bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pain-related fear – Dissociable neural sources of different fear constructs
Authors: Meier Michael Lukas , Vrana Andrea , Humphreys Barry Kim , Seifritz Erich , Stämpfli Philipp , Schweinhardt Petra 
Published: January 2018

AbstractFear of pain demonstrates significant prognostic value regarding the development of persistent musculoskeletal pain and disability. Its assessment often relies on self-report measures of pain-related fear by a variety of questionnaires. However, based either on “fear of movement/(re)injury/kinesiophobia”, “fear avoidance beliefs” or “pain anxiety”, pain-related fear constructs seemingly differ while the potential overlap of the questionnaires is unclear. Furthermore, the relationship to other anxiety measures such as state or trait anxiety remains ambiguous. Because the neural bases of fearful and anxious states are well described, advances in neuroimaging such as machine learning on brain activity patterns recorded by functional magnetic resonance imaging might help to dissect commonalities or differences across pain-related fear constructs. We applied a pattern regression approach in 20 non-specific chronic low back pain patients to reveal predictive relationships between fear-related neural information and different pain-related fear questionnaires. More specifically, the applied Multiple Kernel Learning approach allowed generating models to predict the questionnaire scores based on a hierarchical ranking of fear-related neural patterns induced by viewing videos of potentially harmful activities for the back. We sought to find evidence for or against overlapping pain-related fear constructs by comparing the questionnaire prediction models according to their predictive abilities and associated neural contributors. The results underpin the diversity of pain-related fear constructs by demonstrating evidence of non-overlapping neural predictors within fear processing regions. This neuroscientific approach might ultimately help to further understand and dissect psychological pain-related fear constructs.SignificancePain-related fear, often assessed through self-reports such as questionnaires, has shown prognostic value and clinical utility for a variety of musculoskeletal pain disorders. However, it remains difficult to determine a common underlying construct of pain-related fear due to several proposed constructs among questionnaires. The current study describes a novel neuroscientific approach using machine learning of neural patterns within the fear circuit of chronic low back pain patients that has the potential to identify neural commonalities or differences among the various pain-related fear constructs. Ultimately, this approach might afford a deeper understanding of the suggested constructs and might be also applied to other domains where ambiguity exists between different psychological constructs.
A compact head-mounted endoscope for in vivo calcium imaging in freely-behaving mice
Authors: Jacob Alexander D. , Ramsaran Adam I. , Mocle Andrew J. , Tran Lina M. , Yan Chen , Frankland Paul W. , Josselyn Sheena A 
Published: January 2018

AbstractMiniaturized fluorescence microscopes for imaging calcium transients are a promising tool for investigating the relationship between behaviour and population-level neuronal activity in rodents. However, commercially available miniature microscopes may be costly, and, because they are closed-source, may not be easily modified based on particular experimental requirements. Here, we describe how to build and use a low-cost compact head-mounted endoscope (CHEndoscope) system for in vivo calcium imaging. The CHEndoscope uses an implanted gradient index (GRIN) lens along with the genetically encoded calcium indicator GCaMP6 to image calcium transients from hundreds of neurons simultaneously in awake behaving mice. This system is affordable, open-source, and flexible, permitting modification depending on the particular experiment. This Unit describes in detail the assembly, surgical implantation, data collection, and processing of calcium signals using the CHEndoscope system. The aim of this open framework model is to provide an accessible set of miniaturized calcium imaging tools for the neuroscience research community.Significance StatementThe ability to image calcium transients in awake, behaving rodents using miniature microscopes opens exciting and novel avenues for gaining insights into how information is encoded in neural circuits. The development of this tool has already had a significant impact on neuroscience research. The cost of commercial systems, however, may be prohibitive for many laboratories. Here, we describe an affordable, open-source compact head-mounted endoscope (CHEndoscope) system for performing in vivo calcium imaging in freely-behaving mice. CHEndoscopes may be manufactured by individual laboratories at relatively minor cost. Our hope is that greater availability of affordable, open-source tools (such as the one presented here) will accelerate the pace of discoveries in systems neuroscience.
Prediction and information integration determine subtle anticipatory fixation biases
Authors: Notaro Giuseppe , van Zoest Wieske , Melcher David , Hasson Uri 
Published: January 2018

ABSTRACTA core question underlying neurobiological and computational models of behavior is how individuals learn environmental statistics and use them for making predictions. Treatment of this issue largely relies on reactive paradigms, where inferences about predictive processes are derived by modeling responses to stimuli that vary in likelihood. Here we deployed a novel proactive oculomotor metric to determine how input statistics impact anticipatory behavior, decoupled from stimulus-response. We implemented transition constraints between target locations, and quantified a subtle fixation bias (FB) discernible while individuals fixated a screen center awaiting target presentation. We show that FB is informative with respect the input statistics, reflects learning at different temporal scales, predicts saccade latencies on a trial level, and can be linked to fundamental oculomotor metrics. We also present an extension of this approach to a more complex paradigm. Our work demonstrates how learning impacts strictly predictive processes and presents a novel direction for studying learning and prediction.
WEADE: A Workflow for Enrichment Analysis and Data Exploration
Authors: Trost Nils , Rempel Eugen , Ermakova Olga , Tamirisa Srividya , Pârcălăbescu Letiția , Boutros Michael , Lohmann Jan U. , Lohmann Ingrid 
Published: January 2018

ABSTRACTData analysis based on enrichment of Gene Ontology terms has become an important step in exploring large gene or protein expression datasets and several stand-alone or web tools exist for that purpose. However, a comprehensive and consistent analysis downstream of the enrichment calculation is missing so far. With WEADE we present a free web application that offers an integrated workflow for the exploration of genomic data combining enrichment analysis with a versatile set of tools to directly compare and intersect experiments or candidate gene lists of any size or origin including cross-species data. Lastly, WEADE supports the graphical representation of output data in the form of functional interaction networks based on prior knowledge, allowing users to go from plain expression data to functionally relevant candidate sub-lists in an interactive and consistent manner.
Large-scale two-photon imaging revealed super-sparse population codes in V1 superficial layer of awake monkeys
Authors: Tang Shiming , Zhang Yimeng , Li Zhihao , Li Ming , Liu Fang , Jiang Hongfei , Lee Tai Sing 
Published: January 2018

AbstractEfficient coding has been proposed as a general principle for the sensory systems. The efficient coding hypothesis predicts that neuronal population responses should be sparse, but limited by the measurement techniques, the precise estimates of the population sparseness of visual cortical neurons are still uncertain. Here, we employed large-scale two-photon calcium imaging to examine the neuronal population activities in V1 superficial layers of awake macaques in response to a large set of natural images. We found that only 0.5% of these neurons on average responded strongly to any given natural image with response strength above half of their individual peak responses, which is more than tenfold sparse over those reported by early studies. We further showed that these sparse population activities contain sufficient information for discriminating images with high accuracy. This study provided the first accurate measure of sparseness in V1 neuronal population responses, which support super-sparse neural codes in primates.
In Vivo Measurements of the Frequency-Dependent Impedance of the Spinal Cord
Authors: Utz Marcel , Miller John W. , Reddy Chandan G. , Wilson Saul , Abode-Iyamah Kingsley O. , Fredericks Douglas C. , Gillies George T. , Howard Matthew A. 
Published: January 2018

AbstractImproved knowledge of the electrode-tissue impedance will be useful in optimizing the clinical protocols and resulting efficacy of the existing and emerging approaches to spinal cord stimulation. Toward that end, the complex impedance (amplitude and phase) of in vivo ovine spinal cord tissue was measured at the electrode-pial subdural surface interface from 5 Hz to 1 MHz, and with the bi-polar electrodes oriented both parallel and perpendicular to the rostral-caudal axis of the spinal cord. At stimulation frequencies above 10 kHz, most of the impedance then becomes resistive in nature and the phase diference between the stimulation signal and the resulting current drops to ≈ 10˚, thus maximizing power transfer to the tissues. Also, at these higher frequencies, the current pulse maintains significantly greater fidelity to the shape of the stimulation signal applied across the electrodes. Lastly, there were lower impedances associated with parallel as opposed to perpendicular orientation of the electrodes, thus reflecting the effects of fiber orientation within the spinal cord. Impedance differences of this kind have not been reported with epidural stimulation because of the electrical shunting effects of the intervening layer of relatively high conductivity cerebrospinal fluid. These observations provide a quantitative basis for improved models of spinal cord stimulation and suggest certain advantages for direct intradural stimulation relative to the standard epidural approaches. (Some figures in this article are in colour only in the electronic version)
Dendritic spikes in hippocampal granule cells are necessary for long-term potentiation at the perforant path synapse
Authors: Kim Sooyun , Kim Yoonsub , Lee Suk-Ho , Ho Won-Kyung 
Published: January 2018

AbstractLong-term potentiation (LTP) of synaptic responses is essential for hippocampal memory function. Perforant-path (PP) synapses on hippocampal granule cells (GCs) contribute to the formation of associative memories, which are considered the cellular correlates of memory engrams. However, the mechanisms of LTP at these synapses are not well understood. Due to sparse firing activity and the voltage attenuation in their dendrites, it remains unclear how associative LTP at distal synapses occurs. Here we show that NMDA receptor-dependent LTP can be induced at PP-GC synapses without backpropagating action potentials (bAPs) in acute rat brain slices. Dendritic recordings reveal substantial attenuation of bAPs as well as local dendritic Na + ‐spike generation during PP-GC input. Inhibition of Na+ ‐spikes impairs LTP suggesting that LTP at PP-GC synapse requires local Na + ‐spikes. Thus, dendritic spikes are essential for LTP induction at PP-GC synapse and may constitute a key cellular mechanism for memory formation in the dentate gyrus.
Sex-specific impact of prenatal androgens on intrinsic functional connectivity between social brain default mode subsystems
Authors: V. Lombardo Michael , Auyeung Bonnie , Pramparo Tiziano , Quartier Angélique , Courraud Jérémie , J. Holt Rosemary , Waldman Jack , N. V. Ruigrok Amber , Mooney Natasha , Lai Meng-Chuan , Kundu Prantik , T. Bullmore Edward , Mandel Jean-Louis , Piton Amélie , Baron-Cohen Simon 
Published: January 2018

AbstractMany early-onset neurodevelopmental conditions such as autism affect males more frequently than females and affect corresponding domains such as social cognition, social-communication, language, emotion, and reward. Testosterone is well-known for its role as a sex-related biological mechanism and affects these conditions and domains of functioning. Developmentally, testosterone may sex-differentially impact early fetal brain development by influencing early neuronal development and synaptic mechanisms behind cortical circuit formation, particularly for circuits that later develop specialized roles in such cognitive domains. Here we find that variation in fetal testosterone (FT) exerts sex-specific effects on later adolescent functional connectivity between social brain default mode network (DMN) subsystems. Increased FT is associated with dampening of functional connectivity between DMN subsystems in adolescent males, but has no effect in females. To isolate specific prenatal neurobiological mechanisms behind this effect, we examined changes in gene expression identified following a treatment with a potent androgen, dihydrotestosterone (DHT) in an in-vitro model of human neural stem cell (hNSC). We previously showed that DHT-dysregulates genes enriched with known syndromic causes for autism and intellectual disability. DHT dysregulates genes in hNSCs involved in early neurodevelopmental processes such as neurogenesis, cell differentiation, regionalization, and pattern specification. A significant number of these DHT-dysregulated genes shows spatial expression patterns in the adult brain that highly correspond to the spatial layout of the cortical midline DMN subsystem. These DMN-related and DHT-affected genes (e.g., MEF2C) are involved in a number of synaptic processes, many of which impact excitation/inhibition imbalance. Focusing on MEF2C, we find replicable upregulation of expression after DHT treatment as well as dysregulated expression in induced pluripotent stem cells and neurons of individuals with autism. This work highlights sex-specific prenatal androgen influence on social brain DMN circuitry and autism-related mechanisms and suggests that such influence may impact early neurodevelopmental processes (e.g., neurogenesis, cell differentiation) and later developing synaptic processes.
Neural spiking for causal inference
Authors: Lansdell Benjamin James , Kording Konrad Paul 
Published: January 2018

AbstractWhen 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.
Fast Memory Integration Facilitated by Schema Consistency
Authors: Zhang Qiong , Popov Vencislav , Koch Griffin E. , Calloway Regina C. , Coutanche Marc N. 
Published: January 2018

AbstractMany of our everyday decisions are based not only on memories of direct experiences, but on memories that are integrated across multiple distinct experiences. Sometimes memory integration between existing memories and newly learnt information occurs rapidly, without requiring inference at the time of a decision. Such fast memory integration is known to be supported by the hippocampus but not the neocortex. In this study, we explore an alternative mechanism of fast memory integration, through related prior knowledge (i.e., schema), which is associated with neocortical learning. Paired associates were selected to be schema consistent or inconsistent, and confirmed with a latent semantic analysis of text corpora. We observed that after enabling fast learning by using material that is consistent with a schema, faster memory integration can occur. This result suggests that the hippocampus-mediated integration of new information is not the only available mechanism that supports fast memory integration.
Quality control-based signal drift correction and interpretations of metabolomics/proteomics data using random forest regression
Authors: Luan Hemi , Ji Fenfen , Chen Yu , Cai Zongwei 
Published: January 2018

AbstractLarge-scale mass spectrometry-based metabolomics and proteomics study requires the long-term analysis of multiple batches of biological samples, which often accompanied with significant signal drift and various inter‐ and intra‐ batch variations. The unwanted variations can lead to poor inter‐ and intra-day reproducibility, which is a hindrance to discover real significance. We developed a novel quality control-based random forest signal correction algorithm, being ensemble learning approach to remove inter‐ and intra‐ batches of unwanted variations at feature-level. Our evaluation based on real samples showed the developed algorithm improved the data precision and statistical accuracy for metabolomics and proteomics, which was superior to other common correction methods. We have been able to improve its performance for interpretations of large-scale metabolomics and proteomics data, and to allow the improvement of the data precision for uncovering the real biologically differences.
Valproic acid attenuates hyperglycemia-induced complement and coagulation cascade gene expression
Authors: Felisbino Marina Barreto , Ziemann Mark , Khurana Ishant , de Oliveira Camila Borges Martins , Mello Maria Luiza S. , El-Osta Assam 
Published: January 2018

ABSTRACTAtherothrombosis remains the leading cause of morbidity and mortality in patients diagnosed with diabetes mellitus, but the molecular mechanisms underpinning this remain unresolved. As the liver plays a major role in metabolic homeostasis and secretion of clotting factors and inflammatory innate immune proteins, there is an interest in understanding the mechanisms of hepatic cell activation under hyperglycemia and whether this can be attenuated pharmacologically. We have previously shown that hyperglycemia stimulates major changes in chromatin organisation and metabolism in hepatocytes, and that the histone deacetylase inhibitor valproic acid (VPA; IUPAC: 2-propylpentanoic acid) is able to reverse some of these metabolic changes. In this study, we used deep transcriptome sequencing to show that VPA attenuates hyperglycemia-induced activation of complement and coagulation cascade genes. These findings reveal a novel mechanism of VPA protection against hyperglycemia, which might improve the therapeutic approaches for diabetes.
Monitoring the 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U) modification in eukaryotic tRNAs via the gamma-toxin endonuclease
Authors: Lentini Jenna M. , Ramos Jillian , Fu Dragony 
Published: January 2018

ABSTRACTThe post-transcriptional modification of tRNA at the wobble position is a universal process occurring in all domains of life. In eukaryotes, the wobble uridine of particular tRNAs is transformed to the 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U) modification which is critical for proper mRNA decoding and protein translation. However, current methods to detect mcm5s2U are technically challenging and/or require specialized instrumental expertise. Here, we show that gamma-toxin endonuclease from the yeastKluyveromyces lactiscan be used as a probe for assaying mcm5s2U status in the tRNA of diverse eukaryotic organisms ranging from protozoans to mammalian cells. The assay couples the mcm5s2U-dependent cleavage of tRNA by gamma-toxin with standard molecular biology techniques such as Northern blot analysis or quantitative PCR to monitor mcm5s2U levels in multiple tRNA isoacceptors. The results gained from the gamma-toxin assay reveals the evolutionary conservation of the mcm5s2U modification across eukaryotic species. Moreover, we have employed the gamma-toxin assay to verify uncharacterized eukaryotic Trm9 and Trm112 homologs that catalyze the formation of mcm5s2U. These findings demonstrate the use of gamma-toxin as a detection method to monitor mcm5s2U status in diverse eukaryotic cell types for cellular, genetic and biochemical studies.
High-throughput microcolony growth analysis from suboptimal low-magnification micrographs
Authors: Plavskin Yevgeniy , Li Shuang , Jung Hyun , Sartori Federica M. O. , Buzby Cassandra , Müller Heiko , Ziv Naomi , Levy Sasha F. , Siegal Mark L. 
Published: January 2018

AbstractNew technological advances have enabled high-throughput phenotyping at the single-cell level, yet analyzing the large amount of data generated by high throughput phenotyping experiments automatically and accurately is a considerable challenge. Here we introduce Processing Images Easily (PIE), software that automatically tracks growth of microbial colonies in low-magnification brightfield images by combining adaptive object-center recognition with gradient-based object-outline recognition. PIE recognizes colony outlines very robustly and accurately across a wide range of image brightnesses, focal depths, and organisms. Beyond accurate colony recognition, PIE is designed to easily integrate with complex experiments, allowing colony tracking across multiple experimental phases and classification based on fluorescence intensity. We show that PIE can be used to accurately measure the growth rates of large numbers (>90,000) of bacterial or yeast microcolonies in a single-time-lapse experiment, allowing calculation of population-wide growth properties. Finally, PIE is able to track individual colonies across multiple experimental phases, measuring both growth and fluorescence properties of the microcolonies.Author SummaryHigh-throughput microscopy has enabled automated collection of large amounts of growth and gene-expression data in microbes. Computational methods that can precisely recognize and track organisms in images are essential to performing measurements at scale using automated microscopy. We have developed PIE, software that automatically recognizes microbial colonies in microscopy images, tracks them in imaging time-series, and performs measurements of growth and, potentially, gene expression. PIE is highly effective on low-resolution images, outperforming current state-of-the-art approaches in both speed and accuracy, and works well in microbes of varying shapes and sizes. In addition, PIE allows tracking microcolonies across arbitrary sequences of experimental phases, each collecting data in different modalities. We show that PIE allows measurement of growth and fluorescence properties in tens of thousands of microbial colonies in a single experiment, and that in turn the scale of these measurements can lead to important insights about interindividual differences in growth and stress response. PIE is available as a Python package (https://doi.org/10.5281/zenodo.4987127) with documentation currently at https://pie-image.readthedocs.io/; users can also run analysis on individual images or time-series without the need to install PIE by using our web application, currently available at http://pie.hpc.nyu.edu/.