinks. Front. Neurosci. 9:454. doi: 10.3389/fnins.2015.00454.
Buxton RB, Griffeth VE, Simon AB, Moradi F, Shmuel A (2014) Variability of the coupling of blood flow and oxygen metabolism responses in the brain: a problem for interpreting BOLD studies but potentially a new window on the underlying neural activity. Front Neurosci., 8:139. Corrigendum doi: 10.3389/fnins.2014.00241.
Carbonell F, Bellec P, Shmuel A (2014) Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting-state. Neuroimage, 86:343-53.
Dawson DA, Cha K, Lewis LB, Mendola JD, Shmuel A (2013) Evaluation and calibration of functional network modeling methods based on known anatomical connections. Neuroimage, 67: 331–343.
Sotero RC, Shmuel A (2012) Energy-based stochastic control of neural mass models suggests time-varying effective connectivity in the resting-state. Journal of Computational Neuroscience, 32 (3): 563-576. doi: 10.1007/s10827-011-0370-8
Carbonell F, Bellec P, Shmuel A (2012) Global and system-specific resting-state BOLD fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks. Brain Connectivity, 1 (6): 496-510.
Chaimow D, Yacoub E, Ugurbil K, Shmuel A (2011) Modeling and analysis of mechanisms underlying fMRI-based decoding of information conveyed in cortical columns. Neuroimage, 56 (2): 627-642.
Sotero RC, Bortel A, Martínez-Cancino R, Neupane S, O’Connor P, Carbonell F, Shmuel A (2010) Anatomically-constrained effective connectivity among layers in a cortical column modeled and estimated from local field potentials. Journal of Integrative Neuroscience, 9: 355–379.
Shmuel A, Chaimow D, Raddatz G, Ugurbil K, Yacoub E (2010) Mechanisms underlying decoding at 7 T: Ocular dominance columns, broad structures, and macroscopic blood vessels in V1 convey information on the stimulated eye. Neuroimage, 49:1957–1964.
Shmuel A, Leopold DA (2008) Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: implications for functional connectivity at rest. Human Brain Mapping, 29:751-761.
Keliris G*, Shmuel A* (* co-first authors), Ku SP, Pfeuffer J, Oeltermann A, Steudel T, Ku SP, Logothetis NK (2007), Robust controlled functional MRI in alert monkeys at high magnetic field: effects of jaw and body movements. NeuroImage, 36(3):550-570.
Yacoub E*, Shmuel A* (* co-first authors), Logothetis NK, Ugurbil K (2007), Robust detection of ocular dominance columns in humans using Hahn spin-echo BOLD functional MRI at high field. NeuroImage, 37(4):1161–1177.
Shmuel A, Yacoub E, Chaimow D, Logothetis NK, Ugurbil K (2007), Spatio-temporal point-spread function of fMRI signal in human gray matter at 7 Tesla. NeuroImage, 35(2):539-552.
Shmuel A, Augath M, Oeltermann A, Logothetis NK (2006) Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1. Nature Neuroscience, 9(4):569-577.
Shmuel A, Korman M, Melnik A, Harel M, Ullman S, Malach R, Grinvald A (2005) Retinotopic axis specificity and selective clustering of feedback projections from V2 to V1 in the owl monkey. The Journal of Neuroscience, 25(8):2117-31.
Swindale NV, Grinvald A, Shmuel A (2003) The spatial pattern of response magnitude and selectivity for orientation and direction in cat visual cortex. Cerebral Cortex, 13(3):225-238.
Shmuel A, Yacoub E, Pfeuffer J, Van De Moortele PF, Adriany G, Hu X, Ugurbil K (2002) Sustained negative BOLD, blood flow and oxygen consumption response and its coupling to the positive response in the human brain. Neuron, 36(6):1195-1210.
Shmuel A, Grinvald A (2000) Coexistence of linear zones and pinwheels within orientation maps in cat visual cortex. Proceedings of the National Academy of Sciences USA 97(10):5568- 5573.
Shmuel A, Grinvald A (1996) Functional organization for direction of motion and its relationship to orientation maps in cat area 18. The Journal of Neuroscience, 16(21):6945-6964, and cover illustration.
