Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 67,545 bioRxiv papers from 297,698 authors.
Most downloaded bioRxiv papers, all time
in category neuroscience
11,878 results found. For more information, click each entry to expand.
4,801 downloads neuroscience
Johan Winnubst, Erhan Bas, Tiago A. Ferreira, Zhuhao Wu, Michael N Economo, Patrick Edson, Ben J. Arthur, Christopher Bruns, Konrad Rokicki, David Schauder, Donald J. Olbris, Sean D. Murphy, David G. Ackerman, Cameron Arshadi, Perry Baldwin, Regina Blake, Ahmad Elsayed, Mashtura Hasan, Daniel Ramirez, Bruno Dos Santos, Monet Weldon, Amina Zafar, Joshua T. Dudmann, Charles R Gerfen, Adam W Hantman, Wyatt Korff, Scott M. Sternson, Nelson Spruston, Karel Svoboda, Jayaram Chandrashekar
Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons comprise more than 75 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.
4,795 downloads neuroscience
Serial and parallel processing in visual search have been long debated in psychology but the processing mechanism remains an open issue. Serial processing allows only one object at a time to be processed, whereas parallel processing assumes that various objects are processed simultaneously. Here we present novel neural models for the two types of processing mechanisms based on analysis of simultaneously recorded spike trains using electrophysiological data from prefrontal cortex of rhesus monkeys while processing task-relevant visual displays. We combine mathematical models describing neuronal attention and point process models for spike trains. The same model can explain both serial and parallel processing by adopting different parameter regimes. We present statistical methods to distinguish between serial and parallel processing based on both maximum likelihood estimates and decoding analysis of the attention when two stimuli are presented simultaneously. Results show that both processing mechanisms are in play for the simultaneously recorded neurons, but neurons tend to follow parallel processing in the beginning after the onset of the stimulus pair, whereas they tend to serial processing later on. This could be explained by parallel processing being related to sensory bottom-up signals or feedforward processing, which typically occur in the beginning after stimulus onset, whereas top-down signals related to cognitive modulatory influences guiding attentional effects in recurrent feedback connections occur after a small delay, and is related to serial processing, where all processing capacities are being directed towards the attended object.
4,724 downloads neuroscience
Reconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites. Automated approaches have been developed to perform the tracing, but without costly human proofreading their error rates are too high to obtain reliable circuit diagrams. We present a method for automated segmentation that, like the majority of previous efforts, employs convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of the reconstructed shape of individual neural processes. We used this technique, which we call flood-filling networks, to trace neurons in a data set obtained by serial block-face electron microscopy from a male zebra finch brain. Our method achieved a mean error-free neurite path length of 1.1 mm, an order of magnitude better than previously published approaches applied to the same dataset. Only 4 mergers were observed in a neurite test set of 97 mm path length.
4,652 downloads neuroscience
Morlet wavelets are frequently used for time-frequency analysis of non-stationary time series data, such as neuroelectrical signals recorded from the brain. The crucial parameter of Morlet wavelets is the width of the Gaussian that tapers the sine wave. This width parameter controls the trade-off between temporal precision and frequency precision. It is typically defined as the "number of cycles," but this parameter is opaque, and often leads to uncertainty and suboptimal analysis choices, as well as being difficult to interpret and evaluate. The purpose of this paper is to present alternative formulations of Morlet wavelets in time and in frequency that allow parameterizing the wavelets directly in terms of the desired temporal and spectral smoothing (as full-width at half-maximum). This formulation provides clarity on an important data analysis parameter, and should facilitate proper analyses, reporting, and interpretation of results. MATLAB code is provided.
4,650 downloads neuroscience
CLARITY is a tissue clearing method, which enables immunostaining and imaging of large volumes for 3D-reconstruction. The method was initially time-consuming, expensive and relied on electrophoresis to remove lipids to make the tissue transparent. Since then several improvements and simplifications have emerged, such as passive clearing (PACT) and methods to improve tissue staining. Here, we review advances and compare current applications with the aim of highlighting needed improvements as well as aiding selection of the specific protocol for use in future investigations.
4,641 downloads neuroscience
Genetically encoded fluorescent proteins and immunostainings are widely used to detect cellular or sub-cellular structures in thick biological samples. However, each approach suffers from limitations, including low signal and limited spectral flexibility or slow speed, poor penetration and high background, respectively. Here we overcome these limitations by using transgenically expressed chemical tags for rapid, even and low-background labeling of thick biological tissues. We construct a platform of widely applicable transgenic Drosophila reporter lines, demonstrating that chemical labeling can accelerate staining of whole-mount fly brains by a factor of 100x. Together, this tag-based approach drastically improves the speed and specificity of labeling genetically marked cells in intact and/or thick biological samples.
4,615 downloads neuroscience
Rebecca D Hodge, Trygve E Bakken, Jeremy A Miller, Kimberly A Smith, Eliza R Barkan, Lucas T Graybuck, Jennie L Close, Brian Long, Osnat Penn, Zizhen Yao, Jeroen Eggermont, Thomas Hollt, Boaz P Levi, Soraya I Shehata, Brian Aevermann, Allison Beller, Darren Bertagnolli, Krissy Brouner, Tamara Casper, Charles Cobbs, Rachel Dalley, Nick Dee, Song-Lin Ding, Richard G Ellenbogen, Olivia Fong, Emma Garren, Jeff Goldy, Ryder P Gwinn, Daniel Hirschstein, C Dirk Keene, Mohamed Keshk, Andrew L Ko, Kanan Lathia, Ahmed Mahfouz, Zoe Maltzer, Medea McGraw, Thuc Nghi Nguyen, Julie Nyhus, Jeffrey G Ojemann, Aaron Oldre, Sheana Parry, Shannon Reynolds, Christine Rimorin, Nadiya V Shapovalova, Saroja Somasundaram, Aaron Szafer, Elliot R Thomsen, Michael Tieu, Richard H Scheuermann, Rafael MD Yuste, Susan M Sunkin, Boudewijn Lelieveldt, David Feng, Lydia Ng, Amy Bernard, Michael Hawrylycz, John W Phillips, Bosiljka Tasic, Hongkui Zeng, Allan R Jones, Christof Koch, Ed S Lein
Elucidating the cellular architecture of the human neocortex is central to understanding our cognitive abilities and susceptibility to disease. Here we applied single nucleus RNA-sequencing to perform a comprehensive analysis of cell types in the middle temporal gyrus of human cerebral cortex. We identify a highly diverse set of excitatory and inhibitory neuronal types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to a similar mouse cortex single cell RNA-sequencing dataset revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of human cell type properties. Despite this general conservation, we also find extensive differences between homologous human and mouse cell types, including dramatic alterations in proportions, laminar distributions, gene expression, and morphology. These species-specific features emphasize the importance of directly studying human brain.
4,596 downloads neuroscience
Michael N Economo, Sarada Viswanathan, Bosiljka Tasic, Erhan Bas, Johan Winnubst, Vilas Menon, Lucas T Graybuck, Thuc Nghi Nguyen, Lihua Wang, Charles R Gerfen, Jayaram Chandrashekar, Hongkui Zeng, Loren L Looger, Karel Svoboda
Activity in motor cortex predicts specific movements, seconds before they are initiated. This preparatory activity has been observed in L5 descending "pyramidal tract" (PT) neurons. A key question is how preparatory activity can be maintained without causing movement, and how preparatory activity is eventually converted to a motor command to trigger appropriate movements. We used single cell transcriptional profiling and axonal reconstructions to identify two types of PT neuron. Both types share projections to multiple targets in the basal ganglia and brainstem. One type projects to thalamic regions that connect back to motor cortex. In a delayed-response task, these neurons produced early preparatory activity that persisted until the movement. The second type projects to motor centers in the medulla and produced late preparatory activity and motor commands. These results indicate that two motor cortex output neurons are specialized for distinct roles in motor control.
4,501 downloads neuroscience
Klaus Maier-Hein, Peter Neher, Jean-Christophe Houde, Marc-Alexandre Côté, Eleftherios Garyfallidis, Jidan Zhong, Maxime Chamberland, Fang-Cheng Yeh, Ying-Chia Lin, Qing Ji, Wilburn E. Reddick, John O. Glass, David Qixiang Chen, Yuanjing Feng, Chengfeng Gao, Ye Wu, Jieyan Ma, H Renjie, Qiang Li, Carl-Fredrik Westin, Samuel Deslauriers-Gauthier, J. Omar Ocegueda González, Michael Paquette, Samuel St-Jean, Gabriel Girard, François Rheault, Jasmeen Sidhu, Chantal M.W. Tax, Fenghua Guo, Hamed Y. Mesri, Szabolcs Dávid, Martijn Froeling, Anneriet M. Heemskerk, Alexander Leemans, Arnaud Boré, Basile Pinsard, Christophe Bedetti, Matthieu Desrosiers, Simona Brambati, Julien Doyon, Alessia Sarica, Roberta Vasta, Antonio Cerasa, Aldo Quattrone, Jason Yeatman, Ali R. Khan, Wes Hodges, Simon Alexander, David Romascano, Muhamed Barakovic, Anna Auría, Oscar Esteban, Alia Lemkaddem, Jean-Philippe Thiran, H. Ertan Cetingul, Benjamin L. Odry, Boris Mailhe, Mariappan S. Nadar, Fabrizio Pizzagalli, Gautam Prasad, Julio E. Villalon-Reina, Justin Galvis, Paul M Thompson, Francisco De Santiago Requejo, Pedro Luque Laguna, Luis Miguel Lacerda, Rachel Barrett, Flavio Dell’Acqua, Marco Catani, Laurent Petit, Emmanuel Caruyer, Alessandro Daducci, Tim B Dyrby, Tim Holland-Letz, Claus C Hilgetag, Bram Stieltjes, Maxime Descoteaux
Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.
4,490 downloads neuroscience
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease primarily affecting motor neurons (MNs) to cause progressive paralysis. Ninety percent of cases are sporadic (sALS) and ten percent are familial (fALS). The molecular mechanisms underlying neurodegeneration remain elusive and there is a lack of promising biomarkers that define ALS phenotypes and progression. To date, most expression studies have focused on either complex whole tissues that contain cells other than MNs or induced pluripotent derived MNs (iMNs). Furthermore, as human tissue samples have high variability, estimation of differential gene-expression is not a trivial task. Here, we report a battery of orthogonal computational analyses to discover gene-expression defects in laser capture microdissected and enriched MN RNA pools from sALS patient spinal cords in regions destined for but not yet advanced in neurodegenerative stage. We used total RNA-sequencing (RNA-seq), applied multiple percentile rank (MPR) analysis to analyze MN-specific gene-expression signatures, and used high-throughput qPCR to validate RNA-seq results. Furthermore, we used a systems-level approach that identified molecular networks perturbed in sALS MNs. Weighted gene co-expression correlation network (WGCNA) analysis revealed defects in neurotransmitter biosynthesis and RNA-processing pathways while gene-gene interaction analysis showed abnormalities in networks that pertained to cell-adhesion, immune response and wound healing. We discover gene-expression signatures that distinguish sALS from control MNs and our findings illuminate possible mechanisms of cellular toxicity. Our systematic and comprehensive analysis serves as a framework to reveal expression signatures and disrupted pathways that will be useful for future mechanistic studies and biomarker based therapeutic research. *Corresponding authors: email@example.com, firstname.lastname@example.org
4,479 downloads neuroscience
The global efforts towards the creation of a molecular census of the brain using single-cell transcriptomics is generating a large catalog of molecularly defined cell types lacking spatial information. Thus, new methods are needed to map a large number of cell-specific markers simultaneously on large tissue areas. Here, we developed a cyclic single molecule fluorescence in situ hybridization methodology and defined the cellular organization of the somatosensory cortex using markers identified by single-cell transcriptomics.
4,450 downloads neuroscience
Hanadie Yousef, Cathrin J Czupalla, Davis Lee, Ashley Burke, Michelle Chen, Judith Zandstra, Elisabeth Berber, Benoit Lehallier, Vidhu Mathur, Ramesh V Nair, Liana Bonanno, Taylor Merkel, Markus Schwaninger, Stephen Quake, Eugene C Butcher, Tony Wyss-Coray
An aged circulatory environment can promote brain dysfunction and we hypothesized that the blood-brain barrier (BBB) mediates at least some of these effects. We observe brain endothelial cells (BECs) in the aged mouse hippocampus express an inflammatory transcriptional profile with focal upregulation of Vascular Cell Adhesion Molecule 1 (VCAM1), a protein that facilitates vascular-immune cell interactions. Concomitantly, the shed, soluble form of VCAM1 is prominently increased in the aged circulation of humans and mice, and aged plasma is sufficient to increase VCAM1 expression in cultured BECs and young mouse hippocampi. Systemic anti-VCAM1 antibody or genetic ablation of VCAM1 in BECs counteracts the detrimental effects of aged plasma on young brains and reverses aging aspects in old mouse brains. Thus, VCAM1 is a negative regulator of adult neurogenesis and inducer of microglial reactivity, establishing VCAM1 and the luminal side of the BBB as possible targets to treat age-related neurodegeneration.
4,429 downloads neuroscience
Epilepsy is the most typical neurological disease in the world, and it implies an expensive and specialized diagnosis process based on electroencephalograms and video recordings. We developed a method that only requires the brainwave provided by the difference between two standard-located electrodes. Our proposed technique separates the original signal using a filter array with three different types of filters, and then extracts several features based on information theory and statistical information. In our study, we found that only 10 characteristics, of which the most important are related to higher frequencies, are required to offer an accuracy of 94%, a specificity of 95% and a sensitivity of 87% using C4.5 decision trees.
4,423 downloads neuroscience
Particular deep artificial neural networks (ANNs) are today's most accurate models of the primate brain's ventral visual stream. Here we report that, using a targeted ANN-driven image synthesis method, new luminous power patterns (i.e. images) can be applied to the primate retinae to predictably push the spiking activity of targeted V4 neural sites beyond naturally occurring levels. More importantly, this method, while not yet perfect, already achieves unprecedented independent control of the activity state of entire populations of V4 neural sites, even those with overlapping receptive fields. These results show how the knowledge embedded in today's ANN models might be used to non-invasively set desired internal brain states at neuron-level resolution, and suggest that more accurate ANN models would produce even more accurate control.
4,407 downloads neuroscience
Saskia E. J. de Vries, Jerome Lecoq, Michael A. Buice, Peter A. Groblewski, Gabriel K. Ocker, Michael Oliver, David Feng, Nicholas Cain, Peter Ledochowitsch, Daniel Millman, Kate Roll, Marina Garrett, Tom Keenan, Leonard Kuan, Stefan Mihalas, Shawn R. Olsen, Carol Thompson, Wayne Wakeman, Jack Waters, Derric Williams, Chris Barber, Nathan Berbesque, Brandon Blanchard, Nicholas Bowles, Shiella Caldejon, Linzy Casal, Andrew Cho, Sissy Cross, Chinh Dang, Tim Dolbeare, Melise Edwards, John Galbraith, Nathalie Gaudreault, Fiona Griffin, Perry Hargrave, Robert Howard, Lawrence Huang, Sean Jewell, Nika Keller, Ulf Knoblich, Josh Larkin, Rachael Larsen, Chris Lau, Eric Lee, Felix Lee, Arielle Leon, Lu Li, Fuhui Long, Jennifer Luviano, Kyla Mace, Thuyanh Nguyen, Jed Perkins, Miranda Robertson, Sam Seid, Eric Shea-Brown, Jianghong Shi, Nathan Sjoquist, Cliff Slaughterbeck, David Sullivan, Ryan Valenza, Casey White, Ali Williford, Daniela Witten, Jun Zhuang, Hongkui Zeng, Colin Farrell, Lydia Ng, Amy Bernard, John W Phillips, R. Clay Reid, Christof Koch
To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.
4,373 downloads neuroscience
The internal representations of early deep artificial neural networks (ANNs) were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain. Here we ask, as deep ANNs have continued to evolve, are they becoming more or less brain-like? ANNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. We therefore developed Brain-Score - a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain's mechanisms for core object recognition - and we deployed it to evaluate a wide range of state-of-the-art deep ANNs. Using this scoring system, we here report that: (1) DenseNet-169, CORnet-S and ResNet-101 are the most brain-like ANNs. (2) There remains considerable variability in neural and behavioral responses that is not predicted by any ANN, suggesting that no ANN model has yet captured all the relevant mechanisms. (3) Extending prior work, we found that gains in ANN ImageNet performance led to gains on Brain-Score. However, correlation weakened at >= 70% top-1 ImageNet performance, suggesting that additional guidance from neuroscience is needed to make further advances in capturing brain mechanisms. (4) We uncovered smaller (i.e. less complex) ANNs that are more brain-like than many of the best-performing ImageNet models, which suggests the opportunity to simplify ANNs to better understand the ventral stream. The scoring system used here is far from complete. However, we propose that evaluating and tracking model-benchmark correspondences through a Brain-Score that is regularly updated with new brain data is an exciting opportunity: experimental benchmarks can be used to guide machine network evolution, and machine networks are mechanistic hypotheses of the brain's network and thus drive next experiments. To facilitate both of these, we release Brain-Score.org: a platform that hosts the neural and behavioral benchmarks, where ANNs for visual processing can be submitted to receive a Brain-Score and their rank relative to other models, and where new experimental data can be naturally incorporated.
4,370 downloads neuroscience
When a neuron is driven beyond its threshold it spikes, and the fact that it does not communicate its continuous membrane potential is usually seen as a computational liability. Here we show that this spiking mechanism allows neurons to produce an unbiased estimate of their causal influence, and a way of approximating gradient descent learning. Importantly, neither activity of upstream neurons, which act as confounders, nor downstream non-linearities bias the results. By introducing a local discontinuity with respect to their input drive, we show how spiking enables neurons to solve causal estimation and learning problems.
4,325 downloads neuroscience
There is a vast array of new and improved methods for comparing groups and studying associations that offer the potential for substantially increasing power, providing improved control over the probability of a Type I error, and yielding a deeper and more nuanced under- standing of neuroscience data. These new techniques effectively deal with four insights into when and why conventional methods can be unsatisfactory. But for the non-statistician, the vast array of new and improved techniques for comparing groups and studying associations can seem daunting, simply because there are so many new methods that are now available. The paper briefly reviews when and why conventional methods can have relatively low power and yield misleading results. The main goal is to suggest some general guidelines regarding when, how and why certain modern techniques might be used.
4,300 downloads neuroscience
We propose a novel approach based on modern deep artificial neural networks (DNNs) for understanding how the morpho-electrical complexity of neurons shapes their input/output (I/O) properties at the millisecond resolution in response to massive synaptic input. The I/O of integrate and fire point neuron is accurately captured by a DNN with a single unit and one hidden layer. A fully connected DNN with one hidden layer faithfully replicated the I/O relationship of a detailed model of Layer 5 cortical pyramidal cell (L5PC) receiving AMPA and GABAA synapses. However, when adding voltage-gated NMDA-conductances, a temporally-convolutional DNN with seven layers was required. Analysis of the DNN filters provides new insights into dendritic processing shaping the I/O properties of neurons. This work proposes a systematic approach for characterizing the functional "depth" of a biological neurons, suggesting that cortical pyramidal neurons and the networks they form are computationally much more powerful than previously assumed.
4,278 downloads neuroscience
Jonathan S. Marvin, Yoshiteru Shimoda, Vincent Malgoire, Marco Leite, Takashi Kawashima, Thomas P. Jensen, Erika L Knott, Ondrej Novak, Kaspar Podgorski, Nancy J Leidenheimer, Dmitri A. Rusakov, Misha B Ahrens, Dimitri M Kullmann, Loren L Looger
Current techniques for monitoring GABA, the primary inhibitory neurotransmitter in vertebrates, cannot follow ephemeral transients in intact neural circuits. We applied the design principles used to create iGluSnFR, a fluorescent reporter of synaptic glutamate, to develop a GABA sensor using a protein derived from a previously unsequenced Pseudomonas fluorescens strain. Structure-guided mutagenesis and library screening led to a usable iGABASnFR (maximum DeltaF/F ~ 2.5, Kd ~ 9 micromolar, good specificity, adequate kinetics). iGABASnFR is genetically encoded, detects single action potential-evoked GABA release events in culture, and produces readily detectable fluorescence increases in vivo in mice and zebrafish. iGABASnFR enabled tracking of: (1) mitochondrial GABA content and its modulation by an anticonvulsant; (2) swimming-evoked GABAergic transmission in zebrafish cerebellum; (3) GABA release events during inter-ictal spikes and seizures in awake mice; and (4) GABAergic tone decreases during isoflurane anesthesia. iGABASnFR will permit high spatiotemporal resolution of GABA signaling in intact preparations.
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