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

Jonah Isen, Andrea Perera-Ortega, Sjoerd B. Vos, Roman Rodionov, Baris Kanber, Fahmida A. Chowdhury, John S. Duncan, Parvin Mousavi, Gavin P. Winston*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number102837
JournalNeuroImage: Clinical
Volume32
DOIs
Publication statusPublished - Jan 2021
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported by the Medical Research Council (MR/M00841X/1), Sobell Foundation and National Institute for Health Research Biomedical Research Centre at University College London Hospitals NHS Foundation Trust and University College London. We are grateful to the Epilepsy Society for supporting the Epilepsy Society MRI scanner.

Funding Information:
This research was supported by the Medical Research Council (MR/M00841X/1), Sobell Foundation and National Institute for Health Research Biomedical Research Centre at University College London Hospitals NHS Foundation Trust and University College London. We are grateful to the Epilepsy Society for supporting the Epilepsy Society MRI scanner.

Publisher Copyright:
© 2021 The Authors

Keywords

  • Focal epilepsy
  • Lesion detection
  • MRI-negative
  • Magnetic resonance imaging
  • Non-parametric combination
  • Voxel-based analysis

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