Principal Investigators //
- Corey Hill Allen, PhD >
- Nathaniel Anderson, PhD >
- Vince Calhoun, PhD
- Felicha Candelaria-Cook, PhD >
- Arvind Caprihan, PhD >
- Vince Clark, PhD >
- Eric D. Claus, PhD >
- Aparna Gullapalli, PhD >
- Carla Harenski, PhD >
- Jon Houck, Ph.D. >
- Kent Hutchison, PhD >
- Kent A. Kiehl, PhD >
- Dean O. Kuethe, PhD >
- Jeffrey D. Lewine, PhD >
- J. Michael Maurer, PhD >
- Andrew R. Mayer, PhD >
- John Phillips, MD >
- Sephira Ryman, PhD, MS >
- Julia M. Stephen, PhD >
- Andrei Vakhtin, Ph.D. >
- Claire E. Wilcox, MD >
Vince Calhoun, PhD
Adjunct Professor of Translational Neuroscience
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.
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 //
- Longitudinal epigenetic predictors of amygdala: Hippocampus volume ratio >
- Neuropsychological analysis of auditory verbal hallucinations >
- An information theory framework for dynamic functional domain connectivity >
- Decreased Hemispheric Connectivity and Decreased Intra- and Inter- Hemisphere Asymmetry of Resting State Functional Network Connectivity in Schizophrenia >
- Regional enrichment analyses on genetic profiles for schizophrenia and bipolar disorder >
- Quantifying the Interaction and Contribution of Multiple Datasets in Fusion: Application to the Detection of Schizophrenia >
- Positive symptoms associate with cortical thinning in the superior temporal gyrus via the ENIGMA Schizophrenia consortium >
- Disrupted intrinsic connectivity of the periaqueductal gray in patients with functional dyspepsia: A >
- Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder >
- Resting state networks as simultaneously measured with fMRI and PET >
- Cortical Sensitivity to Guitar Note Patterns: EEG Entrainment to Repetition and Key >
- Default mode network deactivation to smoking cue relative to food cue predicts treatment outcome in nicotine use disorder >
- Cognitive Control, Learning, and Clinical Motor Ratings Are Most Highly Associated with Basal Ganglia Brain Volumes in the Premanifest Huntington’s Disease Phenotype >
- EEG Signatures of Dynamic Functional Network Connectivity States >
- Adaptive independent vector analysis for multi-subject complex-valued fMRI 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.