Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Mary L. Phillips, Mary L. Phillips, C. Ladouceur et al.

A. Larner

Zheng Ye, Yingying Gao, Jiaqi Yuan et al.

The influence of the gut microbiome on the human brain, especially its associations with psychiatric disorders, has emerged as a focal area in contemporary neuroscience and psychiatry research. In this study, we employed a mediation Mendelian randomization approach to delve into the potential causal relationships between gut microbiota and psychiatric disorders, with a focus on the mediating role of brain structural changes. We harnessed genetic data from large - scale genome - wide association studies to analyze how 196 gut microbiota taxa affect ten psychiatric disorders via alterations in 3,143 brain structures. Our key findings revealed significant bidirectional causal relationships. In the gut microbiota - brain structure relationship, certain gut microbiota taxa, such as Bacteroides and Marvinbryantia, were associated with changes in brain activity and white matter integrity respectively. Conversely, brain structures like the right hippocampus and left superior cerebellar peduncle influenced gut microbiota composition. Regarding gut microbiota and psychiatric disorders, we identified numerous associations. For example, the genus Prevotellaceae was significantly associated with an increased risk of Autism Spectrum Disorder, while Ruminococcaceae UCG005 showed a protective effect. In Panic Disorder, Alistipes was positively associated, and for Schizophrenia, both protective (Barnesiella) and risk - associated (Phascolarctobacterium) genera were found. Moreover, through mediation analysis, we found that brain structures mediated the effects of gut microbiota on five psychiatric disorders, including bipolar disorder and anorexia nervosa. In these cases, the influence of gut microbiota on the disorders was fully transmitted through changes in brain structure. Overall, our research clarifies the role of the microbiota - gut - brain axis in mental health. It offers a new perspective on how intestinal microbes impact brain physiology and psychiatric pathology. These findings not only deepen our understanding of the biological interactions between the gut and brain but also suggest that targeted gut microbiota modifications could be novel therapeutic strategies for mental health disorders.

Alan C. Logan, Gregg D. Caruso, Susan L. Prescott

Criminal laws and their deserts-based punishments, particularly in Anglo-American systems, remain grounded in folk psychology assumptions about free will, willpower, and agency. Yet advances in neuropsychiatry, neuromicrobiology, behavioral genetics, multi-omics, and exposome sciences, are revealing how here-and-now decisions are profoundly shaped by antecedent factors. This transdisciplinary evidence increasingly undermines the folk psychology model: some argue it leaves “not a single crack of daylight to shoehorn in free will”, while others suggest the evidence at least reveals far greater constraints on agency than currently acknowledged. Historically, courts and corrections have marginalized brain and behavior sciences, often invoking prescientific notions of monsters and wickedness to explain harmful behavior—encouraging anti-science sentiment and protecting normative assumptions. Earlier disciplinary silos, such as isolated neuroscience or single-gene claims, did little to challenge the system. But today’s integrated sciences—from microbiology and toxicology to nutrition and traumatology, powered by omics and machine learning—pose a threat to the folk psychology fulcrum. Resistance to change is well known in criminal justice, but the accelerating pace of biopsychosocial science makes it unlikely that traditional assumptions will endure. In response to modern science, emergent concepts of reform have been presented. Here, we review the public health quarantine model, an emergent concept that aligns criminal justice with public health principles. The model recognizes human behavior as emergent from complex biological, social, and environmental determinants. It turns away from retribution, while seeking accountability in a way that supports healing and prevention.

Mohammad Azhdari, Ghader Rezazadeh, Raghav Pathak et al.

A comprehensive understanding of heat transfer mechanisms in biological tissues is essential for the advancement of thermal therapeutic techniques and the development of accurate bioheat transfer models. Conventional models often fail to capture the inherently complex thermal behavior of biological media, necessitating more sophisticated approaches for experimental validation and parameter extraction. In this study, the Two-Dimensional Three-Phase Lag (TPL) heat transfer model, implemented via the finite difference method (FDM), was employed to extract key phase lag parameters characterizing heat conduction in bovine skin tissue. Experimental measurements were obtained using a 450 nm laser source and two non-contact infrared sensors. The influence of four critical parameters was systematically investigated: heat flux phase lag ($τ_{q}$), temperature gradient phase lag ($τ_θ$), thermal displacement coefficient ($k^*$), and thermal displacement phase lag ($τ_{v}$). A carefully designed experimental protocol was used to assess each parameter independently. The results revealed that the extracted phase lag values were substantially lower than those previously reported in the literature. This highlights the importance of high-precision measurements and the need to isolate each parameter during analysis. These findings contribute to the refinement of bioheat transfer models and hold potential for improving the efficacy and safety of clinical thermal therapies.

Diego-Martin Lombardo, Christian F Beckmann

The mechanism of neurocognitive failure in Alzheimer's disease remains obscure. While the mainstream hypothesis in the field posits that brain tau pathology is the only process that drives cognitive decline in AD, other complementary mechanisms link vascular brain lesions with beta-amyloid pathology as an important factor leading to neurodegeneration. Recently, it was also proposed that the brain's network's functional imbalance could primarily drive cognitive decline in neurodegenerative diseases. Here, we investigated whether the anticorrelation between the default mode (DMN) and dorsal attention networks (DAN) reveals different pathology burdens in the AD spectrum. We grouped individuals based on their PET amyloid and cognitive status. Using cross-validated regression models, we investigated whether cognitive impairment can be predicted based on rs-fMRI DMN-DAN anticorrelation. We found that the DMN-DAN anticorrelation differentiates between pathology burdens in AD, as quantified by PET amyloid imaging and cognitive performance. We found that an attenuated DMN-DAN anticorrelation predicted cognitive decline, which was controlled by sex, age, education, and brain tau pathology. Education level, measuring cognitive reserve, did not modulate the association between DMN-DAN anticorrelation and cognitive decline. We demonstrate that the attenuation of the anticorrelation between DMN and DAN is associated with a mechanism of cognitive dysfunction independent of tau pathology and proxies of resilience to cognitive decline or cognitive reserve. Our results also suggest the existence of an alternative mechanism of neurocognitive breakdown independent of advanced medial temporal cortex pathology and protective factors of cognitive decline, such as cognitive reserve.

Xinduo Gao, Hanqing Li, Liya Jiang et al.

Perinatal mental health disorders, including anxiety, depression, and post-traumatic stress following childbirth, represent significant neuropsychiatric challenges affecting maternal and infant well-being. Traditional doula support has shown measurable benefits in reducing psychological distress and improving emotional outcomes during childbirth. Recent advancements in digital health have introduced the concept of digital doulas-AI-enhanced, telehealth, or mobile-based systems designed to provide continuous emotional and informational support to birthing individuals. This review synthesizes current evidence on digital doula interventions and their impact on perinatal mental health through the lens of neuropsychiatry. We examine how emerging technologies-such as artificial intelligence, digital biomarkers, predictive modeling, and tele-neuropsychiatry-can extend the reach and precision of doula care. Furthermore, we discuss the neuropsychiatric mechanisms underlying digital emotional support, including stress regulation, oxytocin-mediated bonding, and neural plasticity, alongside ethical and cultural considerations in deploying AI-driven maternal support systems. By integrating neuroscience, psychiatry, and digital innovation, digital doula interventions offer a promising frontier for precision mental healthcare in perinatal populations.

Jan Postberg, Michèle Tina Schubert, Vincent Nin et al.

The debate surrounding nature versus nurture remains a central question in neuroscience, psychology, and in psychiatry, holding implications for both aging processes and the etiology of mental illness. Epigenetics can serve as a bridge between genetic predisposition and environmental influences, thus offering a potential avenue for addressing these questions. Epigenetic clocks, in particular, offer a theoretical framework for measuring biological age based on DNA methylation signatures, enabling the identification of disparities between biological and chronological age. This structured review seeks to consolidate current knowledge regarding the relationship between mental disorders and epigenetic age within the brain. Through a comprehensive literature search encompassing databases such as EBSCO, PubMed, and ClinicalTrials.gov, relevant studies were identified and analyzed. Studies that met inclusion criteria were scrutinized, focusing on those with large sample sizes, analyses of both brain tissue and blood samples, investigation of frontal cortex markers, and a specific emphasis on schizophrenia and depressive disorders. Our review revealed a paucity of significant findings, yet notable insights emerged from studies meeting specific criteria. Studies characterized by extensive sample sizes, analysis of brain tissue and blood samples, assessment of frontal cortex markers, and a focus on schizophrenia and depressive disorders yielded particularly noteworthy results. Despite the limited number of significant findings, these studies shed light on the complex interplay between epigenetic aging and mental illness. While the current body of literature on epigenetic aging in mental disorders presents limited significant findings, it underscores the importance of further research in this area. Future studies should prioritize large sample sizes, comprehensive analyses of brain tissue and blood samples, exploration of specific brain regions such as the frontal cortex, and a focus on key mental disorders. Such endeavors will contribute to a deeper understanding of the relationship between epigenetic aging and mental illness, potentially informing novel diagnostic and therapeutic approaches.

Nicolas Legrand, Lilian A. E. Weber, Peter Thestrup Waade et al.

Bayesian models of cognition have gained considerable traction in computational neuroscience and psychiatry. Their scopes are now expected to expand rapidly to artificial intelligence, providing general inference frameworks to support embodied, adaptable, and energy-efficient autonomous agents. A central theory in this domain is predictive coding, which posits that learning and behaviour are driven by hierarchical probabilistic inferences about the causes of sensory inputs. Biological realism constrains these networks to rely on simple local computations in the form of precision-weighted predictions and prediction errors. This can make this framework highly efficient, but its implementation comes with unique challenges on the software development side. Embedding such models in standard neural network libraries often becomes limiting, as these libraries'compilation and differentiation backends can force a conceptual separation between optimization algorithms and the systems being optimized. This critically departs from other biological principles such as self-monitoring, self-organisation, cellular growth and functional plasticity. In this paper, we introduce \texttt{pyhgf}: a Python package backed by JAX and Rust for creating, manipulating and sampling dynamic networks for predictive coding. We improve over other frameworks by enclosing the network components as transparent, modular and malleable variables in the message-passing steps. The resulting graphs can implement arbitrary computational complexities as beliefs propagation. But the transparency of core variables can also translate into inference processes that leverage self-organisation principles, and express structure learning, meta-learning or causal discovery as the consequence of network structural adaptation to surprising inputs. The code, tutorials and documentation are hosted at: https://github.com/ilabcode/pyhgf.

I. Mark, N. Poole, N. Agrawal

Summary Mainstream psychiatric practice requires a solid grounding in neuroscience, an important part of the biopsychosocial model, allowing for holistic person-centred care. There have been repeated calls for better integration of neuroscience into training, although so far with less focus on implementation for life-long learning. We suggest that such training should be accessible and utilised by all psychiatrists, not solely those with a special interest in neuropsychiatry. By considering recent positive developments within the general psychiatry curricula and neuropsychiatric resource implementation, we propose strategies for how this can be progressed, minimising regional disparities within the growing world of virtual learning.

Asif Naveed Ahmed, Lettie E. Rawlins, Niamat Khan et al.

Abstract Background Hereditary motor and sensory neuropathy (HMSN) refers to a group of inherited progressive peripheral neuropathies characterized by reduced nerve conduction velocity with chronic segmental demyelination and/or axonal degeneration. HMSN is highly clinically and genetically heterogeneous with multiple inheritance patterns and phenotypic overlap with other inherited neuropathies and neurodegenerative diseases. Due to this high complexity and genetic heterogeneity, this study aimed to elucidate the genetic causes of HMSN in Pakistani families using Whole Exome Sequencing (WES) for variant identification and Sanger sequencing for validation and segregation analysis, facilitating accurate clinical diagnosis. Methods Families from Khyber Pakhtunkhwa with at least two members showing HMSN symptoms, who had not previously undergone genetic analysis, were included. Referrals for genetic investigations were based on clinical features suggestive of HMSN by local neurologists. WES was performed on affected individuals from each family, with Sanger sequencing used to validate and analyze the segregation of identified variants among family members. Clinical data including age of onset were assessed for variability among affected individuals, and the success rate of genetic diagnosis was compared with existing literature using proportional differences and Cohen’s h. Results WES identified homozygous pathogenic variants in GDAP1 (c.310 + 4 A > G, p.?), SETX (c.5948_5949del, p.(Asn1984Profs*30), IGHMBP2 (c.1591 C > A, p.(Pro531Thr) and NARS1 (c.1633 C > T, p.(Arg545Cys) as causative for HMSN in five out of nine families, consistent with an autosomal recessive inheritance pattern. Additionally, in families with HMSN, a SETX variant was found to cause cerebellar ataxia, while a NARS1 variant was linked to intellectual disability. Based on American College of Medical Genetics and Genomics criteria, the GDAP1 variant is classified as a variant of uncertain significance, while variants in SETX and IGHMBP2 are classified as pathogenic, and the NARS1 variant is classified as likely pathogenic. The age of onset ranged from 1 to 15 years (Mean = 5.13, SD = 3.61), and a genetic diagnosis was achieved in 55.56% of families with HMSN, with small effect sizes compared to previous studies. Conclusions This study expands the molecular genetic spectrum of HMSN and HMSN plus type neuropathies in Pakistan and facilitates accurate diagnosis, genetic counseling, and clinical management for affected families.

Pablo Rojas, Oreste Piro, Martin E. Garcia

Common models of circadian rhythms are constructed as compartmental reactions of well mixed biochemicals involving a negative-feedback loop containing several intermediate reaction steps in order to enable oscillations. Spatial transport of reactants is mimicked as an extra compartmental reaction step. In this letter, we show that a single activation-repression biochemical reaction pair is enough to produce sustained oscillations, if the sites of both reactions are spatially separated and molecular transport is mediated by diffusion. Our proposed scenario is the simplest possible one in terms of the participating chemical reactions and provides a conceptual basis for understanding biological oscillations and triggering in-vitro assays aimed at constructing minimal clocks.

Martha R. Alvarez S, Ronald G. García, Federico Arturo Silva S

La cefalea por uso excesivo de medicamentos (CUEM) se define como la presencia de cefalea diaria o casi diaria (15 días o más de evolución), que se produce en pacientes con antecedente de cefalea primaria que usan excesivamente medicamentos. Está entidad está asociada a coomorbilidad psiquiátrica, por lo que las características clínicas se hacen más complejas con el paso del tiempo. El manejo fundamental se basa en la suspensión del medicamento sobre el que se centra el abuso (analgésicos, ergotamina, triptanes y opioides). Sin embargo, es necesario tomar en consideración que la suspensión de estos medicamentos puede asociarse a otros problemas como “cefalea de rebote”, síndrome de abstinencia o convulsiones epilépticas; incrementando las tasas de recaída en estos pacientes.

Vinicius de Maria Gadotti, Flavia Tasmin Techera Antunes, Gerald W. Zamponi

Abstract Delta-9-tetrahydrocannabinol (Δ9-THC) is known to produce systemic analgesia that involves CB1 and CB2 cannabinoid receptors. However, there is compelling evidence that Δ9-THC can potently inhibit Cav3.2T-type calcium channels which are highly expressed in dorsal root ganglion neurons and in the dorsal horn of the spinal cord. Here, we investigated whether spinal analgesia produced by Δ9-THC involves Cav3.2 channels vis a vis cannabinoid receptors. We show that spinally delivered Δ9-THC produced dose-dependent and long-lasting mechanical anti-hyperalgesia in neuropathic mice, and showed potent analgesic effects in models of inflammatory pain induced by formalin or Complete Freund’s Adjuvant (CFA) injection into the hind paw, with the latter showing no overt sex differences. The Δ9-THC mediated reversal of thermal hyperalgesia in the CFA model was abolished in Cav3.2 null mice, but was unaltered in CB1 and CB2 null animals. Hence, the analgesic effects of spinally delivered Δ9-THC are due to an action on T-type calcium channels, rather than activation of spinal cannabinoid receptors.

Maksim V. Agarkov, Alexey A. Safuanov, Svetlana T. Evreeva et al.

We describe a case of 72-year-old patient with recurrent transient ischemic attacks in the right internal carotid artery (ICA) territory associated with uncontrolled hypertension. Duplex ultrasonography und carotid angiography showed a 60% stenosis with signs of a vulnerable plaque in the cervical segment, as well as a 90% stenosis in the cavernous segment of the right ICA. After further examination the patient was diagnosed with an 80% renal artery stenosis. First, the patient had a single-stage stenting for extracranial and intracranial stenoses of the right ICA, then left renal artery stenting. No intraoperative and postoperative complications were observed. These results show that this surgical treatment is minimally invasive, safe, and effective in symptomatic patients and may be considered for the disease.

Alexander K. Y. Tam, Matthew J. Simpson

We investigate pattern formation in a two-dimensional (2D) Fisher--Stefan model, which involves solving the Fisher--KPP equation on a compactly-supported region with a moving boundary. By combining the Fisher--KPP and classical Stefan theory, the Fisher--Stefan model alleviates two limitations of the Fisher--KPP equation for biological populations. In this work, we investigate whether the 2D Fisher--Stefan model predicts pattern formation, by analysing the linear stability of planar travelling wave solutions to sinusoidal transverse perturbations. Planar fronts of the Fisher--KPP equation are linearly stable. Similarly, we demonstrate that invading planar fronts ($c > 0$) of the Fisher--Stefan model are linearly stable to perturbations of all wave numbers. However, our analysis demonstrates that receding planar fronts ($c < 0$) of the Fisher--Stefan model are linearly unstable for all wave numbers. This is analogous to unstable solutions for planar solidification in the classical Stefan problem. Introducing a surface tension regularisation stabilises receding fronts for short-wavelength perturbations, giving rise to a range of unstable modes and a most unstable wave number. We supplement linear stability analysis with level-set numerical solutions that corroborate theoretical results. Overall, front instability in the Fisher--Stefan model suggests a new mechanism for pattern formation in receding biological populations.

Paul Carrillo-Mora, Yesenia Lugo Rodríguez, Kenia F. Franyutti-Prado et al.

It is increasingly common for healthy people to seek means to improve their alertness, or to try to get better their performance in some cognitive functions; this with the aim of increasing their performance and productivity in the academic or work environment. Several stimulant drugs have been used for many decades and have recently become very popular especially among young people. However, general practitioners and even specialists are rarely informed of their real benefits or potential adverse effects. This review provides an updated overview of the effects (positive and adverse) of some stimulant drugs that have been used to maintain alertness or improve cognitive performance in healthy subjects. For stimulant drugs, the positive effects improving the subjective symptoms of sleep deprivation are well established. However, the cognitive effects of stimulant drugs are still highly variable and inconsistent, since there are few studies that have been carried out with adequate methodological design. In addition, there are several adverse effects, from mild to severe that can be observed and there is a concern of potential addiction effect to some of them. Some stimulant drugs can improve alertness, but their positive effects improving cognition are not yet fully proven.

Xinyu Cheng, Yi Zhang, Di Zhao et al.

Suicidality in patients with major depressive disorder (MDD) has been an urgent affair during the COVID-19 pandemic. It is well-established that impulsivity and trait anxiety are two risk factors for suicidal ideation. However, literature is still insufficient on the relationships among impulsivity, (state/trait) anxiety and suicidal ideation in individuals with MDD. The present study aims to explore the relationships of these three variables in MDD patients during the COVID-19 pandemic through three scales, including Barrett Impulsivity Scale (BIS), State-Trait Anxiety Scale (STAI) and Self-rating Idea of Suicide Scale (SIOSS). Sixty-three MDD patients (low SIOSS group and high SIOSS group, which were split by the mean score of SIOSS) and twenty-seven well-matched healthy controls were analyzed. Our results showed that the high SIOSS group had higher trait anxiety (p &lt; 0.001, 95% CI = [−19.29, −5.02]) but there was no difference in state anxiety (p = 0.171, 95% CI = [−10.60, 1.25]), compared with the low SIOSS group. And the correlation between impulsivity and suicidal ideation was significant in MDD patients (r = 0.389, p = 0.002), yet it was not significant in healthy controls (r = 0.285, p = 0.167). Further, mediation analysis showed that trait anxiety significantly mediate impulsivity and suicidal ideation in patients with depression (total effect: β = 0.304, p = 0.002, 95% CI = [0.120, 0.489]; direct effect: β = 0.154, p = 0.076, 95% CI = [−0.169, 0.325]), indicating impulsivity influenced suicidal ideation through trait anxiety in MDD patients. In conclusion, our results suggested that trait anxiety might mediate the association of impulsivity and suicidal ideation in MDD patients. Clinicians may use symptoms of trait anxiety and impulsivity for screening when actively evaluating suicidal ideation in MDD patients, especially in the setting of COVID-19 pandemic.

Maximilian J. Grill, Jonas F. Eichinger, Jonas Koban et al.

This article presents a novel computational model to study the selective filtering of biological hydrogels due to the surface charge and size of diffusing particles. It is the first model that includes the random 3D fiber orientation and connectivity of the biopolymer network and that accounts for elastic deformations of the fibers by means of beam theory. As a key component of the model, novel formulations are proposed both for the electrostatic and repulsive steric interactions between a spherical particle and a beam. In addition to providing a thorough validation of the model, the presented computational studies yield new insights into the underlying mechanisms of hindered particle mobility, especially regarding the influence of the aforementioned aspects that are unique to this model. It is found that the precise distribution of fiber and thus charge agglomerations in the network have a crucial influence on the mobility of oppositely charged particles and gives rise to distinct motion patterns. Considering the high practical significance for instance with respect to targeted drug release or infection defense, the provided proof of concept motivates further advances of the model toward a truly predictive computational tool that allows a case- and patient-specific assessment for real (biological) systems.

Sarthak Bagaria, Rickmoy Samanta

We construct a model to explore the hydrodynamic interactions of active inclusions in curved biological membranes. The curved membrane is modelled as a two dimensional layer of highly viscous fluid, surrounded by external solvents of different viscosities. The active inclusions are modelled as point force dipoles. The point dipole limit is taken along a geodesic of the curved geometry, incorporating the change in orientation of the forces due to curvature. We demonstrate this explicitly for the case of a spherical membrane, leading to an analytic solution for the flow generated by a single inclusion. We further show that the flow field features an additional defect of negative index, arising from the membrane topology, which is not present in the planar version of the model. We observe that a mutually perpendicular dipole pair moves along geodesics on the sphere and thus act as "curvature checkers", analogous to vortex dipoles. We finally explore the hydrodynamic interactions of a pair of inclusions in regimes of low and high curvature, as well as situations where the external fluid outside the membrane is confined. Our study suggests aggregation of dipoles in curved biological membranes of both low and high curvatures, under strong confinement. However, very high curvatures tend to destroy dipole aggregation, even under strong confinement.