ArticleBMC psychiatry2025
Frequency-specific alterations in low-frequency functional connectivity in children with ADHD.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThis investigation seeks to elucidate frequency-specific alterations in resting-state functional connectivity (FC) among pediatric subjects with attention-deficit/hyperactivity disorder (ADHD). The study focuses on three distinct sub-bands of slow blood oxygen level-dependent (BOLD) oscillations: Slow3 (0.073–0.198 Hz), Slow4 (0.027–0.073 Hz), and Slow5 (0.010–0.027 Hz). We assess the discriminative capacity of these sub-bands as potential neural biomarkers through the application of machine learning classification techniques. This frequency-resolved methodology addresses a significant lacuna in extant ADHD FC research, which has predominantly treated low-frequency fluctuations as a homogeneous band. By parsing these oscillations into discrete frequency ranges, we aim to provide a more nuanced understanding of the neurophysiological underpinnings of ADHD.
methodsThis study explores the frequency-specific functional connectivity (FC) differences between (ADHD = 40) and healthy control (HC = 45) children, focusing on the Slow3 (0.073–0.198 Hz), Slow4 (0.027–0.073 Hz), and Slow5 (0.010–0.027 Hz) frequency bands. A two-sample t-test was used to identify significant functional connectivity (FC) differences between the two groups. followed by machine learning techniques to evaluate the classification accuracy of these features.
resultsSignificant differences in FC were found between ADHD and HC in specific brain regions. In the Slow3 band, ADHD subjects showed increased connectivity in the right precentral gyrus, which is associated with motor-related neural activity. In the Slow4 and Slow5 bands, higher connectivity was observed in the right inferior frontal orbital region in ADHD compared to HC. Given the identical FC patterns in Slow4 and Slow5, these bands were combined into a single feature for further analysis. Machine learning classification using FC features from Slow3 and the combined Slow4/Slow5 bands demonstrated notable accuracy, with 79% accuracy for ADHD and 82% for HC in the Slow3 band, and 85% accuracy for ADHD and 80% for HC in the combined Slow4/Slow5 band. ROC curve analysis further supported these findings with AUC values of 0.7550 and 0.7830, respectively.
conclusionThese findings suggest that frequency-specific FC patterns may serve as potential biomarkers for ADHD, offering insights into the disorder’s neural mechanisms and aiding in more accurate diagnosis. CLINICAL TRIAL NUMBER: Not applicable.
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