A Foundational Model for Brain Imaging
Faculty: R. Venkatesh Babu (CDS) and Neelam Sinha (CBR)
Understanding the complexities of brain function and structure requires integrating information from multiple neuroimaging modalities, including functional MRI (fMRI) for brain activity, diffusion tensor imaging (DTI) for white matter connectivity, and structural MRI (sMRI) for anatomical insights. Existing computational approaches often focus on single modalities or require extensive labeled data, limiting their generalizability and clinical applicability. This project aims to develop a foundational brain model by leveraging advanced deep learning techniques and multimodal neuroimaging data to create a unified, generalizable representation of brain structure and function.
Objectives and Approach
The goal is to build a large-scale, self-supervised learning model capable of capturing intricate relationships within the brain across different imaging modalities and datasets. The approach involves:
- Multimodal Representation Learning:
- Developing a deep learning framework that integrates fMRI, DTI, and sMRI data.
- Using contrastive and transformer-based models to learn meaningful representations of brain connectivity, function, and structure.
- Exploring graph neural networks (GNNs) for modeling complex inter-regional interactions in the brain.
- Scalability and Generalization:
- Training on diverse, large-scale neuroimaging datasets across multiple populations to improve robustness.
- Implementing domain adaptation techniques to ensure compatibility across different imaging protocols, scanners, and demographic groups.
- Clinical and Computational Applications:
- Fine-tuning the foundational model for downstream tasks such as neurological disease classification, disease progression modeling (e.g., Alzheimer’s, Parkinson’s), cognitive state decoding, and brain-age prediction.
- Enabling personalized diagnostics by identifying patient-specific deviations from normative brain patterns.
- Explainability and Interpretability:
- Integrating explainable AI (XAI) methods to enhance model transparency, allowing neuroscientists and clinicians to interpret the learned representations.
- Using saliency maps, attention mechanisms, and counterfactual analysis to reveal biomarkers of neurological conditions.
Impact and Future Directions
This project aims to establish a benchmark model for computational neuroscience and neuroimaging research, providing a reusable framework for analyzing brain data. The foundational model could accelerate discoveries in neuroscience by enabling rapid, automated analysis of multimodal brain scans and supporting precision medicine applications in neurology and psychiatry.
Future work may extend the model to real-time brain activity decoding, brain-computer interfaces (BCI), and multi-organ neural interaction studies, further bridging the gap between AI and neuroscience.