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Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol
Published on: April 1, 2018
Identifying ADHD children using hemodynamic responses during a working memory task measured by functional
Yue Gu1, Shuo Miao2, Junxia Han3
1School 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.
Insights
Functional near-infrared spectroscopy (fNIRS) effectively identified children with attention-deficit/hyperactivity disorder (ADHD) from healthy controls. This neuroimaging technique shows promise for ADHD assessment in clinical settings.
Area of Science:
- Neuroscience
- Medical Imaging
- Developmental Psychology
Background:
- Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder impacting cognitive functions.
- Previous research indicates functional near-infrared spectroscopy (fNIRS) can detect brain differences in ADHD during working memory tasks.
Purpose of the Study:
- To investigate the efficacy of fNIRS brain activation patterns in distinguishing children with ADHD from healthy controls.
- To explore the potential of fNIRS as a diagnostic tool for ADHD.
Main Methods:
- fNIRS data were collected from 25 children diagnosed with ADHD and 25 typically developing controls during an n-back task.
- Multivariate pattern analysis (MVPA) was employed to classify participants based on their fNIRS signals.
- Permutation testing was used to assess the statistical significance of classification accuracy.
Main Results:
- An 86.0% classification accuracy was achieved in identifying ADHD participants using a leave-one-out cross-validation approach.
- Key brain regions contributing to the classification included the bilateral dorsolateral prefrontal cortex, inferior medial prefrontal cortex, right posterior prefrontal cortex, and right temporal cortex.
Conclusions:
- Multivariate pattern analysis of fNIRS signals can effectively differentiate children with ADHD from healthy controls.
- These findings highlight the potential of fNIRS as a valuable tool for ADHD assessment, even with a small sample size.
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.
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