Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Pralaypati Ta, Sriram Venkatesaperumal, Keerthi Ram et al.

Neuroscience research publications encompass a vast wealth of knowledge. Accurately retrieving existing information and discovering new insights from this extensive literature is essential for advancing the field. However, when knowledge is dispersed across multiple sources, current state-of-the-art retrieval methods often struggle to extract the necessary information. A knowledge graph (KG) can integrate and link knowledge from multiple sources. However, existing methods for constructing KGs in neuroscience often rely on labeled data and require domain expertise. Acquiring large-scale, labeled data for a specialized area like neuroscience presents significant challenges. This work proposes novel methods for constructing KG from unlabeled large-scale neuroscience research corpus utilizing large language models (LLM), neuroscience ontology, and text embeddings. We analyze the semantic relevance of neuroscience text segments identified by LLM for building the knowledge graph. We also introduce an entity-augmented information retrieval algorithm to extract knowledge from the KG. Several experiments were conducted to evaluate the proposed approaches. The results demonstrate that our methods significantly enhance knowledge discovery from the unlabeled neuroscience research corpus. The performance of the proposed entity and relation extraction method is comparable to the existing supervised method. It achieves an F1 score of 0.84 for entity extraction from the unlabeled data. The knowledge obtained from the KG improves answers to over 52% of neuroscience questions from the PubMedQA dataset and questions generated using selected neuroscience entities.

Omar Alhaj Omar, Mesut Yenigün, Farzat Alchayah et al.

<b>Background/Objectives:</b> Brainstem ischemic strokes comprise 10% of ischemic strokes and are challenging to diagnose due to small lesion size and complex presentations. Diffusion-weighted imaging (DWI) is crucial for detecting ischemia, yet it can miss small lesions, especially when only axial slices are employed. This study investigated whether ischemic lesions visible in a single imaging plane correspond to smaller volumes and whether coronal DWI enhances detection compared to axial DWI alone. <b>Methods:</b> This retrospective single-center study examined 134 patients with brainstem ischemic strokes between December 2018 and November 2023. All patients underwent axial and coronal DWI. Clinical data, NIH Stroke Scale (NIHSS) scores, and modified Rankin Scale (mRS) scores were recorded. Diffusion-restricted lesion volumes were calculated using multiple models (planimetric, ellipsoid, and spherical), and lesion visibility per imaging plane was analyzed. <b>Results:</b> Brainstem ischemic strokes were detected in 85.8% of patients. Coronal DWI alone identified 6% of lesions that were undetectable on axial DWI; meanwhile, axial DWI alone identified 6.7%. Combining both improved overall sensitivity to 86.6%. Ischemic lesions visible in only one plane were significantly smaller across all volume models. Higher NIHSS scores were strongly correlated with larger diffusion-restricted lesion volumes. Coronal DWI correlated better with clinical severity than axial DWI, especially in the midbrain and medulla. <b>Conclusions:</b> Coronal DWI significantly improves the detection of small brainstem infarcts and should be incorporated into routine stroke imaging protocols. Infarcts visible in only one plane are typically smaller, yet still clinically relevant. Combined imaging enhances diagnostic accuracy and supports early and precise intervention in posterior circulation strokes.

Farahnaz Yousefi, Mohsen Dadashi, Ali Khadem et al.

A. Kadambi, H. Lu, M. Monti et al.

We recently identified cortical areas in the Action Observation Network that preferentially encoded self actions from minimal kinematic cues (Kadambi et al., 2025). Here, we investigate how identity decoding in these brain areas (Inferior Parietal Lobules, IPL; Inferior Frontal Gyri, IFG; Primary Motor Cortex, M1; Extrastriate Body Area, EBA; Superior Temporal Sulci, STS) relate to motor imagery ability. Using multivariate decoding and localizer analyses, we found that frontoparietal regions (IPL, IFG, and M1) selectively decoded self-identity, while occipitotemporal regions (EBA and STS), did not show such self-specific selectivity, but largely decoded across identities. Participant variability in motor imagery ability was positively associated with self-identity decoding in the IPL, EBA, STS and negatively with other-identity decoding in the IFG. These results introduce functional links between motor imagery and self-action decoding, emerging from frontoparietal and occipitotemporal regions.

Jan Karbowski

This paper provides a perspective on applying the concepts of information thermodynamics, developed recently in non-equilibrium statistical physics, to problems in theoretical neuroscience. Historically, information and energy in neuroscience have been treated separately, in contrast to physics approaches, where the relationship of entropy production with heat is a central idea. It is argued here that also in neural systems information and energy can be considered within the same theoretical framework. Starting from basic ideas of thermodynamics and information theory on a classic Brownian particle, it is shown how noisy neural networks can infer its probabilistic motion. The decoding of the particle motion by neurons is performed with some accuracy and it has some energy cost, and both can be determined using information thermodynamics. In a similar fashion, we also discuss how neural networks in the brain can learn the particle velocity, and maintain that information in the weights of plastic synapses from a physical point of view. Generally, it is shown how the framework of stochastic and information thermodynamics can be used practically to study neural inference, learning, and information storing.

Giulia Baracchini, Ju-Chi Yu, Jenny Rieck et al.

Similarity analyses between multiple correlation or covariance tables constitute the cornerstone of network neuroscience. Here, we introduce covSTATIS, a versatile, linear, unsupervised multi-table method designed to identify structured patterns in multi-table data, and allow for the simultaneous extraction and interpretation of both individual and group-level features. With covSTATIS, multiple similarity tables can now be easily integrated, without requiring a priori data simplification, complex black-box implementations, user-dependent specifications, or supervised frameworks. Applications of covSTATIS, a tutorial with Open Data and source code are provided. CovSTATIS offers a promising avenue for advancing the theoretical and analytic landscape of network neuroscience.

Ahmed ElGazzar, Marcel van Gerven

The unprecedented availability of large-scale datasets in neuroscience has spurred the exploration of artificial deep neural networks (DNNs) both as empirical tools and as models of natural neural systems. Their appeal lies in their ability to approximate arbitrary functions directly from observations, circumventing the need for cumbersome mechanistic modeling. However, without appropriate constraints, DNNs risk producing implausible models, diminishing their scientific value. Moreover, the interpretability of DNNs poses a significant challenge, particularly with the adoption of more complex expressive architectures. In this perspective, we argue for universal differential equations (UDEs) as a unifying approach for model development and validation in neuroscience. UDEs view differential equations as parameterizable, differentiable mathematical objects that can be augmented and trained with scalable deep learning techniques. This synergy facilitates the integration of decades of extensive literature in calculus, numerical analysis, and neural modeling with emerging advancements in AI into a potent framework. We provide a primer on this burgeoning topic in scientific machine learning and demonstrate how UDEs fill in a critical gap between mechanistic, phenomenological, and data-driven models in neuroscience. We outline a flexible recipe for modeling neural systems with UDEs and discuss how they can offer principled solutions to inherent challenges across diverse neuroscience applications such as understanding neural computation, controlling neural systems, neural decoding, and normative modeling.

Emirhan Böge, Yasemin Gunindi, Erchan Aptoula et al.

Neuron importance assessment is crucial for understanding the inner workings of artificial neural networks (ANNs) and improving their interpretability and efficiency. This paper introduces a novel approach to neuron significance assessment inspired by frequency tagging, a technique from neuroscience. By applying sinusoidal contrast modulation to image inputs and analyzing resulting neuron activations, this method enables fine-grained analysis of a network's decision-making processes. Experiments conducted with a convolutional neural network for image classification reveal notable harmonics and intermodulations in neuron-specific responses under part-based frequency tagging. These findings suggest that ANNs exhibit behavior akin to biological brains in tuning to flickering frequencies, thereby opening avenues for neuron/filter importance assessment through frequency tagging. The proposed method holds promise for applications in network pruning, and model interpretability, contributing to the advancement of explainable artificial intelligence and addressing the lack of transparency in neural networks. Future research directions include developing novel loss functions to encourage biologically plausible behavior in ANNs.

Wensi Wu, Mitchell Daneker, Kevin T. Turner et al.

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in using data-driven models to learn full-field mechanical responses such as displacement and strain from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, we propose a physics-informed machine learning approach to identify the elasticity map in nonlinear, large deformation hyperelastic materials. We evaluate the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) by inferring the heterogeneous elasticity maps across three materials with structural complexity that closely resemble real tissue patterns, such as brain tissue and tricuspid valve tissue. We further applied our improved architecture to three additional examples of breast cancer tissue and extended our analysis to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. Our selected network architecture consistently produced highly accurate estimations of heterogeneous elasticity maps, even when there was up to 10% noise present in the training data.

Qiwei Yu, Yan Sun, Xiaowen Ju et al.

Objective: To construct relatively objective, atlas-based multivariate models for predicting early aphasia severity after stroke, using structural magnetic resonance imaging. Methods: We analyzed the clinical and imaging data of 46 patients with post-stroke aphasia. The aphasia severity was identified with a Western Aphasia Battery Aphasia Quotient. The assessments of stroke lesions were indicated by the lesion load of both the cortical language areas (Areas-LL) and four white matter tracts (i.e., the superior longitudinal fasciculus, SLF-LL; the inferior frontal occipital fasciculi, IFOF-LL; the inferior longitudinal, ILF-LL; and the uncinate fasciculi, UF-LL) extracted from human brain atlas. Correlation analyses and multiple linear regression analyses were conducted to evaluate the correlations between demographic, stroke- and lesion-related variables and aphasia severity. The predictive models were then established according to the identified significant variables. Finally, the receiver operating characteristic (ROC) curve was utilized to assess the accuracy of the predictive models. Results: The variables including Areas-LL, the SLF-LL, and the IFOF-LL were significantly negatively associated with aphasia severity (p < 0.05). In multiple linear regression analyses, these variables accounted for 59.4 % of the variance (p < 0.05). The ROC curve analyses yielded the validated area under the curve (AUC) 0.84 both for Areas-LL and SLF-LL and 0.76 for IFOF-LL, indicating good predictive performance (p < 0.01). Adding the combination of SLF-LL and IFOF-LL to this model increased the explained variance to 62.6 % and the AUC to 0.92. Conclusions: The application of atlas-based multimodal lesion assessment may help predict the aphasia severity after stroke, which needs to be further validated and generalized for the prediction of more outcome measures in populations with various brain injuries.

Michela Balconi, Michela Balconi, Roberta A. Allegretta et al.

According to previous research, people influence each other’s emotional states during social interactions via resonance mechanisms and coordinated autonomic rhythms. However, no previous studies tested if the manipulation of the interoceptive focus (focused attention on the breath for a given time interval) in hyperscanning during synchronized tasks may have an impact on autonomic synchrony. Thus, this study aims to assess the psychophysiological synchrony through autonomic measures recording during dyadic linguistic and motor synchronization tasks performed in two distinct interoceptive conditions: the focus and no focus on the breath condition. 26 participants coupled in 13 dyads were recruited. Individuals’ autonomic measures [electrodermal: skin conductance level and response (SCL, SCR); cardiovascular indices: heart rate (HR) and HR variability (HRV)] was continuously monitored during the experiment and correlational coefficients were computed to analyze dyads physiological synchrony. Inter-subject analysis revealed higher synchrony for HR, HRV, SCL, and SCR values in the focus compared to no focus condition during the motor synchronization task and in general more for motor than linguistic task. Higher synchrony was also found for HR, SCL, and SCR values during focus than no focus condition in linguistic task. Overall, evidence suggests that the manipulation of the interoceptive focus has an impact on the autonomic synchrony during distinct synchronization tasks and for different autonomic measures. Such findings encourage the use of hyperscanning paradigms to assess the effect of breath awareness practices on autonomic synchrony in ecological and real-time conditions involving synchronization.

Ximena Palacios-Espinosa, Leonardo Palacios Sánchez

En el 2008, la FDA (Food and Drug Administration) advirtió que los medicamentos antiepilépticos podían desencadenar conductas suicidas en los pacientes con epilepsia. Esto generó diversas reacciones entre académicos, investigadores y clínicos. La presente revisión de tema tuvo como objetivo analizar la situación actual del conocimiento sobre la conducta suicida en las personas con epilepsia, identificar la prevalencia de ésta y los factores de riesgo asociados. Esto se realizó con base en los artículos científicos publicados en bases de datos internacionales. Se encontró que la prevalencia de conductas suicidas en el paciente con epilepsia es diversa, pero ciertamente mayor que en la población general. Dentro de los factores de riesgo médicos, los medicamentos antiepilépticos y el tipo de epilepsia han sido ampliamente identificados como predictores de estas conductas. Entre los factores de riesgo psicológicos están los antecedentes psiquiátricos, especialmente comorbilidad con ansiedad, depresión y antecedentes de suicidio. En cambio, los factores de riesgo sociocultural son escasos y su asociación con la conducta suicida, es aún controvertida. Sin embargo, la edad y el género han sido los factores más asociados con el riesgo suicida. En conclusión, la evidencia confirma la presencia de conducta suicida entre los pacientes con epilepsia; por lo tanto, debe ser objeto de interés y atención por parte de los profesionales del equipo de salud tratante.

Laetitia Lebrun, Lara Absil, Myriam Remmelink et al.

Abstract Introduction COVID-19-infected patients harbour neurological symptoms such as stroke and anosmia, leading to the hypothesis that there is direct invasion of the central nervous system (CNS) by SARS-CoV-2. Several studies have reported the neuropathological examination of brain samples from patients who died from COVID-19. However, there is still sparse evidence of virus replication in the human brain, suggesting that neurologic symptoms could be related to mechanisms other than CNS infection by the virus. Our objective was to provide an extensive review of the literature on the neuropathological findings of postmortem brain samples from patients who died from COVID-19 and to report our own experience with 18 postmortem brain samples. Material and methods We used microscopic examination, immunohistochemistry (using two different antibodies) and PCR-based techniques to describe the neuropathological findings and the presence of SARS-CoV-2 virus in postmortem brain samples. For comparison, similar techniques (IHC and PCR) were applied to the lung tissue samples for each patient from our cohort. The systematic literature review was conducted from the beginning of the pandemic in 2019 until June 1st, 2022. Results In our cohort, the most common neuropathological findings were perivascular haemosiderin-laden macrophages and hypoxic-ischaemic changes in neurons, which were found in all cases (n = 18). Only one brain tissue sample harboured SARS-CoV-2 viral spike and nucleocapsid protein expression, while all brain cases harboured SARS-CoV-2 RNA positivity by PCR. A colocalization immunohistochemistry study revealed that SARS-CoV-2 antigens could be located in brain perivascular macrophages. The literature review highlighted that the most frequent neuropathological findings were ischaemic and haemorrhagic lesions, including hypoxic/ischaemic alterations. However, few studies have confirmed the presence of SARS-CoV-2 antigens in brain tissue samples. Conclusion This study highlighted the lack of specific neuropathological alterations in COVID-19-infected patients. There is still no evidence of neurotropism for SARS-CoV-2 in our cohort or in the literature.

Vincent Millischer, D. Rujescu

V. Sathyanarayanan, D. Shahwar, M. Azeem

Introduction Ireland has been one of the worst affected countries affected by COVID-19 in Europe. Many primary studies from Ireland have documented prevalence of anxiety and depressive disorders during the pandemic and their correlates. Objectives To study the prevalence range of anxiety and depression in Ireland, and their correlates during the pandemic. Methods We systematically searched Pubmed, PsycInfo and the WHO COVID-19 global research database using key words ( January 2020 - September 2021). We removed duplicates and extracted data into an excel database and carried out a narrative synthesis of the extracted data. Results From a total 127 studies, we included 22 studies that met our criteria in our narrative review. Depending on the tool used and the type of population studied, the prevalence of general anxiety disorders varied between 20% and 49.5% while prevalence of depressive disorders ranged between 20.4% and 53.8%. Younger people, health care workers, those who had to give up physical activity, people who had lost income, those who lived alone, infected by COVID-19, or had a higher perceived risk of the disease had a higher prevalence of both anxiety and depression disorders during the pandemic. There was conflicting evidence on prevalence levels among men and women and on whether they had children or not. Conclusions COVID-19 has had a profound effect on the mental health of the Irish population. Some population groups are more affected than the others. Addressing mental health concerns of Irish population during and post pandemic should remain as one of the top public health priorities. Disclosure No significant relationships.

H. Nasrallah

I t’s time for a necessary paradigm shift in re-conceptualizing the nosology, epidemiology, etiology, and treatment of major psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder (MDD), autism spectrum disorder, attentiondeficit/hyperactivity disorder (ADHD), anxiety, obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), and substance use disorders. For a long time, and prior to the neuroscience revolution that enabled probing the human brain and exploring the neurobiology of psychiatric disorders, the field of psychiatry was descriptive and simplistic. It categorized psychiatric disorders essentially as silos, defined by a set of signs and symptoms. If one or more psychiatric conditions co-occurred with a “primary diagnosis,” they were labeled as “comorbidities,” with no implications of a shared etiology or biology. Amazingly, despite the rapid accrual of evidence of shared developmental or genetic etiopathogenesis, shared dysplasia of the same brain regions on neuroimaging, and improvement with the same class of medications, the DSM-5 and its outdated schema remain the diagnostic “bible of psychiatry,” and comorbidities are not being recognized as genetically overlapping disorders. This archaic model is ripe for change and a major update. Highlights of emerging advances that justify the re-conceptualizing of the nosology of major DSM diagnostic entities, and reinterpreting the comorbidities as evidence of the substantial clinical and biological overlap and interconnectivity of psychiatric brain disorders, include: Neurodevelopmental pathology. Disruption of brain development during fetal life has been well-established across the schizophrenia spectrum syndrome and practically all the so-called comorbidities. Genetic pleiotropy. Approximately 50% of the 22,000 proteincoding genes in the human chromosomes are expressed in the brain during development. Schizophrenia and most psychiatric disorders are heavily genetic. Genetic pleiotropy has been identified across several Henry A. Nasrallah, MD University of Cincinnati College of Medicine Cincinnati, Ohio, USA

G. Northoff

Nature or nurture? This is an old debate which re-surfaces in current neuroscience and especially in psychiatry as we gain more and more understanding of the interplay between biological-psychological and social factors. This leaves open the exact nature of their interaction, though. Howdo intrinsic psychological and extrinsic social factors interact? The study by Wong et al. (2021) sheds a novel light on this issue; this is especially pressing in psychiatry as we are confronted with complex scenarios of social, psychological, neuronal, and genetic factors. Wong et al. (2021) explore the interplay of intrinsic and extrinsic psychological factors in ideas of reference (IOR). They investigate the occurrence of IOR in relation to intrinsic factors like thought as operationalized by rumination as well as in the context of extrinsic life events like COVID pandemic and social unrest. Corresponding to their aim of investigating the interplay between intrinsic cognition and extrinsic environment, they distinguish two forms of IOR: attenuated IOR are those that occur within several members of a group (IOR-A) while IOR experienced solely by a single subject are designated as IOR-E. Obtaining subjective ratings on visual analogue scales in a large sample (> 9000 subjects) showed that rumination as related to the events,

L. Hyde, S. Tompson, J. Creswell et al.

T. Shafir

Matt Stack, Paul Macklin, Robert Searles et al.

Computational biology has increasingly turned to agent-based modeling to explore complex biological systems. Biological diffusion (diffusion, decay, secretion, and uptake) is a key driver of biological tissues. GPU computing can vastly accelerate the diffusion and decay operators in the partial differential equations used to represent biological transport in an agent-based biological modeling system. In this paper, we utilize OpenACC to accelerate the diffusion portion of PhysiCell, a cross-platform agent-based biosimulation framework. We demonstrate an almost 40x speedup on the state-of-the-art NVIDIA A100 GPU compared to a serial run on AMD's EPYC 7742. We also demonstrate 9x speedup on the 64 core AMD EPYC 7742 multicore platform. By using OpenACC for both the CPUs and the GPUs, we maintain a single source code base, thus creating a portable yet performant solution. With the simulator's most significant computational bottleneck significantly reduced, we can continue cancer simulations over much longer times.