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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 667 records · Page 37Linked to original sources

bioRxiv2016

On the regulator of spike activity in cortical neurons under hypothermic conditions

In sensorimotor cortical slices of guinea pig in the course of cooling incubating fluid from 34 to 21-22{degrees}C it was shown that hypothermia exerted both increase and decrease of spontaneous activity in different neurons. On hypothermic increase of firing level spike responses of soma to iontophoretic application of glutamate to dendritic locus appeared with shorter latencies and with longer latencies - on hypothermic decrease of spontaneous activity. At the same time hypothermia did not influence on the evoked spike reactions to iontophoretic application of glutamate straight to the soma. It means that hypothermic disorders of neuronal activity are not connected with changes in sensitivity to glutamate but determined by changes of amplitude of glutamatergic excitation while propagating along dendritic branches. The changes in spontaneous activity began at 30{degrees}C along with the decreased spike reactions to iontophoretic applications of acetylcholine and efficacy of dendro-somatic propagation. At the same temperature the fall of spike amplitude was initiated and increased with further hypothermia. It is proposed that the basis for hypothermic changes of neuronal activity is the decreased rate of M-cholinergic process at 27-29{degrees}C which leads both to attenuation of conductive function of dendrites and imbalance of K+ ion homeostasis. Peculiarities of hypothermic regulation of neuronal spike activity depend on individual functional properties of cortical neurons.

Neuroscience↗

bioRxiv2016

Functional Genetic Screen to Identify Interneurons Governing Behaviorally Distinct Aspects of Drosophila Larval Motor Programs

Drosophila larval crawling is an attractive system to study patterned motor output at the level of animal behavior. Larval crawling consists of waves of muscle contractions generating forward or reverse locomotion. In addition, larvae undergo additional behaviors including head casts, turning, and feeding. It is likely that some neurons are used in all these behaviors (e.g. motor neurons), but the identity (or even existence) of neurons dedicated to specific aspects of behavior is unclear. To identify neurons that regulate specific aspects of larval locomotion, we performed a genetic screen to identify neurons that, when activated, could elicit distinct motor programs. We used 165 Janelia CRM-Gal4 lines - chosen for sparse neuronal expression - to express the warmth-inducible neuronal activator TrpA1 and screened for locomotor defects. The primary screen measured forward locomotion velocity, and we identified 63 lines that had locomotion velocities significantly slower than controls following TrpA1 activation (28{degrees}C). A secondary screen was performed on these lines, revealing multiple discrete behavioral phenotypes including slow forward locomotion, excessive reverse locomotion, excessive turning, excessive feeding, immobile, rigid paralysis, and delayed paralysis. While many of the Gal4 lines had motor, sensory, or muscle expression that may account for some or all of the phenotype, some lines showed specific expression in a sparse pattern of interneurons. Our results show that distinct motor programs utilize distinct subsets of interneurons, and provide an entry point for characterizing interneurons governing different elements of the larval motor program.

Neuroscience↗

bioRxiv2016

Inhibitory control of shared variability in cortical networks

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the wide variety of activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

Neuroscience↗

bioRxiv2016

Focused Ultrasound Enhances Decision-Making in Monkeys

Noninvasive brain stimulation using focused ultrasound has many potential applications as a research and clinical tool. Here, we investigated the effect of focused ultrasound (FUS) combined with systemically administered microbubbles on visual-motor decision-making behavior in monkeys. We applied FUS to the putamen in one hemisphere to open the blood-brain barrier, and then tested behavioral performance 3-4 hours later. On days when the monkeys were treated with FUS, their decisions were faster and more accurate than days without sonication. The performance improvement suggested both a shift in the decision criterion and an enhancement of the use of sensory evidence in the decision process. FUS also interacted with the effect of a low dose of haloperidol. The results suggest that a two-minute application of FUS can have a sustained impact on performance of complex cognitive tasks, and may increase the efficacy of psychoactive medications. The results lend further support to the idea that the dorsal striatum plays an integral role in evidence- and reward-based decision-making.

Neuroscience↗

bioRxiv2016

The foraging brain: evidence of Levy dynamics in brain networks

In this research we have analyzed functional magnetic resonance imaging (fMRI) signals of different networks in the brain under resting state condition.\n\nTo such end, the dynamics of signal variation, have been conceived as a stochastic motion, namely it has been modelled through a generalized Langevin stochastic differential equation, which combines a deterministic drift component with a stochastic component where the Gaussian noise source has been replaced with -stable noise.\n\nThe parameters of the deterministic and stochastic parts of the model have been fitted from fluctuating data. Results show that the deterministic part is characterized by a simple, linear decreasing trend, and, most important, the -stable noise, at varying characteristic index , is the source of a spectrum of activity modes across the networks, from those originated by classic Gaussian noise ( = 2), to longer tailed behaviors generated by the more general Levy noise (1 [≤] < 2).\n\nLevy motion is a specific instance of scale-free behavior, it is a source of anomalous diffusion and it has been related to many aspects of human cognition, such as information foraging through memory retrieval or visual exploration.\n\nFinally, some conclusions have been drawn on the functional significance of the dynamics corresponding to different values.\n\nAuthor SummaryIt has been argued, in the literature, that to gain intuition of brain fluctuations one can conceive brain activity as the motion of a random walker or, in the continuous limit, of a diffusing macroscopic particle.\n\nIn this work we have substantiated such metaphor by modelling the dynamics of the fMRI signal of different brain regions, gathered under resting state condition, via a Langevin-like stochastic equation of motion where we have replaced the white Gaussian noise source with the more general -stable noise.\n\nThis way we have been able to show the existence of a spectrum of modes of activity in brain areas. Such modes can be related to the kind of \"noise\" driving the Langevin equation in a specific region. Further, such modes can be parsimoniously distinguished through the stable characteristic index , from Gaussian noise ( = 2) to a range of sharply peaked, long tailed behaviors generated by Levy noise (1 [≤] < 2).\n\nInterestingly enough, random walkers undergoing Levy motion have been widely used to model the foraging behaviour of a range of animal species and, remarkably, Levy motion patterns have been related to many aspects of human cognition.

Neuroscience↗

bioRxiv2016

Learning In Spike Trains: Estimating Within-Session Changes In Firing Rate Using Weighted Interpolation

The electrophysiological study of learning is hampered by modern procedures for estimating firing rates: Such procedures usually require large datasets, and also require that included trials be functionally identical. Unless a method can track the real-time dynamics of how firing rates evolve, learning can only be examined in the past tense. We propose a quantitative procedure, called ARRIS, that can uncover trial-by-trial firing dynamics. ARRIS provides reliable estimates of firing rates based on small samples using the reversible-jump Markov chain Monte Carlo algorithm. Using weighted interpolation, ARRIS can also provide estimates that evolve over time. As a result, both real-time estimates of changing activity, and of task-dependent tuning, can be obtained during the initial stages of learning.

Neuroscience↗

bioRxiv2016

A Dendritic Disinhibitory Circuit Mechanism for Pathway-Specific Gating

In this work we propose that a disinhibitory circuit motif, which recently gained experimental support, can instantiate flexible routing of information flow along selective pathways in a complex system of cortical areas according to behavioral demands (pathway-specific gating). We developed a network model of pyramidal neurons and three classes of interneurons, with connection probabilities constrained by data. If distinct input pathways cluster on separate dendritic branches of pyramidal neurons, then a pathway can be gated-on by disinhibiting targeted dendrites. We show that this branch-specific disinhibition can be achieved despite dense in-terneuronal connectivity, even under the assumption of random connections. We found clustering of input pathways on dendrites can emerge through synaptic plasticity regulated by disinhibition. This gating mechanism in a neural circuit is further demonstrated by performing a context-dependent decision-making task. Our findings suggest a microcircuit architecture that harnesses dendritic computation and diverse inhibitory neuron types to subserve cognitive flexibility.

Neuroscience↗

bioRxiv2016

MECP2 regulates cortical plasticity underlying a learned behavior in adult female mice

Neurodevelopmental disorders begin with the emergence of inappropriate synaptic connectivity early in life, yet how the sustained disruption of experience-dependent plasticity aggravates symptoms in adulthood is unclear. Here we used pup retrieval learning to assay adult cortical plasticity in a female mouse model of Rett syndrome (MeCP2het). We show that auditory cortical plasticity and retrieval learning are impaired in MeCP2het. Specifically, normal MECP2 expression in the adult auditory cortex is required for efficient retrieval learning. In wild-type mice, cohabitation with a mother and her pups triggered transient changes to auditory cortical inhibitory networks, including elevated levels of the GABA-synthesizing enzyme GAD67. However, MeCP2het further exhibited increased expression of parvalbumin (PV) and perineuronal nets (PNNs), events thought to suppress plasticity at the closure of critical periods and in adult learning. Averting these events with genetic and pharmacological manipulations of the GABAergic network restored retrieval behavior. We propose that adult retrieval learning triggers a transient episode of inhibitory plasticity in the auditory cortex that is dysregulated in MeCP2het. This window of heightened sensitivity to social sensory cues reveals a role of MeCP2 mutations in facilitating adult plasticity that is distinct from their effects on early development.

Neuroscience↗

bioRxiv2016

A probability distribution over latent causes in the orbitofrontal cortex

The orbitofrontal cortex (OFC) has been implicated in both the representation of \"state\", in studies of reinforcement learning and decision making, and also in the representation of \"schemas\", in studies of episodic memory. Both of these cognitive constructs require a similar inference about the underlying situation or \"latent cause\" that generates our observations at any given time. The statistically optimal solution to this inference problem is to use Bayes rule to compute a posterior probability distribution over latent causes. To test whether such a posterior probability distribution is represented in the OFC, we tasked human participants with inferring a probability distribution over four possible latent causes, based on their observations. Using fMRI pattern similarity analyses, we found that BOLD activity in OFC is best explained as representing the (log-transformed) posterior distribution over latent causes. Furthermore, this pattern explained OFC activity better than other task-relevant alternatives such as the most probable latent cause, the most recent observation, or the uncertainty over latent causes.\n\nSIGNIFICANCE STATEMENTOur world is governed by hidden (latent) causes that we cannot observe, but which generate the observations that we do see. A range of high-level cognitive processes require inference of a probability distribution (or \"belief distribution\") over the possible latent causes that might be generating our current observations. This is true for reinforcement learning (where the latent cause comprises the true \"state\" of the task), and for episodic memory (where memories are believed to be organized by the inferred situation or \"schema\"). Using fMRI, we show that this belief distribution over latent causes is encoded in patterns of brain activity in the orbitofrontal cortex -- an area that has been separately implicated in the representations of both states and schemas.\n\nCONFLICT OF INTERESTThe authors declare no competing financial interests.

Neuroscience↗

bioRxiv2016

Conformational dynamics of Tau in the cell quantified by an intramolecular FRET biosensor in physiological and pathological context

Impaired interactions of Tau protein with microtubules (MT) and Tau misfolding play a key role in Alzheimer disease (AD) and other neurodegenerative diseases collectively named Tauopathies. However, little is known about the molecular conformational changes that underlie Tau misfolding and aggregation in pathological conditions, due to the difficulty of studying structural aspects of this intrinsically unfolded protein, particularly in the context of living cells.\n\nHere we developed a new Conformational-Sensitive Tau sensor (CST), based on human Tau full length protein, to investigate the changes in 3D conformation and aggregation state of Tau upon modulation of its interactions with MTs in living cells, in physiological and pathological conditions. After showing that the CST fully preserves functional Tau activities in living cells, we demonstrated that MT-bound Tau displays a loop-like conformation, while soluble Tau assumes a relaxed conformation.\n\nThe imaging readout based on CST allowed to discover new conformational properties of full length Tau in living cells, when challenged with Alzheimer-relevant seeds from different sources, and to learn about different ways to induce the self-aggregation of full length Tau in cells. Furthermore, it allowed to investigate the contribution to the pathology of point mutations known to alter Tau/MTs interaction.\n\nSIGNIFICANCEThe microtubule-associated protein Tau regulates the stability of microtubules (MT) and its alteration and misfolding plays an important role in Alzheimer disease (AD) and in several other neurodegenerative diseases collectively named Tauopathies. However, little is known about the molecular mechanism that modulate the conformational changes of Tau in pathological conditions. Here, we developed a conformational sensitive Tau biosensor providing insights into the 3D conformation of Tau in living cells. This new tool showed that MT-bound Tau displays a loop-like conformation, while soluble Tau assume a relaxed conformation, which might be more prone to aggregation. Furthermore the CST was used to develop an assay to screen molecules and mutations involved in Tau self-aggregation in living cells. Remarkably, the imaging tool developed allowed to demonstrate that A{beta} oligomers induced Tau aggregation in living cells.

Neuroscience↗

bioRxiv2016

Sensitive red protein calcium indicators for imaging neural activity

Genetically encoded calcium indicators (GECIs) allow measurement of activity in large populations of neurons and in small neuronal compartments, over times of milliseconds to months. Although GFP-based GECIs are widely used for in vivo neurophysiology, GECIs with red-shifted excitation and emission spectra have advantages for in vivo imaging because of reduced scattering and absorption in tissue, and a consequent reduction in phototoxicity. However, current red GECIs are inferior to the state-of-the-art GFP-based GCaMP6 indicators for detecting and quantifying neural activity. Here we present improved red GECIs based on mRuby (jRCaMP1a, b) and mApple (jRGECO1a), with sensitivity comparable to GCaMP6. We characterized the performance of the new red GECIs in cultured neurons and in mouse, Drosophila, zebrafish and C. elegans in vivo. Red GECIs facilitate deep-tissue imaging, dual-color imaging together with GFP-based reporters, and the use of optogenetics in combination with calcium imaging.

Neuroscience↗

bioRxiv2016

Sharing brain mapping statistical results with the neuroimaging data model

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html.

Neuroscience↗

bioRxiv2016

Evidence for selective attention in the insect brain

The capacity for selective attention appears to be required for any animal responding to an environment containing multiple objects, although this has been difficult to study in smaller animals such as insects. Clear operational characteristics of attention however make study of this crucial brain function accessible to any animal model. Whereas earlier approaches have relied on freely behaving paradigms placed in an ecologically relevant context, recent tethered preparations have focused on brain imaging and electrophysiology in virtual reality environments. Insight into brain activity during attention-like behavior has revealed key elements of attention in the insect brain. Surprisingly, a variety of brain structures appear to be involved, suggesting that even in the smallest brains attention might involve widespread coordination of neural activity.\n\nHighlightsO_LIInsect behavior shows attention-like filtering and allows concurrent neural recording\nC_LIO_LIRecent work finds compelling correlates of visual attention in the central complex\nC_LIO_LIPutative goal-directed stimulus-filtering is present in neuropils across the brain\nC_LI

Neuroscience↗

bioRxiv2016

Coordination of brain wide activity dynamics by dopaminergic neurons

Several neuropsychiatric conditions, such as addiction, schizophrenia, and depression may arise in part from dysregulated activity of ventral tegmental area dopaminergic (THVTA) neurons, as well as from more global maladaptation in neurocircuit function. However, whether THVTA activity affects large-scale brain-wide function remains unknown. Here, we selectively activated THVTA neurons in transgenic rats and measured resulting changes in whole-brain activity using stimulus-evoked functional magnetic resonance imaging (fMRI). Selective optogenetic stimulation of THVTA neurons not only enhanced cerebral blood volume (CBV) signals in striatal target regions in a dopamine receptor dependent fashion, but also engaged many additional anatomically defined regions throughout the brain. In addition, repeated pairing of THVTA neuronal activity with forepaw stimulation, produced an expanded brain-wide sensory representation. These data suggest that modulation of THVTA neurons can impact brain dynamics across many distributed anatomically distinct regions, even those that receive little to no direct THVTA input.

Neuroscience↗

bioRxiv2016

Preprint: A robust peptidomics mass spectrometry platform for measuring oxytocin in plasma and serum

Current approaches to measuring the cyclic peptide oxytocin in plasma/serum are associated with poor selectivity and/or inadequate sensitivity. We here describe a high performance nano liquid chromatography-mass spectrometry platform for measuring OT in human plasma/serum. The platform is extremely robust, allowing laborious sample clean-up steps to be omitted. OT binds strongly to plasma proteins, but a reduction/alkylation procedure breaks this bond, allowing ample detection of total OT. The method showed excellent quantitation properties, and was used to determine total OT levels to 0.5-1.2 ng/mL (evaluated with human plasma and cord serum). The method is compatible with accessible mass spectrometry instrumentation, finally allowing selective and easily comparable oxytocin measurements.

Neuroscience↗

bioRxiv2016

Brain oscillations track the formation of episodic memories in the real world

Despite the well-known influence of environmental context on episodic memory, little has been done to enhance contextual richness within the lab. This leaves a blind spot lingering over the neuronal correlates of episodic memory formation in the real world. To address this, we presented participants with series of words to memorise along a pre-designated route across campus. Meanwhile, a mobile EEG system acquired the associated neural activity. Replicating lab-based subsequent memory effects (SMEs), we identified significant low-frequency power decreases, including beta power decreases over the left inferior frontal gyrus. Additionally, the paradigm enabled us to dissociate the oscillatory correlates of temporal and spatial clustering. Specifically, we found spatially clustered items exhibited significantly greater theta power decreases within the left medial temporal lobe than temporally clustered items. These findings go beyond lab-based studies, which are limited in their capabilities to investigate environmental contextual factors that guide memory formation.

Neuroscience↗

bioRxiv2016

Neural Pattern Change During Encoding of a Narrative Predicts Retrospective Duration Estimates

What mechanisms support our ability to estimate durations on the order of minutes? Behavioral studies in humans have shown that changes in contextual features lead to overestimation of past durations. Based on evidence that the medial temporal lobes and prefrontal cortex represent contextual features, we related the degree of fMRI pattern change in these regions with peoples subsequent duration estimates. After listening to a radio story in the scanner, participants were asked how much time had elapsed between pairs of clips from the story. Our ROI analysis found that the neural pattern distance between two clips at encoding was correlated with duration estimates in the right entorhinal cortex and right pars orbitalis. Moreover, a whole-brain searchlight analysis revealed a cluster spanning the right anterior temporal lobe. Our findings provide convergent support for the hypothesis that retrospective time judgments are driven by \"drift\" in contextual representations supported by these regions.

Neuroscience↗

bioRxiv2016

Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path

Here we use computational modeling of fast neural dynamics to explore the relationship between structural and functional coupling in a population of healthy subjects. We use DTI data to estimate structural connectivity and subsequently model phase couplings from band-limited oscillatory signals derived from multichannel EEG data. Our results show that about 23.4% of the variance in empirical networks of resting-state fast oscillations is explained by the underlying white matter architecture. By simulating functional connectivity using a simple reference model, the match between simulated and empirical functional connectivity further increases to 45.4%. In a second step, we use our modeling framework to explore several technical alternatives along the modeling path. First, we find that an augmentation of homotopic connections in the structural connectivity matrix improves the link to functional connectivity while a correction for fiber distance slightly decreases the performance of the model. Second, a more complex computational model based on Kuramoto oscillators leads to a slight improvement of the model fit. Third, we show that the comparison of modeled and empirical functional connectivity at source level is much more specific for the underlying structural connectivity. However, different source reconstruction algorithms gave comparable results. Of note, as the fourth finding, the model fit was much better if zero-phase lag components were preserved in the empirical functional connectome, indicating a considerable amount of functionally relevant synchrony taking place with near zero or zero-phase lag. The combination of the best performing alternatives at each stage in the pipeline results in a model that explains 54.4% of the variance in the empirical EEG functional connectivity. Our study shows that large-scale brain circuits of fast neural network synchrony strongly rely upon the structural connectome and simple computational models of neural activity can explain missing links in the structure-function relationship.\n\nAuthor SummaryBrain imaging techniques are broadly divided into the two categories of structural and functional imaging. Structural imaging provides information about the static physical connectivity within the brain, while functional imaging provides data about the dynamic ongoing activation of brain areas. Computational models allow to bridge the gap between these two modalities and allow to gain new insights. Specifically, in this study, we use structural data from diffusion tractography recordings to model functional brain connectivity obtained from fast EEG dynamics. First, we present a simple reference procedure which consists of several steps to link the structural to the functional empirical data. Second, we systematically compare several alternative methods along the modeling path in order to assess their impact on the overall fit between simulations and empirical data. We explore preprocessing steps of the structural connectivity and different levels of complexity of the computational model. We highlight the importance of source reconstruction and compare commonly used source reconstruction algorithms and metrics to assess functional connectivity. Our results serve as an important orienting frame for the emerging field of brain network modeling.

Neuroscience↗