Literature DB >> 20582566

Estimating the directed information to infer causal relationships in ensemble neural spike train recordings.

Christopher J Quinn1, Todd P Coleman, Negar Kiyavash, Nicholas G Hatsopoulos.   

Abstract

Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures-most of which are variants of Granger causality-with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger's original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering causal relationships whose directionality are consistent with predictions made from the wave propagation of simultaneously recorded local field potentials.

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Year:  2010        PMID: 20582566      PMCID: PMC3171872          DOI: 10.1007/s10827-010-0247-2

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  45 in total

1.  The time-rescaling theorem and its application to neural spike train data analysis.

Authors:  Emery N Brown; Riccardo Barbieri; Valérie Ventura; Robert E Kass; Loren M Frank
Journal:  Neural Comput       Date:  2002-02       Impact factor: 2.026

2.  A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

Authors:  Wilson Truccolo; Uri T Eden; Matthew R Fellows; John P Donoghue; Emery N Brown
Journal:  J Neurophysiol       Date:  2004-09-08       Impact factor: 2.714

Review 3.  Nonlinear multivariate analysis of neurophysiological signals.

Authors:  Ernesto Pereda; Rodrigo Quian Quiroga; Joydeep Bhattacharya
Journal:  Prog Neurobiol       Date:  2005-11-14       Impact factor: 11.685

4.  Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity.

Authors:  Murat Okatan; Matthew A Wilson; Emery N Brown
Journal:  Neural Comput       Date:  2005-09       Impact factor: 2.026

5.  Propagating waves mediate information transfer in the motor cortex.

Authors:  Doug Rubino; Kay A Robbins; Nicholas G Hatsopoulos
Journal:  Nat Neurosci       Date:  2006-11-19       Impact factor: 24.884

6.  Inferring time-varying network topologies from gene expression data.

Authors:  Arvind Rao; Alfred O Hero; David J States; James Douglas Engel
Journal:  EURASIP J Bioinform Syst Biol       Date:  2007

7.  Dynamic causal modelling.

Authors:  K J Friston; L Harrison; W Penny
Journal:  Neuroimage       Date:  2003-08       Impact factor: 6.556

8.  Analyzing information flow in brain networks with nonparametric Granger causality.

Authors:  Mukeshwar Dhamala; Govindan Rangarajan; Mingzhou Ding
Journal:  Neuroimage       Date:  2008-02-25       Impact factor: 6.556

9.  Dynamic Causal Modeling applied to fMRI data shows high reliability.

Authors:  Brianna Schuyler; John M Ollinger; Terrence R Oakes; Tom Johnstone; Richard J Davidson
Journal:  Neuroimage       Date:  2009-07-18       Impact factor: 6.556

10.  Combined EEG-fMRI and tractography to visualise propagation of epileptic activity.

Authors:  K Hamandi; H W R Powell; H Laufs; M R Symms; G J Barker; G J M Parker; L Lemieux; J S Duncan
Journal:  J Neurol Neurosurg Psychiatry       Date:  2007-12-20       Impact factor: 10.154

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  35 in total

Review 1.  A Tutorial for Information Theory in Neuroscience.

Authors:  Nicholas M Timme; Christopher Lapish
Journal:  eNeuro       Date:  2018-09-11

2.  Information theory in neuroscience.

Authors:  Alexander G Dimitrov; Aurel A Lazar; Jonathan D Victor
Journal:  J Comput Neurosci       Date:  2011-02       Impact factor: 1.621

3.  Granger causality-based synaptic weights estimation for analyzing neuronal networks.

Authors:  Pei-Chiang Shao; Jian-Jia Huang; Wei-Chang Shann; Chen-Tung Yen; Meng-Li Tsai; Chien-Chang Yen
Journal:  J Comput Neurosci       Date:  2015-03-13       Impact factor: 1.621

4.  A study of problems encountered in Granger causality analysis from a neuroscience perspective.

Authors:  Patrick A Stokes; Patrick L Purdon
Journal:  Proc Natl Acad Sci U S A       Date:  2017-08-04       Impact factor: 11.205

5.  Inferring neuronal network functional connectivity with directed information.

Authors:  Zhiting Cai; Curtis L Neveu; Douglas A Baxter; John H Byrne; Behnaam Aazhang
Journal:  J Neurophysiol       Date:  2017-05-03       Impact factor: 2.714

6.  Reconfiguring Motor Circuits for a Joint Manual and BCI Task.

Authors:  Benjamin Lansdell; Ivana Milovanovic; Cooper Mellema; Eberhard E Fetz; Adrienne L Fairhall; Chet T Moritz
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2019-09-27       Impact factor: 3.802

7.  Windowed Granger causal inference strategy improves discovery of gene regulatory networks.

Authors:  Justin D Finkle; Jia J Wu; Neda Bagheri
Journal:  Proc Natl Acad Sci U S A       Date:  2018-02-12       Impact factor: 11.205

8.  Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

Authors:  Alireza Sheikhattar; Sina Miran; Ji Liu; Jonathan B Fritz; Shihab A Shamma; Patrick O Kanold; Behtash Babadi
Journal:  Proc Natl Acad Sci U S A       Date:  2018-04-09       Impact factor: 11.205

9.  Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions.

Authors:  Dong Song; Haonan Wang; Catherine Y Tu; Vasilis Z Marmarelis; Robert E Hampson; Sam A Deadwyler; Theodore W Berger
Journal:  J Comput Neurosci       Date:  2013-05-15       Impact factor: 1.621

10.  Neural Estimator of Information for Time-Series Data with Dependency.

Authors:  Sina Molavipour; Hamid Ghourchian; Germán Bassi; Mikael Skoglund
Journal:  Entropy (Basel)       Date:  2021-05-21       Impact factor: 2.524

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