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In this study, we analyzed brain connectivity profiles from mild traumatic brain injury (mTBI) patients and normal controls. We computed Granger causality measures from magnetoencephalographic (MEG) activity obtained at the resting state, in an attempt to understand how the default network is affected by mTBI. Our results show that all subjects exhibit a dense network of peripheral local connections...
In this study, we analyze brain connectivity based on Granger causality computed from magnetoencephalographic (MEG) activity obtained at the resting state in eight autistic and eight normal subjects along with measures of network connectivity derived from graph theory in an attempt to understand how communication in a human brain network is affected by autism. A connectivity matrix was computed for...
It has frequently been reported in the medical literature that the EEG of Alzheimer disease (AD) patients is less synchronous than in healthy subjects. In this paper, it is explored whether loss in EEG synchrony can be used to diagnose AD at an early stage. Multiple synchrony measures are applied to two different EEG data sets: (1) EEG of pre-dementia patients and control subjects; (2) EEG of mild...
In neurophysiology, it is important to determine the causal relationships between neuronal sites. The major problem with existing methods for quantifying the causality in the brain, e.g. Granger causality, is that they assume an multi-variate autoregressive signal model for the multi-channel EEG signals and do not take the nonlinear dependencies between neuronal oscillations into account. In this...
We assessed directional relationships between short RR interval and systolic arterial pressure (SAP) variability series according to the concept of Granger causality. Causality was quantified as the predictability improvement (PI) of a time series obtained when samples of the other series were used for prediction, i.e. moving from autoregressive (AR) to AR exogenous (ARX) prediction. AR and ARX predictions...
A new method for estimating multivariate autoregressive (MVAR) models of cortical connectivity from surface EEG or MEG measurements is presented. Conventional approaches to this problem first attempt to solve the inverse problem to estimate cortical signals and then fit an MVAR model to the estimated signals. Our new approach expresses the measured data in tens of a hidden state equation describing...
In this paper we propose the use of a time-varying multivariate estimator based on the partial directed coherence (PDC), a frequency-domain estimator able to describe interactions between cortical areas in terms of the concept of Granger causality. Time-varying PDC was obtained by the adaptive recursive fit of an MVAR model with time-dependent parameters, by means of a generalized recursive least-square...
In this paper we propose the use of an adaptive multivariate approach to define time-varying multivariate estimators based on the directed transfer function (DTF) and the partial directed coherence (PDC). DTF and PDC are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. Time-varying DTF and PDC were obtained by the...
This paper re-examines the definition of partial directed coherence (PDC) which was recently introduced as a linear frequency-domain quantifier of the multivariate relationship between simultaneously observed time series for application in functional connectivity inference in neuroscience. The present reappraisal aims at improving PDC's performance under scenarios that involve severely unbalanced...
We have proposed a new ictal source analysis approach by combining a spatio-temporal source localization approach, and causal interaction estimation technique. The FINE approach is used to identify neural electrical sources from spatio-temporal scalp-EEGs. The Granger causality estimation uses source waveforms estimated by FINE to characterize the causal interaction between the neural electrical sources...
We have proposed a new ictal source analysis approach by combining a spatio-temporal source localization approach, and causal interaction estimation technique. The FINE approach is used to identify neural electrical sources from spatio-temporal scalp-EEGs. The Granger causality estimation uses source waveforms estimated by FINE to characterize the causal interaction between the neural electrical sources...
The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during the latest years, as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. Among various methods established during the years, the directed transfer function (DTF), the partial directed coherence (PDC) and the direct DTF...
The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during the latest years, as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. The method of the directed transfer function (DTF) is a frequency-domain approach to this problem, based on a multivariate autoregressive modeling...
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