Yue Gu1, Shuo Miao, Junxia Han, Zhenhu Liang, Gaoxiang Ouyang, Jian Yang, Xiaoli Li. 1. School of Computer Science and Engineering & Key Laboratory of Computer Vision and Systems (Ministry of Education), Tianjin University of Technology, Tianjin 300384, People's Republic of China.
Abstract
OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting children and adults. Previous studies found that functional near-infrared spectroscopy (fNIRS) can reveal significant group differences in several brain regions between ADHD children and healthy controls during working memory tasks. This study aimed to use fNIRS activation patterns to identify ADHD children from healthy controls. APPROACH: FNIRS signals from 25 ADHD children and 25 healthy controls performing the n-back task were recorded; then, multivariate pattern analysis was used to discriminate ADHD individuals from healthy controls, and classification performance was evaluated for significance by the permutation test. MAIN RESULTS: The results showed that 86.0% ([Formula: see text]) of participants can be correctly classified in leave-one-out cross-validation. The most discriminative brain regions included the bilateral dorsolateral prefrontal cortex, inferior medial prefrontal cortex, right posterior prefrontal cortex, and right temporal cortex. SIGNIFICANCE: This study demonstrated that, in a small sample, multivariate pattern analysis can effectively identify ADHD children from healthy controls based on fNIRS signals, which argues for the potential utility of fNIRS in future assessments.
OBJECTIVE:Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting children and adults. Previous studies found that functional near-infrared spectroscopy (fNIRS) can reveal significant group differences in several brain regions between ADHDchildren and healthy controls during working memory tasks. This study aimed to use fNIRS activation patterns to identify ADHDchildren from healthy controls. APPROACH: FNIRS signals from 25 ADHDchildren and 25 healthy controls performing the n-back task were recorded; then, multivariate pattern analysis was used to discriminate ADHD individuals from healthy controls, and classification performance was evaluated for significance by the permutation test. MAIN RESULTS: The results showed that 86.0% ([Formula: see text]) of participants can be correctly classified in leave-one-out cross-validation. The most discriminative brain regions included the bilateral dorsolateral prefrontal cortex, inferior medial prefrontal cortex, right posterior prefrontal cortex, and right temporal cortex. SIGNIFICANCE: This study demonstrated that, in a small sample, multivariate pattern analysis can effectively identify ADHDchildren from healthy controls based on fNIRS signals, which argues for the potential utility of fNIRS in future assessments.
Authors: Wei Shen; Yiheng Tu; Randy L Gollub; Ana Ortiz; Vitaly Napadow; Siyi Yu; Georgia Wilson; Joel Park; Courtney Lang; Minyoung Jung; Jessica Gerber; Ishtiaq Mawla; Suk-Tak Chan; Ajay D Wasan; Robert R Edwards; Ted Kaptchuk; Shasha Li; Bruce Rosen; Jian Kong Journal: Neuroimage Clin Date: 2019-03-14 Impact factor: 4.881
Authors: Evelyne Mercure; Samuel Evans; Laura Pirazzoli; Laura Goldberg; Harriet Bowden-Howl; Kimberley Coulson-Thaker; Indie Beedie; Sarah Lloyd-Fox; Mark H Johnson; Mairéad MacSweeney Journal: Neurobiol Lang (Camb) Date: 2020-04-06