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A novel method for calibrating head models to account for variability in conductivity and its evaluation in a sphere model

Schrader Sophie, Antonakakis Marios, Rampp Stefan, Engwer Christian, Wolters Carsten

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Year 2020
Type of Item Peer-Reviewed Journal Publication
Bibliographic Citation S Schrader, M Antonakakis, S Rampp, C Engwer, and C H Wolters, "A novel method for calibrating head models to account for variability in conductivity and its evaluation in a sphere model", Phys. Med. Biol., vol. 65, no. 24, Dec. 2020. doi: 10.1088/1361-6560/abc5aa
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The accuracy in electroencephalography (EEG) and combined EEG and magnetoencephalography (MEG) source reconstructions as well as in optimized transcranial electric stimulation (TES) depends on the conductive properties assigned to the head model, and most importantly on individual skull conductivity. In this study, we present an automatic pipeline to calibrate head models with respect to skull conductivity based on the reconstruction of the P20/N20 response using somatosensory evoked potentials and fields. In order to validate in a well-controlled setup without interplay with numerical errors, we evaluate the accuracy of this algorithm in a 4-layer spherical head model using realistic noise levels as well as dipole sources at different eccentricities with strengths and orientations related to somatosensory experiments. Our results show that the reference skull conductivity can be reliably reconstructed for sources resembling the generator of the P20/N20 response. In case of erroneous assumptions on scalp conductivity, the resulting skull conductivity parameter counterbalances this effect, so that EEG source reconstructions using the fitted skull conductivity parameter result in lower errors than when using the standard value. We propose an automatized procedure to calibrate head models which only relies on non-invasive modalities that are available in a standard MEG laboratory, measures under in vivo conditions and in the low frequency range of interest. Calibrated head modeling can improve EEG and combined EEG/MEG source analysis as well as optimized TES.