A Nonlinear Effective Connectivity Measure Based on Granger Causality and Volterra Series

Effective connectivity refers to the influence one neural system exerts on another and corresponds to the parameter of a model that tries to explain the observed dependencies. In this sense, effective connectivity corresponds to the intuitive notion of coupling or directed causal influence. Traditional measures to quantify the effective connectivity include model-based methods, such as dynamic causal modeling (DCM), Granger causality (GC), and information-theoretic methods. Directed information (DI) has been a recently proposed information-theoretic measure that captures the causality between two time series. Compared to traditional causality detection methods based on linear models, directed information is a model-free measure and can detect both linear and nonlinear causality relationships. However, the effectiveness of using DI for capturing the causality in different models and neurophysiological data has not been thoroughly illustrated to date. In addition, the advantage of DI compared to model-based measures, especially those used to implement Granger causality, has not been fully investigated. In this paper, we address these issues by evaluating the performance of directed information on both simulated data sets and electroencephalogram (EEG) data to illustrate its effectiveness for quantifying the effective connectivity in the brain.

Download Full-text

Multivariate Granger Causality Analysis of Acupuncture Effects in Mild Cognitive Impairment Patients: An fMRI Study

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2013/127271 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Shangjie Chen ◽

Lijun Bai ◽

Maosheng Xu ◽

Fang Wang ◽

Liang Yin ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Granger Causality ◽

Effective Connectivity ◽

Brain Networks ◽

Brain Regions ◽

Causality Analysis ◽

Granger Causality Analysis ◽

Cognitive States ◽

Dorsolateral Prefrontal

Evidence from clinical reports has indicated that acupuncture has a promising effect on mild cognitive impairment (MCI). However, it is still unknown that by what way acupuncture can modulate brain networks involving the MCI. In the current study, multivariate Granger causality analysis (mGCA) was adopted to compare the interregional effective connectivity of brain networks by varying needling depths (deep acupuncture, DA; superficial acupuncture, SA) and at different cognitive states, which were the MCI and healthy control (HC). Results from DA at KI3 in MCI showed that the dorsolateral prefrontal cortex and hippocampus emerged as central hubs and had significant causal influences with each other, but significant in HC for DA. Moreover, only several brain regions had remarkable causal interactions following SA in MCI and even few brain regions following SA in HC. Our results indicated that acupuncture at KI3 at different cognitive states and with varying needling depths may induce distinct reorganizations of effective connectivities of brain networks, and DA at KI3 in MCI can induce the strongest and more extensive effective connectivities related to the therapeutic effect of acupuncture in MCI. The study demonstrated the relatively functional specificity of acupuncture at KI3 in MCI, and needling depths play an important role in acupuncture treatments.

Download Full-text

A unified framework for dissecting the effects of common signals on functional and effective connectivity analyses: power, coherence, and Granger causality

10.1101/186122 ◽

2017 ◽

Author(s):

Dror Cohen ◽

Naotsugu Tsuchiya

Keyword(s):

Granger Causality ◽

Effective Connectivity ◽

Reference Electrode ◽

Mathematical Framework ◽

Connectivity Analysis ◽

Unified Framework ◽

Special Cases ◽

Recorded Data ◽

The Common ◽

Common Signal

AbstractWhen analyzing neural data it is important to consider the limitations of the particular experimental setup. An enduring issue in the context of electrophysiology is the presence of common signals. For example a non-silent reference electrode adds a common signal across all recorded data and this adversely affects functional and effective connectivity analysis. To address the common signals problem, a number of methods have been proposed, but relatively few detailed investigations have been carried out. We address this gap by analyzing local field potentials recorded from the small brains of fruit flies. We conduct our analysis following a solid mathematical framework that allows us to make precise predictions regarding the nature of the common signals. We demonstrate how a framework that jointly analyzes power, coherence and quantities from the Granger causality framework allows us to detect and assess the nature of the common signals. Our analysis revealed substantial common signals in our data, in part due to a non-silent reference electrode. We further show that subtracting spatially adjacent signals (bipolar rereferencing) largely removes the effects of the common signals. However, in some special cases this operation itself introduces a common signal. The mathematical framework and analysis pipeline we present can readily be used by others to detect and assess the nature of the common signals in their data, thereby reducing the chance of misinterpreting the results of functional and effective connectivity analysis.

Download Full-text

Assessment of Effective Connectivity in Alzheimer’s Disease Using Granger Causality

Converging Clinical and Engineering Research on Neurorehabilitation II - Biosystems & Biorobotics ◽

10.1007/978-3-319-46669-9_125 ◽

2016 ◽

pp. 763-767 ◽

Cited By ~ 3

Author(s):

Celia Juan-Cruz ◽

Carlos Gómez ◽

Jesús Poza ◽

Alberto Fernández ◽

Roberto Hornero

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Granger Causality ◽

Effective Connectivity

Download Full-text

Granger Causality of the Electroencephalogram Reveals Abrupt Global Loss of Cortical Information Flow during Propofol-induced Loss of Responsiveness

Anesthesiology ◽

10.1097/aln.0000000000003398 ◽

2020 ◽

Vol 133 (4) ◽

pp. 774-786 ◽

Cited By ~ 2

Author(s):

Rebecca M. Pullon ◽

Lucy Yan ◽

Jamie W. Sleigh ◽

Catherine E. Warnaby

Keyword(s):

Functional Connectivity ◽

Granger Causality ◽

Information Flow ◽

Behavioral Response ◽

Effective Connectivity ◽

Brain Regions ◽

Motor Tasks ◽

Loss Of Information ◽

Behavioral Responsiveness ◽

Scalp Electroencephalogram

Background It is a commonly held view that information flow between widely separated regions of the cerebral cortex is a necessary component in the generation of wakefulness (also termed “connected” consciousness). This study therefore hypothesized that loss of wakefulness caused by propofol anesthesia should be associated with loss of information flow, as estimated by the effective connectivity in the scalp electroencephalogram (EEG) signal. Methods Effective connectivity during anesthesia was quantified by applying bivariate Granger to multichannel EEG data recorded from 16 adult subjects undergoing a slow induction of, and emergence from, anesthesia with intravenous propofol. During wakefulness they were conducting various auditory and motor tasks. Functional connectivity using EEG coherence was also estimated. Results There was an abrupt, substantial, and global decrease in effective connectivity around the point of loss of responsiveness. Recovery of behavioral responsiveness was associated with a comparable recovery in information flow pattern (expressed as normalized values). The median (interquartile range) change was greatest in the delta frequency band: decreasing from 0.15 (0.21) 2 min before loss of behavioral response, to 0.06 (0.04) 2 min after loss of behavioral response (P < 0.001). Regional decreases in information flow were maximal in a posteromedial direction from lateral frontal and prefrontal regions (0.82 [0.24] 2 min before loss of responsiveness, decreasing to 0.17 [0.05] 2 min after), and least for information flow from posterior channels. The widespread decrease in bivariate Granger causality reflects loss of cortical coordination. The relationship between functional connectivity (coherence) and effective connectivity (Granger causality) was inconsistent. Conclusions Propofol-induced unresponsiveness is marked by a global decrease in information flow, greatest from the lateral frontal and prefrontal brain regions in a posterior and medial direction. Loss of information flow may be a useful measure of connected consciousness. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

Download Full-text