AI-Powered Neuroimaging: Predicting Cognitive Decline Through MRI & fMRI Pattern Recognition

Module 1: Module 1 – Neuroimaging Foundations & Pre‑processing (Day 1)

• Understand T1 relaxation, VBM, and tissue contrast for gray‑ and white‑matter mapping
• Apply BIDS standards, slice‑timing, distortion, and motion correction for robust MRI data
• Execute brain extraction, bias‑field correction, and registration to MNI space using FSL, ANTs, and HD‑BET

Module 2: Module 2 – Classical Machine Learning for MRI/fMRI (Day 2)

• Construct feature matrices from GM volumes, cortical thickness, and functional connectivity edges
• Perform feature selection, nested cross‑validation, and model evaluation (AUC‑ROC, balanced accuracy)
• Implement SVM, Random Forest, and LASSO pipelines on structural and diffusion metrics

Module 3: Module 3 – Deep Learning Architectures (Day 3)

• Build 3D‑CNN, ResNet‑3D, and DenseNet‑3D models for volumetric T1w classification
• Explore Vision Transformers (ViT) and Graph Neural Networks for multimodal fusion
• Apply U‑Net for hippocampal segmentation and interpret models with GradCAM, Integrated Gradients, LIME, SHAP

Module 4: Module 4 – Clinical Translation & Validation

• Learn TRIPOD‑AI reporting, FDA SaMD considerations, and API inference skeletons
• Integrate multi‑site harmonization (ComBat) and external validation on ADNI datasets
• Design deployment pipelines for real‑time cognitive‑decline risk scoring

Module 5: Module 5 – Explainability & Ethical AI

• Implement SHAP DeepExplainer and attention‑map visualisation for model transparency
• Assess bias, fairness, and privacy in neuroimaging AI pipelines
• Document reproducible research workflows using Jupyter and Git

Module 6: Module 6 – Capstone Project

• Apply end‑to‑end pipeline on a real ADNI subset to predict MCI‑to‑AD conversion
• Generate a clinical report with model performance, interpretability visualisations, and deployment script
• Present findings to peer mentors and receive feedback for improvement