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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 37 records · Page 2Linked to original sources

bioRxiv2017

Decoding The Brain: Neural Representation And The Limits Of Multivariate Pattern Analysis In Cognitive Neuroscience

Since its introduction, multivariate pattern analysis (MVPA), or \"neural decoding\", has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoders Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the Dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the Dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the Dictum can be improved on, in order to better justify inferences about neural representation using MVPA.

neuroscience↗

bioRxiv2017

Evaluating Methods of Correcting for Multiple Comparisons Implemented in SPM12 in Social Neuroscience fMRI Studies: An Example from Moral Psychology

In fMRI research, the goal of correcting for multiple comparisons is to identify areas of activity that reflect true effects, and thus would be expected to replicate in future studies. Finding an appropriate balance between trying to minimize false positives (Type I error) while not being too stringent and omitting true effects (Type II error) can be challenging. Furthermore, the advantages and disadvantages of these types of errors may differ for different areas of study. In many areas of social neuroscience that involve complex processes and considerable individual differences, such as the study of moral judgment, effects are typically smaller and statistical power weaker, leading to the suggestion that less stringent corrections that allow for more sensitivity may be beneficial, but also result in more false positives. Using moral judgment fMRI data, we evaluated four commonly used methods for multiple comparison correction implemented in SPM12 by examining which method produced the most precise overlap with results from a meta-analysis of relevant studies and with results from nonparametric permutation analyses. We found that voxel-wise thresholding with family-wise error correction based on Random Field Theory provides a more precise overlap (i.e., without omitting too few regions or encompassing too many additional regions) than either clusterwise thresholding, Bonferroni correction, or false discovery rate correction methods.

neuroscience↗

bioRxiv2017

A guide to robust statistical methods in 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 understanding 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.

neuroscience↗

bioRxiv2017

AFQ-Browser: Supporting reproducible human neuroscience research through browser-based visualization tools

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is an important component of reproducible research, it is not always clear how to usefully share data in a manner that allows other labs to understand and reproduce published findings. Here we describe a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a published diffusion MRI study. Because AFQ-browser is portable -- it runs in any modern web-browser -- it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of a diffusion MRI dataset (anatomy, quantitative diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new scientific discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, sharing and aggregating data across labs, and publishing data alongside manuscripts.

neuroscience↗

bioRxiv2018

Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here we describe a software toolbox--called seqNMF--with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.

neuroscience↗

bioRxiv2018

MemBright: a Family of Fluorescent Membrane Probes for Advanced Cellular Imaging and Neuroscience

The proper staining of the plasma membrane (PM) is critical in bioimaging as it delimits the cell. Herein, we developed MemBright: a family of six cyanine-based fluorescent turn-on PM probes that emit from orange to near-infrared when reaching the PM, and enable homogeneous and selective PM staining with excellent contrast in mono and two-photon microscopy. These probes are compatible with long-term live cell imaging and immunostaining. Moreover, MemBright label neurons in a brighter manner than surrounding cells allowing identification of neurons in acute brain tissue section and neuromuscular-junctions without any use of transfection or transgenic animals. At last, MemBright were used in super-resolution imaging to unravel the dendritic spines neck. 3D multicolor dSTORM in combination with immunostaining revealed en-passant synapse displaying endogenous glutamate receptors clustered at the axonal-dendritic contact site. MemBright probes thus constitute a universal toolkit for cell biology and neuroscience biomembrane imaging with a variety of microscopy techniques.

neuroscience↗

bioRxiv2014

Big Behavioral Data: Psychology, Ethology and the Foundations of Neuroscience

Behavior is a unifying organismal process through which genes, neural function, anatomy and environment converge and interrelate. Here we review the current state and sketch the future impact of accelerating advances in technology for behavioral studies, focusing on rodents as an exemplar. We frame our analysis in three dimensions: (1) degree of experimental constraint, (2) dimensionality of data, and (3) level of description. We argue that ethomics, fueled by \"big behavioral data\", presents challenges proportionate to its promise and describe how these challenges might be met through opportunities afforded by the two rival conceptual legacies of 20th century behavioral science, ethology and psychology. We conclude that although \"ethomes\" are not the goal, big behavioral data has the potential to transform and unify these two disciplines and to solidify the foundations of others, including neuroscience, particularly if the data is as open as it is copious and quantitative.

Animal Behavior and Cognition↗

bioRxiv2017

Bridging Computational Neuroscience and Machine Learning on Non-Stationary Multi-Armed Bandits

Fast adaptation to changes in the environment requires both natural and artificial agents to be able to dynamically tune an exploration-exploitation trade-off during learning. This trade-off usually determines a fixed proportion of exploitative choices (i.e. choice of the action that subjectively appears as best at a given moment) relative to exploratory choices (i.e. testing other actions that now appear worst but may turn out promising later). The problem of finding an efficient exploration-exploitation trade-off has been well studied both in the Machine Learning and Computational Neuroscience fields. Rather than using a fixed proportion, non-stationary multi-armed bandit methods in the former have proven that principles such as exploring actions that have not been tested for a long time can lead to performance closer to optimal - bounded regret. In parallel, researches in the latter have investigated solutions such as progressively increasing exploitation in response to improvements of performance, transiently increasing exploration in response to drops in average performance, or attributing exploration bonuses specifically to actions associated with high uncertainty in order to gain information when performing these actions. In this work, we first try to bridge some of these different methods from the two research fields by rewriting their decision process with a common formalism. We then show numerical simulations of a hybrid algorithm combining bio-inspired meta-learning, kalman filter and exploration bonuses compared to several state-of-the-art alternatives on a set of non-stationary stochastic multi-armed bandit tasks. While we find that different methods are appropriate in different scenarios, the hybrid algorithm displays a good combination of advantages from different methods and outperforms these methods in the studied scenarios.

animal behavior and cognition↗

bioRxiv2018

Impact of Human Behavioral Papers at Journal of Neuroscience

A recent policy change at Journal of Neuroscience (JN) has significantly increased editorial \"desk rejections\", reducing the number of papers that are considered for publication. Survey results from 130 scientists suggested that the new policy may have had a particularly large impact on studies that focused on human behavioral techniques. To quantify the effects of the new policy, we gathered data on all papers ever published in JN (~35,000), as well as all papers that had cited the JN papers (~2.7 million papers). We found that the recent change in editorial policy had disproportionately affected rejection rates of human behavioral papers: since 2015, the number of human behavioral papers as a proportion of all papers published in JN has seen a 30% decline. While there has been a long-term declining trend in the journals impact factor, we found that this declining impact factor was shared by both human behavioral papers as well as other papers in the journal. This suggested that whatever may have been the source of the declining impact factor at JN, this source was affecting the various areas of research equally. That is, it was unlikely that papers in any one field were responsible for the declining impact factor number at JN. Indeed, when impact was measured over the long-term, we found that the average human behavioral paper at JN consistently outperformed other papers, generating a significantly higher number of citations per year at 5 and 10 years post publication.

scientific communication and education↗

bioRxiv2018

A common statistical misunderstanding in Psychology and Neuroscience: Do we need normally distributed independent or dependent variables for linear regression to work?

Our ability to draw conclusions from experiments often relies on the application of inferential statistics, such as those included within the general-linear-modeling framework. The purpose of this commentary is to direct attention a common fallacy that can be observed in paper reviews and professional exchanges in the field of Psychology and Neuroscience pertaining to multiple regression analysis. The fallacy concerns the confusion of the requirements on the residuals of a linear-regression model with requirements on the dependent or independent variables themselves. A simple thought experiment, algebraic demonstration, and simulation study can demonstrate this misconception. It is our hope that consideration of the fallacy may help clarify distinctions between technical statistical issues and conceptual interpretation of research findings.

scientific communication and education↗

bioRxiv2014

Untangling cross-frequency coupling in neuroscience

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

Neuroscience↗

bioRxiv2016

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

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

Neuroscience↗

bioRxiv2017

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

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

neuroscience↗

bioRxiv2017

Probabilistic language models in cognitive neuroscience: promises and pitfalls

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

neuroscience↗

bioRxiv2018

Individual differences in cortical processing speed predict cognitive abilities: a model-based cognitive neuroscience account

Previous research has shown that individuals with greater cognitive abilities display a greater speed of higher-order cognitive processing. These results suggest that speeded neural information-processing may facilitate evidence accumulation during decision making and memory updating and thus yield advantages in general cognitive abilities. We used a hierarchical Bayesian cognitive modeling approach to test the hypothesis that individual differences in the velocity of evidence accumulation mediate the relationship between neural processing speed and cognitive abilities. We found that a higher neural speed predicted both the velocity of evidence accumulation across behavioral tasks as well as cognitive ability test scores. However, only a small part of the association between neural processing speed and cognitive abilities was mediated by individual differences in the velocity of evidence accumulation. The model demonstrated impressive forecasting abilities by predicting 36% of individual variation in cognitive ability test scores in an entirely new sample solely based on their electrophysiological and behavioral data. Our results suggest that individual differences in neural processing speed might affect a plethora of higher-order cognitive processes, that only in concert explain the large association between neural processing speed and cognitive abilities, instead of the effect being entirely explained by differences in evidence accumulation speeds.

neuroscience↗

bioRxiv2018

Establishment of the miniature fish species Danionella translucida as a genetically and optically tractable neuroscience model

Integrative studies of the diverse neuronal networks that govern social behavior are hindered by the lack of methods to record neural activity comprehensively across the entire brain. The recent development of the miniature fish Danionella cerebrum as a model organism offers one potential solution, as the small size and optical transparency of these animals makes it possible to visualize circuit activity throughout the nervous system1-4. Here, we establish the feasibility of using Danionella as a model for social behavior and socially reinforced learning by showing that adult fish exhibit strong affiliative tendencies, and that social interactions can serve as the reinforcer in an appetitive conditioning paradigm. Fish exhibited an acute ability to identify conspecifics and distinguish them from closely related species, which was mediated by both visual and particularly olfactory cues. These behaviors were abolished by pharmacological and genetic interference with oxytocin signaling, demonstrating the conservation of key neural mechanisms observed in other vertebrates5-11. Our work validates Danionella as a tool for understanding the social brain in general, and its modulation by neuropeptide signaling in particular.

neuroscience↗

bioRxiv2017

The Block Object Storage Service (bossDB): A Cloud-Native Approach for Petascale Neuroscience Discovery

Large volumetric neuroimaging datasets have grown in size over the past ten years from gigabytes to terabytes, with petascale data becoming available and more common over the next few years. Current approaches to store and analyze these emerging datasets are insufficient in their ability to scale in both cost-effectiveness and performance. Additionally, enabling large-scale processing and annotation is critical as these data grow too large for manual inspection. We provide a new cloud-native managed service for large and multi-modal experiments, with support for data ingest, storage, visualization, and sharing through a RESTful Application Programming Interface (API) and web-based user interface. Our project is open source and can be easily and cost-effectively used for a variety of modalities and applications.

bioinformatics↗

bioRxiv2018

Beyond Bonferroni Revisited: Concerns over inflated false positives in conservation genetics, genetics, and neuroscience

In 2006, Narum published a paper in Conservation Genetics that was motivated by the stringent nature of the Bonferroni approach for family wise error correction. That work suggested that the approach of Benjamini and Yekutieli in 2001 provided adequate correction and was more biologically relevant. However, there are crucial differences between the original Benjamini and Yekutieli procedure and that described by Narum. After carefully reviewing both papers, we believe that the Narum procedure is both different than the Benjamini and Yekutieli procedure and does not adequately control for family wise error. We provide an overview of approaches for FWE correction as well as evidence for the faulty implementation of the Benjamini and Yekutieli procedure by Narum using the equations from the respective papers, data from both papers, and the results of simulation.

genetics↗