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Neuroscience

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 Preprints are not peer reviewed.

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bioRxiv2022

Suboptimal phenotypic reliability impedes reproducible human neuroscience

Summary ParagraphBiomarkers of behavior and psychiatric illness for cognitive and clinical neuroscience remain out of reach1-4. Suboptimal reliability of biological measurements, such as functional magnetic resonance imaging (fMRI), is increasingly cited as a primary culprit for discouragingly large sample size requirements and poor reproducibility of brain-based biomarker discovery1,5-7. In response, steps are being taken towards optimizing MRI reliability and increasing sample sizes8-11, though this will not be enough. Optimizing biological measurement reliability and increasing sample sizes are necessary but insufficient steps for biomarker discovery; this focus has overlooked the other side of the equation - the reliability of clinical and cognitive assessments - which are often suboptimal or unassessed. Through a combination of simulation analysis and empirical studies using neuroimaging data, we demonstrate that the joint reliability of both biological and clinical/cognitive phenotypic measurements must be optimized in order to ensure biomarkers are reproducible and accurate. Even with best-case scenario high reliability neuroimaging measurements and large sample sizes, we show that suboptimal reliability of phenotypic data (i.e., clinical diagnosis, behavioral and cognitive measurements) will continue to impede meaningful biomarker discovery for the field. Improving reliability through development of novel assessments of phenotypic variation is needed, but it is not the sole solution. We emphasize the potential to improve the reliability of established phenotypic methods through aggregation across multiple raters and/or measurements12-15, which is becoming increasingly feasible with recent innovations in data acquisition (e.g., web- and smart-phone-based administration, ecological momentary assessment, burst sampling, wearable devices, multimodal recordings)16-20. We demonstrate that such aggregation can achieve better biomarker discovery for a fraction of the cost engendered by large-scale samples. Although the current study has been motivated by ongoing developments in neuroimaging, the prioritization of reliable phenotyping will revolutionize neurobiological and clinical endeavors that are focused on brain and behavior.

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bioRxiv2023

Statistical power and false positive rates for interdependent outcomes are strongly influenced by test type: Implications for behavioral neuroscience

Statistical errors in preclinical science are a barrier to reproducibility and translation. For instance, linear models (e.g., ANOVA, linear regression) may be misapplied to data that violate assumptions. In behavioral neuroscience and psychopharmacology, linear models are frequently applied to interdependent or compositional data, which includes behavioral assessments where animals concurrently choose between chambers, objects, outcomes, or types of behavior (e.g., forced swim, novel object, place/social preference). The current study simulated behavioral data for a task with four interdependent choices (i.e., increased choice of a given outcome decreases others) using Monte Carlo methods. 16,000 datasets were simulated (1,000 each of 4 effect sizes by 4 sample sizes) and statistical approaches evaluated for accuracy. Linear regression and linear mixed effects regression (LMER) with a single random intercept resulted in high false positives (>60%). Elevated false positives were attenuated in an LMER with random effects for all choice-levels and a binomial logistic mixed effects regression. However, these models were underpowered to reliably detect effects at common preclinical sample sizes. A Bayesian method using prior knowledge for control subjects increased power by up to 30%. These results were confirmed in a second simulation (8,000 datasets). These data suggest that statistical analyses may often be misapplied in preclinical paradigms, with common linear methods increasing false positives, but potential alternatives lacking power. Ultimately, using informed priors may balance statistical requirements with ethical imperatives to minimize the number of animals used. These findings highlight the importance of considering statistical assumptions and limitations when designing research studies.

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bioRxiv2023

Exploring an EM-algorithm for banded regression in computational neuroscience

Regression is a principal tool for relating brain responses to stimuli or tasks in computational neuroscience. This often involves fitting linear models with predictors that can be divided into groups, such as distinct stimulus feature subsets in encoding models or features of different neural response channels in decoding models. When fitting such models, it can be relevant to allow differential shrinkage of the different groups of regression weights. Here, we explore a framework that allow for straightforward definition and estimation of such models. We present an expectation-maximization algorithm for tuning hyperparameters that control shrinkage of groups of weights. We highlight properties, limitations, and potential use-cases of the model using simulated data. Next, we explore the model in the context of a BOLD fMRI encoding analysis and an EEG decoding analysis. Finally, we discuss cases where the model can be useful and scenarios where regularization procedures complicate model interpretation.

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bioRxiv2023

Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies. We first start with an intuitive discussion of Bayes rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools. Significance StatementBayesian inference has received renewed interest as an alternative to null-significance hypothesis testing for its interpretability, ability to incorporate prior knowledge into current inference, and robust model comparison paradigms. Despite this renewed interest, discussions of Bayesian inference are often obfuscated by undue mathematical complexity and misunderstandings underlying the Bayesian inference process. In this article, we aim to empower neuroscientists to adopt Bayesian statistical inference by providing a practical methodological walkthrough using single and multi-unit recordings from the rodent auditory circuit accompanied by a well-documented and user-friendly toolkit containing regression and ANOVA statistical models commonly encountered in neuroscience.

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bioRxiv2024

Threshold-Free Network-Oriented Statistics in Neuroscience

Network neuroscience has emerged as an indispensable tool for studying brain structure and function. Currently, the network-based statistic (NBS) procedure is widely used for dealing with massive multiple testing/comparison problems in brain networks. However, the NBS requires choosing a hard cluster-forming threshold, lacking objective rules. A powerful and flexible statistical framework is urgently needed with growing interest in finer-grained network explorations across modalities and scales. Here, we introduce a permutation-based framework--"Threshold-Free Network-Oriented Statistics" (TFNOS). It integrates two "threshold-free" pathways: traversing all cluster-forming thresholds (TT) and using predefined clusters (PC). The TT procedure, building upon the threshold-free network-based statistics, requires setting additional parameters. The PC procedures comprise six variants given the degree of freedom in pooling data, null distribution construction, and controlled error rate. Using numerical simulations, we evaluated the performance of the TT procedure under 600 parameter combinations, then benchmarked TFNOS procedures and baselines across different topologies of effects, sample sizes, and effect sizes, and finally provided illustrative examples with real data. We offer recommended parameter values that allow the TT procedure to stably maintain leading power, while empirically controlling the false discovery rate (FDR) beyond only weakly controlling the familywise error rate (FWER). Notably, the relevant parameters commonly employed in the field appear overly liberal. Furthermore, for the PC procedures, FDR-controlling variants showed improved power compared to FWER-controlling variants, and some of them are simple but do not compromise power. The nonparametric PC procedures allow the selection of any test statistics considered appropriate. Overall, the TFNOS is a generalized framework for inference on edges/nodes of undirected/directed brain networks. We provide empirical and principled criteria for selecting appropriate procedures and may enhance the reproducibility and sensitivity of future brain research.

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bioRxiv2024

Spyglass: a data analysis framework for reproducible and shareable neuroscience research

Scientific progress depends on reliable and reproducible results. Progress can be accelerated when data are shared and re-analyzed to address new questions. Current approaches to storing and analyzing neural data involve bespoke formats and software that make replication and reuse of data difficult. To address these challenges, we created Spyglass, an open-source data management and analysis framework written in Python. Spyglass provides reproducible pipelines for common neuroscience analyses and sharing of raw data, intermediate analyses, and final results within and across labs. Spyglass uses the Neurodata Without Borders (NWB) standard and includes pipelines for spectral filtering, spike sorting, pose tracking, and neural decoding. Spyglass can be extended to apply existing and newly developed pipelines to datasets from multiple sources. We demonstrate these features in the context of a cross-laboratory replication by applying advanced state space decoding algorithms to publicly available data. New users can try out Spyglass on a Jupyter Hub hosted by HHMI and 2i2c: https://spyglass.hhmi.2i2c.cloud/.

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bioRxiv2024

Comparing the limbic-frontal connectome across the primate order: conservation of connections and implications for translational neuroscience

The interaction of the limbic system and frontal cortex of the primate brain is important in many affective behaviors. For this reason, it is heavily implicated in a number of psychiatric conditions. This system is often studied in the macaque monkey, the most largely-used non-human primate model species. However, how evolutionary conserved this system is and how well results obtained in any model species translate to the human can only be understood by studying its organization across the primate order. Here, we present an investigation of the topology of limbic-frontal connections across seven species, representing all major branches of the primate family tree. We show that dichotomous organization of amydalofugal and uncinate connections with frontal cortex is conserved across all species. Subgenual connectivity of the cingulum bundle, however, seems less prominent in prosimian and New World monkey brains. These results inform both translational neuroscience and primate brain evolution.

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bioRxiv2024

Systems Neuroscience Computing in Python (SyNCoPy): A Python Package for Large-scale Analysis of Electrophysiological Data

We introduce an open-source Python package for the analysis of large-scale electrophysiological data called SyNCoPy, for Systems Neuroscience Computing in Python. The package includes signal processing analyses across time (e.g. time-lock analysis), frequency (e.g. power spectrum), and connectivity (e.g. coherence) domains. It enables user-friendly data analysis on both laptop-based and high-performance computing systems. SyNCoPy is designed to facilitate trial-parallel workflows (parallel processing of trials) making it an ideal tool for large-scale analysis of electrophysiological data. Based on parallel processing of trials, the software can support very large-scale datasets via innovative out-of-core computation techniques. It also provides seamless interoperability with other standard software packages through a range of file format importers and exporters and open file formats. The naming of the user functions closely follows the well-established FieldTrip framework, which is an open-source Matlab toolbox for advanced analysis of electrophysiological data.

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bioRxiv2024

Nanoporous Platinum Microelectrode Arrays for Neuroscience Applications

Microelectrode arrays are invaluable tools for investigating the electrophysiological behaviour of neuronal networks with high spatiotemporal precision. In recent years, it has become increasingly common to functionalize such electrodes with highly porous platinum to increase their effective surface area, and hence their signal-to-noise ratio. Although such functionalization significantly improves the electrochemical performance of the electrodes, the impact of various electrode morphologies on biocompatibility and electrophysiological performance in cell cultures remains poorly understood. In this study, we introduce reproducible protocols for depositing highly porous platinum with varying morphologies on microelectrodes designed for neural cell cultures. We also evaluate the impact of morphology and electrode size on the signal-to-noise ratio in recordings from rat cortical neurons cultured on these electrodes. Our results indicate that electrodes with a uniform layer of highly nanoporous platinum offer the best trade-off between biocompatibility, electrochemical, and electrophysiological performance. While more microporous electrodes exhibited lower impedance, nanoporous electrodes detected higher extracellular signal amplitudes from neurons, suggesting reduced distance between perisomatic neuronal areas and the electrodes. Additionally, these nanoporous electrodes showed fewer thickness variations at their edges compared to the more porous electrodes. Such edges can be mechanically broken off during cell culturing and contribute to long-term cytotoxic effects, which is highly undesirable. We hope this work will contribute to better standardization in creating and utilizing nanoporous platinum microelectrodes for neuroscience applications. Improving the accessibility and reproducibility of this technology is crucial for enhancing the quality of electrophysiological data and advancing our understanding of neuronal network function and dysfunction.

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bioRxiv2024

Current Reporting Practices in Human Neuroscience Research

Concerns for the replicability, reliability, and generalizability of human neuroimaging research have led to intense debates over sample size and open science practices, with more recent attention on the contributions of sampling and recruitment practices. Key to understanding the state of neuroscience research is an assessment of reporting practices that influence replicability, reliability, and generalizability. In this structured review, we evaluated reporting practice across three domains: (1) demographic (e.g., reporting participant race-ethnicity, age, any measure of socioeconomic position), (2) methodological (e.g., reporting recruitment methods, inclusion and exclusion criteria, why participants were excluded from analyses), and (3) open science and generalizability (e.g., analyses were preregistered, target population was stated). Included were 919 published MRI and fMRI studies from 2019 in nine top-ranked journals (N = 3,856 records screened). Reporting across domains was infrequent, with participant racial or ethnic identity (14.8%), reasons for missing imaging data (31.2%), and identification of a target population (19.4%) being particularly low/underreported. Reporting likelihood varied by study characteristics (e.g., participant age group) and was correlated across domains. The median sample size of studies was 55 participants. Study sample size, reporting frequency, was positively associated with two-year citation counts, providing some evidence that the complete reporting of demographic characteristics, methodological decisions, and open science and generalizability practices may not be as valued as study sample size. Recommendations for structural interventions at the journal level are proposed.

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bioRxiv2024

Higher-Order Interaction Analysis via Hypergraph Models for Studying Multidimensional Neuroscience Data.

Higher-Order Interaction (HOI) theory offers a powerful framework for capturing complex, non-linear relationships within multidimensional systems, moving beyond traditional pairwise graph methods to encompass multi-way interactions. This study applies HOI analysis, specifically using hypergraph theory, to explore intricate connectivity patterns in electrophysiological signals from neuroscience. Hypergraphs were constructed from connectivity data across various frequency bands, characterized through metrics such as spectral entropy, hyperedge centrality, and vertex centrality, and compared using spectral and centrality distance measures. Three distinct neurophysiological datasets were analyzed: intracranial EEG signals from rats during different sleep stages, scalp EEG data to distinguish between epilepsy types, and MEG recordings of seizure dynamics. The findings highlight the effectiveness of hypergraph-based HOI analysis in mapping neural dynamics across normal and pathological brain states. In sleep studies, it reveals distinct connectivity patterns between REM and NREM stages, while in epilepsy, it differentiates seizure types and stages, identifying spectral entropy as a potential marker for seizure onset. Notably, HOI analysis captures differences between primary and secondary generalized epilepsy, suggesting enhanced diagnostic accuracy. This approach provides a powerful tool for understanding complex neural interactions in high-dimensional data.

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bioRxiv2024

CalliCog: an open-source cognitive neuroscience toolkit for freely behaving nonhuman primates

Nonhuman primates (NHPs) are pivotal for unlocking the complexities of human cognition, yet traditional cognitive studies remain constrained to specialized laboratories. To revolutionize this paradigm, we present CalliCog: an open-source, scalable in-cage platform tailored for freely behaving experiments in small primate species such as the common marmoset (Callithrix jacchus). CalliCog includes modular operant chambers that operate autonomously and integrate seamlessly with home cages, eliminating human intervention. Our results showcase the power of CalliCog to train experimentally naive marmosets in touchscreen-based cognitive tasks. Remarkably, across two independent facilities, marmosets achieved touchscreen proficiency within two weeks and successfully completed tasks probing behavioral flexibility and working memory. Moreover, CalliCog enabled precise synchronization of behavioral data with electrocorticography (ECoG) recordings from freely moving animals, opening new frontiers for neurobehavioral research. By making CalliCog openly accessible, we aim to democratize cognitive experimentation with small NHPs, narrowing the translational gap between preclinical models and human cognition. MotivationCognitive neuroscience research involving nonhuman primates (NHPs) has traditionally been confined to a few highly specialized laboratories equipped with advanced infrastructure, expert knowledge, and specialized resources for housing and testing these animals. The common marmoset (Callithrix jacchus), a small NHP species, has gained popularity in cognitive research due to its ability to address some of these challenges. However, behavioral studies in marmosets remain labor-intensive and restricted mainly to experts in the field, making them less accessible to the broader scientific community. To address these barriers, we introduce an open and accessible platform designed for automated cognitive experiments in home cage settings with marmosets. This system supports the integration of cognitive behavioral analysis with wireless neural recordings, is cost-effective, and requires minimal technical expertise to build and operate.

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bioRxiv2025

Doctoral Students as Carbon Accountants: Calculating Carbon Costs of a PhD in Neuroscience

Research is an energy and resource-demanding activity. However, despite increasing awareness and emerging sustainability initiatives, a paucity of data and methodological inconsistency continue to hamper effective and accountable emissions mitigation. With > 250,000 doctoral students graduating annually across all academic disciplines, empowering PhD students to engage in carbon accounting could provide a sizable and robust source of carbon data alongside a powerful generational force for decarbonisation. Here, we demonstrate how doctoral students and other researchers can consistently measure the carbon footprint of their work, using one PhD students research in a neuroscience Drosophila lab as our case study. We present a comprehensive life-cycle assessment of the equivalent carbon dioxide emissions (CO2e) generated by the students research activities, including measurement of scope 1 emissions associated with Drosophila husbandry; calculation of time- and region-specific scope 2 emissions produced by widely used techniques including calcium imaging, electrophysiology, and optogenetics; and estimation of scope 3 emissions associated with procurement and research-related travel. We found that research-related travel and procurement of laboratory supplies were responsible for the majority of annual emissions, up to 1942 kg CO2e and 543 kg CO2e respectively after accounting for aircraft radiative forcing. Using NESOs open-source Carbon Intensity API to account for temporal and geographical variation in the carbon intensity of UK National Grid energy, we found that persistent laboratory energy consumption released 10.99 kg CO2e, with an additional 3.56 kg CO2e scope 2 and 3.6 kg CO2e scope 1 emissions underpinning direct research activities. Finally, we discuss the challenges of accurately carbon foot printing research across disciplines in the UK and beyond, highlighting the value of regionally precise open-source energy mix data and the need for data openness within research supply chains. Overall, we present a common framework for including carbon footprint analyses as Carbon Appendices to PhD theses to generate carbon footprint data across disciplines. Beyond the benefits of such data for informed emissions mitigation, we envision doctoral students carrying insights from carbon appendices forward into academia and industry to catalyse a community-driven decarbonisation of the research sector.

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bioRxiv2025

Canonical Representational Mapping for Cognitive Neuroscience

Understanding neural representations is central to cognitive neuroscience, yet isolating meaningful patterns from noisy or correlated data remains challenging. Canonical Representational Mapping (CRM) is a novel multivariate analysis method to identify neural patterns aligned with specific cognitive hypotheses. CRM maximizes correlations between multivariate datasets - similar to Canonical Correlation Analysis - while controlling for confounding sources of variance, such as shared noise or irrelevant task conditions. We validate CRM with simulations and apply it across diverse neurophysiological datasets: In one application, we use CRM to map large language model activations onto intracranial electroencephalography recordings during story listening, controlling for contextual autocorrelations. In another example, we factorize overlapping representations in functional Magnetic Resonance Imaging into distinct context and episode components. Finally, we uncover frequency-coupled representations shared between hippocampus and medial prefrontal cortex in navigating rodents. Our results introduce CRM as a powerful tool to isolate representations across modalities and species.

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bioRxiv2018

Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here we describe a software toolbox--called seqNMF--with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.

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bioRxiv2018

MemBright: a Family of Fluorescent Membrane Probes for Advanced Cellular Imaging and Neuroscience

The proper staining of the plasma membrane (PM) is critical in bioimaging as it delimits the cell. Herein, we developed MemBright: a family of six cyanine-based fluorescent turn-on PM probes that emit from orange to near-infrared when reaching the PM, and enable homogeneous and selective PM staining with excellent contrast in mono and two-photon microscopy. These probes are compatible with long-term live cell imaging and immunostaining. Moreover, MemBright label neurons in a brighter manner than surrounding cells allowing identification of neurons in acute brain tissue section and neuromuscular-junctions without any use of transfection or transgenic animals. At last, MemBright were used in super-resolution imaging to unravel the dendritic spines neck. 3D multicolor dSTORM in combination with immunostaining revealed en-passant synapse displaying endogenous glutamate receptors clustered at the axonal-dendritic contact site. MemBright probes thus constitute a universal toolkit for cell biology and neuroscience biomembrane imaging with a variety of microscopy techniques.

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bioRxiv2019

Enlarging the Scope of Randomization and Permutation Tests in Neuroimaging and Neuroscience

Especially for the high-dimensional data collected in neuroscience, nonparametric statistical tests are an excellent alternative for parametric statistical tests. Because of the freedom to use any function of the data as a test statistic, nonparametric tests have the potential for a drastic increase in sensitivity by making a biologically-informed choice for a test statistic. In a companion paper (Geerligs & Maris, 2020), we demonstrate that such a drastic increase is actually possible. This increase in sensitivity is only useful if, at the same time, the false alarm (FA) rate can be controlled. However, for some study types (e.g., within-participant studies), nonparametric tests do not control the FA rate (see Eklund, Nichols, & Knutsson, 2016). In the present paper, we present a family of nonparametric randomization and permutation tests of which we prove exact FA rate control. Crucially, these proofs hold for a much larger family of study types than before, and they include both within-participant studies and studies in which the explanatory variable is not under experimental control. The crucial element of this statistical innovation is the adoption of a novel but highly relevant null hypothesis: statistical independence between the biological and the explanatory variable.

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bioRxiv2019

Does higher sampling rate (Multiband + SENSE) benefit the detection of task correlated BOLD for cognitive neuroscience applications at 3T?

Multiband (MB) or Simultaneous multi-slice (SMS) acquisition schemes allow the acquisition of MRI signals from more than one spatial coordinate at a time. Commercial availability has brought this technique within the reach of many neuroscientists and psychologists. Most early evaluation of the performance of MB acquisition employed resting state fMRI or the most basic tasks. In this study, we tested whether the advantages of using MB acquisition schemes generalize to group analyses using a cognitive task more representative of typical cognitive neuroscience applications. Twenty-three subjects were scanned on a Philips 3T scanner using five sequences up to eight-fold acceleration with MB-factors 1 to 4, SENSE factors up to 2 and corresponding TRs of 2.45s down to 0.63s, while they viewed (i) movies showing complex actions with hand object interactions and (ii) control movies without hand object interaction. Using random effects group-level, voxel-wise analysis we found that all sequences were able to detect the basic action observation network known to be recruited by our task. The highest t-values were found for sequences with MB4 acceleration. For the MB1 sequence, a 50% bigger voxel volume was needed to reach comparable t-statistics. The group-level t-values for resting state networks (RSNs) were also highest for MB4 sequences. Here the MB1 sequence with larger voxel size did not perform comparable to the MB4 sequence. Altogether, we can thus recommend the use of MB4 (and SENSE 1.5 or 2) on a Philips scanner when aiming to perform group-level analyses using cognitive block design fMRI tasks and voxel sizes in the range of cortical thickness (e.g. 2.7mm isotropic). While results will not be dramatically changed by the use of multiband, our results suggest that MB will bring a moderate but significant benefit.

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