This project examines dynamic functional connectivity (dFC) within the Default Mode Network (DMN) in children with ADHD using the ADHD-200 dataset. Key methods of analyses include time-varying correlation, clustering of connectivity states, and group comparisons to understand how brain network dynamics differ in ADHD
This project investigates whether there are age-dependent EEG patterns for individuals with ADHD and whether these patterns can predict neurofeedback treatment response. Using the ADHD samples from TDBrain database (n=204), we developed a random forest model to characterize age-related EEG biomarkers and assess treatment prediction across different age groups. Our model achieved AUC=0.865, identifying key EEG signatures including theta-beta ratios and frontal low-frequency patterns that vary with age and treatment response.
Firstly, this project investigates differences in frontoparietal brain connectivity between individuals diagnosed with ADHD and control participants during Spatial Working Memory Task, using fMRI-based connectivity data. In the second part of this project, To classify individuals as either having ADHD or being in control group based on functional connectivity data features machine learning models was tested by using k-fold cross validation.
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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