SEARCH · Search bioRxiv

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,801 records · Page 100Linked to original sources

bioRxiv2017

Neural correlates of perceptual constancy in Auditory Cortex

Perceptual constancy requires neural representations that are selective for object identity, but also tolerant for identity-preserving transformations. How such representations arise in the brain and contribute to perception remains unclear. Here we studied tolerant representations of sound identity in the auditory system by recording multi-unit activity in tonotopic auditory cortex of ferrets discriminating the identity of vowels which co-varied across orthogonal stimulus dimensions (fundamental frequency, sound level, location and voicing). We found that neural decoding of vowel identity was most successful across the same orthogonal dimensions over which animals generalized their behavior. We also decoded orthogonal sound features and behavioral variables including choice and accuracy to show a behaviorally-relevant, multivariate and multiplexed representation of sound, with each variable represented over a distinct time-course. Finally, information content and timing of sound feature encoding was modulated by task-engagement and training, suggesting that tolerant representations during perceptual constancy are attentionally and experience-dependent.

neuroscience↗

bioRxiv2017

Disambiguating brain functional connectivity

Functional connectivity (FC) analyses of correlations of neural activity are used extensively in neuroimaging and electrophysiology to gain insights into neural interactions. However, correlation fails to distinguish sources as different as changes in neural signal amplitudes or noise levels. This ambiguity substantially diminishes the value of FC for inferring system properties and clinical states. Network modelling approaches may avoid ambiguities, but require specific assumptions. We present an enhancement to FC analysis with improved specificity of inferences, minimal assumptions and no reduction in flexibility. The Additive Signal Change (ASC) approach characterises FC changes into certain prevalent classes involving additions of signal. With FMRI data, the approach reveals a rich diversity of signal changes underlying measured changes in FC, bringing into question standard interpretations. The ASC method can also be used to disambiguate other measures of dependency, such as regression and coherence, providing a flexible tool for the analysis of neural data.

neuroscience↗

bioRxiv2017

Pure Correlates of Exploration and Exploitation in the Human Brain

Balancing exploration and exploitation is a fundamental problem in reinforcement learning. Previous neuroimaging studies of the exploration-exploitation dilemma could not completely disentangle these two processes, making it difficult to unambiguously identify their neural signatures. We overcome this problem using a task in which subjects can either observe (pure exploration) or bet (pure exploitation). Insula and dorsal anterior cingulate cortex showed significantly greater activity on observe trials compared to bet trials, suggesting that these regions play a role in driving exploration. A model-based analysis of task performance suggested that subjects chose to observe until a critical evidence threshold was reached. We observed a neural signature of this evidence accumulation process in ventromedial prefrontal cortex. These findings support theories positing an important role for anterior cingulate cortex in exploration, while also providing a new perspective on the roles of insula and ventromedial prefrontal cortex.\n\nSignificance StatementSitting down at a familiar restaurant, you may choose to order an old favorite or sample a new dish. In reinforcement learning theory, this is known as the exploration-exploitation dilemma. The optimal solution is known to be intractable; therefore, humans must use heuristic strategies. Behavioral studies have revealed several candidate strategies, but identifying the neural mechanisms underlying these strategies is complicated due to the fact that exploration and exploitation are not perfectly dissociable in standard tasks. Using an \"observe or bet\" task, we identify for the first time pure neural correlates of exploration and exploitation in the human brain.

neuroscience↗

bioRxiv2017

Spontaneous development of cerebrovascular pathology and microinfarcts in a mouse model of sickle cell disease.

Stroke is a dramatic complication of sickle cell disease (SCD) and is associated with aneurysms, moya moya, intravascular thrombi, cerebral hyperemia and increased vessel tortuosity. We show that aged SCD mice spontaneously develop the characteristics features of cerebral vasculopathy seen in human SCD. Thirteen month old Townes SCD mice and age-matched controls had a cranial windows implanted over the somatosensory cortex. Cortical capillaries were imaged using in vivo two-photon microscopy after the blood plasma was labeled with a fluorescent dye. Results showed that SCD mice compared to controls, had significantly higher red blood cell (RBC) velocity and capillary vessel diameter. SCD mice also had a significantly higher number of occlusive events in the capillary bed, resulting in more stalling of RBC flow. Microvascular topology was also altered, as SCD mice had significantly higher vessel tortuosity and shorter capillary branch lengths. Finally, post-mortem analyses revealed a greater number of cortical microinfarcts, likely caused by vascular occlusion since local tissue hypoxia and blood-brain barrier leakage was prominent. We concluded that aged Townes sickle cell mice spontaneously develop SCD-associated cerebral vasculopathy, and that in vivo two-photon imaging is a powerful approach to investigate the mechanisms of vascular complications in SCD.

neuroscience↗

bioRxiv2017

Heterogeneity of hypothalamic Pro-opiomelanocortin-expressing neurons revealed by single-cell RNA sequencing

Arcuate proopiomelanocortin (POMC) neurons are critical nodes in the control of body weight. Often characterised simply as direct targets for leptin, recent data suggest a more complex architecture. Using single cell RNA sequencing, we have generated an atlas of gene expression in murine POMC neurons. Of 163 neurons, 118 expressed high levels of Pomc with little/no Agrp expression and were considered \"canonical\" POMC neurons (P+). The other 45/163 expressed low levels of Pomc and high levels of Agrp (A+P+). Unbiased clustering analysis of P+ neurons revealed four different classes, each with distinct cell surface receptor gene expression profiles. Further, only 12% (14/118) of P+ neurons expressed the leptin receptor (Lepr) compared with 58% (26/45) of A+P+ neurons. In contrast, the insulin receptor (Insr) was expressed at similar frequency on P+ and A+P+ neurons (64% and 55%, respectively). These data reveal arcuate POMC neurons to be a highly heterogeneous population.

neuroscience↗

bioRxiv2017

Neural noise in the age-varying human brain predicts perceptual decisions

Sensory representations of the physical world and thus human percepts are susceptible to fluctuations in brain state or \"neural irregularity\". Furthermore, aging brains display altered levels of irregularity. We here show that a single, within-trial information-theoretic measure (weighted permutation entropy) captures neural irregularity in the human electroencephalogram as a proxy for both, trait-like differences between individuals of varying age, and state-like fluctuations that bias perceptual decisions. First, the overall level of neural irregularity increased with participants age, paralleled by a decrease in variability over time, likely indexing age-related disintegration on structural and functional levels of brain activity. Second, states of higher neural irregularity were associated with optimized sensory encoding and a subsequently increased probability of choosing the first of two physically identical stimuli. In sum, neural irregularity not only characterizes behaviorally relevant brain states, but also can identify trait-like changes that come with age.

neuroscience↗

bioRxiv2017

In vivo translatome profiling reveals early defects in ribosome biology underlying SMA pathogenesis

BackgroundGenetic alterations impacting on ubiquitously expressed proteins involved in mRNA metabolism often result in neurodegenerative conditions, with increasing evidence suggesting that translational defects can contribute to disease. Spinal Muscular Atrophy (SMA) is a neuromuscular disease caused by low levels of SMN protein, whose role in disease pathogenesis remains unclear.\n\nResultsBy determining in parallel the in vivo transcriptome and translatome in SMA mice we identified a robust decrease in translational efficiency, arising during early stages of disease. Translational defects affected translation-related transcripts, were cell autonomous, and were fully rescued after treatment with antisense oligonucleotides to restore SMN levels. Defects in translation were accompanied by a decrease in the number of ribosomes in motor neurons in vivo.\n\nConclusionOur findings suggest that neuronal tissues and cells are particularly sensitive to perturbations in translation during SMA, and identify ribosome biology as an important, yet largely neglected, factor in motor neuron degeneration.

neuroscience↗

bioRxiv2017

No evidence of early head circumference enlargements in children later diagnosed with autism in Israel

BackgroundLarge controversy exists regarding the potential existence and clinical significance of larger brain volumes in toddlers who later develop autism. Assessing this relationship is important for determining the clinical utility of early head circumference (HC) measures and for assessing the validity of the early overgrowth hypothesis of autism, which suggests that early accelerated brain development may be a hallmark of the disorder\n\nMethodWe performed a retrospective comparison of HC, height, and weight measurements between 66 toddlers who were later diagnosed with autism and 66 matched controls. These toddlers represent an unbiased regional sample from a single health service provider in the southern district of Israel. Using 4-12 measurements between birth and the age of two, we were able to characterize individual HC, height, and weight development with high precision and fit a negative exponential growth model to the data of each toddler with exceptional accuracy\n\nResultsThe analyses revealed that HC sizes and growth rates were not significantly larger in toddlers with autism even when stratifying the autism group based on verbal capabilities at the time of diagnosis. In addition, there were no significant correlations between ADOS scores at the time of diagnosis and HC at any time-point during the first two years of life\n\nConclusionsThese negative results add to accumulating evidence, which suggest that brain volume is not necessarily larger in toddlers who develop autism. While early brain overgrowth may characterize specific individuals with autism, it is not likely to represent a common etiology of the entire autism population

neuroscience↗

bioRxiv2017

A computational toolbox and step-by-step tutorial for the analysis of neuronal population dynamics in calcium imaging data

The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a tutorial for the use of a comprehensive computational toolbox for the analysis of neuronal population activity imaging. It consists of tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters features against surrogate control datasets. They are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain. We demonstrate the suitability of the toolbox for a variety of calcium imaging datasets, and provide a case study to explain its implementation.

neuroscience↗

bioRxiv2017

Sensitivity of Neurons in the Middle Temporal Area of Marmoset Monkeys to Random Dot Motion

Neurons in the Middle Temporal area (MT) of the primate cerebral cortex respond to moving visual stimuli. The sensitivity of MT neurons to motion signals can be characterized by using random-dot stimuli, in which the strength of the motion signal is manipulated by adding different levels of noise (elements that move in random directions). In macaques, this has allowed the calculation of \"neurometric\" thresholds. We characterized the responses of MT neurons in sufentanil/nitrous oxide anesthetized marmoset monkeys, a species which has attracted considerable recent interest as an animal model for vision research. We found that MT neurons show a wide range of neurometric thresholds, and that the responses of the most sensitive neurons could account for the behavioral performance of macaques and humans. We also investigated factors that contributed to the wide range of observed thresholds. The difference in firing rate between responses to motion in the preferred and null directions was the most effective predictor of neurometric threshold, whereas the direction tuning bandwidth had no correlation with the threshold. We also showed that it is possible to obtain reliable estimates of neurometric thresholds using stimuli that were not highly optimized for each neuron, as is often necessary when recording from large populations of neurons with different receptive field concurrently, as was the case in this study. These results demonstrate that marmoset MT shows an essential physiological similarity to macaque MT, and suggest that its neurons are capable of representing motion signals that allow for comparable motion-in-noise judgments.\n\nNew and NoteworthyWe report the activity of neurons in marmoset MT in response to random-dot motion stimuli of varying coherence. The information carried by individual MT neurons was comparable to that of the macaque, and that the maximum firing rates were a strong predictor of sensitivity. Our study provides key information regarding the neural basis of motion perception in the marmoset, a small primate species that is becoming increasingly popular as an experimental model.

neuroscience↗

bioRxiv2017

Gender-specific brain activation during visual art perception

The human body is the most common object of pictorial representation in western art. The goal of this study was to probe its evolutionary basis of visual art perception by investigating neural markers of gender-specific brain activity triggered by paintings of male and female images. Our results show significant activity in brain areas other than those recently associated with visual arts perception. Novel findings concern participant-general as well as gender specific brain activity. Although our participants were fully aware that they were viewing artworks, the inferior parietal lobule - known for its role in the perception of emotional body images - and the somatosensory cortex - which is related to touch - were selectively active for female body paintings in all participants. The most interesting finding as regards gender was that the sight of female bodies activates the subgenual anterior cingulate cortex in males, an area known to subserve autonomic arousal. In contrast, in females the sight of the male body activated reward and control related parts of the dorsal anterior cingulate cortex. This supports the notion that basic evolutionary processes operate when we view body images, also when they are paintings far removed from daily experience.

neuroscience↗

bioRxiv2017

Fiber tractography using machine learning

We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography.

neuroscience↗

bioRxiv2017

Consistent local dynamics in the brain across sessions are revealed by whole brain modeling of resting state activity

Resting state fMRI has been the primary tool for studying the functional organization of the human brain. However, even at so-called \"rest\", ongoing brain activity and its underlying physiological organization is highly dynamic and yet most of the information generated so far comes from group analysis. Here we developed an imaging-based technique capable of portraying information of local dynamics at a single-subject level reliably by using a whole-brain model that estimates a local bifurcation parameter, which reflects if a brain region presents stable, asynchronous or transitory oscillations. Using 50 longitudinal resting state sessions of one single subject and single resting state sessions from a group of 50 participants we demonstrated that individual global and local brain dynamics can be estimated consistently with respect to a reference group using only a scanning time of 15 to 20 minutes. We also showed that brain hubs are closer to a transition point between synchronous and asynchronous oscillatory dynamics and that dynamics in frontal areas have larger variations compared to other regions. Finally, we analyzed the variability and error of these dynamics and found high symmetry between hemispheres, which interestingly was reduced by adding more sessions. The framework presented here can be used to study functional brain dynamics on an individual level, opening new avenues for possible clinical applications.\n\nBullet pointsLocal brain dynamics are consistent across scans.\n\nFour scans of five minutes each are enough to get highly reliable and consistent results.\n\nHub areas are in a transition point between a synchronous and asynchronous regime.\n\nVariability and error of local dynamics presented high symmetry between hemispheres.

neuroscience↗

bioRxiv2017

Multichannel Sleep Spindle Detection Using Sparse Low-Rank Optimization

BackgroundWe propose a multichannel spindle detection method that detects global and local spindle activity across all channels of scalp EEG in a single run\n\nNew MethodUsing a non-linear signal model, which assumes the multichannel EEG to be a sum of a transient component and an oscillatory component, we propose a multichannel transient separation algorithm. Consecutive overlapping blocks of the multichannel oscillatory component are assumed to be low-rank whereas the transient component is assumed to be piecewise constant with a zero baseline. The estimated multichannel oscillatory component is used in conjunction with a bandpass filter and the Teager operator for detecting sleep spindles\n\nResults and comparison with other methodsSeveral examples are shown to illustrate the utility of the proposed method in detecting global and local spindle activity. The proposed method is applied to two publicly available databases and compared with 7 existing single-channel automated detectors. F1 scores for the proposed spindle detection method averaged 0.66 (0.02) and 0.62 (0.06) for the two databases, respectively. For an overnight 6 channel EEG signal, the proposed algorithm takes about 4 minutes to detect sleep spindles simultaneously across all channels with a single setting of corresponding algorithmic parameters\n\nConclusionsThe proposed method aims to mimic and utilize, for better spindle detection, a particular human expert behavior where the decision to mark a spindle event may be subconsciously influenced by the presence of a spindle in EEG channels other than the central channel visible on a digital screen

neuroscience↗

bioRxiv2017

Distinct contributions by frontal and parietal cortices support working memory

Although subregions of frontal and parietal cortex both contribute and coordinate to support working memory (WM) functions, their distinct contributions remain elusive. Here, we demonstrate that perturbations to topographically organized human frontal and parietal cortex during WM maintenance cause distinct but systematic distortions in WM. The nature of these distortions supports theories positing that parietal cortex mainly codes for retrospective sensory information, while frontal cortex codes for prospective action.\n\nAcknowledgementsThe US National Institutes of Health (R01 EY016407 to C.E.C.) and the National Science Foundation (Graduate Research Fellowship Program to W.E.M.) supported the research.\n\nAuthor ContributionsW.E.M and C.E.C. designed the experiment, collected and analyzed the data, and wrote the manuscript.\n\nConflict of interestThe authors declare no competing financial interests.

neuroscience↗

bioRxiv2017

Ultrasound elicits behavioral responses through mechanical effects on neurons and ion channels in a simple nervous system

Focused ultrasound has been shown to stimulate excitable cells, but the biophysical mechanisms behind this phenomenon remain poorly understood. To provide additional insight, we devised a behavioral-genetic assay applied to the well-characterized nervous system of C. elegans nematodes. We found that pulsed ultrasound elicits robust reversal behavior in wild-type animals in a pressure-, duration-, and pulse protocol-dependent manner. Responses were preserved in mutants unable to sense thermal fluctuations and absent in mutants lacking neurons required for mechanosensation. Additionally, we found that the worm s response to ultrasound pulses rests on the expression of MEC-4, a DEG/ENaC/ASIC ion channel required for touch sensation. Consistent with prior studies of MEC-4-dependent currents in vivo, the worms response was optimal for pulses repeated 300 to 1000 times per second. Based on these findings, we conclude that mechanical, rather than thermal stimulation accounts for behavioral responses. Further, we propose that acoustic radiation force governs the response to ultrasound in a manner that depends on the touch receptor neurons and MEC-4-dependent ion channels. Our findings illuminate a complete pathway of ultrasound action, from the forces generated by propagating ultrasound to an activation of a specific ion channel. The findings further highlight the importance of optimizing ultrasound pulsing protocols when stimulating neurons via ion channels with mechanosensitive properties.\n\nSignificance StatementHow ultrasound influences neurons and other excitable cells has remained a mystery for decades. Although it is widely understood that ultrasound can heat tissues and induce mechanical strain, whether or not neuronal activation depends on heat, mechanical force, or both physical factors is not known. We harnessed C. elegans nematodes and their extraordinary sensitivity to thermal and mechanical stimuli to address this question. Whereas thermosensory mutants respond to ultrasound similar to wild-type animals, mechanosensory mutants were insensitive to ultrasound stimulation. Additionally, stimulus parameters that accentuate mechanical effects were more effective than those producing more heat. These findings highlight a mechanical nature of the effect of ultrasound on neurons and suggest specific ways to optimize stimulation protocols in specific tissues.

neuroscience↗

bioRxiv2017

Boredom begets creativity: a solution to the exploitation-exploration trade-off in predictive coding

Here, we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non recurrent problems. We argue that there is a key ingredient that has been overlooked by researchers that needs to be incorporated to understand intelligent behavior in biological and technical systems. This ingredient is boredom. We propose a mathematical model based on the Black-Scholes-Merton equation which provides mechanistic insights into the interplay between boredom and prediction pleasure as the key drivers of behavior.

neuroscience↗

bioRxiv2017

Auditory prediction errors as individual biomarkers of schizophrenia

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible imaging technologies such as electroencephalography (EEG). To achieve this, we used machine learning techniques and a combination of paradigms designed to elicit prediction errors or Mismatch Negativity (MMN) responses. MMN, an EEG component elicited by unpredictable changes in sequences of auditory stimuli, has previously been shown to be reduced in people with schizophrenia and this is arguably one of the most reproducible neurophysiological markers of schizophrenia.\n\nEEG data were acquired from 21 patients with schizophrenia and 22 healthy controls whilst they listened to three auditory oddball paradigms comprising sequences of tones which deviated in 10% of trials from regularly occurring standard tones. Deviant tones shared the same properties as standard tones, except for one physical aspect: 1) duration-the deviant stimulus was twice the duration of the standard; 2) monaural gap-deviants had a silent interval omitted from the standard, or 3) inter-aural timing difference, which caused the deviant location to be perceived as 90{degrees} away from the standards.\n\nWe used multivariate pattern analysis, a machine learning technique implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo) to classify images generated through statistical parametric mapping (SPM) of spatiotemporal EEG data, i.e. event-related potentials measured on the two-dimensional surface of the scalp over time. Using support vector machine (SVM) and Gaussian processes classifiers (GPC), we were able classify individual patients and controls with balanced accuracies of up to 80.48% (p-values = 0.0326, FDR corrected) and an ROC analysis yielding an AUC of 0.87. Crucially, a GPC regression revealed that MMN predicted global assessment of functioning (GAF) scores (correlation = 0.73, R2 = 0.53, p = 0.0006)

neuroscience↗

Refine your search to explore more results.