Classification of children with ADHD through task-related EEG recordings via Swarm-Decomposition-based Phase Locking
Insights
This study introduces a novel EEG analysis framework for Attention Deficit/Hyperactivity Disorder (ADHD), improving the identification of ADHD biomarkers. The new method shows superior performance compared to existing techniques in distinguishing ADHD from typical development.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Computational Psychiatry
Background:
- Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in children.
- Existing Electroencephalography (EEG) biomarkers for ADHD lack clinical establishment despite their importance.
- Understanding alterations in brain activity, specifically Cross-Frequency Coupling (CFC), is crucial for ADHD research.
Purpose of the Study:
- To develop and evaluate a novel framework for analyzing task-related EEG data in children with and without ADHD.
- To investigate Cross-Frequency Coupling (CFC) phenomena as a potential indicator for ADHD.
- To transition from sensor-level to source-level connectivity analysis for improved ADHD detection.
Main Methods:
- Utilized task-related EEG recordings from 61 ADHD and 60 non-ADHD children.
- Employed Swarm Decomposition for personalized brain rhythm extraction into oscillatory modes.
- Applied phase locking value (PLV) for feature extraction and evaluated classification performance using nested 5-fold cross-validation.
Main Results:
- The proposed framework achieved an Area Under the Receiver Operating Characteristics Curve (AUROC) of 0.9166.
- This significantly outperformed the commonly used filter-based brain rhythm extraction method (AUROC: 0.8361).
- Demonstrated superiority over other state-of-the-art functional connectivity approaches for ADHD classification.
Conclusions:
- The novel framework offers enhanced efficiency and superiority in classifying ADHD individuals based on EEG data.
- Provides new insights into brain regions involved in ADHD task-related functions.
- Holds promise for developing objective EEG biomarkers for ADHD by advancing functional connectivity analysis.
Abstract:
Attention Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder mainly affecting children. ADHD children brain activity is reported to present alterations from neurotypically developed children, yet establishment of an EEG biomarker, which is of high importance in clinical practice and research, has not been achieved. In this work, task-related EEG recordings from 61 ADHD and 60 age-matched non-ADHD children are analyzed to examine the underlying Cross-Frequency Coupling phenomena. The proposed framework introduces personalized brain rhythm extraction in the form of oscillatory modes via Swarm Decomposition, allowing for the transition from sensor-level connectivity to source-level connectivity. Oscillatory modes are then subjected to a phase locking value-based feature extraction and the efficiency of the extracted features in separating ADHD from non-ADHD individuals is evaluated by means of a nested 5-fold cross validation scheme. The experimental results of the proposed framework (Area Under the Receiver Operating Characteristics Curve-AUROC: 0.9166) when benchmarked against the commonly used filter-based brain rhythm extraction (AUROC: 0.8361) underscore its efficiency and demonstrate its overall superiority over other state-of-the-art functional connectivity approaches in this classification task for this dataset.Clinical relevance-This framework provides novel insights about brain regions of interest that are involved in ADHD task-related function and holds promise in providing objective ADHD biomarkers by extending classic sensor-level connectivity to source-level.
