The scientist’s investigation covers issues in Neuroscience, Artificial intelligence, Pattern recognition, Resting state fMRI and Brain mapping. His work investigates the relationship between Neuroscience and topics such as Diffusion MRI that intersect with problems in White matter. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Magnetic resonance imaging and Computer vision.
His work in the fields of Pattern recognition, such as Independent component analysis, Principal component analysis, Brain segmentation and Segmentation, overlaps with other areas such as Group comparison. His Resting state fMRI study incorporates themes from Nerve net, Functional connectivity, Functional imaging, Cognitive science and Human brain. His studies in Brain mapping integrate themes in fields like Independence, Interpretability and Network model.
Artificial intelligence, Neuroscience, Pattern recognition, Neuroimaging and Resting state fMRI are his primary areas of study. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Computer vision and Human Connectome Project. All of his Neuroscience and Functional magnetic resonance imaging, Cognition, Brain mapping, Default mode network and Human brain investigations are sub-components of the entire Neuroscience study.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Magnetic resonance imaging and Diffusion MRI. His work in Diffusion MRI covers topics such as White matter which are related to areas like Pathology. He interconnects Connectome and Functional connectivity in the investigation of issues within Resting state fMRI.
His primary scientific interests are in Neuroimaging, Artificial intelligence, Human Connectome Project, Pattern recognition and Neuroscience. He has researched Neuroimaging in several fields, including White matter, Biobank, Cognition and Disease. Artificial intelligence is closely attributed to Machine learning in his study.
His research integrates issues of Resting state fMRI, Diffusion MRI and Connectome in his study of Human Connectome Project. His biological study spans a wide range of topics, including Graphical model, Magnetic resonance imaging and Cortical surface. His work on Functional connectivity, Default mode network, Brain aging and Neurotransmitter as part of general Neuroscience study is frequently linked to Functional change, bridging the gap between disciplines.
The scientist’s investigation covers issues in Neuroimaging, Human Connectome Project, Artificial intelligence, Connectome and Resting state fMRI. His Neuroimaging research includes themes of Biobank and Set. His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
His Independent component analysis study in the realm of Pattern recognition interacts with subjects such as Noise. His Resting state fMRI research incorporates elements of Connectomics, Cognition, Functional connectivity, Functional magnetic resonance imaging and Brain activity and meditation. As a member of one scientific family, he mostly works in the field of Neuroscience, focusing on Multivariate statistics and, on occasion, White matter.
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Advances in functional and structural MR image analysis and implementation as FSL.
S M Smith;M Jenkinson;M W Woolrich;M W Woolrich;C F Beckmann.
IMPROVED OPTIMIZATION FOR THE ROBUST AND ACCURATE LINEAR REGISTRATION AND MOTION CORRECTION OF BRAIN IMAGES
Mark Jenkinson;Peter R. Bannister;Peter R. Bannister;Michael Brady;Stephen M. Smith.
Fast robust automated brain extraction
Stephen M. Smith.
Human Brain Mapping (2002)
Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data.
S M Smith;M Jenkinson;H Johansen-Berg;D Rueckert.
A global optimisation method for robust affine registration of brain images
Mark Jenkinson;Stephen M. Smith.
Medical Image Analysis (2001)
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
Y. Zhang;M. Brady;S. Smith.
IEEE Transactions on Medical Imaging (2001)
SUSAN—A New Approach to Low Level Image Processing
Stephen M. Smith;J. Michael Brady.
International Journal of Computer Vision (1997)
Correspondence of the brain's functional architecture during activation and rest.
Stephen M. Smith;Peter T. Fox;Karla L. Miller;David C. Glahn.
Proceedings of the National Academy of Sciences of the United States of America (2009)
Consistent resting-state networks across healthy subjects
J. S. Damoiseaux;S. A. R. B. Rombouts;F. Barkhof;P. Scheltens.
Proceedings of the National Academy of Sciences of the United States of America (2006)
Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference
Stephen M. Smith;Thomas E. Nichols;Thomas E. Nichols;Thomas E. Nichols.
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