Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity
- Title
- Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity
- Author
- 김성권
- Keywords
- information frontal gyrus; deficit/hyperactivity disorder; prefrontal cortex; clinical utility; children; ADHD; schizophrenia; inhibition; volume; EEG
- Issue Date
- 2021-09
- Publisher
- SPRINGERNATURE
- Citation
- Translational Psychiatry, Vol 11, Iss 1, Pp 1-10 (2021)
- Abstract
- Relatively little is investigated regarding the neurophysiology of adult attention-deficit/hyperactivity disorder (ADHD). Mismatch
negativity (MMN) is an event-related potential component representing pre-attentive auditory processing, which is closely
associated with cognitive status. We investigated MMN features as biomarkers to classify drug-naive adult patients with ADHD and
healthy controls (HCs). Sensor-level features (amplitude and latency) and source-level features (source activation) of MMN were
investigated and compared between the electroencephalograms of 34 patients with ADHD and 45 HCs using a passive auditory
oddball paradigm. Correlations between MMN features and ADHD symptoms were analyzed. Finally, we applied machine learning
to differentiate the two groups using sensor- and source-level features of MMN. Adult patients with ADHD showed significantly
lower MMN amplitudes at the frontocentral electrodes and reduced MMN source activation in the frontal, temporal, and limbic
lobes, which were closely associated with MMN generators and ADHD pathophysiology. Source activities were significantly
correlated with ADHD symptoms. The best classification performance for adult ADHD patients and HCs showed an 81.01%
accuracy, 82.35% sensitivity, and 80.00% specificity based on MMN source activity features. Our results suggest that abnormal MMN
reflects the adult ADHD patients’ pathophysiological characteristics and might serve clinically as a neuromarker of adult ADHD.
- URI
- https://www.proquest.com/docview/2573916398?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/170444
- ISSN
- 2158-3188
- DOI
- 10.1038/s41398-021-01604-3
- Appears in Collections:
- ETC[S] > 연구정보
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