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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 1,531 records · Page 85Linked to original sources

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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bioRxiv2016

Activity-dependent expression of reporter proteins at dendritic spines for synaptic activity mapping and optogenetic stimulation

Increasing evidence points to the importance of dendritic spines in the formation and allocation of memories, and alterations of spine number and physiology are associated to memory and cognitive disorders. Synaptic connections and pathways constitute the physical substrate that conveys information in the brain, and different combinations of active synaptic connections are believed to be responsible for the encoding of specific memories. In addition, modifications of the activity of such subsets of synapses are believed to be crucial for memory establishment, but a way to directly test this hypothesis, by selectively controlling the activity of potentiated spines, is currently lagging behind. Therefore it would be important to develop methods to tag active synapses for mapping functionally active connections and to selectively stimulate or interfere with active synapses. Here we introduce an approach to express light-sensitive membrane channels at synapses in an activity-dependent way by means of RNA and protein regulatory sequences. This approach is based on the local expression of reporter proteins, including optogenetic probes, at activated synapses and will allow the mapping of previously active synapses and the re-activation of the neuron only at these sites. This will allow extending the investigation of memory processes beyond the current neuron tagging technologies, whose resolution is limited at the cellular scale. Thus, it will be possible to unveil and recall the synaptic engram out of the global set of synapses.

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bioRxiv2016

Inter-Subject Alignment of MEG Datasets at the Neural Representational Space

Pooling neural imaging data across subjects requires aligning recordings from different subjects. In magnetoencephalography (MEG) recordings, sensors across subjects are poorly correlated both because of differences in the exact location of the sensors, and structural and functional differences in the brains. It is possible to achieve alignment by assuming that the same regions of different brains correspond across subjects. However, this relies on both the assumption that brain anatomy and function are well correlated, and the strong assumptions that go into solving the underdetermined inverse problem given the high dimensional source space. In this paper, we investigated an alternative method that bypasses source-localization. Instead, it analyzes the sensor recordings themselves and aligns their temporal signatures across subjects. We used a multivariate approach, multi-set canonical correlation analysis (M-CCA), to transform individual subject data to a low dimensional common representational space. We evaluated the robustness of this approach over a synthetic dataset, by examining the effect of different factors that add to the noise and individual differences in the data. On a MEG dataset, we demonstrated that M-CCA performs better than a method that assumes perfect sensor correspondence and a method that applies source localization. Lastly, we described how the standard M-CCA algorithm could be further improved with a regularization term that incorporates spatial sensor information.

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bioRxiv2016

Gene expression patterns associated with neurological disease in HIV infection

To provide new insight into the pathogenesis of neurocognitive impairments (NCI) in HIV infection, we used the Gene Set Enrichment Analysis (GSEA) algorithm to analyze pathway dysregulations in gene expression profiles of HIV-infected patients with or without NCI and HIV encephalitis (HIVE). While HIVE was characterized by widespread inflammation and tissue damage, gene expression evidence of induction of interferon (IFN), cytokines and tissue injury was apparent in all brain regions studied before the emergence of NCI. Various degrees of white matter changes were present in all HIV-infected subjects and were the primary manifestation in patients with NCI in the absence of HIVE. The latter showed a distinct pattern of immune activation with induction of chemokines, cytokines, {beta}-defensins, and limited IFN induction.\n\nAltogether results indicate that significant neuroinflammation and neuronal suffering precede NCI. Patients with NCI without HIVE showed a predominantly white matter dysfunction with a distinct pattern of immune activation.

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bioRxiv2016

Trial-by-trial neural variability is a stable individual trait of adult humans

Numerous studies have shown that neural activity in sensory cortices is remarkably variable over time and across trials even when subjects are presented with an identical repeating stimulus or task. This trial-by-trial neural variability is relatively large in the pre-stimulus period and considerably smaller (quenched) following stimulus presentation. Previous studies have suggested that the magnitude of neural variability affects behavior such that perceptual performance is better on trials and in individuals where variability quenching is larger. To what degree are neural variability magnitudes of individual subjects flexible or static? Here, we used EEG recordings from adult humans to demonstrate that neural variability magnitudes in visual cortex are remarkably consistent across different tasks and recording sessions. While magnitudes of neural variability differed dramatically across individual subjects, they were surprisingly stable across four tasks with different stimuli, temporal structures, and attentional/cognitive demands as well as across experimental sessions separated by one year. These experiments reveal that, in adults, neural variability magnitudes are mostly solidified individual characteristics that change little with task or time, and are likely to predispose individual subjects to exhibit distinct behavioral capabilities.\n\nSignificance statementBrain activity varies dramatically from one moment to the next. Recent research has revealed that humans exhibit different magnitudes of trial-by-trial neural variability, which explain differences in their perceptual performance. How stable are neural variability magnitudes over time and across tasks? Here, subjects performed four different experiments in two experimental sessions separated by one year. The results revealed that neural variability magnitudes were remarkably consistent over time and across tasks, suggesting that the magnitude of neural variability is a solidified neural characteristic that may predispose individual subjects to exhibit different behavioral capabilities.

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bioRxiv2016

Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding

According to the theory of efficient coding, sensory systems are adapted to represent natural scenes with high fidelity and at minimal metabolic cost. Testing this hypothesis for sensory structures performing non-linear computations on high dimensional stimuli is still an open challenge. Here we develop a method to characterize the sensitivity of the retinal network to perturbations of a stimulus. Using closed-loop experiments, we explore selectively the space of possible perturbations around a given stimulus. We then show that the response of the retinal population to these small perturbations can be described by a local linear model. Using this model, we computed the sensitivity of the neural response to arbitrary temporal perturbations of the stimulus, and found a peak in the sensitivity as a function of the frequency of the perturbations. Based on a minimal theory of sensory processing, we argue that this peak is set to maximize information transmission. Our approach is relevant to testing the efficient coding hypothesis locally in any context where no reliable encoding model is known.

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bioRxiv2016

Quantitative mRNA Imaging Throughout the Entire Drosophila Brain

We describe a fluorescence in situ hybridization method that permits detection of the localization and abundance of single mRNAs (smFISH) in cleared whole-mount adult Drosophila brains. The approach is rapid, multiplexable and does not require molecular amplification; it allows facile mRNA expression quantification with subcellular resolution on a standard confocal microscope. Using a custom Bessel Beam-Structured Illumination microscope (BB-SIM), we further demonstrate single-mRNA detection across the entire brain sample.

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bioRxiv2016

The primary motor cortex is critical for the retention of implicit sensorimotor adaptation

Sensorimotor adaptation, or adaptation of movements to external perturbations, is thought to involve the primary motor cortex (M1). In addition to implicit error-driven remapping, explicit re-aiming strategies also contribute to sensorimotor adaptation. However, no studies to date have examined the role of M1 in implicit learning in isolation from explicit strategies. Because the application of explicit strategies requires time, it is possible to emphasise implicit learning by controlling the time available to prepare movement. Here, we examined the role of M1s role in implicit adaptation to rotated visual feedback whilst suppressing the use of explicit re-aiming strategies by limiting movement preparation times to less than 350ms. Perturbing M1 activity via single-pulse TMS during adaptation to a 30 {degrees} rotation of visual feedback did not alter the rate or extent of error compensation, but elicited poorer retention in post-adaptation trials with no perturbation. This work shows that M1 is critical in the retention of new visuomotor maps as a result of implicit adaptation to a perturbation in sensory feedback when strategic error correction processes are suppressed.\n\nHighlightsO_LIAdaptation of movements to perturbations occurs through explicit and implicit processes.\nC_LIO_LIHere, explicit strategies were suppressed by shortening movement preparation time.\nC_LIO_LIPerturbing motor cortex (M1) with TMS selectively impaired retention but not acquisition of sensorimotor adaptation.\nC_LIO_LIM1 plays a crucial role in retention of sensorimotor adaptation obtained via implicit learning.\nC_LI

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bioRxiv2016

Brain connectivity dynamics during social interaction reflect social network structure

Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use functional magnetic resonance imaging data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides novel insight to understand how distributed brain systems respond to social and emotional challenges, and how such brain dynamics might vary based on broader social network characteristics.

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bioRxiv2016

Neuronal gain modulability is determined by dendritic morphology: a computational optogenetic study

The mechanisms by which the gain of the neuronal input-output function may be modulated have been the subject of much investigation. However, little is known of the role of dendrites in neuronal gain control. New optogenetic experimental paradigms based on spatial profiles or patterns of light stimulation offer the prospect of elucidating many aspects of single cell function, including the role of dendrites in gain control. We thus developed a model to investigate how competing excitatory and inhibitory input within the dendritic arbor alters neuronal gain, incorporating kinetic models of opsins into our modeling to ensure it is experimentally testable. To investigate how different topologies of the neuronal dendritic tree affect the neurons input-output characteristics we generate branching geometries which replicate morphological features of most common neurons, but keep the number of branches and overall area of dendrites approximately constant. We found a relationship between a neurons gain modulability and its dendritic morphology, with neurons with bipolar dendrites with a moderate degree of branching being most receptive to control of the gain of their input-output relationship. The theory was then tested and confirmed on two examples of realistic neurons: 1) layer V pyramidal cells - confirming their role in neural circuits as a regulator of the gain in the circuit in addition to acting as the primary excitatory neurons, and 2) stellate cells. In addition to providing testable predictions and a novel application of dual-opsins, our model suggests that innervation of all dendritic subdomains is required for full gain modulation, revealing the importance of dendritic targeting in the generation of neuronal gain control and the functions that it subserves. Finally, our study also demonstrates that neurophysiological investigations which use direct current injection into the soma and bypass the dendrites may miss some important neuronal functions, such as gain modulation.\n\nAuthor SummaryO_LIGain modulability indicated by dendritic morphology\nC_LIO_LIPyramidal cell-like shapes optimally receptive to modulation\nC_LIO_LIAll dendritic subdomains required for gain modulation, partial illumination is insufficient\nC_LIO_LIComputational optogenetic models improve and refine experimental protocols\nC_LI

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bioRxiv2016

Dorsal hippocampus plays a causal role in model-based planning

Planning can be defined as a process of action selection that leverages an internal model of the environment. Such models provide information about the likely outcomes that will follow each selected action, and their use is a key function underlying complex adaptive behavior. However, the neural mechanisms supporting this ability remain poorly understood. In the present work, we adapt for rodents recent advances from work on human planning, presenting for the first time a task for animals which produces many trials of planned behavior per session, allowing the experimental toolkit available for use in trial-by-trial tasks for rodents to be applied to the study of planning. We take advantage of one part of this toolkit to address a perennially controversial issue in planning research: the role of the dorsal hippocampus. Although prospective representations in the hippocampus have been proposed to support model-based planning, intact planning in hippocampally damaged animals has been observed in a number of assays. Combining formal algorithmic behavioral analysis with muscimol inactivation, we provide the first causal evidence directly linking dorsal hippocampus with planning behavior. The results reported, and the methods introduced, open the door to new and more detailed investigations of the neural mechanisms of planning, in the hippocampus and throughout the brain.

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bioRxiv2016

CART neuropeptide modulates the extended amygdalar CeA-vBNST circuit to gate expression of innate fear

Innate fear is critical for the survival of animals and is under tight homeostatic control. Deregulation of innate fear processing is thought to underlie pathological phenotypes including, phobias and panic disorders. Although central processing of conditioned fear has been extensively studied, the circuitry and regulatory mechanisms subserving innate fear remain relatively poorly defined.\n\nIn this study, we identify cocaine- and amphetamine-regulated transcript (CART) neuropeptide signalling in the central amygdala (CeA) - ventral bed nucleus of stria terminalis (vBNST) axis as a key modulator of innate fear expression. TMT, a component of fox faeces, induces a freezing response whose intensity is regulated by the extent of CART-signalling in the CeA neurons. Abrogation of CART activity in the CeA attenuates the freezing response and reduces activation of vBNST neurons. Conversely, ectopically elevated CART signalling in the CeA potentiates the fear response concomitant with enhanced vBNST activation. We show that local levels of CART signaling modulate the activation of CeA neurons by NMDA receptor mediated glutamatergic inputs, in turn, regulating activity in the vBNST.\n\nThis study identifies the extended amygdalar CeA-vBNST circuit as a CART modulated axis encoding innate fear. CART signaling regulates the glutamatergic excitatory drive in the CeA-vBNST circuit, in turn, gating the expression of the freezing response to TMT.

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bioRxiv2016

Dynamics of Cortical Dendritic Membrane Potential and Spikes in Freely Behaving Rats

Neural activity in vivo is primarily measured using extracellular somatic spikes, which provide limited information about neural computation. Hence, it is necessary to record from neuronal dendrites, which generate dendritic action potentials (DAP) and profoundly influence neural computation and plasticity. We measured neocortical sub- and supra-threshold dendritic membrane potential (DMP) from putative distal-most dendrites using tetrodes in freely behaving rats over multiple days with a high degree of stability and sub-millisecond temporal resolution. DAP firing rates were several fold larger than somatic rates. DAP rates were modulated by subthreshold DMP fluctuations which were far larger than DAP amplitude, indicting hybrid, analog-digital coding in the dendrites. Parietal DAP and DMP exhibited egocentric spatial maps comparable to pyramidal neurons. These results have important implications for neural coding and plasticity.\n\nOne Sentence SummaryMeasurement of cortical dendritic membrane potential for several days in freely behaving rats reveals disproportionate dendritic spiking and analog and digital coding.

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bioRxiv2016

Super-resolution Imaging of Synaptic and Extra-synaptic Pools of AMPA Receptors with Different-sized Fluorescent Probes

Whether AMPA receptors (AMPARs) enter into neuronal synapses, by exocytosis from an internal pool, or by diffusion from an external membrane-bound pool, is hotly contested. 3D super-resolution fluorescent nanoscopy to measure the dynamics and placement of AMPAR is a powerful method for addressing this issue. However, probe size and accessibility to tightly packed spaces can be limiting. We have therefore labeled AMPARs with differently sized fluorophores: small organic fluorescent dyes (~ 4 nm), small quantum dots (sQD, ~10 nm in diameter), or big (commercial) quantum dots (bQD, ~ 20 nm in diameter). We then compared their diffusion rate, trajectories, and placement with respect to a postsynaptic density (PSD) protein, Homer 1c. Labeled with the small probes of sQDs or organic fluorophores, we find that AMPARs are located largely within PSDs (~73-93%), and generally reside in \"nanodomains\" with constrained diffusion. In contrast, when labeled with bQDs, only 5-10% of AMPARs are within PSDs. The results can be explained by relatively free access, or lack thereof, to synaptic clefts of the AMPARs when labeled with small or big probes, respectively. This implies that AMPARs primarily enter PSDs soon after their exocytosis and not from a large diffusive pool of extrasynaptic AMPARs.

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