ArticleIEEE transactions on bio-medical engineering2020
N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia.
Article in IEEE transactions on bio-medical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Structural covariance networks in schizophrenia: A systematic review Part I.Schizophrenia research · 2022Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Semantic locality-aware biclustering for brain functional network connectivity.Scientific reports · 2025Article
- Imaging-genomic spatial-modality attentive fusion for studying neuropsychiatric disorders.Human brain mapping · 2024Article
- SpaDE: Semantic Locality Preserving Biclustering for Neuroimaging Data.bioRxiv : the preprint server for biology · 2024Article
- Biclustering data analysis: a comprehensive survey.Briefings in bioinformatics · 2024Review
- Application of hyperalignment to resting state data in individuals with psychosis reveals systematic changes in functional networks and identifies distinct clinical subgroups.Aperture neuro · 2024Article
- Auto-Classification of Parkinson's Disease with Different Motor Subtypes Using Arterial Spin Labelling MRI Based on Machine Learning.Brain sciences · 2023Article
- Statelets: Capturing recurrent transient variations in dynamic functional network connectivity.Human brain mapping · 2022Article
- Biclustering fMRI time series: a comparative study.BMC bioinformatics · 2022Article
- Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals.Brain connectivity · 2022Article
- Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach.Frontiers in human neuroscience · 2022Article
- A Classification-Based Approach to Estimate the Number of Resting Functional Magnetic Resonance Imaging Dynamic Functional Connectivity States.Brain connectivity · 2021Article
- Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth.Biological psychiatry · 2020Review
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22 authors.
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Abstract
objectiveWe 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.
methodsIt 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.
resultsFindings 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.
conclusionN-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.
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