Abstract: The objective of the proposed research is to develop a novel data-driven framework for quantifying individualized Brain Risk Scores (BRS) for brain disorders. Brain disorders exist along a continuum of neural vulnerability, shaped by ongoing disruptions in brain structural integrity and functional coordination. Rather than fitting into distinct categories of health and disease, they represent gradual, system-wide reorganizations of the brain. Functional MRI reveals abnormal interactions among large-scale networks, while structural MRI detects gray-matter atrophy and morphological co-variation. However, each modality offers only a partial view. A comprehensive understanding of brain health, therefore, requires a multimodal approach that combines functional connectivity (both static and dynamic) estimated from resting-state fMRI with structural co-modulation derived from sMRI. In this project, the Brain Risk Score (BRS) will be defined as the difference between an individual’s similarity to control versus patient templates (BRS = R_CN − R_Patient). Higher values indicate a healthier organization; lower or negative values reflect disease-like patterns. Mapping these scores will reveal a continuous risk spectrum and detect subclinical vulnerability. The multimodal BRS (mBRS) will extend this by integrating static and dynamic connectivity with gray-matter co-modulation. The Dynamic Multi-Connectivity (dMC) framework will combine dynamic FNC, dynamic ALFF, and windowed spectra within joint ICA to capture how amplitude, connectivity, and frequency features change together over time. An exploratory EC-ICA module will test whether adding directed causal priors from GIMME improves component stability and interpretability. This project builds on large-scale prior work involving more than 37,000 participants, which demonstrated that mBRS identifies early mild cognitive impairment and predicts Alzheimer’s disease conversion. It also extends a state-guided ICA framework that uncovered reproducible temporal biomarkers of Alzheimer’s pathology. Building on these foundations, the proposed research will construct a comprehensive multimodal continuum model capable of forecasting individualized risk trajectories across disorders. Ultimately, this integrative framework will establish a quantitative map of the brain’s structural–functional–dynamic continuum, enabling early detection, longitudinal monitoring, and precision modeling of neural vulnerability across the lifespan.