MDD-TSVM: A novel semisupervised-based method for major depressive disorder detection using electroencephalogram signals

Effective hyper-connectivity network construction and learning: Application to major depressive disorder identification

Functional connectivity (FC) derived from resting-state fMRI (rs-fMRI) is a primary approach for identifying brain diseases, but it is limited to capturing the pairwise correlation between regions-of-interest (ROIs) in the brain. Thus, hyper-connectivity which describes the higher-order relationship among multiple ROIs is receiving increasing attention. However, most hyper-connectivity methods overlook the directionality of connections. The direction of information flow constitutes a pivotal factor in shaping brain activity and cognitive processes. Neglecting this directional aspect can lead to an incomplete understanding of high-order interactions within the brain. To this end, we propose a novel effective hyper-connectivity (EHC) network that integrates direction detection and hyper-connectivity modeling. It characterizes the high-order directional information flow among multiple ROIs, providing a more comprehensive understanding of brain activity. Then, we develop a directed hypergraph convolutional network (DHGCN) to acquire deep representations from EHC network and functional indicators of ROIs. In contrast to conventional hypergraph convolutional networks designed for undirected hypergraphs, DHGCN is specifically tailored to handle directed hypergraph data structures. Moreover, unlike existing methods that primarily focus on fMRI time series, our proposed DHGCN model also incorporates multiple functional indicators, providing a robust framework for feature learning. Finally, deep representations generated via DHGCN, combined with demographic factors, are used for major depressive disorder (MDD) identification. Experimental results demonstrate that the proposed framework outperforms both FC and undirected hyper-connectivity models, as well as surpassing other state-of-the-art methods. The identification of EHC abnormalities through our framework can enhance the analysis of brain function in individuals with MDD.

AGF-PPIS: A protein-protein interaction site predictor based on an attention mechanism and graph convolutional networks

Protein-protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein-protein interactions sites is crucial. In this paper, we propose a novel predictor for protein-protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph structure), graph convolutional network, and feed-forward neural network. We use the Euclidean distance between each protein residue to generate the corresponding protein graph as the input of AGF-PPIS. On the independent test dataset Test_60, AGF-PPIS achieves superior performance over comparative methods in terms of seven different evaluation metrics (ACC, precision, recall, F1-score, MCC, AUROC, AUPRC), which fully demonstrates the validity and superiority of the proposed AGF-PPIS model. The source codes and the steps for usage of AGF-PPIS are available at https://github.com/fxh1001/AGF-PPIS.

Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals

Purpose

Depression is a global challenge causing psychological and intellectual problems that require efficient diagnosis. Electroencephalogram (EEG) signals represent the functional state of the human brain and can help build an accurate and viable technique for the early prediction and treatment of depression.

Methods

An attention-based gated recurrent units transformer (AttGRUT) time-series model is proposed to efficiently identify EEG perturbations in depressive patients. Statistical, spectral and wavelet features were first extracted from the 60-channel EEG signal data. Then, two feature selection techniques, recursive feature elimination and the Boruta algorithm, both with Shapley additive explanations, were utilised for selecting essential features.

Results

The proposed model outperformed the two baseline and two hybrid time-series models-long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network-LSTM (CNN-LSTM), and CNN-GRU-achieving an accuracy of up to 98.67%. Feature selection considerably increased the performance across all time-series models.

Conclusion

Based on the obtained results, novel feature selection greatly affected the results of the baseline and hybrid time-series models. The proposed AttGRUT can be implemented and tested in other domains by using different modalities for prediction.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-022-00205-8.

Neural energy computations based on Hodgkin-Huxley models bridge abnormal neuronal activities and energy consumption patterns of major depressive disorder

Limited by the current experimental techniques and neurodynamical models, the dysregulation mechanisms of decision-making related neural circuits in major depressive disorder (MDD) are still not clear. In this paper, we proposed a neural coding methodology using energy to further investigate it, which has been proven to strongly complement the neurodynamical methodology. We augmented the previous neural energy calculation method, and applied it to our VTA-NAc-mPFC neurodynamical H-H models. We particularly focused on the peak power and energy consumption of abnormal ion channel (ionic) currents under different concentrations of dopamine input, and investigated the abnormal energy consumption patterns for the MDD group. The results revealed that the energy consumption of medium spiny neurons (MSNs) in the NAc region were lower in the MDD group than that of the normal control group despite having the same firing frequencies, peak action potentials, and average membrane potentials in both groups. Dopamine concentration was also positively correlated with the energy consumption of the pyramidal neurons, but the patterns of different interneuron types were distinct. Additionally, the ratio of mPFC's energy consumption to total energy consumption of the whole network in MDD group was lower than that in normal control group, revealing that the mPFC region in MDD group encoded less neural information, which matched the energy consumption patterns of BOLD-fMRI results. It was also in line with the behavioral characteristics that MDD patients demonstrated in the form of reward insensitivity during decision-making tasks. In conclusion, the model in this paper was the first neural network energy computational model for MDD, which showed success in explaining its dynamical mechanisms with an energy consumption perspective. To build on this, we demonstrated that energy consumption levels can be used as a potential indicator for MDD, which also showed a promising pipeline to use an energy methodology for studying other neuropsychiatric disorders.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

OBJECTIVE

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

EVIDENCE REVIEW

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

FINDINGS

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

CONCLUSIONS AND RELEVANCE

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls

(1) Objective: The aim of this study was to examine the effect of high altitude on inhibitory control processes that underlie sustained attention in the neural correlates of EEG data, and explore whether the EEG data reflecting inhibitory control contain valuable information to classify high-altitude chronic hypoxia and plain controls. (2) Methods: 35 chronic high-altitude hypoxic adults and 32 matched controls were recruited. They were required to perform the go/no-go sustained attention task (GSAT) using event-related potentials. Three machine learning algorithms, namely a support vector machine (SVM), logistic regression (LR), and a decision tree (DT), were trained based on the related ERP components and neural oscillations to build a dichotomous classification model. (3) Results: Behaviorally, we found that the high altitude (HA) group had lower omission error rates during all observation periods than the low altitude (LA) group. Meanwhile, the ERP results showed that the HA participants had significantly shorter latency than the LAs for sustained potential (SP), indicating vigilance to response-related conflict. Meanwhile, event-related spectral perturbation (ERSP) analysis suggested that lowlander immigrants exposed to high altitudes may have compensatory activated prefrontal cortexes (PFC), as reflected by slow alpha, beta, and theta frequency-band neural oscillations. Finally, the machine learning results showed that the SVM achieved the optimal classification F1 score in the later stage of sustained attention, with an F1 score of 0.93, accuracy of 92.54%, sensitivity of 91.43%, specificity of 93.75%, and area under ROC curve (AUC) of 0.97. The results proved that SVM classification algorithms could be applied to identify chronic high-altitude hypoxia. (4) Conclusions: Compared with other methods, the SVM leads to a good overall performance that increases with the time spent on task, illustrating that the ERPs and neural oscillations may provide neuroelectrophysiological markers for identifying chronic plateau hypoxia.

Machine learning approaches for diagnosing depression using EEG: A review

Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.