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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 559 records · Page 31Linked to original sources
bioRxiv2015

Can mindfulness meditation alter consciousness? An integrative interpretation

Mindfulness meditation has been practiced in the East for more than two millennia, but in last years also Western neurscientists drown their attention to it. Mindfulness basically refers to moment to moment awareness. In this review we summarize different hypotheses concerning effects of mindfulness meditation practice and cerebral correlates accounting for these; furthermore we expose some of the most relevant theories dealing with different aspects of consciousness. Finally we propose an integration of mindfulness meditation with consciousness, supported by the identification of brain areas involved in both of them, namely Anterior Cingular Cortex (ACC), Posterior Cingular Cortex (PCC), Insula and Thalamus.

Neuroscience↗
bioRxiv2015

Prediction of primary somatosensory neuron activity during active tactile exploration

Primary sensory neurons form the interface between world and brain. Their function is well-understood during passive stimulation but, under natural behaving conditions, sense organs are under active, motor control. In an attempt to predict primary neuron firing under natural conditions of sensorimotor integration, we recorded from primary mechanosensory neuronsof awake, head-fixed mice as they explored a pole with their whiskers, and simultaneously measured both whisker motion and forces with high-speed videography. Using Generalised Linear Models, we found that primary neuron responses were poorly predicted by kinematics but well-predicted by rotational forces acting on the whisker: both during touch and free-air whisker motion. These results are discrepant with previous studies of passive stimulation, but could be reconciled by differences in the kinematics-force relationship between active and passive conditions. Thus, simple statistical models can predict rich neural activity elicited by natural, exploratory behaviour involving active movement of the sense organs.

Neuroscience↗
bioRxiv2015

Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly nonrandom, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance.

Neuroscience↗
bioRxiv2015

Synergy of AMPA and NMDA receptor currents in dopaminergic neurons: a modeling study

Dopaminergic (DA) neurons display two modes of firing: low-frequency tonic and high-frequency bursts. The high frequency firing within the bursts is attributed to NMDA, but not AMPA receptor activation. In our models of the DA neuron, both biophysical and abstract, the NMDA receptor current can significantly increase their firing frequency, whereas the AMPA receptor current is not able to evoke high-frequency activity and usually suppresses firing. However, both currents are produced by glutamate receptors and, consequently, are often co-activated. Here we consider combined influence of AMPA and NMDA synaptic input in the models of the DA neuron. Different types of neuronal activity (resting state, low frequency, or high frequency firing) are observed depending on the conductance of the AMPAR and NMDAR currents. In two models, biophysical and reduced, we show that the firing frequency increases more effectively if both receptors are co-activated for certain parameter values. In particular, in the more quantitative biophysical model, the maximal frequency is 40% greater than that with NMDAR alone. The dynamical mechanism of such frequency growth is explained in the framework of phase space evolution using the reduced model. In short, both the AMPAR and NMDAR currents flatten the voltage nullcline, providing the frequency increase, whereas only NMDA prevents complete unfolding of the nullcline, providing robust firing. Thus, we confirm a major role of the NMDAR in generating high-frequency firing and conclude that AMPAR activation further significantly increases the frequency.

Neuroscience↗
bioRxiv2015

The human experience with intravenous levodopa

ObjectiveTo compile a comprehensive summary of published human experience with levodopa given intravenously, with a focus on information required by regulatory agencies.\n\nBackgroundWhile safe intravenous use of levodopa has been documented for over 50 years, regulatory supervision for pharmaceuticals given by a route other than that approved by the U.S. Food and Drug Administration (FDA) has become increasingly cautious. If delivering a drug by an alternate route raises the risk of adverse events, an investigational new drug (IND) application is required, including a comprehensive review of toxicity data.\n\nMethodsOver 200 articles referring to intravenous levodopa (IVLD) were examined for details of administration, pharmacokinetics, benefit and side effects.\n\nResultsWe identified 142 original reports describing IVLD use in humans, beginning with psychiatric research in 1959-1960 before the development of peripheral decarboxylase inhibitors. At least 2781 subjects have received IVLD, and reported outcomes include parkinsonian signs, sleep variables, hormones, hemodynamics, CSF amino acid composition, regional cerebral blood flow, cognition, perception and complex behavior. Mean pharmacokinetic variables were summarized for 49 healthy subjects and 190 with Parkinsons disease. Side effects were those expected from clinical experience with oral levodopa and dopamine agonists. No articles reported deaths or induction of psychosis.\n\nConclusionAt least 2781 patients have received i.v. levodopa with a safety profile comparable to that seen with oral administration.

Neuroscience↗
bioRxiv2015

Control of sensory ectopic spike initiation by descending modulatory projection neurons

AO_SCPLOWBSTRACTC_SCPLOWDescending pathways are important modulators of motor networks and allow the dynamic adjustment of behaviors to changing internal and external conditions. Central pattern generating networks (CPG) have been particularly amenable to study the modulation of motor networks and demonstrated that virtually all levels of information processing are controlled by descending projections. CPGs receive sensory feedback and while it is known that sensory activity can be gated by central pathways, we here present for the first time that descending projection neurons modulate action potential initiation in sensory neurons.\n\nWe used the fact that the anterior gastric receptor neuron (AGR), a single-cell bipolar muscle tendon organ in the crustacean stomatogastric nervous system, generates spontaneous ectopic action potentials in its axon. We found that axonal spike initiation is under direct neuromodulatory control by a pair of descending projection neurons. These IV (inferior ventricular) neurons descend from the brain and are known to control CPGs in the stomatogastric ganglion (STG). Activation of the IV neurons elicited a long-lasting decrease in AGR ectopic spike activity. This decrease was only observed when spikes were generated ectopically in the central portion of the axon, i.e. the modulation was specific to the site of spontaneous spike initiation. The decrease could be mimicked by focal application of the IV neuron co-transmitter histamine and IV neuron actions were diminished after blocking H2 receptors, indicating a direct descending modulation of the axonal spike initiation site. In contrast, the propagation dynamics of en-passant action potentials were not affected. Descending modulatory projection neurons therefore control axonal spike initiation in sensory neurons without affecting afferent spike propagation to increase the physiological activity repertoire of sensory pathways.

Neuroscience↗
bioRxiv2015

Tissue-specific tagging of endogenous loci in Drosophila melanogaster

Fluorescent protein tags have revolutionized cell and developmental biology, and in combination with binary expression systems they enable diverse tissue-specific studies of protein function. However these binary expression systems often do not recapitulate endogenous protein expression levels, localization, binding partners, and developmental windows of gene expression. To address these limitations, we have developed a method called T-STEP (Tissue-Specific Tagging of Endogenous Proteins) that allows endogenous loci to be tagged in a tissue specific manner. T-STEP uses a combination of efficient gene targeting and tissue-specific recombinase-mediated tag swapping to temporally and spatially label endogenous proteins. We have employed this method to GFP tag OCRL (a phosphoinositide-5-phosphatase in the endocytic pathway) and Vps35 (a Parkinson's disease-implicated component of the endosomal retromer complex) in diverse Drosophila tissues including neurons, glia, muscles, and hemocytes. Selective tagging of endogenous proteins allows for the first time cell type-specific live imaging and proteomics in complex tissues.

Neuroscience↗
bioRxiv2015

Change Blindness in 3D Virtual Reality

In the present change blindness study subjects explored stereoscopic three dimensional (3D) environments through a virtual reality (VR) headset. A novel method that tracked the subjects head movements was used for inducing changes in the scene whenever the changing object was out of the field of view. The effect of change location (foreground or background in 3D depth) on change blindness was investigated. Two experiments were conducted, one in the lab (n = 50) and the other online (n = 25). Up to 25% of the changes were undetected and the mean overall search time was 27 seconds in the lab study. Results indicated significantly lower change detection success and more change cycles if the changes occurred in the background, with no differences in overall search times. The results confirm findings from previous studies and extend them to 3D environments. The study also demonstrates the feasibility of online VR experiments.

Neuroscience↗
bioRxiv2015

Structural constraints to information flow within cortical circuits: a TMS/EEG-dMRI study

Transcranial magnetic stimulation (TMS) has been used for more than 20 years to investigate brain function by perturbing and observing the consequent behavioral, pathophysiological and electrophysiological modulations. These latter, mainly measured by high-density electroencephalography (hd-EEG), revealed signatures of the functional organization in a brain network. In order to unveil the nature and the underlying mechanism of these signatures, we here mapped TMS-induced hd-EEG changes onto changes in information flow and brain structural architecture, using multimodal modeling of source reconstructed TMS/hd-EEG recordings and diffusion magnetic resonance imaging (dMRI) tractography in a cohort of awake healthy volunteers. We observed that the relationship between information flow and structural connections depend on the stimulation site and on the frequency of the TMS-induced brain rhythms. These findings highlight the importance of taking into account the dynamics of different local oscillations when investigating the mechanisms for integration and segregation of information in the human brain. Our whole-brain analysis sheds light on the function-structure organization of the brain network after TMS, and on the huge variety of information contained in it. TMS/EEG dMRI directed functional connectivity structural connectivity structure-function brain information flow

Neuroscience↗
bioRxiv2015

Long-delay learning in intraoral conditioned taste aversion

The cardinal feature of conditioned taste aversion (CTA) learning is the ability of animals to associate the taste or flavor of a food (the conditioned stimulus; CS) with a subsequent toxic effect (unconditioned stimulus; US), even if the toxicity occurs hours later, i.e. after a long delay. Two experiments were conducted which took advantage of the stimulus control afforded by intraoral catheterization to establish the parameters of long-delay learning in intraoral CTA. First, to determine the range of CS-US intervals which supports intraoral conditioning, rats received infusions of 5% sucrose paired with LiCl (76 mg/kg, ip) across a range of delays (0-6 h). Second, to determine the interaction of US dose and delay, rats were conditioned with sucrose paired with different doses of LiCl (19, 38 or 76 mg/kg) at several CS-US intervals (0, 10, or 60 min). Con-ditioning, assessed during a second infusion of sucrose at 48 h post-conditioning, was optimal at 10 min (although not significantly different at intervals between 0 and 60 min). Effectiveness declined at longer delays, such that CTA was not supported at intervals of 3h or greater. The dose-interval function suggested that an increased US can compensate for a longer CS-US interval. Low doses of LiCl induced a long-term CTA at 0-min (19 and 38 mg/kg) or 10-min delays (38 mg/kg), but were not sufficient to induce CTA at longer delays, which required the highest dose (76 mg/kg).

Neuroscience↗
bioRxiv2015

Quantitative neuroanatomy for connectomics in Drosophila

Neuronal circuit mapping using electron microscopy demands laborious proofreading or reconciliation of multiple independent reconstructions. Here, we describe new methods to apply quantitative arbor and network context to iteratively proofread and reconstruct circuits and create anatomically-enriched wiring diagrams. We measured the morphological underpinnings of connectivity in new and existing reconstructions of Drosophila sensorimotor (larva) and visual (adult) systems. Synaptic inputs were preferentially located on numerous small, microtubule-free \"twigs\" which branch off a single microtubule-containing \"backbone\". Omission of individual twigs accounted for 96% of errors. However, the synapses of highly connected neurons were distributed across multiple twigs. Thus the robustness of a strong connection to detailed twig anatomy was associated with robustness to reconstruction error. By comparing iterative reconstruction to the consensus of multiple reconstructions, we show that our method overcomes the need for redundant effort through the discovery and application of relationships between cellular neuroanatomy and synaptic connectivity.

Neuroscience↗
bioRxiv2015

Stimuli reduce the dimensionality of cortical activity

The activity of ensembles of simultaneously recorded neurons can be represented as a set of points in the space of firing rates. Even though the dimension of this space is equal to the ensemble size, neural activity can be effectively localized on smaller subspaces. The dimensionality of the neural space is an important determinant of the computational tasks supported by the neural activity. Here, we investigate the dimensionality of neural ensembles from the sensory cortex of alert rats during periods of ongoing (intertrial) and stimulus-evoked activity. We find that dimensionality grows linearly with ensemble size, and grows significantly faster during ongoing activity compared to evoked activity. We explain these results using a spiking network model based on a clustered architecture. The model captures the difference in growth rate between ongoing and evoked activity and predicts a characteristic scaling with ensemble size that could be tested in high-density multi-electrode recordings. Moreover, we present a simple theory that predicts the existence of an upper bound on dimensionality. This upper bound is inversely proportional to the amount of pair-wise correlations and, compared to a homogeneous network without clusters, it is larger by a factor equal to the number of clusters. The empirical estimation of such bounds depends on the number and duration of trials and is well predicted by the theory. Together, these results provide a framework to analyze neural dimensionality in alert animals, its behavior under stimulus presentation, and its theoretical dependence on ensemble size, number of clusters, and correlations in spiking network models.

Neuroscience↗
bioRxiv2015

Connectome of a model local cortical circuit flexibly shapes layer-dependent multi-frequency oscillations

The role played by interlayer connections in shaping local responses and their long-range coupling has not yet been fully elucidated. Here, we analyze a rate model of a canonic local circuit with realistic anatomy. We find that this circuit generates a rich repertoire of possible dynamical states, including an oscillatory regime in which gamma-and beta-oscillations dominate in superficial and deep layers, respectively, in agreement with experimental observations. This regime stems from non-linear inter-layer interactions, independently from intrinsic resonance properties of distinct layers. Moreover, by connecting two local circuits via cortico-cortical projections, the emergent phase differences define a flexible and frequency-dependent inter-areal hierarchy. Such dynamic patterns generally do not arise in randomized circuits, and the compatible connectomes are rare, although not unique. Altogether, these results suggest that inter-layer connectivity is homeostatically regulated to make local circuits fit to integrate and multiplex signals from several sources in multiple frequency bands.\n\nAuthor SummaryThe local circuit of mammalian cortex presents a characteristic multilayered structure, with feedforward (and feedback) cortico-cortical connections originating from (and targeting) distinct and well defined layers. Here, we model how such a structure fundamentally shapes the dynamical repertoire of local cortical oscillatory states and their long-range interaction. Experimental evidence, matched by our simulations, suggests that different cortical layers oscillate at different frequencies and that neuronal oscillations at different frequencies are exploited for communication in different directions. While this laminar specificity of oscillations is often explained in terms of multiple inhibitory populations with different resonance properties, we show here that it could alternatively emerge as a byproduct of the collective local circuit dynamics. Our modelling study indicates furthermore that the empirically observed multi-frequency oscillatory patterns cannot be reproduced in presence of an arbitrary interlayer connectivity. In this sense, therefore, we believe that the adopted connectome, derived from neuroanatomical reconstructions, is \"special\". Nevertheless, it is not unique, since other, very different connectomes may also lead to a matching dynamical repertoire. This suggests that a multiplicity of non-random canonical circuit templates may share largely overlapping functions, robustly achieved and maintained via functional homeostasis mechanisms.

Neuroscience↗
bioRxiv2015

Model-free Estimation of Tuning Curves and Their Attentional Modulation, Based on Sparse and Noisy Data

Tuning curves are the functions that relate the responses of sensory neurons to various values within one continuous stimulus dimension (such as the orientation of a bar in the visual domain or the frequency of a tone in the auditory domain). They are commonly determined by fitting a model e.g. a Gaussian or other bell-shaped curves to the measured responses to a small subset of discrete stimuli in the relevant dimension. However, as neuronal responses are irregular and experimental measurements noisy, it is often difficult to determine reliably the appropriate model from the data. We illustrate this general problem by fitting diverse models to representative recordings from area MT in rhesus monkey visual cortex during multiple attentional tasks involving complex composite stimuli. We find that all models can be well-fitted, that the best model generally varies between neurons and that statistical comparisons between neuronal responses across different experimental conditions are affected quantitatively and qualitatively by specific model choices. As a robust alternative to an often arbitrary model selection, we introduce a model-free approach, in which features of interest are extracted directly from the measured response data without the need of fitting any model. In our attentional datasets, we demonstrate that data-driven methods provide descriptions of tuning curve features such as preferred stimulus direction or attentional gain modulations which are in agreement with fit-based approaches when a good fit exists. Furthermore, these methods naturally extend to the frequent cases of uncertain model selection. We show that model-free approaches can identify attentional modulation patterns, such as general alterations of the irregular shape of tuning curves, which cannot be captured by fitting stereotyped conventional models. Finally, by comparing datasets across different conditions, we demonstrate effects of attention that are cell-and even stimulus-specific. Based on these proofs-of-concept, we conclude that our data-driven methods can reliably extract relevant tuning information from neuronal recordings, including cells whose seemingly haphazard response curves defy conventional fitting approaches.

Neuroscience↗
bioRxiv2015

The neurotranscriptome of the Aedes aegypti mosquito

BackgroundA complete genome sequence and the advent of genome editing open up non-traditional model organisms to mechanistic genetic studies. The mosquito Aedes aegypti is an important vector of infectious diseases such as dengue, chikungunya, and yellow fever, and has a large and complex genome, which has slowed annotation efforts. We used comprehensive transcriptomic analysis of adult gene expression to improve the genome annotation and to provide a detailed tissue-specific catalogue of neural gene expression at different adult behavioral states.\n\nResultsWe carried out deep RNA sequencing across all major peripheral male and female sensory tissues, the brain, and (female) ovary. Furthermore, we examined gene expression across three important phases of the female reproductive cycle, a remarkable example of behavioral switching in which a female mosquito alternates between obtaining blood-meals from humans and laying eggs. Using genome-guided alignments and de novo transcriptome assembly, our re-annotation includes 572 new putative protein-coding genes and updates to 13.5% and 50.3% of existing transcripts within coding sequences and untranslated regions, respectively. Using this updated annotation, we detail gene expression in each tissue, identifying large numbers of transcripts regulated by blood-feeding and sexually dimorphic transcripts that may provide clues to the biology of male- and female-specific behaviors, such as mating and blood-feeding, which are areas of intensive study for those interested in vector control.\n\nConclusionsThis neurotranscriptome forms a strong foundation for the study of genes in the mosquito nervous system and investigation of sensory-driven behaviors and their regulation. Furthermore, understanding the molecular genetic basis of mosquito chemosensory behavior has important implications for vector control.

Neuroscience↗
bioRxiv2015

Cortical idiosyncrasies predict the perception of object size

Perception is subjective. Even basic judgments, like those of visual object size, vary substantially between observers and also across the visual field within the same observer. The way in which the visual system determines the size of objects remains unclear, however. We hypothesize that object size is inferred from neuronal population activity in V1 and predict that idiosyncrasies in cortical functional architecture should therefore explain individual differences in size judgments. Indeed, using novel behavioral methods and functional magnetic resonance imaging (fMRI) we demonstrate that biases in size perception are correlated with the spatial tuning of neuronal populations in healthy volunteers. To explain this relationship, we formulate a population read-out model that directly links the spatial distribution of V1 representations to our perceptual experience of visual size. Altogether, we suggest that the individual perception of simple stimuli is warped by idiosyncrasies in visual cortical organization.

Neuroscience↗
bioRxiv2015

Task learning reveals neural signatures of internal models in rodent prefrontal cortex

Cortical population activity may represent sampling from an internal model. Using data from rats learning rules on a maze task, we show that sampling rates of population activity patterns in prefrontal cortex converge between waking and sleep over learning. Sample rate changes were greatest for activity patterns predictive of correct choice and expressed at the choice point. Our results suggest inference-by-sampling is a general computational principle for cortex.

Neuroscience↗
bioRxiv2015

Enhancing burst activation and propagation in cultured neuronal networks by photo-stimulation

Spontaneous bursting activity in cultured neuronal networks is initiated by leader neurons, which constitute a small subset of first-to-fire neurons forming a sub-network that recruits follower neurons into the burst. While the existence and stability of leader neurons is well established, the influence of stimulation on the leader-follower dynamics is not sufficiently understood. By combining multi-electrode array recordings with whole field optical stimulation of cultured Channelrhodopsin-2 transduced hippocampal neurons, we show that fade-in photo-stimulation induces a significant shortening of intra-burst firing rate peak delay of follower electrodes after offset of the stimulation compared to unperturbed spontaneous activity. Our study shows that optogenetic stimulation can be used to change the dynamical fine structure of self-organized network bursts.

Neuroscience↗