Alzheimer's disease (AD) affects an estimated 55 million people worldwide and is projected to nearly double by 2050, creating a need for scalable, non-invasive AI systems that can classify disease stage, support multimodal clinical reasoning, and interact in natural language. Most deep learning work on AD focuses o...
Sohail Haresh Gidwani, Tamoghna Chattopadhyay, S. Thomopoulos et al.· Frontiers in Computational N...· 0 citations
Brain-age prediction from resting-state fMRI provides a quantitative framework for characterizing age-related changes in spontaneous brain dynamics and for identifying functional signatures. Existing studies have linked fractal and multifractal scaling to age and examined the reliability of individual features. However...
Yu Chang, An-Zhe Cheng, Jia-Hao Chen et al.· 0 citations
The findings suggest that genetic influences on brain morphology are expressed across multiple spatial scales, with consequences that may help to guide the design of deep learning methods to discover genomic loci associated with brain structure and brain diseases.
Emma J Gleave, Luis M. García-Marín, Z. Ceja et al.· bioRxiv· 0 citations
Brain-wide maps of polygenic scores (PGS) from loci associated with ten brain volume regions of interest reveal patterns consistent with both localized and distributed genetic influences, offering a novel approach to interpret the genomic architecture of brain structure.
Emma J Gleave, L. García-Marín, Z. Ceja et al.· bioRxiv· 0 citations
TIER-MoE is introduced, a risk-guided subspace mixture-of-experts model that defines sample-specific modality reliability as the prediction loss its unimodal predictor is expected to incur, and demonstrates its superiority over state-of-the-art methods in predictive performance and probability calibration.
