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Find bioRxiv neuroscience preprints. Search neural circuits, brain imaging and behavior in indexed metadata; check the original manuscript and its version. Preprints are not peer reviewed.

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At least 181 records · Page 10Linked to original sources
bioRxiv2021

Moving Beyond Processing and Analysis-Related Variation in Neuroscience

When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional MRI. We show that even when handling identical data, inter-pipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low inter-pipeline agreement mainly becomes appreciable when the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially, we show that when inter-pipeline agreement is compromised, so too are the consistency of insights from brainwide association studies. We highlight the importance of comparing analytic configurations, as both widely discussed and commonly overlooked decisions can lead to marked variation.

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bioRxiv2022

A high-speed, modular display system for diverse neuroscience applications

Visual stimulation of animals in the laboratory is a powerful technique for studying sensory control of complex behaviors. Since commercial displays are optimized for human vision, we established a novel display system based on custom-built modular LED panels that provides millisecond refresh, precise synchronization, customizable color combinations, and varied display configurations. This system simplifies challenging experiments. With variants of this display, we probed the speed limits of motion vision and examined the role of color vision in behavioral experiments with tethered flying Drosophila. Using 2-photon calcium imaging, we comprehensively mapped the tuning of visual projection neurons across the flys field of view. Finally, using real-time behavior analysis, we developed low-latency interactive virtual environments and found that flying flies can independently control their navigation along two dimensions. This display system uniquely addresses most technical challenges of small animal vision experiments and is thoroughly documented for replicability.

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bioRxiv2022

Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information processing. To observe the effect of these processes, we introduce the spatially-embedded recurrent neural network (seRNN). seRNNs learn basic task-related inferences while existing within a 3D Euclidean space, where the communication of constituent neurons is constrained by a sparse connectome. We find that seRNNs, similar to primate cerebral cortices, naturally converge on solving inferences using modular small-world networks, in which functionally similar units spatially configure themselves to utilize an energetically-efficient mixed-selective code. As all these features emerge in unison, seRNNs reveal how many common structural and functional brain motifs are strongly intertwined and can be attributed to basic biological optimization processes. seRNNs can serve as model systems to bridge between structural and functional research communities to move neuroscientific understanding forward.

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bioRxiv2022

Simple Models For Neuroscience Research Discoveries: How Often Are These Models Used In Africa

Simple animal model systems such as Drosophila, Zebrafish, and C. Elegans have enabled numerous breakthroughs in understanding human health and disease. Their conserved biological processes, ever-expanding established procedures for handling, and amenability for molecular and genetic manipulation, in addition to the minimal ethical concerns, have made these models preferred choices in several life science disciplines globally. Owing to their cheap maintenance cost, adopting these model systems will help bridge the research gap between Africa and the Global North and contribute to advancing scientific knowledge in African universities through practical sessions. However, the extent to which these models are used across Africa is unknown. Here, we analysed the use of Drosophila, Zebrafish, and C. elegans model systems in scientific publications from African laboratories from the year 2000 to 2021. Of 1851 PubMed-indexed publications in which at least one simple animal model was mentioned, 168 used at least one of these models for the actual investigation. With an average of 21 articles per country, South Africa, Nigeria, Kenya, Egypt, Morocco, and Tunisia contributed 75% of these studies. The remaining 25% were contributed by seven other countries at 2-7 articles per country. From here, we extracted and analysed information on funding and international collaboration. This revealed that 24.4 % of the studies were exclusively funded locally, 28.57 % exclusively funded internationally, 15.5% received both local and international funding, and the rest (31.5%) were unfunded, revealing that there is satisfactory access to funds for simple animal model studies, especially from external funders. By analysing the pattern of collaborations, we show that most of these studies had international collaborations, while very few collaborated within Africa. Our work provides data on the current state of research using simple model systems in African laboratories and argues that incorporating these models will advance biomedical science research in Africa.

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bioRxiv2023

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher- dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets, the LEMON dataset, containing 213 EEG recordings of healthy participants, and the TDBRAIN dataset, containing 1274 EEG recordings, mainly from patients with a psychiatric condition. Additionally, we performed an exploratory analysis of the LEMON dataset that could inspire biomarkers of healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.

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bioRxiv2023

Permutation-based group sequential analyses for cognitive neuroscience

Cognitive neuroscientists have been grappling with two related experimental design problems. First, the complexity of neuroimaging data (e.g. often hundreds of thousands of correlated measurements) and analysis pipelines demands bespoke, non-parametric statistical tests for valid inference, and these tests often lack an agreed-upon method for performing a priori power analyses. Thus, sample size determination for neuroimaging studies is often arbitrary or inferred from other putatively but questionably similar studies, which can result in underpowered designs - undermining the efficacy of neuroimaging research. Second, when meta-analyses estimate the sample sizes required to obtain reasonable statistical power, estimated sample sizes can be prohibitively large given the resource constraints of many labs. We propose the use of sequential analyses to partially address both of these problems. Sequential study designs - in which the data is analyzed at interim points during data collection and data collection can be stopped if the planned test statistic satisfies a stopping rule specified a priori - are common in the clinical trial literature, due to the efficiency gains they afford over fixed-sample designs. However, the corrections used to control false positive rates in existing approaches to sequential testing rely on parametric assumptions that are often violated in neuroimaging settings. We introduce a general permutation scheme that allows sequential designs to be used with arbitrary test statistics. By simulation, we show that this scheme controls the false positive rate across multiple interim analyses. Then, performing power analyses for seven evoked response effects seen in the EEG literature, we show that this sequential analysis approach can substantially outperform fixed-sample approaches (i.e. require fewer subjects, on average, to detect a true effect) when study designs are sufficiently well-powered. To facilitate the adoption of this methodology, we provide a Python package "niseq" with sequential implementations of common tests used for neuroimaging: cluster-based permutation tests, threshold-free cluster enhancement, t-max, F-max, and the network-based statistic with tutorial examples using EEG and fMRI data.

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bioRxiv2023

A cortical surface template for human neuroscience

Neuroimaging data analysis relies on normalization to standard anatomical templates to resolve macroanatomical differences across brains. Existing human cortical surface templates sample locations unevenly because of distortions introduced by inflation of the folded cortex into a standard shape. Here we present the onavg template, which affords uniform sampling of the cortex. We created the onavg template based on openly-available high-quality structural scans of 1,031 brains--25 times more than existing cortical templates. We optimized the vertex locations based on cortical anatomy, achieving an even distribution. We observed consistently higher multivariate pattern classification accuracies and representational geometry inter-subject correlations based on onavg than on other templates, and onavg only needs 3/4 as much data to achieve the same performance compared to other templates. The optimized sampling also reduces CPU time across algorithms by 1.3%-22.4% due to less variation in the number of vertices in each searchlight.

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bioRxiv2023

Ouvrai: Opening access to remote VR studies of human behavioral neuroscience

Modern virtual reality (VR) devices offer 6 degree-of-freedom kinematic data with high spatial and tem-poral resolution, making them powerful tools for research on sensorimotor and cognitive functions. We introduce Ouvrai, an open-source solution that facilitates the design and execution of remote VR studies, capitalizing on the surge in VR headset ownership. This tool allows researchers to develop sophisticated experiments using cutting-edge web technologies like the WebXR Device API for browser-based VR, with-out compromising on experimental design. Ouvrais features include easy installation, intuitive JavaScript templates, a component library managing front- and back-end processes, and a streamlined workflow. It also integrates APIs for Firebase, Prolific, and Amazon Mechanical Turk and provides data processing utilities for analysis. Unlike other tools, Ouvrai remains free, with researchers managing their web hosting and cloud database via personal Firebase accounts. Through three distinct motor learning experiments, we confirm Ouvrais efficiency and viability for conducting remote VR studies.

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bioRxiv2023

Present: flexible neuroscience- and technology-driven frameworks for the study of classroom engagement

Classroom engagements impact on academic success is crucial. However, the contributions of affective, cognitive, and behavioral components of engagement remain uncertain. We conducted two studies using non-invasive, research-based approaches to clarify these contributions. Study 1 employed portable EEG headsets to measure cognitive engagement, in-class quizzes assessed content retention, and post-class subjective questionnaires indexed affective engagement by measuring feelings of learning and engagement. Content retention predicted subjective measures, while the EEG theta/beta ratio was negatively related to content retention but positively related to subjective measures. Study 2 featured embedded measures of content retention, confidence, engagement, background knowledge, and indexed behavioral engagement looking at nonverbal behavior quantified via video camera recordings. Confidence and engagement were significantly correlated with each other and with particular facial muscle, gaze direction, and head pose movements. We discuss how these approaches enable real-time studies of classroom engagement and can be integrated to develop neurofeedback interventions.

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bioRxiv2023

Generating the sigmoid form in neuroscience: A threshold-crossing model

The conventional interpretation1 of a logistic curve in biology is that it indicates the presence of an infection-like mechanism bounded by a saturation limit e.g. saturation occurs when the whole population has been infected. However, a logistic form may be generated by the fluctuating approach of biological systems to, and then exceeding, an effect threshold. The commonly observed trends in frequency and degree of cognitive loss in neurodegenerative disease (NDD) are consistent with a threshold-crossing model. Whereas direct evidence of real life NDD being driven by an infection-like mechanism remains elusive2,3. Variation in susceptibility to NDD may be explained by variation in the rate of biological ageing (e.g. neurodegeneration rate, or, metabolic interference), height of threshold and initial reserve. Each is potentially modifiable and clinically informative.

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bioRxiv2024

Zapit: Open Source Random-Access Photostimulation For Neuroscience

Optogenetic tools are indispensable for understanding the causal neural mechanisms underlying animal behavior,providing millisecond-precision control over genetically defined neural populations. Random-access laser-scanning optogenetics is a powerful and underutilized method for transiently manipulating cortical activity in mice. Despite the utility of this technique, no general-purpose open-source implementation currently exists, and potential users need expertise across optics, real-time hardware control, and programming. This represents a major barrier to adoption, particularly for newly established research groups. Here we present 'Zapit', the first open-source general-purpose platform for random-access laser-scanning optogenetic experiments in head-fixed mice. Zapit is fully documented, has a user-friendly GUI, works in stereotaxic coordinates, and comes with easy to build hardware options that extend functionality beyond any published system. We validate Zapit's performance through electrophysiological recordings and cortical photoinhibition in behaving mice. Zapit is a novel and innovative tool with the potential to democratize laser-scanning optogenetics and significantly increase uptake throughout the scientific community.

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bioRxiv2024

Multi-species community platform for comparative neuroscience in teleost fish

Studying neural mechanisms in complementary model organisms from different ecological niches in the same animal class can leverage the comparative brain analysis at the cellular level. To advance such a direction, we developed a unified brain atlas platform and specialized tools that allowed us to quantitatively compare neural structures in two teleost larvae, medaka (Oryzias latipes) and zebrafish (Danio rerio). Leveraging this quantitative approach we found that most brain regions are similar but some subpopulations are unique in each species. Specifically, we confirmed the existence of a clear dorsal pallial region in the telencephalon in medaka lacking in zebrafish. Further, our approach allows for extraction of differentially expressed genes in both species, and for quantitative comparison of neural activity at cellular resolution. The web-based and interactive nature of this atlas platform will facilitate the teleost communitys research and its easy extensibility will encourage contributions to its continuous expansion.

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bioRxiv2024

Emo-FilM: A multimodal dataset for affective neuroscience using naturalistic stimuli

The extensive Emo-FilM dataset stands for Emotion research using Films and fMRI in healthy participants. This dataset includes detailed emotion annotations by 44 raters for 14 short films with a combined duration of over 2[1/2] hours, as well as recordings of respiration, heart rate, and functional magnetic resonance imaging (fMRI) from a different sample of 30 individuals watching the same films. The detailed annotations of experienced emotion evaluated 50 items including ratings of discrete emotions and emotion components from the domains of appraisal, motivation, motor expression, physiological response, and feeling. Quality assessment for the behavioural data shows a mean inter-rater agreement of 0.38. The parallel fMRI data was acquired at 3 Tesla in four sessions, accompanied with a high-resolution structural (T1) and resting state fMRI scans for each participant. Physiological recordings during fMRI included heart rate, respiration, and electrodermal activity (EDA). Quality assessment indicators confirm acceptable quality of the MRI data. This dataset is designed, but not limited, to studying the dynamic neural processes involved in emotion experience. A particular strength of this data is the high temporal resolution of behavioural annotations, as well as the inclusion of a validation study in the fMRI sample. This high-quality behavioural data in combination with continuous physiological and MRI measurements makes this dataset a treasure trove for researching human emotion in response to naturalistic stimulation in a multimodal framework.

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bioRxiv2024

Enhancing Statistical Power While Maintaining Small Sample Sizes in Behavioral Neuroscience Experiments Evaluating Success Rates

Studies with low statistical power reduce the probability of detecting true effects and often lead to overestimated effect sizes, undermining the reproducibility of scientific results. While several free statistical software tools are available for calculating statistical power, they often do not account for the specialized aspects of experimental designs in behavioral studies that evaluate success rates. To address this gap, we developed "SuccessRatePower" a free and user-friendly power calculator based on Monte Carlo simulations that takes into account the particular parameters of these experimental designs. Using "SuccessRatePower", we demonstrated that statistical power can be increased by modifying the experimental protocol in three ways: 1) reducing the probability of succeeding by chance (chance level), 2) increasing the number of trials used to calculate subject success rates, and, in some circumstance, 3) employing statistical analyses suited for discrete values. These adjustments enable even studies with small sample sizes to achieve high statistical power. Finally, we performed an associative behavioral task in mice, confirming the simulated statistical advantages of reducing chance levels and increasing the number of trials in such studies

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bioRxiv2024

Generating Synthetic Task-based Brain Fingerprints for Population Neuroscience Using Deep Learning

Task-based functional magnetic resonance imaging (tb-fMRI) reveals individual differences in the neural basis of cognitive functions by linking specific tasks to neural responses. However, scaling tb-fMRI to population-level studies is challenging due to its cognitive demands, variations in task design across studies, and the limited scope of tasks in large datasets. To address this, we propose DeepTaskGen, a deep-learning approach that generates non-acquired task-based contrast maps from resting-state fMRI (rs-fMRI) data. Our approach enables generating synthetic task images for non-acquired tasks within the study protocol. We validate this approach using the Human Connectome Project lifespan data, then generate 47 contrast maps from 7 different cognitive tasks for over 20,000 individuals from UK Biobank. DeepTaskGen outperforms several benchmarks in generating synthetic task-contrast maps, exhibiting superior reconstruction performance while retaining inter-individual variation essential for biomarker development. Notably, we further showed that synthetic task contrast maps achieved similar or greater performance compared to actual task contrast maps and resting-state connectomes for predicting a wide range of demographic, cognitive, and clinical variables. This approach will facilitate the study of individual differences and the generation of task-related biomarkers by enabling the generation of arbitrary functional cognitive tasks from readily available rs-fMRI data.

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bioRxiv2025

Brain Diffusion Transformer for Personalized Neuroscience and Psychiatry

Task-fMRI analyses typically focus on localized activation contrasts between stimuli, neglecting the brains dynamic hierarchy. We introduce Brain Diffusion Transformer (Brain-DiT), a deep generative model capturing recurrent processing underlying individualized neurocognitive state transitions via functional networks. Without prior assumptions, Brain-DiT identifies canonical cognitive regions in the brain and reveals replicable subgroups with distinct neural circuits in large cohorts, offering critical clinical insights overlooked by traditional methods: individuals exhibiting negative emotion bias, linked to language-related regions, had a 12-fold higher likelihood of major depression, and those with maladaptive inhibition strategies, associated with overactive medial frontal regions, showed a 9-fold increased risk of alcohol abuse. By bridging cognitive theory and psychiatric applications, Brain-DiT provides a unified analytical paradigm, paving the way for operational personalized medicine in psychiatry.

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bioRxiv2025

Advancing Mobile Neuroscience: A Novel Wearable Backpack for Multi-Sensor Research in Urban Environments

The rapid global urbanisation has intensified the need to understand the complex interactions and impacts that city environments have on human physical or mental health and well-being. Traditional indoor laboratory-based approaches conduct experiments in well controlled settings but, while advantageous for their controlled conditions, they often lack ecological validity. To address this gap, we present the "eMOTIONAL Cities Walker Backpack" -- a wearable unit developed for synchronously collecting multi-modal data in dynamic real-world settings. Designed for both indoor and outdoor use, the backpack integrates environmental (for microclimate, air pollution and noise) and physiological sensors (including electroencephalography, eye-tracking and wrist-based biosensors for cardiovascular monitoring) to enable the study of human experience in naturalistic urban environments. In this paper, we describe the technical specifications and implementation of this technology during outdoor acquisitions across selected urban locations in the city of Lisbon. We also highlight its potential for methodological comparison with traditional lab-based tasks (particularly through the use of equivalent sensing technologies), and thus advancing the field of translational research in mental health and urban studies.

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