Neuronetwork Approach in the Early Diagnosis of Depression.

BACKGROUND: Depression is a common mental illness, with around 280 million people suffering from depression worldwide. At present, the main way to quantify the severity of depression is through psychometric scales, which entail subjectivity on the part of both patient and clinician. In the last few years, deep (machine) learning is emerging as a more objective approach for measuring depression severity. We now investigate how neural networks might serve for the early diagnosis of depression. SUBJECTS AND METHODS: We searched Medline (Pubmed) for articles published up to June 1, 2023. The search term included Depression AND Diagnostics AND Artificial Intelligence. We did not search for depression studies of machine learning other than neural networks, and selected only those papers attesting to diagnosis or screening for depression. RESULTS: Fifty-four papers met our criteria, among which 14 using facial expression recordings, 14 using EEG, 5 using fMRI, and 5 using audio speech recording analysis, whereas 6 used multimodality approach, two were the text analysis studies, and 8 used other methods. CONCLUSIONS: Research methodologies include both audio and video recordings of clinical interviews, task performance, including their subsequent conversion into text, and resting state studies (EEG, MRI, fMRI). Convolutional neural networks (CNN), including 3D-CNN and 2D-CNN, can obtain diagnostic data from the videos of the facial area. Deep learning in relation to EEG signals is the most commonly used CNN. fMRI approaches use graph convolutional networks and 3D-CNN with voxel connectivity, whereas the text analyses use CNNs, including LSTM (long/short-term memory). Audio recordings are analyzed by a hybrid CNN and support vector machine model. Neural networks are used to analyze biomaterials, gait, polysomnography, ECG, data from wrist wearable devices, and present illness history records. Multimodality studies analyze the fusion of audio features with visual and textual features using LSTM and CNN architectures, a temporal convolutional network, or a recurrent neural network. The accuracy of different hybrid and multimodality models is 78-99%, relative to the standard clinical diagnoses.

Genetic Contributors to PTSD: the Role of SNVs, Gene Interactions and Haplotypes for Developing PTSD Prevention Measures. A Comprehensive Review.

BACKGROUND: Post-traumatic stress disorder (PTSD) is a trauma- or stressor-related mental health condition with high socioeconomic burden. We aimed in this review to identify promising genetic markers predisposing for PTSD, which might serve in the design subsequent studies aiming to develop PTSD prevention and remediation measures. SUBJECTS AND METHODS: Our search queries in the PubMed database yielded 547 articles, of which 20 met our inclusion criteria for further analysis: published between 2018 and 2022, original research, containing molecular-genetic and statistical data, containing diagnosis verification methods, PTSD as a primary condition, and a sample of at least 60 patients. RESULTS: Among the 20 analyzed studies were reports of significant associations between PTSD and: FKBP5 variants rs9470080, regardless of the C or T allele; two FKBP5 haplotypes (A-G-C-C and A-G-C-T); gene-gene DRDхANNK1-COMT (rs1800497 × rs6269) and OXTR-DRD2 (rs2268498 × rs1801028); C-allele of CRHR1 (rs1724402). Other findings, such as the association of FKBP5 haplotypes (A-G-C-C, A-G-C-T) and the FKBP5-CRHR1 genotype, were of lesser statistical significance and less extensively studied. CONCLUSIONS: Although our literature analysis implicates certain genetic factors in PTSD, our understanding of the polygenic nature underlying the disorder remains limited, especially considering the hitherto underexplored epigenetic mechanisms. Future research endeavors should prioritize exploring these aspects to provide a more nuanced understanding of PTSD and its genetic underpinnings.

Post-COVID Neuropsychiatric Complications in Children and Adolescents: a Study Design for Early Diagnosis and Treatment Using Innovative Technologies.

BACKGROUND: The COVID-19 pandemic has had significant impacts on the child and adolescent population, with long-term consequences for physical health, socio-psychological well-being, and cognitive development, which require further investigation. We herein describe a study design protocol for recognizing neuropsychiatric complications associated with pediatric COVID-19, and for developing effective prevention and treatment strategies grounded on the evidence-based findings. METHODS: The study includes two cohorts, each with 163 participants, aged from 7 to 18 years old, and matched by gender. One cohort consisted of individuals with a history of COVID-19, while the other group presents those without such a history. We undertake comprehensive assessments, including neuropsychiatric evaluations, blood tests, and validated questionnaires completed by parents/guardians and by the children themselves. The data analysis is based on machine learning techniques to develop predictive models for COVID-19-associated neuropsychiatric complications in children and adolescents. RESULTS: The first model is focused on a binary classification to distinguish participants with and without a history of COVID-19. The second model clusters significant indicators of clinical dynamics during the follow-up observation period, including the persistence of COVID-19 related somatic and neuropsychiatric symptoms over time. The third model manages the predictors of discrete trajectories in the dynamics of post-COVID-19 states, tailored for personalized prediction modeling of affective, behavioral, cognitive, disturbances (academic/school performance), and somatic symptoms of the long COVID. CONCLUSIONS: The current protocol outlines a comprehensive study design aiming to bring a better understanding of COVID-19-associated neuropsychiatric complications in a population of children and adolescents, and to create a mobile phone-based applications for the diagnosis and treatment of affective, cognitive, and behavioral conditions. The study will inform about the improved management of preventive and personalized care strategies for pediatric COVID-19 patients. Study results support the development of engaging and age-appropriate mobile technologies addressing the needs of this vulnerable population group.

The Efficacy of Mobile Phone-Based Interventions for the Treatment of Depression: A Systematic Meta-Review of Meta-Analyses of Randomized Controlled Trials.

BACKGROUND: Depression is ranked by the World Health Organization as the single largest contributor to global disability. The shortage of health care resources, conditions of social distancing during the present pandemic, and the continuing need of patients with subclinical depression and in remission for supportive therapies, all together motivate a search for new approaches to deliver appropriate and timeous treatment for depression. SUBJECTS AND METHODS: We conducted a systematic literature search of meta-analyses and systematic reviews on the topic of mobile apps for the treatment of depression using the Medline (Pubmed) database during the period ending March 30th, 2022. This review was managed following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and entailed a search strategy using key-words related to depressive states and mobile phone apps for depression treatment and management. RESULTS: A total of 15 full-text articles met the inclusion criteria for the current systematic review. 13 of the 15 studies reported on the effectiveness of mobile apps for treating depression, finding a significant reduction in depressive symptoms with small-to-medium positive effect size. Patients with severe depression experienced greater benefits from a behavioral activation app, whereas those with mild depression responded better to a mindfulness app. The impact of clinicians' support is difficult to isolated completely from the particular interventions' effects. CONCLUSIONS: Mobile-based intervention apps present a convenient tool for prevention and supportive therapy of depression. The use of mobile apps may act as an efficient intervention to reduce depression in adult patients regardless the potential contributing factors of gender or co-morbidities, but the role of mobile apps should be contrasted with other digital interventions.

Educational Initiative about the COVID-19 Pandemic-Related Neuropsychiatry for Early Career Professionals in EU: The Impact of the Novel Virus on Brain, Mind, and Society.

BACKGROUND: The COVID-19 pandemic brought challenges to governments, healthcare systems (including, mental healthcare services), clinicians and researchers in the EU and worldwide. A range of neurological (e.g., brain fog, encephalitis, myalgia) and psychiatric (e.g., affective disorders, delirium, cognitive disturbances) complications of a novel nature have been observed in patients during the acute phase of illness, which often persist as a Long-COVID state for months after the primary recovery. The pandemic has progressed to a psychodemic and syndemic, affecting communities with social distress, panic, fears, increased home violence, and protest movements that derive from conspiracy theories and hostile attitudes towards vaccination and lockdown measures. In response to this complex scenario of major social changes, universities must face the need to equip the new generation of doctors with novel special skills. SUBJECTS AND METHODS: The study course (50 hours duration; 20 lectures, three webinars, three e-discussion forums, five local seminars, two social events, three intermediate assessments and a final test for certification; bilingual Russian/English hybrid format, information materials, video-content, interactive web-page and social media) was developed by the team of the International Centre for Education and Research in Neuropsychiatry (ICERN), and is unique for the EU. The course integrates the most relevant data on SARS-Cov-2-related neuropsychiatry, and COVID-19' pandemic impact on mental health and society, including assignment of the vulnerable groups of students and healthcare professionals. The major topics covered during the course are (i) Novel virus, (ii) Brain, (iii) Society. The project takes place originally in Samara State Medical University. The ICERN Faculty includes academic staff from France, Hungary, Italy, Russia, Switzerland, invited speakers from the WHO Regional Office for Europe and World Psychiatric Association (EU Zones) members, some of them employed at ICERN by remote work contracts. The format of the educational process for students is hybrid suggesting both remote and face-to-face events. Distant learning participants and EU lecturers are to attend on-line via zoom platform, whereas local participants and staff work face-to-face in the ICERN video-conference room. The course is addressed to a broad audience of doctors, undergraduate and postgraduate students, and researchers from EU wishing to upgrade their knowledge in the pandemic-associated neuropsychiatry. RESULTS: The evaluation process supposes three intermediate assessments and a final test for certification. On-line assessment is to be performed at the project web-page - 10 randomly selected questions with scoring from 1 to 10 each. The Pass Score is 70-100. At the end of the course all the participants receive certificates of Samara State Medical University according to the ERASMUS policy book, as planned in 2021. CONCLUSIONS: We formatted this course as essential for the target audience to improve their resources of professional adaptability in the field of neuropsychiatry and mental healthcare management during challenging times. The ICERN course in pandemic-related neuropsychiatry is essential for early career health professionals and targets the principles of "academia without borders" in the context of international medical knowledge exchange. In the conditions of the changing social situation this educational content is necessary for the young doctors to acquire the add-on skills on flexibility to switch toward new professional approaches in the times of need. The long-term outcomes in pandemic-related neuropsychiatry are still to be seen, though the first feedback on the course content is already promising for the academic community.

The Effects of Transcranial Magnetic Stimulation on Cognitive Functioning in Bipolar Depression: A Systematic Review.

BACKGROUND: The features of bipolar affective disorder (BAD) include mood swings, recurring episodes of mania, depression, and mixed states. Numerous studies of people living with BAD have found the presence of cognitive impairments that affect patients' daily social functioning and quality of life. Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique recommended for the treatment of bipolar depression (BD). The effect of TMS on cognitive function in BD patients remains mostly unclear. SUBJECTS AND METHODS: We carried out a systematic search in the databases of PubMed and Scopus for the whole publication period until March 30th, 2022. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) was used to identify all data published in English language and related to the use of TMS in the treatment of depression in BAD and its impact on cognitive function. Articles related to TMS, cognition, and BD were identified using predefined term search algorithms. Articles on clinical trials and case reports were included, but reviews were excluded. The PICOS (Population Intervention Comparison Outputs Study) formula in our review included: P - patients with bipolar depression, I - TMS treatment, C - patients without TMS treatment / placebo TMS, O - changes in cognitive functions, S - all types of original studies. RESULTS: Within the primary screening for assessment of full texts, 25 documents met our selection criteria to test the effect of TMS on cognitive functioning in BD. Based on a secondary screening of the full-text analysis, 10 articles (N=259 patients) were included into the current review. Among these, the majority of articles were based on the randomized controlled trials (RCTs, N=6), whereas the remaining four presented a case report, an open unblinded study, an open-label study, and a pilot study, respectively. Most of the studies produced mixed result. However, the limited data strongly suggested that TMS is without detriment to cognition in BD patients and is indeed beneficial in specific domains of cognitive function, namely (i) verbal fluency, (ii) verbal memory, and (iii) executive functioning. Small sample sizes, heterogeneity across the study designs, lack of the control groups data in some of the trials, different TMS protocols parameters and outcome measures represent significant limitations for comparing and analyzing the available results. CONCLUSIONS: Thus, present data on the effects of TMS in improving cognition in BD patients remains limited. To our mind, in order to evaluate properly the effectiveness of TMS in cognitive functioning improvement in BD, there is need for further randomized controlled trials and the corresponding development of the clinical standards for research recommendations. Such studies could define the appropriate methods for valid assessments of cognitive functions, and guide the selection of optimal TMS protocols when planning RCTs. We suggest that efforts should be expended to organize centralized large-scale clinical trials to determine the optimal parameters of TMS procedures and the range of effects of this treatment on various indicators of cognitive functioning in BD. This applies equally to other socially significant mental disorders marked by perturbations in cognitive functioning.

Virtual Reality-Based Interventions for Treating Depression in the Context of COVID-19 Pandemic: Inducing the Proficit in Positive Emotions as a Key Concept of Recovery and a Path Back to Normality.

BACKGROUND: During the COVID-19 pandemic as much as 40% of the global population reported deterioration in depressive mood, whereas 26% experienced increased need for emotional support. At the same time, the availability of on-site psychiatric care declined drastically because of the COVID-19 preventive social restriction measures. To address this shortfall, telepsychiatry assumes a greater role in mental health care services. Among various on-line treatment modalities, immersive virtual reality (VR) environments provide an important resource for adjusting the emotional state in people living with depression. Therefore, we reviewed the literature on VR-based interventions for depression treatment during the COVID-19 pandemic. SUBJECTS AND METHODS: We searched the PubMed and Scopus databases, as well as the Internet, for full-length articles published during the period of 2020-2022 citing a set of following key words: "virtual reality", "depression", "COVID-19", as well as their terminological synonyms and word combinations. The inclusion criteria were: 1) the primary or secondary study objectives included the treatment of depressive states or symptoms; 2) the immersive VR intervention used a head-mounted display (HMD); 3) the article presented clinical study results and/or case reports 4) the study was urged by or took place during the COVID-19-associated lockdown period. RESULTS: Overall, 904 records were retrieved using the search strategy. Remarkably, only three studies and one case report satisfied all the inclusion criteria elaborated for the review. These studies included 155 participants: representatives of healthy population (n=40), a case report of a patient with major depressive disorder (n=1), patients with cognitive impairments (n=25), and COVID-19 patients who had survived from ICU treatment (n=89). The described interventions used immersive VR scenarios, in combination with other treatment techniques, and targeted depression. The most robust effect, which the VR-based approach had demonstrated, was an immediate post-intervention improvement in mood and the reduction of depressive symptoms in healthy population. However, studies showed no significant findings in relation to both short-term effectiveness in treatment of depression and primary prevention of depressive symptoms. Also, safety issues were identified, such as: three participants developed mild adverse events (e.g., headache, "giddiness", and VR misuse behavior), and three cases of discomfort related to wearing a VR device were registered. CONCLUSIONS: There has been a lack of appropriately designed clinical trials of the VR-based interventions for depression since the onset of the COVID-19 pandemic. Moreover, all these studies had substantial limitations due to the imprecise study design, small sample size, and minor safety issues, that did not allow us making meaningful judgments and conclude regarding the efficacy of VR in the treatment of depression, taking into account those investigations we have retrieved upon the inclusion criteria of our particularistic review design. This may call for randomized, prospective studies of the short-term and long-lasting effect of VR modalities in managing negative affectivity (sadness, anxiety, anhedonia, self-guilt, ignorance) and inducing positive affectivity (feeling of happiness, joy, motivation, self-confidence, viability) in patients suffering from clinical depression.