Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging

Sutera, Antonio; Joly, Arnaud; François-Lavet, Vincent; Qiu, Zixiao Aaron; Louppe, Gilles; Ernst, Damien; Geurts, Pierre

doi:10.1007/978-3-319-53070-3_2

Cited by 13 publications

(30 citation statements)

References 12 publications

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“…We reasoned that many of these errors could be eliminated if the RCNN could make multivariate evaluations that considered the activity across all cells in the graph rather than just pairwise correlations. Partial correlation coefficients (PCs) are multivariate summaries of causality and led the overall competition leaderboard (Sutera et al, 2014). Reasoning that the strengths of PC-based classification and our RCNN model might be complementary, we incorporated the PC for each cell pair into our input structure as a fourth row of data during evaluation and training.…”

Section: Resultsmentioning

confidence: 99%

“…In parallel, several alternative methods for inferring functional connectivity have been published. One method, using a partial correlation coefficient metric estimated from the inverse covariance matrix (Sutera et al, 2014), set the state-of-the-art benchmark at the inaugural Kaggle ChaLearn Connectomics competition, edging out baseline summary statistics like correlation coefficients and entropy-based causality estimations (Orlandi et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

“…An emerging alternative to EM reconstruction is statistical inference of the synaptic connectivity latent in observed neural activity patterns. Various statistical methods for inferring connectivity have been published, ranging from model-free approaches, such as Granger causality (Cadotte et al, 2008;Garofalo et al, 2009), transfer entropy (Stetter et al, 2012), and partial correlation coefficients (Sutera et al, 2014), to model-based Bayesian approaches that seek to invert generative models of neural activity (Mishchenko et al, 2011;Soudry et al, 2015).…”

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confidence: 99%

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Inferring Functional Neural Connectivity with Deep Residual Convolutional Networks

Koo

2017

Preprint

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Measuring synaptic connectivity in large neuronal populations remains a major goal of modern neuroscience. While this connectivity is traditionally revealed by anatomical methods such as electron microscopy, an efficient alternative is to computationally infer functional connectivity from recordings of neural activity. However, these statistical techniques still require further refinement before they can be reliably applied to real data. Here, we report significant improvements to a deep learning method for functional connectomics, as assayed on synthetic ChaLearn Connectomics data. The method, which integrates recent advances in convolutional neural network architecture and model-free partial correlation coefficients, outperforms published methods on competition data and can achieve over 90% precision at 1% recall on validation datasets. This suggests that future application of the model to in vivo whole-brain imaging data in larval zebrafish could reliably recover on the order of 10 6 synaptic connections with a 10% false discovery rate. The model also generalizes to networks with different underlying connection probabilities and should scale well when parallelized across multiple GPUs. The method offers real potential as a statistical complement to existing experiments and circuit hypotheses in neuroscience.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Inferring Functional Neural Connectivity with Deep Residual Convolutional Networks

Koo

2017

Preprint

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“…Furthermore, employment of a network inference method depends on the choice of parameters. It is possible to improve both robustness and accuracy of the connectivity matrix by combining several matrices computed using different parameters and/or different inference methods (Sutera et al 2014;Magrans and Nowe 2014).…”

Section: Post-processingmentioning

confidence: 99%

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions

Yoshimoto

Doya

2018

Neural Networks

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This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions.

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“…Only a few techniques can compete in the challenge of inferring the effective connectivity of a network. Partial-correlation [31,47], which takes into account all neurons in the network, showed best performance in detecting direct associations between neurons and filtering out spurious ones [48]. The most significant limitation of this solution is its high computational cost.…”

Section: Introductionmentioning

confidence: 99%

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

Puppo

PrÐ

Bang

et al. 2020

Preprint

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Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in-vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective -direct and causal -connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then, reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect and apparent links. Our method can be generally applied to the functional characterization of any in-vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in-vitro iPSC-derived neuronal cultures by reconstructing their effective connectivity, thus facilitating future efforts to generate predictive models for neurological disorders, drug testing and neuronal network modeling.

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Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging

Cited by 13 publications

References 12 publications

Inferring Functional Neural Connectivity with Deep Residual Convolutional Networks

Inferring Functional Neural Connectivity with Deep Residual Convolutional Networks

Connectivity inference from neural recording data: Challenges, mathematical bases and research directions

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

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