? The overall goal of this proposed research is to develop, evaluate the performance of, and optimize semi-blind independent component analysis (sblCA) methods for the analysis of multi-group (or multi-paradigm) functional magnetic resonance imaging (fMRI) data. We will examine the application of ICA to fMRI of visual, auditory, and motor paradigms using a proposed synthesis/analysis model [1,2] (and subsequently developed extensions of it) to carefully study the properties of ICA as it is applied to fMRI data and incorporate additional a priori information. We recently published a method for applying independent component analysis (ICA) to groups of subjects [3,4]. We will extend this method to enable comparisons of multiple groups. The use of flexible methods, such as ICA, is of _articular importance when studying cognitive tasks involving a distributed set of brain regions or when studying a disease like schizophrenia, which is known to be a diffuse disorder, affecting many aspects of brain function. We will apply our methods to reanalyze data, previously analyzed with traditional analysis methods, acquired while normal and schizophrenic subjects performed an auditory oddball paradigm.
The specific aims of the proposed research are 1) To develop methods for inter-group/inter-paradigm inference which a) incorporate a priori information about the brain sources, b) provide the probability of source magnitude across and between subjects, c) utilize post hoc parameterization to obtain, e.g., latency valued, and d) enable hypothesis testing when a priori time courses or activation locations are predicted. 2) To optimize and validate ICA of fMRI using """"""""basic"""""""" visual/motor/auditory paradigms, realistic simulations using our model for ICA of fMRI, """"""""hybrid"""""""" data sets containing known sources, and comparisons with SPM99 methods assuming a known form for the hemodynamic response. 3) To apply our methods to analyze previously acquired and well characterized data of normal and schizophrenic subjects performing an auditory oddball task, and 4) To make publicly available, a Matlab software package implementing our methods which can be used stand alone or as a plug-in for a widely used fMRI analysis package, SPM. ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB000840-03
Application #
6847094
Study Section
Diagnostic Imaging Study Section (DMG)
Program Officer
Cohen, Zohara
Project Start
2003-04-01
Project End
2007-01-31
Budget Start
2005-02-01
Budget End
2006-01-31
Support Year
3
Fiscal Year
2005
Total Cost
$299,600
Indirect Cost
Name
Hartford Hospital
Department
Type
DUNS #
065533796
City
Hartford
State
CT
Country
United States
Zip Code
06102
Mennigen, Eva; Miller, Robyn L; Rashid, Barnaly et al. (2018) Reduced higher-dimensional resting state fMRI dynamism in clinical high-risk individuals for schizophrenia identified by meta-state analysis. Schizophr Res 201:217-223
Kong, Xiang-Zhen; Mathias, Samuel R; Guadalupe, Tulio et al. (2018) Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium. Proc Natl Acad Sci U S A 115:E5154-E5163
Yu, Qingbao; Du, Yuhui; Chen, Jiayu et al. (2018) Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs. Proc IEEE Inst Electr Electron Eng 106:886-906
Yu, Qingbao; Du, Yuhui; Chen, Jiayu et al. (2017) Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study. J Neurosci Methods 291:61-68
Calhoun, Vince D; de Lacy, Nina (2017) Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis. Neuroimaging Clin N Am 27:561-579
Calhoun, Vince D; Wager, Tor D; Krishnan, Anjali et al. (2017) The impact of T1 versus EPI spatial normalization templates for fMRI data analyses. Hum Brain Mapp 38:5331-5342
Walton, Esther; Hass, Johanna; Liu, Jingyu et al. (2016) Correspondence of DNA Methylation Between Blood and Brain Tissue and Its Application to Schizophrenia Research. Schizophr Bull 42:406-14
Du, Yuhui; Allen, Elena A; He, Hao et al. (2016) Artifact removal in the context of group ICA: A comparison of single-subject and group approaches. Hum Brain Mapp 37:1005-25
Silva, Rogers F; Plis, Sergey M; Sui, Jing et al. (2016) Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling. IEEE J Sel Top Signal Process 10:1134-1149
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

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