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Journal Articles Journal of Neural Engineering Year : 2020

Structural connectivity to reconstruct brain activation and effective connectivity between brain regions

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Abstract

Understanding how brain regions interact to perform a specific task is very challenging. EEG and MEG are two non-invasive imaging modalities that allow the measurement of brain activation with high temporal resolution. Several works in EEG/MEG source reconstruction show that estimating brain activation can be improved by considering spatio-temporal constraints but only few of them use structural information to do so. In this work, we present a source reconstruction algorithm that uses brain structural connectivity, estimated from diffusion MRI (dMRI), to constrain the EEG/MEG source reconstruction. Contrarily to most source reconstruction methods which reconstruct activation for each time instant, the proposed method estimates an initial reconstruction for the first time instants and a multivariate autoregressive model that explains the data in further time instants. This au-toregressive model can be thought as an estimation of the effective connectivity between brain regions. We called this algorithm iterative Source and Dynamics reconstruction (iSDR). This paper presents the overall iSDR approach and how the proposed model is optimized to obtain both brain activation and brain region interactions. The accuracy of our method is demonstrated using synthetic data in which it shows a good capability to reconstruct both activation and connectivity. iSDR is also tested with real data obtained from [dataset] (face recognition task). The results are in phase with other works published with the same data and others that used different imaging modalities with the same task showing that the choice of using an autoregressive model gives relevant results.
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Dates and versions

hal-02945585 , version 1 (17-02-2021)

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Brahim Belaoucha, Théodore Papadopoulo. Structural connectivity to reconstruct brain activation and effective connectivity between brain regions. Journal of Neural Engineering, 2020, 17 (3), pp.035006. ⟨10.1088/1741-2552/ab8b2b⟩. ⟨hal-02945585⟩
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