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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 109 records · Page 6Linked to original sources

bioRxiv2015

Inferences from a network to a subnetwork and vice versa under an assumption of symmetry

This note summarizes some mathematical relations between the probability distributions for the states of a network of binary units and a subnetwork thereof, under an assumption of symmetry. These relations are standard results of probability theory, but seem to be rarely used in neuroscience. Some of their consequences for inferences between network and subnetwork, especially in connection with the maximum-entropy principle, are briefly discussed. The meanings and applicability of the assumption of symmetry are also discussed.\n\nPACS: 87.19.L-,87.19.lo,05.90.+m\n\nMSC: 03B48,97K50

Neuroscience↗

bioRxiv2015

The Brain Imaging Data Structure: a standard for organizing and describing outputs of neuroimaging experiments

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

Neuroscience↗

bioRxiv2015

Reactivation of reward-related patterns from single past episodes supports memory-based decision making

Rewarding experiences exert a strong influence on later decision making. While decades of neuroscience research have shown how reinforcement gradually shapes preferences, decisions are often influenced by single past experiences. Surprisingly, relatively little is known about the influence of single learning episodes. While recent work has proposed a role for episodes in decision making, it is largely unknown whether and how episodic experiences contribute to value-based decision making and how the values of single episodes are represented in the brain. In multiple behavioral experiments and an fMRI experiment, we tested whether and how rewarding episodes could support later decision making. Participants experienced episodes of high reward or low reward in conjunction with incidental, trial-unique neutral pictures. In a surprise test phase, we found that participants could indeed remember the associated level of reward, as evidenced by accurate source memory for value and preferences to re-engage with rewarded objects. Further, in a separate experiment, we found that high reward objects shown as primes before a gambling task increased financial risk-taking. Neurally, re-exposure to objects in the test phase led to significant reactivation of reward-related patterns. Importantly, individual variability in the strength of reactivation predicted value memory performance. Further, local searchlight analyses identified significant reactivation in the ventromedial PFC. Our results provide a novel demonstration that affect-related neural patterns are reactivated during later experience. Reactivation of value information represents a mechanism by which memory can guide decision making.

Neuroscience↗

bioRxiv2016

Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging

Recent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem of scanning active circuits; and the prospect of scanless microscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotem-poral patterns of neural activity, that scale well with pattern size.

Neuroscience↗

bioRxiv2016

A practical guide for improving transparency and reproducibility in neuroimaging research

Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility.

Neuroscience↗

bioRxiv2016

Cortical feedback to V1 and V2 contains unique information about high-level scene structure.

Human behaviour is dependent on the ability of neuronal circuits to predict the outside world. Neuronal circuits make these predictions based on internal models. Despite our extensive knowledge of the sensory features that drive cortical neurons, we have a limited grasp on the structure of the brains internal models. Substantial progress in neuroscience therefore depends on our ability to replicate the models that the brain creates internally. Here we record human fMRI data while presenting partially occluded visual scenes. Visual occlusion controls sensory input to subregions of visual cortex while internal models continue to influence activity in these regions. Since the observed activity is dependent on internal models, but not on sensory input, we have the opportunity to map the features of the brains internal models. Our results show that internal models in early visual cortex are both categorical and scene-specific. We further demonstrate that behavioural line drawings provide a good description of internal model structure. These findings extend our understanding of internal models by showing that line drawings, which have been effectively used by humans to convey information about the world for thousands of years, provide a window into our brains internal models of vision.

Neuroscience↗

bioRxiv2016

A watershed model of individual differences in fluid intelligence

Fluid intelligence is a crucial cognitive ability that predicts key life outcomes across the lifespan. Strong empirical links exist between fluid intelligence and processing speed on the one hand, and white matter integrity and processing speed on the other. We propose a watershed model that integrates these three explanatory levels in a principled manner in a single statistical model, with processing speed and white matter figuring as intermediate endophenotypes. We fit this model in a large (N=555) adult lifespan cohort from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) using multiple measures of processing speed, white matter health and fluid intelligence. The model fit the data well, outperforming competing models and providing evidence for a many-to-one mapping between white matter integrity, processing speed and fluid intelligence. The model can be naturally extended to integrate other cognitive domains, endophenotypes and genotypes.

Neuroscience↗

bioRxiv2016

Understanding melanopsin using bayesian generative models − an Introduction

1.Understanding biological processes implies a quantitative description. In recent years a new tool set, Bayesian hierarchical modeling, has seen rapid development. We use these methods to model kinetics of a specific protein in a neuroscience context: melanopsin. Melanopsin is a photoactive protein in retinal ganglion cells. Due to its photoactivity, melanopsin is widely used in optogenetic experiments and an important component in the elucidation of neuronal interactions. Thus it is important to understand the relevant processes and develop mechanistic models. Here, with a focus on methodological aspects, we develop, implement, fit and discuss Bayesian generative models of melanopsin dynamics.\n\nWe start with a sketch of a basic model and then translate it into formal probabilistic language. As melanopsin occurs in at least two states, a resting and a firing state, a basic model is defined by a non-stationary two state hidden Markov process. Subsequently we add complexities in the form of (1) a hierarchical extension to fit multiple cells; (2) a wavelength dependency, to investigate the response at different color of light stimulation; (3) an additional third state to investigate whether melanopsin is bi- or tri-stable; (4) differences between different sub-types of melanopsin as found in different species. This application of modeling melanopsin dynamics demonstrates several benefits of Bayesian methods. They directly model uncertainty of parameters, are flexible in the distributions and relations of parameters in the modeling, and allow including prior knowledge, for example parameter values based on biochemical data.

Neuroscience↗

bioRxiv2016

The aptabot: an inducibly affinity-switching, minimally invasive in vivo contrast agent

The ambitions of current neuroscience--understanding neurological disease progression and mapping the connectome--demonstrate a need for safe in vivo tools for creating intricate maps of brain circuitry. Present in vivo contrast agents are often limited by their specificity, uptake, resolvability, and/or clearance.\n\nWe describe an aptamer-functionalized sensor for high-resolution imaging that can switch imaging targets by an induced multi-stage aptamer reaction. Included are synthetic methods as well as calculations of sensor efficacy based on known kinetics. Calculations show that 10 distinct targets may be imaged in a living brain at the submicron scale within 42 hours.

Neuroscience↗

bioRxiv2016

Testing pseudo-linear models of responses to natural scenes in primate retina

A central goal of systems neuroscience is to develop accurate quantitative models of how neural circuits process information. Prevalent models of light response in retinal ganglion cells (RGCs) usually begin with linear filtering over space and time, which reduces the high-dimensional visual stimulus to a simpler and more tractable scalar function of time that in turn determines the model output. Although these pseudo-linear models can accurately replicate RGC responses to stochastic stimuli, it is unclear whether the strong linearity assumption captures the function of the retina in the natural environment. This paper tests how accurately one pseudo-linear model, the generalized linear model (GLM), explains the responses of primate RGCs to naturalistic visual stimuli. Light responses from macaque RGCs were obtained using large-scale multi-electrode recordings, and two major cell types, ON and OFF parasol, were examined. Visual stimuli consisted of images of natural environments with simulated saccadic and fixational eye movements. The GLM accurately reproduced RGC responses to white noise stimuli, as observed previously, but did not generalize to predict RGC responses to naturalistic stimuli. It also failed to capture RGC responses when fitted and tested with naturalistic stimuli alone. Fitted scalar nonlinearities before and after the linear filtering stage were insufficient to correct the failures. These findings suggest that retinal signaling under natural conditions cannot be captured by models that begin with linear filtering, and emphasize the importance of additional spatial nonlinearities, gain control, and/or peripheral effects in the first stage of visual processing.

Neuroscience↗

bioRxiv2016

Ocular Motoneuron Pools Develop Along a Dorsoventral Axis in Zebrafish, Danio rerio

Both spatial and temporal cues determine the fate of immature neurons. A major challenge at the interface of developmental and systems neuroscience is to relate this spatiotempo-ral trajectory of maturation to circuit-level functional organization. This study examined the development of two ocular cranial motor nuclei (nIII and nIV), structures in which a motoneurons identity, or choice of muscle partner, defines its behavioral role. We used retro-orbital dye fills, in combination with fluorescent markers for motoneuron location and birth-date, to probe spatial and temporal organization of the oculomotor (nIII) and trochlear (nIV) nuclei in the larval zebrafish. We described a dorsoventral organization of the four nIII motoneuron pools, in which inferior and medial rectus motoneurons occupy dorsal nIII, while inferior oblique and superior rectus motoneurons occupy distinct divisions of ventral nIII. Dorsal nIII motoneurons are, moreover, born before motoneurons of ventral nIII and nIV. Order of neurogenesis can therefore account for the dorsoventral organization of nIII and may play a primary role in determining motoneuron identity. We propose that the temporal development of ocular motoneurons plays a key role in assembling a functional oculomotor circuit.

Neuroscience↗

bioRxiv2016

Power and sample size calculations for fMRI studies based on the prevalence of active peaks.

Mounting evidence over the last few years suggest that published neuroscience research suffer from low power, and especially for published fMRI experiments. Not only does low power decrease the chance of detecting a true effect, it also reduces the chance that a statistically significant result indicates a true effect (Ioannidis, 2005). Put another way, findings with the least power will be the least reproducible, and thus a (prospective) power analysis is a critical component of any paper. In this work we present a simple way to characterize the spatial signal in a fMRI study with just two parameters, and a direct way to estimate these two parameters based on an existing study. Specifically, using just (1) the proportion of the brain activated and (2) the average effect size in activated brain regions, we can produce closed form power calculations for given sample size, brain volume and smoothness. This procedure allows one to minimize the cost of an fMRI experiment, while preserving a predefined statistical power. The method is evaluated and illustrated using simulations and real neuroimaging data from the Human Connectome Project. The procedures presented in this paper are made publicly available in an online web-based toolbox available at www.neuropowertools.org.

Neuroscience↗

bioRxiv2016

High-yield methods for accurate two-alternative visual psychophysics in head-fixed mice

Research in neuroscience relies increasingly on the mouse, a mammalian species that affords unparalleled genetic tractability and brain atlases. Here we introduce high-yield methods for probing mouse visual decisions. Mice are head-fixed, which facilitates repeatable visual stimulation, eye tracking, and brain access. They turn a steering wheel to make two-alternative choices, forced or unforced. Learning is rapid thanks to intuitive coupling of stimuli to wheel position. The mouse decisions deliver high-quality psychometric curves for detection and discrimination, and conform to the predictions of a simple probabilistic observer model. The task is readily paired with two-photon imaging of cortical activity. Optogenetic inactivation reveals that the task requires the visual cortex. Mice are motivated to perform the task by fluid reward or optogenetic stimulation of dopaminergic neurons. This stimulation elicits larger number of trials and faster learning. These methods provide a platform to accurately probe mouse vision and its neural basis.

Neuroscience↗

bioRxiv2016

Rescuing perishable neuroanatomical information from a threatened biodiversity hotspot: Remote field methods for brain tissue preservation validated by cytoarchitectonic analysis, immunohistochemistry, and X-ray microcomputed tomography

Biodiversity hotspots, which harbor more endemic species than elsewhere on Earth, are increasingly threatened. There is a need to accelerate collection efforts in these regions before threatened or endangered species become extinct. The diverse geographical, ecological, genetic, morphological, and behavioral data generated from the on-site collection of an individual specimen are useful for many scientific purposes. However, traditional methods for specimen preparation in the field do not permit researchers to retrieve neuroanatomical data, disregarding potentially useful data for increasing our understanding of brain diversity. These data have helped clarify brain evolution, deciphered relationships between structure and function, and revealed constraints and selective pressures that provide context about the evolution of complex behavior. Here, we report our field-testing of two commonly used laboratory-based techniques for brain preservation while on a collecting expedition in the Congo Basin and Albertine Rift, two poorly known regions associated with the Eastern Afromontane biodiversity hotspot.\n\nFirst, we found that transcardial perfusion fixation and long-term brain storage, conducted in remote field conditions with no access to cold storage laboratory equipment, had no observable impact on cytoarchitectural features of lizard brain tissue when compared to lizard brain tissue processed under laboratory conditions. Second, field-perfused brain tissue subjected to prolonged post-fixation remained readily compatible with subsequent immunohistochemical detection of neural antigens, with immunostaining that was comparable to that of laboratory-perfused brain tissue. Third, immersion-fixation of lizard brains, prepared under identical environmental conditions, was readily compatible with subsequent iodine-enhanced X-ray microcomputed tomography, which facilitated the non-destructive imaging of the intact brain within its skull.\n\nIn summary, we have validated multiple approaches to preserving intact lizard brains in remote field conditions with limited access to supplies and a high degree of environmental exposure. This protocol should serve as a malleable framework for researchers attempting to rescue perishable and irreplaceable morphological and molecular data from regions of disappearing biodiversity. Our approach can be harnessed to extend the numbers of species being actively studied by the neuroscience community, by reducing some of the difficulty associated with acquiring brains of animal species that are not readily available in captivity.

Neuroscience↗

bioRxiv2016

Motor control by precisely timed spike patterns

A fundamental problem in neuroscience is to understand how sequences of action potentials (\"spikes\") encode information about sensory signals and motor outputs. Although traditional theories of neural coding assume that information is conveyed by the total number of spikes fired (spike rate), recent studies of sensory [1-5] and motor [6] activity have shown that far more information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information carried by spike timing actually plays a causal role in brain function [1]. Here we demonstrate how a precise spike timing code is read out downstream by the muscles to control behavior. We provide both correlative and causal evidence to show that the nervous system uses millisecond-scale variations in the timing of spikes within multi-spike patterns to regulate a relatively simple behavior - respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision, and that significant improvements in applications, such as neural prosthetic devices, can be achieved by using precise spike timing information.

Neuroscience↗

bioRxiv2016

Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

A central goal of cognitive neuroscience is to decode human brain activity--i.e., to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive--i.e., capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a Bayesian decoding framework based on a novel topic model---Generalized Correspondence Latent Dirichlet Allocation---that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to \"seed\" decoder priors with arbitrary images and text--enabling researchers, for the first time, to generative quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

Neuroscience↗

bioRxiv2016

Neurobiology as information physics

This article reviews thermodynamic relationships in the brain in an attempt to consolidate current research in systems neuroscience. The present synthesis supports proposals that thermodynamic information in the brain can be quantified to an appreciable degree of objectivity, that many qualitative properties of information in systems of the brain can be inferred by observing changes in thermodynamic quantities, and that many features of the brains anatomy and architecture illustrate relatively simple information-energy relationships. The brain may provide a unique window into the relationship between energy and information.

Neuroscience↗

bioRxiv2016

Sub-millisecond optogenetic control of neuronal firing with two-photon holographic photoactivation of Chronos

Optogenetic neuronal network manipulation promises to at last unravel a long-standing mystery in neuroscience: how does microcircuit activity causally relate to behavioral and pathological states? The challenge to evoke spikes with high spatial and temporal complexity necessitates further joint development of light-delivery approaches and custom opsins. Two-photon scanning and parallel illumination strategies applied to ChR2- and C1V1-expressing neurons demonstrated reliable, in-depth generation of action potentials both in-vitro and in-vivo, but thus far lack the temporal precision necessary to induce precisely timed spiking events. Here, we show that efficient current integration enabled by two-photon holographic amplified laser illumination of Chronos, a highly light-sensitive and fast opsin, can evoke spikes with submillisecond precision and repeated firing up to 100 Hz. These results pave the way for optogenetic manipulation with the spatial and temporal sophistication necessary to mimic natural microcircuit activity.

Neuroscience↗