Age-Dependent EEG patterns for Predicting Treatment Response in ADHD
In this project we use EEG patterns to predict treatment responses for individuals with ADHD across different age groups. Project reports are incorporated in the BHS website.
Analysing Variability in Frontoparietal Activity in Children with and without ADHD
This study examines dorsolateral prefrontal cortex (dlPFC) and posterior parietal cortex (PPC) connectivity and dlPFC BOLD time series in ADHD versus typically developing (TD) children during the cued stop-signal task (CSST) using fMRI data from OpenNeuro ds005899. It is hypothesised that stronger dlPFC-PPC connectivity will be found in the ADHD group.
CVAE-based ADHD neuroimaging analysis
This project applies a contrastive variational autoencoder (CVAE) to Burner-preprocessed MRI data from the ADHD-200 dataset to disentangle ADHD-specific brain features from shared anatomical variation. We explore latent representations using RSA and clustering to better understand neuroanatomical heterogeneity in ADHD.
Dynamic Functional Connectivity of the Default Mode Network in ADHD
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
Age-Dependent EEG patterns for Predicting Treatment Response 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.
Functional Connectivity in ADHD: Group Differences and Predictive Modeling During Spatial Working Memory Task
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.
ADHD diagnosis prediction using machine learning
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.
Detecting ADHD through fMRI signals using ML classification models
We used the ADHD-200 Sample dataset to implement various machine learning classification models, aimed at diagnosing ADHD through resting-state fMRI signals.
Analyzing variability of working memory and reward processing in children with and without ADHD using fMRI data
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.
Working Memory in Children with and without ADHD
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.