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Updated: May 29, 2025

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
Published on: April 1, 2018
A comparative diagnostic study using clinical and multimodal assessment, including functional neuroimaging and
Guocan Ma1,2, Alexander E Eng3, Soon-Kiat Chiang2,4
1School of Medicine, Tsinghua Medicine, Tsinghua University, Beijing, People's Republic of China.
Aim:
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that may persist into adulthood, with no established objective diagnostic tool yet. This study aims to propose a multimodal objective assessment tool involving clinical assessments, functional neuroimaging, and oculomotricity measurement for ADHD in young adults.
Methods:
Seventy-one medication-naïve patients and 71 healthy controls (HCs) aged 18 to 28 underwent clinical interviews, Conners' Adult ADHD Rating Scale (CAARS) questionnaire, functional near-infrared spectroscopy (fNIRS), oculomotricity task, and Conners' Continuous Performance Task (CPT) 3rd edition. Student's t-tests with Bonferroni's correction were performed to compare the performance between groups, and logistic regression was used for classification.
Results:
ADHD patients had significantly lower frontal hemodynamic response during verbal fluency task (VFT) (P = 0.0003), more anticipatory eye movements during overlap task (P = 0.0006), higher latency (P < 0.0001), anticipatory (P < 0.0001), and errors (P < 0.0001) during anti-saccade task, as well as higher commission errors (P < 0.0001) and standard deviation in hit reaction time (HRT) (P = 0.0018). The multivariate logistic regression model featuring these seven parameters from the three objective tests (fNIRS-VFT, oculomotricity, and CPT) yielded an area under the receiver operating characteristic curve (AUC) value of 0.892 (95% confidence interval (CI): 0.840-0.944), with sensitivity and specificity of 80.28% and 84.51%, respectively.
Conclusion:
This multimodal assessment offered an accurate diagnostic tool for ADHD in young adults and laid the foundation for future machine-learning approaches.
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