Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

These are exciting times for psychiatry and clinical neuroscience. Our knowledge of basic brain function continues to increase at an accelerating pace as the experimental tools available to basic and clinical scientists become ever more powerful and penetrating. After decades of frustration and relatively slow progress, this explosion of knowledge of the brain is at long last beginning to define the etiology and pathophysiology of complex neuropsychiatric disorders that have long defied biological explanations, which in turn is being translated into clinical advances in diagnosis and treatment of an increasing number of these illnesses. This new, sixth edition of Neurobiology of Mental Illness addresses these challenging, yet very promising, times and reflects the continuing reintegration of psychiatry into the mainstream of biomedical science and the increasing synthesis of psychiatry and neurology into a fully integrated clinical neuroscience.

Kerry J. Ressler

A pioneering force in psychiatric neuroscience, Dr. Kerry Ressler divides his time between serving as Chief Scientific Officer at McLean Hospital, Professor of Psychiatry at Harvard Medical School, and translational neuroscientist. Drawing from both molecular biology and human genetics, he has fundamentally changed how we understand fear and anxiety in the brain, especially through his innovative research on the amygdala. Throughout his remarkable career, which includes over 500 published papers, he has uncovered critical insights into the genetic and epigenetic basis of post-traumatic stress disorder (PTSD) and related anxiety disorders. His expertise has earned him membership in the National Academy of Medicine and a term as president of the Society for Biological Psychiatry. Dr. Ressler co-directs the Psychiatric Genomics Consortium PTSD Workgroup and founded the Grady Trauma Project in Atlanta before joining McLean Hospital. This Genomic Press Interview offers an intimate look at the path and perspectives of a scientist who has shaped modern psychiatric research and treatment.

S.B. Jelsma, M. Zijlmans, I.B. Heijink et al.

Epilepsy surgery is usually based on the removal of a local epileptogenic zone. If epilepsy is considered a network disease, a network approach might be more suitable. Insight into patient-specific epileptic brain networks is necessary to establish network-based surgical strategies.We included epilepsy surgery candidates who underwent diffusion-weighted imaging and intracranial EEG implantation with single pulse electrical stimulation (SPES, 0.2 Hz, 1–8 mA, 1 ms, monophasic stimuli) during presurgical evaluation. We reconstructed structural connectivity using fiber tractography taking intracranial electrodes as nodes. We reconstructed effective connectivity with SPES cortico-cortical evoked responses. We determined the inter-modal similarity between structural and effective connectivity with the Jaccard index, and compared network topologies using degree and betweenness centrality. We constructed a linear multilevel model to evaluate the relation between structural and effective connectivity at subject group level. The seizure onset zone nodes (SOZ), node proximity, and the volume of the electrode contact areas (VEA) were added to the model as possible predictors to accommodate for epilepsy and irregular spatial sampling.We included 13 patients (five with electrocorticography, eight with stereo-EEG). The median Jaccard index was 0.25 (IQR: 0.20–0.29), which means there is a higher overlap than expected by chance (median expected Jaccard index = 0.1 (IQR: 0.07–0.17)) with a considerable amount of connections that did not overlap. The structural connectivity degree showed a significant positive correlation with the effective connectivity degree in 9/13 patients and at group level after accommodating for node proximity (β = 0.13, 95 %-CI = [0.04, 0.21], t(852) = 2.79, p = 0.0054). SOZ and VEA were no significant predictors for the correlation between structural and effective connectivity.We showed a moderate overlap between non-invasive structural (measured with DWI) and invasive effective (measured with SPES) connectivity in epileptic brain networks. This overlap supports using non-invasively determined connectivity along with intracranial EEG to understand the epileptic brain. Future research needs to translate these findings towards network-based surgical strategies.

Diego D. Díaz-Guerra, Marena de la C. Hernández-Lugo, Yunier Broche-Pérez et al.

IntroductionEvaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificial intelligence (AI) with the depth of neuropsychological assessments is needed.ObjectivesThis paper presents a novel protocol for AI-assisted neurocognitive assessment aimed at addressing the cognitive, emotional, and functional dimensions of older adults with psychiatric disorders. It also explores potential compensatory mechanisms.MethodologyThe proposed protocol incorporates a comprehensive, personalized approach to neurocognitive evaluation. It integrates a series of standardized and validated psychometric tests with individualized interpretation tailored to the patient’s specific conditions. The protocol utilizes AI to enhance diagnostic accuracy by analyzing data from these tests and supplementing observations made by researchers.Anticipated resultsThe AI-assisted protocol offers several advantages, including a thorough and customized evaluation of neurocognitive functions. It employs machine learning algorithms to analyze test results, generating an individualized neurocognitive profile that highlights patterns and trends useful for clinical decision-making. The integration of AI allows for a deeper understanding of the patient’s cognitive and emotional state, as well as potential compensatory strategies.ConclusionsBy integrating AI with neuro-psychological evaluation, this protocol aims to significantly improve the quality of neurocognitive assessments. It provides a more precise and individualized analysis, which has the potential to enhance clinical decision-making and overall patient care for older adults with psychiatric disorders.

Jiaqi Yao, Jiaqi Yao, Xinjian Lu et al.

BackgroundCompared to single-shell diffusion tensor imaging (DTI), free water (FW) and neurite orientation dispersion and density imaging (NODDI) offer a more comprehensive evaluation of microstructural alterations in cerebral white matter (WM), particularly in detecting crossing fibers. However, research utilizing multi-shell diffusion imaging to investigate thyroid-associated ophthalmopathy (TAO) remains limited. This study employs FW and NODDI to investigate microstructural changes in the white matter of the visual pathways in patients with TAO.MethodsMulti-shell diffusion magnetic resonance imaging (dMRI) scans were performed on 45 patients with TAO and 31 age- and sex-matched healthy controls (HC). Tract-based spatial statistics (TBSS) analysis was conducted using eight FW and NODDI-derived metrics to identify group differences in white matter microstructure. Furthermore, correlations between these microstructural changes and clinical measures were examined.ResultsTBSS analysis revealed that, compared to HC, patients with TAO exhibited lower free-water corrected fractional anisotropy (fwFA) and free-water corrected axial diffusivity (fwAD), while free-water corrected mean diffusivity (fwMD), free-water corrected radial diffusivity (fwRD), and orientation dispersion index (ODI) were significantly increased (p < 0.05, FWE). Notably, ODI demonstrated the highest area under the curve (AUC) among these metrics. Furthermore, fwFA, fwAD, fwMD, fwRD, and ODI showed significant correlations with the Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD), and the Graves’ Orbitopathy Quality of Life Questionnaire (GO-QOL2) scores.ConclusionThis study suggests that abnormalities in the white matter microstructure of TAO patients can be detected through the complementary use of FW and NODDI metrics, and it is revealed that these changes may have an impact on mental health.

Nicolás Parra Bolaños

Cognitive neuroscience has historically worked hand in hand with disciplines such as psychiatry, neuropsychiatry, and, of course, neuropsychology, for the rigorous and scientific study of schizophrenia. Schizophrenia is one of the most challenging conditions within the academic community of health sciences, as very few advances have been made in slowing its progression or curing it. The objective of this review study was to conduct a comprehensive sweep of data from the last decade to compile a theoretical framework of the main developments and progress in schizophrenia research. The study found that the most sophisticated advances continue to be in diagnostic tools, refining and improving these tools to achieve the highest levels of sensitivity. This research concludes that, although many scientific disciplines are involved in the study of schizophrenia, the reality is that we have become overwhelmed with predictive tests and examinations in addition to diagnoses, and very little progress has been made in finding a definitive cure for schizophrenia, so the emergence of new paradigms and disciplines is required, capable of giving a different approach to this condition.

Kiran Nijabat

Aims This study focuses on the North Central London Child and Adolescent Mental Health Services (NCL CAMHS) Co-production workstream, initiated to establish co-production as a foundational method for service planning and delivery in the NCL region. To understand what the CAMHS experts by experience members found useful and did not find useful in co-production projects within Barnet Enfield and Haringey Mental Health NHS Trust and NCL wide co-production. Methods Semi-structured interviews conducted with experts by experience within the Barnet Enfield and Haringey (BEH) NHS Trust aimed to explore their co-production experiences, identifying facilitators and barriers. The study employed an inductive thematic analysis, grounded in a constructionist epistemological position, to analyse qualitative responses from semi-structured interviews. Braun and Clarke's (2006) methodology guided the analysis, consisting of six phases. The researchers emphasized reflexivity, reflection, and maintaining coherence, consistency, and flexibility throughout the recursive process. The voices of the lived experience co-production members played a central role in the research, influencing the entire report. Two members of the NCL CAMHS lived experience group served as “Lived Experience Researchers” and received training on coding reliability based on Braun and Clarke's (2006) guidance. Results Thematic analysis revealed several key findings. Recognition of co-production values within the group highlighted the importance of giving voice to service users, valuing their individual experiences, and promoting power-sharing. Facilitators included good team working, valuing diversity, accessible online sessions, and promoting equality through interactions. Conversely, barriers included inconsistent meeting timings, power imbalances, and a consultation-style dominance. Participants expressed the need for more involved projects and recommended a transformation of BEH's co-production strategy. Conclusion Recommendations for BEH include a comprehensive evaluation of their co-production projects on the ladder of participation, emphasizing the importance of higher-level collaborations. Training for staff on co-production principles is crucial for fostering a mindset shift, and the establishment of a dedicated co-production team, including a co-production lead, is advised by service-users who co-produce. These roles can drive co-production projects, provide organizational structure, and facilitate stakeholder engagement.

A. P. Kotsyubinsky, D. A. Kotsyubinsky

The substantial progress in neurobiological technologies has narrowed the horizons of many psychiatrists, ultimately leading them to focus exclusively on biomedical research, primarily aimed at studying the biological basis of mental illnesses. This has led to an unjustified dominance of the biomedical paradigm in understanding the nature of mental disorders, while virtually ignoring the study of other components of the disease related to the psychosocial maladjustment of patients. This trend, largely associated with advancements in neuroscience employing neuroimaging techniques to study the brain’s activity as a biophysical object, has contributed to the development of such innovative field as evidence-based medicine. The methods of evidence-based medicine are seen as adequate in terms of determining the effectiveness of therapy for predominantly biologically determined components of mental illness (including the selection of medications) and only partially for psychological interventions. However, it seems that the predominant use of evidence-based medicine principles is insufficient for a holistic diagnostic approach, which includes a multilevel (diversified) representation of the criteria of effectiveness for pharmacological and psychological interventions. In this regard, it is promising to establish a scientifically and clinically productive combination of, on the one hand, the evidence-based concept of effectiveness assessments based on high-quality randomized scientific studies, and on the other, expert opinions of highly qualified scientific specialists, as well as practicing physicians with their personal professional experience in individualized therapy. This makes it reasonable to develop a personality-oriented personalized psychiatry, based on a biopsychosocial understanding of the nature of mental disorders, their holistic assessment, and the development of comprehensive therapeutic measures.

Sai Krishna Tikka, Sangha Mitra Godi, M Aleem Siddiqui et al.

Repetitive transcranial magnetic stimulation (rTMS) is potentially effective as an augmentation strategy in the treatment of many neuropsychiatric conditions. Several Indian studies have been conducted in this regard. We aimed to quantitatively synthesize evidence from Indian studies assessing efficacy and safety of rTMS across broad range of neuropsychiatric conditions. Fifty two studies- both randomized controlled and non-controlled studies were included for a series of random-effects meta-analyses. Pre-post intervention effects of rTMS efficacy were estimated in “active only” rTMS treatment arms/groups and “active vs sham” (sham-controlled) studies using pooled Standardized Mean Differences (SMDs). The outcomes were ‘any depression’, depression in unipolar/bipolar depressive disorder, depression in obsessive compulsive disorder (OCD), depression in schizophrenia, schizophrenia symptoms (positive, negative, total psychopathology, auditory hallucinations and cognitive deficits), obsessive compulsive symptoms of OCD, mania, craving/compulsion in substance use disorders (SUDs) and migraine (headache severity and frequency). Frequencies and odds ratios (OR) for adverse events were calculated. Methodological quality of included studies, publication bias and sensitivity assessment for each meta-analyses was conducted. Meta-analyses of “active only” studies suggested a significant effect of rTMS for all outcomes, with moderate to large effect sizes, at both end of treatment as well as at follow-up. However, except for migraine (headache severity and frequency) with large effect sizes at end of treatment only and craving in alcohol dependence where moderate effect size at follow-up only, rTMS was not found to be effective for any outcome in the series of “active vs sham” meta-analyses. Significant heterogeneity was seen. Serious adverse events were rare. Publication bias was common and the sham controlled positive results lost significance in sensitivity analysis. We conclude that rTMS is safe and shows positive results in ‘only active’ treatment groups for all the studied neuropsychiatric conditions. However, the sham-controlled evidence for efficacy is negative from India. Conclusion rTMS is safe and shows positive results in “only active” treatment groups for all the studied neuropsychiatric conditions. However, the sham-controlled evidence for efficacy is negative from India.

Mingfeng Zhai, Shugang Cao, Jinwei Yang et al.

Abstract Introduction This study aimed to investigate the long-term prognostic effects of different alteplase doses on patients with acute ischemic stroke (AIS). Methods In this cohort study, we enrolled 501 patients with AIS treated with intravenous thrombolysis with alteplase, with the primary endpoint event of recurrence of ischemic stroke and the secondary endpoint event of death. The effects of different doses of alteplase on recurrence of ischemic stroke and death were analyzed using a Cox proportional risk model. Results Among 501 patients with AIS treated with thrombolysis, 295 patients (58.9%) and 206 patients (41.1%) were treated with low-dose and standard-dose alteplase, respectively. During the study period, 61 patients (12.2%) had a confirmed recurrence of ischemic stroke. Multivariate Cox proportional risk analysis showed that standard-dose alteplase thrombolysis (HR 0.511, 95% CI 0.288–0.905, P = 0.021) was significantly associated with a reduced risk of long-term recurrence of AIS, whereas atrial fibrillation was associated with an increased risk of long-term recurrence of AIS. Thirty-nine (7.8%) patients died during the study period. Multivariate Cox proportional risk analysis showed that age, baseline National Institutes of Health Stroke Scale (NIHSS) score, and symptomatic steno-occlusion were associated with an increased long-term risk of death from AIS. The alteplase dose was not associated with the risk of death from AIS. Conclusions Standard-dose alteplase treatment reduced the risk of long-term recurrence of AIS after hospital discharge and the alteplase dose was not associated with the long-term risk of death from AIS.

L. Pio-Lopez, Franz Kuchling, Angela Tung et al.

Active inference is a leading theory in neuroscience that provides a simple and neuro-biologically plausible account of how action and perception are coupled in producing (Bayes) optimal behavior; and has been recently used to explain a variety of psychopathological conditions. In parallel, morphogenesis has been described as the behavior of a (non-neural) cellular collective intelligence solving problems in anatomical morphospace. In this article, we establish a link between the domains of cell biology and neuroscience, by analyzing disorders of morphogenesis as disorders of (active) inference. The aim of this article is three-fold. We want to: (i) reveal a connection between disorders of morphogenesis and disorders of active inference as apparent in psychopathological conditions; (ii) show how disorders of morphogenesis can be simulated using active inference; (iii) suggest that active inference can shed light on developmental defects or aberrant morphogenetic processes, seen as disorders of information processing, and perhaps suggesting novel intervention and repair strategies. We present four simulations illustrating application of these ideas to cellular behavior during morphogenesis. Three of the simulations show that the same forms of aberrant active inference (e.g., deficits of sensory attenuation and low sensory precision) that have been used to explain psychopathological conditions (e.g., schizophrenia and autism) also produce familiar disorders of development and morphogenesis when implemented at the level of the collective behavior of a group of cells. The fourth simulation involves two cells with too high precision, in which we show that the reduction of concentration signaling and sensitivity to the signals of other cells treats the development defect. Finally, we present the results of an experimental test of one of the model's predictions in early Xenopus laevis embryos: thioridazine (a dopamine antagonist that may reduce sensory precision in biological systems) induced developmental (anatomical) defects as predicted. The use of conceptual and empirical tools from neuroscience to understand the morphogenetic behavior of pre-neural agents offers the possibility of new approaches in regenerative medicine and evolutionary developmental biology.

Yun-Fei Li, Xu Ren, Liang Zhang et al.

Traumatic brain injury (TBI) is a serious disease that threatens life and health of people. It poses a great economic burden on the healthcare system. Thus, seeking effective therapy to cure a patient with TBI is a matter of great urgency. Microglia are macrophages in the central nervous system (CNS) and play an important role in neuroinflammation. When TBI occurs, the human body environment changes dramatically and microglia polarize to one of two different phenotypes: M1 and M2. M1 microglia play a role in promoting the development of inflammation, while M2 microglia play a role in inhibiting inflammation. How to regulate the polarization direction of microglia is of great significance for the treatment of patients with TBI. The polarization of microglia involves many cellular signal transduction pathways, such as the TLR-4/NF-κB, JAK/STAT, HMGB1, MAPK, and PPAR-γ pathways. These provide a theoretical basis for us to seek therapeutic drugs for the patient with TBI. There are several drugs that target these pathways, including fingolimod, minocycline, Tak-242 and erythropoietin (EPO), and CSF-1. In this study, we will review signaling pathways involved in microglial polarization and medications that influence this process.

Yaoda Xu, Maryam Vaziri-Pashkam

Forming transformation-tolerant object representations is critical to high-level primate vision. Despite its significance, many details of tolerance in the human brain remain unknown. Likewise, despite the ability of convolutional neural networks (CNNs) to exhibit human-like object categorization performance, whether CNNs form tolerance similar to that of the human brain is unknown. Here we provide the first comprehensive documentation and comparison of three tolerance measures in the human brain and CNNs. We measured fMRI responses from human ventral visual areas to real-world objects across both Euclidean and non-Euclidean feature changes. In single fMRI voxels in higher visual areas, we observed robust object response rank-order preservation across feature changes. This is indicative of functional smoothness in tolerance at the fMRI meso-scale level that has never been reported before. At the voxel population level, we found highly consistent object representational structure across feature changes towards the end of ventral processing. Rank-order preservation, consistency, and a third tolerance measure, cross-decoding success (i.e., a linear classifier's ability to generalize performance across feature changes) showed an overall tight coupling. These tolerance measures were in general lower for Euclidean than non-Euclidean feature changes in lower visual areas, but increased over the course of ventral processing for all feature changes. These characteristics of tolerance, however, were absent in eight CNNs pretrained with ImageNet images with varying network architecture, depth, the presence/absence of recurrent processing, or whether a network was pretrained with the original or stylized ImageNet images that encouraged shape processing. CNNs do not appear to develop the same kind of tolerance as the human brain over the course of visual processing.

S. Dalvie, N. Daskalakis

aDepartment of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa; bDepartment of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa; cStanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; dDepartment of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, CA, USA Received: March 24, 2021 Accepted: May 15, 2021 Published online: May 18, 2021

B. Kelly

Objective: To provide an overview of specific aspects of historical and possible future trajectories of psychiatry. Conclusions: Psychiatric treatments alleviate suffering, promote physical health, and are associated with increased longevity. As the biological underpinnings of mental illnesses are slowly uncovered, they generally cease to be primarily part of psychiatry (e.g. epilepsy, anti-NMDA receptor encephalitis). If this process continues, the biological basis of all symptom-based ‘mental illnesses’ might be described, and psychiatry absorbed into neurology and other disciplines. This will be a positive development if it provides better treatment for mental illness and psychiatric symptoms in other conditions, which is psychiatry’s sole concern. Psychiatry’s own survival as a distinct discipline is irrelevant if other disciplines can do the job better, possibly in collaboration. Given the tiny impact of neuroscience on psychiatry to date, the disappearance of psychiatry is unlikely to occur anytime soon, if ever. It is possible that human psychological functioning and psychiatric suffering are sufficiently complex and changeable as to defy complete, fine-grained, neuroscientific explanation. This would leave a role for psychiatry indefinitely, treating the immensely disabling, biologically unexplained clusters of symptoms that we currently call ‘mental illnesses’, increasingly in collaboration with, or absorbed within, other disciplines in medicine.

Daniel Bennett, S. Silverstein, Y. Niv

Computational psychiatry is a rapidly growing field that uses tools from cognitive science, computational neuroscience, and machine learning to address difficult psychiatric questions. Its great promise is that these tools will improve psychiatric diagnosis and treatment while also helping to explain the causes of psychiatric illness.1-3 Within computational psychiatry, there are distinct research cultures with distinct computational tools and research goals: machine learning and explanatory modeling.1 While each can potentially advance psychiatric research, important distinctions between the cultures sometimes go unappreciated in the broader psychiatric research community. We detail these distinctions, referring to Breiman’s influential dichotomy between these cultures of statistical modeling4 to identify limitations on the inferences that each culture can draw. Breiman4 defined the 2 cultures of statistical modeling in terms of a data-generating process that generates output data from input variables. His dichotomy distinguished “algorithmic modeling,”4(p200) which aims to predict what outputs a data-generating process will produce from a given set of inputs while treating the process itself as a black box,2,3 from “data modeling,”4(p199) which uses the pattern of outputs and inputs to explain how the data-generating process works. In psychiatry, the data-generating processes are the psychological and neurobiological mechanisms that produce psychiatric illnesses. The output data produced by these processes are psychiatric outcomes (eg, symptoms, medication response) with input variables including family history, precipitating life events, and others. Breiman’s distinction between prediction and explanation is also what separates machine-learning approaches to computational psychiatry, which aim to predict psychiatric outcomes, from explanatory modeling, which aims to explain the computational-biological mechanisms of psychiatric illnesses. While these approaches have also been termed data-driven and theory-driven,1 we emphasize that the dual cultures of computational psychiatry share an overlapping set of statistical tools and practical methods but differ in whether the end goal is explanation or prediction. A deep neural network, for instance, can be either explanatory (as a biophysically realistic model of psychiatric dysfunction), or predictive (as a classifier used to predict a diagnosis), depending on context. The culture of machine learning typically uses statistical techniques, such as support vector machines or deep neural networks, to predict psychiatric outcomes. These tools can be seen as lying on a continuum with classical statistics such as regression but with the addition of practices designed to reduce overfitting, such as parameter regularization and cross validation. For instance, a study by Webb et al5 has used such tools to predict antidepressant response from a combination of variables, including demographic factors, symptom severity, and cognitive task performance. Despite good predictive performance, the study drew no conclusions about the mechanisms by which these variables were linked to antidepressant response. This is because in machine learning, the parameters of the models that are used to predict psychiatric outcomes are not assumed to correspond to any underlying psychological or neural process; consequently, these parameters cannot be interpreted mechanistically. In comparison, the culture of explanatory modeling focuses on statistical models (expressed as equations) that define interacting processes with parameters that putatively correspond to neural computations. For instance, equations describing value updating in reinforcement-learning models are thought to correspond to corticostriatal synaptic modifications modulated by dopaminergic signaling of reward prediction errors. Consequently, explanatory model parameters fit to behavioral and/or neural data from patients with psychiatric diagnoses can directly inform inferences about dysfunctions in underlying neural computations, subject to several conditions being met. For instance, Huys et al6 have shown that anhedonia is correlated across diagnoses with a model parameter corresponding to the blunting of experienced reward value but not with a parameter controlling the rate of learning from this experienced value, providing evidence against one dopaminergic explanation of depression. Importantly, there are several conditions that must be met before an explanatory model can be used in this way. First, to support the model’s correspondence to the true data-generating process and distinguish between different candidate models, the models must make sufficiently different predictions for the experimental data. Separately, to identify the model parameters accurately, the parameters’ effects on model predictions should be relatively independent, and there must be sufficient data. One approach to testing these conditions is to simulate data from each candidate model and test the ability of a model-fitting routine to recover the true cognitive model and its parameters from these data. Because empirical data will not correspond as perfectly to any of the candidate models, this test is a necessary but not sufficient condition for reliable explanatory modeling. Indeed, a common error is to overinterpret results, forgetting that the best-fitting model is only better than models with which it was compared and parameter values are only estimates reliable to a level of statistical error. A potential limitation of explanatory modeling in computational psychiatry is that theories (ie, models) may be ill-matched to available data, because data collected for other purposes may not distinguish between subtly (but importantly) different hypotheses regardVIEWPOINT

F. Gonçalves Viegas, I. Figueiredo, F. Ferreira et al.

Introduction For almost three decades, botulinum toxin type A (BT-A) has been used for medical purposes. Evidence of the potential use of BT-A is emerging for psychiatric disorders, like unipolar and bipolar depression, borderline personality disorder (BPD), late dyskinesia, amongst others. This may represent a new role of BT-A treatment and could expand the therapeutic arsenal in psychiatry. Objectives The goal is to review current evidence regarding BT-A and psychiatry disorders. Methods Literature review of BT-A use in psychiatric conditions using Medline database. Results There’s evidence supporting the use of BT-A in resistant unipolar depression, with studies showing an 8 and 4 times higher response and remission rates comparing with placebo. Beneficial effects were also found in bipolar depression. Preliminary data suggest that BT-A therapy may also be effective in the treatment of mental disorders characterized by an excess of negative emotions, such as BPD. The underlying mechanism might be the “facial feedback hypothesis”. Hyperhidrosis is a common comorbidity in social anxiety disorder and may itself give rise to depressive or anxiety symptoms. BT-A has proved to be a safe and effective treatment for hyperhidrosis. BT-A can also be safely used for dystonia secondary to the use of psychiatric medication, when there’s an inadequate response to anticholinergic medication. Also, BT-A injections in the salivary glands have been investigated for treating clozapine-induced sialorrhea and studies reported successful reduction in hypersalivation. Conclusions Although more studies are needed to evaluate the potential of BT-A in psychiatry, there is growing evidence of its potential use for some psychiatric conditions.

S. Jesus, A. Costa, J. Alcafache et al.

Introduction Systemic lupus erythematosus (SLE) is a chronic autoimmune disease involving the production of autoantibodies with consequent involvement of multiple organ systems. Although not an uncommon condition, its pleomorphic neuropsychiatric manifestations imply consideration of SLE as a relevant differential diagnosis. As many as 50% of patients with SLE have neurological involvement throughout their disease course and it is associated with impaired quality of life, high morbidity and mortality rates. Objectives Case report study and discussion. Methods The authors present a case of a 50-year old woman without previous psychiatric history presenting to the psychiatric department with suicidal ideation in association with psychotic symptoms of rapid onset. She presented with various somatic symptoms including butterfly rash, alopecia, nail dystrophy and generalized myalgia and arthralgia. After conducting a thorough clinical investigation with subsequent unveiling of various alterations including those in the antibody panels and abnormal magnetic resonance imaging results, a diagnosis of neuropsychiatric lupus was established. Results Improvements in initial psychiatric symptoms were noted after completing pulse corticoid therapy for SLE with adjunct antipsychotic medication. On follow-up, the patient demonstrated a complete return to previous mental functioning with no reported relapses. Conclusions This case demonstrates the heterogeneous presentations that neuropsychiatric lupus can assume. The vast array of psychopathological signs and symptoms in SLE continue to exist as a significant diagnostic and therapeutic challenge. Timely identification resulting from a proactive approach in maintaining lupus as part of our differentials may prevent the significant morbidity and mortality commonly associated with the resultant central nervous system involvement in SLE. Disclosure No significant relationships.

G. Nosachev, I. Nosachev

The article is discussed («Review of psychiatry and medical psychology named after V.M. Bekhterev». 2020; 2: 3-15), which examines the biopsychosocial model as the theoretical basis (scientific, clinical, preventive, therapeutic) of modern psychiatry, in particular, the biological (genetic) domain.The purpose of the discourse: from the standpoint of philosophy and methodology of science, to determine the place of the biological domain (biomedical research) of the biopsychosocial (biopsychosocial—spiritual) (BPS) approach (theory) in Russian psychiatry, in particular, from the standpoint of the subject of psychiatry and its main section-clinical psychiatry.Based on methodology and philosophy, and based on anthropological and holistic approaches, the biological domain of the BPS model, which is based on clinical psychiatry as a practice and, accordingly, theory, is discussed through the subject of psychiatry as a science. The significance and role of the subject of psychiatry (pathology, disorders, abnormalities of mental activity) in the ICD-10 and the components of the biopsychosocial (model) approach are discussed. There are differences in the domains of the model and the difficulties of clinical diagnosis (multi-axis, functional, multidimensional) and, accordingly, the study of the etiopathogenesis of mental disorders, the "bias" of diagnosis and therapy. The article deals with the neurological component of the biological domain and the "expansion" of neurologists into psychiatry, which leads to hidden antipsychiatry. The author emphasizes the independence, contiguity and two-paradigm nature of psychiatry as a science (with its own unity of subject and its own method of research—clinical and psychopathological). In addition to the interdisciplinarity of clinical neuroscience, it is proposed to be multidisciplinary (for the sections of psychiatry), but the future belongs to the transdisciplinary research methodology.

W. Glannon

Psychiatric disorders are often described as disorders of the mind. Major depressive disorder (MDD), generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD), and posttraumatic stress disorder (PTSD) are categorized by varying degrees of psychomotor, cognitive, affective, and volitional impairment (1). Many explain them in psychological terms without referring to an underlying neural substrate (2). This position may be traced to Freud's failed attempt to link neural mechanisms to psychodynamic concepts in his Project for a Scientific Psychology. It led him to abandon neurology in favor of psychoanalysis (3). Karl Jaspers later stated that biological and psychological investigations of the mind are like “the exploration of an unknown continent from opposite directions, where the explorers never meet because of the impenetrable country that intervenes (4).” Jaspers was not endorsing substance dualism, the theory that brain and mind are ontologically distinct material and immaterial substances (5). He wasmaking an epistemological claim, noting that we have an incomplete understanding of the brain andmind and how they interact. Some contemporary psychiatrists seem to interpret the idea of biology and psychology coming from “opposite directions” as suggesting an epistemological and explanatory dualism between neural andmental processes. This appears to be part of an “identity crisis” in psychiatry reflecting disagreement about characterizing psychiatric disorders as disorders of the mind or brain (6). Dualism as such does not preclude mind-brain interaction. But it supports the position that mind and brain can be functionally distinct. I argue that this is not consistent with neuroscience research showing the extent to which mental and neural processes are interdependent and influence each other in maintaining mental health or causing mental illness. Dualistic thinking of the type I have described can limit therapeutic interventions for patients suffering from major psychiatric disorders.