Gian Candrian | Searcharxiv

arXiv2025

Altered oscillatory brain networks during emotional face processing in ADHD: an eLORETA and functional ICA study

Attention-deficit/hyperactivity disorder (ADHD) is characterized by executive dysfunction and difficulties in processing emotional facial expressions, yet the large-scale neural dynamics underlying these impairments remain insufficiently understood. This study applied network-based EEG source analysis to examine oscillatory cortical activity during cognitive and emotional Go/NoGo tasks in individuals with ADHD. EEG data from 272 participants (ADHD n equals 102, controls n equals 170, age range 6 to 60 years) were analyzed using exact low-resolution brain electromagnetic tomography combined with functional independent component analysis, yielding ten frequency-resolved cortical networks. Mixed-effects ANCOVAs were conducted on independent component loadings with Group, Task, and Condition as factors and age and sex as covariates. ADHD participants showed statistically significant but small increases in activation across several networks, including a gamma-dominant inferior temporal component showing a Group effect and a Group by Condition interaction with stronger NoGo-related activation in ADHD. Two additional components showed similar but weaker NoGo-selective patterns. A main effect of Task emerged only for one temporal delta component, with higher activation during the VCPT than the ECPT. No Group by Task interactions were observed. Behavioral results replicated the established ADHD performance profile, with slower responses, greater variability, and higher error rates, particularly during the emotional ECPT. Overall, the findings reveal subtle alterations in oscillatory brain networks during inhibitory processing in ADHD, with modest effect sizes embedded within substantial within-group variability. These results support a dimensional view of ADHD neurobiology and highlight the limited discriminative power of network-level EEG markers.

q-bio.NC↗

arXiv2018

Neurofeedback Tunes Scale-Free Dynamics in Spontaneous Brain Activity

Brain oscillations exhibit long-range temporal correlations (LRTCs), which reflect the regularity of their fluctuations: low values representing more random (decorrelated) while high values more persistent (correlated) dynamics. LRTCs constitute supporting evidence that the brain operates near criticality, a state where neuronal activities are balanced between order and randomness. Here, healthy adults used closed-loop brain training (neurofeedback, NFB) to reduce the amplitude of alpha oscillations, producing a significant increase in spontaneous LRTCs post-training. This effect was reproduced in patients with post-traumatic stress disorder, where abnormally random dynamics were reversed by NFB, correlating with significant improvements in hyperarousal. Notably, regions manifesting abnormally low LRTCs (i.e., excessive randomness) normalized toward healthy population levels, consistent with theoretical predictions about self-organized criticality. Hence, when exposed to appropriate training, spontaneous cortical activity reveals a residual capacity for "self-tuning" its own temporal complexity, despite manifesting the abnormal dynamics seen in individuals with psychiatric disorder. Lastly, we observed an inverse-U relationship between strength of LRTC and oscillation amplitude, suggesting a breakdown of long-range dependence at high/low synchronization extremes, in line with recent computational models. Together, our findings offer a broader mechanistic framework for motivating research and clinical applications of NFB, encompassing disorders with perturbed LRTCs.

q-bio.NC↗