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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

The Block Object Storage Service (bossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery

Large volumetric neuroimaging datasets have grown in size over the past ten years from gigabytes to terabytes, with petascale data becoming available and more common over the next few years. Current approaches to store and analyze these emerging datasets are insufficient in their ability to scale in both cost-effectiveness and performance. Additionally, enabling large-scale processing and annotation is critical as these data grow too large for manual inspection. We provide a new cloud-native managed service for large and multi-modal experiments, with support for data ingest, storage, visualization, and sharing through a RESTful Application Programming Interface (API) and web-based user interface. Our project is open source and can be easily and cost-effectively used for a variety of modalities and applications.

bioinformatics↗
bioRxiv2018

Beyond Bonferroni Revisited: Concerns over inflated false positives in conservation genetics, genetics, and neuroscience

In 2006, Narum published a paper in Conservation Genetics that was motivated by the stringent nature of the Bonferroni approach for family wise error correction. That work suggested that the approach of Benjamini and Yekutieli in 2001 provided adequate correction and was more biologically relevant. However, there are crucial differences between the original Benjamini and Yekutieli procedure and that described by Narum. After carefully reviewing both papers, we believe that the Narum procedure is both different than the Benjamini and Yekutieli procedure and does not adequately control for family wise error. We provide an overview of approaches for FWE correction as well as evidence for the faulty implementation of the Benjamini and Yekutieli procedure by Narum using the equations from the respective papers, data from both papers, and the results of simulation.

genetics↗
bioRxiv2019

Identification of a functional non-coding variant in the GABAA receptor α2 subunit of the C57BL/6J mouse reference genome: Major implications for neuroscience research

GABA type-A (GABA-A) receptors containing the 2 subunit (Gabra2) are expressed in most brain regions and are critical in modulating inhibitory synaptic function. Genetic variation at the GABRA2 locus has been implicated in epilepsy, affective and psychiatric disorders, alcoholism and drug abuse. Gabra2 expression varies as a function of genotype and is modulated by sequence variants in several brain structures and populations, including F2 crosses originating from C57BL/6J (B6J) and the BXD recombinant inbred family derived from B6J and DBA/2J. Here we demonstrate a global reduction of Gabra2 brain mRNA and protein in the B6J strain relative to other inbred strains, and identify and validate the causal mutation in B6J. The mutation is a single base pair intronic deletion located adjacent to a splice acceptor site that only occurs in the B6J reference genome. The deletion became fixed in B6J between 1976 and 1991 and is now pervasive in many engineered lines, BXD strains generated after 1991, the Collaborative Cross, and the majority of consomic lines. Repair of the deletion using CRISPR-Cas9 mediated gene editing on a B6J genetic background completely restored brain levels of Gabra2 mRNA and protein. Comparison of transcript expression in hippocampus, cortex, and striatum between B6J and repaired genotypes revealed alterations in GABA-A receptor subunit expression, especially in striatum. These results suggest that naturally occurring variation in GABRA2 levels between B6J and other substrains or inbred strains may also explain strain differences in anxiety-like or alcohol and drug response traits related to striatal function. Characterization of the B6J private mutation in the Gabra2 gene is of critical importance to molecular genetic studies in neurobiological research as this strain is widely used to generate genetically engineered mice and murine genetic populations, and is the most widely utilized strain for evaluation of anxiety-like, depression-like, pain, epilepsy, and drug response traits that may be partly modulated by Gabra2 function.

genetics↗
bioRxiv2018

A data-driven approach to reduce gender disparity in invited speaker programs at scientific meetings

We present a data-driven approach that uses established metrics of scientific quality to select invited speakers; this approach enables gender parity in conference programs while ensuring high scientific standards. Gender disparity continues to be an issue in STEM, with progress requiring consistent and focused efforts. Here, we present a data-driven approach to promote high quality, gender balanced invited speaker selection for neuroscience conferences. We have targeted invited speaker opportunities because underrepresentation of female speakers at international neuroscience conferences remains a major problem, and such opportunities are critical for career development. First, we audited the top ten neuroscience journals (indexed by SCImago Journal and Country Rank; SJR), identifying (1) highly cited papers, (2) gender of first and last authors, and (3) field-weighted citation impact and total publications of first and last authors. Second, we used these data to establish a database of high quality scientists that could be used to select speakers for conferences. We found that research quality (as indexed by field-weighted citation impact and total publications) of authors of highly cited publications in the top 10 neuroscience journals did not differ significantly for females and males. The comparison between the gender base rate in neuroscience and authors publishing highly cited papers in high-quality neuroscience journals shows that female representation, particularly at last author level, is less than the estimated base rate for neuroscience. In summary, we present a data-driven approach to invited speaker selection that would facilitate gender balanced conference programs while maintaining the highest of scientific standards. This approach minimizes the influence of implicit gender bias in speaker selection decisions by using scientific quality metrics that STEM researchers are familiar with, and indeed use to evaluate their own performance. Having an immediate effect on reducing gender disparity in conference programs, our approach would generate a positive spiral for more long-term reduction of gender disparity in STEM.

scientific communication and education↗
bioRxiv2020

PsychRNN: An Accessible and Flexible Python Package for Training Recurrent Neural Network Models on Cognitive Tasks

Task-trained artificial recurrent neural networks (RNNs) provide a computational modeling framework of increasing interest and application in computational, systems, and cognitive neuroscience. RNNs can be trained, using deep learning methods, to perform cognitive tasks used in animal and human experiments, and can be studied to investigate potential neural representations and circuit mechanisms underlying cognitive computations and behavior. Widespread application of these approaches within neuroscience has been limited by technical barriers in use of deep learning software packages to train network models. Here we introduce PsychRNN, an accessible, flexible, and extensible Python package for training RNNs on cognitive tasks. Our package is designed for accessibility, for researchers to define tasks and train RNN models using only Python and NumPy without requiring knowledge of deep learning software. The training backend is based on TensorFlow and is readily extensible for researchers with TensorFlow knowledge to develop projects with additional customization. PsychRNN implements a number of specialized features to support applications in systems and cognitive neuroscience. Users can impose neurobiologically relevant constraints on synaptic connectivity patterns. Furthermore, specification of cognitive tasks has a modular structure, which facilitates parametric variation of task demands to examine their impact on model solutions. PsychRNN also enables task shaping during training, or curriculum learning, in which tasks are adjusted in closed-loop based on performance. Shaping is ubiquitous in training of animals in cognitive tasks, and PsychRNN allows investigation of how shaping trajectories impact learning and model solutions. Overall, the PsychRNN framework facilitates application of trained RNNs in neuroscience research. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/321752v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1541513org.highwire.dtl.DTLVardef@133c86borg.highwire.dtl.DTLVardef@aaf099org.highwire.dtl.DTLVardef@99d55b_HPS_FORMAT_FIGEXP M_FIG Example workflow for using PsychRNN. First, the task of interest is defined, and a recurrent neural network model is trained to perform the task, optionally with neurobiologically informed constraints on the network. After the network is trained, the researchers can investigate network properties including the synaptic connectivity patterns and the dynamics of neural population activity during task execution, and other studies, e.g. those on perturbations, can be explored. The dotted line shows the possible repetition of this cycle with one network, which allows investigation of training effects of task shaping, or curriculum learning, for closed-loop training of the network on a progression of tasks. C_FIG Significance StatementArtificial recurrent neural network (RNN) modeling is of increasing interest within computational, systems, and cognitive neuroscience, yet its proliferation as a computational tool within the field has been limited due to technical barriers in use of specialized deep-learning software. PsychRNN provides an accessible, flexible, and powerful framework for training RNN models on cognitive tasks. Users can define tasks and train models using the Python-based interface which enables RNN modeling studies without requiring user knowledge of deep learning software. PsychRNNs modular structure facilitates task specification and incorporation of neurobiological constraints, and supports extensibility for users with deep learning expertise. PsychRNNs framework for RNN modeling will increase accessibility and reproducibility of this approach across neuroscience subfields.

neuroscience↗
bioRxiv2018

USE: An integrative suite for temporally-precise psychophysical experiments in virtual environments

11.1 BackgroundThere is a growing interest in complex, active, and immersive behavioral neuroscience tasks. However, the development and control of such tasks present unique challenges.\n\n1.2 New MethodThe Unified Suite for Experiments (USE) is an integrated set of hardware and software tools for the design and control of behavioral neuroscience experiments. The software, developed using the Unity video game engine, supports both active tasks in immersive 3D environments and static 2D tasks used in more traditional visual experiments. The custom USE SyncBox hardware, based around an Arduino Mega2560 board, integrates and synchronizes multiple data streams from different pieces of experimental hardware. The suite addresses three key issues with developing cognitive neuroscience experiments in Unity: tight experimental control, accurate sub-ms timing, and accurate gaze target identification.\n\n1.3 ResultsUSE is a flexible framework to realize experiments, enabling (i) nested control over complex tasks, (ii) flexible use of 3D or 2D scenes and objects, (iii) touchscreen-, button-, joystick- and gaze-based interaction, and (v) complete offline reconstruction of experiments for post-processing and temporal alignment of data streams.\n\n1.4 Comparison with Existing MethodsMost existing experiment-creation tools are not designed to support the development of video-game-like tasks. Those that do use older or less popular video game engines as their base, and are not as feature-rich or enable as precise control over timing as USE.\n\n1.5 ConclusionsUSE provides an integrated, open source framework for a wide variety of active behavioral neuroscience experiments using human and nonhuman participants, and artificially-intelligent agents.\n\n2 GlossaryO_LIActive task: Experimental tasks which involve some combination of realistic, usually moving, stimuli, continuous opportunities for action, ecologically valid tasks, complex behaviours, etc. Here, they are contrasted with static tasks (see below)\nC_LIO_LIArduino: A multi-purpose generic micro-processor, here used to control inter-device communication and time synchronization.\nC_LIO_LIRaycast: A game-engine method that sends a vector between two points in a virtual three-dimensional environment, and returns the first object in that environment it hits. Often used to determine if a character in a game can see or shoot another character.\nC_LIO_LIState Machine (also Finite State Machine): A way of conceptualizing and implementing control in software, such that at any one moment the software is in one, and only one, state. In hierarchical state machines, as used in the present software suite, these can be organized into different levels, such that each level can only be in one state, but a state can pass control to a lower level.\nC_LIO_LIStatic task: Experimental tasks like those traditionally used in the cognitive neurosciences. Simple, usually stationary, stimuli, limited opportunities for action, simple behaviours, etc. Here, they are contrasted with active tasks (see above).\nC_LIO_LIUnity: One of the most popular video game engines. Freely available.\nC_LIO_LIVideo game engine: A software development kit designed to handle many of the common issues involved in creating video games, such as interfacing with controllers, simulating physical collisions and lighting, etc.\nC_LI

neuroscience↗
bioRxiv2016

Could a neuroscientist understand a microprocessor?

There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.\n\nAuthor SummaryNeuroscience is held back by the fact that it is hard to evaluate if a conclusion is correct; the complexity of the systems under study and their experimental inaccessability make the assessment of algorithmic and data analytic technqiues challenging at best. We thus argue for testing approaches using known artifacts, where the correct interpretation is known. Here we present a microprocessor platform as one such test case. We find that many approaches in neuroscience, when used na*vely, fall short of producing a meaningful understanding.

Neuroscience↗
bioRxiv2020

Connectome subgraph isomorphisms and graph queries with DotMotif

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.

neuroscience↗
bioRxiv2020

Significance tests for Leave-One-Out Inter-Subject Correlations in fMRI

The inter-subject correlation (ISC) of fMRI data of different subjects performing the same task is a powerful way to localize and differentiate neural processes caused by a stimulus from those that spontaneously or idiosyncratically take place in each subject. The wider adoption of this method has however been impeded by the lack of widely available tools to assess the significance of the observed correlations. Several non-parametric approaches have been proposed, but these approaches are computationally intensive, challenging to implement, and sensitive methods to correct for multiple comparison across voxels are not yet well established. More widely available, and computationally simple, parametric methods have been criticized theoretically on the basis that dependencies in the data could inflate false positives. Here, we therefore endeavored to assess the actual performance of parametric tests on leave-one-out ISC values in two ways. First, we assess whether parametric tests protect against Type I error by assessing how often they find significant clusters of synchronized activity in publicly available datasets, in which such synchronization should not occur. This includes three resting state datasets, and one dataset in which participants did view movies, but where we randomly select segments that were not taken at the same time in the same movie. Contrary to what has been suspected, we find that parametric tests with corrections for multiple comparisons do protect appropriately against Type I error in that data. This was true for FDR correction at the voxel level at q < 0.05, with a minimum cluster-size of k = 20 voxels, FWE correction at the voxel level at < 0.05, with a minimum cluster-size of k = 5, and for correction at the cluster-level with punc < 0.001 with k = max(20, FWEc). Second, we assessed how these parametric tests compare with non-parametric methods when it comes to detecting ISC when participants actually did watch the same movies. We used a dataset including 150 participants viewing two movies, and used a bootstrapping thresholding of the ISC in the entire dataset to outline our best guess of the network of brain regions that truly synchronize while viewing the movies. We then drew subsamples of between 10 and 50 participants from the entire dataset, calculated the ISC, and thresholded it using our candidate methods. We find that FDR thresholding with k = 20 in particular, was substantially more sensitive than bootstrapping methods in detecting this network even in smallish samples of N = 20 participants typical of cognitive neuroscience studies, while at the same time retaining appropriate specificity. Because the parametric tests we show to perform well are more readily available to the neuroscience community than the non-parametric tests previously championed, we trust that this finding paves the way to a wider adoption of ISC, and empowers a wider range of neuroimagers to use ISC to tackle the challenges of naturalistic neuroscience. In particular in the context of often limited sample sizes and modest effect sizes in cognitive neuroscience, we trust that using FDR correction in particular will help neuroimagers identify the contribution of higher brain regions that process stimuli in more loosely timed fashions, more effectively than non-parametric alternatives.

neuroscience↗
bioRxiv2020

Topological Augmentation of Latent Information Streams in Feed-Forward Neural Networks

Network neuroscience has yielded crucial insights into the systems-level organisation of the brain, however the indirect nature of neuroimaging recordings has rendered the discovery of generative mechanisms for a given function inherently challenging. In parallel, neural network machine-learning models have exhibited breakthrough performance in tackling a range of complex problems, however the principles that govern learning-induced modifications to network structure remain poorly understood, in part due to a lack of analytic tools to quantify the dynamics of network structure. While the question of how network reconfiguration supports learning is mirrored in machine learning and network neuroscience, the different contexts of these fields provides a timely opportunity to bring them together synergistically to investigate the problem. Here we combine these two approaches to reveal connections between the brains network structure and the emerging network structure of an artificial neural network. Specifically, we train a shallow, feedforward neural network to classify handwritten digits and then used a combination of systems neuroscience and information theoretic tools to perform virtual brain analytics on the resultant edge weights and activity patterns of each node. We identify three distinct phases of network reconfiguration across learning, each of which are characterised by unique topological and information-theoretic signatures. Each phase involves aligning the connections of the neural network with patterns of information contained in the input dataset or preceding layers (as relevant). We also observe a process of low-dimensional category separation in the network as a function of learning. Our results offer a systems-level perspective of how artificial neural networks function - in terms of multi-stage reorganization of edge weights and activity patterns to effectively exploit the information content of input data during edge-weight training - while simultaneously enriching our understanding of the methods used by systems neuroscience.

neuroscience↗
bioRxiv2020

Information Confusion Reveals an Innate Limit of the Information Processing by Neurons

Information experiences complex transformation processes in the brain, involving various errors. A daunting and critical challenge in neuroscience is to understand the origin of these errors and their effects on neural information processing. While previous efforts have made substantial progresses in studying the information errors in bounded, unreliable and noisy transformation cases, it still remains elusive whether the neural system is inherently error-free under an ideal and noise-free condition. This work brings the controversy to an end with a negative answer. We propose a novel neural information confusion theory, indicating the widespread presence of information confusion phenomenon after the end of transmission process, which originates from innate neuron characteristics rather than external noises. Then, we reformulate the definition of zero-error capacity under the context of neuroscience, presenting an optimal upper bound of the zero-error transformation rates determined by the tuning properties of neurons. By applying this theory to neural coding analysis, we unveil the multi-dimensional impacts of information confusion on neural coding. Although it reduces the variability of neural responses and limits mutual information, it controls the stimulus-irrelevant neural activities and improves the interpretability of neural responses based on stimuli. Together, the present study discovers an inherent and ubiquitous precision limitation of neural information transformation, which shapes the coding process by neural ensembles. These discoveries reveal that the neural system is intrinsically error-prone in information processing even in the most ideal cases. Author summaryOne of the most central challenges in neuroscience is to understand the information processing capacity of the neural system. Decades of efforts have identified various errors in nonideal neural information processing cases, indicating that the neural system is not optimal in information processing because of the widespread presences of external noises and limitations. These incredible progresses, however, can not address the problem about whether the neural system is essentially error-free and optimal under ideal information processing conditions, leading to extensive controversies in neuroscience. Our work brings this well-known controversy to an end with a negative answer. We demonstrate that the neural system is intrinsically error-prone in information processing even in the most ideal cases, challenging the conventional ideas about the superior neural information processing capacity. We further indicate that the neural coding process is shaped by this innate limit, revealing how the characteristics of neural information functions and further cognitive functions are determined by the inherent limitation of the neural system.

neuroscience↗
bioRxiv2021

Modeling spectral changes following stimulation: Is there an event-related shift in 1/f activity?

Research into the nature of 1/f-like, non-oscillatory electrophysiological activity has grown exponentially in recent years in cognitive neuroscience. The shape of this activity has been linked to the balance between excitatory and inhibitory neural circuits, which is thought to be important for information processing. However, to date, it is not known whether the presentation of a stimulus induces changes in the parameters of 1/f activity, which are separable from the emergence of event-related potentials (ERPs). Here, we analyze event-related broadband changes in scalp-recorded EEG both before and after removing ERPs to demonstrate their confounding effect, and to establish whether there are genuine stimulus-induced changes in 1/f. Using data from a passive and an active auditory task (n=23), we found that the shape of the post-event spectra differed significantly from the pre-event spectra even after removing the frequency-content of ERPs. Further, a significant portion of this difference could be accounted for by a rotational shift in 1/f activity, manifesting as an increase in low and a decrease in high frequencies. Importantly, the magnitude of this rotational shift was related to the attentional demands of the task. This change in 1/f is consistent with increased inhibition following the onset of a stimulus, and likely reflects a disruption of ongoing excitatory activity proportional to processing demands. Finally, these findings contradict the central assumption of baseline normalization strategies in time-frequency analyses, namely that background EEG activity is stationary across time. As such, they have far-reaching consequences that cut across several subfields of neuroscience. Significance statementInterest in the functional role of the 1/f-like background brain activity has been growing exponentially in neuroscience. Yet, no study to date has demonstrated a clear relationship between information processing and 1/f activity by investigating event-related effects on its parameters. Here, we demonstrate for the first time that stimuli induce rotational changes in 1/f activity, independent from the occurrence of event-related potentials. Not only do these findings suggest that the assumption of stationary background activity in the analysis of neural oscillations is untenable, but they also suggest the presence of large-scale inhibition following stimulus onset, the magnitude of which is dependent on the demands of the task. These findings have far-reaching consequences that cut across several subfields of neuroscience.

neuroscience↗
bioRxiv2022

Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks

We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Although substantial progress has been made toward understanding the dynamics of learning in the brain, neuroscience-derived models of learning have yet to demonstrate the same performance capabilities as methods in deep learning such as gradient descent. Inspired by the successes of machine learning using gradient descent, we demonstrate that models of neuromodulated synaptic plasticity from neuroscience can be trained in Spiking Neural Networks (SNNs) with a framework of learning to learn through gradient descent to address challenging online learning problems. This framework opens a new path toward developing neuroscience inspired online learning algorithms.

neuroscience↗
bioRxiv2022

Class imbalance should not throw you off balance: Choosing classifiers and performance metrics for brain decoding with imbalanced data

Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG) and magnetoencephalography (MEG). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.

neuroscience↗
bioRxiv2022

Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural Networks

Mechanistic and computational models in neuroscience usually take the form of systems of differential or time-recursive equations. The spatio-temporal behavior of such systems is the subject of dynamical systems theory (DST). DST provides a powerful mathematical toolbox for describing and analyzing neurobiological processes at any level, from molecules to behavior, and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) became a popular machine learning tool for studying the nonlinear dynamics underlying neural or behavioral observations. By training RNNs on the same behavioral tasks as employed for animal subjects and dissecting their inner workings, insights and hypotheses about the neuro-computational underpinnings of behavior could be generated. Alternatively, RNNs may be trained directly on the physiological and behavioral time series at hand. Ideally, the once trained RNN would then be able to generate data with the same temporal and geometrical properties as those observed. This is called dynamical systems reconstruction, a burgeoning field in machine learning and nonlinear dynamics. Through this more powerful approach the trained RNN becomes a surrogate for the experimentally probed system, as far as its dynamical and computational properties are concerned. The trained system can then be systematically analyzed, probed and simulated. Here we will review this highly exciting and rapidly expanding field, including recent trends in machine learning that may as yet be less well known in neuroscience. We will also discuss important validation tests, caveats, and requirements of RNN-based dynamical systems reconstruction. Concepts and applications will be illustrated with various examples from neuroscience.

neuroscience↗
bioRxiv2022

Demographic Reporting and Phenotypic Exclusion in fNIRS

1.Functional near-infrared spectroscopy (fNIRS) promises to be a leading non-invasive neuroimaging method due to its portability and low cost. However, concerns are rising over its inclusivity of all skin tones and hair types (Parker and Ricard 2022, Webb et al 2022). Functional NIRS relies on direct contact of light-emitting optodes to the scalp, which can be blocked more by longer, darker, and especially curlier hair. Additionally, NIR light can be attenuated by melanin, which is accounted for in neither fNIRS hardware nor analysis methods. Recent work has shown that overlooking these considerations in other modalities like EEG leads to the disproportionate exclusion of individuals with these phenotypes - especially Black people - in both clinical and research literature (Bradford et al 2022, Choy 2020). In this article, we sought to determine if (1) biomedical optics developers and researchers report fNIRS performance variability between skin tones and hair textures, (2a) fNIRS neuroscience practitioners report phenotypic and demographic details in their articles, and thus, (2b) is a similar pattern of participant exclusion found in EEG also present in the fNIRS literature. We present a literature review of top Biomedical Optics and Human Neuroscience journals, showing that demographic and phenotypic reporting is unpopular in both fNIRS development and neuroscience applications. We conclude with a list of recommendations to the fNIRS community including examples of Black researchers addressing these issues head-on, inclusive best practices for fNIRS researchers, and recommendations to funding and regulatory bodies to achieve an inclusive neuroscience enterprise in fNIRS and beyond.

neuroscience↗
bioRxiv2024

eLemur: A cellular-resolution 3D atlas of the mouse lemur brain

The gray mouse lemur (Microcebus murinus), one of the smallest living primates, emerges as a promising model organism for neuroscience research. This is due to its genetic similarity to humans, its evolutionary position between rodents and humans, and its primate-like features encapsulated within a rodent-sized brain. Despite its potential, the absence of a comprehensive reference brain atlas impedes the progress of research endeavors in this species, particularly at the microscopic level. Existing references have largely been confined to the macroscopic scale, lacking detailed anatomical information. Here, we present eLemur, a new resource, comprising a repository of high-resolution brain-wide images immunostained with multiple cell type and structural markers, elucidating the cyto- and chemoarchitecture of the mouse lemur brain. Additionally, it encompasses a segmented two-dimensional (2D) reference and 3D anatomical brain atlas delineated into cortical, subcortical, and other vital regions. Furthermore, eLemur includes a comprehensive 3D cell atlas, providing densities and spatial distributions of non-neuronal and neuronal cells across the mouse lemur brain. Accessible via a web-based viewer (https://eeum-brain.com/#/lemurdatasets), the eLemur resource streamlines data sharing and integration, fostering the exploration of new hypotheses and experimental designs using the mouse lemur as a model organism. Moreover, in conjunction with the growing 3D datasets for rodents, non-human primates, and humans, our eLemur 3D digital framework enhances the potential for comparative analysis and translation research, facilitating the integration of extensive rodent study data into human studies. Significance StatementThe gray mouse lemur (Microcebus murinus) represents a promising model for neuroscience research, offering insights into brain structure and function due to its genetic and evolutionary proximity to humans and rodents. Our development of eLemur, a comprehensive 3D digital brain atlas, fills critical gaps in microscopic data availability, enabling nuanced investigations at cellular resolutions. This resource not only enhances our understanding of the mouse lemur brain but also catalyzes broader neuroscience endeavors. By facilitating data sharing and integration, eLemur empowers researchers to explore novel hypotheses and experimental designs. Moreover, its compatibility with existing neuroanatomy frameworks and the growing repository of 3D datasets positions eLemur as a pivotal tool for advancing basic and translational neuroscience studies.

neuroscience↗
bioRxiv2025

Following the Robot's Lead: Predicting Human and Robot Movement from EEG in a Motor Learning HRI Task

Human-robot interaction (HRI) offers unique opportunities to study the neuroscience of human motor control through controlled and reproducible sensory stimuli. In this study, we introduce an innovative neuroscience-HRI framework inspired by the Serial Reaction Time (SRT) task, that combines EEG with a task where a humanoid robot performs preprogrammed movement sequences that are mirrored by a human participant in real time. The use of a humanoid robot ensures precise and repeatable sensory-motor stimuli in the 3D peripersonal space of the participant, providing experimental conditions that may be challenging to replicate with traditional methods. Behavioral performance is assessed by measuring the temporal lag between human and robot movements, which decreases with training, reflecting motor sequence learning. Concurrently, EEG data from the human participant is analyzed to reveal neural correlates of learning and movement dynamics. Event-Related Spectral Perturbations (ERSP) in theta, mu, and beta frequency bands demonstrate distinct patterns associated with rest, fixation, and movement. Furthermore, the ERSP changes over successive trials reflect the progression of sequence learning, highlighting the relationship between neural oscillations and motor learning. A Markov-Switching Linear Regression model further decodes EEG signals to predict movement parameters including both human and robot position and velocity in a time-resolved manner. Our findings highlight the potential of HRI as a robust platform for neuroscience research and underscore the value of EEG-based neural decoding in understanding motor sequence learning. This work suggests further advances for integrating robotics into neuroscience and rehabilitation research.

neuroscience↗