Reduced intra-frontal functional connectivity during a verbal fluency task in depressed individuals with autistic traits: an fNIRS study.

BACKGROUND: Depressive symptoms accompanied by autistic traits represent an important source of clinical heterogeneity. While aberrant functional connectivity (FC) within prefrontal networks has been implicated as a shared neural feature of both autism spectrum disorder (ASD) and depression, task-evoked FC abnormalities in individuals experiencing both remain poorly understood. This study employed functional near-infrared spectroscopy (fNIRS) to evaluate task-evoked FC during a verbal fluency task (VFT), with a focus on prefrontal network organization. METHODS: Fifty neurotypical controls and 43 individuals experiencing depressive symptoms were enrolled. The depressive-state cohort was further subdivided into high autistic traits (DP_AQ-high) and low autistic traits (DP_AQ-low) subgroups based on Autism-Spectrum Quotient (AQ) scores. FC among channels in frontal, temporal, and inferior parietal regions was calculated using zero-lag Pearson correlation analysis of oxyhemoglobin (HbO₂) and deoxyhemoglobin (HbR) signals during the VFT. Between-group FC differences were evaluated using ANOVA for the Control versus depressive-state comparison, and medication-adjusted ANCOVA for the DP_AQ-high versus DP_AQ-low comparison, with diazepam-equivalent anxiolytic dosage and imipramine-equivalent antidepressant dosage as covariates. RESULTS: Relative to neurotypical controls, the depressive-state group exhibited decreased interhemispheric frontal FC across both chromophores, alongside network-specific FC increases and decreases. Within the depressive-state group, the DP_AQ-high subgroup consistently demonstrated increased interhemispheric frontoparietal FC across both chromophores, accompanied by localized intra-frontal FC reductions, compared with the DP_AQ-low subgroup. CONCLUSION: These findings suggest that autistic traits modulate task-evoked large-scale network organization within a clinically relevant depressive-state cohort and support the utility of fNIRS-based network analysis for characterizing neurobiological heterogeneity associated with depressive symptoms.