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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2020

Solving the where problem in neuroanatomy: a generative framework with learned mappings to register multimodal, incomplete data into a reference brain

A current focus of research in neuroscience is to enumerate, map and annotate neuronal cell types in whole vertebrate brains using different modalities of data acquisition. Mapping these molecular and anatomical datasets into a common reference space remains a key challenge. While several brain-to-atlas mapping workflows exist, they do not adequately address challenges of modern high throughput neuroimaging, including multimodal and multiscale signals, missing data or non reference signals, and geometric quantification of individual variation. Our solution is to implement a generative statistical model that describes the likelihood of imaging data given a sequence of transforms of an atlas image, and a framework for maximum a posteriori estimation of unknown parameters capturing the issues listed above. The key idea in our approach is to minimize the difference between synthetic image volumes and real data over these parameter. Rather than merely using mappings as a "normalization" step, we implement tools for using their local metric changes as an opportunity for geometric quantification of technical and biological sources of variation in an unprecedented manner. While the framework is used to compute pairwise mappings, our approach particularly allows for easy compositions across chains of multimodality datasets. We apply these methods across a broad range of datasets including various combinations of in-vivo and ex-vivo MRI, 3D STP and fMOST data sets, 2D serial histology sections, and brains processed for snRNAseq with tissue partially removed. We show biological utility by quantifying cell density and diffeomorphic characterization of brain shape fluctuations across biological covariates. We note that the magnitude of individual variation is often greater than differences between different sample preparation techniques. To facilitate community accessibility, we implement our algorithm as open source, include a web based framework, and implement input and output dataset standards. Our work establishes a quantitative, scalable and streamlined workflow for unifying a broad spectrum of multi-modal whole-brain light microscopic data volumes into a coordinate-based atlas framework. This work enables large scale integration of whole brain data sets that are essential in modern neuroscience.

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bioRxiv2020

A new sulcal landmark identifying anatomical and functional gradients in human lateral prefrontal cortex

Understanding the relationship between neuroanatomy and function in portions of human cortex that are expanded compared to other mammals such as lateral prefrontal cortex (LPFC) is of major interest in systems and cognitive neuroscience. When considering neuroanatomical-functional relationships in LPFC, shallow indentations in cortex known as tertiary sulci have been largely ignored. Here, by implementing a multi-modal approach and manually defining 936 neuroanatomical structures in 72 hemispheres (males and females), we show that a subset of these overlooked tertiary sulci serve as a meso-scale link between microstructural (myelin content) and functional (network connectivity) properties of human LPFC in individuals. For example, the posterior middle frontal sulcus (pmfs) is a tertiary sulcus with three components that differ in their myelin content, resting state connectivity profiles, and engagement across meta-analyses of 83 cognitive tasks. Further, generating microstructural profiles of myelin content across cortical depths for each pmfs component and the surrounding middle frontal gyrus (MFG) shows that both gyral and sulcal components of the MFG have greater myelin content in deeper compared to superficial layers and that the myelin content in superficial layers of the gyral components is greater than sulcal components. These findings support a classic, yet largely unconsidered theory that tertiary sulci may serve as landmarks in association cortices, as well as a modern cognitive neuroscience theory proposing a functional hierarchy in LPFC. As there is a growing need for computational tools that automatically define tertiary sulci throughout cortex, we share pmfs probabilistic sulcal maps with the field. Significance statementLateral prefrontal cortex (LPFC) is critical for higher-order cognitive control and goal-directed behavior and is disproportionately expanded in the human brain. However, relationships between fine-scale neuroanatomical structures largely specific to hominoid cortex and functional properties of LPFC remain elusive. Here, we show that these structures, which have been largely neglected throughout history, surprisingly serve as markers for anatomical and functional organization in human LPFC. These findings have theoretical, methodological, developmental, and evolutionary implications for improved understanding of neuroanatomical-functional relationships not only in LPFC, but also in association cortices more broadly. Finally, these findings ignite new questions regarding how morphological features of these neglected neuroanatomical structures contribute to functions of association cortices that are critical for human-specific aspects of cognition.

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bioRxiv2020

Functional MRI of large scale activity in behaving mice

Behaviour involves complex dynamic interactions across many brain regions. Detecting whole-brain activity in mice performing sophisticated behavioural tasks could facilitate insights into distributed processing underlying behaviour, guide local targeting, and help bridge the disparate spatial scales between rodent and human studies. Here, we present a comprehensive approach for recording brain-wide activity with functional magnetic resonance imaging (fMRI) compatible with a wide range of behavioural paradigms and neuroscience questions. We introduce hardware and procedural advances to allow multi-sensory, multi-action behavioural paradigms in the scanner. We identify signal artefacts arising from task-related body movements and propose novel strategies to reduce them. We validate and explore our approach in a 4-odour classical conditioning and a visually-guided operant task, illustrating how it can be used to extract information so far inaccessible to rodent behaviour studies. Our work paves the way for future studies combining fMRI and local circuit techniques during complex behaviour to tackle multi-scale behavioural neuroscience questions.

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bioRxiv2020

Comparative performance of mutual information and transfer entropy for analyzing the balance of information flow and energy consumption at synapses

Information theory has become an essential tool of modern neuroscience. It can however be difficult to apply in experimental contexts when acquisition of very large datasets is prohibitive. Here, we compare the relative performance of two information theoretic measures, mutual information and transfer entropy, for the analysis of information flow and energetic consumption at synapses. We show that transfer entropy outperforms mutual information in terms of reliability of estimates for small datasets. However, we also show that a detailed understanding of the underlying neuronal biophysics is essential for properly interpreting the results obtained with transfer entropy. We conclude that when time and experimental conditions permit, mutual information might provide an easier to interpret alternative. Finally, we apply both measures to the study of energetic optimality of information flow at thalamic relay synapses in the visual pathway. We show that both measures recapitulate the experimental finding that these synapses are tuned to optimally balance information flowing through them with the energetic consumption associated with that synaptic and neuronal activity. Our results highlight the importance of conducting systematic computational studies prior to applying information theoretic tools to experimental data. Author summaryInformation theory has become an essential tool of modern neuroscience. It is being routinely used to evaluate how much information flows from external stimuli to various brain regions or individual neurons. It is also used to evaluate how information flows between brain regions, between neurons, across synapses, or in neural networks. Information theory offers multiple measures to do that. Two of the most popular are mutual information and transfer entropy. While these measures are related to each other, they differ in one important aspect: transfer entropy reports a directional flow of information, as mutual information does not. Here, we proceed to a systematic evaluation of their respective performances and trade-offs from the perspective of an experimentalist looking to apply these measures to binarized spike trains. We show that transfer entropy might be a better choice than mutual information when time for experimental data collection is limited, as it appears less affected by systematic biases induced by a relative lack of data. Transmission delays and integration properties of the output neuron can however complicate this picture, and we provide an example of the effect this has on both measures. We conclude that when time and experimental conditions permit, mutual information - especially when estimated using a method referred to as the direct method - might provide an easier to interpret alternative. Finally, we apply both measures in the biophysical context of evaluating the energetic optimality of information flow at thalamic relay synapses in the visual pathway. We show that both measures capture the original experimental finding that those synapses are tuned to optimally balance information flowing through them with the concomitant energetic consumption associated with that synaptic and neuronal activity.

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bioRxiv2020

Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data

Transfer entropy (TE) is a widely used measure of directed information flows in a number of domains including neuroscience. Many real-world time series in which we are interested in information flows come in the form of (near) instantaneous events occurring over time, including the spiking of biological neurons, trades on stock markets and posts to social media. However, there exist severe limitations to the current approach to TE estimation on such event-based data via discretising the time series into time bins: it is not consistent, has high bias, converges slowly and cannot simultaneously capture relationships that occur with very fine time precision as well as those that occur over long time intervals. Building on recent work which derived a theoretical framework for TE in continuous time, we present an estimation framework for TE on event-based data and develop a k-nearest-neighbours estimator within this framework. This estimator is provably consistent, has favourable bias properties and converges orders of magnitude more quickly than the discrete-time estimator on synthetic examples. We also develop a local permutation scheme for generating null surrogate time series to test for the statistical significance of the TE and, as such, test for the conditional independence between the history of one point process and the updates of another -- signifying the lack of a causal connection under certain weak assumptions. Our approach is capable of detecting conditional independence or otherwise even in the presence of strong pairwise time-directed correlations. The power of this approach is further demonstrated on the inference of the connectivity of biophysical models of a spiking neural circuit inspired by the pyloric circuit of the crustacean stomatogastric ganglion, succeeding where previous related estimators have failed. AUTHOR SUMMARYTransfer Entropy (TE) is an information-theoretic measure commonly used in neuroscience to measure the directed statistical dependence between a source and a target time series, possibly also conditioned on other processes. Along with measuring information flows, it is used for the inference of directed functional and effective networks from time series data. The currently-used technique for estimating TE on neural spike trains first time-discretises the data and then applies a straightforward or "plug-in" information-theoretic estimation procedure. This approach has numerous drawbacks: it is very biased, it cannot capture relationships occurring on both fine and large timescales simultaneously, converges very slowly as more data is obtained, and indeed does not even converge to the correct value. We present a new estimator for TE which operates in continuous time, demonstrating via application to synthetic examples that it addresses these problems, and can reliably differentiate statistically significant flows from (conditionally) independent spike trains. Further, we also apply it to more biologically-realistic spike trains obtained from a biophysical model of the pyloric circuit of the crustacean stomatogastric ganglion; our correct inference of the underlying connection structure here provides an important validation for our approach where similar methods have previously failed

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bioRxiv2020

Probing machine-learning classifiers using noise, bubbles, and reverse correlation

BackgroundMany scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. However, such tools often remain black-boxes: they lack interpretability. A lack of interpretability has obvious ethical implications for clinical applications, but it also limits the usefulness of these tools to formulate new theoretical hypotheses. New methodWe propose a simple and versatile method to help characterize the information used by a classifier to perform its task. Specifically, noisy versions of training samples or, when the training set is unavailable, custom-generated noisy samples, are fed to the classifier. Multiplicative noise, so-called "bubbles", or additive noise are applied to the input representation. Reverse correlation techniques are then adapted to extract either the discriminative information, defined as the parts of the input dataset that have the most weight in the classification decision, and represented information, which correspond to the input features most representative of each category. ResultsThe method is illustrated for the classification of written numbers by a convolutional deep neural network; for the classification of speech versus music by a support vector machine; and for the classification of sleep stages from neurophysiological recordings by a random forest classifier. In all cases, the features extracted are readily interpretable. Comparison with Existing MethodsQuantitative comparisons show that the present method can match state-of-the art interpretation methods for convolutional neural networks. Moreover, our method uses an intuitive and well-established framework in neuroscience, reverse correlation. It is also generic: it can be applied to any kind of classifier and any kind of input data. ConclusionsWe suggest that the method could provide an intuitive and versatile interface between neuroscientists and machine-learning tools. HighlightsO_LIThe heuristics of black-box classifiers can be probed with noisy inputs C_LIO_LIThe relevant features can be visualised in the input representation space C_LIO_LIThe method applies to any kind of data such as 2D images or 1D time series C_LIO_LIIt applies to any classifier such as deep neural networks, support vector machines, random forests C_LI

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bioRxiv2020

Characterisation of Geometric Variance in the Epithelial Nerve Net of the Ctenophore Pleurobrachia pileus

Neuroscience currently lacks a diverse repertoire of model organisms, resulting in an incomplete understanding of what principles of neural function generalise and what are species-specific. Ctenophores display many neurobiological and experimental features which make them a promising candidate to fill this gap. They possess a nerve net distributed across their outer body surface, just beneath the epithelial layer. There is a long-held assumption that nerve nets are simple and random while lacking distinct organisational principles. We want to challenge this assumption and determine how stereotyped the structure of this network really is. We validated an approach to estimate body surface area in Pleurobrachia pileus using custom Optical Projection Tomography and Light Sheet Morphometry imaging systems. We used an antibody against tyrosylated -tubulin to visualise the nerve net in situ. We used an automated segmentation approach to extract the morphological features of the nerve net. We characterised organisational rules of the epithelial nerve net in P. pileus in animals of different sizes and at different regions of the body. We found that specific morphological features within the nerve net are largely un-changed during growth. These properties must be essential to the functionality of the nervous system and therefore are maintained during a change in body size. We have also established the principles of organisation of the network and showed that some of the geometric properties are variable across different parts of the body. This suggests that there may be different functions occurring in regions with different structural characteristics. This is the most comprehensive structural description of a nerve net to date. This study also demonstrates the amenability of the ctenophore P. pileus for whole organism network analysis and shows their promise as a model organism for neuroscience, which may provide insights into the foundational principles of nervous systems.

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bioRxiv2020

Systematic comparison and automated validation of detailed models of hippocampal neurons

Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and concluded that each of these models provides a good match to experimental results in some domains but not in others. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community. Author summary Anatomically and biophysically detailed neuronal models are useful tools in neuroscience because they allow the prediction of the behavior and the function of the studied cell type under circumstances that are hard to investigate experimentally. However, most detailed biophysical models have been built to capture a few selected properties of the real neuron, and it is often unknown how they would behave under different circumstances, or whether they can be used to successfully answer different scientific questions. To help the modeling community develop better neural models, and make the process of model building more reproducible and transparent, we developed a test suite that enables the comparison of the behavior of models of neurons in the rat hippocampus and their evaluation against experimental data. Applying our tests to several models available in the literature, we show that each model is able to capture some of the important properties of the real neuron but fails to match experimental data in other domains. We also use the test suite in the model development workflow of the European Human Brain Project to aid the construction of better models of hippocampal neurons and networks.

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bioRxiv2020

Cell Type-Specific Chromatin Accessibility Analysis in the Mouse and Human Brain

The Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) is becoming increasingly popular in the neuroscience field where chromatin regulation is thought to be involved in neurodevelopment, activity-dependent gene regulation, hormonal and environmental responses, and the pathophysiology of neuropsychiatric disorders. The advantages of using this assay include a small amount of material needed, relatively simple and fast protocol, and the ability to capture a range of gene regulatory elements with a single assay. However, with increasing interest in chromatin research, it is an imperative to have feasible, reliable assays that are compatible with a range of neuroscience study designs in both animals and humans. Here we tested three different protocols for neuronal chromatin accessibility analysis, including a varying brain tissue freezing method followed by fluorescent-activated nuclei sorting (FANS) and the ATAC-seq analysis. Our study shows that the cryopreservation method impacts the number of open chromatin regions that can be identified from frozen brain tissue using the cell-type specific ATAC-seq assay. However, we show that all three protocols generate consistent and robust data and enable the identification of functional regulatory elements, promoters and enhancers, in neuronal cells. Our study also implies that the broad biological interpretation of chromatin accessibility data is not significantly affected by the freezing condition. In comparison to the mouse brain analysis, we reveal the additional challenges of doing chromatin analysis on post mortem human brain tissue. However, we also show that these studies are revealing important cell type-specific information about gene regulation in the human brain. Overall, the ATAC-seq coupled with FANS is a powerful method to capture cell-type specific chromatin accessibility information in the mouse and human brain. Our study provides alternative brain preservation methods that generate high quality ATAC-seq data while fitting in different study designs, and further encourages the use of this method to uncover the role of epigenetic (dys)regulation in healthy and malfunctioning brain.

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bioRxiv2020

DNNBrain: a unifying toolbox for mapping deep neural networks and brains

Deep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks through an end-to-end deep learning strategy. Deep learning gives rise to data representations with multiple levels of abstraction; however, it does not explicitly provide any insights into the internal operations of DNNs. Its success appeals to neuroscientists not only to apply DNNs to model biological neural systems, but also to adopt concepts and methods from cognitive neuroscience to understand the internal representations of DNNs. Although general deep learning frameworks such as PyTorch and TensorFlow could be used to allow such cross-disciplinary studies, the use of these frameworks typically requires high-level programming expertise and comprehensive mathematical knowledge. A toolbox specifically designed for cognitive neuroscientists to map DNNs and brains is urgently needed. Here, we present DNNBrain, a Python-based toolbox designed for exploring internal representations in both DNNs and the brain. By integrating DNN software packages and well-established brain imaging tools, DNNBrain provides application programming and command line interfaces for a variety of research scenarios, such as extracting DNN activation, probing DNN representations, mapping DNN representations onto the brain, and visualizing DNN representations. We expect that our toolbox will accelerate scientific research in applying DNNs to model biological neural systems and utilizing paradigms of cognitive neuroscience to unveil the black box of DNNs.

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bioRxiv2020

Bayesian inference of population prevalence

Within neuroscience, psychology and neuroimaging, the most frequently used statistical approach is null-hypothesis significance testing (NHST) of the population mean. An alternative approach is to perform NHST within individual participants and then infer, from the proportion of participants showing an effect, the prevalence of that effect in the population. We propose a novel Bayesian method to estimate such population prevalence that offers several advantages over population mean NHST. This method provides a population-level inference that is currently missing from study designs with small participant numbers, such as in traditional psychophysics and in precision imaging. Bayesian prevalence delivers a quantitative population estimate with associated uncertainty instead of reducing an experiment to a binary inference. Bayesian prevalence is widely applicable to a broad range of studies in neuroscience, psychology, and neuroimaging. Its emphasis on detecting effects within individual participants can also help address replicability issues in these fields.

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bioRxiv2020

Structure-informed functional connectivity driven by identifiable and state-specific control regions

AO_SCPLOWBSTRACTC_SCPLOWA challenge in neuroscience is to describe the contribution of the brain anatomical wiring to the emergence of coordinated neural activity underlying complex behavior. Indeed, patterns of remote coactivations that adjust with the ongoing task-demand do not systematically match direct, static anatomical links. Here, we propose that observed coactivation patterns, known as Functional Connectivity (FC), can be explained by a linear diffusion dynamics defined on the brain architecture and driven by control regions. Our model, termed structure-informed FC, is based on a novel interpretation of functional connectivity according to which different sets of brain regions controlling the information flow on a fixed anatomical wiring enable the emergence of state-specific FC. This observation leads us to introduce a framework for the identification of potential control centers in the brain. We find that well-defined, sparse and robust sets of control regions, which partially overlap across several task conditions and resting-state, produce FC patterns comparable to empirical ones. In conclusion, this work introduces a principled method for identifying brain regions underlying the task-specific control of brain activity. Significance statementUnderstanding how brain anatomy promotes particular patterns of coactivations among neural regions is a key challenge in neuroscience. This challenge can be addressed using network science and systems theory. Here, we propose that coactivations result from the diffusion of information through the network of anatomical links connecting brain regions, with certain regions controlling the dynamics. We translate this hypothesis into a model called structure-informed functional connectivity, and we introduce a framework for identifying control regions based on empirical data. We find that our model produces coactivation patterns comparable to empirical ones, and that distinct sets of control regions are associated with different functional states. These findings suggest that controllability is an important feature allowing the brain to reach different states.

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bioRxiv2020

The Contribution of Environmental Complexity to Phenotypic Variation in Mice and Rats

The reproducibility and translation of neuroscience research is assumed to be undermined by introducing environmental complexity and heterogeneity. Rearing laboratory animals with minimal (if any) environmental stimulation is thought to control for biological variability but may not adequately test the robustness of our animal models. Standard laboratory housing is associated with reduced demonstrations of species typical behaviors and changes in neurophysiology that may impact the translation of research results. Modest increases in environmental enrichment (EE), mitigate against insults used to induce animal models of disease, directly calling into question the translatability of our work. This may in part underlie the disconnect between preclinical and clinical research findings. Enhancing environmental stimulation for our model organisms promotes ethological natural behaviors but may simultaneously increase phenotypic trait variability. To test this assumption, we conducted a systematic review and evaluated coefficients of variation between EE and standard housed mice and rats. Given findings of suboptimal reporting of animal laboratory housing conditions, we also developed a methodological reporting table for enrichment use in neuroscience research. Our data show that animals housed in environmental enrichment were not more variable than those in standard housing. Therefore, environmental heterogeneity introduced into the laboratory, in the form of enrichment, does not compromise data integrity. Overall, human life is complicated and by embracing such nuanced complexity into our laboratories we may paradoxically improve upon the rigor and reproducibility of our research. Significance StatementEnvironmental complexity is thought to increase phenotypic variability, undermining research translation. We conducted a systematic review and compared coefficients of variation between environmentally enriched and standard housed laboratory animals. Despite there being no differences in variability across several phenotypic traits, there are stark contrasts in the display of ethological natural behaviors between these housing conditions. Environmental enrichment is recognized as being beneficial for animal welfare and mitigates against insults used to induce animal models of disease. In contrast, standard laboratory cages are recognized as being impoverished and unnatural. From these observations, it is apparent that our current "gold standard" caging system is not a true control condition as it does not adequately test the robustness of our animal models.

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bioRxiv2020

SNT: A Unifying Toolbox for Quantification of Neuronal Anatomy

Quantification of neuronal morphology is essential for understanding neuronal connectivity and many software tools have been developed for neuronal reconstruction and morphometry. However, such tools remain domain-specific, tethered to specific imaging modalities, and were not designed to accommodate the rich metadata generated by recent whole-brain cellular connectomics. To address these limitations, we created SNT: a unifying framework for neuronal morphometry and analysis of single-cell connectomics for the widely used Fiji and ImageJ platforms. We demonstrate that SNT can be used to tackle important problems in contemporary neuroscience, validate its utility, and illustrate how it establishes an end-to-end platform for tracing, proof-editing, visualization, quantification, and modeling of neuroanatomy. With an open and scriptable architecture, a large user base, and thorough community-based documentation, SNT is an accessible and scalable resource for the broad neuroscience community that synergizes well with existing software.

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bioRxiv2020

An electrodiffusive neuron-extracellular-glia model with somatodendritic interactions

Computational modeling in neuroscience has largely focused on simulating the electrical activity of neurons, while ignoring other components of brain tissue, such as glial cells and the extracellular space. As such, most existing models can not be used to address pathological conditions, such as spreading depression, which involves dramatic changes in ion concentrations, large extracellular potential gradients, and glial buffering processes. We here present the electrodiffusive neuron-extracellular-glia (edNEG) model, which we believe is the first model to combine multicompartmental neuron modeling with an electrodiffusive framework for intra- and extracellular ion concentration dynamics in a local piece of neuro-glial brain tissue. The edNEG model (i) keeps track of all intraneuronal, intraglial, and extracellular ion concentrations and electrical potentials, (ii) accounts for neuronal somatic action potentials, and dendritic calcium spikes, (iii) contains a neuronal and glial homeostatic machinery that gives physiologically realistic ion concentration dynamics, (iv) accounts for electrodiffusive transmembrane, intracellular, and extracellular ionic movements, and (v) accounts for glial and neuronal swelling caused by osmotic transmembrane pressure gradients. We demonstrate that the edNEG model performs realistically as a local and closed system, i.e., that it maintains a steady state for moderate neural activity, but experiences concentration-dependent effects, such as altered firing patterns and homeostatic breakdown, when the activity level becomes too intense. Furthermore, we study the role of glia in making the neuron more tolerable to hyperactive firing and in limiting neuronal swelling. Finally, we discuss how the edNEG model can be integrated with previous spatial continuum models of spreading depression to account for effects of neuronal morphology, action potential generation, and dendritic Ca2+ spikes which are currently not included in these models. Author summaryNeurons communicate by electrical signals mediated by the movement of ions across the cell membranes. The ionic flow changes the ion concentrations on both sides of the cell membranes, but most modelers of neurons assume ion concentrations to remain constant. Since the neuronal membrane contains structures called ion pumps and cotransporters that work to maintain close-to baseline ion concentrations, and the brain contains a cell type called astrocytes that contribute in keeping an appropriate ionic environment for neurons, the assumption is justifiable in many scenarios. However, for several pathological conditions, such as epilepsy and spreading depression, the ion concentrations may vary dramatically. To study these scenarios, we need models that account for changes in ion concentrations. In this paper, we present what we call the electrodiffusive neuron-extracellular-glia model (edNEG), which keeps track of all ions in a closed system containing a neuron, the extracellular space surrounding it, and an astrocytic "domain". The edNEG model ensures a complete and consistent relationship between ion concentrations and charge conservation. We envision that the model can be used to study a range of pathological conditions such as spreading depression and, hence, be of great value for the field of neuroscience.

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bioRxiv2020

P2X receptors in Aplysia californica: Chemosensory systems, bio-energetic and development

ATP and its ionotropic P2X receptors are components of one of the most ancient signaling systems. However, little is known about the distribution and function of purinergic transmission in invertebrates. Here, we cloned, expressed, and pharmacologically characterized P2X receptors in the sea slug Aplysia californica - a prominent model in cellular and system neuroscience. We showed that ATP and P2X receptors are essential signaling components within the unique bioenergetic center located in the CNS of Aplysia, also known as the cerebral F-cluster of insulin-containing neurons. Functional P2X receptors were successfully expressed in Xenopus oocytes to characterize their ATP-dependence (EC50=306M), two-phased kinetics, ion selectivity (Na+-dependence), sensitivity to the ATP analog Bz-ATP (~20% compare to ATP) and antagonists (with PPADS as a more potent inhibitor compared to suramin). Next, using RNA-seq, we characterized the expression of P2X receptors across more than a dozen Aplysia peripheral tissues and developmental stages. We showed that P2X receptors are predominantly expressed in chemosensory structures and during early cleavage stages. The localization and pharmacology of P2X receptors in Aplysia highlight the evolutionary conservation of bioenergetic sensors and chemosensory purinergic transmission across animals. This study also provides a foundation to decipher homeostatic mechanisms in development and neuroendocrine systems. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=124 SRC="FIGDIR/small/243196v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@d5c2faorg.highwire.dtl.DTLVardef@1f3dab3org.highwire.dtl.DTLVardef@9b49daorg.highwire.dtl.DTLVardef@10dc71f_HPS_FORMAT_FIGEXP M_FIG C_FIG We show that ATP and its ligand-gated P2X receptors are essential signaling components within both the chemosensory systems and the unique bioenergetic center, present in the CNS of the sea slug Aplysia californica - a prominent model in neuroscience. Expression and pharmacology of P2X receptors in Aplysia confirms the preservation of evolutionary conserved bioenergetic sensors across animals and provide new tools to decipher homeostatic mechanisms in neuro-endocrine systems in general.

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bioRxiv2020

Mean-field modeling of brain-scale dynamics for the evaluation of EEG source-space networks

Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called "electroencephalography (EEG) source connectivity" has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted for to avoid pitfalls. Thus, a validation toolbox that provides flexible "ground truth" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the "EEG source connectivity" pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.

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bioRxiv2020

A single-mode associates global patterns of brain network structure and behavior across the human lifespan

Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r=0.5808). Individual variability within the single association axis well predicted the age of the subject (r=0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach. [147] Data availabilityThe source data are provided by the Cambridge Aging Neuroscience Project https://camcan-archive.mrc-cbu.cam.ac.uk/. Brain data derived as part of this project and used as features for all the analyses are available on brainlife.io/pubs: Code availabilityCode is available on github at https://github.com/bcmcpher/cca_aging and as web services reproducing the analyes at

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