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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 1,657 records · Page 92Linked to original sources
bioRxiv2016

Electro-diffusion modulation of synaptic input in dendritic spines using deconvolved voltage sensor time series

The inward current flowing inside the post-synaptic terminal of a neuron modulates transiently the membrane voltage potential. Most of the excitatory connections are made on dendritic spines characterized by a large variability in their geometry.How the voltage in a spine is modulated by geometry remains elusive due in part to the absence of direct measurements. To understand the spine voltage-current relation, we develop here a model for the voltage and we use it to extract electrical properties from live cell imaging data. We first deconvolve the genetically encoded voltage sensor expressed in hippocampal neurons and then use electro-diffusion theory, to compute the electric field and the ionic flows induced in a dendritic spine.The ionic flow is driven by the electric field coupled to the charge densities that interact through the non-cylindrical spine geometry. We determine the I-V relation and conclude that the spine effective resistance is mostly determined by the neck geometry. This modulation of synaptic inputs by the spine neck is significantly larger than what was expected from traditional cable models. Thus modulating the postsynaptic current can be achieved by changing the number of receptors or by altering the spine geometry which independently affects the transformation of current into voltage.\n\nSignificance statementDendritic spines are geometrical structures receiving most of the excitatory transmission, yet how they modulate voltage from the synaptic current is not clear due to their submicron small size and specific non-cylindrical geometry. We study here the conversion of the synaptic current into voltage modulated by the spine geometry. Our approach is based on the electro-diffusion theory and we show that the spine neck is the main resistance filter, while the voltage is maintained constant in the head. Finally, we extract the effective resistance using a deconvolution of the genetically encoded voltage indicators expressed in hippocampal neurons. The present approach allows studying the electrical properties of many other structures such as glial small protrusions, cilia, and many others.

neuroscience↗
bioRxiv2016

Spatial specificity of the functional MRI blood oxygenation response relative to neuronal activity

Previous attempts at characterizing the spatial specificity of the blood oxygenation level dependent functional MRI (BOLD fMRI) response by estimating its point-spread function (PSF) have conventionally relied on spatial representations of visual stimuli in area V1. Consequently, their estimates were confounded by the width and scatter of receptive fields of V1 neurons. Here, we circumvent these limits by instead using the inherent cortical spatial organization of ocular dominance columns (ODCs) to determine the PSF for both Gradient Echo (GE) and Spin Echo (SE) BOLD imaging at 7 Tesla. By applying Markov Chain Monte Carlo sampling on a probabilistic generative model of imaging ODCs, we quantified the PSFs that best predict the spatial structure and magnitude of differential ODCs responses. Prior distributions for the ODC model parameters were determined by analyzing published data of cytochrome oxidase patterns from post-mortem histology of human V1 and of neurophysio-logical ocular dominance indices. The most probable PSF full-widths at halfmaximum were 0.82 mm (SE) and 1.02 mm (GE). Our results provide a quantitative basis for the spatial specificity of BOLD fMRI at ultra-high fields, which can be used for planning and interpretation of high-resolution differential fMRI of fine-scale cortical organizations.

neuroscience↗
bioRxiv2017

α7 nicotinic acetylcholine receptor signalling modulates the inflammatory and iron homeostasis in fetal brain microglia

Neuroinflammation in utero may result in life-long neurological disabilities. Microglia play a pivotal role, but the mechanisms are poorly understood. No early postnatal treatment strategies exist to enhance neuroprotective potential of microglia. We hypothesized that agonism on 7 nicotinic acetylcholine receptor (7nAChR) in fetal microglia will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of 7nAChR will achieve the opposite. Using an in vivo - in vitro model of developmental programming of neuroinflammation induced by lipopolysaccharide (LPS), we validated this hypothesis in primary fetal sheep microglia cultures re-exposed to LPS in presence of a selective 7nAChR agonist or antagonist. Our RNAseq and protein level findings show that a pro-inflammatory microglial phenotype acquired in vitro by LPS stimulation is reversed with 7nAChR agonistic stimulation. Conversely, antagonistic 7nAChR stimulation potentiates the pro-inflammatory microglial phenotype. Surprisingly, under conditions of LPS double-hit an interference of a postulated 7nAChR - ferroportin signaling pathway may impede this mechanism. These results suggest a therapeutic potential of 7nAChR agonists in early re-programming of microglia in neonates exposed to in utero inflammation via an endogenous cerebral cholinergic anti-inflammatory pathway. Future studies will assess the role of interactions between inflammation-triggered microglial iron sequestering and 7nAChR signaling in neurodevelopment.

neuroscience↗
bioRxiv2016

Generating in vivo somatic mouse mosaics with locus-specific, stably-integrated transgenic elements

Viral vectors and electroporation (EP)-mediated gene transfers are efficient means of inducing somatic mosaicism in mice, but they lack the exquisite control over transgene copy number, gene zygosity, and genomic-locus specificity that genetically engineered mouse models (GEMMs) provide. Here, we develop and demonstrate a simple and generalizable in vivo method, mosaic analysis by dual recombinase-mediated cassette exchange (MADR). MADR allows for stable labeling of mutant cells express transgenic elements from a precisely-defined chromosomal locus. To test our method, we generated reporter-labeled lineages from stem and progenitor cells in a well-defined Rosa26mTmG mouse. We demonstrate the power and versatility of MADR by creating novel glioma models with mixed, reporter-defined zygosity or with \"personalized\" driver mutations from pediatric glioma--each manipulation altering the profile of resulting tumors. Thus, MADR provides a high-throughput genetic platform for the dissection of development and disease, and this rapid method can be applied to the thousands of existing gene-trap mice.

neuroscience↗
bioRxiv2016

Comparison of linear spatial filters for identifying oscillatory activity in multichannel data

BackgroundLarge-scale synchronous neural activity produces electrical fields that can be measured by electrodes outside the head, and volume conduction ensures that neural sources can be measured by many electrodes. However, most data analyses in M/EEG research are univariate, meaning each electrode is considered as a separate measurement. Several multivariate linear spatial filtering techniques have been introduced to the cognitive electrophysiology literature, but these techniques are not commonly used; comparisons across filters would be beneficial to the field.\n\nNew methodThe purpose of this paper is to evaluate and compare the performance of several linear spatial filtering techniques, with a focus on those that use generalized eigendecomposition to facilitate dimensionality reduction and signal-to-noise ratio maximization.\n\nResultsSimulated and empirical data were used to assess the accuracy, signal-to-noise ratio, and interpretability of the spatial filter results. When the simulated signal is powerful, different spatial filters provide convergent results. However, more subtle signals require carefully selected analysis parameters to obtain optimal results.\n\nComparison with existing methodsLinear spatial filters can be powerful data analysis tools in cognitive electrophysiology, and should be applied more often; on the other hand, spatial filters can latch onto artifacts or produce uninterpretable results.\n\nConclusionsHypothesis-driven analyses, careful data inspection, and appropriate parameter selection are necessary to obtain high-quality results when using spatial filters.

neuroscience↗
bioRxiv2016

When Attended and Conscious Perception Deactivates Fronto-Parietal Regions

Conscious and attended perception is commonly thought to elicit fronto-parietal activity. However, supportive evidence comes largely from studies which involve detecting a target and reporting its visibility. This approach confounds conscious perception with goal completion of either the perceptual task of detection or the metacognitive task of introspective reporting. In contrast, in real life such perceptions are a means of achieving goals and rarely a goal in themselves, and almost never involve explicit metacognitive reports. It therefore remains unclear if fronto-parietal activity is indeed a correlate of conscious perception or is the result of confounds related to goal completion. Here we show that conscious and attended perception when delinked from goals does not increase fronto-parietal activity, and when inconsequential for the goal may even deactivate these regions. In experiments 1 and 2 participants attended to a highly visible stream of letters to detect the occasional targets in their midst. The non-target letters, in spite of being visible and attended to, deactivated fronto-parietal regions. In experiment 3 we looked at the activity elicited by a loud auditory cue that had to be kept in memory for up to 9 s and used to select the correct rule for completing the goal. Even such a salient, attended and remembered event did not elicit prefrontal activity. Across these experiments conscious and attended perception only activated the relevant sensory regions while goal completion events activated fronto-parietal regions.\n\nSignificance statementConsciousness and attended perception has been seen to correlate with fronto-parietal activity. This informs key theories of consciousness and attention, e.g. widespread availability of incoming information or its higher level representation causes perceptual awareness, or that top down attention during perception broadcasts incoming sensations into frontal and parietal regions. However such experiments unwittingly conflate attended and conscious perception with some form of goal completion, whereas such perception in our daily life mostly serves as a means of goal completion and not a goal in itself. Here we show that such perception when delinked from goal completion does not activate fronto-parietal regions, and may even deactivate these regions if the percept is inconsequential for goal completion.

neuroscience↗
bioRxiv2016

Parallel streams define the temporal dynamics of speech processing across human auditory cortex

To derive meaning from speech, we must extract multiple dimensions of concurrent information from incoming speech signals, including phonetic and prosodic cues. Equally important is the detection of acoustic cues that give structure and context to the information we hear, such as sentence boundaries. How the brain organizes this information processing is unknown. Here, using data-driven computational methods on an extensive set of high-density intracranial recordings, we reveal a large-scale partitioning of the entire human speech cortex into two spatially distinct regions that detect important cues for parsing natural speech. These caudal (Zone 1) and rostral (Zone 2) regions work in parallel to detect onsets and prosodic information, respectively, within naturally spoken sentences. In contrast, local processing within each region supports phonetic feature encoding. These findings demonstrate a fundamental organizational property of the human auditory cortex that has been previously unrecognized.

neuroscience↗
bioRxiv2016

Contributions of local speech encoding and functional connectivity to audio-visual speech integration

Seeing a speakers face enhances speech intelligibility in adverse environments. We investigated the underlying network mechanisms by quantifying local speech representations and directed connectivity in MEG data obtained while human participants listened to speech of varying acoustic SNR and visual context. During high acoustic SNR speech encoding by entrained brain activity was strong in temporal and inferior frontal cortex, while during low SNR strong entrainment emerged in premotor and superior frontal cortex. These changes in local encoding were accompanied by changes in directed connectivity along the ventral stream and the auditory-premotor axis. Importantly, the behavioural benefit arising from seeing the speaker's face was not predicted by changes in local encoding but rather by enhanced functional connectivity between temporal and inferior frontal cortex. Our results demonstrate a role of auditory-motor interactions in visual speech representations and suggest that functional connectivity along the ventral pathway facilitates speech comprehension in multisensory environments.

neuroscience↗
bioRxiv2017

Envelope-tACS modulates intelligibility of speech in noise

Cortical entrainment of the auditory cortex to the broad-band temporal envelope of a speech signal is crucial for speech comprehension. This entrainment results in phases of high and low neural excitability which structure and decode the incoming speech signal. Entrainment to speech is strongest in the theta frequency range (4-8 Hz), the average frequency of the speech envelope. If a speech signal is degraded, for example masked by irrelevant information such as noise, entrainment to the speech envelope is weaker and speech intelligibility declines.\n\nBesides perceptually evoked cortical entrainment, transcranial alternating current stimulation (tACS) can entrain neural oscillations by applying an electric signal to the brain. Accordingly, tACS-induced entrainment in auditory cortex has been shown to improve auditory perception. The aim of the current study was to externally modulate speech intelligibility by means of tACS such that the electric current corresponds to the envelope of the presented speech stream.\n\nParticipants performed the Oldenburg sentence test with sentences presented in noise in combination with tACS. Critically, the time lag between sentence presentation and tACS was manipulated from 0 to 250 ms in 50-ms steps (auditory stimuli were simultaneous to or preceded tACS).\n\nFirst, we were able to show that envelope-tACS modulated sentence comprehension such that on average sentence comprehension at the time lag of the best performance was significantly better than sentence comprehension of the worst performance. Second, sentence comprehension across time lags was modulated sinusoidally.\n\nIn sum, envelope tACS modulates intelligibility of speech in noise presumably by enhancing (time lag with in-phase stimulation) and disrupting (time lag with out-of-phase stimulation) cortical entrainment to the speech envelope in auditory cortex.

neuroscience↗
bioRxiv2017

Theoretical principles of multiscale spatiotemporal control of neuronal networks: a complex systems perspective

Success in the fine control of the nervous system depends on a deeper understanding of how neural circuits control behavior. There is, however, a wide gap between the components of neural circuits and behavior. We advance the idea that a suitable approach for narrowing this gap has to be based on a multiscale information-theoretic description of the system. We evaluate the possibility that brain-wide complex neural computations can be dissected into a hierarchy of computational motifs that rely on smaller circuit modules interacting at multiple scales. In doing so, we draw attention to the importance of formalizing the goals of stimulation in terms of neural computations so that the possible implementations are matched in scale to the underlying circuit modules.

neuroscience↗
bioRxiv2017

Neural precursors of decisions that matter--an ERP study of deliberate and arbitrary choice

The readiness potential (RP)--a key ERP correlate of upcoming action--is known to precede subjects reports of their decision to move. Some view this as evidence against a causal role for consciousness in human decision-making and thus against free-will. Yet those studies focused on arbitrary decisions--purposeless, unreasoned, and without consequences. It remains unknown to what degree the RP generalizes to deliberate, more ecological decisions. We directly compared deliberate and arbitrary decision-making during a $1000-donation task to non-profit organizations. While we found the expected RPs for arbitrary decisions, they were strikingly absent for deliberate ones. Our results and drift-diffusion model are congruent with the RP representing accumulation of noisy, random fluctuations that drive arbitrary--but not deliberate--decisions. They further point to different neural mechanisms underlying deliberate and arbitrary decisions, challenging the generalizability of studies that argue for no causal role for consciousness in decision-making to real-life decisions.\n\nSignificance StatementThe extent of human free will has been debated for millennia. Previous studies demonstrated that neural precursors of action--especially the readiness potential--precede subjects reports of deciding to move. Some viewed this as evidence against free-will. However, these experiments focused on arbitrary decisions--e.g., randomly raising the left or right hand. We directly compared deliberate (actual $1000 donations to NPOs) and arbitrary decisions, and found readiness potentials before arbitrary decisions, but--critically--not before deliberate decisions. This supports the interpretation of readiness potentials as byproducts of accumulation of random fluctuations in arbitrary but not deliberate decisions and points to different neural mechanisms underlying deliberate and arbitrary choice. Hence, it challenges the generalizability of previous results from arbitrary to deliberate decisions.

neuroscience↗
bioRxiv2017

Wiring variations that enable and constrain neural computation in a sensory microcircuit

Neural network function can be shaped by varying the strength of synaptic connections. One way to achieve this is to vary connection structure. To investigate how structural variation among synaptic connections might affect neural computation, we examined primary afferent connections in the Drosophila olfactory system. We used large-scale serial section electron microscopy to reconstruct all the olfactory receptor neuron (ORN) axons that target a left-right pair of glomeruli, as well as all the projection neurons (PNs) postsynaptic to these ORNs. We found three variations in ORN[->]PN connectivity. First, we found a systematic co-variation in synapse number and PN dendrite size, suggesting total synaptic conductance is tuned to postsynaptic excitability. Second, we discovered that PNs receive more synapses from ipsilateral than contralateral ORNs, providing a structural basis for odor lateralization behavior. Finally, we found evidence of imprecision in ORN[->]PN connections and show how this can diminish network performance.

neuroscience↗
bioRxiv2017

Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration

The human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network whose topology evolves with time. Despite the nontrivial dynamics that are germaine to this networked system, experimental evidence demonstrates that functional interactions organize into putative brain systems that facilitate different facets of cognitive computation. We hypothesize that such dynamic functional networks are organized around a set of rules that constrain their spatial architecture - which brain regions may functionally interact - and their temporal architecture - how these interactions fluctuate over time. To objectively uncover these organizing principles, we apply an unsupervised machine learning approach called nonnegative matrix factorization to time-evolving, resting state functional networks in 20 healthy subjects. This machine-learning approach automatically clusters temporally co-varying functional interactions into subgraphs that represent putative topological modes of dynamic functional architecture. We find that subgraphs are stratified based on both the underlying modular organization and the topographical distance of their strongest interactions: while many subgraphs are largely contained within modules, others span between modules and are expressed differently over time. The relationship between dynamic subgraphs and modular architecture is further highlighted by the ability of time-varying subgraph expression to explain inter-individual differences in module reorganization. Collectively, these results point to the critical role subgraphs play in constraining the topography and topology of functional brain networks. More broadly, this machine learning approach opens a new door for understanding the architecture of dynamic functional networks during both task and rest states, and for probing alterations of that architecture in disease.

neuroscience↗
bioRxiv2017

Red Bull Gives You Incentive Motivation: Understanding Placebo Effects of Energy Drinks on Human Cognitive Performance

The consumption of cognitive enhancers like energy drinks (EnD) is on the rise, but do they really improve cognitive performance, and, if yes, why? We examined two novel psychological mechanisms. First, we dissociated the role of expectations and actual consumption by crossing what people consumed--Red Bull Silver Edition or a similar-tasting Sprite soda--and what they thought they consumed. We found that participants performed better in a numerical Stroop task when they believed that they had consumed an EnD, irrespective of what they had actually consume, Second, we investigated the role of motivation for such a placebo effect of EnD. We found that expected, but not actual, consumption of EnD increased the effects of incentives on cognitive performance. Our results suggest that believing that one consumes an EnD increased participants motivation to perform and thus enhanced their performance.\n\nSignificance statementAcademic doping has become increasingly popular on campuses around the globe over the last years. However, it remains unknown if and how commercially available cognitive enhancers such as energy drinks (EnD) impact cognitive performance. We varied actual and believed consumption of an EnD and measured cognitive performance and its allocation according to the magnitude of points to earn. We found that the belief that one has consumed an EnD but not its actual consumption, increased cognitive performance specifically in high point trials compared to low point trials. These findings provide novel insights into motivational processes underpinning marketing-led expectancy effects, and contribute to the current public policy debate on the use and abuse of increased alertness and concentration claims for EnD.

neuroscience↗
bioRxiv2017

Your favorite color makes learning more adaptable and precise

Learning from reward feedback is essential for survival but can become extremely challenging with myriad choice options. Here, we propose that learning reward values of individual features can provide a heuristic for estimating reward values of choice options in dynamic, multidimensional environments. We hypothesized that this feature-based learning occurs not just because it can reduce dimensionality, but more importantly because it can increase adaptability without compromising precision of learning. We experimentally tested this hypothesis and found that in dynamic environments, human subjects adopted feature-based learning even when this approach does not reduce dimensionality. Even in static, low-dimensional environments, subjects initially adopted feature-based learning and gradually switched to learning reward values of individual options, depending on how accurately objects values can be predicted by combining feature values. Our computational models reproduced these results and highlight the importance of neurons coding feature values for parallel learning of values for features and objects.

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bioRxiv2017

Adaptive Cortical Parcellations for Source Reconstructed EEG/MEG Connectomes

There is growing interest in the rich temporal and spectral properties of the brain's functional connectome that are provided by Electro- and Magnetoencephalography (EEG/MEG). However, the problem of leakage between brain sources that arises when reconstructing brain activity from EEG/MEG recordings outside the head makes it difficult to distinguish true connections from spurious connections, even when connections are based on measures that ignore zero-lag dependencies. In particular, standard anatomical parcellations for potential cortical sources tend to over- or under-sample the real spatial resolution of EEG/MEG. By using information from cross-talk functions (CTFs) that objectively describe leakage for a given sensor configuration and distributed source reconstruction method, we introduce methods for optimising the number of parcels while simultaneously minimising the leakage between them. More specifically, we compare two image segmentation algorithms: 1) a split-and-merge (SaM) algorithm based on standard anatomical parcellations and 2) a region growing (RG) algorithm based on all the brain vertices with no prior parcellation. Interestingly, when applied to minimum-norm reconstructions for EEG/MEG configurations from real data, both algorithms yielded approximately 70 parcels despite their different starting points, suggesting that this reflects the resolution limit of this particular sensor configuration and reconstruction method. Importantly, when compared against standard anatomical parcellations, resolution matrices of adaptive parcellations showed notably higher sensitivity and distinguishability of parcels. Furthermore, extensive simulations of realistic networks under various circumstances revealed significant improvements in network reconstruction accuracies, particularly in reducing false leakage-induced connections. Adaptive parcellations therefore allow a more accurate reconstruction of functional EEG/MEG connectomes.\n\nHighlightsO_LIWe introduce adaptive cortical parcellation algorithms for E/MEG source estimation.\nC_LIO_LIAlgorithms are based on cross-talk functions and image segmentation methods.\nC_LIO_LIThe resulting parcellations yielded ~70 parcels regardless of starting point.\nC_LIO_LISensitivity and distinguishability improved compared to anatomical parcellations.\nC_LIO_LIAccuracy of realistic whole-brain network reconstruction improved significantly.\nC_LI

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bioRxiv2017

Masking of a circadian behavior in larval zebrafish involves the thalamo-habenula pathway

Light has the ability to disrupt or mask behavior that is normally controlled by the circadian clock. In mammals, masking requires melanopsin-expressing retinal ganglion cells that detect blue light and project to the thalamus. It is not known whether masking is wavelength-dependent in other vertebrates, nor is it clear what higher circuits are involved. Here, we address these questions in zebrafish. We find that diel vertical migration, a circadian behavior in larval zebrafish, is effectively masked by blue, but not by red light. Two-photon calcium imaging reveals that a retino-recipient thalamic nucleus and a downstream structure, the habenula, are tuned to blue light. Lesioning the habenula inhibits light-evoked climbing. These data suggest that a thalamo-habenula pathway may be involved in the ability of blue light to mask circadian behavior.

neuroscience↗
bioRxiv2017

Individual movement variability magnitudes are predicted by cortical neural variability

Humans exhibit considerable motor variability even across trivial reaching movements. This variability can be separated into specific kinematic components such as extent and direction, which are thought to be governed by distinct neural processes. Here, we report that individual subjects exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements to different targets and regardless of which arm (right or left) was used to perform the movements. Simultaneous fMRI recordings revealed that the same subjects also exhibited different magnitudes of fMRI variability across movements in a variety of motor system areas. These fMRI variability magnitudes were also consistent across movements to different targets when performed with either arm. Cortical fMRI variability in the posterior-parietal cortex of individual subjects predicted their movement-extent variability. This relationship was apparent only in posterior-parietal cortex and not in other motor system areas, thereby suggesting that individuals with more variable movement preparation exhibit larger kinematic variability. We, therefore, propose that neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities.

neuroscience↗