Functional Connectivity Based Classification of ADHD Using Different Atlases

TL;DR

This study develops a brain connectivity-based model using fMRI data and various brain atlases to improve ADHD diagnosis accuracy, employing feature extraction and machine learning classification.

Contribution

It introduces a novel approach combining LBEM and HELM for ADHD classification using multiple brain atlases, highlighting CC400's superior performance.

Findings

CC400 atlas yields highest classification accuracy

The proposed model effectively distinguishes ADHD from normal controls

Using multiple atlases helps identify the most informative brain regions

Abstract

These days, computational diagnosis strategies of neuropsychiatric disorders are gaining attention day by day. It's critical to determine the brain's functional connectivity based on Functional-Magnetic-Resonance-Imaging(fMRI) to diagnose the disorder. It's known as a chronic disease, and millions of children amass the symptoms of this disease, so there is much vacuum for the researcher to formulate a model to improve the accuracy to diagnose ADHD accurately. In this paper, we consider the functional connectivity of a brain extracted using various time templates/Atlases. Local-Binary Encoding-Method (LBEM) algorithm is utilized for feature extraction, while Hierarchical- Extreme-Learning-Machine (HELM) is used to classify the extracted features. To validate our approach, fMRI data of 143 normal and 100 ADHD affected children is used for experimental purpose. Our experimental results are based on comparing various Atlases given as CC400, CC200, and AAL. Our model achieves high performance with CC400 as compared to other Atlases