The algorithm and data analysis (ADA) core of this phase II COBRE will fulfill the need for centralized image analysis resources that will be used to support all five projects. These resources include tools designed for measurement and analysis of sMRI, MRS, fMRI, DTI, genetics, EEG and MEG data. The ADA Core will play a leading role in developing and providing software that is needed to solve basic image analysis problems that arise when working with MR and MEG/EEG data. This will be accomplished by providing a core set of tools and approaches for analysis of imaging and genetic data. The core set of resources includes expertise and tools for analyzing all first level-imaging data (automated pipeline preprocessing) as well as advanced algorithms for network-based functional and structural connectivity measures to address in a comprehensive way the scientific questions being asked in each of the projects. We will work with the tools developed locally as well as widely-used tools developed by other groups to enable network-based analysis, data-fusion of multimodal data, and prediction/classification approaches. Importantly, a key aspect of this COBRE and the ADA core is focused on combining multimodal data as each project will work with two or more modalities. An additional area of emphasis will be on the development of realistic simulation approaches, to enable comparisons of algorithms, optimization of parameters, and to provide intuition about how new algorithms work. Finally, the ADA core will also provide essential training to junior investigators about data analysis of brain imaging and genetic data. This will ensure junior investigators are informed about the various algorithms, understand how to make analysis choices given a particular hypothesis, and have a basic idea of how to implement such algorithms themselves. The director of the ADA Core is Dr. Calhoun, who has over 20 years of experience in developing tools and approaches for working with unimodal and multimodal imaging and genetics data. Codirector Dr. Cheryl Aine has extensive experience in unimodal and multimodal imaging with MEG/EEG and codirector Dr. Julia Stephen, a graduate of the phase I COBRE, is currently the director of the MEG facility at MRN and has considerable experience in combining MEG and fMRI data, as well as EEG and MEG data in clinical groups.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Exploratory Grants (P20)
Project #
Application #
Study Section
Special Emphasis Panel (ZGM1-TWD-Y)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
The Mind Research Network
United States
Zip Code
Zille, Pascal; Calhoun, Vince D; Stephen, Julia M et al. (2017) Fused estimation of sparse connectivity patterns from rest fMRI. Application to comparison of children and adult brains. IEEE Trans Med Imaging :
Meng, Xing; Jiang, Rongtao; Lin, Dongdong et al. (2017) Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data. Neuroimage 145:218-229
Bernard, Jessica A; Goen, James R M; Maldonado, Ted (2017) A case for motor network contributions to schizophrenia symptoms: Evidence from resting-state connectivity. Hum Brain Mapp 38:4535-4545
Gupta, Cota Navin; Castro, Eduardo; Rachkonda, Srinivas et al. (2017) Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia. Front Psychiatry 8:179
Arbabshirani, Mohammad R; Plis, Sergey; Sui, Jing et al. (2017) Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage 145:137-165
He, Hao; Sui, Jing; Du, Yuhui et al. (2017) Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders. Brain Struct Funct 222:4051-4064
Vergara, Victor M; Mayer, Andrew R; Damaraju, Eswar et al. (2017) The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA. Neuroimage 145:365-376
Faghiri, Ashkan; Stephen, Julia M; Wang, Yu-Ping et al. (2017) Changing brain connectivity dynamics: From early childhood to adulthood. Hum Brain Mapp :
de Lacy, N; Doherty, D; King, B H et al. (2017) Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum. Neuroimage Clin 15:513-524
Lerman-Sinkoff, Dov B; Sui, Jing; Rachakonda, Srinivas et al. (2017) Multimodal neural correlates of cognitive control in the Human Connectome Project. Neuroimage 163:41-54

Showing the most recent 10 out of 168 publications