Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

Overview

Journal Neural Comput

Publisher MIT Press

Specialty Medical Informatics

Date 2017 Aug 5

PMID 28777719

Citations 5

Authors

Tiger W Lin

Anup Das

Giri P Krishnan

Maxim Bazhenov

Terrence J Sejnowski

Affiliations

Soon will be listed here.

Abstract

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008 ), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005 ; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005 ; Pillow et al., 2008 ), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals.

Citing Articles

Analyzing asymmetry in brain hierarchies with a linear state-space model of resting-state fMRI data.

Benozzo D, Baggio G, Baron G, Chiuso A, Zampieri S, Bertoldo A Netw Neurosci. 2024; 8(3):965-988.

PMID: 39355437 PMC: 11424037. DOI: 10.1162/netn_a_00381.

Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times.

Chen Y, Bukhari Q, Lin T, Sejnowski T Netw Neurosci. 2022; 6(2):614-633.

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Dynamical differential covariance recovers directional network structure in multiscale neural systems.

Chen Y, Rosen B, Sejnowski T Proc Natl Acad Sci U S A. 2022; 119(24):e2117234119.

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Rethinking Measures of Functional Connectivity via Feature Extraction.

Mohanty R, Sethares W, Nair V, Prabhakaran V Sci Rep. 2020; 10(1):1298.

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Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions.

Bielczyk N, Llera A, Buitelaar J, Glennon J, Beckmann C Netw Neurosci. 2019; 3(4):1009-1037.

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