Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Hyomin Kim, Sang-Yeon Hwang, Jaechang Lim et al.

Predicting gene regulation responses to biological perturbations requires reasoning about underlying biological causalities. While large language models (LLMs) show promise for such tasks, they are often overwhelmed by the entangled nature of high-dimensional perturbation results. Moreover, recent works have primarily focused on genetic perturbations in single-cell experiments, leaving bulk-cell chemical perturbations, which is central to drug discovery, largely unexplored. Motivated by this, we present LINCSQA, a novel benchmark for predicting target gene regulation under complex chemical perturbations in bulk-cell environments. We further propose PBio-Agent, a multi-agent framework that integrates difficulty-aware task sequencing with iterative knowledge refinement. Our key insight is that genes affected by the same perturbation share causal structure, allowing confidently predicted genes to contextualize more challenging cases. The framework employs specialized agents enriched with biological knowledge graphs, while a synthesis agent integrates outputs and specialized judges ensure logical coherence. PBio-Agent outperforms existing baselines on both LINCSQA and PerturbQA, enabling even smaller models to predict and explain complex biological processes without additional training.

Oleksii S. Nekhlopochyn, Vadim V. Verbov, Ievgen V. Cheshuk et al.

Introduction: Burst fractures of the thoracolumbar junction (TLJ, T10–L2) are common spinal injuries associated with a high risk of neurological complications. Transpedicular fixation is one of the most effective treatment methods; however, the optimal choice of fixation configuration remains unresolved. This study aims to analyze the stress-strain state of various short-segment transpedicular fixation configurations for Th12 vertebra burst fractures under lateral flexion loading. Materials and methods: A finite element model of the Th9–L5 spinal segment with a simulated Th12 burst fracture was created. Four fixation configurations were considered: M1 – short screws in Th11 and L1 (without intermediate screws), M2 – long screws in Th11 and L1 (without intermediate screws), M3 – short screws in Th11 and L1 with intermediate screws in Th12, and M4 – long screws in Th11 and L1 with intermediate screws in Th12. The models were analyzed using CosmosM software, assessing equivalent von Mises stress at 18 control points. Loads simulated physiological lateral trunk bending. Results: Models with long screws (M2, M4) demonstrated lower maximum stresses in connecting rods (315.5–321.0 MPa) compared to short screws (324.8–324.9 MPa). The inclusion of intermediate screws (M3, M4) significantly reduced stress in the fractured Th12 vertebra (by up to 28%), in adjacent vertebral endplates (by 18–25%), and at screw entry points into vertebral arches (up to 28%). The lowest fixation screw stresses were observed in the model with long and intermediate screws (up to 38% lower compared to the baseline model M1). However, intermediate screws minimally influenced stresses in the connecting rods (up to 1.2%). Conclusions: The optimal short-segment transpedicular fixation configuration is the use of long screws in adjacent vertebrae combined with intermediate fixation in the fractured vertebra (M4). This approach provides optimal load distribution, reduces the risk of construct failure, and preserves mobility of adjacent segments. Long screws improve overall system stiffness, while intermediate screws effectively stabilize the damaged segment and significantly unload critical areas of the construct and adjacent anatomical structures.

Gavin P. Schmitz, Yi-Ting Chiu, Mia L. Foglesong et al.

Abstract Psilocybin, and its active metabolite psilocin, have seen renewed interest due to studies suggesting potential therapeutic utility. 5-Hydroxytryptamine2A receptors (5-HT2ARs) are primary mediators of the psychoactive effects of psychedelics in animals and humans, but the underlying neurobiological mechanisms remain poorly understood. Functional magnetic resonance imaging identified significant psilocin-induced increases in medial prefrontal cortex (mPFC) activity, a site of enriched 5-HT2AR expression. We identified a population of 5-HT2AR neurons in the prelimbic/anterior cingulate mPFC. Psilocin and the 5-HT2AR-selective compound 25-CN-NBOH increased excitability, and stimulated firing across a range of current injections in these neurons that was both 5-HT2AR and Gαq dependent. Similar effects were observed with a novel, non-hallucinogenic psychedelic compound. These findings provide valuable insight into the specific role of 5-HT2AR-containing neurons in psychedelic-associated plasticity in mPFC regions that are likely implicated in the clinical effects of psychedelics and further identify membrane-bound 5-HT2ARs and subsequent intracellular Gαq signaling as therapeutic targets.

Teresa Lesiuk, Kaitlyn Dillon, Giulia Ripani et al.

IntroductionResearchers have shown that music-evoked autobiographical memories (MEAMs) can stimulate long-term memory mechanisms while requiring little retrieval effort and may therefore be used in promising non-pharmacological interventions to mitigate memory deficits. Despite an increasing number of studies on MEAMs, few researchers have explored how MEAMs are bound in the brain.MethodsIn the current study activation indexed by fractional amplitude of low frequency fluctuations (fALFF) during familiar and unfamiliar MEAM retrieval was compared in a sample of 24 healthy older adults. Additionally, we aimed to investigate the impact of age-related gray matter volume (GMV) reduction in key regions associated with MEAM-related activation. In addition to a T1 structural scan, neuroimaging data were collected while participants listened to familiar music (MEAM retrieval) versus unfamiliar music.ResultsWhen listening to familiar compared to unfamiliar music, greater fALFF activation patterns were observed in the right parahippocampal gyrus, controlling for age and GMV. The current findings for the familiar (MEAM) condition have implications for cognitive aging as persons experiencing age-related memory decline are particularly susceptible to volumetric reduction in the parahippocampal cortex. Post-hoc analyses to explore correlations between brain activity and the content of MEAMs were performed using the text analysis program Linguistic Inquiry and Word Count.DiscussionOur findings suggest that MEAM-related activation of the parahippocampal cortex is evident in normative older adults. However, it is yet to be determined whether such brain states are attainable in older adult populations diagnosed with mild cognitive impairment and/or prodromal Alzheimer’s disease.

Ludovico Iannello, Luca Ciampi, Gabriele Lagani et al.

In this paper, we introduce a paradigm for reservoir computing (RC) that leverages a pool of cultured biological neurons as the reservoir substrate, creating a biological reservoir computing (BRC). This system operates similarly to an echo state network (ESN), with the key distinction that the neural activity is generated by a network of cultured neurons, rather than being modeled by traditional artificial computational units. The neuronal activity is recorded using a multi-electrode array (MEA), which enables high-throughput recording of neural signals. In our approach, inputs are introduced into the network through a subset of the MEA electrodes, while the remaining electrodes capture the resulting neural activity. This generates a nonlinear mapping of the input data to a high-dimensional biological feature space, where distinguishing between data becomes more efficient and straightforward, allowing a simple linear classifier to perform pattern recognition tasks effectively. To evaluate the performance of our proposed system, we present an experimental study that includes various input patterns, such as positional codes, bars with different orientations, and a digit recognition task. The results demonstrate the feasibility of using biological neural networks to perform tasks traditionally handled by artificial neural networks, paving the way for further exploration of biologically-inspired computing systems, with potential applications in neuromorphic engineering and bio-hybrid computing.

A. Martínez Gimeno, J. Jiménez, R. Baviera et al.

Ebenezer Daniel, Frank Deng, Sunita K. Patel et al.

PurposeThe objective of this study was to assess changes in hippocampal volume and shape in older long-term breast cancer survivors who were exposed to chemotherapy 5–15 years prior.MethodsThis study recruited female long-term breast cancer survivors aged 65 years or older with a history of chemotherapy (C+), age-matched breast cancer survivors who did not receive chemotherapy (C−), and healthy controls (HC). The participants were recruited 5–15 years after chemotherapy at time point 1 (TP1) and were followed up for 2 years at time point 2 (TP2). Assessments included hippocampal volume and shape from brain MRI scans and neuropsychological (NP) tests.ResultsAt TP1, each of the three groups was comprised of 20 participants. The C+ group exhibited a hippocampal volume loss estimated in proportion with total intracranial volume (ICV) in both the left and right hemispheres from TP1 to TP2. Regarding the hippocampal shape at TP1, the C+ group displayed inward changes compared to the control groups. Within the C+ group, changes in right hippocampal volume adjusted with ICV were positively correlated with crystalized composite scores (R = 0.450, p = 0.044). Additionally, in C+ groups, chronological age was negatively correlated with right hippocampal volume adjusted with ICV (R = −0.585, p = 0.007).ConclusionThe observed hippocampal volume reduction and inward shape deformation within the C+ group may serve as neural basis for cognitive changes in older long-term breast cancer survivors with history of chemotherapy treatment.

M. Krupa, A. Balogh-Pécsi

Introduction Following the pandemic, we can find many new communication situations. Social relationships have changed a lot and are developing differently due to digital development, new lifestyles, and the effects of COVID-19. These components: social media, the transformation of interpersonal relationships, and the use of the platforms provided by the internet can lead to addictive disorders as risk factors. Objectives In this presentation, we review studies investigating the relationship between the new digital techniques, social connection, and communication development of adults with addictive disorders. We attempt to provide a summary of new theories and the areas currently being researched around the topic. Another aim of our research is to present the new drama-based therapy theories and methods in adults with addictive disorders. Methods To learn about recent international results, we conducted a literature search in 3 databases (PubMed, Medline, Web of Science) using the following keywords: drama therapy, addiction, emotion regulation, and adults, over the past 5 years. Empirical journal articles in English were used to prepare the literature review. Exclusion criteria were: the appearance publication before the year 2017 and the adolescent population. Results Changes in social behavior, emotion regulation, and addictive disorder were correlated. The studies examined social communications and loneliness in primarily cross-sectional studies design. The escapism from interpersonal relations and low self-esteem is the highest motivation to start regular videogame playing or using social media without control which becomes an addictive disorder. Conclusions Problematic social media use and changes in social connection threaten adults’ mental health. The diagnosis of emotion dysregulation, low self-esteem, and social disconnection is the detection of risk factors for addictive disorders. The new methods and tools of drama-based therapy are new prevention possibilities for these risk factors. In this way, it is a relevant issue in the field of education science. Disclosure of Interest None Declared

Moein Khajehnejad, Forough Habibollahi, Aswin Paul et al.

How do biological systems and machine learning algorithms compare in the number of samples required to show significant improvements in completing a task? We compared the learning efficiency of in vitro biological neural networks to the state-of-the-art deep reinforcement learning (RL) algorithms in a simplified simulation of the game `Pong'. Using DishBrain, a system that embodies in vitro neural networks with in silico computation using a high-density multi-electrode array, we contrasted the learning rate and the performance of these biological systems against time-matched learning from three state-of-the-art deep RL algorithms (i.e., DQN, A2C, and PPO) in the same game environment. This allowed a meaningful comparison between biological neural systems and deep RL. We find that when samples are limited to a real-world time course, even these very simple biological cultures outperformed deep RL algorithms across various game performance characteristics, implying a higher sample efficiency. Ultimately, even when tested across multiple types of information input to assess the impact of higher dimensional data input, biological neurons showcased faster learning than all deep reinforcement learning agents.

Rajat Saxena, Bruce L. McNaughton

Continual learning (CL) refers to an agent's capability to learn from a continuous stream of data and transfer knowledge without forgetting old information. One crucial aspect of CL is forward transfer, i.e., improved and faster learning on a new task by leveraging information from prior knowledge. While this ability comes naturally to biological brains, it poses a significant challenge for artificial intelligence (AI). Here, we suggest that environmental enrichment (EE) can be used as a biological model for studying forward transfer, inspiring human-like AI development. EE refers to animal studies that enhance cognitive, social, motor, and sensory stimulation and is a model for what, in humans, is referred to as 'cognitive reserve'. Enriched animals show significant improvement in learning speed and performance on new tasks, typically exhibiting forward transfer. We explore anatomical, molecular, and neuronal changes post-EE and discuss how artificial neural networks (ANNs) can be used to predict neural computation changes after enriched experiences. Finally, we provide a synergistic way of combining neuroscience and AI research that paves the path toward developing AI capable of rapid and efficient new task learning.

Konstantin Holzhausen, Mia Merlid, Håkon Olav Torvik et al.

Backpropagation-optimized artificial neural networks, while precise, lack robustness, leading to unforeseen behaviors that affect their safety. Biological neural systems do solve some of these issues already. Unlike artificial models, biological neurons adjust connectivity based on neighboring cell activity. Understanding the biological mechanisms of robustness can pave the way towards building trust worthy and safe systems. Robustness in neural representations is hypothesized to correlate with the smoothness of the encoding manifold. Recent work suggests power law covariance spectra, which were observed studying the primary visual cortex of mice, to be indicative of a balanced trade-off between accuracy and robustness in representations. Here, we show that unsupervised local learning models with winner takes all dynamics learn such power law representations, providing upcoming studies a mechanistic model with that characteristic. Our research aims to understand the interplay between geometry, spectral properties, robustness, and expressivity in neural representations. Hence, we study the link between representation smoothness and spectrum by using weight, Jacobian and spectral regularization while assessing performance and adversarial robustness. Our work serves as a foundation for future research into the mechanisms underlying power law spectra and optimally smooth encodings in both biological and artificial systems. The insights gained may elucidate the mechanisms that realize robust neural networks in mammalian brains and inform the development of more stable and reliable artificial systems.

Uros Sutulovic, Daniele Proverbio, Rami Katz et al.

Robustness analysis is very important in biology and neuroscience, to unravel behavioural patterns of systems that are conserved despite large parametric uncertainties. To make studies of probabilistic robustness more efficient and scalable when addressing complex models in neuroscience, we propose an alternative to computationally expensive Monte Carlo (MC) methods by introducing and analysing the generalised polynomial chaos (gPC) framework for uncertainty quantification. We consider both intrusive and non-intrusive gPC approaches, which turn out to be scalable and allow for a fast comprehensive exploration of parameter spaces. Focusing on widely used models of neural dynamics as case studies, we explore the trade-off between efficiency and accuracy of gPC methods, and we adopt the proposed methodology to investigate parametric uncertainties in models that feature multiple dynamic regimes.

Zhen Li, Rong Chen, Dachuan Liu et al.

Previous studies have demonstrated that low-intensity transcranial ultrasound stimulation (TUS) can eliminate hippocampal neural activity. However, until now, it has remained unclear how ultrasound modulates theta and gamma oscillations in the hippocampus under different behavioral states. In this study, we used ultrasound to stimulate the CA1 in mice in anesthesia, awake and running states, and we simultaneously recorded the local field potential of the stimulation location. We analyzed the power spectrum, phase-amplitude coupling (PAC) of theta and gamma oscillations, and their relationship with ultrasound intensity. The results showed that (i) TUS significantly enhanced the absolute power of theta and gamma oscillations under anesthesia and in the awake state. (ii) The PAC strength between theta and gamma oscillations is significantly enhanced under the anesthesia and awake states but is weakened under the running state with TUS. (iii) Under anesthesia, the relative power of theta decreases and that of gamma increases as ultrasound intensity increases, and the result under the awake state is opposite that under the anesthesia state. (iv) The PAC index between theta and gamma increases as ultrasound intensity increases under the anesthesia and awake states. The above results demonstrate that TUS can modulate theta and gamma oscillations in the CA1 and that the modulation effect depends on behavioral states. Our study provides guidance for the application of ultrasound in modulating hippocampal function.

Felipe José Corrêa de Oliveira

Lisa Topolnik, Graziella Di Cristo, Elsa Rossignol

Takayuki KIKUCHI, Yasushi TAKAGI, Jyoji NAKAGAWARA et al.

Cognitive impairment in adult patients with moyamoya disease (MMD) is sometimes overlooked and can occur in patients with no ischemic or hemorrhagic lesions. Better profiling and reliable diagnostic methods that characterize the group and associate the impairments and pathology of MMD are required in order to deliver appropriate treatments and support. The potential of 123I-iomazenil single-photon emission computed tomography (SPECT) for this issue has been reported in some studies, but the universality of this method remains unclear. A multicenter study of adult patients (aged 18-60 years) with MMD who experienced difficulties in social lives despite normal activities of daily living was implemented to delineate the common characteristics of this group of patients. In this study, iomazenil SPECT, besides patient characteristics, cognitive functions, and conventional imaging, was acquired to examine whether this method is suitable as a universal diagnostic tool. A total of 36 patients from 12 institutes in Japan were included in this study. Domain scores of world health organization quality of life 26 indicated low self-rating in physical health and psychological domains. The percentages of patients who had <85 in each index were 27.8%-33.3% in the WAIS-III and 16.7%-47.2% in the Wechsler Memory Scale-Revised. The group analysis of iomazenil SPECT demonstrated a decreased accumulation in the bilateral medial frontal areas in comparison with the normal control, whereas there were no specific characteristics on conventional imaging in the cohort. Iomazenil SPECT is a possible universal diagnostic method for the extraction of patients with cognitive impairment in MMD.

Muhammad Hassan, Hao Zhang, Ahmed Fateh Ameen et al.

Fundus image captures rear of an eye, and which has been studied for the diseases identification, classification, segmentation, generation, and biological traits association using handcrafted, conventional, and deep learning methods. In biological traits estimation, most of the studies have been carried out for the age prediction and gender classification with convincing results. However, the current study utilizes the cutting-edge deep learning (DL) algorithms to estimate biological traits in terms of age and gender together with associating traits to retinal visuals. For the traits association, our study embeds aging as the label information into the proposed DL model to learn knowledge about the effected regions with aging. Our proposed DL models, named FAG-Net and FGC-Net, correspondingly estimate biological traits (age and gender) and generates fundus images. FAG-Net can generate multiple variants of an input fundus image given a list of ages as conditions. Our study analyzes fundus images and their corresponding association with biological traits, and predicts of possible spreading of ocular disease on fundus images given age as condition to the generative model. Our proposed models outperform the randomly selected state of-the-art DL models.

Maurice HT Ling

Modeling and simulation are recognized as important aspects of the scientific method for more than 70 years but its adoption in biology has been slow. Debates on its representativeness, usefulness, and whether the effort spent on such endeavors is worthwhile, exist to this day. Here, I argue that most of learning is modeling; hence, arriving at a contradiction if models are not useful. Representing biological systems through mathematical models can be difficult but the modeling procedure is a process in itself that follows a semi-formal set of rules. Although seldom reported, failure in modeling is not a rare event but I argue that this is usually a result of erroneous underlying knowledge or misapplication of a model beyond its intended purpose. I argue that in many biological studies, simulation is the only experimental tool. In others, simulation is a means of reducing possible combinations of experimental work; thereby, presenting an economical case for simulation; thus, worthwhile to engage in this endeavor. The representativeness of simulation depends on the validation, verification, assumptions, and limitations of the underlying model. This will be illustrated using the inter-relationship between population, samples, probability theory, and statistics.

Mohamed Maama, Ajay Jasra, Kengo Kamatani

In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus upon the case of neuroscience. The data are assumed to be observed regularly in time and driven by the SDE model with unknown parameters. In practice the SDE may not have an analytically tractable solution and this leads naturally to a time-discretization. We adapt the multilevel Markov chain Monte Carlo method of [11], which works with a hierarchy of time discretizations and show empirically and theoretically that this is preferable to using one single time discretization. The improvement is in terms of the computational cost needed to obtain a pre-specified numerical error. Our approach is illustrated on models that are found in neuroscience.

Chengwei Fu, Chengwei Fu, Yue Zhang et al.

BackgroundMigraine is a common disorder, affecting many patients. However, for one thing, lacking objective biomarkers, misdiagnosis, and missed diagnosis happen occasionally. For another, though transcutaneous vagus nerve stimulation (tVNS) could alleviate migraine symptoms, the individual difference of tVNS efficacy in migraineurs hamper the clinical application of tVNS. Therefore, it is necessary to identify biomarkers to discriminate migraineurs as well as select patients suitable for tVNS treatment.MethodsA total of 70 patients diagnosed with migraine without aura (MWoA) and 70 matched healthy controls were recruited to complete fMRI scanning. In study 1, the fractional amplitude of low-frequency fluctuation (fALFF) of each voxel was calculated, and the differences between healthy controls and MWoA were compared. Meaningful voxels were extracted as features for discriminating model construction by a support vector machine. The performance of the discriminating model was assessed by accuracy, sensitivity, and specificity. In addition, a mask of these significant brain regions was generated for further analysis. Then, in study 2, 33 of the 70 patients with MWoA in study 1 receiving real tVNS were included to construct the predicting model in the generated mask. Discriminative features of the discriminating model in study 1 were used to predict the reduction of attack frequency after a 4-week tVNS treatment by support vector regression. A correlation coefficient between predicted value and actual value of the reduction of migraine attack frequency was conducted in 33 patients to assess the performance of predicting model after tVNS treatment. We vislized the distribution of the predictive voxels as well as investigated the association between fALFF change (post-per treatment) of predict weight brain regions and clinical outcomes (frequency of migraine attack) in the real group.ResultsA biomarker containing 3,650 features was identified with an accuracy of 79.3%, sensitivity of 78.6%, and specificity of 80.0% (p &lt; 0.002). The discriminative features were found in the trigeminal cervical complex/rostral ventromedial medulla (TCC/RVM), thalamus, medial prefrontal cortex (mPFC), and temporal gyrus. Then, 70 of 3,650 discriminative features were identified to predict the reduction of attack frequency after tVNS treatment with a correlation coefficient of 0.36 (p = 0.03). The 70 predictive features were involved in TCC/RVM, mPFC, temporal gyrus, middle cingulate cortex (MCC), and insula. The reduction of migraine attack frequency had a positive correlation with right TCC/RVM (r = 0.433, p = 0.021), left MCC (r = 0.451, p = 0.016), and bilateral mPFC (r = 0.416, p = 0.028), and negative with left insula (r = −0.473, p = 0.011) and right superior temporal gyrus/middle temporal gyrus (r = −0.684, p &lt; 0.001), respectively.ConclusionsBy machine learning, the study proposed two potential biomarkers that could discriminate patients with MWoA and predict the efficacy of tVNS in reducing migraine attack frequency. The pivotal features were mainly located in the TCC/RVM, thalamus, mPFC, and temporal gyrus.