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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

Network Neuroscience of Human Multitasking: Local Features Matter

The neural basis of multitasking costs is subject to continuing debate. Cognitive theories assume that overlap of task representations may lead to between-task crosstalk in concurrent task processing and thus requires cognitive control. Recent research suggests that modality-based crosstalk contributes to multitasking costs, involving central overlap of modality-specific representations. Consistently increased costs for specific modality pairings (visual-vocal and auditory-manual vs. visual-manual and auditory-vocal) were demonstrated (modality-compatibility effect), which were recently linked to representational overlap in the auditory cortex. However, it remains unclear whether modality-based crosstalk emerges from overlapping patterns of global brain connectivity and whether resolving it requires additional involvement of cognitive control as reflected in the fronto-parietal control network. This preregistered functional imaging study investigates these questions in 64 healthy, young human adults. Specifically, we focus on the modality-compatibility effect in multitasking by employing functional connectivity (FC) analysis. First, we tested the FC similarity between the single-task networks. Second, we compared the strength of the control network in whole-brain FC between dual tasks. We found no evidence for different FC similarities of single-task networks between modality pairings and no additional involvement of the control network during dual tasks. However, post-hoc connectivity analysis revealed a brain-behavior correlation for the modality-compatibility effect in dual tasks. This effect was locally restricted to FC between lateral frontal and sensory auditory regions, providing evidence for the modality-based crosstalk theory. More generally, the findings suggest that robust behavioral differences in multitasking are not necessarily related to global functional connectivity differences but to local connectivity changes. SignificanceOur lives are dominated by multitasking. Understanding the neurocognitive mechanisms of multitasking and its associated limitations is relevant for safety-relevant consequences. Here, we investigate functional brain-connectivity patterns associated with modality-based crosstalk in multitasking, describing unintentional exchange of information between two tasks, leading to robust dual-task costs. Our network analyses revealed no significant difference between single or dual tasks with varying degrees of modality overlap on the whole-brain level. However, specific connections between cognitive control-related frontal and sensory-related regions were associated with individual multitasking performance, emphasizing that behavioral differences can arise from specific neural interactions rather than from widespread network reconfigurations. These findings advance our understanding of the underlying mechanisms responsible for modality-based crosstalk, providing practical implications for human-machine interactions.

neuroscience↗

bioRxiv2025

Evaluating scientific theories as predictive models in language neuroscience

Modern data-driven encoding models are highly effective at predicting brain responses to language stimuli. However, these models struggle to explain the underlying phenomena, i.e. what features of the stimulus drive the response? We present Question Answering encoding models, a method for converting qualitative theories of language selectivity into highly accurate, interpretable models of brain responses. QA encoding models annotate a language stimulus by using a large language model to answer yes-no questions corresponding to qualitative theories. A compact QA encoding model that uses only 35 questions outperforms existing baselines at predicting brain responses in both fMRI and ECoG data. The model weights also provide easily interpretable maps of language selectivity across cortex; these maps show quantitative agreement with meta-analyses of the existing literature and selectivity maps identified in a follow-up fMRI experiment. These results demonstrate that LLMs can bridge the widening gap between qualitative scientific theories and data-driven models.

neuroscience↗

bioRxiv2025

Enabling wider access to human molecular neuroscience research in pain: A simple preservation method for human dorsal root ganglion neurons in Hibernate A media

The use of human dorsal root ganglion (DRG) from organ donors opens the door for research into the molecular biology and physiology of human nociceptors; however, there are barriers to working with this tissue including logistical difficulties and limited access. We present an approach using Hibernate media to temporarily store either whole DRGs or dissociated DRG neurons prior to culturing and functional testing. Dissociation of DRGs following temporary storage (4-16hrs) in Hibernate media resulted in similar neuronal and immune cell yield as acutely dissociated DRGs. Neurons derived from DRGs stored in Hibernate media prior to dissociation exhibited similar electrophysiological properties and capsaicin responses as acutely dissociated DRG neurons. Similarly, neurons from acutely dissociated DRGs stored in Hibernate media (>24hrs) and shipped to geographically distant laboratories produced neuronal cultures displaying comparable electrophysiological properties as acutely cultured neurons. This approach overcomes insurmountable logistical burdens and increases access to freshly recovered human DRGs.

neuroscience↗

bioRxiv2025

netneurotools: a trainee-oriented approach to network neuroscience

Brain imaging is an increasingly inter-disciplinary field, encompassing multiple data types and multiple analytic traditions. Projects typically involve many moving parts, such as building customized preprocessing pipelines, transforming between data formats, preparing datasets for analysis, and ultimately displaying results. The field is conventionally built on highly specialized software packages that solve these individual challenges well, but are not necessarily designed to be interoperable. Trainees new to the field are therefore often left to come up with isolated heuristics and workarounds to complete a project. Here we present a way to navigate the increasingly complex informatics ecosystem of brain imaging. netneurotools is our labs internal Python toolkit that has been continuously developed and maintained by the labs trainees. The philosophy of the toolkit is that it should be the Swiss army knife of the lab: functions and routines that we often use but that are not part of any established pipeline or package. Since its inception, the toolkit has been open and welcomes contribution from neuroscientists across the globe. netneurotools presents a necessary counterweight to out-of-the-box software packages and highlights the importance of smaller, ad hoc functions for implementing projects. By opening a window into the inner workings of a lab, netneurotools also presents an opportunity to begin a new type of discourse among groups and establish tangible links within the community.

neuroscience↗

bioRxiv2025

The neuroscience of highly stable, positive, and refined states of consciousness during jhana-type advanced concentration absorption meditation (ACAM-J)

Humans can experience a rich array of conscious experience, including highly stable, positive, and refined states of non-ordinary perception that can be elicited through advanced meditation. Here, we present the first group-level, ultra-high-field (7T) fMRI study of jhana advanced concentration absorption meditation (ACAM-J). We combined local (regional homogeneity), mesoscale (connectivity gradients), and global (geometric eigenmodes) human brain mapping with diverse and detailed phenomenology, meditative traits, behaviorally assessed cognitive functions, and extensive publicly available psychobehavioral affinity maps. Across eight successive states of ACAM-J, we observed reproducible neural trajectories marked by anterior-to-posterior reorganization, flattening of cortical hierarchies, nonlinear reconfiguration of global harmonics, and tight coupling between brain metrics and equanimity, attentional stability, and behavior. Neurosynth decoding further associate ACAM-J with reduced suffering-related processes and more with attentional monitoring. These findings suggest ACAM-J is a distinct, structured mode of awareness that persists despite radically reduced narrative thought and sensory content. Our findings also inform boundary conditions for current models of consciousness. More broadly, this research indicates that advanced meditation is a powerful framework for understanding psychological transformation, and for potential new opportunities for supporting human well-being and flourishing.

neuroscience↗

bioRxiv2018

Individual differences in cortical processing speed predict cognitive abilities: a model-based cognitive neuroscience account

Previous research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information-processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used a hierarchical Bayesian cognitive modeling approach to test the hypothesis that individual differences in the velocity of evidence accumulation mediate the relationship between neural processing speed and cognitive abilities. We found that a higher neural speed predicted both the velocity of evidence accumulation across behavioral tasks as well as cognitive ability test scores. However, only a small part of the association between neural processing speed and cognitive abilities was mediated by individual differences in the velocity of evidence accumulation. The model demonstrated impressive forecasting abilities by predicting 36% of individual variation in cognitive ability test scores in an entirely new sample solely based on their electrophysiological and behavioral data. Our results suggest that individual differences in neural processing speed might affect a plethora of higher-order cognitive processes, that only in concert explain the large association between neural processing speed and cognitive abilities, instead of the effect being entirely explained by differences in evidence accumulation speeds.

neuroscience↗

bioRxiv2018

Establishment of the miniature fish species Danionella translucida as a genetically and optically tractable neuroscience model

Integrative studies of the diverse neuronal networks that govern social behavior are hindered by the lack of methods to record neural activity comprehensively across the entire brain. The recent development of the miniature fish Danionella cerebrum as a model organism offers one potential solution, as the small size and optical transparency of these animals makes it possible to visualize circuit activity throughout the nervous system1-4. Here, we establish the feasibility of using Danionella as a model for social behavior and socially reinforced learning by showing that adult fish exhibit strong affiliative tendencies, and that social interactions can serve as the reinforcer in an appetitive conditioning paradigm. Fish exhibited an acute ability to identify conspecifics and distinguish them from closely related species, which was mediated by both visual and particularly olfactory cues. These behaviors were abolished by pharmacological and genetic interference with oxytocin signaling, demonstrating the conservation of key neural mechanisms observed in other vertebrates5-11. Our work validates Danionella as a tool for understanding the social brain in general, and its modulation by neuropeptide signaling in particular.

neuroscience↗

bioRxiv2019

A toolbox of nanobodies developed and validated for diverse neuroscience research applications

Nanobodies (nAbs) are small, minimal antibodies that have distinct attributes that make them uniquely suited for certain biomedical research, diagnostic and therapeutic applications. Prominent uses include as intracellular antibodies or intrabodies to bind and deliver cargo to specific proteins and/or subcellular sites within cells, and as nanoscale immunolabels for enhanced tissue penetration and improved spatial imaging resolution. Here, we report the generation and validation of nAbs against a set of proteins prominently expressed at specific subcellular sites in brain neurons. We describe a novel hierarchical validation pipeline to systematically evaluate nAbs isolated by phage display for effective and specific use as intrabodies and immunolabels in mammalian cells including brain neurons. These nAbs form part of a robust toolbox for targeting proteins with distinct and highly spatially-restricted subcellular localization in mammalian brain neurons, allowing for visualization and/or modulation of structure and function at those sites.

neuroscience↗

bioRxiv2019

Disentangling competence from performance in behavioral measures of learning: A lesson for cognitive neuroscience

A crucial question in skill learning research is how instruction affects the performance or the underlying representations. However, a little is known about its effect on one critical aspect of skill leaning, namely, picking-up statistical regularities. More specifically, how pre-learning speed vs. accuracy instructions affect the acquisition of non-adjacent second-order dependencies. Here, we trained two groups of participants on an implicit probabilistic sequence learning task: one group focusing on being fast and the other on being accurate. As expected, we detected strong instruction effect: accuracy instruction resulted in a nearly errorless performance, while speed instruction caused short reaction times. Despite the differences in the average reaction times and accuracy scores, we found a similar level of statistical learning in the training phase. After the training phase, we tested the two groups under the same instruction (focusing on both speed and accuracy), and they showed comparable performance, suggesting a similar level of underlying statistical representations. Our findings support that skill learning can result in robust representations, and they highlight that this form of knowledge may appear with almost errorless performance.

neuroscience↗

bioRxiv2026

Application of Explainable AI in Neuroscience: Enhancing Autism Screening

The main challenges in the life of a child with autism are difficulties in communication, behavior, and social interaction. Early diagnosis of this neurodevelopmental disorder improves patient outcomes by enabling more effective, personalized interventions. This diagnosis can sometimes be difficult, especially in very young children. Non-invasive, relatively accessible, and able to reflect neural function in real time, electroencephalography (EEG) shows promise in the detection of Autism spectrum disorders (ASD). However, because EEG data is still difficult for experts to understand, machine learning and artificial intelligence (AI) are beginning to be used in this field as well. In this paper, a ResNet+BiLSTM hybrid deep network was applied and achieved high accuracy in distinguishing individuals with autism from neurotypical subjects. Since AI models typically provide predictions without clear explanations, this study employs explainable AI (XAI) methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to clarify their decision-making.Delta, theta, alpha, beta, and gamma waves, as well as ERP components P100, N100, P200, MMN, and P600, were analyzed in the two neurotypical and autistic groups that were compared in this study using EEG recordings. By integrating SHAP and LIME, the system achieved both accurate classification and transparent explanations, pointing to EEG- and ERP-based features as reliable biomarkers for ASD.

neuroscience↗

bioRxiv2026

A Scalable fMRI Estimate of Basal Ganglia Brain Tissue Iron for Use in Developmental and Translational Neuroscience

Dopaminergic (DA) function and basal ganglia neurobiology are central to reward learning, motivation, and cognitive control, and dysregulation of these systems contributes to neuropsychiatric conditions that emerge during development. Adolescence is marked by profound reorganization of DAergic basal ganglia circuitry, yet direct in vivo assessment of the DA system remains limited in youth. Brain tissue iron is a developmentally sensitive marker of DA-related neurobiology that can be measured non-invasively via magnetic resonance imaging (MRI). Iron is an essential co-factor for DA synthesis and a foundational metabolic resource that supports cellular metabolism, myelination, and energetic demands of the basal ganglia. T2*-weighted echo-planar imaging (EPI), collected during functional MRI (fMRI), is sensitive to magnetic susceptibility of non-heme brain iron. Leveraging this property, we demonstrate the validity and broad applicability of an iron-sensitive metric that can be derived from conventional single-echo fMRI: {Delta}R2*. In a longitudinal developmental dataset (N = 151; age range 12-31), {Delta}R2* showed high reliability, strong longitudinal stability, and validity via robust convergence with established quantitative relaxometry-based iron measures (R2* and R2). Critically, {Delta}R2* can be retrospectively estimated from extant fMRI data and derived in large-scale consortium data repositories, demonstrated here in the Adolescent Brain and Cognitive Development (ABCD) baseline cohort (N = 8,366; ages 9-11). We show that {Delta}R2* captures known age-related increases in basal ganglia iron, highlighting neurodevelopmental sensitivity at population-scale. Together, these findings establish {Delta}R2* as a reliable, widely accessible marker of basal ganglia iron, enabling scalable investigation of lifespan trajectories and neuropsychiatric risk in existing and future datasets.

neuroscience↗

bioRxiv2026

A Systems Neuroscience Approach Identifies IL1B-CASP3 Signaling as a Molecular Link Between Polystyrene Exposure and Alzheimer's Disease

The widespread presence of microplastics and nanoplastics has emerged as a significant environmental concern, with increasing evidence suggesting potential adverse effects on neurological health. However, the molecular mechanisms linking polystyrene exposure to Alzheimers disease (AD) remain poorly understood. In this study, an integrative systems biology framework was employed to investigate the molecular interplay between environmental polystyrene exposure and AD pathogenesis. AD-associated genes were retrieved from the Comparative Toxicogenomics Database (CTD) and DisGeNET, while polystyrene-responsive genes were obtained from CTD. Integration of these datasets identified 16 shared genes potentially connecting polystyrene exposure with AD. Transcriptomic analysis of the hippocampal dataset GSE29378 revealed significant differential expression of several overlapping genes between AD and healthy controls. Functional enrichment analyses demonstrated that these genes are predominantly involved in oxidative stress, inflammatory signaling, apoptosis, and synaptic function, all of which are central to AD pathology. Weighted gene co-expression network analysis (WGCNA) further identified disease-associated modules containing multiple intersecting genes strongly correlated with AD clinical traits. Protein-protein interaction analysis highlighted IL1B, CASP3, BCL2, ACHE, and APOE as key hub genes, indicating their potential roles in integrating environmental stress responses with neurodegenerative pathways. Independent validation using the GSE48350 dataset confirmed the robust diagnostic performance of several hub genes in discriminating AD from control samples. Collectively, these findings suggest that environmental polystyrene exposure may promote AD progression through neuroinflammation, oxidative stress, apoptosis, and synaptic dysfunction, providing novel mechanistic insights and identifying promising molecular targets for future experimental, clinical, and epidemiological investigations.

neuroscience↗

bioRxiv2026

Breadboard Neuroscience: Exploring Neuronal Dynamics and Computation with Simple Electronic Circuits

Simple electronic circuits provide a tangible means of exploring the electrical principles underlying neuronal signalling and computation. Here is presented a sequence of experiments using inexpensive, readily available components assembled on solderless breadboards to investigate passive membrane properties and analogue neural computation. A resistor-capacitor membrane model demonstrates how membrane resistance, capacitance, and excitatory conductance determine response amplitude and charging and discharging time constants. Multiple inputs illustrate sublinear summation and shunting inhibition. Connecting membrane compartments through axial resistances reproduces spatial attenuation and temporal delay. We then construct an elementary motion detector to demonstrate how simple electrical operations can produce direction-selective responses to visual motion. Together, this approach offers an accessible and flexible platform for exploring neuronal biophysics, allowing students to relate mathematical descriptions to observable electrical behaviour whilst developing practical electronics skills and engaging creatively in the design of their own experiments.

neuroscience↗

bioRxiv2020

NeuroSuites-BNs: An open web framework for massive Bayesian networks focused on neuroscience

BayeSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks (BNs) that can scale to tens of thousands of nodes while providing fast and friendly user experience. All the necessary features that enable this are reviewed in this paper; these features include scalability, extensibility, interoperability, ease of use, and interpretability. Scalability is the key factor in learning and processing massive networks within reasonable time; for a maintainable software open to new functionalities, extensibility and interoperability are necessary. Ease of use and interpretability are fundamental aspects of model interpretation, fairly similar to the case of the recent explainable artificial intelligence trend. We present the capabilities of our proposed framework by highlighting a real example of a BN learned from genomic data obtained from Allen Institute for Brain Science. The extensibility properties of the software are also demonstrated with the help of our BN-based probabilistic clustering implementation, together with another genomic-data example.

bioinformatics↗

bioRxiv2023

A data-efficient deep learning tool for scRNA-Seq label transfer in neuroscience

Large single-cell RNA datasets have contributed to unprecedented biological insight. Often, these take the form of cell atlases and serve as a reference for automating cell labeling of newly sequenced samples. Yet, classification algorithms have lacked the capacity to accurately annotate cells, particularly in complex datasets. Here we present SIMS (Scalable, Interpretable Ma-chine Learning for Single-Cell), an end-to-end data-efficient machine learning pipeline for discrete classification of single-cell data that can be applied to new datasets with minimal coding. We benchmarked SIMS against common single-cell label transfer tools and demonstrated that it performs as well or better than state of the art algorithms. We then use SIMS to classify cells in one of the most complex tissues: the brain. We show that SIMS classifies cells of the adult cerebral cortex and hippocampus at a remarkably high accuracy. This accuracy is maintained in trans-sample label transfers of the adult hu-man cerebral cortex. We then apply SIMS to classify cells in the developing brain and demonstrate a high level of accuracy at predicting neuronal sub-types, even in periods of fate refinement, shedding light on genetic changes affecting specific cell types across development. Finally, we apply SIMS to single cell datasets of cortical organoids to predict cell identities and unveil genetic variations between cell lines. SIMS identifies cell-line differences and misannotated cell lineages in human cortical organoids derived from different pluripotent stem cell lines. When cell types are obscured by stress signals, label transfer from primary tissue improves the accuracy of cortical organoid annotations, serving as a reliable ground truth. Altogether, we show that SIMS is a versatile and robust tool for cell-type classification from single-cell datasets.

bioinformatics↗

bioRxiv2024

Time-Domain Diffuse Optical Tomography for Precision Neuroscience

AbstractRecent years have witnessed a rise in research utilizing neuroimaging for precision neuromedicine, but clinical translation has been hindered by scalability and cost. Time Domain functional Near Infrared Spectroscopy (TD-fNIRS), the gold standard of optical neuroimaging techniques, offers a unique opportunity in this domain since it provides superior depth sensitivity and enables resolution of absolute properties unlike its continuous wave counterparts. However, current TD systems have limited commercial availability, slow sampling rates, and sparse head coverage. Our team has overcome the technical challenges involved in developing a whole-head time-domain diffuse optical tomography (TD-DOT) system. Here, we present the system characterization results using standardized protocols and compare them to the state-of-the-art. Furthermore, we showcase the system performance in retrieving cortical activation maps during standard hemodynamic, sensory, and motor tasks. A combination of the system performance, signal quality, and ease-of-use can enable future studies aimed at investigating TD-DOT clinical applications.

bioengineering↗

bioRxiv2025

BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience

Human-induced pluripotent stem cells (iPSCs) offer transformative potential for biomedical research, with iPSC-derived organoids providing more physiologically relevant models than traditional 2D cell cultures. Among these, brain organoids are particularly valuable for drug screening, disease modeling, and investigations into molecular pathways. Accurate representation of brain morphology is critical, as more complex organoid structures better mimic the human brain. Deep learning (DL) and machine learning (ML) approaches have become integral to analyzing organoid morphology, yet tools for comprehensive, time-resolved assessments are scarce. Here, we introduce BrAIn, a DL-based application for analyzing the developmental progression of brain organoids. BrAIn tracks their evolution from embryoid bodies and quantifies parameters including area, Feret diameter, perimeter, roundness, and circularity. It also classifies budding and abnormal morphologies of 3D organoids and detects monolayer neural rosette structures, key features of neuronal differentiation. Designed with accessibility in mind, BrAIn provides a no-code interface, enabling researchers of all technical backgrounds to conduct advanced morphological analyses with ease. Our study demonstrates the application of BrAIn to evaluate the effects of different growth conditions - static, orbital shaker, and microfluidic chip-based - on brain organoid development. Orbital shaker cultures resulted in the largest organoids, while chip-based systems achieved more homogeneous growth. Both conditions produced organoids with greater morphological complexity compared to static culture. BrAIn emerges as a robust, user-friendly tool to quantify brain organoid development and explore how versatile growth conditions influence their morphology and maturation.

bioengineering↗

bioRxiv2025

EthoPy: Reproducible Behavioral Neuroscience Made Simple

As brain activity is tightly coupled to behavior, an accurate understanding of neural function necessitates consideration of behavioral tasks that capture the complexity and variety animals encounter. Nevertheless, animal training for behavioral experiments is often labor-intensive, costly, and difficult to standardize. To overcome these challenges, we developed EthoPy, an open-source, Python-based behavioral control framework that integrates stimulus presentation, hardware management, and data logging. EthoPy supports diverse behavioral paradigms, stimulus modalities, and experimental systems, from homecage to head-fixed configurations, while operating on affordable hardware, such as Raspberry Pi. Its modular architecture and database integration enable scalable, high-throughput automatic behavioral training with minimal experimenter involvement while ensuring reproducibility through comprehensive metadata tracking. By automating training workflows, EthoPy makes it feasible to implement sophisticated behavioral paradigms that are traditionally difficult to achieve. EthoPy thus provides an accessible, extensible framework to study behavior and the underlying neural activity.

animal behavior and cognition↗