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.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory Grants (P20)
Project #
2P20GM103472-06
Application #
8602555
Study Section
Special Emphasis Panel (ZGM1-TWD-Y (C2))
Project Start
Project End
Budget Start
2013-08-01
Budget End
2014-04-30
Support Year
6
Fiscal Year
2013
Total Cost
$147,391
Indirect Cost
$38,518
Name
The Mind Research Network
Department
Type
DUNS #
098640696
City
Albuquerque
State
NM
Country
United States
Zip Code
87106
Miller, Robyn L; Yaesoubi, Maziar; Calhoun, Vince D (2016) Cross-Frequency rs-fMRI Network Connectivity Patterns Manifest Differently for Schizophrenia Patients and Healthy Controls. IEEE Signal Process Lett 23:1076-1080
Yu, Qingbao; Wu, Lei; Bridwell, David A et al. (2016) Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study. Front Hum Neurosci 10:476
Chen, Jiayu; Calhoun, Vince D; Pearlson, Godfrey D et al. (2016) Independent component analysis of SNPs reflects polygenic risk scores for schizophrenia. Schizophr Res :
Mayer, Andrew R; Hanlon, Faith M; Dodd, Andrew B et al. (2016) Proactive response inhibition abnormalities in the sensorimotor cortex of patients with schizophrenia. J Psychiatry Neurosci 41:312-21
Fink, Brandi C; Steele, Vaughn R; Maurer, Michael J et al. (2016) Brain potentials predict substance abuse treatment completion in a prison sample. Brain Behav 6:e00501
Nomi, Jason S; Farrant, Kristafor; Damaraju, Eswar et al. (2016) Dynamic functional network connectivity reveals unique and overlapping profiles of insula subdivisions. Hum Brain Mapp 37:1770-87
Hanlon, Faith M; Shaff, Nicholas A; Dodd, Andrew B et al. (2016) Hemodynamic response function abnormalities in schizophrenia during a multisensory detection task. Hum Brain Mapp 37:745-55
van Erp, T G M; Hibar, D P; Rasmussen, J M et al. (2016) Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol Psychiatry 21:547-53
Bridwell, David A; Rachakonda, Srinivas; Silva, Rogers F et al. (2016) Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data. Brain Topogr :
Xu, Jiansong; Potenza, Marc N; Calhoun, Vince D et al. (2016) Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses. Neurosci Biobehav Rev 71:83-100

Showing the most recent 10 out of 118 publications