Background/Objectives: Electroencephalography (EEG) is considered a standard but powerful tool for the diagnosis of neurological and psychiatric diseases. With modern imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and magnetoencephalography (MEG), source localization can be improved, especially with low-resolution brain electromagnetic tomography (LORETA). The aim of this review is to explore the variety of modern techniques with emphasis on the efficacy of LORETA in detecting brain activity patterns in schizophrenia. The study’s novelty lies in the comprehensive survey of EEG methods and detailed exploration of LORETA in schizophrenia research. This evaluation aligns with clinical objectives and has been performed for the first time. Methods: The study is split into two sections. Part I examines different EEG methodologies and adjuncts to detail brain activity in deep layers in articles published between 2018 and 2023 in PubMed. Part II focuses on the role of LORETA in investigating structural and functional changes in schizophrenia in studies published between 1999 and 2024 in PubMed. Results: Combining imaging techniques and EEG provides opportunities for mapping brain activity. Using LORETA, studies of schizophrenia have identified hemispheric asymmetry, especially increased activity in the left hemisphere. Cognitive deficits were associated with decreased activity in the dorsolateral prefrontal cortex and other areas. Comparison of the first episode of schizophrenia and a chronic one may help to classify structural change as a cause or as a consequence of the disorder. Antipsychotic drugs such as olanzapine or clozapine showed a change in P300 source density and increased activity in the delta and theta bands. Conclusions: Given the relatively low spatial resolution of LORETA, the method offers benefits such as accessibility, high temporal resolution, and the ability to map depth layers, emphasizing the potential of LORETA in monitoring the progression and treatment response in schizophrenia.

Background: Post-traumatic stress disorder (PTSD) is a severe psychiatric condition prevalent among combat veterans. Its diagnosis is challenging due to the heterogeneity of clinical presentations and the complex interplay of pathogenic factors. Objective: This study aimed to develop and validate a diagnostic algorithm for combat-related PTSD by integrating clinical data with a panel of biological markers associated with blood–brain barrier disruption (anti-GFAP and anti-NSE antibodies), HPA axis dysfunction (cortisol), and neuroinflammation (IL-6, IL-8). Methods: A total of 721 male participants were enrolled: 434 veterans with PTSD (F43.1), 147 combat veterans without PTSD, and 140 non-combat military controls. All participants underwent clinical and psychometric assessment (Likert scale, HADS). Serum levels of biomarkers were measured using ELISA. Statistical analysis included non-parametric tests, correlation analysis, and binary logistic regression with Wald’s method to build a predictive model. Results: The binary logistic regression model identified cortisol and IL-6 as the most significant predictors of PTSD. The final algorithm, based on a cortisol level below 199.8 nmol/L and an IL-6 level above 0.002438 pg/mL, correctly classified 78% of patients (AUC = 0.724, 95% CI [0.669, 0.779]). Furthermore, levels of IL-4, IL-8, and cortisol positively correlated with the severity of combat stress factors, independent of physical injuries. Conclusions: We developed a novel diagnostic algorithm for combat-related PTSD based on cortisol and IL-6 levels, demonstrating high accuracy. The correlation between neuroinflammatory markers and the severity of combat exposure suggests their role as primary indicators of stress response, highlighting their utility for early risk identification and targeted interventions.

BACKGROUND: Anxiety and depressive disorders are the most common mental disorders. Detecting a disorder at an early stage can prevent the development of severe disorders and preserve the patient’s functioning ability. Simple and reliable screening tools based on self-completion of questionnaires can be used for this purpose. However, it is not always the case that the scores of the self-questionnaire align with those of the clinician. AIM: To estimate the prevalence of anxiety-depressive disorders using the GAD-7 and HADS self-report questionnaires compared to psychiatrist assessment. METHODS: The study included individuals aged 18 to 65 years, living in Moscow, Russia, without psychiatric disorders, who participated in an online study using the HADS (Hospital Anxiety and Depression Scale, HADS-A and HADS-D) and GAD-7 (Generalized Anxiety Disorder 7-item scale). Anxiety disorder was diagnosed when the total score was ≥10 on the GAD-7 and/or ≥10 on the HADS-A scale, and depression was defined when the total score was ≥9 on the HADS-D scale. Then, 82 randomly selected participants attended an anonymous consultation with a psychiatrist. RESULTS: The study included 1,097 individuals (72% female), median age 29 (23; 37) years. As a result of testing, anxiety disorder was found in 168 (15%); depressive disorder — in 152 (14%) respondents. At medical verification, anxiety was diagnosed in 18 (22%); depression — in 19 (23%) people. The sensitivity of the HADS-D subscale for physician-diagnosed cases of depression was 61%, and specificity was 73%. The sensitivity of the HADS-A and GTR-7 subscale in identifying cases of anxiety disorder was 58%, specificity 59%. Sixteen percent were first diagnosed with a personality disorder or schizotypal disorder. CONCLUSION: The level of anxiety and depression in our sample of the population of Moscow, Russia, was higher than the global level. Self-assessment based on the questionnaire seems to not fully reflect the real state of a patient, as evidenced by the differences with the psychiatrist’s assessment.

BACKGROUND: Studies suggest that the components of brain-evoked potentials (EPs) may serve as biomarkers of the post-traumatic stress disorder (PTSD) caused by participation in combat operations; however, to date, research remains fragmented, with no studies that have attempted to combine different paradigms. In addition, the mismatch negativity component has not been studied in a Russian sample of veterans with PTSD. AIM: To identify objective neurophysiological markers of combat-related PTSD using the method of auditory-evoked potentials in active and passive listening paradigms. METHODS: The study included a recording of auditory EPs in an oddball paradigm in three settings: 1) directed attention to auditory stimuli, 2) passive listening while viewing a neutral video sequence, and 3) viewing a video sequence associated with a traumatic event. Combatants diagnosed with PTSD (18 people) were compared with mentally healthy civilian volunteers (22 people). RESULTS: An increase in the latency period of the early components of auditory EP (N100 and P200), an increase in the amplitude of the P200 component to a deviant stimulus, and a decrease to a standard one in the active listening paradigm were established in the PTSD group. There were no significant differences in the parameters of the P300 component. The characteristics of mismatch negativity in the passive paradigm were revealed: an increase in the phenomenon amplitude, both when shown a video sequence associated with a traumatic event and when shown a neutral video sequence. A binary logistic regression model constructed using the selected parameters showed that the identified characteristics can potentially be considered as diagnostic markers of PTSD in combatants, as the classification accuracy stood at 87% (sensitivity — 81%, specificity — 91%). CONCLUSION: Potential neurophysiological markers of PTSD are the following: the amplitude and latency of early components of auditory EPs in the paradigm of directed attention to stimuli and the amplitude of mismatch negativity during passive attention.

The default mode network (DMN), also referred to as the “Passive Mode Brain Network” (PMBN), serves as a network of active brain regions while

Given the high growth rates of cognitive decline among the elderly population and the lack of effective etiological treatments, early diagnosis of cognitive impairment progression is an imperative task for modern science and medicine. It is of particular interest to identify predictors of an unfavorable subsequent course of cognitive disorders, specifically, rapid progression. Our study assessed the informative role of various risk factors on the dynamics of cognitive impairment among mild cognitive impairment (MCI) patients. The study included patients with MCI (N = 338) who underwent neuropsychological assessment, magnetic resonance imaging (MRI) examination, blood sampling for general and biochemical analysis, APOE genotyping, and polygenic risk score (PRS) evaluation. The APOE ε4/ε4 genotype was found to be associated with a diminished overall cognitive scores initial assessment and negative cognitive dynamics. No associations were found between cognitive changes and the PRS. The progression of cognitive impairment was associated with the width of the third ventricle and hematological parameters, specifically, hematocrit and erythrocyte levels. The absence of significant associations between the dynamics of cognitive decline and PRS over three years can be attributed to the provided suitable medical care for the prevention of cognitive impairment. Adding other risk factors and their inclusion in panels assessing the risk of progression of cognitive impairment should be considered.