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Nitrous oxide has rapid antidepressant effects in patients with treatment-resistant depression (TRD), but its underlying mechanisms of therapeutic actions are not well understood. Moreover, most of the current studies lack objective biological indicators to evaluate the changes of nitrous oxide-induced brain function for TRD. Therefore, this study assessed the effect of nitrous oxide on brain function for TRD based on event-related potential (ERP) components and functional connectivity networks (FCNs) methods. In this randomized, longitudinal, placebo-controlled trial, all TRD participants were divided into two groups to receive either a 1-hour inhalation of nitrous oxide or a placebo treatment, and they took part in the same task-state electroencephalogram (EEG) experiment before and after treatment. The experimental results showed that nitrous oxide improved depressive symptoms better than placebo in terms of 17-Hamilton Depression Rating Scale score (HAMD-17). Statistical analysis based on ERP components showed that nitrous oxide-induced significant differences in amplitude and latency of N1, P1, N2, P2. In addition, increased brain functional connectivity was found after nitrous oxide treatment. And the change of network metrics has a significant correlation with decreased depressive symptoms. These findings may suggest that nitrous oxide improves depression symptoms for TRD by modifying brain function., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2023. Published by Elsevier B.V.)

Studies have shown that attention bias can affect behavioral indicators in patients with depression, but it is still unclear how this bias affects the brain network topology of patients with mild depression (MD). Therefore, a novel functional brain network analysis and hierarchical clustering methods were used to explore the abnormal brain topology of MD patients based on EEG signals during the visual search paradigm. The behavior results showed that the reaction time of MD group was significantly higher than that of normal group. The results of functional brain network indicated significant differences in functional connections between the two groups, the amount of inter-hemispheric long-distance connections are much larger than intra-hemispheric short-distance connections. Patients with MD showed significantly lower local efficiency and clustering coefficient, destroyed community structure of frontal lobe and parietal-occipital lobe, frontal asymmetry, especially in beta band. In addition, the average value of long-distance connections between left frontal and right parietal-occipital lobes presented significant correlation with depressive symptoms. Our results suggested that MD patients achieved long-distance connections between the frontal and parietal-occipital regions by sacrificing the connections within the regions, which might provide new insights into the abnormal cognitive processing mechanism of depression.

At present, depression has become a main health burden in the world. However, there are many problems with the diagnosis of depression, such as low patient cooperation, subjective bias and low accuracy. Therefore, reliable and objective evaluation method is needed to achieve effective depression detection. Electroencephalogram (EEG) and eye movements (EMs) data have been widely used for depression detection due to their advantages of easy recording and non-invasion. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, both static and dynamic CBEM were discussed. In the proposed model, EEG or EMs dataset was divided into subsets by the context of the experiments, and then a majority vote strategy was used to determine the subjects' label. The validation of the method is testified on two datasets which included free viewing eye tracking and resting-state EEG, and these two datasets have 36,34 subjects respectively. For these two datasets, CBEM achieves accuracies of 82.5% and 92.65% respectively. The results show that CBEM outperforms traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and provide an effective method for identificationof depression, which in the future could be used for the auxiliary diagnosis of depression.

The diagnosis of depression almost exclusively depends on doctor-patient communication and scale analysis, which have the obvious disadvantages such as patient denial, poor sensitivity, subjective biases and inaccuracy. An objective, automated method that predicts clinical outcomes in depression is essential for increasing the accuracy of depression recognition and treatments. This paper aims at better recognizing depression using the transformation of EEG features and machine learning methods. An experiment based on emotional face stimuli task was conducted, and twenty-eight subjects' EEG data were recorded from 128-channel HydroCel Geodesic Sensor Net (HCGSN) by Net Station software. The Mini International Neuropsychiatric Interview (MINI) was used by psychiatrists as the criterion for diagnosis of depression patients. The power spectral density and activity were respectively extracted as original features using Auto-regress model and Hjorth algorithm with different time windows. Two separate approaches processed the features: ensemble learning and deep learning. For the ensemble learning, a deep forest transformed the original features to new features that potentially improve feature engineering and a support vector machine (SVM) that was applied as classifier. For deep learning method, we added spatial information of EEG caps to both features by image conversion and adopted convolutional neural network (CNN) to recognize them. The performance of both methods was evaluated for separated and total frequency bands. As a result, the best accuracy obtained was 89.02% when we used the ensemble model and power spectral density. The best accuracy of deep learning method was 84.75% using the activity. These experimental results prove the efficiency of the proposed methods and show that EEG could be used as a reliable indicator for depression recognition, which makes it possible for EEG-based portable system design and application in auxiliary depression recognition in the future., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Electroencephalography (EEG)-based studies focus on depression recognition using data mining methods, while those on mild depression are yet in infancy, especially in effective monitoring and quantitative measure aspects. Aiming at mild depression recognition, this study proposed a computer-aided detection (CAD) system using convolutional neural network (ConvNet). However, the architecture of ConvNet derived by trial and error and the CAD system used in clinical practice should be built on the basis of the local database; we therefore applied transfer learning when constructing ConvNet architecture. We also focused on the role of different aspects of EEG, i.e., spectral, spatial, and temporal information, in the recognition of mild depression and found that the spectral information of EEG played a major role and the temporal information of EEG provided a statistically significant improvement to accuracy. The proposed system provided the accuracy of 85.62% for recognition of mild depression and normal controls with 24-fold cross-validation (the training and test sets are divided based on the subjects). Thus, the system can be clinically used for the objective, accurate, and rapid diagnosis of mild depression. Graphical abstract The EEG power of theta, alpha, and beta bands is calculated separately under trial-wise and frame-wise strategies and is organized into three input forms of deep neural networks: feature vector, images without electrode location (spatial information), and images with electrode location. The role of EEG's spectral and spatial information in mild depression recognition is investigated through ConvNet, and the role of EEG's temporal information is investigated using different architectures to aggregate temporal features from multiple frames. The ConvNet and models for aggregating temporal features are transferred from the state-of-the-art model in mental load classification.

Background and Objective: Depression has become a major health burden worldwide, and effectively detection of such disorder is a great challenge which requires latest technological tool, such as Electroencephalography (EEG). This EEG-based research seeks to find prominent frequency band and brain regions that are most related to mild depression, as well as an optimal combination of classification algorithms and feature selection methods which can be used in future mild depression detection., Methods: An experiment based on facial expression viewing task (Emo_block and Neu_block) was conducted, and EEG data of 37 university students were collected using a 128 channel HydroCel Geodesic Sensor Net (HCGSN). For discriminating mild depressive patients and normal controls, BayesNet (BN), Support Vector Machine (SVM), Logistic Regression (LR), k-nearest neighbor (KNN) and RandomForest (RF) classifiers were used. And BestFirst (BF), GreedyStepwise (GSW), GeneticSearch (GS), LinearForwordSelection (LFS) and RankSearch (RS) based on Correlation Features Selection (CFS) were applied for linear and non-linear EEG features selection. Independent Samples T-test with Bonferroni correction was used to find the significantly discriminant electrodes and features., Results: Data mining results indicate that optimal performance is achieved using a combination of feature selection method GSW based on CFS and classifier KNN for beta frequency band. Accuracies achieved 92.00% and 98.00%, and AUC achieved 0.957 and 0.997, for Emo_block and Neu_block beta band data respectively. T-test results validate the effectiveness of selected features by search method GSW. Simplified EEG system with only FP1, FP2, F3, O2, T3 electrodes was also explored with linear features, which yielded accuracies of 91.70% and 96.00%, AUC of 0.952 and 0.972, for Emo_block and Neu_block respectively., Conclusions: Classification results obtained by GSW + KNN are encouraging and better than previously published results. In the spatial distribution of features, we find that left parietotemporal lobe in beta EEG frequency band has greater effect on mild depression detection. And fewer EEG channels (FP1, FP2, F3, O2 and T3) combined with linear features may be good candidates for usage in portable systems for mild depression detection., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism. • Cross-sectional and longitudinal papers of resting-state dynamic functional connectivity (dFC) in depression were reviewed. • DFC holds a close relation to pathophysiology and finer delineation of major depressive disorder (MDD). • Abnormal brain regions, functional networks and brain state were detected with dFC. Relevant recognition was also enhanced. • Default mode network with its subregions exhibited close relation to the pathological mechanism of MDD. [ABSTRACT FROM AUTHOR]