TY - JOUR
T1 - Applied Machine Learning Method to Predict Children With ADHD Using Prefrontal Cortex Activity
T2 - A Multicenter Study in Japan
AU - Yasumura, Akira
AU - Omori, Mikimasa
AU - Fukuda, Ayako
AU - Takahashi, Junichi
AU - Yasumura, Yukiko
AU - Nakagawa, Eiji
AU - Koike, Toshihide
AU - Yamashita, Yushiro
AU - Miyajima, Tasuku
AU - Koeda, Tatsuya
AU - Aihara, Masao
AU - Tachimori, Hisateru
AU - Inagaki, Masumi
N1 - Funding Information:
We would like to thank the following people for their cooperation in collecting data: Dr. Shingo Oana, Department of Pediatrics, Tokyo Medical University; Dr. Yoshimi Kaga, Department of Pediatrics, University of Yamanashi; Dr. Kotaro Yuge, Department of Pediatrics, Kurume University; Dr. Yoriko Okamoto, Graduate School of Regional Sciences, Tottori University; and Dr. Chikaho Naka, Department of Special Needs Education, Tokyo Gakugei University. Cactus Communications provided editorial support in the form of writing based on the authors’ detailed directions, collating author comments, copyediting, fact checking, and formatting. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by an Intramural Research Grant (25-6; Clinical Research for Diagnostic and Therapeutic Innovations in Developmental Disorders) for Neurological and Psychiatric Disorders of the National Center of Neurology and Psychiatry (NCNP); a Grant-in-Aid for Young Scientists (A) from the Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant number 15H05405 to Akira Yasumura), and a Grant-in-Aid for Challenging Exploratory Research from JSPS KAKENHI (Grant number 15K13167 to Akira Yasumura).The sources had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
Publisher Copyright:
© The Author(s) 2017.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Objective: To establish valid, objective biomarkers for ADHD using machine learning. Method: Machine learning was used to predict disorder severity from new brain function data, using a support vector machine (SVM). A multicenter approach was used to collect data for machine learning training, including behavioral and physiological indicators, age, and reverse Stroop task (RST) data from 108 children with ADHD and 108 typically developing (TD) children. Near-infrared spectroscopy (NIRS) was used to quantify change in prefrontal cortex oxygenated hemoglobin during RST. Verification data were from 62 children with ADHD and 37 TD children from six facilities in Japan. Results: The SVM general performance results showed sensitivity of 88.71%, specificity of 83.78%, and an overall discrimination rate of 86.25%. Conclusion: A SVM using an objective index from RST may be useful as an auxiliary biomarker for diagnosis for children with ADHD.
AB - Objective: To establish valid, objective biomarkers for ADHD using machine learning. Method: Machine learning was used to predict disorder severity from new brain function data, using a support vector machine (SVM). A multicenter approach was used to collect data for machine learning training, including behavioral and physiological indicators, age, and reverse Stroop task (RST) data from 108 children with ADHD and 108 typically developing (TD) children. Near-infrared spectroscopy (NIRS) was used to quantify change in prefrontal cortex oxygenated hemoglobin during RST. Verification data were from 62 children with ADHD and 37 TD children from six facilities in Japan. Results: The SVM general performance results showed sensitivity of 88.71%, specificity of 83.78%, and an overall discrimination rate of 86.25%. Conclusion: A SVM using an objective index from RST may be useful as an auxiliary biomarker for diagnosis for children with ADHD.
KW - ADHD
KW - developmental disorder
KW - machine learning
KW - near-infrared spectroscopy
UR - http://www.scopus.com/inward/record.url?scp=85041583467&partnerID=8YFLogxK
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U2 - 10.1177/1087054717740632
DO - 10.1177/1087054717740632
M3 - Article
C2 - 29154696
AN - SCOPUS:85041583467
SN - 1087-0547
VL - 24
SP - 2012
EP - 2020
JO - Journal of Attention Disorders
JF - Journal of Attention Disorders
IS - 14
ER -