Multimodal MRI Integration for Predicting Brain Disease Progression

Our research aims to build a comprehensive framework for understanding brain health and disease by integrating advanced multimodal MRI with blood-based biomarkers and cognitive performance measures. By combining structural, microstructural, mechanical, vascular, and functional information, we seek to develop sensitive indicators of early brain alterations and robust predictors of disease progression.

We leverage a broad spectrum of MRI techniques, including:

• Anatomical MRI (T1-weighted): volumetric analysis, cortical thickness, and fractal dimension to characterize macrostructural brain integrity.
• Diffusion MRI:
  • DTI (fractional anisotropy, FA; mean diffusivity, MD),
  • NODDI (neurite density index, NDI; orientation dispersion index, ODI; free water, FW),
  • IVIM (diffusion coefficients D and D*; perfusion fraction f),
     to assess microstructural and microvascular properties.
  • Tensor-valued encoding —uFA, MKi, and MKa—to capture microscopic anisotropy and tissue heterogeneity.
• MR Elastography: measurements of tissue stress and strain to characterize biomechanical properties.
• Functional MRI: evaluation of cerebrovascular reactivity (CVR) and functional dynamics.
• Perfusion MRI: quantification of cerebral blood flow (CBF).
• Quantitative Susceptibility Mapping (QSM): assessment of iron content and tissue composition.
• Myelin Water Imaging: estimation of myelin integrity.

These imaging-derived metrics are integrated with blood biomarkers of inflammation, vascular health, and neurodegeneration, as well as comprehensive cognitive assessments. Using machine learning and predictive modeling, we aim to:

• Identify early signatures of neurological disease,
• Predict current disease status, and
• Forecast longitudinal disease progression across follow-up visits.

This multimodal, data-driven approach enables a deeper understanding of the biological mechanisms underlying brain disorders and supports the development of personalized tools for early detection, monitoring, and intervention. Target applications include HIV infection, COVID-19, Alzheimer’s disease, stroke, multiple sclerosis, and vascular dementia, with the ultimate goal of translating research insights into improved patient outcomes.