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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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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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bioRxiv2016

Peripheral Htt silencing does not ameliorate central signs of disease in the B6.HttQ111/+ mouse model of Huntington’s Disease

Huntingtons disease (HD) is an autosomal dominant neurodegenerative disease whose neuropathological signature is a selective loss of medium spiny neurons in the striatum. Despite this selective neuropathology, the mutant protein (huntingtin) is found in virtually every cell so far studied, and, consequently, phenotypes are observed in a wide range of organ systems both inside and outside the central nervous system. We, and others, have suggested that peripheral dysfunction could contribute to the rate of progression of striatal phenotypes of HD. To test this hypothesis, we lowered levels of huntingtin by treating mice with antisense oligonucleotides (ASOs) targeting the murine Huntingtin gene. To study the relationship between peripheral huntingtin levels and striatal HD phenotypes, we utilized a knock-in model of the human HD mutation (the B6.HttQ111/+ mouse). We treated mice with ASOs from 2-10 months of age, a time period over which significant HD-relevant signs progressively develop in the brains of HttQ111+ mice. Peripheral treatment with ASOs led to persistent reduction of huntingtin protein in peripheral organs, including liver, brown and white adipose tissues. This reduction was not associated with alterations in the severity of HD-relevant signs in the striatum of HttQ111/+ mice at the end of the study, including transcriptional dysregulation, the accumulation of neuronal intranuclear inclusions, and behavioral changes such as subtle hypoactivity and reduced exploratory drive. These results suggest that the amount of peripheral reduction achieved in the current study does not significantly impact the progression of HD-relevant signs in the central nervous system.

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bioRxiv2016

Delivery of BACE1 siRNA mediated by TARBP-BTP fusion protein reduces β-amyloid deposits in a transgenic mouse model of Alzheimers disease

Systemic delivery of nucleic acids to the central nervous system (CNS) is a major challenge for the development of RNA interference-based therapeutics due to absence of stability, target specificity, non-permeability to the blood-brain barrier (BBB), and mainly due to lack of suitable carriers. Using a designed bi-functional fusion protein TARBP-BTP, very recently we demonstrated knockdown of target genes in the brain of both A{beta}PP-PS1 (Alzheimers disease, AD) and wildtype C57BL/6 mice upon systemic delivery of a single dose of siRNA. In this report, we further substantiate this hypothesis through an extended study in A{beta}PP-PS1 mice, which upon treatment with seven doses of {beta}-secretase APP cleaving Enzyme 1 (BACE1) siRNA, led to target-specific effects in the mouse brain. Concomitant gene silencing and consequent reduction in plaque load in the cerebral cortex and hippocampus (>60%) in mice treated with TARBP-BTP:siRNA complex further led to improvement in spatial learning and memory, which was assessed and verified through Morris Water Maze test that revealed significant improvement in cognitive function. Moreover, the treatment did not induce any adverse effects as revealed by the histopathology of different organs. The work validates the efficiency of TARBP-BTP fusion protein as an efficient mediator of RNAi giving considerable scope for future intervention of neurodegenerative disorders of the CNS through the use of short nucleic acids as gene specific inhibitors.

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bioRxiv2016

A more accurate account of the effect of k-space sampling and signal decay on the effective spatial resolution in functional MRI

The effects of k-space sampling and signal decay on the effective spatial resolution of MRI and functional MRI (fMRI) are commonly assessed by means of the magnitude point-spread function (PSF), defined as the absolute values (magnitudes) of the complex MR imaging PSF. It is commonly assumed that this magnitude PSF signifies blurring, which can be quantified by its full-width at half-maximum (FWHM). Here we show that the magnitude PSF fails to accurately represent the true effects of k-space sampling and signal decay.\n\nFirstly, a substantial part of the width of the magnitude PSF is due to MRI sampling per se. This part is independent of any signal decay and its effect depends on the spatial frequency composition of the imaged object. Therefore, it cannot always be expected to introduce blurring. Secondly, MRI reconstruction is typically followed by taking the absolute values (magnitude image) of the reconstructed complex image. This introduces a non-linear stage into the process of image formation. The complex imaging PSF does not fully describe this process, since it does not reflect the stage of taking the magnitude image. Its corresponding magnitude PSF fails to correctly describe this process, since convolving the original pattern with the magnitude PSF is different from the true process of taking the absolute following a convolution with the complex imaging PSF. Lastly, signal decay can have not only a blurring, but also a high-pass filtering effect. This cannot be reflected by the strictly positive width of the magnitude PSF.\n\nAs an alternative, we propose to first approximate the MRI process linearly. We then model the linear approximation by decomposing it into a signal decay-independent MR sampling part and an approximation of the signal decay effect. We approximate the latter as a convolution with a Gaussian PSF or, if the effect is that of high-pass filtering, as reversing the effect of a convolution with a Gaussian PSF. We show that for typical high-resolution fMRI at 7 Tesla, signal decay in Spin-Echo has a moderate blurring effect (FWHM = 0.89 voxels, corresponds to 0.44 mm for 0.5 mm wide voxels). In contrast, Gradient-Echo acts as a moderate high-pass filter that can be interpreted as reversing a Gaussian blurring with FWHM = 0.59 voxels (0.30 mm for 0.5 mm wide voxels). Our improved approximations and findings hold not only for Gradient-Echo and Spin-Echo fMRI but also for GRASE and VASO fMRI. Our findings support the correct planning, interpretation, and modeling of high-resolution fMRI.

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bioRxiv2016

The hippocampus as a predictive map

A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity, and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore, we argue that entorhinal grid cells encode a low-dimensional basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.

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bioRxiv2016

Fast Assembling of Neuron Fragments in Serial 3D Sections

AbstractReconstructing neurons from 3D image-stacks of serial sections of thick brain tissue is very time-consuming and often becomes a bottleneck in high-throughput brain mapping projects. We developed NeuronStitcher, a software suite for stitching non-overlapping neuron fragments reconstructed in serial 3D image sections. With its efficient algorithm and user-friendly interface, NeuronStitcher has been used successfully to reconstruct very large and complex human and mouse neurons.

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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.

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