N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia
Year: 2019
Type: article
Abstract: OBJECTIVE: We propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia. METHODS: It uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa. RESULTS: Findings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively. CONCLUSION: N-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications. SIGNIFICANCE: The use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.
Authors: Md Abdur Rahaman, Daniel Mathalon, Hyo Jong Lee, Wenhao Jiang, Bryon A. Mueller, Ole Andreassen, Ingrid Agartz, Scott R. Sponheim, Andrew R. Mayer, Julia Stephen, Rex E. Jung, Jessica A. Turner, Jose Canive, Juan Bustillo, Vince D. Calhoun, Cota Navin Gupta, Srinivas Rachakonda, Jiayu Chen, Jingyu Liu, Theo G. M. van Erp, Steven Potkin, Judith Ford
Institutions: University of New Mexico, University of California, San Francisco, Jeonbuk National University, Georgia State University, University of Minnesota, Oslo University Hospital, University of Oslo, Karolinska Institutet, Diakonhjemmet Hospital, Minneapolis VA Health Care System, Minneapolis VA Medical Center, Mind Research Network, University of New Mexico Health Sciences Center, University of California System
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Function BIRN, Unified multivariate data-driven solutions for static and dynamic brain connectivity, Neural Mechanism of Schizophrenia: Use of Multiple Neuroimaging Tools to Examine, Integration of fMRI imaging, genomics, network and biological knowledge, Mining the Genomewide Scan: Genetic Profiles of Structural Loss in Schizophrenia, Integration of brain imaging with genomic and epigenomic data, 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, Flexible multivariate models for linking multi-scale connectome and genome data in Alzheimer's disease and related disorders, Integration of multiscale genomic data for comprehensive analysis of complex dise, Multimodal Imaging of Neuropsychiatric Disorders (MIND): Mechanisms &, Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders, Multivariate methods for identifying multitask/multimodal brain imaging biomarkers
OpenAlex ID: W2932886604