Detecting ADHD Based on Brain Functional Connectivity Using Resting-State MEG Signals

Nastaran  Hamedi; Ali  Khadem; Mehdi  Delrobaei; Abbas  Babajani-Feremi

doi:10.18502/fbt.v9i2.8850

Nastaran Hamedi Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Ali Khadem Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Mehdi Delrobaei Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Abbas Babajani-Feremi Dell Medical School, University of Texas at Austin, Austin, TX, USA

DOI: https://doi.org/10.18502/fbt.v9i2.8850

Keywords: Attention Deficit Hyperactivity Disorder; Resting-State Magnetoencephalography; Functional Connectivity; Coherence; Neighborhood Component Analysis; Machine Learning.

Abstract

Purpose: Attention Deficit Hyperactivity Disorder (ADHD) is now recognized as the most common childhood behavioral disorder. This disorder causes school problems and social incompatibility. Thus an accurate diagnosis can help diminish such problems. In this paper, we propose a brain connectomics approach based on eyes-open resting state Magnetoencephalography (rs-MEG) to diagnose subjects with ADHD from Healthy Controls (HC).

Materials and Methods: We used the eyes-open rs-MEG signals recorded from 25 subjects with ADHD and 25 HC. We calculated Coherence (COH) between the MEG sensors in the conventional frequency bands (i.e., delta, theta, alpha, beta, and gamma), selected the most discriminative COH measures by the Neighborhood Component Analysis (NCA), and fed them to three classifiers, including Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel, K-Nearest Neighbors (KNN), and Decision Tree to classify ADHD and HC.

Results: We achieved the best average accuracy of 91.1% for a single-band classifier based on the COH in the delta-band as an input feature of the SVM. However, when we integrated the COH values of all frequency bands as input features, the average accuracy was slightly improved to 92.7% using the SVM classifier.

Conclusion: Our results demonstrate the capability of a functional connectomics approach based on rs-MEG for the diagnosis of ADHD. It is noteworthy that, to the best of our knowledge, COH has not yet been used to diagnose ADHD using rs-MEG data. Furthermore, there is no study on diagnosing ADHD using eyes-open rs-MEG. Thus, a novelty of our proposed method is to use COH and eyes-open rs-MEG data to diagnose ADHD. Moreover, our proposed method showed promising results compared with previous rs-MEG studies for the diagnosis of ADHD.