scholarly journals Inference of Synaptic Connectivity and External Variability in Neural Microcircuits

2019 ◽  
Author(s):  
Cody Baker ◽  
Emmanouil Froudarakis ◽  
Dimitri Yatsenko ◽  
Andreas S. Tolias ◽  
Robert Rosenbaum
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Network Models ◽  
Ground Truth ◽  
External Input ◽  
Synaptic Connectivity ◽  
Imaging Data ◽  
Common Input ◽  
Simplifying Assumptions

AbstractA major goal in neuroscience is to estimate neural connectivity from large scale extracellular recordings of neural activity in vivo. This is challenging in part because any such activity is modulated by the unmeasured external synaptic input to the network, known as the common input problem. Many different measures of functional connectivity have been proposed in the literature, but their direct relationship to synaptic connectivity is often assumed or ignored. For in vivo data, measurements of this relationship would require a knowledge of ground truth connectivity, which is nearly always unavailable. Instead, many studies use in silico simulations as benchmarks for investigation, but such approaches necessarily rely upon a variety of simplifying assumptions about the simulated network and can depend on numerous simulation parameters. We combine neuronal network simulations, mathematical analysis, and calcium imaging data to address the question of when and how functional connectivity, synaptic connectivity, and latent external input variability can be untangled. We show numerically and analytically that, even though the precision matrix of recorded spiking activity does not uniquely determine synaptic connectivity, it is often closely related to synaptic connectivity in practice under various network models. This relation becomes more pronounced when the spatial structure of neuronal variability is considered jointly with precision.

Integrating –omics data into genome-scale metabolic network models: principles and challenges

2018 ◽  
Vol 62 (4) ◽  
pp. 563-574 ◽  
Author(s):  
Charlotte Ramon ◽  
Mattia G. Gollub ◽  
Jörg Stelling
Keyword(s):  
Data Integration ◽  
Large Scale ◽  
Network Models ◽  
Omics Data ◽  
Scale Models ◽  
Common Framework ◽  
Genome Scale ◽  
Constraint Based Models ◽  
Omics Data Integration

At genome scale, it is not yet possible to devise detailed kinetic models for metabolism because data on the in vivo biochemistry are too sparse. Predictive large-scale models for metabolism most commonly use the constraint-based framework, in which network structures constrain possible metabolic phenotypes at steady state. However, these models commonly leave many possibilities open, making them less predictive than desired. With increasingly available –omics data, it is appealing to increase the predictive power of constraint-based models (CBMs) through data integration. Many corresponding methods have been developed, but data integration is still a challenge and existing methods perform less well than expected. Here, we review main approaches for the integration of different types of –omics data into CBMs focussing on the methods’ assumptions and limitations. We argue that key assumptions – often derived from single-enzyme kinetics – do not generally apply in the context of networks, thereby explaining current limitations. Emerging methods bridging CBMs and biochemical kinetics may allow for –omics data integration in a common framework to provide more accurate predictions.

scholarly journals Network models of primary melanoma microenvironments identify key melanoma regulators underlying prognosis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Won-Min Song ◽  
Praveen Agrawal ◽  
Richard Von Itter ◽  
Barbara Fontanals-Cirera ◽  
Minghui Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Primary Melanoma ◽  
Network Models ◽  
Transcriptional Networks ◽  
Melanoma Progression ◽  
Skin Malignancy

AbstractMelanoma is the most lethal skin malignancy, driven by genetic and epigenetic alterations in the complex tumour microenvironment. While large-scale molecular profiling of melanoma has identified molecular signatures associated with melanoma progression, comprehensive systems-level modeling remains elusive. This study builds up predictive gene network models of molecular alterations in primary melanoma by integrating large-scale bulk-based multi-omic and single-cell transcriptomic data. Incorporating clinical, epigenetic, and proteomic data into these networks reveals key subnetworks, cell types, and regulators underlying melanoma progression. Tumors with high immune infiltrates are found to be associated with good prognosis, presumably due to induced CD8+ T-cell cytotoxicity, via MYO1F-mediated M1-polarization of macrophages. Seventeen key drivers of the gene subnetworks associated with poor prognosis, including the transcription factor ZNF180, are tested for their pro-tumorigenic effects in vitro. The anti-tumor effect of silencing ZNF180 is further validated using in vivo xenografts. Experimentally validated targets of ZNF180 are enriched in the ZNF180 centered network and the known pathways such as melanoma cell maintenance and immune cell infiltration. The transcriptional networks and their critical regulators provide insights into the molecular mechanisms of melanomagenesis and pave the way for developing therapeutic strategies for melanoma.

scholarly journals Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012884
Author(s):  
Hugo Vrenken ◽  
Mark Jenkinson ◽  
Dzung Pham ◽  
Charles R.G. Guttmann ◽  
Deborah Pareto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Data Sharing ◽  
Large Scale ◽  
Personal Data ◽  
Human Observer ◽  
Imaging Data ◽  
Large Scale Data ◽  
Scale Data

Multiple sclerosis (MS) patients have heterogeneous clinical presentations, symptoms and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data-sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using magnetic resonance imaging (MRI).First, development of validated MS-specific image analysis methods can be boosted by verified reference, test and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy or functional network changes) to large multi-domain datasets (imaging, cognition, clinical disability, genetics, etc.).After reviewing data-sharing and artificial intelligence, this paper highlights three areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding of MS.

scholarly journals Scale-resolved analysis of brain functional connectivity networks with spectral entropy

2019 ◽  
Author(s):  
Carlo Nicolini ◽  
Giulia Forcellini ◽  
Ludovico Minati ◽  
Angelo Bifone
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Brain Activity ◽  
Ground Truth ◽  
Structural Features ◽  
Von Neumann Entropy ◽  
Complete Graphs ◽  
Theoretic Approach ◽  
Physiological Noise ◽  
Brain Functional Connectivity

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between an empirical functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure.We apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks.

scholarly journals A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Pierre Yger ◽  
Giulia LB Spampinato ◽  
Elric Esposito ◽  
Baptiste Lefebvre ◽  
Stéphane Deny ◽  
...  
Keyword(s):  
Large Scale ◽  
Ground Truth ◽  
Multielectrode Arrays ◽  
Signal To Noise ◽  
Ground Truth Data ◽  
Sorting Problem ◽  
Expected Performance ◽  
Large Ensembles

In recent years, multielectrode arrays and large silicon probes have been developed to record simultaneously between hundreds and thousands of electrodes packed with a high density. However, they require novel methods to extract the spiking activity of large ensembles of neurons. Here, we developed a new toolbox to sort spikes from these large-scale extracellular data. To validate our method, we performed simultaneous extracellular and loose patch recordings in rodents to obtain ‘ground truth’ data, where the solution to this sorting problem is known for one cell. The performance of our algorithm was always close to the best expected performance, over a broad range of signal-to-noise ratios, in vitro and in vivo. The algorithm is entirely parallelized and has been successfully tested on recordings with up to 4225 electrodes. Our toolbox thus offers a generic solution to sort accurately spikes for up to thousands of electrodes.

scholarly journals A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

2021 ◽  
pp. 1-28
Author(s):  
Daniel A Llano ◽  
Chihua Ma ◽  
Umberto Di Fabrizio ◽  
Aynaz Taheri ◽  
Kevin A. Stebbings ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Slices ◽  
Dynamic Network ◽  
Network Connectivity ◽  
Analytical Tool ◽  
Autofluorescence Imaging ◽  
Imaging Data ◽  
Cortical Networks ◽  
Network Metrics

Abstract Network analysis of large-scale neuroimaging data is a particularly challenging computational problem. Here, we adapt a novel analytical tool, the community dynamic inference method (CommDy), for brain imaging data from young and aged mice. CommDy, which was inspired by social network theory, has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network metrics in the auditory and motor cortices using flavoprotein autofluorescence imaging in brain slices and in vivo. We observed that auditory cortical networks in slices taken from aged brains were highly fragmented compared to networks observed in young animals. CommDy network metrics were then used to build a random-forests classifier based on NMDA-receptor blockade data, which successfully reproduced the aging findings, suggesting that the excitatory cortical connections may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity in the awake motor cortex, suggesting that the findings in the auditory cortex reflect general mechanisms during aging. These data suggest that CommDy provides a new dynamic network analytical tool to study the brain and that aging is associated with fragmentation of intracortical networks.

scholarly journals Cell Tracking Profiler – a user-driven analysis framework for evaluating 4D live-cell imaging data

2020 ◽  
Vol 133 (22) ◽  
pp. jcs241422
Author(s):  
Claire Mitchell ◽  
Lauryanne Caroff ◽  
Jose Alonso Solis-Lemus ◽  
Constantino Carlos Reyes-Aldasoro ◽  
Alessandra Vigilante ◽  
...  
Keyword(s):  
Image Analysis ◽  
Cell Tracking ◽  
Cell Function ◽  
Ground Truth ◽  
Time Lapse ◽  
Automated Image Analysis ◽  
Imaging Data ◽  
Cell Behaviour ◽  
Muscle Stem Cell

ABSTRACTAccurate measurements of cell morphology and behaviour are fundamentally important for understanding how disease, molecules and drugs affect cell function in vivo. Here, by using muscle stem cell (muSC) responses to injury in zebrafish as our biological paradigm, we established a ‘ground truth’ for muSC behaviour. This revealed that segmentation and tracking algorithms from commonly used programs are error-prone, leading us to develop a fast semi-automated image analysis pipeline that allows user-defined parameters for segmentation and correction of cell tracking. Cell Tracking Profiler (CTP) is a package that runs two existing programs, HK Means and Phagosight within the Icy image analysis suite, to enable user-managed cell tracking from 3D time-lapse datasets to provide measures of cell shape and movement. We demonstrate how CTP can be used to reveal changes to cell behaviour of muSCs in response to manipulation of the cell cytoskeleton by small-molecule inhibitors. CTP and the associated tools we have developed for analysis of outputs thus provide a powerful framework for analysing complex cell behaviour in vivo from 4D datasets that are not amenable to straightforward analysis.

scholarly journals Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Teddy J. Akiki ◽  
Chadi G. Abdallah
Keyword(s):  
Large Scale ◽  
Community Organization ◽  
Hierarchical Architecture ◽  
Imaging Data ◽  
Large Scale Network ◽  
Human Connectome Project ◽  
Neuronal Connections ◽  
Scale Network ◽  
Cortical Regions

AbstractOptimal integration and segregation of neuronal connections are necessary for efficient large-scale network communication between distributed cortical regions while allowing for modular specialization. This dynamic in the cortex is enabled at the network mesoscale by the organization of nodes into communities. Previous in vivo efforts to map the mesoscale architecture in humans had several limitations. Here we characterize a consensus multiscale community organization of the functional cortical network. We derive this consensus from the clustering of subject-level networks. We applied this analysis to magnetic resonance imaging data from 1003 healthy individuals part of the Human Connectome Project. The hierarchical atlas and code will be made publicly available for future investigators.

scholarly journals Inferring large-scale structural connectivity from local activity in a model of the mouse large-scale cortical network

2021 ◽  
Author(s):  
Ronaldo V. Nunes ◽  
Marcelo Bussotti Reyes ◽  
Jorge F. Mejias ◽  
Raphael Y. de Camargo
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Structural Connectivity ◽  
Ground Truth ◽  
Brain Regions ◽  
Cortical Network ◽  
Ground Truth Data ◽  
Cortical Areas ◽  
Electrophysiological Measurements ◽  
Flow Of Information

AbstractInferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the Generalized Partial Directed Correlation (GPDC), provide estimates of the causal influence between areas. However, such methods have a limitation because their estimates depend on the number of brain regions simultaneously recorded. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale mouse cortical network. The model contains 19 cortical areas modeled using spiking neural populations, and directed weights for long-range projections were obtained from a tract-tracing cortical connectome. We show that the GPDC estimates correlate positively with structural connectivity. Moreover, the correlation between structural and directed functional connectivity is comparable even when using only a few cortical areas for GPDC estimation, a typical scenario for electro-physiological recordings. Finally, GPDC measures also provided a measure of the flow of information among cortical areas.

scholarly journals Directed functional and structural connectivity in a large-scale model for the mouse cortex

2021 ◽  
pp. 1-25
Author(s):  
Ronaldo V. Nunes ◽  
Marcelo B. Reyes ◽  
Jorge F. Mejias ◽  
Raphael Y. de Camargo
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Pearson Correlation ◽  
Structural Connectivity ◽  
Ground Truth ◽  
Scale Model ◽  
Electrophysiological Recording ◽  
Ground Truth Data ◽  
Mouse Cortex ◽  
Electrophysiological Measurements

Abstract Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the Generalized Partial Directed Coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. The model contains 19 cortical areas comprised of spiking neurons, with areas connected by long-range projections with weights obtained from a tract-tracing cortical connectome. We show that GPDC values provide a reasonable estimate of structural connectivity, with an average Pearson correlation over simulations of 0.74. Moreover, even in a typical electrophysiological recording scenario containing five areas, the mean correlation was above 0.6. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

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