A recent study from Concordia University in Montreal, Canada, discusses new findings on mental disorders detection using an Adaptive Constrained Ivamggmm approach. The research focuses on analyzing fMRI data to capture population patterns while preserving individual uniqueness, proposing models that relax the independence assumption of Independent Component Analysis (ICA). The study validates these models on various datasets, including Alzheimer's, Schizophrenia, EEG, and ADHD, showing superior performance over base models. For more information, readers can refer to the article "Adaptive Constrained Ivamggmm: Application To Mental Disorders Detection" published in IEEE Transactions on Emerging Topics in Computational Intelligence in 2024. [Extracted from the article]