Research Projects

Scientists investigating TBI are constantly looking for novel treatments, including drugs and therapies, to aid in the successful rehabilitation of TBI victims. Accurate diagnosis of TBI is a key area of research because it enables medical professionals to act quickly to aid the suffering person. Understanding the best therapies for TBI and what can aid or hinder a person’s healing is another aim of TBI research. In India, there is a significant gap and opportunities for research to advance the understanding of impacts of traumatic brain injury (TBI). In this project we will focus on the application of graph theory along with statistical learning algorithms to diagnose the aforementioned brain disorders associated with TBI. Therefore, with this proposed project we aim to look for answers to the following Research Questions (RQ): RQ 1: How to do statistical reasoning of neurological communication from the brain network of TBI subjects using scarcely labeled training data samples? RQ 2: How to learn and differentiate dynamic neurological patterns of TBI subjects from control participants? RQ 3: How can the massive scale brain network be handled efficiently while using it as input to the pattern analysis models? RQ 4: How to simultaneously incorporate complementary information from different neural modalities to obtain a better performance of statistical models in identifying the neural patterns of TBI patients? RQ 5: How to train statistical learning models from different medical sites for better generalization and reproducibility for precision medicine? In pursuit of answering the RQs, we aim to develop a graph-based unified framework for the analysis and diagnosis of subjects with TBI. To the best of our knowledge, the proposed research project is the first to develop a unified framework for the analysis of TBI-related cases. The framework can be in general applied for different neurological disorders and hence is most suitable for future-generation neurocomputing systems.