Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Agnes Korcsak-Gorzo, Jesús A. Espinoza Valverde, Jonas Stapmanns et al.

Despite remarkable technological advances, AI systems may still benefit from biological principles, such as recurrent connectivity and energy-efficient mechanisms. Drawing inspiration from the brain, we present a biologically plausible extension of the eligibility propagation (e-prop) learning rule for recurrent spiking networks. By translating the time-driven update scheme into an event-driven one, we integrate the learning rule into a simulation platform for large-scale spiking neural networks and demonstrate its applicability to tasks such as neuromorphic MNIST. We extend the model with prominent biological features such as continuous dynamics and weight updates, strict locality, and sparse connectivity. Our results show that biologically grounded constraints can inform the design of computationally efficient AI algorithms, offering scalability to millions of neurons without compromising learning performance. This work bridges machine learning and computational neuroscience, paving the way for sustainable, biologically inspired AI systems while advancing our understanding of brain-like learning.

ehsan golestani, Akbar Atadokht, Niloofar Mikaeili et al.

This study designed an educational board game and investigated its effectiveness on the symptoms of attention deficit and hyperactivity disorder (ADHD) in children. The population included 40 children aged seven to nine years in Baharestan County, Tehran Province in 2023 and selected by purposeful sampling and divided into two experimental and control groups randomly. The educational game was employed for 10 sessions in the experimental group. The instruments included the SNAP-IV questionnaire and diagnostic interview. Data was analyzed using repeated measures variance test in SPSS 23 software. The results showed that the educational board game had a significant effect on reducing children's hyperactivity symptoms and there was a significant difference between the experimental and control groups. Also, these effects remained stable in the follow-up phase.​​​​​​​​​ According to the findings, it can be generally concluded that game therapy, including physical games and cognitive games such as educational board games, can be used alongside first-line treatments as a complement or as an independent intervention for these children.

J. Oltra Cucarella, C. Iñesta Carrizosa, B. Bonete López et al.

Antonios Panayotopoulos

For the generation of acyclic biological diagrams, from a graph-theoretical perspective, we introduce the relative diagrams of cyclic permutations with ramphoid and keratoid vertices of degree 2, which correspond to Motzkin and Dyck words/paths. The relation between these two types of diagrams, defines the generation of the first via the permutations of the second, which may be of assistance in the study and treatment of several biological problems.

Paolo Burelli, Laurits Dixen

Videogames have been a catalyst for advances in many research fields, such as artificial intelligence, human-computer interaction or virtual reality. Over the years, research in fields such as artificial intelligence has enabled the design of new types of games, while games have often served as a powerful tool for testing and simulation. Can this also happen with neuroscience? What is the current relationship between neuroscience and games research? what can we expect from the future? In this article, we'll try to answer these questions, analysing the current state-of-the-art at the crossroads between neuroscience and games and envisioning future directions.

Claus Kadelka, David Murrugarra

Biological networks such as gene regulatory networks possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structural and dynamical features that evolution has incorporated in biological networks. A recent meta-analysis of published, expert-curated Boolean biological network models has revealed several such features, often referred to as design principles. Among others, the biological networks are enriched for certain recurring network motifs, the dynamic update rules are more redundant, more biased and more canalizing than expected, and the dynamics of biological networks are better approximable by linear and lower-order approximations than those of comparable random networks. Since most of these features are interrelated, it is paramount to disentangle cause and effect, that is, to understand which features evolution actively selects for, and thus truly constitute evolutionary design principles. Here, we show that approximability is strongly dependent on the dynamical robustness of a network, and that increased canalization in biological networks can almost completely explain their recently postulated high approximability.

Mark Gaertner, Mark Gaertner, Christopher M. DeGiorgio et al.

Xuan Kan, Zimu Li, Hejie Cui et al.

Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biological networks usually faces severe overfitting. In this work, we propose R-MIXUP, a Mixup-based data augmentation technique that suits the symmetric positive definite (SPD) property of adjacency matrices from biological networks with optimized training efficiency. The interpolation process in R-MIXUP leverages the log-Euclidean distance metrics from the Riemannian manifold, effectively addressing the swelling effect and arbitrarily incorrect label issues of vanilla Mixup. We demonstrate the effectiveness of R-MIXUP with five real-world biological network datasets on both regression and classification tasks. Besides, we derive a commonly ignored necessary condition for identifying the SPD matrices of biological networks and empirically study its influence on the model performance. The code implementation can be found in Appendix E.

Erman Şentürk, Behçet Coşar, Zehra Arıkan

Cryptocurrencies has been considered as both an investment tool and a great invention that will replace money and change the world order. Although crypto currency trading has been investigated in many aspects, the psychological dimension that directly affects investors has often been ignored. Control of cryptocurrency trading is in the hands of investors rather than a central authority or institution. Thus, the value of cryptocurrencies changes with the reactions of investors. This situation suggests that psychological factors may be more prominent in cryptocurrency trading. Cryptocurrency trading has many similarities with gambling and betting, such as risk taking, getting quick returns, extreme gains or losses. Some significant components of behavioral addiction are also seen in individuals who spend so much time with cryptocurrency trading. The purpose of this article is to provide a better understanding of the psychological effects of cryptocurrency trading, which has entered our lives over a relatively brief period of time and reached millions of investors.

Hamed Ekhtiari

Yuto Hosaka, Shigeyuki Komura

Biological nanomachines are nanometer-size macromolecular complexes that catalyze chemical reactions in the presence of substrate molecules. The catalytic functions carried out by such nanomachines in the cytoplasm, and biological membranes are essential for cellular metabolism and homeostasis. During catalytic reactions, enzymes undergo conformational changes induced by substrate binding and product release. In recent years, these conformational dynamics have been considered to account for the nonequilibrium transport phenomena such as diffusion enhancement, chemotaxis, and substantial change in rheological properties, which are observed in biological systems. In this review article, we shall give an overview of the recent theoretical and experimental investigations that deal with nonequilibrium transport phenomena induced by biological nanomachines such as enzymes or proteins.

Hadas Benisty, Alexander Song, Gal Mishne et al.

Functional optical imaging in neuroscience is rapidly growing with the development of new optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. In this review, we cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.

Louis Fabrice Tshimanga, Manfredo Atzori, Federico Del Pup et al.

In recent years, deep learning revolutionized machine learning and its applications, producing results comparable to human experts in several domains, including neuroscience. Each year, hundreds of scientific publications present applications of deep neural networks for biomedical data analysis. Due to the fast growth of the domain, it could be a complicated and extremely time-consuming task for worldwide researchers to have a clear perspective of the most recent and advanced software libraries. This work contributes to clarify the current situation in the domain, outlining the most useful libraries that implement and facilitate deep learning application to neuroscience, allowing scientists to identify the most suitable options for their research or clinical projects. This paper summarizes the main developments in Deep Learning and their relevance to Neuroscience; it then reviews neuroinformatic toolboxes and libraries, collected from the literature and from specific hubs of software projects oriented to neuroscience research. The selected tools are presented in tables detailing key features grouped by domain of application (e.g. data type, neuroscience area, task), model engineering (e.g. programming language, model customization) and technological aspect (e.g. interface, code source). The results show that, among a high number of available software tools, several libraries are standing out in terms of functionalities for neuroscience applications. The aggregation and discussion of this information can help the neuroscience community to devolop their research projects more efficiently and quickly, both by means of readily available tools, and by knowing which modules may be improved, connected or added.

Suman K. Guha, Yanaira Alonso-Caraballo, Gillian S. Driscoll et al.

IntroductionRates of relapse to drug use during abstinence are among the highest for opioid use disorder (OUD). In preclinical studies, reinstatement to drug-seeking has been extensively studied as a model of relapse–but the work has been primarily in males. We asked whether biological sex contributes to behaviors comprising self-administration of the prescription opioid oxycodone in rats, and we calculated the relative contribution of these behavioral measures to reinstatement in male and female rats.Materials and methodsRats were trained to self-administer oxycodone (8 days, training phase), after which we examined oxycodone self-administration behaviors for an additional 14 days under three conditions in male and female rats: short access (ShA, 1 h/d), long access (LgA, 6 h/d), and saline self-administration. All rats were then tested for cue-induced reinstatement of drug-seeking after a 14-d forced abstinence period. We quantified the # of infusions, front-loading of drug intake, non-reinforced lever pressing, inter-infusion intervals, escalation of intake, and reinstatement responding on the active lever.ResultsBoth male and female rats in LgA and ShA conditions escalated oxycodone intake to a similar extent. However, males had higher levels of non-reinforced responding than females under LgA conditions, and females had greater levels of reinstatement responding than males. We then correlated each addiction-related measure listed above with reinstatement responding in males and females and ranked their respective relative contributions. Although the majority of behavioral measures associated with oxycodone self-administration did not show sex differences on their own, when analyzed together using partial least squares regression, their relative contributions to reinstatement were sex-dependent. Front-loading behavior was calculated to have the highest relative contribution to reinstatement in both sexes, with long and short inter-infusion intervals having the second greatest contribution in females and males, respectively.DiscussionOur results demonstrate sex differences in some oxycodone self-administration measures. More importantly, we demonstrate that a sex- dependent constellation of self-administration behaviors can predict the magnitude of reinstatement, which holds great promise for relapse prevention in people.

Wenzheng Zhang, Ya Zhang, Naixia Hu et al.

This study aimed to explore the potential molecular pathways and targets of Alzheimer's disease leading to osteoporosis using bioinformatics tools. The Alzheimer's and osteoporosis microarray gene expression data were retrieved from the Gene Expression Omnibus, and differentially expressed genes in the blood microenvironment related to Alzheimer's disease and osteoporosis were identified. The intersection of the three datasets (GSE97760, GSE168813, and GSE62402) was used to obtain 21 co-expressed targets in the peripheral blood samples in patients with Alzheimer's disease and osteoporosis. Based on the degree algorithm, the top 10 potential core target genes related to these diseases were identified, which included CLEC4D, PROK2, SIGLEC7, PDGFB, PTCRA, ECH1, etc. Two differentially expressed mRNAs, Prokineticin 2 (PROK2) and three colony-stimulating factor 3 (CSF3), were screened in the GSE62402 dataset associated with osteoporosis. Protein–protein rigid docking with ZDOCK revealed that PROK2 and CSF3 could form a stable protein docking model. The interaction of PROK2 and CSF3, core genes related to osteoporosis inflammation, plays an important role in the mechanism of osteoporosis in patients with Alzheimer's. Therefore, abnormalities or alterations in the inflammatory pathways in the peripheral blood samples of Alzheimer's patients may affect the course of osteoporosis.

E. Chkonia

Since gaining independence in 1991, Georgia has struggled to transform the old-Soviet mental health care structure into a humane system to meet basic human rights standards.

Clara Muñoz-Castro, Ayush Noori, Colin G. Magdamo et al.

Abstract Background Astrocytes and microglia react to Aβ plaques, neurofibrillary tangles, and neurodegeneration in the Alzheimer’s disease (AD) brain. Single-nuclei and single-cell RNA-seq have revealed multiple states or subpopulations of these glial cells but lack spatial information. We have developed a methodology of cyclic multiplex fluorescent immunohistochemistry on human postmortem brains and image analysis that enables a comprehensive morphological quantitative characterization of astrocytes and microglia in the context of their spatial relationships with plaques and tangles. Methods Single FFPE sections from the temporal association cortex of control and AD subjects were subjected to 8 cycles of multiplex fluorescent immunohistochemistry, including 7 astroglial, 6 microglial, 1 neuronal, Aβ, and phospho-tau markers. Our analysis pipeline consisted of: (1) image alignment across cycles; (2) background subtraction; (3) manual annotation of 5172 ALDH1L1+ astrocytic and 6226 IBA1+ microglial profiles; (4) local thresholding and segmentation of profiles; (5) machine learning on marker intensity data; and (6) deep learning on image features. Results Spectral clustering identified three phenotypes of astrocytes and microglia, which we termed “homeostatic,” “intermediate,” and “reactive.” Reactive and, to a lesser extent, intermediate astrocytes and microglia were closely associated with AD pathology (≤ 50 µm). Compared to homeostatic, reactive astrocytes contained substantially higher GFAP and YKL-40, modestly elevated vimentin and TSPO as well as EAAT1, and reduced GS. Intermediate astrocytes had markedly increased EAAT2, moderately increased GS, and intermediate GFAP and YKL-40 levels. Relative to homeostatic, reactive microglia showed increased expression of all markers (CD68, ferritin, MHC2, TMEM119, TSPO), whereas intermediate microglia exhibited increased ferritin and TMEM119 as well as intermediate CD68 levels. Machine learning models applied on either high-plex signal intensity data (gradient boosting machines) or directly on image features (convolutional neural networks) accurately discriminated control vs. AD diagnoses at the single-cell level. Conclusions Cyclic multiplex fluorescent immunohistochemistry combined with machine learning models holds promise to advance our understanding of the complexity and heterogeneity of glial responses as well as inform transcriptomics studies. Three distinct phenotypes emerged with our combination of markers, thus expanding the classic binary “homeostatic vs. reactive” classification to a third state, which could represent “transitional” or “resilient” glia.

Zhen Liu, Zhen Liu, Yi-Xuan Liu et al.

Restoring the correct or realistic color of a cultural heritage object is a crucial problem for imaging techniques. Digital images often have undesired color casts due to adverse effects caused by unstable illuminant conditions, vignetting, and color changes due to camera settings. In this work, we present an improved color correction method for color cast images that makes the color appear more realistic. It is based on a computational model of the human visual system that perceives objects by color constancy theory; it realizes illumination non-uniformity compensation and chromaticity correction for color cast images by taking into account the color stability of some pigments. This approach has been used to correct the color in Cave 465 of the Mogao Grottoes. The experimental results demonstrate that the proposed method is able to “adaptively correct” color cast images with widely varying lighting conditions and improve the consistency efficaciously. It can achieve improved consistency in the mean CIEDE2000 color difference compared with the images before correction. This colorimetric correction methodology is sufficiently accurate in color correction implementation for cast images of murals captured in the early years.

T. Luor, Anies Al-Hroub, Hsi-Peng Lu et al.

ABSTRACT This study used the bibliographic scattering analysis to explore the scientific publications trends on gifted individuals with autism spectrum disorders (ASD) over the past 23 years. The study examined the applicability and appropriateness of Bradford’s and Lotka’s laws of scattering to measure the impact factors of journals, institutions, countries, researchers, and personal publications on the area of research. After examining 95 research papers published in 55 Social Sciences Citation Indexed (SSCI) journals from 1998 to 2020. The rank list was prepared to identify the core journals in education. Themost frequent venues of journals in descending order of times cited are, Philosophical Transactions of the Royal Society B-Biological Science (USA) with 33.8% of citation, followed by the Journal of Autism and Developmental Disorders (USA) with 10.3% of citation and Journal of the American Academy of Child and Adolescent Psychiatry (USA) with 8.2% of citation. The top four categories of research were psychology with 37.13% of publications, followed by psychiatry (13.77%), neurosciences and neurology (11.38%), and education (8.38%). The study found that Asperger syndrome (AS) is still used in academic studies, even after it was immersed in the ASD by DSM-5 criteria. The findings and limitations were presented and discussed.

Joseph J. Cooper, Ashley E. Walker

Psychiatric education has struggled to move past dualistic notions separating mind from brain, and embrace the field's identity as a clinical neuroscience discipline. To modernize our educational systems, we must integrate neuroscience perspectives into every facet of our clinical work. To do this effectively, neuroscience education should be clinically relevant, informed by adult learning theory, and tailored to the individualized needs of learners. Classic neuropsychiatry skills can help us better understand our patients' brain function at the bedside. Integrating neuroscience perspectives alongside the other rich perspectives in psychiatry will help trainees appreciate the relevance of neuroscience to modern medical practice.