Exploring Functional Connectivity in Attention Deficit/Hyperactivity Disorder: A Functional Near-Infrared...

Area of Science:

• Neuroscience
• Medical Imaging

Background:

• Attention deficit/hyperactivity disorder (ADHD) is associated with cognitive challenges and altered brain activity.
• Research on adult ADHD, especially under task conditions, is less extensive than in children.
• Functional connectivity investigation offers insights into the neural characteristics of ADHD.

Purpose of the Study:

• To investigate functional connectivity in adult ADHD patients compared to healthy controls under task-state conditions.
• To explore the potential of functional connectivity metrics as ADHD biomarkers.

Main Methods:

• Utilized a functional near-infrared spectroscopy (fNIRS) dataset of 75 healthy controls and 75 medication-naïve adults with ADHD.
• Compared network characteristics (density, global clustering coefficient, efficiency, betweenness centrality) during a verbal fluency task.
• Employed machine learning classifiers, including linear support vector machine, to assess diagnostic potential.

Main Results:

• Significant differences in functional connectivity density were observed between ADHD patients and controls (p<0.001).
• Machine learning models, particularly linear SVM, demonstrated high classification performance (accuracy ~0.80).
• Distinct functional connectivity patterns were identified in adults with ADHD.

Conclusions:

• fNIRS-derived functional connectivity metrics show promise as objective biomarkers for adult ADHD.
• Task-state fNIRS analysis can differentiate between adults with and without ADHD.
• Further research can validate fNIRS for ADHD diagnosis.