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This work was supported by an Engineering and Physical Sciences Research Council (EPSRC) Healthcare Impact Partnership Grant (EP/V047264/1). GCO was supported by funding from EPSRC (EP/T001046/1) from the Quantum Technology hub in sensing and timing (sub-award QTPRF02). TMT is funded by a fellowship from Epilepsy Research UK and Young Epilepsy (FY2101). MS is supported by a Wellcome Technology development award (223736/Z/21/Z). K.W. was supported by the Wellcome Trust (215901/Z/19/Z). The MELD project was supported by the Rosetrees Trust (A2665). The Wellcome Centre for Human Neuroimaging is supported by core funding from Wellcome (203147/Z/16/Z).

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Combining OPM and lesion mapping data for epilepsy surgery planning: a simulation study

Publicat a:Scientific Reports. 14 (1): 2882- - 2024-02-04 14(1), DOI: 10.1038/s41598-024-51857-3

Autors: Mellor, Stephanie; Timms, Ryan C; O'Neill, George C; Tierney, Tim M; Spedden, Meaghan E; Ripart, Mathilde; Chari, Aswin; Tisdall, Martin; Cross, J Helen; Baldeweg, Torsten; Adler, Sophie; Brookes, Matthew J; Wagstyl, Konrad; Barnes, Gareth R

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When planning for epilepsy surgery, multiple potential sites for resection may be identified through anatomical imaging. Magnetoencephalography (MEG) using optically pumped sensors (OP-MEG) is a non-invasive functional neuroimaging technique which could be used to help identify the epileptogenic zone from these candidate regions. Here we test the utility of a-priori information from anatomical imaging for differentiating potential lesion sites with OP-MEG. We investigate a number of scenarios: whether to use rigid or flexible sensor arrays, with or without a-priori source information and with or without source modelling errors. We simulated OP-MEG recordings for 1309 potential lesion sites identified from anatomical images in the Multi-centre Epilepsy Lesion Detection (MELD) project. To localise the simulated data, we used three source inversion schemes: unconstrained, prior source locations at centre of the candidate sites, and prior source locations within a volume around the lesion location. We found that prior knowledge of the candidate lesion zones made the inversion robust to errors in sensor gain, orientation and even location. When the reconstruction was too highly restricted and the source assumptions were inaccurate, the utility of this a-priori information was undermined. Overall, we found that constraining the reconstruction to the region including and around the participant's potential lesion sites provided the best compromise of robustness against modelling or measurement error.

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