EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies.

Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.

Mentalizing impairments and hypermentalizing bias in individuals with first-episode schizophrenia-spectrum disorder and at-risk mental state: the differential roles of neurocognition and social anxiety.

Mentalizing, or theory of mind (ToM), impairments and self-referential hypermentalizing bias are well-evident in schizophrenia. However, findings compared to individuals with at-risk mental states (ARMS) are inconsistent, and investigations into the relationship between social cognitive impairments and social anxiety in the two populations are scarce. This study aimed to examine and compare these deficits in first-episode schizophrenia-spectrum disorder (FES) and ARMS, and to explore potential specific associations with neurocognition and symptomatology. Forty patients with FES, 40 individuals with ARMS, and 40 healthy controls (HC) completed clinical assessments, a battery of neurocognitive tasks, and three social cognitive tasks. The comic strip and hinting tasks were used to measure non-verbal and verbal mentalizing abilities, and the gaze perception task was employed to assess self-referential hypermentalizing bias. FES and ARMS showed comparable mentalizing impairments and self-referential hypermentalizing bias compared to HC. However, only ambiguous self-referential gaze perception (SRGP) bias remained significantly different between three groups after controlling for covariates. Findings suggested that self-referential hypermentalizing bias could be a specific deficit and may be considered a potential behavioral indicator in early-stage and prodromal psychosis. Moreover, working memory and social anxiety were related to the social cognitive impairments in ARMS, whereas higher-order executive functions and positive symptoms were associated with the impairments in FES. The current study indicates the presence of stage-specific mechanisms of mentalizing impairments and self-referential hypermentalizing bias, providing insights into the importance of personalized interventions to improve specific neurocognitive domains, social cognition, and clinical outcomes for FES and ARMS.

Evaluation of EEG Signals by Spectral Peak Methods and Statistical Correlation for Mental State Discrimination Induced by Arithmetic Tasks.

Bringing out brain activity through the interpretation of EEG signals is a challenging problem that involves combined methods of signal analysis. The issue of classifying mental states induced by arithmetic tasks can be solved through various classification methods, using diverse characteristic parameters of EEG signals in the time, frequency, and statistical domains. This paper explores the results of an experiment that aimed to highlight arithmetic mental tasks contained in the PhysioNet database, performed on a group of 36 subjects. The majority of publications on this topic deal with machine learning (ML)-based classification methods with supervised learning support vector machine (SVM) algorithms, K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Decision Trees (DTs). Also, there are frequent approaches based on the analysis of EEG data as time series and their classification with Recurrent Neural Networks (RNNs), as well as with improved algorithms such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BLSTM), and Gated Recurrent Units (GRUs). In the present work, we evaluate the classification method based on the comparison of domain limits for two specific characteristics of EEG signals: the statistical correlation of pairs of signals and the size of the spectral peak detected in theta, alpha, and beta bands. This study provides some interpretations regarding the electrical activity of the brain, consolidating and complementing the results of similar research. The classification method used is simple and easy to apply and interpret. The analysis of EEG data showed that the theta and beta frequency bands were the only discriminators between the relaxation and arithmetic calculation states. Notably, the F7 signal, which used the spectral peak criterion, achieved the best classification accuracy (100%) in both theta and beta bands for the subjects with the best results in performing calculations. Also, our study found the Fz signal to be a good sensor in the theta band for mental task discrimination for all subjects in the group with 90% accuracy.

Effectiveness of a two-stage strategy using the 16-item Prodromal Questionnaire (PQ16/fPQ16) and the Comprehensive Assessment of At Risk Mental States (CAARMS) in the early detection of Ultra High Risk (UHR) patients in Lebanon: a cross-sectional pilot study.

OBJECTIVES: This study aims to identify the prevalence of Ultra High Risk (UHR) adults in a sample of patients in Lebanon and to compare screening and diagnostic tools. METHODS: This is a cross-sectional pilot study which targeted patients aged 17-30 years willingly seeking psychiatric care in a university hospital in Beirut, Lebanon. Participants were invited to fill either the English or the French version of the Prodromal Questionnaire (respectively, PQ16 or fPQ16). The abbreviated version of the "Comprehensive Assessment of At-Risk Mental States" (CAARMS) was then administered to all participants. The latter were subsequently sorted into one of the three UHR groups - vulnerability group, attenuated psychosis (APS), intermittent psychosis (BLIPS) - or were diagnosed as suffering from a psychotic disorder. RESULTS: Thirty-one patients participated in this study. The prevalence of positive screening on the PQ16/fPQ16 and positive diagnosis of UHR on the CAARMS were respectively of 61.29% and 45%. The APS group was the most prevalent (71.42%). A positive psychosis screening on PQ16/fPQ16 was statistically related to a UHR diagnosis on CAARMS (p-value: 0.011 on Chi2 test), OR=8.5 (95% CI: 1.4-50.9; p-value: 0.018). No relation was found between PQ16/fPQ16 results and risk stratification or between the number of "True" responses on PQ16/fPQ16 and the intensity of symptoms on CAARMS. PQ16/fPQ16 statements 5, 9, 11 and 16 predicted a UHR diagnosis on CAARMS (p-value of 0.045, 0.006, 0.045 and 0.045, respectively). CONCLUSION: This two-stage strategy for identifying UHR patients can be adopted in a tertiary health care center.

Stations Along the Via Dolorosa Towards Good-Enough Endings.

This article focuses on the prevailing aspiration to reach a "good-enough ending" in analysis, a concept that is partly realistic and partly illusional. I discuss some of the obstacles that interfere with achieving this yearned for goal, and lead to endings that are far from the misleading illusion of the good-enough termination, that many of us believe we have achieved and are many more than it is commonly reported. I describe characteristics, obstacles, blockages, dreads within the analysand, within the analyst and in the space in between, which lead to endings which are far from good enough, by any criteria we might choose. These obstacles include the failure to distinguish between "real" versus "similar to"; emotional excess; emptying out of internal resources and toxemia of therapy/analysis; a fascination with certain levels of mind versus a neglect of others; osmotic pressure for oneness and the terror of perfection; and malignant nostalgia. Reflecting on such complex facets in the analytic process is relevant not only for a deeper understanding of illusions that we and our analysands hold with regard to endings, but also, implicitly, to the understanding of illusions, beliefs, and myths we and our patients have regarding beginnings.

Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability.

Predicting pilots' mental states is a critical challenge in aviation safety and performance, with electroencephalogram data offering a promising avenue for detection. However, the interpretability of machine learning and deep learning models, which are often used for such tasks, remains a significant issue. This study aims to address these challenges by developing an interpretable model to detect four mental states-channelised attention, diverted attention, startle/surprise, and normal state-in pilots using EEG data. The methodology involves training a convolutional neural network on power spectral density features of EEG data from 17 pilots. The model's interpretability is enhanced via the use of SHapley Additive exPlanations values, which identify the top 10 most influential features for each mental state. The results demonstrate high performance in all metrics, with an average accuracy of 96%, a precision of 96%, a recall of 94%, and an F1 score of 95%. An examination of the effects of mental states on EEG frequency bands further elucidates the neural mechanisms underlying these states. The innovative nature of this study lies in its combination of high-performance model development, improved interpretability, and in-depth analysis of the neural correlates of mental states. This approach not only addresses the critical need for effective and interpretable mental state detection in aviation but also contributes to our understanding of the neural underpinnings of these states. This study thus represents a significant advancement in the field of EEG-based mental state detection.

Multimodal Approach for Pilot Mental State Detection Based on EEG.

The safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in pilots using electroencephalography (EEG) signals. Our approach includes an advanced automated preprocessing pipeline to remove artefacts from the EEG data, a feature extraction method based on Riemannian geometry analysis of the cleaned EEG data, and a hybrid ensemble learning technique that combines the results of several machine learning classifiers. The proposed approach provides improved accuracy compared to existing methods, achieving an accuracy of 86% when tested on cleaned EEG data. The EEG dataset was collected from 18 pilots who participated in flight experiments and publicly released at NASA's open portal. This study presents a reliable and efficient solution for detecting mental states in pilots and highlights the potential of EEG signals and ensemble learning algorithms in developing cognitive cockpit systems. The use of an automated preprocessing pipeline, feature extraction method based on Riemannian geometry analysis, and hybrid ensemble learning technique set this work apart from previous efforts in the field and demonstrates the innovative nature of the proposed approach.

Neuroergonomics on the Go: An Evaluation of the Potential of Mobile EEG for Workplace Assessment and Design.

OBJECTIVE: We demonstrate and discuss the use of mobile electroencephalogram (EEG) for neuroergonomics. Both technical state of the art as well as measures and cognitive concepts are systematically addressed. BACKGROUND: Modern work is increasingly characterized by information processing. Therefore, the examination of mental states, mental load, or cognitive processing during work is becoming increasingly important for ergonomics. RESULTS: Mobile EEG allows to measure mental states and processes under real live conditions. It can be used for various research questions in cognitive neuroergonomics. Besides measures in the frequency domain that have a long tradition in the investigation of mental fatigue, task load, and task engagement, new approaches-like blink-evoked potentials-render event-related analyses of the EEG possible also during unrestricted behavior. CONCLUSION: Mobile EEG has become a valuable tool for evaluating mental states and mental processes on a highly objective level during work. The main advantage of this technique is that working environments don't have to be changed while systematically measuring brain functions at work. Moreover, the workflow is unaffected by such neuroergonomic approaches.

Memories without Survival: Personal Identity and the Ascending Reticular Activating System.

Lockean views of personal identity maintain that we are essentially persons who persist diachronically by virtue of being psychologically continuous with our former selves. In this article, I present a novel objection to this variant of psychological accounts, which is based on neurophysiological characteristics of the brain. While the mental states that constitute said psychological continuity reside in the cerebral hemispheres, so that for the former to persist only the upper brain must remain intact, being conscious additionally requires that a structure originating in the brainstem-the ascending reticular activating system-be functional. Hence, there can be situations in which even small brainstem lesions render individuals irreversibly comatose and thus forever preclude access to their mental states, while the neural correlates of the states themselves are retained. In these situations, Lockeans are forced to regard as fulfilled their criterion of diachronic persistence since psychological continuity, as they construe it, is not disrupted. Deeming an entity that is never again going to have any mental experiences to be a person, however, is an untenable position for a psychological account to adopt. In their current form, Lockean views of personal identity are therefore incompatible with human neurophysiology.

Agency and Identity in the Collective Self.

Contemporary research on human sociality is heavily influenced by the social identity approach, positioning social categorization as the primary mechanism governing social life. Building on the distinction between agency and identity in the individual self ("I" vs. "Me"), we emphasize the analogous importance of distinguishing collective agency from collective identity ("We" vs. "Us"). While collective identity is anchored in the unique characteristics of group members, collective agency involves the adoption of a shared subjectivity that is directed toward some object of our attention, desire, emotion, belief, or action. These distinct components of the collective self are differentiated in terms of their mental representations, neurocognitive underpinnings, conditions of emergence, mechanisms of social convergence, and functional consequences. Overall, we show that collective agency provides a useful complement to the social categorization approach, with unique implications for multiple domains of human social life, including collective action, responsibility, dignity, violence, dominance, ritual, and morality.

Testing Function Focused Care for Acute Care using the Evidence Integration Triangle: Protocol description.

This manuscript provides the protocol for a National Institute of Aging-funded cluster randomized clinical trial that focuses on helping nurses in acute care to engage patients with dementia in physical activity while hospitalized using an approach referred to as function-focused care. Physical activity is defined as bodily movement produced by skeletal muscles resulting in the expenditure of energy and includes functional tasks such as bathing and dressing, leisure activity, ambulation, and moderate and vigorous intensity physical activity such as dancing, bike riding, or walking upstairs. The development of Function Focused Care for Acute Care (FFC-AC) was based on the Social Ecological Model and Social Cognitive Theory and includes four steps: (1) Environment and Policy Assessments; (2) Education; (3) Establishing Patient Goals; and (4) Mentoring and Motivating of Staff (all levels of nursing staff), Patients, and Families. Function-focused care activities include motivating older patients to participate in bed mobility; personal care activities such as bathing, dressing, ambulating as they are able; and other types of physical activities. The integration of the intervention among the nurses on the units is guided by the Evidence Integration Triangle (EIT), which includes the participation of a stakeholder team and practical outcome measures. The intervention is therefore referred to as FFC-AC-EIT. In addition to describing the protocol developed to test the effectiveness and feasibility of FFC-AC-EIT, a description of ways to overcome some of the barriers and challenges that can be encountered with this study is provided.

Delirium detection using GAMMA wave and machine learning: A pilot study.

Delirium occurs in as many as 80% of critically ill older adults and is associated with increased long-term cognitive impairment, institutionalization, and mortality. Less than half of delirium cases are identified using currently available subjective assessment tools. Electroencephalogram (EEG) has been identified as a reliable objective measure but has not been feasible. This study was a prospective pilot proof-of-concept study, to examine the use of machine learning methods evaluating the use of gamma band to predict delirium from EEG data derived from a limited lead rapid response handheld device. Data from 13 critically ill participants aged 50 or older requiring mechanical ventilation for more than 12 h were enrolled. Across the three models, accuracy of predicting delirium was 70 or greater. Stepwise discriminant analysis provided the best overall method. While additional research is needed to determine the best cut points and efficacy, use of a handheld limited lead rapid response EEG device capable of monitoring all five cerebral lobes of the brain for predicting delirium hold promise.

Three-month Chan-Chuang qigong program improves physical performance and quality of life of patients with cognitive impairment: A randomized controlled trial.

This randomized controlled trial was conducted to evaluate the effects of a 3-month-long Chan-Chuang qigong program on patients' physical performance and quality of life while excluding the influence caused by the progression of their cognitive impairment. Patients with mild to moderate cognitive impairment were recruited from two dementia daycare centers in Taiwan. The control group (n = 41) received the standardized plan of treatment, and the qigong group (n = 39) received the standardized plan of treatment plus the Chan-Chuang qigong program. The outcomes were muscle strength, muscle endurance, exercise capacity, and quality of life. After controlling for the progression of cognitive impairment, the qigong group showed significant improvements over the control group and baseline in muscle strength and exercise capacity at Months 2 and 3 (p < 0.05) and in muscle endurance at Months 1, 2, and 3 (p < 0.05). The Cognitron test scores were significantly associated with muscle strength (p = 0.03), whereas the Corsi block-tapping test scores were significantly associated with exercise capacity (p = 0.001). Furthermore, a significant between-group difference was detected in the physical (p = 0.01), not mental (p = 0.83), component of quality of life. The 3-month Chan-Chuang qigong program can be applied for patients with mild to moderate cognitive impairment as complementary therapy to improve their muscle strength, muscle endurance, exercise capacity, and physical quality of life. This program should be practiced for at least 2 months to achieve satisfactory results.

Neurocognitive mechanisms of poor social connection in posttraumatic stress disorder: Evidence for abnormalities in social working memory.

BACKGROUND: Poor social connection is a central feature of posttraumatic stress disorder (PTSD), but little is known about the neurocognitive processes associated with social difficulties in this population. We examined recruitment of the default network and behavioral responses during social working memory (SWM; i.e., maintaining and manipulating social information on a moment-to-moment basis) in relation to PTSD and social connection. METHODS: Participants with PTSD (n = 31) and a trauma-exposed control group (n = 21) underwent functional magnetic resonance imaging while completing a task in which they reasoned about two or four people's relationships in working memory (social condition) and alphabetized two or four people's names in working memory (nonsocial condition). Participants also completed measures of social connection (e.g., loneliness, social network size). RESULTS: Compared to trauma-exposed controls, individuals with PTSD reported smaller social networks (p = .032) and greater loneliness (p = .038). Individuals with PTSD showed a selective deficit in SWM accuracy (p = .029) and hyperactivation in the default network, particularly in the dorsomedial subsystem, on trials with four relationships to consider. Moreover, default network hyperactivation in the PTSD group (vs. trauma-exposed group) differentially related to social network size and loneliness (p's < .05). Participants with PTSD also showed less resting state functional connectivity within the dorsomedial subsystem than controls (p = .002), suggesting differences in the functional integrity of a subsystem key to SWM. CONCLUSIONS: SWM abnormalities in the default network may be a basic mechanism underlying poorer social connection in PTSD.

A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare.

Current telemedicine and remote healthcare applications foresee different interactions between the doctor and the patient relying on the use of commercial and medical wearable sensors and internet-based video conferencing platforms. Nevertheless, the existing applications necessarily require a contact between the patient and sensors for an objective evaluation of the patient's state. The proposed study explored an innovative video-based solution for monitoring neurophysiological parameters of potential patients and assessing their mental state. In particular, we investigated the possibility to estimate the heart rate (HR) and eye blinks rate (EBR) of participants while performing laboratory tasks by mean of facial-video analysis. The objectives of the study were focused on: (i) assessing the effectiveness of the proposed technique in estimating the HR and EBR by comparing them with laboratory sensor-based measures and (ii) assessing the capability of the video-based technique in discriminating between the participant's resting state (Nominal condition) and their active state (Non-nominal condition). The results demonstrated that the HR and EBR estimated through the facial-video technique or the laboratory equipment did not statistically differ (p > 0.1), and that these neurophysiological parameters allowed to discriminate between the Nominal and Non-nominal states (p < 0.02).

STUDY OF THE MENTAL STATE OF STUDENTS IN THE PROCESS OF ONLINE EDUCATION DURING THE CORONAVIRUS PANDEMIC.

OBJECTIVE: The aim: Exploring the specifics of the underlying mental states of university students in the context of online learning during the COVID-19 pandemic and identifying their relationship to mental health. PATIENTS AND METHODS: Materials and methods: The study of mental states was carried out using three valid methods, namely: a clinical questionnaire for the detection and evaluation of neurotic states (K. Yakhin, D. Mendelevich), a questionnaire for diagnosing the level of social frustration (L. Wasserman, adaptation of V. Bojko), State-Trait Anxiety Inventory (STAI) (Ch. Spielberger, adaptation by Yu. Khanin). The author's questionnaire (L. Borysenko) was used to study the connection between the factors of online learning and emotional states of students. The obtained empirical data were subjected to qualitative and quantitative processing. RESULTS: Results: The analysis of the obtained data allows to state the fact of predominance of high and increased level of situational and personal anxiety in the subjects in the conditions of online learning. In the process of psychodiagnosis of neurotic states, it was found that most students have a level of painful or borderline mental conditions such as anxiety, neurotic depression, asthenia and autonomic disorders. It was found that students have an elevated level and high level of social frustration. The main factors of online learning, which, according to students, cause them to deteriorate mental health are: forced to stay in a confined space, lack of direct social contacts, prolonged stay in a static position, increased study load and volume of tasks, anxiety for academic success and quality professional training, concern for their health and the health of their loved ones. CONCLUSION: Conclusions: The forced transition to online learning due to the COVID-19 pandemic has negatively affected the mental state of many students. In most respondents, this manifested itself in increased anxiety, depression, autonomic reactions due to limited opportunities to communicate with teachers and classmates, new learning conditions and increased workload.

Mental health ubiquitous monitoring supported by social situation awareness: A systematic review.

Traditionally, the process of monitoring and evaluating social behavior related to mental health has based on self-reported information, which is limited by the subjective character of responses and various cognitive biases. Today, however, there is a growing amount of studies that have provided methods to objectively monitor social behavior through ubiquitous devices and have used this information to support mental health services. In this paper, we present a Systematic Literature Review (SLR) to identify, analyze and characterize the state of the art about the use of ubiquitous devices to monitor users' social behavior focused on mental health. For this purpose, we performed an exhaustive literature search on the six main digital libraries. A screening process was conducted on 160 peer-reviewed publications by applying suitable selection criteria to define the appropriate studies to the scope of this SLR. Next, 20 selected studies were forwarded to the data extraction phase. From an analysis of the selected studies, we recognized the types of social situations identified, the process of transforming contextual data into social situations, the use of social situation awareness to support mental health monitoring, and the methods used to evaluate proposed solutions. Additionally, we identified the main trends presented by this research area, as well as open questions and perspectives for future research. Results of this SLR showed that social situation-aware ubiquitous systems represent promising assistance tools for patients and mental health professionals. However, studies still present limitations in methodological rigor and restrictions in experiments, and solutions proposed by them have limitations to be overcome.

Predicting the knowledge-recklessness distinction in the human brain.

Criminal convictions require proof that a prohibited act was performed in a statutorily specified mental state. Different legal consequences, including greater punishments, are mandated for those who act in a state of knowledge, compared with a state of recklessness. Existing research, however, suggests people have trouble classifying defendants as knowing, rather than reckless, even when instructed on the relevant legal criteria. We used a machine-learning technique on brain imaging data to predict, with high accuracy, which mental state our participants were in. This predictive ability depended on both the magnitude of the risks and the amount of information about those risks possessed by the participants. Our results provide neural evidence of a detectable difference in the mental state of knowledge in contrast to recklessness and suggest, as a proof of principle, the possibility of inferring from brain data in which legally relevant category a person belongs. Some potential legal implications of this result are discussed.

Aggressive behavior and metacognitive functions: a longitudinal study on patients with mental disorders.

BACKGROUND: Metacognitive functions play a key role in understanding which psychological variables underlying the personality might lead a person with a severe mental disorder to commit violent acts against others. The aims of this study were to: (a) investigate the differences between patients with poor metacognitive functioning (PM group) and patients with good metacognitive functioning (GM group) in relation to a history of violence; (b) investigate the differences between the two groups in relation to aggressive behavior during a 1-year follow-up; and (c) analyze the predictors of aggressive behavior. METHODS: In a prospective cohort study, patients with severe mental disorders with and without a lifetime history of serious violence were assessed with a large set of standardized instruments and were evaluated bi-monthly with MOAS in order to monitor any aggressive behavior. The total sample included 180 patients: 56% outpatients and 44% inpatients, and the majority were male (75%) with a mean age of 44 (± 9.8) years, and half of them had a history of violence. The sample was split into two groups: poor metacognition (PM) group and good metacognition (GM) group, according to MAI evaluation scores. RESULTS: The PM patients reported a history of violence more frequently than GM patients, during the 1-year follow-up, but no differences between groups in aggressive and violent behavior were found. The strongest predictors of aggressive behavior were: borderline and passive-aggressive personality traits and a history of violence, anger, and hostility. The metacognitive functions alone did not predict aggressive behavior, but metacognitive functions interacted with hostility and angry reactions in predicting aggressive behavior. CONCLUSIONS: This study led to some important conclusions: (a) some aspects closely related to violence are predictive of aggressive behavior only in patients with poor metacognition, thus good metacognition is a protective factor; (b) poor metacognition is associated with a history of violence, which in turn increases the risk of committing aggressive behavior.

ALEXITHYMIA FORMATION AS AN ADAPTATION TO EVERYDAY STRESS IS DETERMINED BY THE PROPERTIES OF THE NERVOUS SYSTEM.

OBJECTIVE: The aim of the study was to determine the psychological nature and mechanisms of alexithymia formation by way of the analysis of its relation to the properties of the nervous system, mental states, and characteristics of the emotional sphere of the personality. PATIENTS AND METHODS: Materials and methods: In the process of the study, for the diagnostics of alexithymia, we used the 26-item Toronto Alexithymia Scale (TAS-26) developed by G.J. Taylor and a block of psycho-diagnostic methods aimed at the diagnostics of properties of the nervous systems, the emotional sphere and mental states of respondents. The relationships were evaluated using Spearman's rank correlation coefficient and Pearson's correlation coefficient. RESULTS: Results: The main factors related to alexithymia were weak nervous system, low stress resistance and such characteristics of the emotional sphere as marked extraversion, high level of trait anxiety, neuroticism, indirect verbal aggression, low levels of aggressiveness. The emotional exhaustion and reduction of personal achievements, the Resistance Phase, chronic fatigue and depression were the most pronounced within the alexithymia group. The alexithymic personality type demonstrated less developed spatial anticipation. CONCLUSION: Conclusion: In accordance with our results, the weakness of the nervous system and high Trait Anxiety facilitate the adaption to stressful situations by avoiding and crowding out negative emotions, lead to the inability of verbal description and expression of emotions. A low level of stress resistance conduces to neurotization, chronic fatigue, and emotional burnout. The predominance of refractory and dysphoric reactions causes a negative vision of the situation and can provoke the development of psychosomatic disorders.