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Results for “Neuroscience”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 271 records · Page 15Linked to original sources
bioRxiv2017

Increasing sensitivity and accuracy of brain-wide quantitative studies in light-sheet microscopy

Light-sheet microscopy (LSM) has proven a useful tool in neuroscience to image whole brains with high frame rates at cellular resolution. LSM is employed either in combination with tissue clearing to reconstruct the cyto-architecture over the entire mouse brain or with intrinsically transparent samples like zebrafish larvae for functional imaging. Inherently to LSM, however, residual opaque objects cause stripe artifacts, which obscure features of interest and, during functional imaging, modulate fluorescence variations related to neuronal activity. Here, we report how Bessel beams reduce streaking artifacts and produce high-fidelity structural data. Furthermore, using Bessel beams, we demonstrate a fivefold increase in sensitivity to calcium transients and a 20 fold increase in accuracy in the detection of activity correlations in functional imaging. Our results demonstrate the contamination of data by systematic and random errors through Gaussian illumination and furthermore quantify the increase in fidelity of such data when using Bessel beams.

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bioRxiv2017

Ultrasonic Neuromodulation Causes Widespread Cortical Activation via an Indirect Auditory Mechanism

Ultrasound has received widespread attention as an emerging technology for targeted, non-invasive neuromodulation based on its ability to evoke electrophysiological and motor responses in animals. However, little is known about the spatiotemporal pattern of ultrasound-induced brain activity that could drive these responses. Here, we address this question by combining focused ultrasound with wide-field optical imaging of calcium signals in transgenic mice. Surprisingly, we find cortical activity patterns consistent with indirect activation of auditory pathways rather than direct neuromodulation at the ultrasound focus. Ultrasound-induced activity is similar to that evoked by audible sound. Furthermore, both ultrasound and audible sound elicit motor responses consistent with a startle reflex, with both responses reduced by chemical deafening. These findings reveal an indirect auditory mechanism for ultrasound-induced cortical activity and movement requiring careful consideration in future development of ultrasonic neuromodulation as a tool in neuroscience research.

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bioRxiv2017

Fully unsupervised online spike sorting based on an artificial spiking neural network

Spike sorting is a crucial step of neural data processing widely used in neuroscience and neuroprosthetics. However, current methods remain not fully automatic and require heavy computations making them not embeddable in implantable devices. To overcome these limitations, we propose a novel method based on an artificial spiking neural network designed to process neural data online and completely automatically. An input layer continuously encodes the data stream into artificial spike trains, which are then processed by two further layers to output artificial trains of spikes reproducing the real spiking activity present in the input signal. The proposed method can be adapted to process several channels simultaneously in the case of tetrode recordings. It outperforms two existing algorithms at low SNR and has the advantage to be compatible with neuromorphic computing and the perspective of being embedded in very low-power analog systems for future implantable devices serving neurorehabilitation applications.

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bioRxiv2017

MEG/EEG group study with MNE: recommendations, quality assessments and best practices

Cognitive neuroscience questions are commonly tested with experiments that involve a cohort of subjects. The cohort can consist of a handful of subjects for small studies to hundreds or thousands of subjects in open datasets.\n\nWhile there exist various online resources to get started with the analysis of magnetoencephalography (MEG) or electroencephalography (EEG) data, such educational materials are usually restricted to the analysis of a single subject. This is in part because data from larger group studies are harder to share, but also analyses of such data are often require subject-specific decisions which are hard to document.\n\nThis work presents the results obtained by the reanalysis of an open dataset from Wakeman and Henson (2015) using the MNE software package. The analysis covers preprocessing steps, quality assurance steps, sensor space analysis of evoked responses, source localization, and statistics in both sensor and source space. Results with possible alternative strategies are presented and discussed at different stages such as the use of high-pass filtering versus baseline correction, tSSS versus SSS, the use of a minimum norm inverse versus LCMV beamformer, and the use of univariate or multivariate statistics. This aims to provide a comparative study of different stages of M/EEG analysis pipeline on the same dataset, with open access to all of the scripts necessary to reproduce this analysis.

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bioRxiv2018

Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks

Primates--including humans--can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks--such as those obtained here--could serve as direct guides for discovering such models.\n\nSIGNIFICANCE STATEMENTRecently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.

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bioRxiv2017

Three-photon fluorescence microscopy with an axially elongated Bessel focus

Volumetric imaging tools that are simple to adopt, flexible, and robust, are in high demand in the field of neuroscience, where the ability to image neurons and their networks with high spatiotemporal resolution is essential. Using an axially elongated focus approximating a Bessel beam, in combination with two-photon fluorescence microscopy, has proven successful at such an endeavor. Here we demonstrate three-photon fluorescence imaging with an axially extended Bessel focus. We use an axicon-based module which allowed for the generation of Bessel foci of varying numerical aperture and axial length, and apply this volumetric imaging tool to image mouse brain slices and for in vivo imaging of the mouse brain.

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bioRxiv2017

A topographic axis of transcriptional identity in thalamus

Uncovering common principles by which diverse modalities of information are processed is a fundamental goal in neuroscience. In mammalian brain, thalamus is the central processing station for inputs from sensory systems, subcortical motor systems, and cortex; a function subserved by over 30 defined nuclei1,2. Multiple thalamic nuclei send convergent information to each region of the forebrain, but whether there is a conserved architecture across the set of thalamic pathways projecting to each forebrain area has remained unresolved3-5. To uncover organizational principles of thalamic pathways, we produced a near-comprehensive transcriptomic atlas of thalamus. This revealed a common logic for thalamic nuclei serving all major cortical modalities. We found that almost all nuclei belong to one of three major profiles, with a given cortical area getting input from each of these profiles. These profiles lie on a single axis of variance aligned with the mediolateral axis of thalamus, and this axis is strongly enriched in genes encoding receptors and ion channels. We further show that each projection profile exhibits different electrophysiological signatures. Single-cell profiling revealed that rather than forming discrete classes, thalamic neurons lie on a spectrum, with intermediate cells existing between profiles. Thus, in contrast to canonical models of thalamus that suggest it is a switchboard primarily concerned with routing distinct modalities of information to distinct cortical regions, we show that the thalamocortical system is more akin to a molecularly-defined filter bank repeatedly applied across modality. Together, we reveal striking covariation in the organization of thalamic pathways serving all input modalities and output targets, establishing a simple and comprehensive thalamic functional architecture.

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bioRxiv2018

The influence of cortical depth on neuronal responses in mouse visual cortex

With the advent of two-photon imaging as a tool for systems neuroscience, the mouse has become a preeminent model system for studying sensory processing. One notable difference that has been found however, between mice and traditional model species like cats and primates is the responsiveness of the cortex. In the primary visual cortex of cats and primates, nearly all neurons respond to simple visual stimuli like drifting gratings. In contrast, imaging studies in mice consistently find that only around half of the neurons respond to such stimuli. Here we show that visual responsiveness is strongly dependent on the cortical depth of neurons. Moving from superficial layer 2 down to layer 4, the percentage of responsive neurons increases dramatically, ultimately reaching levels similar to what is seen in other species. Over this span of cortical depth, neuronal response amplitude also increases and orientation selectivity moderately decreases. These depth dependent response properties may be explained by the distribution of thalamic inputs in mouse V1. Unlike in cats and primates where thalamic projections to the granular layer are constrained to layer 4, in mice they spread up into layer 2/3, qualitatively matching the distribution of response properties we see. These results show that the analysis of neural response properties must take into consideration not only the overall cortical lamina boundaries but also the depth of recorded neurons within each cortical layer. Furthermore, the inability to drive the majority of neurons in superficial layer 2/3 of mouse V1 with grating stimuli indicates that there may be fundamental differences in the role of V1 between rodents and other mammals.

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bioRxiv2018

Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimers disease or depression have adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.

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bioRxiv2018

A computational framework for converting high-throughput DNA sequencing data into neural circuit connectivity.

There is growing interest in determining the connectivity of neural circuits-the "connectome"-at single neuron resolution. Most approaches to circuit mapping rely on either microscopy or physiology, but these approaches have very limited throughput. We have recently proposed BOINC (Barcoding of Individual Neuronal Connectivity), a radically different approach to connectivity mapping based on high-throughput DNA sequencing. Here we describe the set of computational algorithms that serve to convert sequencing data into neural connectivity. We apply our computational pipeline to the results of proof-of-principle experiments illustrating an implementation of BOINC based on pseudorabies virus (PRV). PRV is capable of traversing individual synapses and carry DNA barcodes from one cell to another. Using this high-throughput sequencing data, we obtain 456-by-486 connectivity matrix between putative neurons. An inexpensive high-throughput technique for establishing circuit connectivity at single neuron resolution would represent a major advance in neuroscience.

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bioRxiv2018

Sampling Neuron Morphologies

The intricate morphology of neurons has fascinated since the dawn of neuroscience, and yet, it is hard to synthesize them. Current algorithms typically define a growth process with parameters that allow matching aspects of the morphologies. However, such algorithmic growth processes are far simpler than the biological ones. What is needed is an algorithm that, given a database of morphologies, produces more of those. Here, we introduce a generator for neuron morphologies that is based on a statistical sampling process. Our Reversible Jump Markov chain Monte Carlo (RJMCMC) method starts with a trivial neuron and iteratively perturbs the morphology bringing the features close to those of the database. By quantifying the statistics of the generated neurons, we find that it outperforms growth-based models for many features. Good generative models for neuron morphologies promise to be important both for neural simulations and for morphology reconstructions from imaging data.

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bioRxiv2018

Finding decodable information that is read out in behaviour

Multivariate decoding methods applied to neuroimaging data have become the standard in cognitive neuroscience for unravelling statistical dependencies between brain activation patterns and experimental conditions. The current challenge is to demonstrate that information decoded as such by the experimenter is in fact used by the brain itself to guide behaviour. Here we demonstrate a promising approach to do so in the context of neural activation during object perception and categorisation behaviour. We first localised decodable information about visual objects in the human brain using a spatially-unbiased multivariate decoding analysis. We then related brain activation patterns to behaviour using a machine-learning based extension of signal detection theory. We show that while there is decodable information about visual category throughout the visual brain, only a subset of those representations predicted categorisation behaviour, located mainly in anterior ventral temporal cortex. Our results have important implications for the interpretation of neuroimaging studies, highlight the importance of relating decoding results to behaviour, and suggest a suitable methodology towards this aim.

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bioRxiv2018

Facilitated event-related power-modulations during transcranial alternating current stimulation (tACS) revealed by concurrent tACS-MEG

Non-invasive approaches to modulate oscillatory activity in the brain receive growing popularity in the scientific community. Transcranial alternating current stimulation (tACS) has been shown to modulate neural oscillations in a frequency specific manner. Due to a massive stimulation artifact at the targeted frequency, only little is known about effects of tACS during stimulation. I.e. it remains unclear how the continuous application of tACS affects event-related oscillations during cognitive tasks. Depending on whether tACS merely affects pre- or post-stimulus oscillations or both, stimulation can alter patterns of event-related oscillatory dynamics in various directions or may not affect them at all. Thus, knowledge about these directions is crucial to plan, predict and understand outcomes of solely behavioral tACS experiments. Here, a recently proposed procedure to suppress tACS artifacts by projecting MEG data into source space using spatial filtering was utilized to recover event-related power modulations in the alpha band during a mental rotation task. MEG of twenty-five volunteers was continuously recorded. After 10 minutes of baseline measurement, they received either 20 minutes of tACS at individual alpha frequency or sham stimulation. Another 40 minutes of MEG were acquired thereafter. Data were projected into source space and carefully examined for residual artifacts. Results revealed strong facilitation of event-related power modulations in the alpha band during tACS application. Data provide first direct evidence, that tACS does not counteract top-down suppression of intrinsic oscillations, but rather enhances pre-existent power modulations within the range of the individual alpha (=stimulation) frequency.\n\nSignificanceTranscranial alternating current stimulation (tACS) is increasingly used in cognitive neuroscience to study the causal role of brain oscillations and cognition. However, online effects of tACS so far largely remain a black box due to an intense electromagnetic artifact encountered during stimulation. The current study is the first to employ a spatial filtering approach to recover and systematically study event-related oscillatory dynamics during tACS, which can potentially be altered in various directions. TACS facilitated pre-existing patterns of oscillatory dynamics during the employed mental rotation task, but does not counteract or overwrite them. In addition, control analysis and a measure to quantify tACS artifact suppression are provided that can enrich future studies investigating tACS online effects.

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bioRxiv2018

Multi-scale mapping along the auditory hierarchy using high-resolution functional UltraSound in the awake ferret

A major challenge in neuroscience is to longitudinally monitor whole brain activity across multiple spatial scales in the same animal. Functional UltraSound (fUS) is an emerging technology that offers images of cerebral blood volume over large brain portions. Here we show for the first time its capability to resolve the functional organization of sensory systems at multiple scales in awake animals, both within structures by precisely mapping sensory responses, and between structures by elucidating the connectivity scheme of top-down projections. We demonstrate that fUS provides stable (over days), yet rapid, highly-resolved 3D tonotopic maps in the auditory pathway of awake ferrets, with unprecedented sharp functional resolution (100m). This was performed in four different brain regions, including small (1-2mm3 size), subcortical (8mm deep) and previously undescribed structures in the ferret. Furthermore, we used fUS to map longdistance projections from frontal cortex, a key source of sensory response modulation, to auditory cortex.

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bioRxiv2018

Rate fluctuations not steps dominate LIP activity during decision-making

The idea that lateral intraparietal cortex (LIP) integrates information for and against a decision, is one of the most popular models in neuroscience. However, a recent statistical analysis has suggested that LIP does not integrate information but that individual neurons activities jump. The result was based on a model comparison, which is often hard to interpret. There are two worries that can render comparisons problematic. (1) Important aspects of variance are contained in neither model. (2) The analysis is complicated, making it hard to verify. We thus followed up with a simple approach for model comparison: crossvalidation. We find evidence that baseline fluctuations describe much of the variance, which are properly modeled by neither the original papers drift-diffusion model, nor simple ramp or step models. Moreover, we find that our straightforward analysis strategy prefers ramping models, both with and without trial-by-trial baseline fluctuations. Our analysis, implementable in a few lines of code, suggests the importance of simple analyses.

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bioRxiv2018

Locally distributed abstraction of temporal distance in human parietal cortex

An enduring puzzle in the neuroscience of memory is how the brain parsimoniously situates past events by their order in relation to time. By combining functional MRI, and representational similarity analysis, we reveal a multivoxel representation of time intervals separating pairs of episodic event-moments in the posterior medial memory system, especially when the events were experienced within a similar temporal context. We further show such multivoxel representations to be vulnerable to disruption through targeted repetitive transcranial magnetic stimulation and that perturbation to the mnemonic abstraction alters the neural--behavior relationship across the wider parietal memory network. Our findings establish a mnemonic \"pattern-based\" code of temporal distances in the human brain, a fundamental neural mechanism for supporting the temporal structure of past events, assigning the precuneus as a locus of flexibly effecting the manipulation of physical time during episodic memory retrieval.

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bioRxiv2018

High precision automated detection of labelednuclei in terabyte-scale whole-brain volumetricimage data of mouse

There is a need in modern neuroscience for accurate and automated image processing techniques for analyzing the large volume of neuroanatomical data. For e.g., the use of light microscopy to image whole mouse brains in a mesoscopic scale produces individual neuroanatomical data volumes in the TerraByte range.. A fundamental task involves the detection and quantification of objects of a given type, e.g. neuronal nuclei or somata, in whole mouse brains. Traditionally this quantification is performed by human visual inspection with high accuracy, that is not scalable.. When state-of-the-art CNN and SVM-based methods are used to solve this classification problem, they achieve accuracy levels between 85 - 92%. However, higher rates of precision and recall, close to that of humans are necessary. In this paper, we describe an unsupervised, iterative algorithm, which provides a high close to human performance for a specific problem of broad interest, i.e. detection of Green Fluorescent Protein labeled nuclei in whole mouse brains. The algorithm judiciously combines classical computer vision (CV) techniques and is focused on the complex problem of decomposing strong overlapped objects (nuclei). Our proposed iterative method uses features detected on ridge lines over distance transformation and an arc based iterative spatial-filling method to solve the problem. We demonstrate our results on two whole mouse brain data sets of Gigabyte resolution and compare it with manual annotation of the brains. Our results show that an aptly designed CV algorithm with classical feature extractors when tailored to this problem of interest achieves near-ideal human-like performance. Quantitative analysis, when compared with the manually annotated ground truth, reveals that our approach performs better on whole mouse brain scans than general purpose machine learning (including deep CNN) methods.

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bioRxiv2018

Lost in Space: The Impact of Traditional Neuroimaging Methods on the Spatial Localization of Cortical Areas

Localizing human brain functions is a long-standing goal in systems neuroscience. Towards this goal, neuroimaging studies have traditionally used volume-based smoothing, registered data to volume-based standard spaces, and reported results relative to volume-based parcellations. A novel 360-area surface-based cortical parcellation was recently generated using multimodal data from the Human Connectome Project (HCP), and a volume-based version of this parcellation has frequently been requested for use with traditional volume-based analyses. However, given the major methodological differences between traditional volumetric and HCP-style processing, the utility and interpretability of such an altered parcellation must first be established. By starting from automatically generated individual-subject parcellations and processing them with different methodological approaches, we show that traditional processing steps, especially volume-based smoothing and registration, substantially degrade cortical area localization when compared to surface-based approaches. We also show that surface-based registration using features closely tied to cortical areas, rather than to folding patterns alone, improves the alignment of areas, and that the benefits of high resolution acquisitions are largely unexploited by traditional volume-based methods. Quantitatively, we show that the most common version of the traditional approach has spatial localization that is only 35% as good as the best surface-based method as assessed with two objective measures (peak areal probabilities and captured area fraction for maximum probability maps). Finally, we demonstrate that substantial challenges exist when attempting to accurately represent volume-based group analysis results on the surface, which has important implications for the interpretability of studies, both past and future, that use these volume-based methods.\n\nSignificance StatementMost human brain imaging studies have traditionally used low-resolution images, inaccurate methods of cross-subject alignment, and extensive blurring. Recently, a high-resolution approach with more accurate alignment and minimized blurring was used by the Human Connectome Project to generate a multi-modal map of human cortical areas in hundreds of individuals. Starting from this data, we systematically compared these two approaches, showing that the traditional approach is nearly three times worse than the HCPs improved approach in two objective measures of spatial localization of cortical areas. Further, we demonstrate considerable challenges in comparing data across the two approaches, and, as a result, argue that there is an urgent need for the field to adopt more accurate methods of data acquisition and analysis.

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