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Multimodal functional and structural brain connectivity analysis in autism: A preliminary integrated approach with EEG, fMRI and DTI

Multimodal functional and structural brain connectivity analysis in autism: A preliminary integrated approach with EEG, fMRI and DTI
Multimodal functional and structural brain connectivity analysis in autism: A preliminary integrated approach with EEG, fMRI and DTI
This paper proposes a novel approach of integrating different neuroimaging techniques to characterize an autistic brain. Different techniques like EEG, fMRI and DTI have traditionally been used to find biomarkers for autism, but there have been very few attempts for a combined or multimodal approach of EEG, fMRI and DTI to understand the neurobiological basis of autism spectrum disorder (ASD). Here, we explore how the structural brain network correlate with the functional brain network, such that the information encompassed by these two could be uncovered only by using the latter. In this paper, source localization from EEG using independent component analysis (ICA) and dipole fitting has been applied first, followed by selecting those dipoles that are closest to the active regions identified with fMRI. This allows translating the high temporal resolution of EEG to estimate time varying connectivity at the spatial source level. Our analysis shows that the estimated functional connectivity between two active regions can be correlated with the physical properties of the structure obtained from DTI analysis. This constitutes a first step towards opening the possibility of using pervasive EEG to monitor the long-term impact of ASD treatment without the need for frequent expensive fMRI or DTI investigations.
Functional brain connectivity, structural connectivity network, EEG, fMRI, DTI, multimodal analysis
Cociu, Bogdan Alexandru
ee01d12d-3108-489e-82fe-3eaa86ba28ec
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Billeci, Lucia
3131ac8d-7479-460f-9212-73e32acb1abb
Jamal, Wasifa
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Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Calderoni, Sara
453cc1b4-0424-4217-904a-6223dd1f6ed6
Narzisi, Antonio
2b96a3bd-85c4-4a49-8989-ac6934a3bd57
Muratori, Filippo
72a8f12f-671c-40e6-b3bc-219d497b6d76
Cociu, Bogdan Alexandru
ee01d12d-3108-489e-82fe-3eaa86ba28ec
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Billeci, Lucia
3131ac8d-7479-460f-9212-73e32acb1abb
Jamal, Wasifa
3f70176e-843e-46b7-8447-4eefaef104f1
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Calderoni, Sara
453cc1b4-0424-4217-904a-6223dd1f6ed6
Narzisi, Antonio
2b96a3bd-85c4-4a49-8989-ac6934a3bd57
Muratori, Filippo
72a8f12f-671c-40e6-b3bc-219d497b6d76

Cociu, Bogdan Alexandru, Das, Saptarshi, Billeci, Lucia, Jamal, Wasifa, Maharatna, Koushik, Calderoni, Sara, Narzisi, Antonio and Muratori, Filippo (2017) Multimodal functional and structural brain connectivity analysis in autism: A preliminary integrated approach with EEG, fMRI and DTI. IEEE Transactions on Cognitive and Developmental Systems, (99). (doi:10.1109/TCDS.2017.2680408).

Record type: Article

Abstract

This paper proposes a novel approach of integrating different neuroimaging techniques to characterize an autistic brain. Different techniques like EEG, fMRI and DTI have traditionally been used to find biomarkers for autism, but there have been very few attempts for a combined or multimodal approach of EEG, fMRI and DTI to understand the neurobiological basis of autism spectrum disorder (ASD). Here, we explore how the structural brain network correlate with the functional brain network, such that the information encompassed by these two could be uncovered only by using the latter. In this paper, source localization from EEG using independent component analysis (ICA) and dipole fitting has been applied first, followed by selecting those dipoles that are closest to the active regions identified with fMRI. This allows translating the high temporal resolution of EEG to estimate time varying connectivity at the spatial source level. Our analysis shows that the estimated functional connectivity between two active regions can be correlated with the physical properties of the structure obtained from DTI analysis. This constitutes a first step towards opening the possibility of using pervasive EEG to monitor the long-term impact of ASD treatment without the need for frequent expensive fMRI or DTI investigations.

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07875078 - Accepted Manuscript
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Accepted/In Press date: 7 March 2017
e-pub ahead of print date: 9 March 2017
Keywords: Functional brain connectivity, structural connectivity network, EEG, fMRI, DTI, multimodal analysis
Organisations: Electronic & Software Systems

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Local EPrints ID: 411118
URI: https://eprints.soton.ac.uk/id/eprint/411118
PURE UUID: ea9658ae-5891-40b1-aec4-43552efbca34

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Date deposited: 14 Jun 2017 16:31
Last modified: 16 Sep 2019 17:29

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