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      Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder

      Author(s): , , , , , ,
      Publication date Created:
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      Journal: Brain Sciences
      Publisher: MDPI AG

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          Abstract

          Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that interferes with normal brain development. Brain connectivity may serve as a biomarker for ASD in this respect. This study enrolled a total of 179 children aged 3−10 years (90 typically developed (TD) and 89 with ASD). We used a weighted phase lag index and a directed transfer function to investigate the functional and effective connectivity in children with ASD and TD. Our findings indicated that patients with ASD had local hyper-connectivity of brain regions in functional connectivity and simultaneous significant decrease in effective connectivity across hemispheres. These connectivity abnormalities may help to find biomarkers of ASD.

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          Investigating Causal Relations by Econometric Models and Cross-spectral Methods

          C. W. J. Granger (1969)
          Article 0 comments Cited 1555 times – based on 0 reviews     Review now
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            Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources.

            Cornelis Stam, Guido Nolte, Andreas Daffertshofer (2007)
            To address the problem of volume conduction and active reference electrodes in the assessment of functional connectivity, we propose a novel measure to quantify phase synchronization, the phase lag index (PLI), and compare its performance to the well-known phase coherence (PC), and to the imaginary component of coherency (IC). The PLI is a measure of the asymmetry of the distribution of phase differences between two signals. The performance of PLI, PC, and IC was examined in (i) a model of 64 globally coupled oscillators, (ii) an EEG with an absence seizure, (iii) an EEG data set of 15 Alzheimer patients and 13 control subjects, and (iv) two MEG data sets. PLI and PC were more sensitive than IC to increasing levels of true synchronization in the model. PC and IC were influenced stronger than PLI by spurious correlations because of common sources. All measures detected changes in synchronization during the absence seizure. In contrast to PC, PLI and IC were barely changed by the choice of different montages. PLI and IC were superior to PC in detecting changes in beta band connectivity in AD patients. Finally, PLI and IC revealed a different spatial pattern of functional connectivity in MEG data than PC. The PLI performed at least as well as the PC in detecting true changes in synchronization in model and real data but, at the same token and like-wise the IC, it was much less affected by the influence of common sources and active reference electrodes. Copyright 2007 Wiley-Liss, Inc.
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              An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias.

              Martin Vinck, Robert Oostenveld, Marijn van Wingerden … (2011)
              Phase-synchronization is a manifestation of interaction between neuronal groups measurable from LFP, EEG or MEG signals, however, volume conduction can cause the coherence and the phase locking value to spuriously increase. It has been shown that the imaginary component of the coherency (ImC) cannot be spuriously increased by volume-conduction of independent sources. Recently, it was proposed that the phase lag index (PLI), which estimates to what extent the phase leads and lags between signals from two sensors are nonequiprobable, improves on the ImC. Compared to ImC, PLI has the advantage of being less influenced by phase delays. However, sensitivity to volume-conduction and noise, and capacity to detect changes in phase-synchronization, is hindered by the discontinuity of the PLI, as small perturbations turn phase lags into leads and vice versa. To solve this problem, we introduce a related index, namely the weighted phase lag index (WPLI). Differently from PLI, in WPLI the contribution of the observed phase leads and lags is weighted by the magnitude of the imaginary component of the cross-spectrum. We demonstrate two advantages of the WPLI over the PLI, in terms of reduced sensitivity to additional, uncorrelated noise sources and increased statistical power to detect changes in phase-synchronization. Another factor that can affect phase-synchronization indices is sample-size bias. We show that, when directly estimated, both PLI and the magnitude of the ImC have typically positively biased estimators. To solve this problem, we develop an unbiased estimator of the squared PLI, and a debiased estimator of the squared WPLI. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Journal ID (publisher-id): BSRCCS
                Title: Brain Sciences
                Abbreviated Title: Brain Sciences
                Publisher: MDPI AG
                ISSN (Electronic): 2076-3425
                Publication date Created: January 2023
                Publication date (Electronic): January 12 2023
                Volume: 13
                Issue: 1
                Page: 130
                Article
                DOI: 10.3390/brainsci13010130
                PubMed ID: 36672111
                SO-VID: 2c447bcb-b492-4bed-83d8-3ca9534f91b5
                Copyright © © 2023
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                https://creativecommons.org/licenses/by/4.0/

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