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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 1,621 records · Page 90Linked to original sources

bioRxiv2016

The contribution of area MT to visual motion perception depends on training

SummaryPerceptual decisions require the transformation of raw sensory inputs into cortical representations suitable for stimulus discrimination. One of the best-known examples of this transformation involves the middle temporal area (MT) of the primate visual cortex. Area MT provides a robust representation of stimulus motion, and previous work has shown that it contributes causally to performance on motion discrimination tasks. Here we report that the strength of this contribution can be highly plastic: Depending on the recent training history, pharmacological inactivation of MT can severely impair motion discrimination, or it can have little detectable influence. Similarly, depending on training, microstimulation can bias motion perception or simply introduce noise. Further analysis of neural and behavioral data suggests that training shifts the readout of motion information between MT and lower-level cortical areas. These results show that the contribution of individual brain regions to conscious perception can shift flexibly depending on sensory experience.

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bioRxiv2016

Stabilized supralinear network dynamics account for stimulus-induced changes of noise variability in the cortex

Variability and correlations in cortical activity are ubiquitously modulated by stimuli. Correlated variability is quenched following stimulus onset across multiple cortical areas, suppressing low-frequency components of the LFP and of Vm-LFP coherence. Modulation of Fano factors and correlations in area MT is tuned for stimulus direction. What circuit mechanisms underly these behaviors? We show that a simple model circuit, the stochastic Stabilized Supralinear Network (SSN), robustly explains these results. Stimuli modulate variability by modifying two forms of effective connectivity between activity patterns that characterize excitatory-inhibitory (E/I) circuits. Increases in the strength with which activity patterns inhibit themselves reduce correlated variability, while increases in feedforward connections between patterns (transforming E/I imbalance into balanced fluctuations) increase variability. These results suggest an operating regime of cortical dynamics that involves fast fluctuations and fast responses to stimulus changes, unlike previous models of variability suppression through suppression of chaos or networks with multiple attractors.

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bioRxiv2017

Rapid cerebrovascular reactivity mapping: Enabling vascular reactivity information to be routinely acquired

Cerebrovascular reactivity mapping (CVR), using magnetic resonance imaging (MRI) and carbon dioxide as a stimulus, provides useful information on how cerebral blood vessels react under stress. This information has proven to be useful in the study of vascular disorders, dementia and healthy ageing. However, clinical adoption of this form of CVR mapping has been hindered by relatively long scan durations of 7 to 12 minutes. By replacing the conventional block presentation of carbon dioxide enriched air with a sinusoidally modulated stimulus, the aim of this study was to investigate whether more clinically acceptable scan durations are possible. Firstly, the conventional block protocol was compared with a sinusoidal protocol of the same duration of 7 minutes. Estimates of the magnitude of the CVR signal (CVR magnitude) and the relative timing of the CVR response (CVR phase) were found to be in good agreement between the stimulus protocols. Secondly, data from the sinusoidal protocol was reanalysed using decreasing amounts of data in the range 1 to 6 minutes. The CVR magnitude was found to tolerate this reduction in scan duration better than CVR phase. However, these analyses indicate that scan durations in the range of 3 to 5 minutes produce robust data.

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bioRxiv2016

The Bayesian-Laplacian Brain

We outline what we believe could be an improvement in future discussions of the brain acting as a Bayesian-Laplacian system. We do so by distinguishing between two broad classes of priors on which the brains inferential systems operate: in one category are biological priors ({beta} priors) and in the other artifactual ones ( priors). We argue that {beta} priors, of which colour categories and faces are good examples, are inherited or acquired very rapidly after birth, are highly or relatively resistant to change through experience, and are common to all humans. The consequence is that the probability of posteriors generated from {beta} priors having universal assent and agreement is high. By contrast, priors, of which man-made objects are examples, are acquired post-natally and modified at various stages throughout post-natal life; they are much more accommodating of, and hospitable to, new experiences. Consequently, posteriors generated from them are less likely to find universal assent. Taken together, in addition to the more limited capacity of experiment and experience to alter the {beta} priors compared to priors, another cardinal distinction between the two is that the probability of posteriors generated from {beta} priors having universal agreement is greater than that for priors. The two categories are not, however, always totally distinct and can merge into one another to varying extents, resulting in posteriors that draw upon both categories.

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bioRxiv2016

Dynamically controlled light delivery over large brain volumes through tapered optical fibers

Optogenetics promises spatiotemporal precise control of neural processes using light. However, the spatial extent of illumination within the brain is difficult to control and cannot be adjusted using standard fiber optics. We demonstrate that optical fibers with tapered tips can be used to illuminate either large brain volumes or dynamically selectable subregions. Remotely adjusting the light input angle to the fiber varies the light-emitting portion of the taper over several millimeters without movement of the implant. We use this mode to activate dorsal versus ventral striatum of individual mice and reveal different effects of each manipulation on motor behavior. Conversely injecting light over the full numerical aperture of the fiber results in light emission from the entire taper surface, achieving broader and more efficient optogenetic activation of neurons when compared to the standard flat-faced fiber stimulation. Thus, tapered fibers permit focal or broad illumination that can be precisely and dynamically matched to experimental needs.

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bioRxiv2016

CORTICO-CORTICAL FEEDBACK CONTROLS SPATIAL SUMMATION IN PRIMATE VISUAL CORTEX

In the cerebral cortex, sensory information travels along feedforward connections through a hierarchy of areas, which, in turn, send a denser network of feedback connections to lower-order areas. Feedback has been implicated in attention, expectation, and sensory context, but the cellular mechanisms underlying these diverse feedback functions are unknown. Using specific optogenetic inactivation of feedback connections in the primate visual cortex, we have identified the cellular mechanisms of feedback-mediated modulations of early sensory processing. Specifically, we found that feedback modulates receptive field size, surround suppression and response gain, similar to the modulatory effects of visual spatial attention. A recurrent network model captured these effects. These feedback-mediated modulations allow higher-order cortical areas to dynamically regulate spatial resolution, sensitivity to image features, and efficiency of coding natural images in lower-order cortical areas.

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bioRxiv2016

Dissecting the role of non-coding RNAs in the accumulation of amyloid and tau neuropathologies in Alzheimer’s disease

BackgroundGiven multiple studies of brain microRNA (miRNA) in relation to Alzheimers disease (AD) with few consistent results and the heterogeneity of this disease, the objective of this study was to explore their mechanism by evaluating their relation to different elements of Alzheimers disease pathology, confounding factors and mRNA expression data from the same subjects in the same brain region.\n\nResultsWe report analyses of expression profiling of miRNA (n=700 subjects) and lincRNA (n=540 subjects) from the dorsolateral prefrontal cortex of individuals participating in two longitudinal cohort studies of aging. Evaluating well-established (miR-132, miR-129), we confirm their association with pathologic AD in our dataset, and then characterize their in disease role in terms of neuritic {beta}-amyloid plaques and neurofibrillary tangle pathology. Additionally, we identify one new miRNA (miR-99) and four lincRNA that are associated with these traits. Many other previously reported associations of microRNA with AD are associated with the confounders quantified in our longitudinal cohort. Finally, by performing analyses integrating both miRNA and RNA sequence data from the same individuals (525 samples), we characterize the impact of AD associated miRNA on human brain expression: we show that the effects of miR-132 and miR-129-5b converge on certain genes such as EP300 and find a role for miR200 and its target genes in AD using an integrated miRNA/mRNA analysis.\n\nConclusionsOverall, miRNAs play a modest role in human AD, but we observe robust evidence that a small number of miRNAs are responsible for specific alterations in the cortical transcriptome that are associated with AD.

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bioRxiv2016

Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision making

Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into behavioral decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of neural oscillations. Yet, the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalography signals in human listeners who judged acoustic stimuli made of carefully titrated clouds of tone sweeps. These stimuli were used under two task contexts where the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of source-projected neural oscillations on a trial-by-trial basis using power envelope correlations and graph-theoretical network discovery. Under both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (~16-28 Hz) oscillations. We also uncovered brain network states that promoted faster decisions and emerged from lower-order auditory and higher-order control brain areas. Specifically, decision speed in judging tone-sweep direction critically relied on nodal network configurations of anterior temporal, cingulate and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations.\n\nAuthor SummaryThe speed at which we make perceptual decisions varies. This translation of sensory information into behavioral decisions hinges on dynamic changes in neural oscillatory activity. However, the large-scale neural network embodiment supporting perceptual decision-making is unclear. Alavash et al. address this question by experimenting two auditory perceptual decision-making situations. Using graph-theoretical network discovery, they trace the large-scale network dynamics of coupled neural oscillations to uncover brain network states supporting the speed of auditory perceptual decisions. They find that higher network segregation of coupled beta-band oscillations supports faster auditory perceptual decisions over trials. Moreover, when auditory perceptual decisions are relatively difficult, the decision speed benefits from higher segregation of frontal cortical areas, but lower segregation and integration of auditory cortical areas.

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bioRxiv2016

Second Generation Drosophila Chemical Tags: Sensitivity, Versatility and Speed

Labeling and visualizing cells and sub-cellular structures within thick tissues, whole organs and even intact animals is key to studying biological processes. This is particularly true for studies of neural circuits where neurons form sub-micron synapses but have arbors that may span millimeters in length. Traditionally labeling is achieved by immunofluorescence; however diffusion of antibody molecules (>100 kDa) is slow and often results in uneven labeling with very poor penetration into the centre of thick specimens; these limitations can be partially addressed by extending staining protocols to over a week (Drosophila brain) and months (mice). Recently we developed an alternative approach using genetically encoded chemical tags CLIP, SNAP, Halo and TMP for tissue labeling; this resulted in >100 fold increase in labeling speed in both mice and Drosophila, at the expense of a considerable drop in absolute sensitivity when compared to optimized immunofluorescence staining. We now present a second generation of UAS and LexA responsive CLIP, SNAPf and Halo chemical labeling reagents for flies. These multimerized tags with translational enhancers display up to 64 fold increase in sensitivity over first generation reagents. In addition we developed a suite of conditional reporters (4xSNAPf tag and CLIP-SNAP-Halo) that are activated by the DNA recombinase Bxb1. Our new reporters can be used with weak and strong GAL4 and LexA drivers and enable stochastic, intersectional and multicolor Brainbow labeling. These improvements in sensitivity and experimental versatility, while still retaining the substantial speed advantage that is a signature of chemical labeling, should significantly increase the scope of this technology.

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bioRxiv2016

Distributed alpha networks underlie individual differences in memory performance

Episodic memory, our ability to remember specific events, varies considerably across individuals. However, little is known about the neural basis of this variability. To address this issue, we investigated the role of distributed networks of oscillatory activity, as measured through electroencephalography (EEG). We observed that individual differences in alpha network structure reliably predict individual memory capacity. Specifically, individuals whose network profiles during encoding were most different from their resting state networks exhibited greatest subsequent memory performance, suggesting that optimal information processing requires substantial shifts in large-scale oscillatory organization. Furthermore, these results were not observed in circumscribed topographical regions or individual connections, indicating that distributed network approaches were more sensitive to functional processes than more conventional methods. These findings uncover a physiological correlate of individual differences in episodic memory and demonstrate the utility of multivariate EEG techniques to uncover brain-behavior correlates.

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bioRxiv2016

Transcranial stimulation enhances memory-relevant sleep oscillations and their functional coupling in mild cognitive impairment

Alzheimers disease (AD) not only involves loss of memory functions but also prominent deterioration of sleep physiology, already evident in the stage of mild cognitive impairment (MCI). Cortical slow oscillations (SO, 0.5-1 Hz) and thalamo-cortical spindle activity (12-15 Hz) during sleep, and their temporal coordination, are considered critical for memory formation. We investigated the potential of slow oscillatory transcranial direct current stimulation (so-tDCS), applied during a daytime nap in a sleep state-dependent manner, to modulate these activity patterns and sleep-related memory consolidation in 16 patients with MCI.\n\nStimulation significantly increased overall SO and spindle power, amplified spindle power during SO up-phases, and led to stronger synchronization between SO and spindle power fluctuations in electroencephalographic recordings. Moreover, visual declarative memory was improved by so-tDCS compared to sham stimulation, associated with stronger synchronization. These findings indicate a well-tolerated therapeutic approach for disordered sleep physiology and deficits in memory consolidation in MCI patients.

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bioRxiv2016

Multivariate pattern analysis of MEG and EEG: a comparison of representational structure in time and space

1Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in early visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research.

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bioRxiv2016

Online control of prehension predicts performance on a standardised motor assessment test in 8-12 year-old children

Goal-directed hand movements are guided by sensory information and may be adjusted online, during the movement. If the target of a movement unexpectedly changes position, trajectory corrections can be initiated in as little as 100ms in adults. This rapid visual online control is impaired in children with developmental coordination disorder (DCD), and potentially in other neurodevelopmental conditions. We investigated the visual control of hand movements in children in a centre-out double-step reaching and grasping task, and examined how parameters of this visuomotor control co-vary with performance on standardised motor tests often used with typically and atypically developing children. Two groups of children aged 8-12 years were asked to reach and grasp an illuminated central ball on a vertically oriented board. On a proportion of trials, and at movement onset, the illumination switched unpredictably to one of four other balls in a centre-out configuration (left, right, up, or down). When the target moved, all but one of the children were able to correct their movements before reaching the initial target, at least on some trials, but the latencies to initiate these corrections were longer than those typically reported in the adult literature, ranging from 211 to 581 ms. These later corrections may be due to less developed motor skills in children, or to the increased cognitive and biomechanical complexity of switching movements in four directions. In the first group (n=187), reaching and grasping parameters significantly predicted standardised movement scores on the MABC-2, most strongly for the aiming and catching component. In the second group (n=85), these same parameters did not significantly predict scores on the DCDQ-07 parent questionnaire. Our reaching and grasping task provides a sensitive and continuous measure of movement skill that predicts scores on standardized movement tasks used to screen for DCD.

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bioRxiv2016

How to improve parameter estimates in GLM-based fMRI data analysis: cross-validated Bayesian model averaging

In functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: \"How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection\", NeuroImage, vol. 141, pp. 469-489; DOI: 10.1016/j. neuroimage.2016.07.047), we have introduced cross-validated Bayesian model selection (cvBMS) to infer the best model for a group of subjects and use it to guide second-level analysis. While this is the optimal approach given that the same GLM has to be used for all subjects, there is a much more efficient procedure when model selection only addresses nuisance variables and regressors of interest are included in all candidate models. In this work, we propose cross-validated Bayesian model averaging (cvBMA) to improve parameter estimates for these regressors of interest by combining information from all models using their posterior probabilities. This is particularly useful as different models can lead to different conclusions regarding experimental effects and the most complex model is not necessarily the best choice. We find that cvBMS can prevent not detecting established effects and that cvBMA can be more sensitive to experimental effects than just using even the best model in each subject.

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bioRxiv2016

BAYESIAN NON-CENTRAL CHI REGRESSION FOR NEUROIMAGING

We propose a regression model for non-central{chi} (NC-{chi}) distributed functional magnetic resonance imaging (fMRI) and diffusion weighted imaging (DWI) data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the NC-{chi} distribution to be linked to explanatory variables, with the relevant covariates automatically chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed for simulating from the joint Bayesian posterior distribution of all model parameters and the binary covariate selection indicators. Simulated fMRI data is used to demonstrate that the Rician model is able to localize brain activity much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the theoretically correct Rician model.

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bioRxiv2016

Variable penetrance of the 15q11.2 BP1-BP2 microduplication in a family with cognitive and language impairment

The 15q11.2 BP1-BP2 region is found duplicated or deleted in people with cognitive, language, and behavioral impairment. Case presentation. We report on a family (the father and three male twin siblings) who presents with a duplication of the 15q11.2 BP1-BP2 region and a variable phenotype: whereas the father and the fraternal twin are normal carriers, the monozygotic twins exhibit severe language and cognitive delay and behavioral disturbances. The genes located within the duplicated region are involved in brain development and function, and some of them are related to language processing. Conclusions. The probands phenotype may result from changes in the expression level of some of these genes important for cognitive development.

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bioRxiv2016

The Constancy of Colored After-Images

We undertook psychophysical experiments to determine whether the color of the after-image produced by viewing a colored patch which is part of a complex multi-colored scene depends on the wavelength-energy composition of the light reflected from that patch. Our results show that, just as the color of a patch which is part of a complex scene is independent of the wavelength-energy composition of the light coming from it alone, but depends as well on the wavelength-energy composition of the light coming from its surrounds, so is the color of its after-image. Hence, traditional accounts of after-images as being the result of retinal adaptation or the perceptual result of physiological opponency, are inadequate. We propose instead that the color of after-images is generated after colors themselves are generated in the visual brain.

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bioRxiv2016

Behavioural, modeling, and electrophysiological evidence for domain-generality in human metacognition

Metacognition, or the capacity to introspect on ones own mental states, has been mostly characterized through confidence reports in visual tasks. A pressing question is to what extent the results from visual studies generalize to other domains. Answering this question allows determining whether metacognition operates through shared, domain-general mechanisms, or through idiosyncratic, domain-specific mechanisms. Here, we report three new lines of evidence for decisional and post-decisional mechanisms arguing for the domain-generality of metacognition. First, metacognitive efficiency correlated between auditory, tactile, visual, and audiovisual tasks. Second, confidence in an audiovisual task was best modeled using supramodal formats based on integrated representations of auditory and visual signals. Third, confidence in correct responses involved similar electrophysiological markers for visual and audiovisual tasks that are associated with motor preparation preceding the perceptual judgment. We conclude that the domain-generality of metacognition relies on supramodal confidence estimates and decisional signals that are shared across sensory modalities.

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