Big Data Analytics for Uncovering Voxel Connectivity Patterns in Attention Deficit Hyperactivity Disorder

Area of Science:

• Neuroscience
• Computational Psychiatry
• Medical Imaging Analysis

Background:

• Attention Deficit Hyperactivity Disorder (ADHD) presents complex, heterogeneous brain activity patterns.
• Identifying specific brain regions linked to ADHD is challenging due to neuroimaging data complexity.

Purpose of the Study:

• To apply advanced machine learning for uncovering critical features in ADHD diagnosis.
• To improve classification performance using neuroimaging data.

Main Methods:

• Analysis of 5937 brain voxels from ADHD patient neuroimaging records.
• Feature selection using Boruta, Random Forest with DALEX, and Neural Networks.
• Dimensionality reduction (PCA) and clustering (KMeans, MCLUST) for pattern exploration; evaluation of activation functions (ReLU, Sigmoid, Tanh) in deep neural networks.

Main Results:

• Identification of significant ADHD predictors: Fusiform Gyrus, Thalamus, and Superior Temporal Gyrus.
• Machine learning integration improved classification accuracy.
• ReLU-based neural networks showed superior performance across most metrics.

Conclusions:

• A robust machine learning framework can analyze high-dimensional neuroimaging data for ADHD.
• Identified brain regions serve as biologically relevant markers for ADHD.
• Findings support data-driven approaches for neuropsychiatric diagnosis and personalized interventions.