Vince Calhoun, PhD
Jane and Bud Smith Chair
Technology is opening new windows of understanding about the brain’s incredible ability to change, grow and adapt, putting us on the cusp of major advances. Dr. Vince Calhoun leverages data in unprecedented ways to help improve individual cognitive function and optimize the performance of the human brain.
As one of the world’s foremost experts in human brain imaging and analysis, Dr. Calhoun’s research focuses on such areas as: 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; neuroinformatics; and imaging genomics/epigenomics.
A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery
Dr. Calhoun developed a widely used approach for estimating brain networks from fMRI data and creating a data-driven functional "connectome" of the brain.
Feature-based Fusion of Medical Imaging Data
This presents a novel framework for combining information from multiple types of neuroimaging data – a way to ‘fuse’ the data to provide something that is greater than the sum of the parts.
COINS: An innovative informatics and neuroimaging tool suite built for large heterogeneous datasets
COINS is a neuroinformatics platform used by researchers around the world which provides a comprehensive system for capture, management and analysis of multiple types of neuroimaging and assessment data.
Deep learning for neuroimaging: a validation study
This paper was one of the first that developed and applied deep learning approaches to neuroimaging data. The use of deep learning/AI tools to study the brain has since exploded, with exciting new research findings coming out all the time.
Brain-Based Biomarkers: A Focus on the Heterogeneous by Enhancing Sensitivity to Brain Disorders and Change The use of neuroimaging to study mental and neurological disorder has shown to be a powerful tool to capture information on the underlying brain changes. However diagnostic heterogeneity is a major issue as the field struggles to learn from the brain imaging data. One important aspect which has not been well explored is the use of rich, high-dimensional brain data to guide us through this complex territory. We show that by focusing on more similar subsets of data, identified via advanced algorithmic strategies, we can facilitate an apples-to-apples comparison, enhance sensitivity to mental illness, and provide a framework for improved stratification.
Vince Calhoun, author of the Cerebrum story, “The Promise of Big Data Imaging for Mental Health,” discusses his pioneering research and the evolution of a growing field that has enormous potential to have an impact of both mental health and neurodegenerative treatment.