This project trains machine learning classification models to make predictions of adhd diagnosis from brain fRMI connectivity measures which are obtained from a resting state ADHD dataset . The main goals of this project are to get more practice with machine learning tools and to learn how work with brain data more precisely fMRI data.
We used the ADHD-200 Sample dataset to implement various machine learning classification models, aimed at diagnosing ADHD through resting-state fMRI signals.
The focus of our project was to gain experience using neuroimaging tools to preprocess, analyze, and visualize functional MRI data. We aimed to explore differential variability in brain connectivity among children with and without ADHD. Project reports are incorporated on the BHS website.
This project aims to using both fMRI data and the behavioral data during a n-back task to compare the difference between ADHD children and the heathly control ones.
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