Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia
Year: 2019
Type: article
Abstract: There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.
Source: Human Brain Mapping
DOI: 10.1002/hbm.24632
Authors: Shile Qi, Jing Sui, Jiayu Chen, Jingyu Liu, Rongtao Jiang, Rogers Silva, Armin Iraji, Eswar Damaraju, Mustafa Salman, Dongdong Lin, Zening Fu, Dongmei Zhi, Jessica A. Turner, Juan Bustillo, Judith M. Ford, Daniel H. Mathalon, James Voyvodic, Sarah McEwen, Adrian Preda, Aysenil Belger, Steven G. Potkin, Bryon A. Mueller, Tulay Adali, Vince D. Calhoun
Institutions: Georgia Institute of Technology, Mind Research Network, Center for Translational Research in Neuroimaging and Data Science, Chinese Academy of Sciences, Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, University of Chinese Academy of Sciences, Georgia State University, University of New Mexico, University of California, San Francisco, Duke University, University of California San Diego, University of California, Irvine, University of North Carolina at Chapel Hill, University of Minnesota, University of Maryland, Baltimore, University of Maryland, Baltimore County
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
Funders: National Science Foundation, Stanley Medical Research Institute, National Natural Science Foundation of China, Chinese Academy of Sciences, Beijing Municipal Science and Technology Commission, National Institutes of Health, National Institute of Mental Health, National Institute of General Medical Sciences, National Institute of Biomedical Imaging and Bioengineering
Awards: G1098917480, G1290398358, G1452663721, G1510064630, G1635530505, Unified multivariate data-driven solutions for static and dynamic brain connectivity, G3065752336, Mining the Genomewide Scan: Genetic Profiles of Structural Loss in Schizophrenia, G330011291, Informed Data-Driven Fusion of Behavior, Brain Function, and Genes, RII Track-2 FEC: Developmental Chronnecto-Genomics (Dev-CoG): A Next Generation Framework for Quantifying Brain Dynamics and Related Genetic Factors in Childhood, Phase III COBRE: Multimodal Imaging of Neuropsychiatric Disorders (MIND), Multimodal Imaging of Neuropsychiatric Disorders (MIND): Mechanisms &, G527314314, G5910022598, G595497569, Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders, G6521376908, G6605091644, G6715088289, G772009288, G7935258156, G8423037688, G8512316527, G9000095469
OpenAlex ID: W2927337949