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Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum

Social and communicative impairments are among the core symptoms of autism spectrum disorders (ASD), and a great deal of evidence supports the notion that these impairments are associated with aberrant functioning and connectivity of various cortical networks. The present study explored the links be...

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Autores principales: Datko, Michael, Gougelet, Robert, Huang, Ming-Xiong, Pineda, Jaime A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899470/
https://www.ncbi.nlm.nih.gov/pubmed/27375419
http://dx.doi.org/10.3389/fnins.2016.00258
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author Datko, Michael
Gougelet, Robert
Huang, Ming-Xiong
Pineda, Jaime A.
author_facet Datko, Michael
Gougelet, Robert
Huang, Ming-Xiong
Pineda, Jaime A.
author_sort Datko, Michael
collection PubMed
description Social and communicative impairments are among the core symptoms of autism spectrum disorders (ASD), and a great deal of evidence supports the notion that these impairments are associated with aberrant functioning and connectivity of various cortical networks. The present study explored the links between sources of MEG amplitude in various frequency bands and functional connectivity MRI in the resting state. The goal of combining these modalities was to use sources of neural oscillatory activity, measured with MEG, as functionally relevant seed regions for a more traditional pairwise fMRI connectivity analysis. We performed a seed-based connectivity analysis on resting state fMRI data, using seed regions derived from frequency-specific amplitude sources in resting state MEG data in the same nine subjects with ASD (10–17 years of age). We then compared fMRI connectivity among these MEG-source-derived regions between participants with autism and typically developing, age-matched controls. We used a source modeling technique designed for MEG data to detect significant amplitude sources in six frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low gamma (30–60 Hz), and high gamma (60–120 Hz). MEG-derived source maps for each participant were co-registered in standard MNI space, and group-level source maps were obtained for each frequency. For each frequency band, the 10 largest clusters resulting from these t-tests were used as regions of interest (ROIs) for the fMRI functional connectivity analysis. Pairwise BOLD signal correlations were obtained between each pair of these ROIs for each frequency band. Each pairwise correlation was compared between the ASD and TD groups using t-tests. We also constrained these pairwise correlations to known network structures, resulting in a follow-up set of correlation matrices specific to each network we considered. Frequency-specific MEG sources had distinct patterns of fMRI resting state functional connectivity in the ASD group, but perhaps the most significant was a finding of hypoconnectivity between many sources of low and high gamma activity. These novel findings suggest that in ASD there are differences in functionally defined networks as shown in previous fMRI studies, as well as between sets of regions defined by magnetoencephalographic neural oscillatory activity.
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spelling pubmed-48994702016-07-01 Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum Datko, Michael Gougelet, Robert Huang, Ming-Xiong Pineda, Jaime A. Front Neurosci Psychiatry Social and communicative impairments are among the core symptoms of autism spectrum disorders (ASD), and a great deal of evidence supports the notion that these impairments are associated with aberrant functioning and connectivity of various cortical networks. The present study explored the links between sources of MEG amplitude in various frequency bands and functional connectivity MRI in the resting state. The goal of combining these modalities was to use sources of neural oscillatory activity, measured with MEG, as functionally relevant seed regions for a more traditional pairwise fMRI connectivity analysis. We performed a seed-based connectivity analysis on resting state fMRI data, using seed regions derived from frequency-specific amplitude sources in resting state MEG data in the same nine subjects with ASD (10–17 years of age). We then compared fMRI connectivity among these MEG-source-derived regions between participants with autism and typically developing, age-matched controls. We used a source modeling technique designed for MEG data to detect significant amplitude sources in six frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low gamma (30–60 Hz), and high gamma (60–120 Hz). MEG-derived source maps for each participant were co-registered in standard MNI space, and group-level source maps were obtained for each frequency. For each frequency band, the 10 largest clusters resulting from these t-tests were used as regions of interest (ROIs) for the fMRI functional connectivity analysis. Pairwise BOLD signal correlations were obtained between each pair of these ROIs for each frequency band. Each pairwise correlation was compared between the ASD and TD groups using t-tests. We also constrained these pairwise correlations to known network structures, resulting in a follow-up set of correlation matrices specific to each network we considered. Frequency-specific MEG sources had distinct patterns of fMRI resting state functional connectivity in the ASD group, but perhaps the most significant was a finding of hypoconnectivity between many sources of low and high gamma activity. These novel findings suggest that in ASD there are differences in functionally defined networks as shown in previous fMRI studies, as well as between sets of regions defined by magnetoencephalographic neural oscillatory activity. Frontiers Media S.A. 2016-06-09 /pmc/articles/PMC4899470/ /pubmed/27375419 http://dx.doi.org/10.3389/fnins.2016.00258 Text en Copyright © 2016 Datko, Gougelet, Huang and Pineda. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Datko, Michael
Gougelet, Robert
Huang, Ming-Xiong
Pineda, Jaime A.
Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum
title Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum
title_full Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum
title_fullStr Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum
title_full_unstemmed Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum
title_short Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum
title_sort resting state functional connectivity mri among spectral meg current sources in children on the autism spectrum
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899470/
https://www.ncbi.nlm.nih.gov/pubmed/27375419
http://dx.doi.org/10.3389/fnins.2016.00258
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