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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

Inhibitory neuron diversity originates from cardinal classes shared across germinal zones.

Diverse subsets of cortical interneurons play a particularly important role in the stability of the neural circuits underlying cognitive and higher order brain functions, yet our understanding of how this diversity is generated is far from complete. We applied massively parallel single-cell RNA-seq to profile a developmental time course of interneuron development, measuring the transcriptomes of over 60,000 progenitors during their maturation in the ganglionic eminences and embryonic migration into the cortex. While diversity within mitotic progenitors is largely driven by cell cycle and differentiation state, we observed sparse eminence-specific transcription factor expression, which seeds the emergence of later cell diversity. Upon becoming postmitotic, cells from all eminences pass through one of three precursor states, one of which represents a cortical interneuron ground state. By integrating datasets across developmental timepoints, we identified transcriptomic heterogeneity in interneuron precursors representing the emergence of four cardinal classes (Pvalb, Sst, Id2 and Vip), which further separate into subtypes at different timepoints during development. Our analysis revealed that the ASD-associated transcription factor Mef2c discriminates early Pvalb-precursors in E13.5 cells, and removal of Mef2c confirms its essential role for Pvalb interneuron development. These findings shed new light on the molecular diversification of early inhibitory precursors, and suggest gene modules that may link developmental specification with the etiology of neuropsychiatric disorders.

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bioRxiv2017

Effective synaptic interactions in subsampled nonlinear networks with strong coupling

A major obstacle to understanding neural coding and computation is the fact that experimental recordings typically sample only a small fraction of the neurons in a circuit. Measured neural properties are skewed by interactions between recorded neurons and the \"hidden\" portion of the network. To properly interpret neural data and determine how biological structure gives rise to neural circuit function, we thus need a better understanding of the relationships between measured effective neural properties and the true underlying physiological properties. Here, we focus on how the effective spatiotemporal dynamics of the synaptic interactions between neurons are reshaped by coupling to unobserved neurons. We find that the effective interactions from a pre-synaptic neuron r' to a post-synaptic neuron r can be decomposed into a sum of the true interaction from r' to r plus corrections from every directed path from r' to r through unobserved neurons. Importantly, the resulting formula reveals when the hidden units have--or do not have--major effects on reshaping the interactions among observed neurons. As a particular example of interest, we derive a formula for the impact of hidden units in random networks with \"strong\" coupling--connection weights that scale with [Formula], where N is the network size, precisely the scaling observed in recent experiments. With this quantitative relationship between measured and true interactions, we can study how network properties shape effective interactions, which properties are relevant for neural computations, and how to manipulate effective interactions.

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bioRxiv2017

Filter-banks and artificial intelligence in seizure detection using electroencephalograms

Epilepsy is the most typical neurological disease in the world, and it implies an expensive and specialized diagnosis process based on electroencephalograms and video recordings. We developed a method that only requires the brainwave provided by the difference between two standard-located electrodes. Our proposed technique separates the original signal using a filter array with three different types of filters, and then extracts several features based on information theory and statistical information. In our study, we found that only 10 characteristics, of which the most important are related to higher frequencies, are required to offer an accuracy of 94%, a specificity of 95% and a sensitivity of 87% using C4.5 decision trees.

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bioRxiv2017

A population MRI brain template and analysis tools for the macaque

The use of standard anatomical templates is common in human neuroimaging, as it facilitates data analysis and comparison across subjects and studies. For non-human primates, previous in vivo templates have lacked sufficient contrast to reliably validate known anatomical brain regions and have not provided tools for automated single-subject processing. Here we present the \"National Institute of Mental Health Macaque Template\", or NMT for short. The NMT is a high-resolution in vivo MRI template of the average macaque brain generated from 31 subjects, as well as a neuroimaging tool for improved data analysis and visualization. From the NMT volume, we generated maps of tissue segmentation and cortical thickness. Surface reconstructions and transformations to previously published digital brain atlases are also provided. We further provide an analysis pipeline using the NMT that automates and standardizes the time-consuming processes of brain extraction, tissue segmentation, and morphometric feature estimation for anatomical scans of individual subjects. The NMT and associated tools thus provide a common platform for precise single-subject data analysis and for characterizations of neuroimaging results across subjects and studies.

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bioRxiv2017

Cocaine place conditioning strengthens location-specific hippocampal inputs to the nucleus accumbens

Conditioned place preference (CPP) is a widely used model of addiction-related behavior whose underlying mechanism is not understood. In this study, we used dual site silicon probe recordings in freely moving mice to examine interactions between the hippocampus and nucleus accumbens in cocaine CPP. We found that CPP was associated with recruitment of nucleus accumbens medium spiny neurons to fire in the cocaine-paired location, and this recruitment was driven predominantly by selective strengthening of hippocampal inputs arising from place cells that encode the cocaine-paired location. These findings provide in vivo evidence that the synaptic potentiation in the accumbens caused by repeated cocaine administration preferentially affects inputs that were active at the time of drug exposure. This provides a potential physiological mechanism by which drug use becomes associated with specific environmental contexts.

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bioRxiv2017

How activity dependent feedback inhibition may maintain head direction signals in mouse presubiculum

Orientation in space is represented in specialized brain circuits. Persistent head direction signals are transmitted from anterior thalamus to the presubiculum, but the identity of the presubicular target neurons, their connectivity and function in local microcircuits are unknown. Here we examine how thalamic afferents recruit presubicular principal neurons and Martinotti interneurons and the ensuing synaptic interactions between these cells. Pyramidal neuron activation of Martinotti cells in superficial layers is strongly facilitating such that high frequency head directional stimulation efficiently unmutes synaptic excitation. Martinotti cell feedback plays a dual role: precisely timed spikes may not inhibit the firing of in-tune head direction cells, while exerting lateral inhibition. Autonomous attractor dynamics emerge from a modeled network implementing wiring motifs and timing sensitive synaptic interactions in the pyramidal - Martinotti cell feedback loop. This inhibitory microcircuit is therefore tuned to refine and maintain head direction information in the presubiculum.

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bioRxiv2017

Mindfulness and dynamic functional neural connectivity in children and adolescents

BackgroundMindfulness is a non-judgmental, present-centered awareness and acceptance of ones thoughts, feelings, and bodily sensations. Interventions that promote mindfulness consistently show salutatory effects on cognition and psychological wellbeing in adults, and more recently, in children. Understanding the neurobiological mechanisms underlying mindfulness in children may allow for more judicious application of these techniques in clinical and educational settings.\n\nMethodsUsing multi-echo/multi-band fMRI, we measured resting-state connectivity and tested the hypothesis that the association between mindfulness and anxiety in children (N=42) will relate to static and dynamic interactions between large-scale neural networks considered central to neurocognitive functioning and implicated in mindfulness in adults (default mode [DMN], salience and emotion [SEN], and central executive networks [CEN]).\n\nResultsMindfulness was related to dynamic but not static connectivity in children. Specifically, more mindful children transitioned more between brain states over the course of the scan, spent overall less time in a certain connectivity state (state 2), and showed a state-specific reduction in SEN-right CEN connectivity (state 4). Results of a separate measure of present-focused thought during the resting-state were consistent with these results, suggesting state-trait convergence. Finally, the number of state transitions mediated the link between higher mindfulness and lower anxiety, suggesting that flexibility in transitioning between neural states may bridge the well-established link between mindfulness and anxiety in children.\n\nConclusionsResults provide new insights into neural mechanisms underlying benefits of mindfulness on psychological health in children, and suggest that mindfulness relates to functional neural dynamics and interactions between neurocognitive networks, over time.

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bioRxiv2017

Very Early Responses to Colour Stimuli Detected in Prestriate Visual Cortex by Magnetoencephalography (MEG)

Previous studies with the visual motion and form systems show that visual stimuli belonging to these categories trigger much earlier latency responses from the visual cortex than previously supposed and that the source of the earliest signals can be located in either the prestriate cortex or in both the striate (V1) and prestriate cortex. This is consistent with the known anatomical connections since, in addition to the classical retino-geniculo-striate cortex input, there are direct anatomical inputs from both the lateral geniculate nucleus and the pulvinar that reach the prestriate visual cortex without passing through V1. In pursuing our studies, we thought it especially interesting to study another cardinal visual attribute, namely colour, to learn whether colour stimuli also provoke very early responses, at less than 50 ms, from visual cortex. To address the question, we asked participants to view stimuli that changed in colour and used magneto-encephalography to detect very early responses (< 50 ms) in the occipital visual cortex. Our results show that coloured stimuli also provoke an early cortical response (M30), with an average peak time at 30.8 ms, thus bringing the colour system into line with the visual motion and form systems. We conclude that colour signals reach visual cortex, including prestriate visual cortex, earlier than previously supposed.\n\nKey points summaryO_LIWe measured visual evoked responses to colour stimuli using magnetoencephalography.\nC_LIO_LIAn early response was identified, at around 30 ms after stimulation.\nC_LIO_LIThe sources of the response were estimated to be in prestriate cortex.\nC_LIO_LIColour signals thus appear to evoke very early cortical responses, just like form and motion signals.\nC_LI\n\nAbbreviations list

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bioRxiv2017

Multiple roles of PIWIL1 in mouse neocorticogenesis

PIWI-interacting RNAs (piRNAs) and their associated PIWI proteins play an important role in repressing transposable elements in animal germlines. However, little is known about the function of PIWI proteins and piRNAs in the developing brain. Here, we investigated the role of an important PIWI family member, Piwi-like protein 1 (Piwil1; also known as Miwi in mouse) in the developing mouse neocortex. Using a Piwil1 knock-out (Piwil1 KO) mouse strain, we found that Piwil1 is essential for several steps of neocorticogenesis, including neocortical cell cycle, neuron migration and dendritogenesis. Piwil1 deletion resulted in increased cell cycle re-entry at embryonic day 17 (E17) when predominantly intracortically projecting neurons are being produced. Prenatal Piwil1 deletion increased the number of Pax6+ radial glia at postnatal day 0 (P0). Furthermore, Piwil1 deletion disrupted migration of Satb2+ neurons within deep layers at E17, P0 and P7. Satb2+ neurons showed increased co-localization with Bcl11b (also known as Ctip2), marker of subcortically projecting neurons. Piwil1 knockouts had disrupted neocortical circuitry represented by thinning of the corpus callosum and altered dendritogenesis. We further investigated if Piwil1 deletion disrupted expression levels of neocortical piRNAs by small RNA-sequencing in neocortex. We did not find differential expression of piRNAs in the neocortices of Piwil1 KO, while differences were observed in other Piwil1 KO tissues. This result suggests that Piwil1 may act independently of piRNAs and have novel roles in higher cognitive centers, such as neocortex. In addition, we report a screen of piRNAs derived from tRNA fragments in developing neocortices. Our result is the first report of selective subsets of piRNAs and tRNA fragments in developing prenatal neocortices and helps clarify some outstanding questions about the role of the piRNA pathway in the brain.

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bioRxiv2017

The GridCAT: A toolbox for automated analysis of human grid cell codes in fMRI

Human fMRI studies examining the putative firing of grid cells (i.e., the grid code) suggest that this cellular mechanism supports not only spatial navigation, but also more abstract cognitive processes. This research area, however, remains relatively unexplored, perhaps us to the complexities of data analysis. To overcome this, we have developed the Matlab-based Grid Code Analysis Toolbox (GridCAT), providing a graphical user interface, and open-source code, for the analysis of fMRI data. The GridCAT performs all analyses, from estimation and fitting of the grid code in the general linear model, to the generation of grid code metrics and plots. Moreover, it is flexible in allowing the specification of bespoke analysis pipelines; example data are provided to demonstrate the GridCATs main functionality. We believe the GridCAT is essential to opening this research area to the imaging community, and helping to elucidate the role of human grid codes in higher-order cognitive processes.\n\nHighlightsO_LIThe putative firing of grid cells (i.e., the grid code) can be examined using fMRI\nC_LIO_LINecessary steps for grid code analysis are reviewed\nC_LIO_LIThe Matlab-based grid code analysis toolbox (GridCAT) is introduced\nC_LIO_LIAutomated grid code analysis can be conducted either via a graphical user interface or open-source code\nC_LIO_LIA detailed manual and an example dataset are provided\nC_LI

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bioRxiv2017

Reward and punishment enhance motor adaptation in stroke

The effects of motor learning, such as motor adaptation, in stroke rehabilitation are often transient, thus mandating approaches that enhance the amount of learning and retention. Previously, we showed in young individuals that reward-and punishment-feedback have dissociable effects on motor adaptation, with punishment improving adaptation and reward enhancing retention. If these findings were able to generalise to stroke patients, they would provide a way to optimize motor learning in these patients. Therefore, we tested this in 45 chronic stroke patients allocated in three groups. Patients performed reaching movements with their paretic arm with a robotic manipulandum. After training (day 1), day 2 involved adapting to a novel force-field. During this adaptation phase, patients received performance-based feedback according to the group they were allocated: reward, punishment or no feedback (neutral). On day 3, patients readapted to the force-field but all groups now received neutral feedback. All patients adapted, with reward and punishment groups displaying greater adaptation and readaptation than the neutral group, irrespective of demographic, cognitive or functional differences. Remarkably, the reward and punishment groups adapted to similar degree as healthy controls. Finally, the reward group showed greater retention. This study provides, for the first time, evidence that reward and punishment can enhance motor adaptation in stroke patients. Further research on reinforcement-based motor learning regimes is warranted to translate these promising results into clinical practice and improve motor rehabilitation outcomes in stroke patients.

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bioRxiv2017

Medial prefrontal cortical NMDA receptors regulate depression-like behavior and dictate limbic thalamus innervation

Depression is a pervasive and debilitating neuropsychiatric disorder. A single, low dose of the NMDA receptor (NMDAR) antagonist ketamine elicits a long-lasting antidepressant response in patients with treatment-resistant major depressive disorder. Developing mechanistic understanding of how NMDAR antagonism alters synapse and circuit function is pivotal to developing translatable, circuit-based therapies for depression. Here using viral vectors, anatomical tracing, fMRI, and optogenetic-assisted circuit analysis, we assessed the role of the NMDAR subunit GluN2B in regulating cellular, synaptic, and circuit-level function and depression-related behavior. We demonstrate that post-developmental deletion of GluN2B from pyramidal neurons in medial prefrontal cortex enhances action potential output in a synaptic activity-dependent manner. GluN2B deletion dictates functional connectivity between mPFC and limbic thalamus but not ventral hippocampus and elicits antidepressant-like behavior. Our findings demonstrate that postsynaptic GluN2B exerts input-specific control of pyramidal neuron innervation, and identify a novel circuit for regulating depression-like behaviors in mice.

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bioRxiv2017

Visibility graphs for fMRI data: multiplex temporal graphs and their modulations across resting state networks

Visibility graphs have proved a convenient tool to describe the dynamics of time series, finding applications across several disciplines. Recently an approach has been proposed to extend this framework to multivariate time series, allowing a novel way to describe collective dynamics. Here we test their application to fMRI time series and give some explanation on why we think that its an interesting thing to do.

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bioRxiv2017

Synaptic input sequence discrimination on behavioral time-scales mediated by reaction-diffusion chemistry in dendrites

Sequences of events are ubiquitous in sensory, motor, and cognitive function. Key computational operations, including pattern recognition, event prediction, and plasticity, involve neural discrimination of spatio-temporal sequences. Here we show that synaptically-driven reaction-diffusion pathways on dendrites can perform sequence discrimination on behaviorally relevant time-scales. We used abstract signaling models to show that this selectivity arises when inputs at successive locations are aligned with, and amplified by, propagating chemical waves triggered by previous inputs. We incorporated biological detail using sequential synaptic input onto spines in morphologically, electrically, and chemically detailed pyramidal neuronal models. Again, sequences were recognized, and local channel modulation on the length-scale of sequence input could elicit changes in neuronal firing. We predict that dendritic sequence-recognition zones occupy 5 to 20 microns and recognize time-intervals of 0.2 to 5s. We suggest that this mechanism provides highly parallel and selective neural computation in a functionally important time range.

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bioRxiv2017

Neural Encoding of Auditory Features during Music Perception and Imagery: Insight into the Brain of a Piano Player

It remains unclear how the human cortex represents spectrotemporal sound features during auditory imagery, and how this representation compares to auditory perception. To assess this, we recorded electrocorticographic signals from an epileptic patient with proficient music ability in two conditions. First, the participant played two piano pieces on an electronic piano with the sound volume of the digital keyboard on. Second, the participant replayed the same piano pieces, but without auditory feedback, and the participant was asked to imagine hearing the music in his mind. In both conditions, the sound output of the keyboard was recorded, thus allowing precise time-locking between the neural activity and the spectrotemporal content of the music imagery. For both conditions, we built encoding models to predict high gamma neural activity (70-150Hz) from the spectrogram representation of the recorded sound. We found robust similarities between perception and imagery - in frequency and temporal tuning properties in auditory areas.\n\nAbbreviations

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bioRxiv2017

BRAPH: A Graph Theory Software for the Analysis of Brain Connectivity

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH - BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimers disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinsons patients with mild cognitive impairment.

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bioRxiv2017

A probabilistic approach to discovering dynamic full-brain functional connectivity patterns

Recent research shows that the covariance structure of functional magnetic resonance imaging (fMRI) data - commonly described as functional connectivity - can change as a function of the participants cognitive state (for review see [35]). Here we present a Bayesian hierarchical matrix factorization model, termed hierarchical topographic factor analysis (HTFA), for efficiently discovering full-brain networks in large multi-subject neuroimaging datasets. HTFA approximates each subjects network by first re-representing each brain image in terms of the activities of a set of localized nodes, and then computing the covariance of the activity time series of these nodes. The number of nodes, along with their locations, sizes, and activities (over time) are learned from the data. Because the number of nodes is typically substantially smaller than the number of fMRI voxels, HTFA can be orders of magnitude more efficient than traditional voxel-based functional connectivity approaches. In one case study, we show that HTFA recovers the known connectivity patterns underlying a collection of synthetic datasets. In a second case study, we illustrate how HTFA may be used to discover dynamic full-brain activity and connectivity patterns in real fMRI data, collected as participants listened to a story. In a third case study, we carried out a similar series of analyses on fMRI data collected as participants viewed an episode of a television show. In these latter case studies, we found that the HTFA-derived activity and connectivity patterns can be used to reliably decode which moments in the story or show the participants were experiencing. Further, we found that these two classes of patterns contained partially non-overlapping information, such that decoders trained on combinations of activity-based and dynamic connectivity-based features performed better than decoders trained on activity or connectivity patterns alone. We replicated this latter result with two additional (previously developed) methods for efficiently characterizing full-brain activity and connectivity patterns.

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bioRxiv2017

Robust and efficient coding with grid cells

The neuronal code arising from the coordinated population activity of grid cells in the rodent entorhinal cortex can uniquely represent space across large distances but the precise conditions for efficient coding are unknown. Here we present a number-theoretic analysis of grid coding and derive an upper bound on the distance that a population of grid cells can represent without error. We show that in the absence of neuronal noise, the capacity of the system would be extremely sensitive to the choice of the grid periods. However, when the accuracy of the representation is limited by neuronal noise, the capacity becomes gradually more robust against the choice of grid scales as the number of modules increases and remains near optimal even for random scale choices. Our study reveals that robust and efficient coding can be achieved without parameter tuning in the case of grid cell representation.

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