Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

Ilan Gold, Felix Fischer, Lucas Arnoldt et al.

The scale of biological datasets now routinely exceeds system memory, making data access rather than model computation the primary bottleneck in training machine-learning models. This bottleneck is particularly acute in biology, where widely used community data formats must support heterogeneous metadata, sparse and dense assays, and downstream analysis within established computational ecosystems. Here we present annbatch, a mini-batch loader native to anndata that enables out-of-core training directly on disk-backed datasets. Across single-cell transcriptomics, microscopy and whole-genome sequencing benchmarks, annbatch increases loading throughput by up to an order of magnitude and shortens training from days to hours, while remaining fully compatible with the scverse ecosystem. Annbatch establishes a practical data-loading infrastructure for scalable biological AI, allowing increasingly large and diverse datasets to be used without abandoning standard biological data formats. Github: https://github.com/scverse/annbatch

Shaowei Li, Jianying Zhang, Jiehui Li et al.

Optogenetics and chemogenetics are emerging neuromodulation techniques that have attracted significant attention in recent years. These techniques enable the precise control of specific neuronal types and neural circuits, allowing researchers to investigate the cellular mechanisms underlying depression. The advancement in these techniques has significantly contributed to the understanding of the neural circuits involved in depression; when combined with other emerging technologies, they provide novel therapeutic targets and diagnostic tools for the clinical treatment of depression. Additionally, these techniques have provided theoretical support for the development of novel antidepressants. This review primarily focuses on the application of optogenetics and chemogenetics in several brain regions closely associated with depressive-like behaviors in rodent models, such as the ventral tegmental area, nucleus accumbens, prefrontal cortex, hippocampus, dorsal raphe nucleus, and lateral habenula and discusses the potential and challenges of optogenetics and chemogenetics in future research. Furthermore, this review discusses the potential and challenges these techniques pose for future research and describes the current state of research on sonogenetics and odourgenetics developed based on optogenetics and chemogenetics. Specifically, this study aimed to provide reliable insights and directions for future research on the role of optogenetics and chemogenetics in the neural circuits of depressive rodent models.

Sushmitha Kota, Rakesh Jayantilal Shah, Hitesh Chandrakant Sheth

Background: Adverse childhood experiences (ACEs) are the potentially traumatic events that occur in childhood. The past literature has shown ACEs that have been linked with negative physical and mental health outcomes in adulthood that may influence on quality of life (QoL) in adulthood. This study assesses the association of various types of ACEs with QoL in adulthood among offspring of schizophrenia patients. Materials and Methods: The study was conducted at hospital for mental health, Vadodara, for 6 months using ACEs–International Questionnaires and World Health Organization–BREF QoL in 66 participants, and independent t-test and Mann–Whitney U-test were used to assess the association between domains of childhood adversities with domains of QoL using SPSS software version 20 for their analysis. Results: The overall childhood adversities noticed were 87.88% (n = 58), and the mean scores of physical domain, psychological domain, social domain, and environmental domain (domains of QoL) were 53.59 ± 13.38, 46.12 ± 9.29, 39.94 ± 19.98, and 49.41 ± 15.44, respectively. We found that there was a significant association of physical abuse, household treating violently, physical neglect, and bullying with physical domain and psychological domain; bullying and household treating violently with the social domain and physical abuse, physical neglect, household treating violently and bullying with environmental domain and bullying with health-related QoL. Conclusions: There is a negative correlation of childhood adversities faced by adult offsprings of schizophrenia patients with QoL. This emphasizes the significance of childhood adversities faced by children of schizophrenia patients.

J. Isern Cabrero, A. Pellisé Guinjoan, P. Rodríguez Parajua et al.

Ylvi Müller, Sebastian Bühner, Victoria Fincke et al.

Abstract Atypical teratoid rhabdoid tumors (AT/RT) are characterized by a poor prognosis and a manifestation within the first 2 years of life. Genetic hallmark of these tumors is the homozygous inactivation of SMARCB1 or, in some rare cases, of SMARCA4. While heterozygous pathogenic variants of SMARCA4 have been described, inter alia, in the context of other CNS malignancies such as medulloblastoma or glioblastoma, the co-occurrence of pathogenic variants in both, SMARCB1 and SMARCA4, in the same AT/RT has to our knowledge not been reported previously. Liquid biopsy, a rapidly developing and promising technique measuring cell-free DNA (cfDNA) in body fluids such as the cerebrospinal fluid (CSF), offers a minimally invasive method to assess disease status. It has yet to be established as a standard procedure in the diagnostic workup of CNS tumors. We present the case of a three-year-old male diagnosed with an AT/RT that exhibits both biallelic alterations of SMARCB1 due to a frameshift mutation and loss of heterozygosity as well as a heterozygous missense variant in SMARCA4 presenting with early disease progression. We employed liquid biopsy successfully to monitor disease progression throughout treatment and the subsequent relapse. We highlight the ramifications that simultaneous alterations in two chromatin-modifying genes may have for tumor biology and clinical course.

Yibo Qiu, Zan Huang, Zhiyu Wang et al.

Large language models (LLMs) and vision-language models (VLMs) have the potential to transform biological research by enabling autonomous experimentation. Yet, their application remains constrained by rigid protocol design, limited adaptability to dynamic lab conditions, inadequate error handling, and high operational complexity. Here we introduce BioMARS (Biological Multi-Agent Robotic System), an intelligent platform that integrates LLMs, VLMs, and modular robotics to autonomously design, plan, and execute biological experiments. BioMARS uses a hierarchical architecture: the Biologist Agent synthesizes protocols via retrieval-augmented generation; the Technician Agent translates them into executable robotic pseudo-code; and the Inspector Agent ensures procedural integrity through multimodal perception and anomaly detection. The system autonomously conducts cell passaging and culture tasks, matching or exceeding manual performance in viability, consistency, and morphological integrity. It also supports context-aware optimization, outperforming conventional strategies in differentiating retinal pigment epithelial cells. A web interface enables real-time human-AI collaboration, while a modular backend allows scalable integration with laboratory hardware. These results highlight the feasibility of generalizable, AI-driven laboratory automation and the transformative role of language-based reasoning in biological research.

Ana M. Lyons, Saul Kato

We present the case for developing the tardigrade (Hypsibius exemplaris) into a model organism for systems neuroscience. These microscopic, transparent animals (~300-500 microns) are among the smallest known to possess both limbs (eight) and eyes (two), with a nervous system of only a few hundred neurons organized into a multi-lobed brain, ventral nerve cord, and a series of ganglia along the body. Despite their neuroanatomical simplicity, tardigrades exhibit complex behaviors, including multi-limbed walking gaits, individual limb grasping, phototaxis, and transitions between active and dormant states. These behaviors position tardigrades as a uniquely powerful system for addressing certain fundamental questions in systems neuroscience, such as: How do nervous systems coordinate multi-limbed behaviors? How are top-down and bottom-up motor control systems integrated? How is stereovision-guided navigation implemented? What mechanisms underlie neural resilience and recovery during environmental stress? We review current knowledge of tardigrade neuroanatomy, behavior, and genomics, and we identify opportunities and challenges for leveraging their unique biology. We propose developing essential neuroscientific tools for tardigrades, including genetic engineering and live neuroimaging, alongside behavioral assays linking neural activity to outputs. Leveraging their evolutionary ties to Caenorhabditis elegans and Drosophila melanogaster, we can adapt existing toolkits to accelerate tardigrade research - providing a bridge between simpler invertebrate systems and more complex neural architectures.

Francesco Madeddu, Lucia Testa, Gianluca De Carlo et al.

The intrinsic complexity of human biology presents ongoing challenges to scientific understanding. Researchers collaborate across disciplines to expand our knowledge of the biological interactions that define human life. AI methodologies have emerged as powerful tools across scientific domains, particularly in computational biology, where graph data structures effectively model biological entities such as protein-protein interaction (PPI) networks and gene functional networks. Those networks are used as datasets for paramount network medicine tasks, such as gene-disease association prediction, drug repurposing, and polypharmacy side effect studies. Reliable predictions from machine learning models require high-quality foundational data. In this work, we present a comprehensive multi-purpose biological knowledge graph constructed by integrating and refining multiple publicly available datasets. Building upon the Drug Repurposing Knowledge Graph (DRKG), we define a pipeline tasked with a) cleaning inconsistencies and redundancies present in DRKG, b) coalescing information from the main available public data sources, and c) enriching the graph nodes with expressive feature vectors such as molecular fingerprints and gene ontologies. Biologically and chemically relevant features improve the capacity of machine learning models to generate accurate and well-structured embedding spaces. The resulting resource represents a coherent and reliable biological knowledge graph that serves as a state-of-the-art platform to advance research in computational biology and precision medicine. Moreover, it offers the opportunity to benchmark graph-based machine learning and network medicine models on relevant tasks. We demonstrate the effectiveness of the proposed dataset by benchmarking it against the task of drug repurposing, PPI prediction, and side-effect prediction, modeled as link prediction problems.

Ziheng Zhang, Xinyue Ma, Arpita Chowdhury et al.

This work investigates descriptive captions as an additional source of supervision for biological multimodal foundation models. Images and captions can be viewed as complementary samples from the latent morphospace of a species, each capturing certain biological traits. Incorporating captions during training encourages alignment with this shared latent structure, emphasizing potentially diagnostic characters while suppressing spurious correlations. The main challenge, however, lies in obtaining faithful, instance-specific captions at scale. This requirement has limited the utilization of natural language supervision in organismal biology compared with many other scientific domains. We complement this gap by generating synthetic captions with multimodal large language models (MLLMs), guided by Wikipedia-derived visual information and taxon-tailored format examples. These domain-specific contexts help reduce hallucination and yield accurate, instance-based descriptive captions. Using these captions, we train BioCAP (i.e., BioCLIP with Captions), a biological foundation model that captures rich semantics and achieves strong performance in species classification and text-image retrieval. These results demonstrate the value of descriptive captions beyond labels in bridging biological images with multimodal foundation models.

Florin Leon

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.

Steven A. Frank

Organisms gain by anticipating future changes in the environment. Those environmental changes often follow stochastic trends. The greater the slope of the trend, the more likely the trend's momentum carries the future trend in the same direction. This article presents a simple biological circuit that measures the momentum, providing a prediction about future trend. The circuit calculates the momentum by the difference between a short-term and a long-term exponential moving average. The time lengths of the two moving averages can be adjusted by changing the decay rates of state variables. Different time lengths for those averages trade off between errors caused by noise and errors caused by lags in predicting a change in the direction of the trend. Prior studies have emphasized circuits that make similar calculations about trends. However, those prior studies embedded their analyses in the details of particular applications, obscuring the simple generality and wide applicability of the approach. The model here contributes to the topic by clarifying the great simplicity and generality of anticipation for stochastic trends. This article also notes that, in financial analysis, the difference between moving averages is widely used to predict future trends in asset prices. The financial measure is called the moving average convergence-divergence (MACD) indicator. Connecting the biological problem to financial analysis opens the way for future studies in biology to exploit the variety of highly developed trend models in finance.

Eliana Brehaut, Dipika Neupane, Brooke Levis et al.

Abstract Objectives Optimal cutoff thresholds are selected to separate ‘positive’ from ‘negative’ screening results. We evaluated how depression screening tool studies select optimal cutoffs. Methods We included studies from previously conducted meta‐analyses of Patient Health Questionnaire‐9, Edinburgh Postnatal Depression Scale, or Hospital Anxiety and Depression Scale—Depression accuracy. Outcomes included whether an optimal cutoff was selected, method used, recommendations made, and reporting guideline and protocol citation. Results Of 212 included studies, 172 (81%) attempted to identify an optimal cutoff, and 147 of these 172 (85%) reported one or more methods. Methods were heterogeneous with Youden's J (N = 35, 23%) most common. Only 23 of 147 (16%) studies described a rationale for their method. Rationales focused on balancing sensitivity and specificity without describing why desirable. 131 of 172 studies (76%) identified an optimal cutoff other than the standard; most did not make use recommendations (N = 56; 43%) or recommended using a non‐standard cutoff (N = 53; 40%). Only 4 studies cited a reporting guideline, and 4 described a protocol with optimal cutoff selection methods, but none used the protocol method in the published study. Conclusions Research is needed to guide how selection of cutoffs for depression screening tools can be standardized and reflect clinical considerations.

Kazuma DOI, Satoshi TANI, Toshiyuki OKAZAKI et al.

Cylindrical cages were known to cause subsidence after anterior cervical discectomy and fusion (ACDF); hence, they were gradually replaced by box-shaped cages. However, this phenomenon has been inconclusive due to limited information and short-term results. Therefore, this study aimed to clarify risk factors for subsidence after ACDF using titanium double cylindrical cages with mid-term follow-up periods. This retrospective study included 49 patients (76 segments) diagnosed with cervical radiculopathy or myelopathy caused by disc herniation, spondylosis, and ossification of the posterior longitudinal ligament. These patients underwent ACDF using these cages from January 2016 to March 2020 in a single institution. Patient demographics and neurological outcomes were also examined. Subsidence was defined as a 3-mm segmental disc height decrease at the final follow-up lateral X-ray compared to that on the next day postoperatively. Subsidence occurred in 26 of 76 segments (34.7%) within the follow-up periods of approximately three years. Multivariate analysis using a logistic regression model demonstrated that multilevel surgery was significantly associated with subsidence. The majority of patients achieved good clinical outcomes based on the Odom criteria. This study demonstrated that multilevel surgery was the only risk factor of subsidence post-ACDF with double cylindrical cages. Despite the relatively high subsidence rates, the clinical outcome was almost good at least during the mid-term period.

Shubham Saini, Neha Bagri

Cerebrotendinous xanthomatosis is a rare autosomal recessive disorder that occurs due to defective bile acid biosynthesis, causing unusual cholesterol and cholestanol deposition in multiple soft tissues. It is manifested by various neurological and non-neurological symptoms. The characteristic imaging features and clinical symptoms can help to make an early diagnosis and induce timely treatment to prevent neurological sequelae. The authors present two adults with differing clinical symptoms, whose imaging provided pivotal cues in diagnosing cerebrotendinous xanthomatosis.

Maryam Sharifi, Shahrbanoo Oryan, Alireza Komaki et al.

There is growing evidence that the hippocampus comprises diverse neural circuits that exhibit longitudinal variation in their properties, however, the intermediate region of the hippocampus has received comparatively little attention. Therefore, this study was designed to compared short- and long-term synaptic plasticity between the dorsal and intermediate regions of the hippocampus in normal and PTZ-kindled rats. Short-term plasticity was assessed by measuring the ratio of field excitatory postsynaptic potentials’ (fEPSPs) slope in response to paired-pulse stimulation at three different inter-pulse intervals (20, 80, and 160 ms), while long-term plasticity was assessed using primed burst stimulation (PBS). The results showed that the basal synaptic strength differed between the dorsal and intermediate regions of the hippocampus in both control and kindled rats. In the control group, paired-pulse stimulation of Schaffer collaterals resulted in a significantly lower fEPSP slope in the intermediate part of the hippocampus compared to the dorsal region. Additionally, the magnitude of long-term potentiation (LTP) was significantly lower in the intermediate part of the hippocampus compared to the dorsal region. In PTZ-kindled rats, both short-term facilitation and long-term potentiation were impaired in both regions of the hippocampus. Interestingly, there was no significant difference in synaptic plasticity between the dorsal and intermediate regions in PTZ-kindled rats, despite impairments in both regions. This suggests that seizures eliminate the regional difference between the dorsal and intermediate parts of the hippocampus, resulting in similar electrophysiological activity in both regions in kindled animals. Future studies should consider this when investigating the responses of the dorsal and intermediate regions of the hippocampus following PTZ kindling.

Eli Newby, Jorge Gómez Tejeda Zañudo, Réka Albert

Because the attractors of biological networks reflect stable behaviors (e.g., cell phenotypes), identifying control interventions that can drive a system towards its attractors (attractor control) is of particular relevance when controlling biological systems. Driving a network's feedback vertex set (FVS) by node-state override into a state consistent with a target attractor is proven to force every system trajectory to the target attractor, but in biological networks, the FVS is typically larger than can be realistically manipulated. External control of a subset of a biological network's FVS was proposed as a strategy to drive the network to its attractors utilizing fewer interventions; however, the effectiveness of this strategy was only demonstrated on a small set of Boolean models of biological networks. Here, we extend this analysis to ensembles of biologically-inspired Boolean networks. On these models, we use three structural metrics -- PRINCE propagation, modified PRINCE propagation, and CheiRank -- to rank FVS subsets by their predicted attractor control strength. We validate the accuracy of these rankings using three dynamical measures: To Control, Away Control, and logical domain of influence. We also calculate the propagation metrics on effective graphs, which incorporate each Boolean model's functional information into edge weights. While this additional information increases the predicting power of structural metrics, we find that the increase with respect to the unweighted network is limited. The propagation metrics in conjunction with the FVS can be used to identify realizable driver node sets by emulating the dynamics that are prevalent in biological networks. This approach only uses the network's structure, and the driver sets are shown to be robust to the specific dynamical model.

Rachel K. Luu, Markus J. Buehler

The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge has been systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model was finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further enhanced with enhanced reasoning ability, as well as with retrieval-augmented generation to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has been shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model showed impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

Aween Karim, Azad Amen, Waleed Aziz

The aim of this study is to analyze the integrability problem of Lotka--Volterra three species biological system. The system which considered in this work is a biological plausibility or a chemical model. The system has a complex dynamical behavior because it is chaotic system. We, first show that the system is a complete integrable when two of the involved parameters in the system are zero. Second, thorough invariant algebraic surfaces and exponential factors, the nonintegrability problems have been investigated. Particularly, we show the non-existence of polynomial, rational, formal series, and Darboux first integrals when parameters are strictly positive.

Eya Khadhraoui, Sebastian Johannes Müller, Niels Hansen et al.

Abstract Background Dementia with Lewy bodies (DLB) is the second most common dementia type in patients older than 65 years. Its atrophy patterns remain unknown. Its similarities to Parkinson's disease and differences from Alzheimer's disease are subjects of current research. Methods The aim of our study was (i) to form a group of patients with DLB (and a control group) and create a 3D MRI data set (ii) to volumetrically analyze the entire brain in these groups, (iii) to evaluate visual and manual metric measurements of the innominate substance for real-time diagnosis, and (iv) to compare our groups and results with the latest literature. We identified 102 patients with diagnosed DLB in our psychiatric and neurophysiological archives. After exclusion, 63 patients with valid 3D data sets remained. We compared them with a control group of 25 patients of equal age and sex distribution. We evaluated the atrophy patterns in both (1) manually and (2) via Fast Surfers segmentation and volumetric calculations. Subgroup analyses were done of the CSF data and quality of 3D T1 data sets. Results Concordant with the literature, we detected moderate, symmetric atrophy of the hippocampus, entorhinal cortex and amygdala, as well as asymmetric atrophy of the right parahippocampal gyrus in DLB. The caudate nucleus was unaffected in patients with DLB, while all the other measured territories were slightly too moderately atrophied. The area under the curve analysis of the left hippocampus volume ratio (< 3646mm3) revealed optimal 76% sensitivity and 100% specificity (followed by the right hippocampus and left amygdala). The substantia innominata’s visual score attained a 51% optimal sensitivity and 84% specificity, and the measured distance 51% optimal sensitivity and 68% specificity in differentiating DLB from our control group. Conclusions In contrast to other studies, we observed a caudate nucleus sparing atrophy of the whole brain in patients with DLB. As the caudate nucleus is known to be the last survivor in dopamine-uptake, this could be the result of an overstimulation or compensation mechanism deserving further investigation. Its relative hypertrophy compared to all other brain regions could enable an imaging based identification of patients with DLB via automated segmentation and combined volumetric analysis of the hippocampus and amygdala.

P. Gadjradj, S. Rubinstein, W. Peul et al.