RESUMO
Continuous challenges have been imposed on mental health science by Anxiety and Depression disorders as the most prevalent and debilitating psychiatric conditions worldwide. Pharmacologic and cognitive behavioral therapies, either alone or in combination, have been considered as the first-line therapies, however, resistant symptomatology is prevalent in comorbid conditions with symptoms remaining after interventions. The demand for new therapeutic solutions has given space to the development of non-invasive brain stimulation techniques (NIBS), and the transmagnetic direct current stimulation (tDCS) has been reported as a safe and well-tolerated technique for the treatment of several mental health conditions, including Anxiety and Depression disorders. Relying on quantitative electroencephalography(qEEG)- tDCS approach, the current study aims to inspect the effect of tDCS intervention on patients who suffer from anxiety-depression comorbidity, in particular, the impact of tDCS intervention on qEEG spectral power activity and resting-state connectivity organization during eyes closed and eyes open protocols. QEEG data were acquired from eight patients suffering from moderate to severe anxiety-depression comorbid symptoms along with poor coping skills to manage stress and negative affect. Twelve control subjects allocated in the control group exhibiting low to moderate symptoms in both anxiety and depression conditions went also through the qEEG data acquisition. In addition, a sham-controlled study was conducted, and the patient group went through resting-state qEEG-tDCS neuromodulation once a week for ten weeks. Various-stage qEEG recordings were performed to inspect the efficacy of tDCS treatment during the modulation of brain regions involved in the regulation of affective responses. Our results demonstrated that after tDCS neuromodulation, the patients' groups exhibited decreased absolute power abnormalities over the left anterior cingulate cortex and reduced abnormal activity in the alpha band over posterior regions; improved functional connectivity indexes; decreased anxiety and depressive scores while positive affect score was improved. Besides the promising improvements, our study did not find a significant tDCS effect on perceived stress and negative affect scores. Consistently, significant differences in absolute spectral power over the left anterior cingulate cortex were detected among the patient group, as compared to the controls, as expected. Therefore, our study offers preliminary data to understand the neuroplasticity changes that potentially result from the manipulation of cortical excitability during affective regulation protocols followed by the consequent decrease of comorbid anxiety and depressive symptomatology. The pilot study was followed by prospective registration with ChiCTR2200062142.
RESUMO
Neuroimaging studies have revealed that insomnia is characterized by aberrant neuronal connectivity in specific brain regions, but the topological disruptions in the white matter (WM) structural connectivity networks remain largely unknown in insomnia. The current study uses diffusion tensor imaging (DTI) tractography to construct the WM structural networks and graph theory analysis to detect alterations of the brain structural networks. The study participants comprised 30 healthy subjects with insomnia symptoms (IS) and 62 healthy subjects without IS. Both the two groups showed small-world properties regarding their WM structural connectivity networks. By contrast, increased local efficiency and decreased global efficiency were identified in the IS group, indicating an insomnia-related shift in topology away from regular networks. In addition, the IS group exhibited disrupted nodal topological characteristics in regions involving the fronto-limbic and the default-mode systems. To our knowledge, this is the first study to explore the topological organization of WM structural network connectivity in insomnia. More importantly, the dysfunctions of large-scale brain systems including the fronto-limbic pathways, salience network and default-mode network in insomnia were identified, which provides new insights into the insomnia connectome. Topology-based brain network analysis thus could be a potential biomarker for IS.