Exploring Functional Connectivity in Attention Deficit/Hyperactivity Disorder: A Functional Near-Infrared Spectroscopy Study With Machine Learning Analysis
Bibliographic record
Abstract
Functional near-infrared spectroscopy (fNIRS) has shown potential in attention deficit/hyperactivity disorder (ADHD) research, though it is not yet widely used as a primary diagnostic tool. While most previous studies have focused on children and resting-state conditions, research on adult ADHD, particularly under task-state conditions, is increasing but still limited compared to studies on children. Since ADHD is associated with cognitive challenges and alterations in brain activity, investigating functional connectivity during a task can provide a better understanding of its neural characteristics. In this study, we aim to investigate functional connectivity in adult patients with ADHD by comparing them with healthy controls under task-state conditions. We used the fNIRS dataset, which comprised 75 healthy controls and 75 medication-naïve individuals with ADHD. The network characteristics of functional connectivity were compared during a verbal fluency task, specifically focusing on density, global clustering coefficient, efficiency, and average betweenness centrality. By statistical analysis between the two groups, statistical significance was observed in density (p<0.001, t = 5.39, η2 = 0.443). Additionally, various machine learning classifiers were employed to assess the potential of functional connectivity metrics in classifying the two groups. The linear support vector machine achieved accuracy and precision of 0.800, recall of 0.808, and F1-score of 0.799, representing the highest performance among five different classifiers. In conclusion, our findings reveal distinct functional connectivity patterns among the groups, highlighting the potential of fNIRS-derived functional connectivity metrics as biomarkers for ADHD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".