Vol 4, No 3 (2023)

BACKGROUND: The search for biological markers for the differential diagnosis of recurrent depression and bipolar depression is an important undertaking in modern psychiatry. Electroencephalography (EEG) is one of the promising tools in addressing this challenge.

AIM: To identify differences in the quantitative characteristics of the electroencephalographic alpha band activity in patients with a depressive episode within the framework of recurrent depression and bipolar depression.

METHODS: Two groups of patients (all women) were formed: one consisting of subjects with recurrent depressive disorder and one with subjects experiencing a current mild/moderate episode (30 patients), and subjects with bipolar affective disorder or a current episode of mild or moderate depression (30 patients). The groups did not receive pharmacotherapy and did not differ in their socio-demographic parameters or total score on the Hamilton depression scale. A baseline electroencephalogram was recorded, and the quantitative characteristics of the alpha band activity were analyzed, including the absolute spectral power, interhemispheric coherence, and EEG activation.

RESULTS: The patients with recurrent depressive disorder demonstrated statistically significantly lower values of the average absolute spectral power of the alpha band (z=2.481; p=0.042), as well as less alpha attenuation from eyes closed to eyes open (z=2.573; p=0.035), as compared with the patients with bipolar affective disorder.

CONCLUSION: The presented quantitative characteristics of alpha activity are confirmation that patients with affective disorders of different origins also display distinctive electrophysiological features which can become promising biomarkers and could help separate bipolar depression from the recurrent type.

BACKGROUND: The COVID-19 global pandemic exposed gaps in the treatment of common physical and mental disorders that had to do with things like lockdowns, poor convenience, fear of contracting COVID, and economic constraints. Hence, to address these treatment gaps while also limiting exposure to the COVID-19 infection, telemedicine in the form of telephone and internet consultations has increasingly become the recourse around the world. Our center adopted this trend and also launched a telepsychiatry initiative in order to better cater to the needs of patients with pre-existing mental health disorders and to ensure regular follow-ups and compliance with prescription regiments.

AIM: The present study aimed to assess the level of patient satisfaction with the online psychiatric services/telepsychiatry.

METHODS: The sample consisted of 100 patients with pre-existing mental health disorders. This was a cross-sectional study lasting 6 months. The DigiDoc app by Hospital Information Software (HIS) software, which is used to manage a patient’s appointment schedule, relevant clinical and lab details, along with follow-up prescriptions, was used to follow the selected patients for the purpose of this study. This software also provides a digital platform for video calls for online consultation. The Client Satisfaction Questionnaires-8 (CSQ-8) was employed to collect patient data for analysis.

RESULTS: The mean total CSQ-8 score of the study sample was 21.01±5.80 (8–32), which corresponds to a low-to-moderate level of satisfaction with online psychiatric services/telepsychiatry. Most patients (45%) reported low satisfaction levels, followed by 37% who reported moderate levels of satisfaction. Only 18% of patients reported higher satisfaction with telepsychiatry.

CONCLUSION: Despite the psychiatrists ability to provide adequate professional advice and psychoeducation through online psychiatric services, patients’ level of satisfaction proved moderate-to-low. This suggests a need to design standard protocols and guidelines in the search and provision of consultation services on online psychiatric service platforms that could help enhance patients’ levels of satisfaction.

BACKGROUND: Computational psychiatry is an area of scientific knowledge which lies at the intersection of neuroscience, psychiatry, and computer science. It employs mathematical models and computational simulations to shed light on the complexities inherent to mental disorders.

AIM: The aim of this narrative review is to offer insight into the current landscape of computational psychiatry, to discuss its significant challenges, as well as the potential opportunities for the field’s growth.

METHODS: The authors have carried out a narrative review of the scientific literature published on the topic of computational psychiatry. The literature search was performed in the PubMed, eLibrary, PsycINFO, and Google Scholar databases. A descriptive analysis was used to summarize the published information on the theoretical and practical aspects of computational psychiatry.

RESULTS: The article relates the development of the scientific approach in computational psychiatry since the mid-1980s. The data on the practical application of computational psychiatry in modeling psychiatric disorders and explaining the mechanisms of how psychopathological symptomatology develops (in schizophrenia, attention-deficit/hyperactivity disorder, autism spectrum disorder, anxiety disorders, obsessive-compulsive disorder, substance use disorders) are summarized. Challenges, limitations, and the prospects of computational psychiatry are discussed.

CONCLUSION: The capacity of current computational technologies in psychiatry has reached a stage where its integration into psychiatric practice is not just feasible but urgently needed. The hurdles that now need to be addressed are no longer rooted in technological advancement, but in ethics, education, and understanding.

BACKGROUND: The use of remote forms of mental health care has become widespread during the period of epidemiological restrictions due to the COVID-19 pandemic. Methodological and organizational issues remain insufficiently developed, including the level of equivalence of the use of telemedicine technologies in the diagnosis of autistic spectrum disorders.

AIM: Study of the equivalence of diagnostic tools in the framework of telemedicine and face-to-face consultations in children with autistic spectrum disorders according to modern scientific literature.

METHODS: A descriptive review of scientific studies published between January 2017 and May 2023 was carried out. The papers presented in the electronic databases PubMed, Web of Science, and eLibrary were analyzed. Descriptive analysis was used to summarize the obtained data.

RESULTS: The conducted analysis convincingly indicates sufficient equivalence of remote tools used in different countries for level I screening, assessment scales, and structured procedures for diagnosing autistic spectrum disorders with a high level of specificity from 60.0 to 94.4%, sensitivity from 75 dog 98.4%, and satisfaction of patients and their legal representatives.

CONCLUSION: The widespread use of validated telemedicine diagnostic systems in clinical practice contributes to the early detection of autistic spectrum disorders, increasing the timeliness and effectiveness of medical, corrective psychological, pedagogical, and habilitation interventions.

As one of the largest contributors of morbidity and mortality, psychiatric disorders are anticipated to triple in prevalence over the coming decade or so. Major obstacles to psychiatric care include stigma, funding constraints, and a dearth of resources and psychiatrists. The main thrust of our present-day discussion has been towards the direction of how machine learning and artificial intelligence could influence the way that patients experience care. To better grasp the issues regarding trust, privacy, and autonomy, their societal and ethical ramifications need to be probed. There is always the possibility that the artificial mind could malfunction or exhibit behavioral abnormalities. An in-depth philosophical understanding of these possibilities in both human and artificial intelligence could offer correlational insights into the robotic management of mental disorders in the future. This article looks into the role of artificial intelligence, the different challenges associated with it, as well as the perspectives in the management of such mental illnesses as depression, anxiety, and schizophrenia.

There is an error occurred in the article “Schizophrenia: a narrative review of etiological and diagnostic issues” published in the Consortium Psychiatricum journal (Volume 3 Issue 3) by Sofia Oskolkova. Due to a technical error on author’s and editorial parts and without any malicious intent, the “Errors in diagnostics of schizophrenia” chapter links to incorrect references.

The publisher made changes to the electronic version of the published article (PDF and HTML) on the journal’s website instead of the version with errors. 

The authors team and the editorial board of the journal are sure that the mistakes could not significantly affect the perception and interpretation of the published work by readers, and should not become the reason for retraction.
The authors team and the editorial board apologize to the readers for the mistakes made.