Adaptive Constrained IVAMGGMM: Application to Mental Disorders Detection

Abstract

The demand for adaptable approaches to analyze extensive fMRI data is growing, focusing on capturing population patterns while preserving individual uniqueness. Independent component analysis (ICA) is increasingly used to uncover spatio-temporal patterns in brain imaging but struggles with separating correlated sources in multivariate data like fMRI. For that, we propose an ICA-based multivariate generalized Gaussian mixture model combined with the constrained ICA to form the cICA-MGGMM. This model relaxes the independence assumption of ICA. Also, we propose the adaptive constrained ICA-MGGMM (acICA-MGGMM) to adaptively control the association between reference signals and estimated sources. Independent vector analysis (IVA) calculates global spatial and temporal patterns from multi-subject fMRI data while preserving individual variability but performs poorly with large datasets and weak component correlations. This paper proposes integrating reference signals into the formulation to address the problem and provide guidance in high-dimensional situations. For that, we propose cIVA-MGGMM to address ICA limitations for multivariate data, offering a framework for references but relying on user-defined constraint parameters to enforce reference-estimated sources associations. To tackle these limitations, we introduce the adaptive cIVA-MGGMM (acIVA-MGGMM) to adapt and separate the activated brain sources. This model employs a full covariance matrix, which consider the feature correlation. Our four constrained methods incorporate prior information about the sources into the ICA and IVA models to address the limitations of ICA and IVA in high-dimensional data. We validate our models on simulation, Alzheimer's, Schizophrenia, EEG, and ADHD datasets, demonstrating superior performance over base models.