Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism?

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Abstract

Neurodynamics is poorly understood and has raised interest of neuroscientists over the past decade. When a brain pathology cannot be described through structural or functional brain analyses, neurodynamics based descriptors might be the only option to understand a pathology and maybe predict its symptomatic evolution. For example, adolescents or adults with autism have shown mixed results when their intrinsic structural and functional connectivity parameters in the brain at rest were assessed. To visualize neurodynamics parameters we use wavelet coherence maps, which show when and at which frequency two large-scale resting-state networks (RSNs) co-vary and display phase-locked behavior. Here the wavelet-based coherence coefficients are extracted from fMRI of adolescents with and without autism. More specifically, we introduce a novel metric: ‘time of in- phase coherence’ between pairs of resting-state networks. Results show that wavelet coherence maps can be used as neurodynamics maps, and that features such as ‘time of in-phase coherence’ can be calculated between pairs of resting-state networks. This wavelet-based metric shows actually weaker coherent patterns between the ventral stream and the executive control network in patient with autism.
1
Original languageEnglish
Title of host publicationProceedings of the 37th WIC Symposium on Information Theory in the Benelux, and The 6th Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux., Louvain-la-Neuve, Belgium, May 19-20, 2016
Place of PublicationLouvain-la-Neuve
PublisherKatholieke Universiteit Leuven
Pages58-65
Publication statusPublished - May 2016
Event37th WIC Symposium on Information Theory in the Benelux (SITB 2016) and 6th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux - Université catholique de Louvain, Louvan-la Neuve, Belgium
Duration: 19 May 201620 May 2016

Conference

Conference37th WIC Symposium on Information Theory in the Benelux (SITB 2016) and 6th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux
CountryBelgium
CityLouvan-la Neuve
Period19/05/1620/05/16

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Autistic Disorder
Brain
Pathology
Executive Function
Magnetic Resonance Imaging

Cite this

Bernas, A., Barendse, E. M., Zinger, S., & Aldenkamp, A. P. (2016). Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism? In Proceedings of the 37th WIC Symposium on Information Theory in the Benelux, and The 6th Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux., Louvain-la-Neuve, Belgium, May 19-20, 2016 (pp. 58-65). Louvain-la-Neuve: Katholieke Universiteit Leuven.
Bernas, Antoine ; Barendse, Evelien M ; Zinger, Svitlana ; Aldenkamp, Albert P. / Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism?. Proceedings of the 37th WIC Symposium on Information Theory in the Benelux, and The 6th Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux., Louvain-la-Neuve, Belgium, May 19-20, 2016. Louvain-la-Neuve : Katholieke Universiteit Leuven, 2016. pp. 58-65
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title = "Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism?",
abstract = "Neurodynamics is poorly understood and has raised interest of neuroscientists over the past decade. When a brain pathology cannot be described through structural or functional brain analyses, neurodynamics based descriptors might be the only option to understand a pathology and maybe predict its symptomatic evolution. For example, adolescents or adults with autism have shown mixed results when their intrinsic structural and functional connectivity parameters in the brain at rest were assessed. To visualize neurodynamics parameters we use wavelet coherence maps, which show when and at which frequency two large-scale resting-state networks (RSNs) co-vary and display phase-locked behavior. Here the wavelet-based coherence coefficients are extracted from fMRI of adolescents with and without autism. More specifically, we introduce a novel metric: ‘time of in- phase coherence’ between pairs of resting-state networks. Results show that wavelet coherence maps can be used as neurodynamics maps, and that features such as ‘time of in-phase coherence’ can be calculated between pairs of resting-state networks. This wavelet-based metric shows actually weaker coherent patterns between the ventral stream and the executive control network in patient with autism.1",
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Bernas, A, Barendse, EM, Zinger, S & Aldenkamp, AP 2016, Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism? in Proceedings of the 37th WIC Symposium on Information Theory in the Benelux, and The 6th Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux., Louvain-la-Neuve, Belgium, May 19-20, 2016. Katholieke Universiteit Leuven, Louvain-la-Neuve, pp. 58-65, 37th WIC Symposium on Information Theory in the Benelux (SITB 2016) and 6th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Louvan-la Neuve, Belgium, 19/05/16.

Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism? / Bernas, Antoine; Barendse, Evelien M; Zinger, Svitlana; Aldenkamp, Albert P.

Proceedings of the 37th WIC Symposium on Information Theory in the Benelux, and The 6th Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux., Louvain-la-Neuve, Belgium, May 19-20, 2016. Louvain-la-Neuve : Katholieke Universiteit Leuven, 2016. p. 58-65.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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T1 - Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism?

AU - Bernas, Antoine

AU - Barendse, Evelien M

AU - Zinger, Svitlana

AU - Aldenkamp, Albert P.

PY - 2016/5

Y1 - 2016/5

N2 - Neurodynamics is poorly understood and has raised interest of neuroscientists over the past decade. When a brain pathology cannot be described through structural or functional brain analyses, neurodynamics based descriptors might be the only option to understand a pathology and maybe predict its symptomatic evolution. For example, adolescents or adults with autism have shown mixed results when their intrinsic structural and functional connectivity parameters in the brain at rest were assessed. To visualize neurodynamics parameters we use wavelet coherence maps, which show when and at which frequency two large-scale resting-state networks (RSNs) co-vary and display phase-locked behavior. Here the wavelet-based coherence coefficients are extracted from fMRI of adolescents with and without autism. More specifically, we introduce a novel metric: ‘time of in- phase coherence’ between pairs of resting-state networks. Results show that wavelet coherence maps can be used as neurodynamics maps, and that features such as ‘time of in-phase coherence’ can be calculated between pairs of resting-state networks. This wavelet-based metric shows actually weaker coherent patterns between the ventral stream and the executive control network in patient with autism.1

AB - Neurodynamics is poorly understood and has raised interest of neuroscientists over the past decade. When a brain pathology cannot be described through structural or functional brain analyses, neurodynamics based descriptors might be the only option to understand a pathology and maybe predict its symptomatic evolution. For example, adolescents or adults with autism have shown mixed results when their intrinsic structural and functional connectivity parameters in the brain at rest were assessed. To visualize neurodynamics parameters we use wavelet coherence maps, which show when and at which frequency two large-scale resting-state networks (RSNs) co-vary and display phase-locked behavior. Here the wavelet-based coherence coefficients are extracted from fMRI of adolescents with and without autism. More specifically, we introduce a novel metric: ‘time of in- phase coherence’ between pairs of resting-state networks. Results show that wavelet coherence maps can be used as neurodynamics maps, and that features such as ‘time of in-phase coherence’ can be calculated between pairs of resting-state networks. This wavelet-based metric shows actually weaker coherent patterns between the ventral stream and the executive control network in patient with autism.1

M3 - Conference contribution

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BT - Proceedings of the 37th WIC Symposium on Information Theory in the Benelux, and The 6th Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux., Louvain-la-Neuve, Belgium, May 19-20, 2016

PB - Katholieke Universiteit Leuven

CY - Louvain-la-Neuve

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Bernas A, Barendse EM, Zinger S, Aldenkamp AP. Wavelet-based coherence between large-scale resting-state networks : neurodynamics marker for autism? In Proceedings of the 37th WIC Symposium on Information Theory in the Benelux, and The 6th Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux., Louvain-la-Neuve, Belgium, May 19-20, 2016. Louvain-la-Neuve: Katholieke Universiteit Leuven. 2016. p. 58-65