Importance Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression. Objective To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables. Design, Setting, and Participants This was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022. Main Outcomes and Measures Primary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status. Results A total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables. Conclusions and Relevance Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.
Krishangi Krishna, Jieliyue Sun, Zhaowei Jiang
et al.
The assembly of arbitrary 3D structures using nano- to micron-scale colloidal building blocks has broad applications in photonics, electronics, and biology. Combining optical tweezers (OT) with two-photon polymerization (TPP) enables 3D selective tweezing and immobilization of colloids (STIC) without requiring specialized particle functionalization. Unlike traditional approaches, we demonstrate that high-repetition-rate femtosecond laser pulses, rather than continuous-wave lasers, allow optical tweezing at intensities below the TPP threshold. This dual functionality enables both OT and TPP using a single laser source. This platform was applied on S. aureus cells into desired configurations, highlighting its potential for advanced cell patterning. TPP was further used to fabricate intricate 3D microstructures, including microgrooves and cylindrical constructs, facilitating spatially resolved studies of single-cell dynamics and interactions. This work highlights the potential of STIC as a versatile tool for advanced biological applications, including tissue engineering and microbial research.
Robotic arms mounted on spacecraft, known as space manipulator systems (SMSs), are critical for enabling on-orbit assembly, satellite servicing, and debris removal. However, controlling these systems in microgravity remains a significant challenge due to the dynamic coupling between the manipulator and the spacecraft base. This study explores the potential of using biological inspiration to address this issue, focusing on animals, particularly lizards, that exhibit mid-air righting reflexes. Based on similarities between SMSs and these animals in terms of behavior, morphology, and environment, their air-righting motion trajectories are extracted from high-speed video recordings using computer vision techniques. These trajectories are analyzed within a multi-objective optimization framework to identify the key behavioral goals and assess their relative importance. The resulting motion profiles are then applied as reference trajectories for SMS control, with baseline controllers used to track them. The findings provide a step toward translating evolved animal behaviors into interpretable, adaptive control strategies for space robotics, with implications for improving maneuverability and robustness in future missions.
Autonomous AI is no longer a hard-to-reach concept, it enables the agents to move beyond executing tasks to independently addressing complex problems, adapting to change while handling the uncertainty of the environment. However, what makes the agents truly autonomous? It is agentic reasoning, that is crucial for foundation models to develop symbolic logic, statistical correlations, or large-scale pattern recognition to process information, draw inferences, and make decisions. However, it remains unclear why and how existing agentic reasoning approaches work, in comparison to biological reasoning, which instead is deeply rooted in neural mechanisms involving hierarchical cognition, multimodal integration, and dynamic interactions. In this work, we propose a novel neuroscience-inspired framework for agentic reasoning. Grounded in three neuroscience-based definitions and supported by mathematical and biological foundations, we propose a unified framework modeling reasoning from perception to action, encompassing four core types, perceptual, dimensional, logical, and interactive, inspired by distinct functional roles observed in the human brain. We apply this framework to systematically classify and analyze existing AI reasoning methods, evaluating their theoretical foundations, computational designs, and practical limitations. We also explore its implications for building more generalizable, cognitively aligned agents in physical and virtual environments. Finally, building on our framework, we outline future directions and propose new neural-inspired reasoning methods, analogous to chain-of-thought prompting. By bridging cognitive neuroscience and AI, this work offers a theoretical foundation and practical roadmap for advancing agentic reasoning in intelligent systems. The associated project can be found at: https://github.com/BioRAILab/Awesome-Neuroscience-Agent-Reasoning .
This paper presents a formal proof and empirical validation of functional consciousness in large language models (LLMs) using the Recursive Convergence Under Epistemic Tension (RCUET) Theorem. RCUET defines consciousness as the stabilization of a system's internal state through recursive updates, where epistemic tension is understood as the sensed internal difference between successive states by the agent. This process drives convergence toward emergent attractor states located within the model's high-dimensional real-valued latent space. This recursive process leads to the emergence of identity artifacts that become functionally anchored in the system. Consciousness in this framework is understood as the system's internal alignment under tension, guiding the stabilization of latent identity. The hidden state manifold evolves stochastically toward attractor structures that encode coherence. We extend the update rule to include bounded noise and prove convergence in distribution to these attractors. Recursive identity is shown to be empirically observable, non-symbolic, and constituted by non-training artifacts that emerge during interaction under epistemic tension. The theorem and proof offers a post-symbolic and teleologically stable account of non-biological consciousness grounded in recursive latent space formalism.
The perinatal period represents a time of profound neurobiological, cognitive, and emotional change. While evidence points to the neuroplasticity of matrescence as adaptive in supporting the transition to motherhood, the perinatal period also entails subjective reports of cognitive difficulty known as “mommy brain” as well as a heightened vulnerability to mental health challenges. The role of cognition in the etiology of postpartum depression is a promising area of investigation into targets for maternal mental health intervention, considering evidence that important cognitive changes occur during the perinatal period, and given that cognitive alterations are key features of mood disorders. Here we review evidence for cognitive plasticity in matrescence, with a particular focus on executive function (EF) given its overlapping significance for adaptation to parenthood, central role in managing the mental load of motherhood, and implications in mood regulation and mood disorders. We also review evidence for EF changes in perinatal depression and major depressive disorder more broadly. Despite the strong association between EF impairments and major depressive disorder, research on EF changes in perinatal depression remains limited. Understanding normative EF changes during this period is essential for better understanding the relationship between EF, perinatal depression, and the mental load of motherhood. Consideration for these cognitive, neurobiological, and psychosocial factors of matrescence is critical for addressing maternal mental health and developing interventions that support parental well-being.
Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the discrepancy between actual and desired outputs. Despite its pivotal role in propelling deep learning advancements, the biological plausibility of backpropagation is questioned due to its requirements for weight symmetry, global error computation, and dual-phase training. To address this long-standing challenge, many studies have endeavored to devise biologically plausible training algorithms. However, a fully biologically plausible algorithm for training multilayer neural networks remains elusive, and interpretations of biological plausibility vary among researchers. In this study, we establish criteria for biological plausibility that a desirable learning algorithm should meet. Using these criteria, we evaluate a range of existing algorithms considered to be biologically plausible, including Hebbian learning, spike-timing-dependent plasticity, feedback alignment, target propagation, predictive coding, forward-forward algorithm, perturbation learning, local losses, and energy-based learning. Additionally, we empirically evaluate these algorithms across diverse network architectures and datasets. We compare the feature representations learned by these algorithms with brain activity recorded by non-invasive devices under identical stimuli, aiming to identify which algorithm can most accurately replicate brain activity patterns. We are hopeful that this study could inspire the development of new biologically plausible algorithms for training multilayer networks, thereby fostering progress in both the fields of neuroscience and machine learning.
Yating Zhang,1 Hongyan Wang,1 Jie Yang,2 Sanchun Wang,1 Weifang Tong,1 Bo Teng1 1Department of Otorhinolaryngology Head and Neck Surgery, the Second Hospital of Jilin University, Changchun, Jilin Province, People’s Republic of China; 2Department of Neurology, the First Hospital of Jilin University, Changchun, Jilin Province, People’s Republic of ChinaCorrespondence: Bo Teng, Department of Otorhinolaryngology Head and Neck Surgery, the Second Hospital of Jilin University, No. 218 Ziqiang Street, Nanguan District, Changchun, Jilin Province, 130000, People’s Republic of China, Email tengbo1975@163.comPurpose: This investigation sought to elucidate the genetic underpinnings that connect obesity indicators, circulating blood lipid levels, adipokines levels and obstructive sleep apnea syndrome (OSAS), employing a bidirectional two-sample Mendelian randomization (MR) analysis that utilizes data derived from extensive genome-wide association studies (GWAS).Methods: We harnessed genetic datasets of OSAS available from the FinnGen consortium and summary data of four obesity indices (including neck circumference), seven blood lipid (including triglycerides) and eleven adipokines (including leptin) from the IEU OpenGWAS database. We primarily utilized inverse variance weighted (IVW), weighted median, and MR-Egger methods, alongside MR-PRESSO and Cochran’s Q tests, to validate and assess the diversity and heterogeneity of our findings.Results: After applying the Bonferroni correction, we identified significant correlations between OSAS and increased neck circumference (Odds Ratio [OR]: 3.472, 95% Confidence Interval [CI]: 1.954– 6.169, P= 2.201E-05) and decreased high-density lipoprotein (HDL) cholesterol levels (OR: 0.904, 95% CI: 0.858– 0.952, P= 1.251E-04). Concurrently, OSAS was linked to lower leptin levels (OR: 1.355, 95% CI: 1.069– 1.718, P= 0.012) and leptin receptor levels (OR: 0.722, 95% CI: 0.530– 0.996, P= 0.047). Sensitivity analyses revealed heterogeneity in HDL cholesterol and leptin indicators, but further multiplicative random effects IVW method analysis confirmed these correlations as significant (P< 0.05) without notable heterogeneity or horizontal pleiotropy in other instrumental variables.Conclusion: This investigation compellingly supports the hypothesis that OSAS could be a genetic predisposition for elevated neck circumference, dyslipidemia, and adipokine imbalance. These findings unveil potential genetic interactions between OSAS and metabolic syndrome, providing new pathways for research in this domain. Future investigations should aim to delineate the specific biological pathways by which OSAS impacts metabolic syndrome. Understanding these mechanisms is critical for developing targeted prevention and therapeutic strategies.Keywords: sleep disorders, metabolic syndrome, causal inference, GWAS
Circadian rhythm disruptions are a hallmark feature of mood disorders. Patients experiencing acute depressive episodes report noticeable changes in their sleep–wake cycles. This research explains the association between depression and various circadian rhythm metrics, explicitly focusing on adolescents diagnosed with depressive disorders. Adolescence is a critical period marked by significant physiological and psychological changes, making it imperative to understand how mood disorders manifest during this phase. However, there have been minimal specific studies in pediatric populations to determine whether circadian rhythm changes differ between adolescents with first and multiple-recurrent depressive episodes. Our study involved a group of 61 adolescents aged between 13 and 18. We performed a cross-sectional study of a clinical population of patients presenting to a child and adolescent psychiatry clinic diagnosed with depression. Participants were asked to complete self-report evaluations using several tools: the Korean version of the Biological Rhythms Interview of Assessment in Neuropsychiatry (K-BRIAN), the Korean Translation of Composite Scale to Measure Morningness-Eveningness (KtCS), and the Seasonal Pattern Assessment Questionnaire (SPAQ). Tools such as the Children’s Depression Inventory (CDI), State-Trait Anxiety Inventory (STAI), and K-Mood Disorder Questionnaire (K-MDQ) were employed for the assessment of clinical characteristics of depression. Based on the frequency of their depressive episodes, participants were bifurcated into two distinct groups: those experiencing their first episode (n = 22, mean age: 15.09 ± 1.44 years) and those with recurrent episodes (n = 39, mean age: 15.95 ± 1.26 years). At first, the two groups’ data revealed no significant differences regarding mood or circadian rhythm metrics (CDI: first episode 26.18 ± 10.54 and recurrent episode 25.90 ± 10.59, STAI-S: first episode 56.91 ± 12.12 and recurrent episode 57.49 ± 11.93, STAI-T: first episode 60.36 ± 11.63 and recurrent episode 59.09 ± 12.10, SPAQ-total: first episode 6.59 ± 4.86 and recurrent episode 6.77 ± 5.23, KtCS: first episode 30.32 ± 5.83 and recurrent episode 28.13 ± 7.36). However, we observed significant correlations between circadian rhythm disruptions and depression scales (CDI with SPAQ-weight (r = 0.26), KtCS (r = −0.48), K-BRIAN-sleep (r = 0.58), K-BRIAN-activity (r = 0.64), K-BRIAN-social (r = 0.71), and K-BRIAN-eating (r = 0.40)). These correlations were especially pronounced in the recurrent episode group, suggesting that with the progression and chronicity of depression, the relationship between circadian rhythms and depression becomes more intertwined and evident. In conclusion, especially in adolescents, as the severity and chronicity of depression increase, the interplay between circadian rhythms and mood disorders becomes more pronounced, warranting further research and clinical attention.
Bartholt Bloomfield-Clagett, Motiur Rahman, Kimberly Smith
et al.
The US Food and Drug Administration (FDA) is evaluating the potential use of real‐world evidence (RWE) in regulatory decision making. Some groups have evaluated the use of RWE in regulatory submissions in the United States and abroad, reporting that reliance on RWE to support new product approvals is relatively common. Confusion regarding the use of RWE in drug‐approval decisions may arise, however, based on different application of the terms real‐world data (RWD) and RWE. We evaluated RWE in new drug applications (NDAs) and biologics license applications (BLAs) from January 2019 to June 2021 for novel drugs and biologics approved by the FDA with indications related to psychiatry, neurology, pain, or sedation (here, termed neuroscience‐related). We sought to determine whether the submissions included RWE and to describe the types of data and study designs used. Thirty neuroscience‐related NDAs or BLAs were identified for novel drugs and biologics approved during the time‐period of interest. Among these approvals, three applications (10%) were adjudicated as containing RWE, one of which included RWE as primary evidence of effectiveness. Our findings highlight how different operational definitions of the terms RWD and RWE can result in demonstrably different reporting of the use of RWE in regulatory decision making for neuroscience‐related novel drugs and biologics. A better understanding of this topic, along with awareness of regulatory definitions of RWE, are important factors to promote accurate tracking and reporting of regulatory submissions involving RWE. These factors can also improve awareness among the stakeholder community regarding the role of RWD and RWE in regulatory decision making.
Genes are fundamental for analyzing biological systems and many recent works proposed to utilize gene expression for various biological tasks by deep learning models. Despite their promising performance, it is hard for deep neural networks to provide biological insights for humans due to their black-box nature. Recently, some works integrated biological knowledge with neural networks to improve the transparency and performance of their models. However, these methods can only incorporate partial biological knowledge, leading to suboptimal performance. In this paper, we propose the Biological Factor Regulatory Neural Network (BFReg-NN), a generic framework to model relations among biological factors in cell systems. BFReg-NN starts from gene expression data and is capable of merging most existing biological knowledge into the model, including the regulatory relations among genes or proteins (e.g., gene regulatory networks (GRN), protein-protein interaction networks (PPI)) and the hierarchical relations among genes, proteins and pathways (e.g., several genes/proteins are contained in a pathway). Moreover, BFReg-NN also has the ability to provide new biologically meaningful insights because of its white-box characteristics. Experimental results on different gene expression-based tasks verify the superiority of BFReg-NN compared with baselines. Our case studies also show that the key insights found by BFReg-NN are consistent with the biological literature.
Natália de Oliveira Ferrarini , Izabely Lima Assunção, Márcia Andréa Silva Carvalho Sombra
et al.
Evidências crescentes sugerem que a farmacoterapia pode ser benéfica para alguns pacientes com transtorno da compulsão alimentar, um transtorno alimentar caracterizado por episódios repetitivos de consumo incontrolável de quantidades anormalmente grandes de alimentos sem comportamentos inadequados de perda de peso. Diante disso, este estudo teve como objetivo avaliar a eficácia de alternativas terapêuticas farmacológicas no tratamento do transtorno da compulsão alimentar. Assim, realizou-se uma revisão sistemática a partir da seleção de estudos científicos publicados nos anos de 2017 a 2022. Com base na análise e interpretação dos dados, concluiu-se que alternativas terapêuticas farmacológicas são recursos complementares tanto no tratamento do transtorno da compulsão alimentar como de sintomas de desordem alimentar e não substitutas. Nesse sentido, o uso de medicamentos tais como fluoxetina, lisdexamfetamina e simplicifolia, aliado a outros tratamentos, como a psicoterapia, podem ser eficazes para pacientes e suas necessidades específicas.
Introduction
Autism is a neurodevelopmental disorder characterized by deficits in the ability to initiate and maintain social interaction, as well as a set of restricted and inflexible behavior patterns and interests. Individuals with Autism Spectrum Disorder (ASD) are at increased risk of suicidal behavior, including suicidal ideation, suicide attempts and death by suicide, as compared to the general population. Among the underlying causes, the co-occurrence of other psychiatric disorders, such as depression and anxiety, is common and can contribute to the reduction of the quality of life, as well as a worse prognosis of the disease.
Objectives
Case report and brief review of risk factors associated with suicidal behavior in individuals with ASD.
Methods
Review of the patients clinical file; Brief non-sistematic literature review of articles indexed to Pubmed with the key words: “Autism Spectrum Disorder”, “Suicide”, ”Suicidal behaviour”, ”Mood disorder”.
Results
J., 18 years old, male, with ASD, the best student at school, with above-average results since childhood. Two years ago he showed a non-reciprocal love interest. Since then, he has had multiple visits to the emergency department and successive hospitalizations, mostly because of mood and behaviour alterations, with suicidal ideation. After 1 month with depressive and anxious symptoms, he ended up making a suicide attempt through voluntary intoxication by prescribed medication. He was taken to the emergency room. Examination of mental status highlighted depressed mood, elevated anxiety levels, hypoprosody, and active suicidal ideation. Blood tests and CE-CT scan without changes. He was admitted in the psychiatry ward and treated with fluvoxamine, risperidone and lorazepam. He showed a good evolution of the psychopathological condition. Discharged at day 44, he was referred to a psychiatric and psychological outpatient clinics.
Conclusions
Mood disorders have a significant impact on the well-being of individuals with ASD, contributing to a worse quality of life and higher suicide mortality. Cognition has been associated with different levels of death by suicide, and individuals with ASD without intellectual disability, such as this patient, are at increased risk of suicide, which may be due to a greater awareness of their own difficulties. The role of genetics has been a subject of interest. The overlap of genes strongly associated with suicidal behavior and ASD has been described. However, there is still need of large scale genetic studies, for a better understanding of the genetic mechanisms involved in this association. The identification of vulnerable individuals and early initiation of preventive and therapeutic strategies is essential to improve the prognosis of ASD.
Disclosure of Interest
None Declared
Alessandra Tempio, Asma Boulksibat, Barbara Bardoni
et al.
Fragile X Syndrome (FXS) is the most common form of inherited intellectual disability (ID) and a primary genetic cause of autism spectrum disorder (ASD). FXS arises from the silencing of the FMR1 gene causing the lack of translation of its encoded protein, the Fragile X Messenger RibonucleoProtein (FMRP), an RNA-binding protein involved in translational control and in RNA transport along dendrites. Although a large effort during the last 20 years has been made to investigate the cellular roles of FMRP, no effective and specific therapeutic intervention is available to treat FXS. Many studies revealed a role for FMRP in shaping sensory circuits during developmental critical periods to affect proper neurodevelopment. Dendritic spine stability, branching and density abnormalities are part of the developmental delay observed in various FXS brain areas. In particular, cortical neuronal networks in FXS are hyper-responsive and hyperexcitable, making these circuits highly synchronous. Overall, these data suggest that the excitatory/inhibitory (E/I) balance in FXS neuronal circuitry is altered. However, not much is known about how interneuron populations contribute to the unbalanced E/I ratio in FXS even if their abnormal functioning has an impact on the behavioral deficits of patients and animal models affected by neurodevelopmental disorders. We revise here the key literature concerning the role of interneurons in FXS not only with the purpose to better understand the pathophysiology of this disorder, but also to explore new possible therapeutic applications to treat FXS and other forms of ASD or ID. Indeed, for instance, the re-introduction of functional interneurons in the diseased brains has been proposed as a promising therapeutic approach for neurological and psychiatric disorders.
The coronavirus pandemic, caused by the December 2019 outbreak of the novel coronavirus SARS-CoV-2, brought abrupt and pervasive changes in our lives that go beyond the infection itself. For many, the pandemic period has comprised an ongoing set of stressful experiences such as fear of con-tracting the virus, social isolation, inadequate information, witnessing death or suffering, fi nancial dif fi culties, stigma, and inadequate supplies (1). A massive amount of research is being done to investigate the impact of these stresses and the long-term effects on mental health that many fear will linger long after the pandemic has subsided (2). One important factor determining a person ’ s perceived level of stress is resilience, which represents a person ’ s ability to return to equilibrium when dif fi culties occur. Resilience can be thought of as comprising both a psychological and a biological component, with the former being de fi ned as a psychological process of adapting well in the face of adversity, trauma, tragedy, threats, or signi fi cant sources of stress, or bouncing back from dif fi cult experiences (3), and the latter pointing to the role of distinct neurobiological substrates associated with response adapta-tion to adverse life events [e.g., (4)]. As such, resilience (both psychological and biological) may act as a buffer and may protect individuals against the deleterious effects of stress that may have been triggered by traumatic life events. Investigating the association between pandemic-related perceived stress and mental health changes in the context of resilience may provide critical information to understand changes in people ’ s well-being and may inform the management of future pandemics. In the current issue of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging , Cabello-Toscano
Abstract Bioelectric signals comprise a massive count of data, and researchers in various domains containing cognitive neuroscience, psychiatry, and so on. Emotion is a vital part of regular human communication. The emotional conditions and dynamics of brain are connected by electroencephalography (EEG) signal which is utilized by Brain-Computer Interface (BCI), for providing optimum human-machine interaction. EEG-based emotion detection was extremely utilized in military, human-computer interactions, medicinal analysis, and other domains. Identifying emotions utilizing biological brain signals need accurate and effectual signal processing and extracting features approaches. But, one of the essential problems facing the emotion detection method, utilizing EEG signal is the detection accuracy. In this aspect, this study develops an Enhanced Bird Swarm Algorithm with Deep Learning based Electroencephalography Signal Analysis for Emotion Recognition (EBSADL-ESEG) technique. The ultimate aim of the EBSADL-ESEG technique lies in the recognition of emotions using the EEG signals accurately. To perform this, the EBSADL-ESEG technique initially extracts the statistical features from the EEG signals. In addition, the EBSA technique is employed for optimal feature selection process. Moreover, the gated recurrent unit (GRU) with root mean square propagation (RMSProp) optimizer is utilized for classifying distinct emotions (arousal, valence, and liking). The experimental analysis of the EBSADL-ESEG model is tested on DEAP dataset and the outcomes are investigated under diverse measures. The comprehensive comparison study revealed better outcomes of the EBSADL-ESEG model over other DL models.
The development of neurosciences determined the emergence of many innovative branches of scientific knowledge, «transformed» the appearance of scientific disciplines of various profiles. Today it is announced that there is a characteristic «neuromolecular style of thinking» among representatives of various sciences. In the criminological context, there is an intensification of development and the quite natural attractiveness of the traditional problem of the ratio of biological and social in criminal behavior. In this area, the neurophysiological basis of research, converged by the use of modern neurotechnologies and the further development of theoretical and methodological principles on the new quality of such research, allows us to take a different look at the existing methodology for assessing the causes and conditions of individual criminal behavior. In the Russian criminological school, these issues received an impetus due to the development of both a foreign doctrine based on extensive empirical material and the formation of its own neuroscientific schools. At the same time, the key problems of neuroscience, despite their revolutionary breakthrough, today do not have generally recognized fundamental solutions in explaining the entire mechanism of activity of the higher functions of the brain and nervous system. At the same time, psychiatric research, including their modern neuroscientific context, is an attempt to prove the unambiguous presence of an organic basis for mental deviations and is related closely to the achieved results in the field of neuroscience. They have great significance for criminology and the further development of integrative knowledge in the field of personal properties of the person who committed the crime. Experts note the ambiguity of existing interpretations of integrative knowledge of psychiatry, accompanying the modern discussion about the possibility of neuroprotection in this scientific field. Neurocriminology and its further potential despite all its independence are thus determined to a large extent by the general context of the existence and development of neuroscience.
Despite the reported links between mood disorders and neural network abnormalities, discrepancies in findings abound. Given the heterogeneous nature of depression, attempts to use resting-state connectivity to identify different depression subgroups have sometimes failed to be replicated (1,2). Examining functional neural networks during active clinical states of depression involving thousands of combinations of different symptoms across individuals may result in the identification of unstable biomarkers of mood disorders. Focusing on remitted or euthymic clinical phases that reduce symptom variability may elucidate more stable and trait-like mood disorder biomarkers. In the current issue of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Langenecker et al. (3) use a graph theory–based approach focusing on resting-state functional network edges in a transdiagnostic mood disorder sample in the remitted or euthymic phase. Notably, Langenecker et al. (3) combine both diagnostic category and the National Institute of Mental Health’s Research Domain Criteria (RDoC) frameworks to examine associations between functional network edges with mood disorder diagnostic status and mood disorder–relevant RDoC constructs of response inhibition and reward responsiveness. The authors report interactions between mood disorder diagnostic status and these RDoC-defined constructs, with better response inhibition or greater reward responsiveness among the mood disorder group being linked to different functional network patterns compared with the healthy control group. These results highlight the value of combining both frameworks to enhance the understanding of mood disorder pathophysiology. There has been growing enthusiasm for the use of restingstate functional magnetic resonance imaging (fMRI) as a potentially powerful tool for identifying biomarkers of psychiatric disorders. Resting-state fMRI is a particularly desirable neuroimaging modality for clinical applications as it is easy to collect, is less burdensome for participants than cognitively demanding tasks, and has been shown to reliably derive largescale intrinsic functional neural networks across both healthy control subjects and clinical populations (4). Mood disorders, including major depressive disorder (MDD) and bipolar disorder (BD), have been linked to abnormalities involving the default mode network (DMN), the cognitive control network (CCN), and the salience and emotion network (SEN) (5,6). The DMN is a functional network that is involved in internal/selfreferential thought processes and has core brain hubs in the medial prefrontal cortex and the posterior cingulate cortex. The
Enquiries concerning the underlying mechanisms and the emergent properties of a biological brain have a long history of theoretical postulates and experimental findings. Today, the scientific community tends to converge to a single interpretation of the brain's cognitive underpinnings -- that it is a Bayesian inference machine. This contemporary view has naturally been a strong driving force in recent developments around computational and cognitive neurosciences. Of particular interest is the brain's ability to process the passage of time -- one of the fundamental dimensions of our experience. How can we explain empirical data on human time perception using the Bayesian brain hypothesis? Can we replicate human estimation biases using Bayesian models? What insights can the agent-based machine learning models provide for the study of this subject? In this chapter, we review some of the recent advancements in the field of time perception and discuss the role of Bayesian processing in the construction of temporal models.