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<b><i>Introduction:</i></b> Functional connectivity is attracting increasing attention for understanding the pathophysiology of depression and predicting the therapeutic efficacy of antidepressants. In this study, we evaluated effective connectivity using isolated effective coherence (iCoh), an effective functional connectivity analysis method developed from low-resolution brain electromagnetic tomography (LORETA) and estimated its practical usefulness for predicting the reaction to antidepressants in theta and alpha band iCoh values. <b><i>Methods:</i></b> We enrolled 25 participants from a depression treatment randomized study (the GUNDAM study) in which electroencephalography was performed before treatment. We conducted iCoh between the rostral anterior cingulate cortex (rACC) and anterior insula (AI), which are associated with the salience network. The patients were divided into responder and nonresponder groups at 4 weeks after the start of treatment, and iCoh values were compared between the two groups. Additionally, the sensitivity and specificity of iCoh were calculated using the receiver-operating characteristic (ROC) curve. <b><i>Results:</i></b> The Mann-Whitney U test showed significantly weaker connectivity flow from the rACC to the left AI in the alpha band in the responder group. The ROC curve for the connectivity flow from the rACC to the left AI in the alpha band showed 82% sensitivity and 86% specificity. <b><i>Discussion/Conclusion:</i></b> These findings suggest the pathological importance of effective connectivity flow from the rACC to the left AI in the alpha and theta bands and suggest its usefulness as a biomarker to distinguish responders to antidepressants.

Background: Cognitive dysfunction is a persistent residual symptom in major depressive disorders (MDDs) that hinders social and occupational recovery. Cognitive inflexibility is a typical cognitive dysfunction in MDD and refers to difficulty in switching tasks, which requires two subcomponents: forgetting an old task and adapting to a new one. Here, we aimed to disentangle the subcomponents of cognitive inflexibility in MDD and investigate whether they can be improved by transcranial direct current stimulation (tDCS) on the prefrontal cortex. Methods:The current study included 20 patients with MDD (seven females) and 22 age-matched healthy controls (HCs) (seven females). The participants received anodal tDCS on either the dorsomedial prefrontal cortex (DMPFC) or dorsolateral prefrontal cortex (DLPFC) in a crossover design. Before and after the application of tDCS, the participants performed a modified Wisconsin Card Sorting Test, in which the task-switching rules were explicitly described and proactive interference from a previous task rule was occasionally released.Results: We found that the behavioral cost of a task switch was increased in patients with MDD, but that of proactive interference was comparable between patients with MDD and HCs. The response time for anodal DMPFC tDCS was decreased compared with that for anodal tDCS on the DLPFC in MDD.Conclusions: These findings suggest that cognitive inflexibility in MDD is primarily explained by the difficulty to adapt to a new task and environment, and that tDCS on the DMPFC improves behavioral performance during cognitively demanding tasks that require conflict resolution.

<b><i>Introduction:</i></b> It is critical to develop accurate and universally available biomarkers for dementia diseases to appropriately deal with the dementia problems under world-wide rapid increasing of patients with dementia. In this sense, electroencephalography (EEG) has been utilized as a promising examination to screen and assist in diagnosing dementia, with advantages of sensitiveness to neural functions, inexpensiveness, and high availability. Moreover, the algorithm-based deep learning can expand EEG applicability, yielding accurate and automatic classification easily applied even in general hospitals without any research specialist. <b><i>Methods:</i></b> We utilized a novel deep neural network, with which high accuracy of discrimination was archived in neurological disorders in the previous study. Based on this network, we analyzed EEG data of healthy volunteers (HVs, <i>N</i> = 55), patients with Alzheimer’s disease (AD, <i>N</i> = 101), dementia with Lewy bodies (DLB, <i>N</i> = 75), and idiopathic normal pressure hydrocephalus (iNPH, <i>N</i> = 60) to evaluate the discriminative accuracy of these diseases. <b><i>Results:</i></b> High discriminative accuracies were archived between HV and patients with dementia, yielding 81.7% (vs. AD), 93.9% (vs. DLB), 93.1% (vs. iNPH), and 87.7% (vs. AD, DLB, and iNPH). <b><i>Conclusion:</i></b> This study revealed that the EEG data of patients with dementia were successfully discriminated from HVs based on a novel deep learning algorithm, which could be useful for automatic screening and assisting diagnosis of dementia diseases.