Major depressive disorder in 188 adolescents was associated with significantly weaker temporal variability of the dorsal attention network compared to 94 healthy controls.
Observational (n=282)
What are the dynamic neural network characteristics associated with attention deficits in adolescents with MDD, and how do they respond to treatment?
Dynamic network characteristics, specifically EEG microstate transitions and fMRI dorsal attention network variability, mediate attention deficits in adolescents with MDD and may serve as clinical biomarkers.
Attention deficit is a critical symptom that impairs social functioning in adolescents with major depressive disorder (MDD). In this study, we aimed to explore the dynamic neural network activity associated with attention deficits and its relationship with clinical outcomes in adolescents with MDD. We included 188 adolescents with MDD and 94 healthy controls. By combining psychophysics, resting-state electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) techniques, we aimed to identify dynamic network features through the investigation of EEG microstate characteristics and related temporal network features in adolescents with MDD. At baseline, microstate analysis revealed that the occurrence of Microstate C in the patient group was lower than that in healthy controls, whereas the duration and coverage of Microstate D increased in the MDD group. Mediation analysis revealed that the probability of transition from Microstate C to D mediated anhedonia and attention deficits in the MDD group. fMRI results showed that the temporal variability of the dorsal attention network (DAN) was significantly weaker in patients with MDD than in healthy controls. Importantly, the temporal variability of DAN mediated the relationship between anhedonia and attention deficits in the patient group. After acute-stage treatment, the response prediction group (RP) showed improvement in Microstates C and D compared to the nonresponse prediction group (NRP). For resting-state fMRI data, the temporal variability of DAN was significantly higher in the RP group than in the NRP group. Overall, this study enriches our understanding of the neural mechanisms underlying attention deficits in patients with MDD and provides novel clinical biomarkers.
Wen et al. (Fri,) conducted a observational in Major depressive disorder (n=282). Major depressive disorder vs. Healthy controls was evaluated on Dynamic neural network activity (EEG microstates and fMRI temporal variability of the dorsal attention network). Major depressive disorder in 188 adolescents was associated with significantly weaker temporal variability of the dorsal attention network compared to 94 healthy controls.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: