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Sci Rep. 2019 Dec 27;9(1):20028. doi: 10.1038/s41598-019-56398-8.

Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders.

Author information

1
Department of Neurology, Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany. mmuthura@uni-mainz.de.
2
Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany.
3
Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Autism Research and Intervention Center of Excellence, University Hospital Frankfurt am Main, Goethe University, Frankfurt am Main, Germany.
4
Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Ev. Hospital Bielefeld, Bielefeld, Germany.
5
Department of Neurology, Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.

Abstract

Functional and effective connectivity measures for tracking brain region interactions that have been investigated using both electroencephalography (EEG) and magnetoencephalography (MEG) bringing up new insights into clinical research. However, the differences between these connectivity methods, especially at the source level, have not yet been systematically studied. The dynamic characterization of coherent sources and temporal partial directed coherence, as measures of functional and effective connectivity, were applied to multimodal resting EEG and MEG data obtained from 11 young patients (mean age 13.2 ± 1.5 years) with attention-deficit/hyperactivity disorder (ADHD) and age-matched healthy subjects. Additionally, machine-learning algorithms were applied to the extracted connectivity features to identify biomarkers differentiating the two groups. An altered thalamo-cortical connectivity profile was attested in patients with ADHD who showed solely information outflow from cortical regions in comparison to healthy controls who exhibited bidirectional interregional connectivity in alpha, beta, and gamma frequency bands. We achieved an accuracy of 98% by combining features from all five studied frequency bands. Our findings suggest that both types of connectivity as extracted from EEG or MEG are sensitive methods to investigate neuronal network features in neuropsychiatric disorders. The connectivity features investigated here can be further tested as biomarkers of ADHD.

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