2014 - Fellow of the Indian National Academy of Engineering (INAE)
2013 - IEEE Fellow For contributions to data-driven processing of multimodal brain imaging and genetic data
2012 - Fellow of the American Association for the Advancement of Science (AAAS)
Neuroscience, Artificial intelligence, Functional magnetic resonance imaging, Independent component analysis and Brain mapping are his primary areas of study. As part of his studies on Neuroscience, he often connects relevant areas like Schizophrenia. His Artificial intelligence research includes themes of Algorithm, Machine learning, Electroencephalography and Pattern recognition.
His Functional magnetic resonance imaging course of study focuses on Schizophrenia and Voxel. He has researched Independent component analysis in several fields, including Communication, Speech recognition, Principal component analysis, Sensor fusion and Component. His Brain mapping study combines topics in areas such as Nerve net, Event-related potential, Event, Cerebral cortex and Sensory system.
Vince D. Calhoun focuses on Artificial intelligence, Neuroscience, Functional magnetic resonance imaging, Pattern recognition and Independent component analysis. His Artificial intelligence research is multidisciplinary, incorporating elements of Schizophrenia, Machine learning and Neuroimaging. Many of his studies on Neuroscience apply to Schizophrenia as well.
The study incorporates disciplines such as Brain activity and meditation and Magnetic resonance imaging in addition to Functional magnetic resonance imaging. His biological study spans a wide range of topics, including Cluster analysis, Blind signal separation, Sensor fusion and Electroencephalography. His research integrates issues of Infomax, Data mining, Speech recognition and Component in his study of Independent component analysis.
Vince D. Calhoun spends much of his time researching Artificial intelligence, Pattern recognition, Functional magnetic resonance imaging, Neuroimaging and Neuroscience. His Artificial intelligence study incorporates themes from Resting state fMRI, Machine learning and Cognition. Vince D. Calhoun studies Independent component analysis, a branch of Pattern recognition.
He is interested in Dynamic functional connectivity, which is a field of Functional magnetic resonance imaging. His Dynamic functional connectivity research integrates issues from Sliding window protocol and Algorithm. His Neuroscience study frequently links to adjacent areas such as Schizophrenia.
The scientist’s investigation covers issues in Artificial intelligence, Neuroimaging, Neuroscience, Functional magnetic resonance imaging and Resting state fMRI. His work deals with themes such as Machine learning, Schizophrenia and Pattern recognition, which intersect with Artificial intelligence. His Neuroimaging research incorporates elements of Cognition, Neuropsychology, Cortical surface, Clinical psychology and Autism spectrum disorder.
The Neuroscience study which covers Magnetic resonance imaging that intersects with Schizophrenia spectrum. His work carried out in the field of Functional magnetic resonance imaging brings together such families of science as Graphical model, Statistics and Hidden Markov model. The various areas that Vince D. Calhoun examines in his Resting state fMRI study include Classifier, Sliding window protocol, Functional networks, Functional connectivity and Basal ganglia.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A method for making group inferences from functional MRI data using independent component analysis
V. D. Calhoun;V. D. Calhoun;T. Adali;G. D. Pearlson;J. J. Pekar;J. J. Pekar.
Human Brain Mapping (2001)
Dynamic functional connectivity: Promise, issues, and interpretations
R. Matthew Hutchison;Thilo Womelsdorf;Elena A. Allen;Elena A. Allen;Peter A. Bandettini.
Tracking Whole-Brain Connectivity Dynamics in the Resting State
Elena A. Allen;Elena A. Allen;Eswar Damaraju;Sergey M. Plis;Erik B. Erhardt.
Cerebral Cortex (2014)
Aberrant "default mode" functional connectivity in schizophrenia.
Abigail G. Garrity;Godfrey D. Pearlson;Kristen McKiernan;Dan Lloyd.
American Journal of Psychiatry (2007)
Selective changes of resting-state networks in individuals at risk for Alzheimer's disease
Christian Sorg;Valentin Riedl;Valentin Riedl;Mark Mühlau;Vince D. Calhoun.
Proceedings of the National Academy of Sciences of the United States of America (2007)
A Baseline for the Multivariate Comparison of Resting-State Networks
Elena A. Allen;Erik B. Erhardt;Eswar Damaraju;William Gruner;William Gruner.
Frontiers in Systems Neuroscience (2011)
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.
Vince D. Calhoun;Jingyu Liu;Jingyu Liu;Tülay Adalı.
A Method for Functional Network Connectivity Among Spatially Independent Resting-State Components in Schizophrenia
Madiha J. Jafri;Godfrey D. Pearlson;Michael C. Stevens;Vince D. Calhoun.
Estimating the number of independent components for functional magnetic resonance imaging data.
Yi Ou Li;Tülay Adali;Vince D. Calhoun.
Human Brain Mapping (2007)
Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms.
V.D. Calhoun;T. Adali;G.D. Pearlson;J.J. Pekar;J.J. Pekar.
Human Brain Mapping (2001)
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