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. 2021 Mar;62(3):807-816.
doi: 10.1111/epi.16836. Epub 2021 Feb 10.

Detection of covert lesions in focal epilepsy using computational analysis of multimodal magnetic resonance imaging data

Baris Kanber  1   2   3   4 , Sjoerd B Vos  1   2   3   4   5 , Jane de Tisi  2 , Tobias C Wood  6 , Gareth J Barker  6 , Roman Rodionov  2   3 , Fahmida Amin Chowdhury  2 , Maria Thom  2   7 , Daniel C Alexander  1   4 , John S Duncan  2   3   4 , Gavin P Winston  2   3   8
Affiliations

Detection of covert lesions in focal epilepsy using computational analysis of multimodal magnetic resonance imaging data

Baris Kanber et al. Epilepsia. 2021 Mar.

Abstract

Objective: To compare the location of suspect lesions detected by computational analysis of multimodal magnetic resonance imaging data with areas of seizure onset, early propagation, and interictal epileptiform discharges (IEDs) identified with stereoelectroencephalography (SEEG) in a cohort of patients with medically refractory focal epilepsy and radiologically normal magnetic resonance imaging (MRI) scans.

Methods: We developed a method of lesion detection using computational analysis of multimodal MRI data in a cohort of 62 control subjects, and 42 patients with focal epilepsy and MRI-visible lesions. We then applied it to detect covert lesions in 27 focal epilepsy patients with radiologically normal MRI scans, comparing our findings with the areas of seizure onset, early propagation, and IEDs identified at SEEG.

Results: Seizure-onset zones (SoZs) were identified at SEEG in 18 of the 27 patients (67%) with radiologically normal MRI scans. In 11 of these 18 cases (61%), concordant abnormalities were detected by our method. In the remaining seven cases, either early seizure propagation or IEDs were observed within the abnormalities detected, or there were additional areas of imaging abnormalities found by our method that were not sampled at SEEG. In one of the nine patients (11%) in whom SEEG was inconclusive, an abnormality, which may have been involved in seizures, was identified by our method and was not sampled at SEEG.

Significance: Computational analysis of multimodal MRI data revealed covert abnormalities in the majority of patients with refractory focal epilepsy and radiologically normal MRI that co-located with SEEG defined zones of seizure onset. The method could help identify areas that should be targeted with SEEG when considering epilepsy surgery.

Keywords: MRI; covert; epilepsy; lesion; stereoelectroencephalography.

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Conflict of interest statement

GJB receives honoraria for teaching from GE Healthcare. The other authors have no disclosures or conflicts of interest to report. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Figures

FIGURE 1
FIGURE 1
The focal epilepsy lesion detection workflow. (A) MRI images (eg, 3D T1, 3D FLAIR) are used as input to the workflow, alongside MRI‐derived maps (eg, DESPOT T1, NODDI NDI, DTI FA). These images, rigidly registered (RR) to the 3D T1, together with the brain parcellation (BRpar) obtained from the 3D T1, are used for regional feature extraction and comprise the input to the regional abnormality detector (C1reg). The output from this stage is a probabilistic map of regional brain abnormalities (RAP). (B) This output, together with the original input data, are nonrigidly registered to the MNI152 space (FFR), and alongside the cortical thickness map (XTmap) obtained from the 3D T1 in the same space are used for voxelwise feature extraction and utilised as input to the voxelwise abnormality detector (C2vox). An additional input, not shown in the figure, is the GIF parcellation of the MNI152 brain atlas. The output from C2vox is a voxelwise, probabilistic map of brain abnormalities in MNI152 space, which is converted back to the original 3D T1 space by inverse transformation (FFRinv) and constitutes the final output of the pipeline
FIGURE 2
FIGURE 2
Illustration, in cross‐section, of the extraction of the boundary volumes of a brain region. The brain region shown in this example occupies the volume given by the union of B and C. Volume C is the regional volume deflated by 1 mm around its boundary, whereas the union of A, B, and C is the same volume inflated by 1 mm around the regional boundary. The boundary regions of this brain region are then taken as B, and A, corresponding to a 1 mm thick section of tissue internal to the regional boundary, and a 1 mm thick section of tissue external to the regional boundary, respectively
FIGURE 3
FIGURE 3
An MRI‐positive case: a 27‐year‐old female patient, with right amygdala dysembryoplastic neuroepithelial tumor. Images on the left show the manually drawn lesion mask in blue, whereas the images on the right show the probabilistic abnormality map detected by C2vox in red (lowest probability) to yellow scale (highest probability). The lowest and highest probabilities of abnormality found in the lesional area detected in this case were 3% and 91%, respectively, with a mean over the lesion of 36%
FIGURE 4
FIGURE 4
The agreement between SEEG and the abnormal areas detected by C2vox along with surgical outcomes. SEEG was conclusive in 18 of 27 cases. In 11 of these 18 cases, a SoZ was found at SEEG, which collocated with the abnormal areas detected by C2vox
FIGURE 5
FIGURE 5
Structured comparison of the anatomical coordinates of abnormalities detected by C2vox (blue), with the results of SEEG in an example case (case 1). SEEG electrodes are shown in gray, whereas the contacts that were active during the seizure onset, early propagation, and interictal activity are shown in red, orange, and yellow, respectively. The assessment in this case was that there was seizure onset, early seizure spread, and interictal discharges within the abnormal areas detected by C2vox but there were also abnormal areas detected by C2vox which were sampled at SEEG but not found to be involved in seizure onset
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