The objective was to determine the optimal combination of multimodal imaging methods (IMs) for localizing the epileptogenic zone (EZ) in patients with MR-negative drug-resistant epilepsy. Data from 25 patients with MR-negative focal epilepsy (age 30 ± 10 years, 16M/9F) who underwent surgical resection of the EZ and from 110 healthy controls (age 31 ± 9 years; 56M/54F) were used to evaluate IMs based on 3T MRI, FDG-PET, HD-EEG, and SPECT. Patients with successful outcomes and/or positive histological findings were evaluated. From 38 IMs calculated per patient, 13 methods were selected by evaluating the mutual similarity of the methods and the accuracy of the EZ localization. The best results in postsurgical patients for EZ localization were found for ictal/ interictal SPECT (SISCOM), FDG-PET, arterial spin labeling (ASL), functional regional homogeneity (ReHo), gray matter volume (GMV), cortical thickness, HD electrical source imaging (ESI-HD), amplitude of low-frequency fluctuation (ALFF), diffusion tensor imaging, and kurtosis imaging. Combining IMs provides the method with the most accurate EZ identification in MR-negative epilepsy. The PET, SISCOM, and selected MRI-post-processing techniques are useful for EZ localization for surgical tailoring.

• MeSH
• dospělí MeSH
• elektroencefalografie MeSH
• epilepsie * diagnostické zobrazování   chirurgie   MeSH
• fluorodeoxyglukosa F18 * MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• mladý dospělý MeSH
• neurozobrazování metody   MeSH
• zobrazování difuzních tenzorů MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladý dospělý MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• fluorodeoxyglukosa F18 * MeSH

Many methods applied to data acquired by various imaging modalities have been evaluated for their benefit in localizing lesions in magnetic resonance (MR) negative epilepsy patients. No approach has proven to be a stand-alone method with sufficiently high sensitivity and specificity. The presented study addresses the potential benefit of the automated fusion of results of individual methods in presurgical evaluation. We collected electrophysiological, MR, and nuclear imaging data from 137 patients with pharmacoresistant MR-negative/inconclusive focal epilepsy. A subgroup of 32 patients underwent surgical treatment with known postsurgical outcomes and histopathology. We employed a Gaussian mixture model to reveal several classes of gray matter tissue. Classes specific to epileptogenic tissue were identified and validated using the surgery subgroup divided into two disjoint sets. We evaluated the classification accuracy of the proposed method at a voxel-wise level and assessed the effect of individual methods. The training of the classifier resulted in six classes of gray matter tissue. We found a subset of two classes specific to tissue located in resected areas. The average classification accuracy (i.e., the probability of correct classification) was significantly higher than the level of chance in the training group (0.73) and even better in the validation surgery subgroup (0.82). Nuclear imaging, diffusion-weighted imaging, and source localization of interictal epileptic discharges were the strongest methods for classification accuracy. We showed that the automatic fusion of results can identify brain areas that show epileptogenic gray matter tissue features. The method might enhance the presurgical evaluations of MR-negative epilepsy patients.

• Klíčová slova
• data fusion, neuroimaging, nonlesional epilepsy, seizure onset zone,
• MeSH
• dospělí MeSH
• elektroencefalografie metody   MeSH
• epilepsie parciální diagnostické zobrazování   MeSH
• jednofotonová emisní výpočetní tomografie metody   MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• multimodální zobrazování MeSH
• neurozobrazování metody   MeSH
• pozitronová emisní tomografie metody   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH