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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 Preprints are not peer reviewed.

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At least 163 records · Page 9Linked to original sources

bioRxiv2026

Technology-enhanced learning in undergraduate neuroscience education: tractography-based virtual dissection in psychology

Background: Neuroanatomy poses a significant challenge for Psychology students due to its spatial and conceptual complexity. Educational approaches that enhance the relevance and visualization of neuroanatomical content may improve students learning experiences. This study implemented a tractography-based activity focused on the virtual dissection of the arcuate fasciculus, a major white matter pathway, in undergraduate Psychology students and examined the relationships between perceived learning and students perceptions of utility, difficulty and handling, and organizational aspects of the activity. Methods: First-year undergraduate Psychology students participated in a two-session tractography-based activity combining instruction on white matter anatomy and diffusion tractography with a hands-on virtual dissection of the arcuate fasciculus using research-grade software routinely employed in neuroscience research. Following the activity, students completed an anonymous questionnaire assessing perceived learning, utility, difficulty and handling, and organizational aspects of the activity. Pearson correlations, multiple regression analyses, and relative importance analyses were performed. Results: Sixty-eight students completed the questionnaire. Students reported generally positive perceptions of the activity across the evaluated dimensions, with perceived learning receiving the highest mean score (M = 3.44, SD = .85). Perceived utility showed the strongest association with perceived learning (r = .64, p < .001). The regression model explained 41% of the variance in perceived learning (R2 = .41, adjusted R2 = .38, p < .001). Perceived utility was the only significant predictor in the model ({beta} = .59, p = .001), accounting for 67.9% of the explained variance. Conclusions: The findings support the feasibility of integrating authentic neuroimaging tools into undergraduate neuroanatomy teaching. Students who perceived the activity as more useful also reported higher perceived learning outcomes, with perceived utility emerging as the strongest predictor of perceived learning. In contrast, perceived difficulty and handling, and organizational aspects did not make significant independent contributions. These results suggest that students perceptions of educational relevance may play an important role in technology-enhanced STEM learning experiences.

scientific communication and education↗

bioRxiv2014

Untangling cross-frequency coupling in neuroscience

Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.\n\nHighlightsFundamental caveats and confounds in the methodology of assessing CFC are discussed.\n\nSignificant CFC can be observed without any underlying physiological coupling.\n\nNon-stationarity of a time-series leads to spectral correlations interpreted as CFC.\n\nWe offer practical recommendations, which can relieve some of the current confounds.\n\nFurther theoretical and experimental work is needed to ground the CFC analysis.

Neuroscience↗

bioRxiv2016

PackIO and EphysViewer: software tools for acquisition and analysis of neuroscience data

We present an open-source synchronization software package, PackIO, that can record and generate voltage signals to enable complex experimental paradigms across multiple devices. This general purpose package is built on National Instruments data acquisition and generation hardware and has temporal precision up to the limit of the hardware. PackIO acts as a flexibly programmable master clock that can record experimental data (e.g. voltage traces), timing data (e.g. event times such as imaging frame times) while generating stimuli (e.g. voltage waveforms, voltage triggers to drive other devices, etc.). PackIO is particularly useful to record from and synchronize multiple devices, for example when simultaneously acquiring electrophysiology while generating and recording imaging timing data. Experimental control is easily enabled by an intuitive graphical user interface. We also release an open-source data visualisation and analysis tool, EphysViewer, written in MATLAB, as well as a module to import data into Python. These flexible and programmable tools allow experimenters to configure and set up customised input and output protocols in a synchronized fashion for controlling, recording, and analysing experiments.

Neuroscience↗

bioRxiv2017

Comparative transcriptome profiling of the human and mouse dorsal root ganglia: An RNA-seq-based resource for pain and sensory neuroscience research

Molecular neurobiological insight into human nervous tissues is needed to generate next generation therapeutics for neurological disorders like chronic pain. We obtained human Dorsal Root Ganglia (DRG) samples from organ donors and performed RNA-sequencing (RNA-seq) to study the human DRG (hDRG) transcriptional landscape, systematically comparing it with publicly available data from a variety of human and orthologous mouse tissues, including mouse DRG (mDRG). We characterized the hDRG transcriptional profile in terms of tissue-restricted gene co-expression patterns and putative transcriptional regulators, and formulated an information-theoretic framework to quantify DRG enrichment. Our analyses reveal an hDRG-enriched protein-coding gene set (~140), some of which have not been described in the context of DRG or pain signaling. A majority of these show conserved enrichment in mDRG, and were mined for known drug - gene product interactions. Comparison of hDRG and tibial nerve transcriptomes suggest pervasive mRNA transport of sensory neuronal genes to axons in adult hDRG, with potential implications for mechanistic insight into chronic pain in patients. Relevant gene families and pathways were also analyzed, including transcription factors (TFs), g-protein coupled receptors (GCPRs) and ion channels. We present our work as an online, searchable repository (http://www.utdallas.edu/bbs/painneurosciencelab/DRGtranscriptome), creating a valuable resource for the community. Our analyses provide insight into DRG biology for guiding development of novel therapeutics, and a blueprint for cross-species transcriptomic analyses.\n\nSummaryWe generated RNA sequencing data from human DRG samples and comprehensively compared this transcriptome to other human tissues and a matching panel of mouse tissues. Our analysis uncovered functionally enriched genes in the human and mouse DRG with important implications for understanding sensory biology and pain drug discovery.

neuroscience↗

bioRxiv2017

Probabilistic language models in cognitive neuroscience: promises and pitfalls

Cognitive neuroscientists of language comprehension study how neural computations relate to cognitive computations during comprehension. On the cognitive part of the equation, it is important that the computations and processing complexity are explicitly defined. Probabilistic language models can be used to give a computationally explicit account of language complexity during comprehension. Whereas such models have so far predominantly been evaluated against behavioral data, only recently have the models been used to explain neurobiological signals. Measures obtained from these models emphasize the probabilistic, information-processing view of language understanding and provide a set of tools that can be used for testing neural hypotheses about language comprehension. Here, we provide a cursory review of the theoretical foundations and example neuroimaging studies employing probabilistic language models. We high-light the advantages and potential pitfalls of this approach and indicate avenues for future research.

neuroscience↗

bioRxiv2020

Opto-E-Dura: a Soft, Stretchable ECoG Array for Multimodal, Multi-scale Neuroscience

Soft, stretchable materials hold great promise for the fabrication of biomedical devices due to their capacity to integrate gracefully with and conform to biological tissues. Conformal devices are of particular interest in the development of brain interfaces where rigid structures can lead to tissue damage and loss of signal quality over the lifetime of the implant. Interfaces to study brain function and dysfunction increasingly require multimodal access in order to facilitate measurement of diverse physiological signals that span the disparate temporal and spatial scales of brain dynamics. Here we present the Opto-e-Dura, a soft, stretchable, 16-channel electrocorticography array that is optically transparent. We demonstrate its compatibility with diverse optical and electrical readouts enabling multimodal studies that bridge spatial and temporal scales. The device is chronically stable for weeks, compatible with wide-field and 2-photon calcium imaging and permits the repeated insertion of penetrating multi-electrode arrays. As the variety of sensors and effectors realizable on soft, stretchable substrates expands, similar devices that provide large-scale, multimodal access to the brain will continue to improve fundamental understanding of brain function.

neuroscience↗

bioRxiv2020

Collegiate athlete brain data for white matter mapping and network neuroscience

We describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group. The dataset shares minimally processed diffusion-weighted magnetic resonance imaging (dMRI) data, three models of the diffusion signal in the voxel, full-brain tractograms, segmentation of the major white matter tracts as well as structural connectivity matrices. There is currently a paucity of similar datasets openly shared. Furthermore, major challenges are associated with collecting this type of data. The data and derivatives shared here can be used as a reference to study the effects of long-term exposure to collegiate athletics, such as the effects of repetitive head impacts. We use advanced anatomical and dMRI data processing methods publicly available as reproducible web services at brainlife.io.

neuroscience↗

bioRxiv2020

Removing independent noise in systems neuroscience data using DeepInterpolation

Progress in nearly every scientific discipline is hindered by the presence of independent noise in spatiotemporally structured datasets. Three widespread technologies for measuring neural activity--calcium imaging, extracellular electrophysiology, and fMRI--all operate in domains in which shot noise and/or thermal noise deteriorate the quality of measured physiological signals. Current denoising approaches sacrifice spatial and/or temporal resolution to increase the Signal-to-Noise Ratio of weak neuronal events, leading to missed opportunities for scientific discovery. Here, we introduce DeepInterpolation, a general-purpose denoising algorithm that trains a spatio-temporal nonlinear interpolation model using only noisy samples from the original raw data. Applying DeepInterpolation to in vivo two-photon Ca2+ imaging yields up to 6 times more segmented neuronal segments with a 15 fold increase in single pixel SNR, uncovering network dynamics at the single-trial level. In extracellular electrophysiology recordings, DeepInterpolation recovered 25% more high-quality spiking units compared to a standard data analysis pipeline. On fMRI datasets, DeepInterpolation increased the SNR of individual voxels 1.6-fold. All these improvements were attained without sacrificing spatial or temporal resolution. DeepInterpolation could well have a similar impact in other domains for which independent noise is present in experimental data.

neuroscience↗

bioRxiv2020

More than the sum of its parts: Merging network psychometrics and network neuroscience with application in autism

Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach to understanding brain function in neuroimaging research. Combining these parallel approaches would enable new insights into the interaction between behaviours and their brain-level correlates. In this paper, we introduce a methodology for combining network psychometrics and network neuro-science. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10-21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. In addition, because the shared variance between behavioural measures is controlled for in the approach, the methodology enables us to isolate mechanisms at the brain-level that are unique to particular behavioural variables. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates.

neuroscience↗

bioRxiv2020

Nonsense correlations in neuroscience

Many neurophysiological signals exhibit slow continuous trends over time. Because standard correlation analyses assume that all samples are independent, they can yield apparently significant "nonsense correlations" even for signals that are completely unrelated. Here we compare the performance of several methods for assessing correlations between timeseries, using simulated slowly drifting signals with and without genuine correlations. The best performance was obtained from a "pseudosession method", which relies on one of the signals being randomly generated by the experimenter, or a "session perturbation" method which requires multiple recordings under the same conditions. If neither of these is applicable, a "linear shift" method can be used when one of the signals is stationary. Methods based on cross-validation, circular shifting, phase randomization, or detrending gave up to 100% false positive rates in our simulations. We conclude that analysis of neural timeseries is best performed when stationarity and randomization is built into the experimental design.

neuroscience↗

bioRxiv2021

pyControl: Open source, Python based, hardware and software for controlling behavioural neuroscience experiments.

Laboratory behavioural tasks are an essential research tool. As questions asked of behaviour and brain activity become more sophisticated, the ability to specify and run richly structured tasks becomes more important. An increasing focus on reproducibility also necessitates accurate communication of task logic to other researchers. To these ends we developed pyControl, a system of open source hardware and software for controlling behavioural experiments comprising; a simple yet flexible Python-based syntax for specifying tasks as extended state machines, hardware modules for building behavioural setups, and a graphical user interface designed for efficiently running high throughput experiments on many setups in parallel, all with extensive online documentation. These tools make it quicker, easier and cheaper to implement rich behavioural tasks at scale. As important, pyControl facilitates communication and reproducibility of behavioural experiments through a highly readable task definition syntax and self-documenting features. ResourcesDocumentation: https://pycontrol.readthedocs.io Repositories: https://github.com/pyControl User support: https://groups.google.com/g/pycontrol

neuroscience↗

bioRxiv2021

Cellular, physiological, and behavioral validation of a CRFR1:Cre-tdTomato transgenic rat for use in basic neuroscience research

Corticotropin-releasing factor type-1 (CRF1) receptors are critical to stress responses because they allow neurons to respond to CRF released in response to stress. Our understanding of the precise role of CRF1-expressing neuronal populations in CRF-mediated behaviors has been largely limited to mouse experiments due to the lack of genetic tools available to selectively visualize and manipulate CRF1+ cells in rats. Here, we describe the generation and validation of a transgenic CRF1:Cre-tdTomato rat, which expresses a bicistronic iCre-2A-tdTomato transgene directed by 200kb of promoter and enhacer sequence surrounding the Crhr1 cDNA present within a BAC clone, that has been transgenically inserted into the rat genome. We report that Crhr1 and Cre mRNA expression are highly colocalized in CRF1:Cre-tdTomato rats within both the central amygdala (CeA), composed of mostly GABAergic neurons, and in the basolateral amygdala (BLA), composed of mostly glutamatergic neurons. In the CeA, membrane properties, inhibitory synaptic transmission, and responses to CRF bath application in tdTomato+ neurons are similar to those previously reported in GFP+ cells in CRFR1-GFP mice. We show that stimulatory DREADD receptors can be selectively targeted to CeA CRF1+ cells via virally delivered Cre-dependent transgenes, that transfected Cre/tdTomato+ cells are activated by clozapine-n-oxide in vitro and in vivo, and that activation of these cells in vivo increases anxiety-like behavior and nocifensive responses. Given the accuracy of expression in the CRF1:Cre rat, modern genetic techniques used to investigate the anatomy, physiology, and behavioral function of CRF1+ neurons and circuits can now be performed in assays that require the use of rats as the model organism. Impact StatementA novel transgenic rat for studying the role of specific corticotropin-releasing factor type-1 receptor-expressing cell populations in physiology and behavior.

neuroscience↗

bioRxiv2021

Rapid generation of conditional knockout mice using the CRISPR-CAS9 system and electroporation for neuroscience research

The Cre/Loxp-based conditional knockout technology is a powerful tool for gene function analyses by allowing region-time-specific gene manipulation. However, inserting a pair of LoxP cassettes for generating conditional knock-out can be technically challenging and thus time- and resource-consuming. This study proposes an efficient, low-cost method to generate floxed mice using the in vitro fertilization and the CRISPR-Cas9 system over two consecutive generations. This method allowed us to produce floxed mice targeting exon 5 to exon 6 of CaMK1 in a short period, 125 days, using only 16 mice. The efficiency of generating floxed mice was 10%, significantly higher than the conventional ES cell-based method. We directly edited the genome of C57BL/6N fertilized eggs, our target genetic background, to eliminate additional backcrossing steps. We confirmed that the genome of this floxed mouse is responsive to Cre protein. This low-cost, highly efficient method for generating conditional knock-out will facilitate comprehensive, tissue-specific genome analyses.

neuroscience↗

bioRxiv2021

A Neuroscience-Inspired Spiking Neural Network for Auditory Spatial Attention Detection Using Single-Trial EEG

Recently, studies have shown that the alpha band (8-13 Hz) EEG signals enable the decoding of auditory spatial attention. However, deep learning methods typically requires a large amount of training data. Inspired by "sparse coding" in cortical neurons, we propose a spiking neural network model for auditory spatial attention detection. The model is composed of three neural layers, two of them are spiking neurons. We formulate a new learning rule that is based on firing rate of pre-synaptic and post-synaptic neurons in the first layer and the second layer of spiking neurons. The third layer consists of 10 spiking neurons that the pattern of their firing rate after training is used in test phase of the method. The proposed method extracts the patterns of recorded EEG of leftward and rightward attention, independently, and uses them to train network to detect the auditory spatial attention. In addition, a computational approach is presented to find the best single-trial EEG data as training samples of leftward and rightward attention EEG. In this model, the role of using low connectivity rate of the layers and specific range of learning parameters in sparse coding is studied. Importantly, unlike most prior model, our method requires 10% of EEG data as training data and has shown 90% accuracy in average. This study suggests new insights into the role of sparse coding in both biological networks and brain-inspired machine learning.

neuroscience↗

bioRxiv2021

PiRATeMC: A highly flexible, scalable, and affordable system for obtaining high quality video recordings for behavioral neuroscience.

With the recent development and rapidly accelerating adoption of machine-learning based rodent behavioral tracking tools such as DeepLabCut, there is an unmet need for a method of acquiring video data that is scalable, flexible, and affordable. Many experimenters use webcams, GoPros, or other commercially available cameras that are not only relatively expensive, but offer very little flexibility over recording parameters. These cameras are not ideal for recording many types of behavioral experiments, and can lead to suboptimal video quality. Furthermore when using relatively affordable commercially available products, it is a challenge, if not impossible, to synchronize multiple cameras with each other, or to interface with third-party equipment (for example, receiving a simple trigger to simultaneously start recording, or acting as a microcontroller for closed-loop experiments). We have developed an affordable ecosystem of behavioral recording equipment, PiRATeMC (Pi-based Remote Acquisition Technology for Motion Capture), that relies on Raspberry Pi Camera Boards that are able to acquire high quality recordings in bright light, low light, or dark conditions under infrared light. PiRATeMC offers users control over nearly every recording parameter, and can be fine-tuned to produce optimal video data in any behavioral arena. This setup can easily be scaled up and synchronously controlled in clusters via a self-contained network to record a large number of simultaneous behavioral sessions without burdening institutional network infrastructure. Furthermore, the Raspberry Pi is an excellent platform for novice and inexperienced programmers interested in using an open-source recording system, with a large online community that is very active in developing novel open-source tools. It easily interfaces with Arduinos and other microcontrollers, allowing simple synchronization and interfacing of video recording with nearly any behavioral equipment using GPIO pins to send or receive 3.3V or 5V (TTL) signals, I2C, or serial communication.

neuroscience↗

bioRxiv2021

Characterising the Photoplethysmography Pulse Waveform for Use in Human Neuroscience: The Hybrid Excess and Decay (HED) Model

Photoplethysmography (PPG) offers a widely-used, convenient and non-invasive approach to monitoring basic indices of cardiovascular function such as heart rate and blood oxygenation. However, while the pulse waveform, generated by PPG comprises features that are shaped by physiological and psychological factors, it is frequently overlooked in analyses of such data. We suggest that studies could be enriched by exploiting the possibilities afforded by a systematic analysis of PPG waveforms. To do this we initially require a robust and automated means of characterising it, thereby allowing us to examine variations across individuals and between different physiological and psychological contexts. We present a psychophysiologically-relevant model, the Hybrid Excess and Decay (HED) Model, which characterises pulse wave morphology in terms of three underlying pressure waves and a decay function. We show that these parameters capture PPG data with a high degree of precision and, moreover, are sensitive to specific, physiologically-relevant changes within individuals. We present the theoretical and practical basis for the model and demonstrate its performance when applied to a pharmacological dataset of 105 participants receiving intravenous administrations of the sympathomimetic drug isoproterenol (Isoprenaline). We conclude by discussing the possible value in using the HED model to complement standard measures of PPG outputs.

neuroscience↗

bioRxiv2021

Home alone: A population neuroscience investigation of brain morphology substrates

As a social species, ready exchange with peers is a pivotal asset - our "social capital". Yet, single-person households have come to pervade metropolitan cities worldwide, with unknown consequences in the long run. Here, we systematically explore the morphological manifestations associated with singular living in [~]40,000 UK Biobank participants. The uncovered population-level signature spotlights the highly associative default mode network, in addition to findings such as in the amygdala central, cortical and corticoamygdaloid nuclei groups, as well as the hippocampal fimbria and dentate gyrus. Sex-stratified analyses revealed male-specific neural substrates, including somatomotor, saliency and visual systems, while female-specific neural substrates centred on the dorsomedial prefrontal cortex. In line with our demographic profiling results, the discovered neural imprint of living alone is potentially linked to alcohol and tobacco consumption, anxiety, sleep quality as well as daily TV watching. The secular trend for solitary living will require new answers from public-health decision makers.

neuroscience↗

bioRxiv2021

Towards real-world neuroscience using mobile EEG and augmented reality

Our visual environment impacts multiple aspects of cognition including perception, attention and memory, yet most studies traditionally remove or control the external environment. As a result, we have a limited understanding of neurocognitive processes beyond the controlled lab environment. Here, we aim to study neural processes in real-world environments, while also maintaining a degree of control over perception. To achieve this, we combined mobile EEG (mEEG) and augmented reality (AR), which allows us to place virtual objects into the real world. We validated this AR and mEEG approach using a well-characterised cognitive response - the face inversion effect. Participants viewed upright and inverted faces in three EEG tasks (1) a lab-based computer task, (2) walking through an indoor environment while seeing face photographs, and (3) walking through an indoor environment while seeing virtual faces. We find greater low frequency EEG activity for inverted compared to upright faces in all experimental tasks, demonstrating that cognitively relevant signals can be extracted from mEEG and AR paradigms. This was established in both an epoch-based analysis aligned to face events, and a GLM-based approach that incorporates continuous EEG signals and face perception states. Together, this research helps pave the way to exploring neurocognitive processes in real-world environments while maintaining experimental control using AR.

neuroscience↗