Machine learning and brain imaging techniques are promising for understanding and classifying mental health conditions. However, the application of machine learning in mental health and integrating brain imaging markers into clinical practice is still limited. Our research aims to bridge this gap by leveraging machine learning algorithms to analyze brain imaging data, including structural MRI, functional MRI, and diffusion tensor imaging.
Our primary goal is to extract brain imaging endophenotypes strongly associated with psychiatric diagnoses and mental health-related characteristics. We will employ conventional techniques and advanced deep learning algorithms like graph neural networks and Transformers to explore these brain imaging phenotypes. By identifying and categorizing these endophenotypes, we can develop new classifications for individuals with mental health issues, leading to improved understanding and targeted interventions. Additionally, these endophenotypes will be instrumental in predicting treatment response over time, facilitating the translation of research findings into clinical practice.
We will conduct two additional analyses to validate the utility of the brain imaging endophenotypes. First, we will investigate associations between these endophenotypes and genetic data using genome-wide association studies (GWAS) and single nucleotide polymorphisms (SNPs). Second, we will develop predictive treatment response and prognosis models using external longitudinal data.
The proposed research holds substantial value in studying and analyzing the structure and function of the brain, offering new perspectives and classifications related to mental health beyond existing diagnostic frameworks. By introducing brain imaging phenotypes and exploring their applications, our findings can have significant implications for future studies and potential clinical use. In a field where heritable and stable endophenotypes are scarce, this exploratory research has the potential to be a groundbreaking endeavor.
