Principal Investigators //
- David Bridwell, PhD >
- Vince Calhoun, PhD >
- Arvind Caprihan, PhD >
- Zikuan Chen, PhD >
- Vince Clark, PhD >
- Eric D. Claus, PhD >
- Carla Harenski, PhD >
- Kent Hutchison, PhD >
- Kent A. Kiehl, PhD >
- Jeffrey D. Lewine, PhD >
- Jingyu Liu, PhD >
- Andrew R. Mayer, PhD >
- John Phillips, MD >
- Sergey Plis, PhD >
- Matthew Shane, PhD >
- Julia M. Stephen, PhD >
- Jing Sui, PhD >
- Jessica Turner, PhD >
- Qingbao Yu, PhD >
Vince Calhoun, PhD
Executive Science Officer and Director, Image Analysis and MR Research
Professor of Translational Neuroscience
The Mind Research Network
Distinguished Professor, Departments of Electrical and Computer Engineering (primary),
Neurosciences, Computer Science, and Psychiatry
The University of New Mexico
Dr. Calhoun develops techniques for making sense of complex brain imaging data. Because each imaging modality has limitations, the integration of these data is needed to understand the healthy and especially the disordered human brain.
Dr. Calhoun has created algorithms that map dynamic networks of brain function, structure and genetics, and how these are affected while being stimulated by various tasks or in individuals with mental illness such as schizophrenia.
For more information on Dr. Calhoun, please refer to his Curriculum Vitae or visit the Medical Image Analysis Lab. His CV includes his academic career, as well as grant history, professional service, a partial list of publications and a full bibliography.
Congratulations
Dr. Calhoun recently received fellowship designations for both the American Association for the Advancement of Science (AAAS) and the Institute of Electrical and Electronics Engineers (IEEE).
The organizations recognize Dr. Calhoun for his contributions to human brain research. One of Calhoun’s most significant accomplishments is his development of advanced algorithms that identify how brain regions ‘talk’ to one another either during a specific task or when at rest.
He also recently earned the A. Earl Walker Neuroscience Research Award, which recognizes outstanding contributions to basic or clinical research in neuroscience by a member of the faculty in any UNM department.
Selected Publications //
- The effect of preprocessing pipelines in subject classification and detection of abnormal resting… >
- Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls >
- Multimodal fusion of brain imaging data: A key to finding the missing link(s) in complex mental… >
- The role of diversity in complex ICA algorithms for fMRI analysis. >
- In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia >
- An Introductory Review of Parallel Independent Component Analysis (p-ICA) and a Guide to Applying… >
- The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery >
- Harnessing modern web application technology to create intuitive and efficient data visualization… >
- Deep learning for neuroimaging: a validation study >
- Multivariate analysis reveals genetic associations of the resting default mode network in psychotic >
- A three-way parallel ICA approach to analyze links among genetics, brain structure and brain… >
- Guided exploration of genomic risk for gray matter abnormalities in schizophrenia using parallel… >
- Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and… >
- Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode… >
- COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets. >
- A method for making group inferences from functional MRI data using independent component analysis >
- Feature-based fusion of medical imaging data >
Fusing multi-task and multi-modal brain imaging data and indentify the potential biological markers
Each brain imaging modality reports on a different aspect of the brain with different strengths and weaknesses and there are now literally thousands of putative imaging biomarkers. This project will develop multivariate methods which use higher order statistics to combine diverse information in a scalable manner, identify correspondence among data types and also provide a sophisticated data sharing and management system.
Exploring similarity and differences among schizophrenia and bipolar disorder by combining fMRI and
This project will develop an exploratory data fusion model which combines 2 multivariate methods and is able to identify correspondence among multiple data types. We aim to apply this model to schizophrenia and bipolar disorder via an fMRI-DTI fusion, which can identify both shared and disease-specific brain abnormalities from multiple perspectives (brain function and structure).
Brain Connectivity Changes in Individual Subjects with Neuropsychological Disease
Methods to provide better characterization of functional and structural brain network connectivity in patients with schizophrenia and addiction are being developed.(Calhoun et al., 2009; Greicius et al., 2007; Lynall et al., 2010; van den Heuvel et al., 2010) The goal is to provide accurate markers of disease progression in individual subjects with neuropsychological diseases associated with brain connectivity alterations.
