This project aims to develop a machine learning (ML) model for predicting children’s attention ability using features extracted from over 10,000 MRI images from the Adolescent Brain Cognitive Development (ABCD) study. The project will cover diverse data science topics, including big data in neuroimaging, network analysis, complex systems, feature selection, visualization, and cloud computing. The proposed features encompass those derived from T1 and T2 MRI scans, structural connectivity via diffusion, functional connectivity via resting and task-based fMRI, and non-linear metrics like fractal dimensions and Lyapunov exponents. The ABCD database provides predictive labels, including self-report surveys, clinical assessments (e.g., NIH Toolbox Flanker Inhibitory Control), and ADHD-related diagnosis and symptoms. Model interpretability is a priority. Feature selection should be transparent, and their respective contributions should be reportable and visualized. This project is part of a broader study on brain-computer interfaces and neural plasticity.

The student will have the opportunity to work with large-scale neuroimaging data, MRI/fMRI preprocessing, experimentation with feature selection methods and ML and/or deep learning models. They will have access to cloud computing resources and Google Vertex AI tools. The student will be supported by doctoral trainees and staff engineers in the lab. The expected deliverables will be:

• A deep learning model trained on 10,000 MRI images from the ABCD study to predict children’s attention ability.
• A feature visualization tool to qualitatively and/or quantitatively describe what brain areas and measures are physiologically relevant to attention in children.