Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Classification of mental workload using brain connectivity and machine learning on electroencephalogram data

Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).

Unified Convolutional Sparse Transformer for Disease Diagnosis, Monitoring, Drug Development, and Therapeutic Effect Prediction from EEG Raw Data

Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw waveform inputs. It aims to address the challenges of manual feature engineering and the neglect of spatial interrelationships in existing methodologies. Specifically, a spatial channel attention module is introduced to emphasize the critical inter-channel dependencies in EEG signals through channel statistics aggregation and multi-layer perceptron operations. Furthermore, a sparse transformer encoder is used to leverage selective sparse attention in order to efficiently process long EEG sequences while reducing computational complexity. Distilling convolutional layers further concatenates the temporal features and retains only the salient patterns. As it was rigorously evaluated on key EEG datasets, our model consistently accomplished a superior performance over the current approaches in detection and classification assignments. By accounting for both spatial and temporal relationships in an end-to-end paradigm, this work facilitates a versatile, automated EEG understanding across diseases, subjects, and objectives through a singular yet customizable architecture. Extensive empirical validation and further architectural refinement may promote broader clinical adoption prospects.

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

Deep learning methods are increasingly being applied to raw electroencephalogram (EEG) data. However, if these models are to be used in clinical or research contexts, methods to explain them must be developed, and if these models are to be used in research contexts, methods for combining explanations across large numbers of models must be developed to counteract the inherent randomness of existing training approaches. Model visualization-based explainability methods for EEG involve structuring a model architecture such that its extracted features can be characterized and have the potential to offer highly useful insights into the patterns that they uncover. Nevertheless, model visualization-based explainability methods have been underexplored within the context of multichannel EEG, and methods to combine their explanations across folds have not yet been developed. In this study, we present two novel convolutional neural network-based architectures and apply them for automated major depressive disorder diagnosis. Our models obtain slightly lower classification performance than a baseline architecture. However, across 50 training folds, they find that individuals with MDD exhibit higher β power, potentially higher δ power, and higher brain-wide correlation that is most strongly represented within the right hemisphere. This study provides multiple key insights into MDD and represents a significant step forward for the domain of explainable deep learning applied to raw EEG. We hope that it will inspire future efforts that will eventually enable the development of explainable EEG deep learning models that can contribute both to clinical care and novel medical research discoveries.

Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data

Objective: Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful for aiding in diagnosis, although prior neuroimaging studies were unable to provide such biomarkers. Method: In this study, patients with OCD were classified from healthy controls using 2 different hybrid deep learning models: one-dimensional convolutional neural networks (1DCNN) together with long-short term memory (LSTM) and gradient recurrent units (GRU), respectively. Results: Both models exhibited exceptional classification accuracies in cross-validation and external validation phases. The mean classification accuracies in the cross-validation stage were 90.88% and 85.91% for the 1DCNN-LSTM and 1DCNN-GRU models, respectively. The inferior frontal, temporal, and occipital electrodes were predominant in providing discriminative features. Conclusion: Our findings underscore the potential of hybrid deep learning architectures utilizing EEG data to effectively differentiate patients with OCD from healthy controls. This promising approach holds implications for advancing clinical decision-making by offering valuable insights into diagnostic markers for OCD.

Dementia classification using a graph neural network on imaging of effective brain connectivity

Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms of neurodegenerative diseases. The literature suggests that effective brain connectivity (EBC) has the potential to track differences between AD, PD and healthy controls (HC). However, how to effectively use EBC estimations for the research of disease diagnosis remains an open problem. To deal with complex brain networks, graph neural network (GNN) has been increasingly popular in very recent years and the effectiveness of combining EBC and GNN techniques has been unexplored in the field of dementia diagnosis. In this study, a novel directed structure learning GNN (DSL-GNN) was developed and performed on the imaging of EBC estimations and power spectrum density (PSD) features. In comparison to the previous studies on GNN, our proposed approach enhanced the functionality for processing directional information, which builds the basis for more efficiently performing GNN on EBC. Another contribution of this study is the creation of a new framework for applying univariate and multivariate features simultaneously in a classification task. The proposed framework and DSL-GNN are validated in four discrimination tasks and our approach exhibited the best performance, against the existing methods, with the highest accuracy of 94.0% (AD vs. HC), 94.2% (PD vs. HC), 97.4% (AD vs. PD) and 93.0% (AD vs. PD vs. HC). In a word, this research provides a robust analytical framework to deal with complex brain networks containing causal directional information and implies promising potential in the diagnosis of two of the most common neurodegenerative conditions.

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram

In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early stages to stop the side effects. Early detection is critical to identify a variety of serious conditions. In order to provide safe and effective protection to MDD patients, it is crucial to automate diagnoses and make decision-making tools widely available. Although there are various classification systems for the diagnosis of MDD, no reliable, secure method that meets these requirements has been established to date. In this paper, a federated deep learning-based multimodal system for MDD classification using electroencephalography (EEG) and audio datasets is presented while meeting data privacy requirements. The performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. The study began by extracting features from several pre-trained models and ultimately decided to use bidirectional short-term memory (Bi-LSTM) as the base model, as it had the highest validation accuracy of 91% compared to a convolutional neural network and LSTM with 85% and 89% validation accuracy on audio data, respectively. The Bi-LSTM model also achieved a validation accuracy of 98.9% for EEG data. The FL method was then used to perform experiments on IID and non-IID datasets. The FL-based multimodal model achieved an exceptional training and validation accuracy of 99.9% when trained and evaluated on both IID and non-IIID datasets. These results show that the FL multimodal system performs almost as well as the Bi-LSTM multimodal system and emphasize its suitability for processing IID and non-IIID data. Several clients were found to perform better than conventional pre-trained models in a multimodal framework for federated learning using EEG and audio datasets. The proposed framework stands out from other classification techniques for MDD due to its special features, such as multimodality and data privacy for edge machines with limited resources. Due to these additional features, the framework concept is the most suitable alternative approach for the early classification of MDD patients.

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data

As the field of deep learning has grown in recent years, its application to the domain of raw resting-state electroencephalography (EEG) has also increased. Relative to traditional machine learning methods or deep learning methods applied to manually engineered features, there are fewer methods for developing deep learning models on small raw EEG datasets. One potential approach for enhancing deep learning performance, in this case, is the use of transfer learning. While a number of studies have presented transfer learning approaches for manually engineered EEG features, relatively few approaches have been developed for raw resting-state EEG. In this study, we propose a novel EEG transfer learning approach wherein we first train a model on a large publicly available single-channel sleep stage classification dataset. We then use the learned representations to develop a classifier for automated major depressive disorder diagnosis with raw multichannel EEG. Statistical testing reveals that our approach significantly improves the performance of our model (p < 0.05), and we also find that the performance of our approach exceeds that of many previous studies using both engineered features and raw EEG. We further examine how transfer learning affected the representations learned by the model through a pair of explainability analyses, identifying key frequency bands and channels utilized across models. Our proposed approach represents a significant step forward for the domain of raw resting-state EEG classification and has broader implications for use with other electrophysiology and time-series modalities. Importantly, it has the potential to expand the use of deep learning methods across a greater variety of raw EEG datasets and lead to the development of more reliable EEG classifiers.

EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions

The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be used for this purpose. In this article, we present a comprehensive review of the main factors and parameters that affect the performance of deep neural networks in classifying different neuropsychiatric disorders using EEG signals. We also analyze the EEG features used for improving classification performance. Our analysis includes 82 scientific journal papers that applied deep neural networks for subject-wise classification based on EEG signals. We extracted information on the EEG dataset and types of disorders, deep neural network structures, performance, and hyperparameters. The results show that most studies have focused on clinical classification, achieving an average accuracy of 91.83 ± 7.34, with convolutional neural networks (CNNs) being the most frequently used network architecture and resting-state EEG signals being the most commonly used data type. Additionally, the review reveals that depression (N = 18), Alzheimer's (N = 11), and schizophrenia (N = 11) were studied more frequently than other types of neuropsychiatric disorders. Our review provides insight into the performance of deep neural networks in EEG classification and highlights the importance of EEG feature extraction in improving classification accuracy. By identifying the main factors and parameters that affect deep neural network performance in EEG classification, our review can guide future research in this area. We hope that our findings will encourage further exploration of deep learning methods for EEG classification and contribute to the development of more accurate and effective methods for diagnosing and monitoring neuropsychiatric disorders using EEG signals.

Prediction of response to repetitive transcranial magnetic stimulation for major depressive disorder using hybrid Convolutional recurrent neural networks and raw Electroencephalogram Signal

Major Depressive Disorder (MDD) is a high prevalence disease that needs an effective and timely treatment to prevent its progress and additional costs. Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment option for MDD patients which uses strong magnetic pulses to stimulate specific regions of the brain. However, some patients do not respond to this treatment which causes the waste of multiple weeks as treatment time and clinical resources. Therefore developing an effective way for the prediction of response to the rTMS treatment of depression is necessary. In this work, we proposed a hybrid model created by pre-trained Convolutional Neural Networks (CNN) models and Bidirectional Long Short-Term Memory (BLSTM) cells to predict response to rTMS treatment from raw EEG signal. Three pre-trained CNN models named VGG16, InceptionResNetV2, and EffecientNetB0 were utilized as Transfer Learning (TL) models to construct hybrid TL-BLSTM models. Then an ensemble of these models was created using weighted majority voting which the weights were optimized by Differential Evolution (DE) optimization algorithm. Evaluation of these models shows the superior performance of the ensemble model by the accuracy of 98.51%, sensitivity of 98.64%, specificity of 98.36%, F1-score of 98.6%, and AUC of 98.5%. Therefore, the ensemble of the proposed hybrid convolutional recurrent networks can efficiently predict the treatment outcome of rTMS using raw EEG data.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

BACKGROUND

The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.

METHODS

Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses.

RESULTS

A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β =  - 2.75, p < .001, R²_adj = 0.40; r =  - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF₁₀ > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance.

CONCLUSIONS

Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

A convolutional recurrent neural network with attention for response prediction to repetitive transcranial magnetic stimulation in major depressive disorder

Prediction of response to Repetitive Transcranial Magnetic Stimulation (rTMS) can build a very effective treatment platform that helps Major Depressive Disorder (MDD) patients to receive timely treatment. We proposed a deep learning model powered up by state-of-the-art methods to classify responders (R) and non-responders (NR) to rTMS treatment. Pre-treatment Electro-Encephalogram (EEG) signal of public TDBRAIN dataset and 46 proprietary MDD subjects were utilized to create time-frequency representations using Continuous Wavelet Transform (CWT) to be fed into the two powerful pre-trained Convolutional Neural Networks (CNN) named VGG16 and EfficientNetB0. Equipping these Transfer Learning (TL) models with Bidirectional Long Short-Term Memory (BLSTM) and attention mechanism for the extraction of most discriminative spatiotemporal features from input images, can lead to superior performance in the prediction of rTMS treatment outcome. Five brain regions named Frontal, Central, Parietal, Temporal, and occipital were assessed and the highest evaluated performance in 46 proprietary MDD subjects was acquired for the Frontal region using the TL-LSTM-Attention model based on EfficientNetB0 with accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 97.1%, 97.3%, 97.0%, and 0.96 respectively. Additionally, to test the generalizability of the proposed models, these TL-BLSTM-Attention models were evaluated on a public dataset called TDBRAIN and the highest accuracy of 82.3%, the sensitivity of 80.2%, the specificity of 81.9% and the AUC of 0.83 were obtained. Therefore, advanced deep learning methods using a time-frequency representation of EEG signals from the frontal brain region and the convolutional recurrent neural networks equipped with the attention mechanism can construct an accurate platform for the prediction of response to the rTMS treatment.

Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifier

(1) Background: Epilepsy is a neurological disorder that causes repeated seizures. Since electroencephalogram (EEG) patterns differ in different states (inter-ictal, pre-ictal, and ictal), a seizure can be detected and predicted by extracting various features. However, the brain connectivity network, a two-dimensional feature, is rarely studied. We aim to investigate its effectiveness for seizure detection and prediction. (2) Methods: Two time-window lengths, five frequency bands, and five connectivity measures were used to extract image-like features, which were fed into a support vector machine for the subject-specific model (SSM) and a convolutional neural networks meet transformers (CMT) classifier for the subject-independent model (SIM) and cross-subject model (CSM). Finally, feature selection and efficiency analyses were conducted. (3) Results: The classification results on the CHB-MIT dataset showed that a long window indicated better performance. The best detection accuracies of SSM, SIM, and CSM were 100.00, 99.98, and 99.27%, respectively. The highest prediction accuracies were 99.72, 99.38, and 86.17%, respectively. In addition, Pearson Correlation Coefficient and Phase Lock Value connectivity in the β and γ bands showed good performance and high efficiency. (4) Conclusions: The proposed brain connectivity features showed good reliability and practical value for automatic seizure detection and prediction, which expects to develop portable real-time monitoring equipment.

Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques

The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals' complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. In this project, resting EEG readings of 128 channels are considered. According to CNN, training with 25 epoch iterations had a 97% accuracy rate. The patient's status has to be divided into two basic categories: major depressive disorder (MDD) and healthy control. Additional MDD include the following six classes: obsessive-compulsive disorders, addiction disorders, conditions brought on by trauma and stress, mood disorders, schizophrenia, and the anxiety disorders discussed in this paper are a few examples of mental illnesses. According to the study, a natural combination of EEG signals and demographic data is promising for the diagnosis of depression.

Breakdown of long-range spatial correlations of infraslow amplitude fluctuations of EEG oscillations in patients with current and past major depressive disorder

Introduction

Identifying biomarkers for depression from brain activity is important for the diagnosis and treatment of depression disorders. We investigated spatial correlations of the amplitude fluctuations of electroencephalography (EEG) oscillations as a potential biomarker of depression. The amplitude fluctuations of EEG oscillations intrinsically reveal both temporal and spatial correlations, indicating rapid and functional organization of the brain networks. Amid these correlations, long-range temporal correlations are reportedly impaired in patients with depression, exhibiting amplitude fluctuations closer to a random process. Based on this occurrence, we hypothesized that the spatial correlations of amplitude fluctuations would also be altered by depression.

Methods

In the present study, we extracted the amplitude fluctuations of EEG oscillations by filtering them through infraslow frequency band (0.05-0.1 Hz).

Results

We found that the amplitude fluctuations of theta oscillations during eye-closed rest depicted lower levels of spatial correlation in patients with major depressive disorder (MDD) compared to control individuals. This breakdown of spatial correlations was most prominent in the left fronto - temporal network, specifically in patients with current MDD rather than in those with past MDD. We also found that the amplitude fluctuations of alpha oscillations during eye-open rest exhibited lower levels of spatial correlation in patients with past MDD compared to control individuals or patients with current MDD.

Discussion

Our results suggest that breakdown of long-range spatial correlations may offer a biomarker for the diagnosis of depression (current MDD), as well as the tracking of the recovery from depression (past MDD).

Depression screening using hybrid neural network

Depression is a common cause of increased suicides worldwide, and studies have shown that the number of patients suffering from major depressive disorder (MDD) increased several-fold during the COVID-19 pandemic, highlighting the importance of disease detection and depression management, while increasing the need for effective diagnostic tools. In recent years, machine learning and deep learning methods based on electroencephalography (EEG) have achieved significant results in the field of automatic depression detection. However, most current studies have focused on a small number of EEG signal channels, and experimental data require special processing by professionals. In this study, 128 channels of EEG signals were simply filtered and 24-fold leave-one-out cross-validation experiments were performed using 2DCNN-LSTM classifier, support vector machine, K-nearest neighbor and decision tree. The current results show that the proposed 2DCNN-LSTM model has an average classification accuracy of 95.1% with an AUC of 0.98 for depression detection of 6-second participant EEG signals, and the model is much better than 72.05%, 79.7% and 79.49% for support vector machine, K nearest neighbor and decision tree. In addition, we found that the model achieved a 100% probability of correctly classifying the EEG signals of 300-second participants.

Precision Medicine Approaches to Mental Health Care

Developing a more comprehensive understanding of the physiological underpinnings of mental illness, precision medicine has the potential to revolutionize psychiatric care. With recent breakthroughs in next-generation multi-omics technologies and data analytics, it is becoming more feasible to leverage multimodal biomarkers, from genetic variants to neuroimaging biomarkers, to objectify diagnostics and treatment decisions in psychiatry and improve patient outcomes. Ongoing work in precision psychiatry will parallel progress in precision oncology and cardiology to develop an expanded suite of blood- and neuroimaging-based diagnostic tests, empower monitoring of treatment efficacy over time, and reduce patient exposure to ineffective treatments. The emerging model of precision psychiatry has the potential to mitigate some of psychiatry's most pressing issues, including improving disease classification, lengthy treatment duration, and suboptimal treatment outcomes. This narrative-style review summarizes some of the emerging breakthroughs and recurring challenges in the application of precision medicine approaches to mental health care.

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.

Depression recognition using a proposed speech chain model fusing speech production and perception features

BACKGROUND

Increasing depression patients puts great pressure on clinical diagnosis. Audio-based diagnosis is a helpful auxiliary tool for early mass screening. However, current methods consider only speech perception features, ignoring patients' vocal tract changes, which may partly result in the poor recognition.

METHODS

This work proposes a novel machine speech chain model for depression recognition (MSCDR) that can capture text-independent depressive speech representation from the speaker's mouth to the listener's ear to improve recognition performance. In the proposed MSCDR, linear predictive coding (LPC) and Mel-frequency cepstral coefficients (MFCC) features are extracted to describe the processes of speech generation and of speech perception, respectively. Then, a one-dimensional convolutional neural network and a long short-term memory network sequentially capture intra- and inter-segment dynamic depressive features for classification.

RESULTS

We tested the MSCDR on two public datasets with different languages and paradigms, namely, the Distress Analysis Interview Corpus-Wizard of Oz and the Multi-modal Open Dataset for Mental-disorder Analysis. The accuracy of the MSCDR on the two datasets was 0.77 and 0.86, and the average F1 score was 0.75 and 0.86, which were better than the other existing methods. This improvement reveals the complementarity of speech production and perception features in carrying depressive information.

LIMITATIONS

The sample size was relatively small, which may limit the application in clinical translation to some extent.

CONCLUSION

This experiment proves the good generalization ability and superiority of the proposed MSCDR and suggests that the vocal tract changes in patients with depression deserve attention for audio-based depression diagnosis.

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.

Attention-Based Convolutional Recurrent Deep Neural Networks for the Prediction of Response to Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder

Repetitive Transcranial Magnetic Stimulation (rTMS) is proposed as an effective treatment for major depressive disorder (MDD). However, because of the suboptimal treatment outcome of rTMS, the prediction of response to this technique is a crucial task. We developed a deep learning (DL) model to classify responders (R) and non-responders (NR). With this aim, we assessed the pre-treatment EEG signal of 34 MDD patients and extracted effective connectivity (EC) among all electrodes in four frequency bands of EEG signal. Two-dimensional EC maps are put together to create a rich connectivity image and a sequence of these images is fed to the DL model. Then, the DL framework was constructed based on transfer learning (TL) models which are pre-trained convolutional neural networks (CNN) named VGG16, Xception, and EfficientNetB0. Then, long short-term memory (LSTM) cells are equipped with an attention mechanism added on top of TL models to fully exploit the spatiotemporal information of EEG signal. Using leave-one subject out cross validation (LOSO CV), Xception-BLSTM-Attention acquired the highest performance with 98.86% of accuracy and 97.73% of specificity. Fusion of these models as an ensemble model based on optimized majority voting gained 99.32% accuracy and 98.34% of specificity. Therefore, the ensemble of TL-LSTM-Attention models can predict accurately the treatment outcome.

Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review

Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.

Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals

Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.

Effectiveness of Artificial Intelligence Methods in Personalized Aggression Risk Prediction within Inpatient Psychiatric Treatment Settings—A Systematic Review

Aggression risk assessments are vital to prevent injuries and morbidities amongst patients and staff in psychiatric settings. More recent studies have harnessed artificial intelligence (AI) methods such as machine learning algorithms to determine factors associated with aggression in psychiatric treatment settings. In this review, using Cooper’s five-stage review framework, we aimed to evaluate the: (1) predictive accuracy, and (2) clinical variables associated with AI-based aggression risk prediction amongst psychiatric inpatients. Databases including PubMed, Cochrane, Scopus, PsycINFO, CINAHL were searched for relevant articles until April 2022. The eight included studies were independently evaluated using critical appraisal tools for systematic review developed by Joanna Briggs Institute. Most of the studies (87.5%) examined health records in predicting aggression and reported acceptable to excellent accuracy with specific machine learning algorithms employed (area under curve range 0.75–0.87). No particular machine learning algorithm outperformed the others consistently across studies (area under curve range 0.61–0.87). Relevant factors identified with aggression related to demographic and social profile, past aggression, forensic history, other psychiatric history, psychopathology, challenging behaviors and management domains. The limited extant studies have highlighted a potential role for the use of AI methods to clarify factors associated with aggression in psychiatric inpatient treatment settings.

Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features

Accurate and early diagnosis of mild cognitive impairment (MCI) is necessary to prevent the progress of Alzheimer's and other kinds of dementia. Unfortunately, the symptoms of MCI are complicated and may often be misinterpreted as those associated with the normal ageing process. To address this issue, many studies have proposed application of machine learning techniques for early MCI diagnosis based on electroencephalography (EEG). In this study, a machine learning framework for MCI diagnosis is proposed in this study, which extracts spectral, functional connectivity, and nonlinear features from EEG signals. The sequential backward feature selection (SBFS) algorithm is used to select the best subset of features. Several classification models and different combinations of feature sets are measured to identify the best ones for the proposed framework. A dataset of 16 and 18 EEG data of normal and MCI subjects is used to validate the proposed system. Metrics including accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) are evaluated using 10-fold crossvalidation. An average AC of 99.4%, SE of 98.8%, SP of 100%, F1 of 99.4%, and FDR of 0% have been provided by the best performance of the proposed framework using the linear support vector machine (LSVM) classifier and the combination of all feature sets. The acquired results confirm that the proposed framework provides an accurate and robust performance for recognizing MCI cases and outperforms previous approaches. Based on the obtained results, it is possible to be developed in order to use as a computer-aided diagnosis (CAD) tool for clinical purposes.

Directed network analysis reveals changes in cortical and muscular connectivity caused by different standing balance tasks

Objective.Standing balance forms the basis of daily activities that require the integration of multi-sensory information and coordination of multi-muscle activation. Previous studies have confirmed that the cortex is directly involved in balance control, but little is known about the neural mechanisms of cortical integration and muscle coordination in maintaining standing balance.Approach.We used a direct directed transfer function (dDTF) to analyze the changes in the cortex and muscle connections of healthy subjects (15 subjects: 13 male and 2 female) corresponding to different standing balance tasks.Main results.The results show that the topology of the EEG brain network and muscle network changes significantly as the difficulty of the balancing tasks increases. For muscle networks, the connection analysis shows that the connection of antagonistic muscle pairs plays a major role in the task. For EEG brain networks, graph theory-based analysis shows that the clustering coefficient increases significantly, and the characteristic path length decreases significantly with increasing task difficulty. We also found that cortex-to-muscle connections increased with the difficulty of the task and were significantly stronger than the muscle-to-cortex connections.Significance.These results show that changes in the difficulty of balancing tasks alter EEG brain networks and muscle networks, and an analysis based on the directed network can provide rich information for exploring the neural mechanisms of balance control.

Dopamine-Mediated Major Depressive Disorder in the Neural Circuit of Ventral Tegmental Area-Nucleus Accumbens-Medial Prefrontal Cortex: From Biological Evidence to Computational Models

Major depressive disorder (MDD) is a serious psychiatric disorder, with an increasing incidence in recent years. The abnormal dopaminergic pathways of the midbrain cortical and limbic system are the key pathological regions of MDD, particularly the ventral tegmental area- nucleus accumbens- medial prefrontal cortex (VTA-NAc-mPFC) neural circuit. MDD usually occurs with the dysfunction of dopaminergic neurons in VTA, which decreases the dopamine concentration and metabolic rate in NAc/mPFC brain regions. However, it has not been fully explained how abnormal dopamine concentration levels affect this neural circuit dynamically through the modulations of ion channels and synaptic activities. We used Hodgkin-Huxley and dynamical receptor binding model to establish this network, which can quantitatively explain neural activity patterns observed in MDD with different dopamine concentrations by changing the kinetics of some ion channels. The simulation replicated some important pathological patterns of MDD at the level of neurons and circuits with low dopamine concentration, such as the decreased action potential frequency in pyramidal neurons of mPFC with significantly reduced burst firing frequency. The calculation results also revealed that NaP and KS channels of mPFC pyramidal neurons played key roles in the functional regulation of this neural circuit. In addition, we analyzed the synaptic currents and local field potentials to explain the mechanism of MDD from the perspective of dysfunction of excitation-inhibition balance, especially the disinhibition effect in the network. The significance of this article is that we built the first computational model to illuminate the effect of dopamine concentrations for the NAc-mPFC-VTA circuit between MDD and normal groups, which can be used to quantitatively explain the results of existing physiological experiments, predict the results for unperformed experiments and screen possible drug targets.

Artificial intelligence and Psychiatry: An overview

The burden of mental illness both in world and India is increasing at an alarming rate. Adding to it, there has been an increase in mental health challenges during covid-19 pandemic with a rise in suicide, loneliness and substance use. Artificial intelligence can act as a potential solution to address this shortage. The use of artificial intelligence is increasingly being employed in various fields of mental health like affective disorders, psychosis, and geriatric psychiatry. The benefits are various like lower costs, wider reach but at the same time it comes with its own disadvantages. This article reviews the current understanding of artificial intelligence, the types of Artificial intelligence, its current use in various mental health disorders, current status in India, advantages, disadvantages and future potentials. With the passage of time and digitalization of the modern age, there will be an increase in the use of artificial intelligence in psychiatry hence a detailed understanding will be thoughtful. For this, we searched PubMed, Google Scholar, and Science Direct, China national Knowledge Infrastructure (CNKI), Globus Index Medicus search engines by using keywords. Initial searches involved the use of each individual keyword while the later searches involved the use of more than one word in different permutation combinations.

Brain functional and effective connectivity based on electroencephalography recordings: A review

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Continuous Scoring of Depression from EEG Signals via a Hybrid of Convolutional Neural Networks

Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative scoring of depression has also been achieved by analyzing and classifying pre-recorded electroencephalography (EEG) signals. Here, we go one step further and apply raw EEG signals to a proposed hybrid convolutional and temporal-convolutional neural network (CNN-TCN) to continuously estimate the BDI score. In this research, the EEG signals of 119 individuals are captured by 64 scalp electrodes through successive eyes-closed and eyes-open intervals. Moreover, all the subjects take the BDI test and their scores are determined. The proposed CNN-TCN provides mean squared error (MSE) of 5.64±1.6 and mean absolute error (MAE) of 1.73±0.27 for eyes-open state and also provides MSE of 9.53±2.94 and MAE of 2.32±0.35 for the eyes-closed state, which significantly surpasses state-of-the-art deep network methods. In another approach, conventional EEG features are elicited from the EEG signals in successive frames and apply them to the proposed CNN-TCN in conjunction with known statistical regression methods. Our method provides MSE of 10.81±5.14 and MAE of 2.41±0.59 that statistically outperform the statistical regression methods. Moreover, the results with raw EEG are significantly better than those with EEG features.

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

Major depressive disorder (MDD) is a common mental illness characterized by persistent feeling of depressed mood and loss of interest. It would cause, in a severe case, suicide behaviors. In clinical settings, automatic MDD detection is mainly based on electroencephalogram (EEG) signals with supervised learning techniques. However, supervised-based MDD detection methods encounter two ineviTable bottlenecks: firstly, such methods rely heavily on an EEG training dataset with MDD labels annotated by a physical therapist, leading to subjectivity and high cost; secondly, most of EEG signals are unlabeled in a real scenario. In this paper, a novel semisupervised-based MDD detection method named MDD-TSVM is presented. Specifically, the MDD-TSVM utilizes the semisupervised method of transductive support vector machine (TSVM) as its backbone, further dividing the unlabeled penalty item of the TSVM objective function into two pseudo-labeled penalty items with or without MDD. By such improvement, the MDD-SVM can make full use of labeled and unlabeled datasets as well as alleviate the class imbalance problem. Experiment results showed that our proposed MDD-TSVM achieved F1 score of 0.85 ± 0.05 and accuracy of 0.89 ± 0.03 on identifying MDD patients, which is superior to the state-of-the-art methods.

Mental Health in Women Victims of Gender Violence: Descriptive and Multivariate Analysis of Neuropsychological Functions and Depressive Symptomatology

Female victims of abuse, as well as suffering from psychopathological disorders such as depression, can have neuropsychological sequelae affecting memory and attention, with serious consequences, both physical and psychological, in their daily lives. Therefore, the objective of this study is to analyse these sequelae that affect attention and memory, as well as the possible association of these sequelae to depression. A total of 68 women, victims of gender-based violence, between the ages of 15 and 62 participated in this study. The Luria DNA Battery (Neuropsychological Diagnosis of Adults) by Manga and Ramos (2000); and the Beck Depression Inventory (2011) were applied. It is shown that female victims of gender-based violence present poor short-term memory, attentional control, and score low on the Luria-DNA battery. Of these women, 60% suffer from some relevant type of depression. Through HJ-Biplot analysis, a direct relationship was found between memory and attentional control with the total score of the Luria battery. However, an inverse relationship was found between short-term memory and depression. In addition, three well-differentiated clusters of female victims of gender-based violence were identified. It is concluded that a lower rate of depression is observed in female victims of abuse when they have a more intact short-term memory.

Prediction of Probable Major Depressive Disorder in the Taiwan Biobank: An Integrated Machine Learning and Genome-Wide Analysis Approach

In light of recent advancements in machine learning, personalized medicine using predictive algorithms serves as an essential paradigmatic methodology. Our goal was to explore an integrated machine learning and genome-wide analysis approach which targets the prediction of probable major depressive disorder (MDD) using 9828 individuals in the Taiwan Biobank. In our analysis, we reported a genome-wide significant association with probable MDD that has not been previously identified: FBN1 on chromosome 15. Furthermore, we pinpointed 17 single nucleotide polymorphisms (SNPs) which show evidence of both associations with probable MDD and potential roles as expression quantitative trait loci (eQTLs). To predict the status of probable MDD, we established prediction models with random undersampling and synthetic minority oversampling using 17 eQTL SNPs and eight clinical variables. We utilized five state-of-the-art models: logistic ridge regression, support vector machine, C4.5 decision tree, LogitBoost, and random forests. Our data revealed that random forests had the highest performance (area under curve = 0.8905 ± 0.0088; repeated 10-fold cross-validation) among the predictive algorithms to infer complex correlations between biomarkers and probable MDD. Our study suggests that an integrated machine learning and genome-wide analysis approach may offer an advantageous method to establish bioinformatics tools for discriminating MDD patients from healthy controls.

An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals

In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.

Deep-Asymmetry: Asymmetry Matrix Image for Deep Learning Method in Pre-Screening Depression

To have an objective depression diagnosis, numerous studies based on machine learning and deep learning using electroencephalogram (EEG) have been conducted. Most studies depend on one-dimensional raw data and required fine feature extraction. To solve this problem, in the EEG visualization research field, short-time Fourier transform (STFT), wavelet, and coherence commonly used as method s for transferring EEG data to 2D images. However, we devised a new way from the concept that EEG's asymmetry was considered one of the major biomarkers of depression. This study proposes a deep-asymmetry methodology that converts the EEG's asymmetry feature into a matrix image and uses it as input to a convolutional neural network. The asymmetry matrix image in the alpha band achieved 98.85% accuracy and outperformed most of the methods presented in previous studies. This study indicates that the proposed method can be an effective tool for pre-screening major depressive disorder patients.