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Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 83,434 bioRxiv papers from 359,620 authors.

Most downloaded bioRxiv papers, all time

in category neuroscience

14,562 results found. For more information, click each entry to expand.

101: Single-cell in situ transcriptomic map of astrocyte cortical layer diversity
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Posted to bioRxiv 03 Oct 2018

Single-cell in situ transcriptomic map of astrocyte cortical layer diversity
4,268 downloads neuroscience

Omer Ali Bayraktar, Theresa Bartels, Damon Polioudakis, Staffan Holmqvist, Lucile Ben Haim, Adam M.H. Young, Udo Birk, Alexander Brown, Mercedes F. Paredes, Riki Kawaguchi, John Stockley, Khalida Sabeur, Sandra M. Chang, Eric Huang, Peter Hutchinson, Erik M. Ullian, Daniel H. Geschwind, Giovanni Coppola, David H. Rowitch

During organogenesis, patterns and gradients of gene expression underlie organization and diversified cell specification to generate complex tissue architecture. While the cerebral cortex is organized into six excitatory neuronal layers, it is unclear whether glial cells are diversified to mimic neuronal laminae or show distinct layering. To determine the molecular architecture of the mammalian cortex, we developed a high- content pipeline that can quantify single-cell gene expression in situ. The Large-area Spatial Transcriptomic (LaST) map confirmed expected cortical neuron layer organization and also revealed a novel neuronal identity signature. Screening 46 candidate genes for astrocyte diversity across the cortex, we identified grey matter superficial, mid and deep astrocyte identities in gradient layer patterns that were distinct from neurons. Astrocyte layers formed in early postnatal cortex and mostly persisted in adult mouse and human cortex. Mutations that shifted neuronal post-mitotic identity or organization were sufficient to alter glial layering, indicating an instructive role for neuronal cues. In normal mouse cortex, astrocyte layer patterns showed area diversity between functionally distinct cortical regions. These findings indicate that excitatory neurons and astrocytes cells are organized into distinct lineage-associated laminae, which give rise to higher order neuroglial complexity of cortical architecture.

102: Crossvalidation in brain imaging analysis
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Posted to bioRxiv 01 Apr 2015

Crossvalidation in brain imaging analysis
4,221 downloads neuroscience

Nikolaus Kriegeskorte

Crossvalidation is a method for estimating predictive performance and adjudicating between multiple models. On each of k folds of the process, k-1 of k independent subsets of the data (training set) are used to fit the parameters of each model and the left-out subset (test set) is used to estimate predictive performance. The method is statistically efficient, because training data are reused for testing and performance estimates combined across folds. The method requires no assumptions, provides nearly unbiased (slightly conservative) estimates of predictive performance, and is generally applicable because it amounts to a direct empirical test of each model.

103: Classification of electrophysiological and morphological types in mouse visual cortex
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Posted to bioRxiv 17 Jul 2018

Classification of electrophysiological and morphological types in mouse visual cortex
4,212 downloads neuroscience

Nathan W. Gouwens, Staci A. Sorensen, Jim Berg, Changkyu Lee, Tim Jarsky, Jonathan Ting, Susan M. Sunkin, David Feng, Costas Anastassiou, Eliza Barkan, Kris Bickley, Nicole Blesie, Thomas Braun, Krissy Brouner, Agata Budzillo, Shiella Caldejon, Tamara Casper, Dan Casteli, Peter Chong, Kirsten Crichton, Christine Cuhaciyan, L. Daigle Tanya, Rachel Dalley, Nick Dee, Tsega Desta, Samuel Dingman, Alyse Doperalski, Nadezhda Dotson, Tom Egdorf, Michael Fisher, Rebecca A de Frates, Emma Garren, Marissa Garwood, Amanda Gary, Nathalie Gaudreault, Keith Godfrey, Melissa Gorham, Thuc Nghi Nguyen, Caroline Habel, Kristen Hadley, James Harrington, Julie Harris, Alex Henry, DiJon Hill, Sam Josephsen, Sara Kebede, Lisa Kim, Matthew Kroll, Brian Lee, Tracy Lemon, Xiaoxiao Liu, Brian Long, Rusty Mann, Medea McGraw, Stefan Mihalas, Alice Mukora, Gabe J. Murphy, Lindsay Ng, Kiet Ngo, Philip R. Nicovich, Aaron Oldre, Daniel Park, Sheana Parry, Jed Perkins, Lydia Potekhina, David Reid, Miranda Robertson, David Sandman, Martin Schroedter, Cliff Slaughterbeck, Gilberto Soler-Llavina, Josef Sulc, Aaron Szafer, Bosiljka Tasic, Naz Taskin, Corinne Teeter, Nivretta Thatra, Herman Tung, Wayne Wakeman, Grace Williams, Rob Young, Zhi Zhou, Colin Farrell, Hanchuan Peng, Michael J. Hawrylycz, Ed Lein, Lydia Ng, Anton Arkhipov, Amy Bernard, John W. Phillips, Hongkui Zeng, Christof Koch

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To profile morpho-electric properties of mammalian neurons systematically, we established a single cell characterization pipeline using standardized patch clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly-accessible online database, the Allen Cell Types Database, to display these data sets. Intrinsic physiological and morphological properties were measured from over 1,800 neurons from the adult laboratory mouse visual cortex. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We establish a taxonomy of morphologically- and electrophysiologically-defined cell types for this region of cortex with 17 e-types and 35 m-types, as well as an initial correspondence with previously-defined transcriptomic cell types using the same transgenic mouse lines.

104: Towards reconstructing intelligible speech from the human auditory cortex
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Posted to bioRxiv 19 Jun 2018

Towards reconstructing intelligible speech from the human auditory cortex
4,208 downloads neuroscience

Hassan Akbari, Bahar Khalighinejad, Jose L Herrero, Ashesh D. Mehta, Nima Mesgarani

Auditory stimulus reconstruction is a technique that finds the best approximation of the acoustic stimulus from the population of evoked neural activity. Reconstructing speech from the human auditory cortex creates the possibility of a speech neuroprosthetic to establish a direct communication with the brain and has been shown to be possible in both overt and covert conditions. However, the low quality of the reconstructed speech has severely limited the utility of this method for brain-computer interface (BCI) applications. To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear regression methods and the acoustic representation that is used as the target of reconstruction, including spectrogram and speech synthesis parameters. In addition, we compared the reconstruction accuracy from low and high neural frequency ranges. Our results show that a deep neural network model that directly estimates the parameters of a speech synthesizer from all neural frequencies achieves the highest subjective and objective scores on a digit recognition task, improving the intelligibility by 65% over the baseline. These results demonstrate the efficacy of deep learning and speech synthesis algorithms for designing the next generation of speech BCI systems, which not only can restore communications for paralyzed patients but also have the potential to transform human-computer interaction technologies.

105: Predictive Coding of Novel versus Familiar Stimuli in the Primary Visual Cortex
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Posted to bioRxiv 03 Oct 2017

Predictive Coding of Novel versus Familiar Stimuli in the Primary Visual Cortex
4,152 downloads neuroscience

Jan Homann, Sue Ann Koay, Alistair M. Glidden, David W. Tank, Michael J. Berry

To explore theories of predictive coding, we presented mice with repeated sequences of images with novel im- ages sparsely substituted. Under these conditions, mice could be rapidly trained to lick in response to a novel image, demonstrating a high level of performance on the first day of testing. Using 2-photon calcium imaging to record from layer 2/3 neurons in the primary visual cor- tex, we found that novel images evoked excess activity in the majority of neurons. When a new stimulus se- quence was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presenta- tions, which then decayed to almost zero activity. The decay time of these transient responses was not fixed, but instead scaled with the length of the stimulus sequence. However, at the same time, we also found a small fraction of the neurons within the population (~2%) that contin- ued to respond strongly and periodically to the repeated stimulus. Decoding analysis demonstrated that both the transient and sustained responses encoded information about stimulus identity. We conclude that the layer 2/3 population uses a two-channel predictive code: a dense transient code for novel stimuli and a sparse sustained code for familiar stimuli. These results extend and unify existing theories about the nature of predictive neural codes.

106: Developmental diversification of cortical inhibitory interneurons.
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Posted to bioRxiv 02 Feb 2017

Developmental diversification of cortical inhibitory interneurons.
4,152 downloads neuroscience

Christian Mayer, Christoph Hafemeister, Rachel C Bandler, Robert Machold, Kathryn Allaway, Xavier Jaglin, Renata Batista Brito, Andrew Butler, Gord Fishell, Rahul Satija

Diverse subsets of cortical interneurons play a particularly important role in the stability of the neural circuits underlying cognitive and higher order brain functions, yet our understanding of how this diversity is generated is far from complete. We applied massively parallel single-cell RNA-seq to profile a developmental time course of interneuron development, measuring the transcriptomes of over 60,000 progenitors during their maturation in the ganglionic eminences and embryonic migration into the cortex. While diversity within mitotic progenitors is largely driven by cell cycle and differentiation state, we observed sparse eminence-specific transcription factor expression, which seeds the emergence of later cell diversity. Upon becoming postmitotic, cells from all eminences pass through one of three precursor states, one of which represents a cortical interneuron ground state. By integrating datasets across developmental timepoints, we identified transcriptomic heterogeneity in interneuron precursors representing the emergence of four cardinal classes (Pvalb, Sst, Id2 and Vip), which further separate into subtypes at different timepoints during development. Our analysis revealed that the ASD-associated transcription factor Mef2c discriminates early Pvalb-precursors in E13.5 cells, and removal of Mef2c confirms its essential role for Pvalb interneuron development. These findings shed new light on the molecular diversification of early inhibitory precursors, and suggest gene modules that may link developmental specification with the etiology of neuropsychiatric disorders.

107: Lipid droplet accumulating microglia represent a dysfunctional and pro-inflammatory state in the aging brain
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Posted to bioRxiv 06 Aug 2019

Lipid droplet accumulating microglia represent a dysfunctional and pro-inflammatory state in the aging brain
4,123 downloads neuroscience

Julia Marschallinger, Tal Iram, Macy Zardeneta, Song E Lee, Benoit Lehallier, Michael S. Haney, John V. Pluvinage, Vidhu Mathur, Oliver Hahn, David W Morgens, Justin Kim, Julia Tevini, Thomas K. Felder, Heimo Wolinski, Carolyn R. Bertozzi, Michael C. Bassik, Ludwig Aigner, Tony Wyss-Coray

Microglia become progressively activated and seemingly dysfunctional with age, and genetic studies have linked these cells to the pathogenesis of a growing number of neurodegenerative diseases. Here we report a striking buildup of lipid droplets in microglia with aging in mouse and human brains. These cells, which we call lipid droplet-accumulating microglia (LAM), are defective in phagocytosis, produce high levels of reactive oxygen species, and secrete pro-inflammatory cytokines. RNA sequencing analysis of LAM revealed a transcriptional profile driven by innate inflammation distinct from previously reported microglial states. An unbiased CRISPR-Cas9 screen identified genetic modifiers of lipid droplet formation; surprisingly, variants of several of these genes, including progranulin, are causes of autosomal dominant forms of human neurodegenerative diseases. We thus propose that LAM contribute to age-related and genetic forms of neurodegeneration.

108: Fast two-photon volumetric imaging of an improved voltage indicator reveals electrical activity in deeply located neurons in the awake brain
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Posted to bioRxiv 17 Oct 2018

Fast two-photon volumetric imaging of an improved voltage indicator reveals electrical activity in deeply located neurons in the awake brain
4,120 downloads neuroscience

Mariya Chavarha, Vincent Villette, Ivan K. Dimov, Lagnajeet Pradhan, Stephen W. Evans, Dongqing Shi, Renzhi Yang, Simon Chamberland, Jonathan Bradley, Benjamin Mathieu, Francois St-Pierre, Mark J Schnitzer, Guoqiang Bi, Katalin Toth, Jun Ding, Stéphane Dieudonné, Michael Z. Lin

Imaging of transmembrane voltage deep in brain tissue with cellular resolution has the potential to reveal information processing by neuronal circuits in living animals with minimal perturbation. Multi-photon voltage imaging in vivo, however, is currently limited by speed and sensitivity of both indicators and imaging methods. Here, we report the engineering of an improved genetically encoded voltage indicator, ASAP3, which exhibits up to 51% fluorescence responses in the physiological voltage range, sub-millisecond activation kinetics, and full responsivity under two-photon illumination. We also introduce an ultrafast local volume excitation (ULOVE) two-photon scanning method to sample ASAP3 signals in awake mice at kilohertz rates with increased stability and sensitivity. ASAP3 and ULOVE allowed continuous single-trial tracking of spikes and subthreshold events for minutes in deep locations, with subcellular resolution, and with repeated sampling over multiple days. By imaging voltage in visual cortex neurons, we found evidence for cell type-dependent subthreshold modulation by locomotion. Thus, ASAP3 and ULOVE enable continuous high-speed high-resolution imaging of electrical activity in deeply located genetically defined neurons during awake behavior.

109: Exploring the effect of microdosing psychedelics on creativity in an open-label natural setting
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Posted to bioRxiv 08 Aug 2018

Exploring the effect of microdosing psychedelics on creativity in an open-label natural setting
4,087 downloads neuroscience

Luisa Prochazkova, Dominique P Lippelt, Lorenza S Colzato, Martin Kuchar, Zsuzsika Sjoerds, Bernhard Hommel

Introduction: Recently popular sub-perceptual doses of psychedelic substances such as truffles, referred to as microdosing, allegedly have multiple beneficial effects including creativity and problem solving performance, potentially through targeting serotonergic 5-HT2A receptors and promoting cognitive flexibility, crucial to creative thinking. Nevertheless, enhancing effects of microdosing remain anecdotal, and in the absence of quantitative research on microdosing psychedelics it is impossible to draw definitive conclusions on that matter. Here, our main aim was to quantitatively explore the cognitive-enhancing potential of microdosing psychedelics in healthy adults. Methods: During a microdosing event organized by the Dutch Psychedelic Society, we examined the effects of psychedelic truffles (which were later analyzed to quantify active psychedelic alkaloids) on two creativity-related problem-solving tasks: the Picture Concept Task assessing convergent thinking, and the Alternative Uses Task assessing divergent thinking. A short version of the Ravens Progressive Matrices task assessed potential changes in fluid intelligence. We tested once before taking a microdose and once while the effects were manifested. Results: We found that both convergent and divergent thinking performance was improved after a non-blinded microdose, whereas fluid intelligence was unaffected. Conclusion: While this study provides quantitative support for the cognitive enhancing properties of microdosing psychedelics, future research has to confirm these preliminary findings in more rigorous placebo-controlled study designs. Based on these preliminary results we speculate that psychedelics might affect cognitive metacontrol policies by optimizing the balance between cognitive persistence and flexibility. We hope this study will motivate future microdosing studies with more controlled designs to test this hypothesis.

110: Modulation of Body Mass Composition using Vestibular Nerve Stimulation
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Posted to bioRxiv 14 Nov 2016

Modulation of Body Mass Composition using Vestibular Nerve Stimulation
4,078 downloads neuroscience

Paul D. McGeoch, Jason McKeown, Hans Peterson, V.S. Ramachandran

There is increasing evidence of a set-point for body weight in the brain, that is regulated by the hypothalamus. This system modifies feeding behavior and metabolic rate, to keep body fat within predetermined parameters. It is also known that animals subjected to chronic centrifugation show a reduction in body fat. Experiments with mutant mice found that this loss of fat appears to be mediated by a vestibulo-hypothalamic pathway. Vestibular nerve stimulation (VeNS), also known as galvanic vestibular stimulation, involves non-invasively stimulating the vestibular system by applying a small electrical current between two electrodes placed over the mastoid processes. We suggest that any means of repeatedly stimulating the otolith organs in humans would cause a reduction in total body fat, and that VeNS would be a useful technique to use in this regard. Below we provide pilot data to support this idea.

111: When null hypothesis significance testing is unsuitable for research: a reassessment
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Posted to bioRxiv 20 Dec 2016

When null hypothesis significance testing is unsuitable for research: a reassessment
4,064 downloads neuroscience

Denes Szucs, John PA Ioannidis

Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of psychology, cognitive neuroscience and biomedical science in general. We review these shortcomings and suggest that, after about 60 years of negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. Different inferential methods (NHST, likelihood estimation, Bayesian methods, false-discovery rate control) may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Studies should optimally be pre-registered and raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out. Instead, we should encourage either more in-depth statistical training of more researchers and/or more widespread involvement of professional statisticians in all research.

112: Evolving super stimuli for real neurons using deep generative networks
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Posted to bioRxiv 17 Jan 2019

Evolving super stimuli for real neurons using deep generative networks
4,061 downloads neuroscience

Carlos R. Ponce, Will Xiao, Peter F. Schade, Till S. Hartmann, Gabriel Kreiman, Margaret S. Livingstone

Finding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.

113: Light Sheet Theta Microscopy for High-resolution Quantitative Imaging of Large Biological Systems
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Posted to bioRxiv 22 Mar 2017

Light Sheet Theta Microscopy for High-resolution Quantitative Imaging of Large Biological Systems
4,051 downloads neuroscience

Bianca Migliori, Malika S Datta, Christophe Dupre, Mehmet C Apak, Shoh Asano, Ruixuan Gao, Edward S Boyden, Ola Hermanson, Rafael Yuste, Raju Tomer

Advances in tissue clearing and molecular labelling methods are enabling unprecedented optical access to large intact biological systems. These advances fuel the need for high-speed microscopy approaches to image large samples quantitatively and at high resolution. While Light Sheet Microscopy (LSM), with its high planar imaging speed and low photo-bleaching, can be effective, scaling up to larger imaging volumes has been hindered by the use of orthogonal light-sheet illumination. To address this fundamental limitation, we have developed Light Sheet Theta Microscopy (LSTM), which uniformly illuminates samples from same side as the detection objective, thereby eliminating limits on lateral dimensions without sacrificing imaging resolution, depth and speed. We present detailed characterization of LSTM, and show that this approach achieves rapid high-resolution imaging of large intact samples with superior uniform high-resolution than LSM. LSTM is a significant step in high-resolution quantitative mapping of structure and function of large intact biological systems.

114: Variational Autoencoder: An Unsupervised Model for Modeling and Decoding fMRI Activity in Visual Cortex
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Posted to bioRxiv 05 Nov 2017

Variational Autoencoder: An Unsupervised Model for Modeling and Decoding fMRI Activity in Visual Cortex
4,033 downloads neuroscience

Kuan Han, Haiguang Wen, Junxing Shi, Kun-Han Lu, Yizhen Zhang, Zhongming Liu

Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE), as a computational model of the visual cortex. We trained a VAE with a five-layer encoder and a five-layer decoder to learn visual representations from a diverse set of unlabeled images. Inspired by the "free-energy" principle in neuroscience, we modeled the brain's bottom-up and top-down pathways using the VAE's encoder and decoder, respectively. Following such conceptual relationships, we used VAE to predict or decode cortical activity observed with functional magnetic resonance imaging (fMRI) from three human subjects passively watching natural videos. Compared to CNN, VAE resulted in relatively lower accuracies for predicting the fMRI responses to the video stimuli, especially for higher-order ventral visual areas. However, VAE offered a more convenient strategy for decoding the fMRI activity to reconstruct the video input, by first converting the fMRI activity to the VAE's latent variables, and then converting the latent variables to the reconstructed video frames through the VAE's decoder. This strategy was more advantageous than alternative decoding methods, e.g. partial least square regression, by reconstructing both the spatial structure and color of the visual input. Findings from this study support the notion that the brain, at least in part, bears a generative model of the visual world.

115: Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans
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Posted to bioRxiv 29 Apr 2019

Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans
3,995 downloads neuroscience

Scott Linderman, Annika Nichols, David Blei, Manuel Zimmer, Liam Paninski

Modern recording techniques enable large-scale measurements of neural activity in a variety of model organisms. The dynamics of neural activity shed light on how organisms process sensory information and generate motor behavior. Here, we study these dynamics using optical recordings of neural activity in the nematode C. elegans. To understand these data, we develop state space models that decompose neural time-series into segments with simple, linear dynamics. We incorporate these models into a hierarchical framework that combines partial recordings from many worms to learn shared structure, while still allowing for individual variability. This framework reveals latent states of population neural activity, along with the discrete behavioral states that govern dynamics in this state space. We find stochastic transition patterns between discrete states and see that transition probabilities are determined by both current brain activity and sensory cues. Our methods automatically recover transition times that closely match manual labels of different behaviors, such as forward crawling, reversals, and turns. Finally, the resulting model can simulate neural data, faithfully capturing salient patterns of whole brain dynamics seen in real data.

116: Simple Framework for Constructing Functional Spiking Recurrent Neural Networks
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Posted to bioRxiv 15 Mar 2019

Simple Framework for Constructing Functional Spiking Recurrent Neural Networks
3,969 downloads neuroscience

Robert Kim, Yinghao Li, Terrence J. Sejnowski

Cortical microcircuits exhibit complex recurrent architectures that possess dynamically rich properties. The neurons that make up these microcircuits communicate mainly via discrete spikes, and it is not clear how spikes give rise to dynamics that can be used to perform computationally challenging tasks. In contrast, continuous models of rate-coding neurons can be trained to perform complex tasks. Here, we present a simple framework to construct biologically realistic spiking recurrent neural networks (RNNs) capable of learning a wide range of tasks. Our framework involves training a continuous-variable rate RNN with important biophysical constraints and transferring the learned dynamics and constraints to a spiking RNN in a one-to-one manner. The proposed framework introduces only one additional parameter to establish the equivalence between rate and spiking RNN models. We also study other model parameters related to the rate and spiking networks to optimize the one-to-one mapping. By establishing a close relationship between rate and spiking models, we demonstrate that spiking RNNs could be constructed to achieve similar performance as their counterpart continuous rate networks.

117: Measuring narrative engagement: The heart tells the story
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Posted to bioRxiv 20 Jun 2018

Measuring narrative engagement: The heart tells the story
3,958 downloads neuroscience

Daniel C. Richardson, Nicole K. Griffin, Lara Zaki, Auburn Stephenson, Jiachen Yan, Thomas Curry, Richard Noble, John Hogan, Jeremy I. Skipper, Joseph T. Devlin

Stories play a fundamental role in human culture. They provide a mechanism for sharing cultural identity, imparting knowledge, revealing beliefs, reinforcing social bonds and providing entertainment that is central to all human societies. Here we investigated the extent to which the delivery medium (audio or visual) of a story affected conscious and sub-conscious engagement with the narrative. Although participants self-reported greater involvement for watching video relative to listening to auditory scenes, they had stronger physiological responses for auditory stories including higher heart rates, greater electrodermal activity, and even higher body temperatures. We interpret these findings as physiological evidence that the stories were more cognitively and emotionally engaging when presented in an auditory format. This may be because listening to a story is a more active process of co-creation (i.e. via imagination) than watching a video.

118: A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging
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Posted to bioRxiv 30 May 2016

A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging
3,957 downloads neuroscience

N. J. Sofroniew, D Flickinger, J. King, K Svoboda

Imaging is used to map activity across populations of neurons. Microscopes with cellular resolution have small (< 1 millimeter) fields of view and cannot simultaneously image activity distributed across multiple brain areas. Typical large field of view microscopes do not resolve single cells, especially in the axial dimension. We developed a 2-photon random access mesoscope (2p-RAM) that allows high-resolution imaging anywhere within a volume spanning multiple brain areas (∅ 5 mm x 1 mm cylinder). 2p-RAM resolution is near diffraction limited (lateral, 0.66 μm, axial 4.09 μm at the center; excitation wavelength = 970 nm; numerical aperture = 0.6) over a large range of excitation wavelengths. A fast three-dimensional scanning system allows efficient sampling of neural activity in arbitrary regions of interest across the entire imaging volume. We illustrate the use of the 2p-RAM by imaging neural activity in multiple, non-contiguous brain areas in transgenic mice expressing protein calcium sensors.

119: Mapping the human brain's cortical-subcortical functional network organization
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Posted to bioRxiv 19 Oct 2017

Mapping the human brain's cortical-subcortical functional network organization
3,955 downloads neuroscience

Jie Lisa Ji, Marjolein Spronk, Kaustubh Kulkarni, Grega Repovš, Alan Anticevic, Michael W Cole

Understanding complex systems such as the human brain requires characterization of the system's architecture across multiple levels of organization - from neurons, to local circuits, to brain regions, and ultimately large-scale brain networks. Here we focus on characterizing the human brain's comprehensive large-scale network organization, as it provides an overall framework for the organization of all other levels. We leveraged the Human Connectome Project dataset to identify network communities across cortical regions, replicating well-known networks and revealing several novel but robust networks, including a left-lateralized language network. We expanded these cortical networks to subcortex, revealing 288 highly-organized subcortical segments that take part in forming whole-brain functional networks. This whole-brain network atlas - released as an open resource for the neuroscience community - places all brain structures across both cortex and subcortex in a single large-scale functional framework, substantially advancing existing atlases to provide a brain-wide functional network characterization in humans.

120: A self-initiated two-alternative forced choice paradigm for head-fixed mice
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Posted to bioRxiv 08 Sep 2016

A self-initiated two-alternative forced choice paradigm for head-fixed mice
3,937 downloads neuroscience

Fred Marbach, Anthony M. Zador

Psychophysical tasks for non-human primates have been instrumental in studying circuits underlying perceptual decision-making. To obtain greater experimental flexibility, these tasks have subsequently been adapted for use in freely moving rodents. However, advances in functional imaging and genetic targeting of neuronal populations have made it critical to develop similar tasks for head-fixed mice. Although head-fixed mice have been trained in two-alternative forced choice tasks before, these tasks were not self-initiated, making it difficult to attribute error trials to perceptual or decision errors as opposed to mere lapses in task engagement. Here, we describe a paradigm for head-fixed mice with three lick spouts, analogous to the well-established 3-port paradigm for freely moving rodents. Mice readily learned to initiate trials on the center spout and performed around 200 self-initiated trials per session, reaching good psychometric performance within two weeks of training. We expect this paradigm will be useful to study the role of defined neural populations in sensory processing and decision-making.

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