Testing non-linearity and directedness of interactions between neural groups in the macaque inferotemporal cortex

Freiwald, Winrich A.; Valdés, P.; Bosch, J.; Biscay, Rolando J.; Jiménez, Juan Carlos; Rodríguez, Luis M.; Rodrı́guez, Valia; Kreiter, Andreas K.; Singer, Wolf

doi:10.1016/s0165-0270(99)00129-6

Cited by 116 publications

(98 citation statements)

References 66 publications

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“…Previous invasive electrophysiological studies have shown that statistical techniques based on VAR-modeling and Granger causality are capable of detecting directed interactions between neuronal populations as reflected in the dynamic structure of LFP signals (Bernasconi and Konig, 1999;Bernasconi et al, 2000;Brovelli et al, 2004;Freiwald et al, 1999). We used simulations to investigate whether and to what extent this capability is preserved with fMRI measurements.…”

Section: Simulationsmentioning

confidence: 99%

“…Granger causality analysis has been previously applied to electrophysiological animal data, specifically Local Field Potentials (LFPs) to detect influences at the neuronal population level (Baccala and Sameshima, 2001;Bernasconi and Konig, 1999;Bernasconi et al, 2000;Brovelli et al, 2004;Freiwald et al, 1999). More recently, Granger causality and related frequency domain measures have also been applied to human EEG data (Hesse et al, 2003;Kaminski et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Mapping directed influence over the brain using Granger causality and fMRI

2005

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We propose Granger causality mapping (GCM) as an approach to explore directed influences between neuronal populations (effective connectivity) in fMRI data. The method does not rely on a priori specification of a model that contains pre-selected regions and connections between them. This distinguishes it from other fMRI effective connectivity approaches that aim at testing or contrasting specific hypotheses about neuronal interactions. Instead, GCM relies on the concept of Granger causality to define the existence and direction of influence from information in the data. Temporal precedence information is exploited to compute Granger causality maps that identify voxels that are sources or targets of directed influence for any selected region-of-interest. We investigated the method by simulations and by application to fMRI data of a complex visuomotor task. The presented exploratory approach of mapping influences between a region of interest and the rest of the brain can form a useful complement to existing models of effective connectivity. D 2004 Elsevier Inc. All rights reserved.

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Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping directed influence over the brain using Granger causality and fMRI

2005

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“…Today, there are many variations which exist including a sparse multivariate autoregressive model (Valdes-Sosa et al, 2005), nonlinear models (Freiwald et al, 1999), time-varying models for non-stationary data (Havlicek et al, 2010;Hesse et al, 2003), and models with the structure of non-parametric spectral factorization (Dhamala et al, 2008) (Stephan andRoebroeck, 2012). The use of these methods with functional MRI data has been criticized partly because of a mismatch in the time frames of the studied neuronal communications and the scanner image acquisition time David et al, 2008).…”

Section: Lag Based Modelsmentioning

confidence: 99%

Evaluation and calibration of functional network modeling methods based on known anatomical connections

et al. 2013

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1-Abstract EnglishRecent studies have identified large scale brain networks based on the spatio-temporal structure of spontaneous fluctuations in resting-state fMRI data. It is expected that functional connectivity based on resting-state data is reflective of-but not identical to the underlying anatomical connectivity.However, which functional connectivity analysis methods reliably predict the network structure remains unclear. Here we tested and compared network connectivity analysis methods by applying them to fMRI restingstate time-series obtained from the human visual cortex. The methods evaluated here are those previously tested against simulated data in Smith et al. (Neuroimage, 2011).To this end, we defined regions within retinotopic visual areas V1, V2, and V3 according to their eccentricity in the visual field, delineating central, intermediate, and peripheral eccentricity regions of interest (ROIs). These ROIs served as nodes in the models we study. We based our evaluation -anatomical connectivity in the monkey visual cortex. For each evaluated method, we computed the fractional rate of detecting connections known to -th percentile of the 4 distribution of interaction magnitudes of those connections not expected to exist.Under optimal conditions, including session duration of 68 minutes, a relatively small network consisting of 9 nodes and artifact-free regression of the global effect, each of the top methods predicted the expected connections with 75%-83% c-sensitivity. Partial Correlation performed best (PCorr; 83%), followed by Regularized Inverse Covariance (ICOV; 79%), Bayesian Network methods (BayesNet; 77%), Correlation (75%), andGeneral Synchronization measures (75%). With decreased session duration, these top methods saw decreases in c-sensitivities, achieving 66%-78% and 60%-70% for 34 and 17 minute sessions, respectively. With a short resting-state fMRI scan of 8.5 minutes (TR = 2s), none of the methods predicted the real network well, with ICOV (53%) and PCorr (51%) performing best. With increased complexity of the network from 9 to 36 nodes, multivariate methods including PCorr and BayesNet saw a decrease in performance. However, this decrease became small when using data from a long (68 minutes) session. Artifact-free regression of the global effect significantly increased the c-sensitivity of all top-performing methods. In an overall evaluation across all tests we performed, PCorr, ICOV and BayesNet set themselves somewhat above all other methods. 5We propose that data-based calibration based on known anatomical connections be integrated into future network studies, in order to maximize sensitivity and reduce false positives. Studies have also demonstrated changes in fMRI-measured brain activity when the subjects are in a state of rest. Importantly, it has been shown that these spontaneous (i.e. without any external stimulation)fluctuations in fMRI signals demonstrate functional connectivity over large parts of the human brain (Xiong et al., 1999). These large parts of the brain correspo...

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“…A number of studies have appeared in the neuroscience literature where the issue of causal effects in neural data is examined [6,8,9,10,11,12,13,14,15]. Three of these studies [9,10,15] used the measures presented in this article.…”

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confidence: 99%

Granger Causality: Basic Theory and Application to Neuroscience

Ding

Chen

Bressler

2006

Handbook of Time Series Analysis

535

521

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Multi-electrode neurophysiological recordings produce massive quantities of data. Multivariate time series analysis provides the basic framework for analyzing the patterns of neural interactions in these data. It has long been recognized that neural interactions are directional. Being able to assess the directionality of neuronal interactions is thus a highly desired capability for understanding the cooperative nature of neural computation. Research over the last few years has shown that Granger causality is a key technique to furnish this capability. The main goal of this article is to provide an expository introduction to the concept of Granger causality. Mathematical frameworks for both bivariate Granger causality and conditional Granger causality are developed in detail with particular emphasis on their spectral representations. The technique is demonstrated in numerical examples where the exact answers of causal influences are known.It is then applied to analyze multichannel local field potentials recorded from monkeys performing a visuomotor task. Our results are shown to be physiologically interpretable and yield new insights into the dynamical organization of large-scale oscillatory cortical networks.

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Testing non-linearity and directedness of interactions between neural groups in the macaque inferotemporal cortex

Cited by 116 publications

References 66 publications

Mapping directed influence over the brain using Granger causality and fMRI

Mapping directed influence over the brain using Granger causality and fMRI

Evaluation and calibration of functional network modeling methods based on known anatomical connections

Granger Causality: Basic Theory and Application to Neuroscience

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