Microstructural brain abnormalities, affective temperaments, and suicidal behavior in patients with major depression

According to magnetic resonance imaging (MRI) studies, brain white matter (WM) abnormalities have been suggested to play a critical role in the pathogenesis of major depressive disorder (MDD) and related suicidal behavior. However, MRI findings may be limited by low spatial resolution; therefore, an important contribution to the understanding of the role and significance of WM alterations derived by the development of the most recent magnetic resonance techniques, such as diffusion tensor imaging (DTI). Several DTI studies reported an association between altered WM integrity and MDD/suicidal behavior. Microstructural WM abnormalities may be located in neural circuits critically implicated in emotional processes and mood regulation resulting in enhanced vulnerability to psychiatric morbidity. WM abnormalities detected using DTI may contribute to functional deficits and help to clarify the pathophysiological mechanisms underlying MDD as well as suicidal behavior. By a clinical point of view, research also suggested that affective temperaments may play a relevant role in the psychopathological characteristics of mood disorders, clinical trajectory of episodes and polarity, long-term outcome and suicidality. Unfortunately, only few studies investigated the association between affective temperaments and WM abnormalities and discussed their possible implications in patients with MDD and suicidal behavior. Using a comprehensive search of Medline database, the aim of the present study was to critically review the current literature on the association between WM alterations as assessed by MRI and DTI techniques, affective temperaments, MDD and suicidal behavior

A Neuro-Symbolic Approach to Structured Event Recognition

Events are structured entities with multiple components: the event type, the participants with their roles, the outcome, the sub-events etc. A fully end-to-end approach for event recognition from raw data sequence, therefore, should also solve a number of simpler tasks like recognizing the objects involved in the events and their roles, the outcome of the events as well as the sub-events. Ontological knowledge about event structure, specified in logic languages, could be very useful to solve the aforementioned challenges. However, the majority of successful approaches in event recognition from raw data are based on purely neural approaches (mainly recurrent neural networks), with limited, if any, support for background knowledge. These approaches typically require large training sets with detailed annotations at the different levels in which recognition can be decomposed (e.g., video annotated with object bounding boxes, object roles, events and sub-events). In this paper, we propose a neuro-symbolic approach for structured event recognition from raw data that uses "shallow" annotation on the high-level events and exploits background knowledge to propagate this supervision to simpler tasks such as object classification. We develop a prototype of the approach and compare it with a purely neural solution based on recurrent neural networks, showing the higher capability of solving both the event recognition task and the simpler task of object classification, as well as the ability to generalize to events with unseen outcomes

A Neuro-Symbolic Approach for Real-World Event Recognition from Weak Supervision

Events are structured entities involving different components (e.g, the participants, their roles etc.) and their relations. Structured events are typically defined in terms of (a subset of) simpler, atomic events and a set of temporal relation between them. Temporal Event Detection (TED) is the task of detecting structured and atomic events within data streams, most often text or video sequences, and has numerous applications, from video surveillance to sports analytics. Existing deep learning approaches solve TED task by implicitly learning the temporal correlations among events from data. As consequence, these approaches often fail in ensuring a consistent prediction in terms of the relationship between structured and atomic events. On the other hand, neuro-symbolic approaches have shown their capability to constrain the output of the neural networks to be consistent with respect to the background knowledge of the domain. In this paper, we propose a neuro-symbolic approach for TED in a real world scenario involving sports activities. We show how by incorporating simple knowledge involving the relative order of atomic events and constraints on their duration, the approach substantially outperforms a fully neural solution in terms of recognition accuracy, when little or even no supervision is available on the atomic events

the role of glutamate excitotoxicity and neuroinflammation in depression and suicidal behavior focus on microglia cells

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Interval Logic Tensor Networks

In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of real-featured data. We interpret connectives using fuzzy logic, event durations using trapezoidal fuzzy intervals, and fuzzy temporal relations using relationships between the intervals' areas. We propose Interval Logic Tensor Networks (ILTN), a neuro-symbolic system that learns by propagating gradients through IRL. In order to support effective learning, ILTN defines smoothened versions of the fuzzy intervals and temporal relations of IRL using softplus activations. We show that ILTN can successfully leverage knowledge expressed in IRL in synthetic tasks that require reasoning about events to predict their fuzzy durations. Our results show that the system is capable of making events compliant with background temporal knowledge

How to save a life. From neurobiological underpinnings to psychopharmacotherapies in the prevention of suicide

The impact of suicide on our societies, mental healthcare, and public health is beyond questionable. Every year approximately 700 000 lives are lost due to suicide around the world (WHO, 2021); more people die by suicide than by homicide and war. Although suicide is a key issue and reducing suicide mortality is a global imperative, suicide is a highly complex biopsychosocial phenomenon, and in spite of several suicidal models developed in recent years and a high number of suicide risk factors identified, we still have neither a sufficient understanding of underpinnings of suicide nor adequate management strategies to reduce its prevalence. The present paper first overviews the background of suicidal behavior including its epidemiology, age and gender correlations, and its association with neuropsychiatric disorders as well as its clinical assessment. Then we give an overview of the etiological background, including its biopsychosocial contexts, genetics and neurobiology. Based on the above, we then provide a critical overview of the currently available intervention options to manage and reduce risk of suicide, including psychotherapeutic modalities, traditional medication classes also providing an up-to-date overview on the antisuicidal effects of lithium, as well as novel molecules such as esketamine and emerging medications and further molecules in development. Finally we give a critical overview on our current knowledge on using neuromodulatory and biological therapies, such as ECT, rTMS, tDCS, and other options.(c) 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/)

White matter abnormalities : Insights into the pathophysiology of major affective disorders

Abstract The presence of white matter hyperintensities (WMHs) has been commonly associated with poor outcome in subjects with major affective disorders. Unfortunately, WMHs may be frequently confounded by the use of psychoactive medications and duration of illness. Although findings from the current literature are quite conflicting, we proposed that subjects with WMHs may be at higher suicidal risk when compared to other subgroups without. Based on the Fazekas modified scale, the severity of WMHs may serve as a trait marker of disease. Interestingly, the presence of WMHs may represent a neurobiological marker between the underlying vulnerability and clinical presentation of major affective disorders

The Role of Neuropeptides in Suicidal Behavior: A Systematic Review

There is a growing evidence that neuropeptides may be involved in the pathophysiology of suicidal behavior. A critical review of the literature was conducted to investigate the association between neuropeptides and suicidal behavior. Only articles from peer-reviewed journals were selected for the inclusion in the present review. Twenty-six articles were assessed for eligibility but only 22 studies were included. Most studies have documented an association between suicidality and some neuropeptides such as corticotropin-releasing factor (CRF), VGF, cholecystokinin, substance P, and neuropeptide Y (NPY), which have been demonstrated to act as key neuromodulators of emotional processing. Significant differences in neuropeptides levels have been found in those who have attempted or completed suicide compared with healthy controls or those dying from other causes. Despite cross-sectional associations between neuropeptides levels and suicidal behavior, causality may not be inferred. The implications of the mentioned studies were discussed in this review paper

Gene variants with suicidal risk in a sample of subjects with chronic migraine and affective temperamental dysregulation

BACKGROUND: Risk factors for suicide are at least partially heritable and functional polymorphisms of targeted genes have been suggested to be implicated in the pathogenesis of this phenomenon. However, other studies examining the association between specific gene variants and suicide revealed inconsistent findings. We aims to evaluate the possible association between MAO-A3, CYP1A2*1F and GNB3 gene variants, hopelessness and suicidal risk in a sample of subjects with chronic migraine and affective temperamental dysregulation. METHODS: 56 women were genotyped for MAO-A3, CYP1A2*1F and GNB3 gene variants. Participants were also assessed using Beck Hopelessness Scale (BHS), the Temperament Evaluation of the Memphis, Pisa, Paris and San Diego-Autoquestionnaire (TEMPS-A), and the Suicidal History Self-Rating Screening Scale (SHSS). RESULTS: Patients with higher total scores on affective dysregulated temperaments are more likely to have higher BHS (11.27 +/- 5.54 vs. 5.73 +/- 3.81; t19.20 = -3.57; p = 9 indicating high levels of hopelessness. No association was found between MAO-A3, CYP1A2*1F and GNB3 gene variants and suicidal risk as assessed by BHS and SHSS. CONCLUSIONS: This study did not sustain the association between MAO-A3, CYP1A2*1F and GNB3 gene variants and increased suicidal risk in patients with chronic migraine and affective temperamental dysregulation. Further studies investigating the gene-environment interaction or focusing on other genetic risk factors involved in suicidal behaviour are needed

Extreme sensory processing patterns show a complex association with depression, and impulsivity, alexithymia, and hopelessness

INTRODUCTION: The involvement of extreme sensory processing patterns, impulsivity, alexithymia, and hopelessness was hypothesized to contribute to the complex pathophysiology of major depression and bipolar disorder. However, the nature of the relation between these variables has not been thoroughly investigated. AIMS: This study aimed to explore the association between extreme sensory processing patterns, impulsivity, alexithymia, depression, and hopelessness. METHODS: We recruited 281 euthymic participants (mean age=47.4+/-12.1) of which 62.3% with unipolar major depression and 37.7% with bipolar disorder. All participants completed the Adolescent/Adult Sensory Profile (AASP), Toronto Alexithymia Scale (TAS-20), second version of the Beck Depression Inventory (BDI-II), Barratt Impulsivity Scale (BIS), and Beck Hopelessness Scale (BHS). RESULTS: Lower registration of sensory input showed a significant correlation with depression, impulsivity, attentional/motor impulsivity, and alexithymia. It was significantly more frequent among participants with elevated hopelessness, and accounted for 22% of the variance in depression severity, 15% in greater impulsivity, 36% in alexithymia, and 3% in hopelessness. Elevated sensory seeking correlated with enhanced motor impulsivity and decreased non-planning impulsivity. Higher sensory sensitivity and sensory avoiding correlated with depression, impulsivity, and alexithymia. LIMITATIONS: The study was limited by the relatively small sample size and cross-sectional nature of the study. Furthermore, only self-report measures that may be potentially biased by social desirability were used. CONCLUSION: Extreme sensory processing patterns, impulsivity, alexithymia, depression, and hopelessness may show a characteristic pattern in patients with major affective disorders. The careful assessment of sensory profiles may help in developing targeted interventions and improve functional/adaptive strategies