Dr. Calhoun received a bachelor’s degree in electrical engineering (EE) from the University of Kansas, Lawrence, Kansas in 1991, master’s degrees in biomedical engineering and information systems from Johns Hopkins University, Baltimore in 1993 and 1996, and the Ph.D. degree in EE from the University of Maryland Baltimore County, Baltimore in 2002. He worked as a research engineer in the psychiatric neuroimaging laboratory at Johns Hopkins from 1993 until 2002. He then served as the director of medical image analysis at the Olin Neuropsychiatry Research Center and as an associate professor at Yale University. Most recently, he was a Distinguished Professor at the University of New Mexico and the President of the Mind Research Network. Dr. Calhoun is the founding director of the tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS; http://www.trendscenter.org), a joint effort between Georgia State, Georgia Tech, and Emory University, which is focused on improving our understanding of the human brain using advanced analytic approaches with an emphasis on translational research such as the development of predictive biomarkers for mental and neurological disorders. The use of big data approaches and neuroinformatics tools to capture, manage, analyze, and share data is also a major emphasis. Dr. Calhoun also enjoys playing tennis and playing clarinet with his children.
- Image and signal processing, data fusion
- Multimodal brain imaging (MRI, EEG, MEG, etc.)
- Identification of biomarkers for brain health and disease
- Machine learning/deep learning
- Imaging genomics/epigenomics
- Fellow of the Institute of Electrical and Electronic Engineers, the Association for the Advancement of Science, the American Institute of Biomedical and Medical Engineers, the International Society of Magnetic Resonance in Medicine, and the American College of Neuropsychopharmacology
- Chair of the Organization for Human Brain Mapping
- Chief section editor for Frontiers in Brain Imaging Methods
- 2016 Outstanding Alumnus of the Year, University of Maryland, Baltimore County
- 2014 IEEE Outstanding Engineer Award
- 2015 IEEE Southwest Area Outstanding Educator Award
R. Jiang, C. C. Abbott, T. Jiang, Y. Du, R. Espinoza, K. L. Narr, B. Wade, Q. Yu, M. Song, D. Lin, J. Chen, T. Jones, M. Argyelan, G. Petrides, J. Sui, and V. D. Calhoun, "SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets," Neuropsychopharmacology, vol. 43, pp. 1078-1087, Apr. 2018, PMC5854791.
J. Sui, S. L. Qi, T. G. M. van Erp, J. Bustillo, R. T. Jiang, D. D. Lin, J. A. Turner, E. Damaraju, A. R. Mayer, Y. Cui, Z. N. Fu, Y. H. Du, J. Y. Chen, S. G. Potkin, A. Preda, D. H. Mathalon, J. M. Ford, J. Voyvodic, B. A. Mueller, A. Belger, S. C. McEwen, D. S. O'Leary, A. McMahon, T. Z. Jiang, and V. D. Calhoun, "Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion," Nature Communications, vol. 9, Aug. 2, 2018.
V. D. Calhoun and J. Sui, "Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental illness," Biol Psychiatry Cogn Neurosci Neuroimaging, vol. 1, pp. 230-244, May 2016, PMC4917230.
S. M. Plis, A. D. Sarwate, D. Wood, C. Dieringer, D. Landis, C. Reed, S. R. Panta, J. A. Turner, J. M. Shoemaker, K. W. Carter, P. Thompson, K. Hutchison, and V. D. Calhoun, "COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data," Front Neurosci, vol. 10, p. 365, Aug. 19, 2016, PMC4990563.
G. D. Pearlson, J. Liu, and V. D. Calhoun, "An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders," Front Genet, vol. 6, p. 276, 2015, PMC4561364.
J. Sui, G. D. Pearlson, Y. Du, Q. Yu, T. R. Jones, J. Chen, T. Jiang, J. Bustillo, and V. D. Calhoun, "In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia," Biol Psychiatry, vol. 78, pp. 794-804, Dec. 1, 2015, PMC4547923.
V. D. Calhoun, R. Miller, G. Pearlson, and T. Adali, "The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery," Neuron, vol. 84, pp. 262-274, Oct. 22, 2014, PMC4372723.
S. M. Plis, D. R. Hjelm, R. Salakhutdinov, E. A. Allen, H. J. Bockholt, J. D. Long, H. J. Johnson, J. S. Paulsen, J. A. Turner, and V. D. Calhoun, "Deep learning for neuroimaging: a validation study," Front Neurosci, vol. 8, p. 229, Aug. 20, 2014, 4138493.
S. A. Meda, B. Narayanan, J. Liu, N. I. Perrone-Bizzozero, M. C. Stevens, V. D. Calhoun, D. C. Glahn, L. Shen, S. L. Risacher, A. J. Saykin, and G. D. Pearlson, "A large scale multivariate parallel ICA method reveals novel imaging-genetic relationships for Alzheimer's disease in the ADNI cohort," Neuroimage, vol. 60, pp. 1608-1621, Apr. 15, 2012, PMC3312985.
V. D. Calhoun and T. Adali, "Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery," IEEE Rev Biomed Eng, vol. 5, pp. 60-73, 2012, PMC4433055.
V. D. Calhoun and T. Adalı, "Analysis of Complex-Valued Functional Magnetic Resonance Imaging Data: Are We Just Going Through a Phase?," Special Issue of the Bulletin of the Polish Academy of Sciences, vol. 60, pp. 371-418, 2012.
J. Sui, T. Adalı, Q. Yu, and V. D. Calhoun, "A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data," Journal of Neuroscience Methods, vol. 204, pp. 68-81, 2012, PMC3690333.
A. Scott, W. Courtney, D. Wood, R. de la Garza, S. Lane, M. King, R. Wang, J. Roberts, J. A. Turner, and V. D. Calhoun, "COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets," Front Neuroinform, vol. 5, p. 33, 2011, 3250631.
V. D. Calhoun and T. Adalı, "Feature-based Fusion of Medical Imaging Data," IEEE Transactions on Information Technology in Biomedicine, vol. 13, pp. 1-10, 2009, PMC2737598.
Last revised January 14, 2019