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Results for “neuroscience”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 415 records · Page 23Linked to original sources
bioRxiv2018

Smart Region-Growing: a novel algorithm for the segmentation of 3D clarified confocal image stacks

MotivationAccurately mapping the brain at the micro-scale is still a challenge in cellular neuroscience. While notable success has been reached in the field of tissue clarification and confocal imaging to obtain high-fidelity maps of three-dimensional neuron organization, neuron segmentation is still far away of ground-truth and manual segmentation performed by experts may be needed. The need of an expert is in part related to the limited success of the algorithms and tools performing single-neuron segmentation from 3D microscopic image data available in the State of Art, in part to the non-complete information given by these methods, which typically perform neuron tracing and thus limit the interpret-ability of results.\n\nResultsIn this paper, a novel algorithm for segmenting single neurons in their own arrangement within the brain is presented. The algorithm performs a region growing procedure with local thresholds based on the pixel intensity statistics typical of confocal acquisitions of biological samples and described by a mixture model. The algorithm is developed and tested on 3D confocal datasets obtained from clarified tissues. We compare the result of our algorithm with those obtained by manual segmentation performed by 6 different experts in terms of neuron surface area, volume and Sholl profiles. Statistical analysis performed on morphologic features extracted from the segmented structures confirms the feasibility of our approach.\n\nAvailabilityThe Smart Region Growing (SmRG) algorithm used in the analysis along with test confocal image stacks is available on request to the authors.\n\nContactalejandrocallara@gmail.com\n\nSupplementary informationSupplementary data are available on request to the authors.

bioinformatics↗
bioRxiv2018

G2P: Using machine learning to understand and predict genes causing rare neurological disorders

To facilitate precision medicine and neuroscience research, we developed a machine-learning technique that scores the likelihood that a gene, when mutated, will cause a neurological phenotype. We analysed 1126 genes relating to 25 subtypes of Mendelian neurological disease defined by Genomics England (March 2017) together with 154 gene-specific features capturing genetic variation, gene structure and tissue-specific expression and co-expression. We randomly re-sampled genes with no known disease association to develop bootstrapped decision-tree models, which were integrated to generate a decision tree-based ensemble for each disease subtype. Genes generating larger numbers of distinct transcripts and with higher probability of having missense mutations in normal individuals were significantly more likely to cause neurological diseases. Using mouse-mutant phenotypic data we tested the accuracy of gene-phenotype predictions and found that for 88% of all disease subtypes there was a significant enrichment of relevant phenotypic abnormalities when predicted genes were mutated in mice and in many cases mutations produced specific and matching phenotypes. Furthermore, using only newly identified genes included in the Genomics England November 2017 release, we assessed our gene-phenotype predictions and showed an 8.3 fold enrichment relative to chance for correct predictions. Thus, we demonstrate both the explanatory and predictive power of machine-learning-based models in neurological disease.

genetics↗
bioRxiv2018

Fast calcium transients in neuronal spines is driven by extreme statistics

Extreme statistics describe the distribution of rare events that can define the timescales of transduction within cellular microdomains. We combine biophysical modeling and analysis of live-cell calcium imaging to explain the fast calcium transient in spines. We show that in the presence of a spine apparatus (SA), which is an extension of the smooth endoplasmic reticulum (ER), calcium transients during synaptic inputs rely on rare and extreme calcium ion trajectories. Using numerical simulations, we predicted the asymmetrical distributions of Ryanodine receptors and SERCA pumps that we confirmed experimentally. When calcium ions are released in the spine head, the fastest ions arriving at the base determine the transient timescale through a calcium-induced calcium release mechanism. In general, the fastest particles arriving at a small target are likely to be a generic mechanism that determines the timescale of molecular transduction in cellular neuroscience.\n\nSignificance statementIntrigued by fast calcium transients of few milliseconds in dendritic spines, we investigated its underlying biophysical mechanism. We show here that it is generated by the diffusion of the fastest calcium ions when the spine contains a Spine Apparatus, an extension of the endoplasmic reticulum. This timescale is modulated by the initial number of released calcium ions and the asymmetric distribution of its associated calcium release associated Ryanodyne receptors, present only at the base of a spine. This novel mechanism of calcium signaling that we have unraveled here is driven by the fastest particles. To conclude, the rate of arrival of the fastest particles (ions) to a small target receptor defines the timescale of activation instead of the classical forward rate of chemical reactions introduced by von Smoluchowski in 1916. Applying this new rate theory to transduction should refine our understanding of the biophysical mechanisms underlying molecular signaling.

cell biology↗
bioRxiv2018

Can the presence of neural probes be neglected in computational modeling of extracellular potentials?

AbstractO_ST_ABSObjectiveC_ST_ABSMechanistic modeling of neurons is an essential component of computational neuroscience that enables scientists to simulate, explain, and explore neural activity. The conventional approach to simulation of extracellular neural recordings first computes transmembrane currents using the cable equation and then sums their contribution to model the extracellular potential. This two-step approach relies on the assumption that the extracellular space is an infinite and homogeneous conductive medium, while measurements are performed using neural probes. The main purpose of this paper is to assess to what extent the presence of the neural probes of varying shape and size impacts the extracellular field and how to correct for them.\n\nApproachWe apply a detailed modeling framework allowing explicit representation of the neuron and the probe to study the effect of the probes and thereby estimate the effect of ignoring it. We use meshes with simplified neurons and different types of probe and compare the extracellular action potentials with and without the probe in the extracellular space. We then compare various solutions to account for the probes presence and introduce an efficient probe correction method to include the probe effect in modeling of extracellular potentials.\n\nMain resultsOur computations show that microwires hardly influence the extracellular electric field and their effect can therefore be ignored. In contrast, Multi-Electrode Arrays (MEAs) significantly affect the extracellular field by magnifying the recorded potential. While MEAs behave similarly to infinite insulated planes, we find that their effect strongly depends on the neuron-probe alignment and probe orientation.\n\nSignificanceIgnoring the probe effect might be deleterious in some applications, such as neural localization and parameterization of neural models from extracellular recordings. Moreover, the presence of the probe can improve the interpretation of extracellular recordings, by providing a more accurate estimation of the extracellular potential generated by neuronal models.

bioengineering↗
bioRxiv2018

Acoustic Calibration in an Echoic Environment

Background: The sound fed to a loudspeaker may significantly differ from that reaching the ear of the listener. The transformation from one to the other consists of spectral distortions with strong dependence on the relative locations of the speaker and the listener as well as on the geometry of the environment. With the increased importance of research in awake, freely-moving animals in large arenas, it becomes important to understand how animal location influences the corresponding spectral distortions.\n\nNew Method: We describe a full calibration pipeline that includes spatial sampling and estimation of the spectral distortions. We estimated the impulse responses of the environment using Golay complementary sequences.\n\nUsing those sequences, we also describe an acoustic 3D localization method for freely moving animals.\n\nResults: In our arena, the impulse responses are dominated by a small number of strong reflections. We use this understanding to provide guidelines for designing the geometry of the environment as well as the presented sounds, in order to provide more uniform sound levels throughout the environment. Our 3D localization method achieves a 1 cm precision through the utilization of sound cues only.\n\nComparison with Existing Methods: To our knowledge, this is the first description of a large-scale acoustic calibration pipeline with acoustic localization for neuroscience studies.\n\nConclusions: Principled sampling of large arena allows for better design and control of the acoustic information provided to freely-moving animals.

animal behavior and cognition↗
bioRxiv2018

Robust Mouse Tracking in Complex Environments using Neural Networks

The ability to track animals accurately is critical for behavioral experiments. For video-based assays, this is often accomplished by manipulating environmental conditions to increase contrast between the animal and the background, in order to achieve proper foreground/background detection (segmentation). However, as behavioral paradigms become more sophisticated with ethologically relevant environments, the approach of modifying environmental conditions offers diminishing returns, particularly for scalable experiments. Currently, there is a need for methods to monitor behaviors over long periods of time, under dynamic environmental conditions, and in animals that are genetically and behaviorally heterogeneous. To address this need, we developed a state-of-the-art neural network-based tracker for mice, using modern machine vision techniques. We test three different neural network architectures to determine their performance on genetically diverse mice under varying environmental conditions. We find that an encoder-decoder segmentation neural network achieves high accuracy and speed with minimal training data. Furthermore, we provide a labeling interface, labeled training data, tuned hyperparameters, and a pre-trained network for the mouse behavior and neuroscience communities. This general-purpose neural network tracker can be easily extended to other experimental paradigms and even to other animals, through transfer learning, thus providing a robust, generalizable solution for biobehavioral research.

animal behavior and cognition↗
bioRxiv2018

Cryo-EM structure of the benzodiazepine-sensitive α1β1γ2 heterotrimeric GABAA receptor in complex with GABA illuminates mechanism of receptor assembly and agonist binding

Fast inhibitory neurotransmission in the mammalian nervous system is largely mediated by GABAA receptors, chloride-selective members of the superfamily of pentameric Cys-loop receptors. Native GABAA receptors are heteromeric assemblies sensitive to many important drugs, from sedatives to anesthetics and anticonvulsive agents, with mutant forms of GABAA receptors implicated in multiple neurological diseases, including epilepsy. Despite the profound importance of heteromeric GABAA receptors in neuroscience and medicine, they have proven recalcitrant to structure determination. Here we present the structure of the triheteromeric 1{beta}1{gamma}2EM GABAA receptor in complex with GABA, determined by single particle cryo-EM at 3.1-3.8 [A] resolution, elucidating the molecular principles of receptor assembly and agonist binding. Remarkable N-linked glycosylation on the 1 subunit occludes the extracellular vestibule of the ion channel and is poised to modulate receptor assembly and perhaps ion channel gating. Our work provides a pathway to structural studies of heteromeric GABAA receptors and a framework for the rational design of novel therapeutic agents.

biophysics↗
bioRxiv2018

In a temporally segmented experience hippocampal neurons represent temporally drifting context but not discrete segments.

There is widespread agreement that episodic memory is organized into a timeline of past experiences. Recent work suggests that the hippocampus may parse the flow of experience into discrete episodes separated by event boundaries. A complementary body of work suggests that context changes gradually as experience unfolds. We recorded from hippocampal neurons as male long evans rats performed 6 blocks of an object discrimination task in sets of 15 trials. Each block was separated by removal from the testing chamber for a delay to enable segmentation. The reward contingency reversed from one block to the next to incentivize segmentation. We expected animals to hold two distinct, recurring representations of context to match the two distinct rule contingencies. Instead, we found that overtrained rats began each block neither above nor below chance but by guessing randomly. While many units had clear firing fields selective to the conjunction of objects in places, a significant population also reflected a continuously drifting code both within block and across blocks. Despite clear boundaries between blocks, we saw no neural evidence for event segmentation in this experiment. Rather, the hippocampal ensemble drifted continuously across time. This continuous drift in the neural representation was consistent with the lack of segmentation observed in behavior.\n\nSignificance StatementThe neuroscience literature yet to reach consensus as to how hippocampal firing fields support the organizing of events across time in episodic memory. Initial reports of hippocampal activity focused on discrete episodes within which representations were stable, and across which representations remapped. However, it remains unclear whether this segmentation of representations is merely an artifact of cue responsivity. More recently, research has shown that a proportion of the population codes for temporal aspects of context by exhibiting varying degrees of drift in their firing fields. Drift is hypothesized to represent a continually evolving temporal context, however it is unclear whether this drift is continuous or is also a mere artifact of changing experiences. We recorded from the dorsal hippocampus of rats performing an object discrimination task that involved contexts that were segmented in time. Overtrained rats were unable to anticipate the identity of the upcoming context, but may have used context boundaries to their advantage. Event segmentation theory predicts that hippocampal ensembles would alternate between behaviorally-relevant segments. Contrary to these predictions, animals showed weak evidence of context segmentation, even across blocks with different reward contingencies. Hippocampal ensembles showed neither evidence of alternating between stable contexts nor sensitivity to context boundaries, but did show robust temporal drift.

animal behavior and cognition↗
bioRxiv2018

Diffusion-weighted magnetic resonance spectroscopy enables cell-specific monitoring of astrocyte reactivity in vivo

The diffusion of brain intracellular metabolites, as measured using diffusion-weighted magnetic resonance spectroscopy in vivo, is thought to specifically depend on the cellular structure constraining them. However, it has never been established that variations of metabolite diffusion, e.g. as observed in some diseases, could indeed be linked to alterations of cellular morphology. Here we demonstrate, in a mouse model of reactive astrocytes, that advanced diffusion-weighted magnetic resonance spectroscopy acquisition and modeling techniques enable non-invasive detection of reactive astrocyte hypertrophy (increased soma radius, increased fiber radius and length), as inferred from variations of myo-inositol diffusion, and as confirmed by confocal microscopy ex vivo. This establishes that specific alterations of intracellular metabolite diffusion can be measured and related to cell-specific morphological alterations. Furthermore, as reactive astrocytes are a hallmark of many brain pathologies, this work opens exciting perspectives for neuroscience and clinical research.

bioinformatics↗
bioRxiv2018

Mapping Mouse Brain Slice Sequence to a Reference Brain Without 3D Reconstruction

Histological brain slices are widely used in neuroscience to study anatomical organization of neural circuits. Since data from many brains are collected, mapping the slices to a reference atlas is often the first step in interpreting results. Most existing methods rely on an initial reconstruction of the volume before registering it to a reference atlas. Because these slices are prone to distortion during sectioning process and often sectioned with nonstandard angles, reconstruction is challenging and often inaccurate. We propose a framework that maps each slice to its corresponding plane in the atlas to build a plane-wise mapping and then perform 2D nonrigid registration to build pixel-wise mapping. We use the L2 norm of the Histogram of Oriented Gradients (HOG) of two patches as the similarity metric for both steps, and a Markov Random Field formulation that incorporates tissue coherency to compute the nonrigid registration. To fix significantly distorted regions that are misshaped or much smaller than the control grids, we trained a context-aggregation network to segment and warp them to their corresponding regions with thin plate spline. We have shown that our method generates results comparable to an expert neuroscientist and is significantly better than reconstruction-first approaches.

bioinformatics↗
bioRxiv2018

Long term impact of intensive post graduate laboratory training at the Cold Spring Harbor Neurobiology of Drosophila summer course

Intensive postgraduate courses provide an opportunity for junior and senior level scientists to learn concepts and techniques that will advance their training and research programs. It is commonly assumed that short intensive courses have positive impacts within fields of research; however, these assumptions are rarely tested. Here we describe the framework of a long running postgraduate summer course at Cold Spring Harbor and attempt to quantify the impact made over its history. For over three decades, the Drosophila Neurobiology: Genes, Circuits & Behavior Summer Course at Cold Spring Harbor Laboratories (CSHL) has provided participants with intense instruction on a wide variety of topics and techniques in integrative neuroscience using Drosophila as a model organism. Students are introduced to the latest approaches for studying nervous system development, activity and connectivity, as well as complex behaviors and diseases. The course has a long history of successful alumni, many of whom describe participation in the course as foundational to their training. Student surveys of recent participants indicate a high level of satisfaction, improved career outcomes, and direct impact on publications. Analysis of student success reveals that over 64% of participants obtain independent faculty positions. Further, we describe ongoing efforts to enhance diversity and encourage access to scientific research at undergraduate-focused institutions. Together, our findings suggest that laboratory-intensive postgraduate courses provide a highly effective mechanism for scientific training that has lasting positive impacts on trainees.

scientific communication and education↗
bioRxiv2018

COMPASS: An Open-Source, General-Purpose Software Toolkit for Computational Psychiatry

Mathematical modeling of behavior during psychophysical tasks, referred to as \"computational psychiatry\", could greatly improve our understanding of mental disorders. One barrier to broader adoption of computational methods is that they often require advanced programming skills. We developed the Computational Psychiatry Adaptive State-Space (COMPASS) toolbox, an open-source MATLAB-based software package. After specifying a few parameters in a small set of user-friendly functions, COMPASS allows the user to efficiently fit of a wide range of computational behavioral models. The model output can be analyzed as an experimental outcome or used as a regressor for neural data, and can be tested using goodness-of-fit methods. Here, we demonstrate that COMPASS can replicate two computational behavior analyses from different groups. COMPASS replicates and, in one case, slightly improves on the original modeling results. This flexible, general-purpose toolkit should accelerate the use of computational modeling in psychiatric neuroscience.

bioengineering↗
bioRxiv2018

A single-cell level and connectome-derived computational model of the Drosophila brain

Computer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits. Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible. Supported by FlyCircuit (http://www.flycircuit.tw), one of the largest Drosophila neuron image databases, we began a long-term project with the goal to construct a whole-brain spiking network model of the Drosophila brain. In this paper, we report the outcome of the first phase of the project. We developed the Flysim platform, which 1) identifies the polarity of each neuron arbor, 2) predicts connections between neurons, 3) translates morphology data from the database into physiology parameters for computational modeling, 4) reconstructs a brain-wide network model, which consists of 20,089 neurons and 1,044,020 synapses, and 5) performs computer simulations of the resting state. We compared the reconstructed brain network with a randomized brain network by shuffling the connections of each neuron. We found that the reconstructed brain can be easily stabilized by implementing synaptic short-term depression, while the randomized one exhibited seizure-like firing activity under the same treatment. Furthermore, the reconstructed Drosophila brain was structurally and dynamically more diverse than the randomized one and exhibited both Poisson-like and patterned firing activities. Despite being at its early stage of development, this single-cell level brain model allows us to study some of the fundamental properties of neural networks including network balance, critical behavior, long-term stability, and plasticity.

bioinformatics↗
bioRxiv2018

Genetic characterization of outbred Sprague Dawley rats and utility for genome-wide association studies

Sprague Dawley (SD) rats are among the most widely used outbred laboratory rat populations. Despite this, the genetic characteristics of SD rats have not been clearly described, and SD rats are rarely used for experiments aimed at exploring genotype-phenotype relationships. In order to use SD rats to perform a genome-wide association study (GWAS), we collected behavioral data from 4,625 SD rats that were predominantly obtained from two commercial vendors, Charles River Laboratories and Harlan Sprague Dawley Inc. Using double-digest genotyping-by-sequencing (ddGBS), we obtained dense, high-quality genotypes at 291,438 SNPs across 4,061 rats. This genetic data allowed us to characterize the variation present in Charles River vs. Harlan SD rats. We found that the two populations are highly diverged (FST > 0.4). Furthermore, even for rats obtained from the same vendor, there was strong population structure across breeding facilities and even between rooms at the same facility. We performed multiple separate GWAS by fitting a linear mixed model that accounted population structure and using meta-analysis to jointly analyze all cohorts. Our study examined Pavlovian conditioned approach (PavCA) behavior, which assesses the propensity for rats to attribute incentive salience to reward-associated cues. We identified 46 significant associations for the various metrics used to define PavCA. The surprising degree of population structure among SD rats from different sources has important implications for their use in both genetic and non-genetic studies. Author SummaryOutbred Sprague Dawley rats are among the most commonly used rats for neuroscience, physiology and pharmacological research; in the year 2020, 4,188 publications contained the keyword "Sprague Dawley". Rats identified as "Sprague Dawley" are sold by several commercial vendors, including Charles River Laboratories and Harlan Sprague Dawley Inc. (now Envigo). Despite their widespread use, little is known about the genetic diversity of SD. We genotyped more than 4,000 SD rats, which we used for a genome-wide association study (GWAS) and to characterize genetic differences between SD rats from Charles River Laboratories and Harlan. Our analysis revealed extensive population structure both between and within vendors. The GWAS for Pavlovian conditioned approach (PavCA) identified a number of genome-wide significant loci for that complex behavioral trait. Our results demonstrate that, despite sharing an identical name, SD rats that are obtained from different vendors are very different. Future studies should carefully define the exact source of SD rats being used and may exploit their genetic diversity for genetic studies of complex traits.

genetics↗
bioRxiv2018

Foraging as an evidence accumulation process

A canonical foraging task is the patch-leaving problem, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. Evidence accumulation models belong to the class of drift diffusion processes and have been used to model decision making in different contexts especially in cognitive and systems neuroscience. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different \"strategies\", for example an increment-decrement or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To account for sub-optimal decisions, we introduce an energy-dependent utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch leaving decisions.

animal behavior and cognition↗
bioRxiv2018

Predictive Modelling of The Dynamic Patterns of Thinkingin Attention-Deficit/Hyperactivity Disorder: DiagnosticAccuracy of Spatiotemporal Fractal Measures

BackgroundAttention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental condition characterized by executive function (EF) dynamics disturbances. Notwithstanding, current advances in translational neuroscience, no ADHD objective, clinically useful, diagnostic marker is available to date.\n\nObjectivesUsing a customized definition of EF and a new clinical paradigm, we performed a prospective diagnostic accuracy trial to assess the diagnostic value of several fractal measures from the thinking processes or inferences in a cohort of ADHD children and typically developing controls.\n\nMethodWe included children from age five to twelve diagnosed with a reference standard based on case history, physical and neurological examination, Conners 3rd Edition, and DSM-V. The index test consisted of a computer-based inference task with a set of eight different instances of the \"Battleships\" game to be solved. A consecutive series of 18 cases and 18 controls (n = 36) recruited at the primary paediatrics service from the Nelson Marlborough Health in New Zealand underwent the reference standard and the index test. Several fractal measures were obtained from the inference task to produce supervised classification models.\n\nResultsNotably, the summarized logistic regressions predicted probabilities from the eight games played by each children yielded a 100% classification accuracy, sensitivity and specificity in both a training and an independent testing/validating cohort.\n\nConclusionsFrom a translational vantage point the expeditious method and the robust results make this technique a promising candidate to develop a screening, diagnostic and monitoring system for ADHD, and may serve to assess other EF disturbances.

clinical trials↗
bioRxiv2018

Novel diamond shuttle to deliver flexible bioelectronics with reduced tissue compression

The ability to deliver flexible biosensors through the toughest membranes of the central and peripheral nervous system is an important challenge in neuroscience and neural engineering. Bioelectronic devices implanted through dura mater and thick epineurium would ideally create minimal compression and acute damage as they reach the neurons of interest. We demonstrate that a three-dimensional diamond shuttle can be easily made with a vertical support to deliver ultra-compliant polymer microelectrodes (4.5 m thick) in-vivo through dura mater and thick epineurium. The diamond shuttle has 54% less cross-sectional area than an equivalently stiff silicon shuttle, which we simulated will result in a 37% reduction in blood vessel damage. We also discovered that higher frequency oscillation of the shuttle (200 Hz) significantly reduced tissue compression regardless of the insertion speed, while slow speeds also independently reduced tissue compression. Insertion and recording performance are demonstrated in rat and feline models, but the large design space of these tools are suitable for research in a variety of animal models and nervous system targets.

bioengineering↗
bioRxiv2018

Dynamic Brain Connectivity Alternation Detection via Matrix-variate Differential Network Model

MotivationNowadays brain connectivity analysis has attracted tremendous attention and has been at the foreground of neuroscience research. Brain functional connectivity reveals the synchronization of brain systems through correlations in neurophysiological measures of brain activity. Growing evidence now suggests that the brain connectivity network experiences alternations with the presence of numerous neurological disorders, thus differential brain network analysis may provides new insights into disease pathologies. For the matrix-valued data in brain connectivity analysis, existing graphical model estimation methods assume a vector normal distribution that in essence requires the columns of the matrix data to be independent. It is obviously not true, they have limited applications. Among the few solutions on graphical model estimation under a matrix normal distribution, none of them tackle the estimation of differential graphs across different populations. This motivates us to consider the differential network for matrix-variate data to detect the brain connectivity alternation.\n\nResultsThe primary interest is to detect spatial locations where the connectivity, in terms of the spatial partial correlation, differ across the two groups. To detect the brain connectivity alternation, we innovatively propose a Matrix-Variate Differential Network (MVDN) model. MVDN assumes that the matrix-variate data follows a matrix-normal distribution. We exploit the D-trace loss function and a Lasso-type penalty to directly estimate the spatial differential partial correlation matrix where the temporal information is fully excavated. We propose an ADMM algorithm for the Lasso penalized D-trace loss optimization problem. We investigate theoretical properties of the estimator. We show that under mild and regular conditions, the proposed method can identify all differential edges accurately with probability tending to 1 in high-dimensional setting where dimensions of matrix-valued data p, q and sample size n are all allowed to go to infinity. Simulation studies demonstrate that MVDN provides more accurate differential network estimation than that achieved by other state-of-the-art methods. We apply MVDN to Electroencephalography (EEG) dataset, which consists of 77 alcoholic individuals and 45 controls. The hub genes and differential interaction patterns identified are consistent with existing experimental studies.\n\nContactheyong@sdufe.edu.cn\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗