Non-parametric combination of multimodal MRI for lesion detection in focal epilepsy.

Autor:	Isen J; School of Computing, Queen's University, Kingston, Canada., Perera-Ortega A; School of Computing, Queen's University, Kingston, Canada., Vos SB; Centre for Medical Image Computing, University College London, London, UK; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St Peter, UK; National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK., Rodionov R; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St Peter, UK., Kanber B; Centre for Medical Image Computing, University College London, London, UK; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St Peter, UK; National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK., Chowdhury FA; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK., Duncan JS; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St Peter, UK; National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK., Mousavi P; School of Computing, Queen's University, Kingston, Canada., Winston GP; School of Computing, Queen's University, Kingston, Canada; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; MRI Unit, Epilepsy Society, Chalfont St Peter, UK; National Institute for Health Research Biomedical Research Centre at University College London and University College London NHS Foundation Trust, London, UK; Department of Medicine, Division of Neurology & Centre for Neuroscience Studies, Queen's University, Kingston, Canada. Electronic address: gavin.winston@queensu.ca.
Jazyk:	angličtina
Zdroj:	NeuroImage. Clinical [Neuroimage Clin] 2021; Vol. 32, pp. 102837. Date of Electronic Publication: 2021 Sep 25.
DOI:	10.1016/j.nicl.2021.102837
Abstrakt:	One third of patients with medically refractory focal epilepsy have normal-appearing MRI scans. This poses a problem as identification of the epileptogenic region is required for surgical treatment. This study performs a multimodal voxel-based analysis (VBA) to identify brain abnormalities in MRI-negative focal epilepsy. Data was collected from 69 focal epilepsy patients (42 with discrete lesions on MRI scans, 27 with no visible findings on scans), and 62 healthy controls. MR images comprised T1-weighted, fluid-attenuated inversion recovery (FLAIR), fractional anisotropy (FA) and mean diffusivity (MD) from diffusion tensor imaging, and neurite density index (NDI) from neurite orientation dispersion and density imaging. These multimodal images were coregistered to T1-weighted scans, normalized to a standard space, and smoothed with 8 mm FWHM. Initial analysis performed voxel-wise one-tailed t-tests separately on grey matter concentration (GMC), FLAIR, FA, MD, and NDI, comparing patients with epilepsy to controls. A multimodal non-parametric combination (NPC) analysis was also performed simultaneously on FLAIR, FA, MD, and NDI. Resulting p-maps were family-wise error rate corrected, threshold-free cluster enhanced, and thresholded at p < 0.05. Sensitivity was established through visual comparison of results to manually drawn lesion masks or seizure onset zone (SOZ) from stereoelectroencephalography. A leave-one-out cross-validation with the same analysis protocols was performed on controls to determine specificity. NDI was the best performing individual modality, detecting focal abnormalities in 38% of patients with normal MRI and conclusive SOZ. GMC demonstrated the lowest sensitivity at 19%. NPC provided superior performance to univariate analyses with 50% sensitivity. Specificity in controls ranged between 96 and 100% for all analyses. This study demonstrated the utility of a multimodal VBA utilizing NPC for detecting epileptogenic lesions in MRI-negative focal epilepsy. Future work will apply this approach to datasets from other centres and will experiment with different combinations of MR sequences. (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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