Differentiation chronic post traumatic stress disorder patients from healthy subjects using objective and subjective sleep-related parameters

Applications of artificial intelligence-machine learning for detection of stress: a critical overview

Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.

The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis

Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.

Systematic review of machine learning in PTSD studies for automated diagnosis evaluation

Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.

ENIGMA-Sleep: Challenges, opportunities, and the road map

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine.

Neuroimaging insights into the link between depression and Insomnia: A systematic review

BACKGROUND

Insomnia is a common symptom of Major Depressive Disorder (MDD) and genome-wide association studies pointed to their strong genetic association. Although the prevalence of insomnia symptoms in MDD is noticeable and evidence supports their strong bidirectional association, the number of available neuroimaging findings on patients of MDD with insomnia symptoms is limited. However, such neuroimaging studies could verily improve our understanding of their shared pathophysiology and advance corresponding theories.

METHODS

Based on the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guideline, we have conducted a literature search using PubMed, EMBASE, and Scopus databases and systematically explored 640 studies using various neuroimaging modalities in MDD patients with different degrees of insomnia symptoms.

RESULTS

Despite inconsistencies, current findings from eight studies suggested structural and functional disturbances in several brain regions including the amygdala, prefrontal cortex and anterior cingulate cortex and insula. The aberrant functional connectivity within and between the main hubs of the salience and default mode networks could potentially yield new insights into the link between MDD and insomnia, which needs further assessment.

LIMITATIONS

The number of studies reviewed herein is limited. The applied methods for assessing structural and functional neural mechanisms of insomnia and depression were variable.

CONCLUSION

Neuroimaging methods demonstrated the overlapping underlying neural mechanisms between MDD and insomnia. Future studies may facilitate better understanding of their pathophysiology to allow development of specific treatment.

Changes in sleep architecture in German Armed Forces personnel with posttraumatic stress disorder compared with depressed and healthy control subjects

BACKGROUND

This study compares the sleep architecture of patients with posttraumatic stress disorder (PTSD) with that of both patients with depression and subjects with no mental disorder.

METHOD

45 German armed forces personnel with PTSD, 72 German armed forces personnel with depression and 24 healthy control subjects underwent 24-hour polysomnography. The effects of group membership, medication and the statistical interaction of group and medication were analysed for the following variables: sleep onset latency, REM sleep latency, slow-wave sleep and REM sleep percentages.

RESULTS

Sleep onset latency was significantly prolonged in both the PTSD and the depression group. Moreover, psychotropic medication was associated with significantly prolonged REM sleep latency.

CONCLUSION

The impact on sleep onset latency is of special clinical relevance in that according to preliminary studies, it is of major importance for subjective sleep quality. In contrast to the other parameters, an increase in sleep onset latency results in a subjective reduction in sleep quality which can lead to hyperarousal and increased preoccupation with sleep, which in turn may lead to dysfunctional sleep patterns.

Link between insomnia and perinatal depressive symptoms: A meta-analysis

Evidence shows the possible link between insomnia and perinatal depressive symptoms. In order to find a convergent quantitative answer, we collected data via the search of Medline, EMBASE and reference tracking, which included nine studies (a total sample of 1,922 women). An aggregate effect size estimate (correlation coefficient) was generated using the comprehensive meta-analysis software. For the meta-analytic procedure, a random effects model was set a priori. Moderating factors, including study design, method of assessment of depression, geographical origin of data, publication year, mean age, % married, breastfeeding rate, quality and type of data, % primiparous and history of depression, were examined via categorical or univariate mixed-effects (method of moments) meta-regression methods. Heterogeneity and publication bias were examined using standard meta-analytic approaches. We found a significant, medium-size relationship between insomnia and perinatal depressive symptoms (point estimate, 0.366; 95% confidence interval [CI], 0.205-0.508; p < 0.001; n = 9) and this was significantly heterogeneous (Q, 118.77; df, 8; p < 0.001; I² , 93.26%). The effect size estimate was significant for studies reporting no history of depression (point estimate, 0.364; 95% CI, 0.035-0.622; p < 0.05; n = 5) and for study design. With meta-regression, no moderating factor (age, marriage rate, breastfeeding rate, pregnancy history or publication year) significantly mediated the effect size estimate. The depression assessment scale used, but not other categorical variables, explained the magnitude of heterogeneity. We found that insomnia during the perinatal period is associated with depressive symptoms, which warrants screening pregnant mothers for insomnia and depression.

Machine Learning for Prediction of Posttraumatic Stress and Resilience Following Trauma: An Overview of Basic Concepts and Recent Advances

Posttraumatic stress responses are characterized by a heterogeneity in clinical appearance and etiology. This heterogeneity impacts the field's ability to characterize, predict, and remediate maladaptive responses to trauma. Machine learning (ML) approaches are increasingly utilized to overcome this foundational problem in characterization, prediction, and treatment selection across branches of medicine that have struggled with similar clinical realities of heterogeneity in etiology and outcome, such as oncology. In this article, we review and evaluate ML approaches and applications utilized in the areas of posttraumatic stress, stress pathology, and resilience research, and present didactic information and examples to aid researchers interested in the relevance of ML to their own research. The examined studies exemplify the high potential of ML approaches to build accurate predictive and diagnostic models of posttraumatic stress and stress pathology risk based on diverse sources of available information. The use of ML approaches to integrate high-dimensional data demonstrates substantial gains in risk prediction even when the sources of data are the same as those used in traditional predictive models. This area of research will greatly benefit from collaboration and data sharing among researchers of posttraumatic stress disorder, stress pathology, and resilience.

A lack of consistent brain alterations in insomnia disorder: An activation likelihood estimation meta-analysis

Insomnia disorder is a prevalent sleep disorder, which affects about 10% of general population. However, its neural mechanisms are poorly understood. Recently, several structural and functional neuroimaging studies have been conducted in patients with insomnia disorder, but these studies have yielded diverse findings. Here, we aimed to identify consistent patterns of abnormal brain alterations in insomnia disorder by performing a quantitative coordinate-based meta-analysis. Following the preferred reporting for systematic reviews and meta-analyses statement, we searched PubMed database and used reference tracking and finally retrieved 19 eligible studies (six task-based functional magnetic resonance imaging, eight resting-state functional magnetic resonance imaging, three voxel-based morphometry, and two positron emission tomography). We extracted peak coordinates from these studies and tested for convergence using the activation likelihood estimation method. Using this method, we found no significant convergent evidence for combination of structural atrophy and functional disturbances across previous studies (p = 0.914). Inconsistencies across these studies might be related to heterogonous clinical populations, the explorative nature of these studies in combination with small sample sizes, different experimental designs, and various preprocessing and statistical approaches. Future neuroimaging studies on insomnia disorder should include larger well-characterized samples, as well as standard imaging and analysis protocols.