MAREČEK, Radek, Pavel ŘÍHA, Michaela BARTOŇOVÁ, Martin KOJAN, Martin LAMOŠ, Martin GAJDOŠ, Lubomír VOJTÍŠEK, Michal MIKL, Marek BARTOŇ, Irena DOLEŽALOVÁ, Martin PAIL, Ondřej STRÝČEK, Marta PAŽOURKOVÁ, Milan BRÁZDIL and Ivan REKTOR. Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging. Human Brain mapping. Hoboken: WILEY-BLACKWELL, vol. 42, No 9, p. 2921-2930. ISSN 1065-9471. doi:10.1002/hbm.25413. 2021.
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Basic information
Original name Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging
Authors MAREČEK, Radek (203 Czech Republic, belonging to the institution), Pavel ŘÍHA (203 Czech Republic, belonging to the institution), Michaela BARTOŇOVÁ (703 Slovakia, belonging to the institution), Martin KOJAN (703 Slovakia, belonging to the institution), Martin LAMOŠ (203 Czech Republic, belonging to the institution), Martin GAJDOŠ (203 Czech Republic, belonging to the institution), Lubomír VOJTÍŠEK (203 Czech Republic, belonging to the institution), Michal MIKL (203 Czech Republic, belonging to the institution), Marek BARTOŇ (203 Czech Republic, belonging to the institution), Irena DOLEŽALOVÁ (203 Czech Republic, belonging to the institution), Martin PAIL (203 Czech Republic, belonging to the institution), Ondřej STRÝČEK (203 Czech Republic, belonging to the institution), Marta PAŽOURKOVÁ (203 Czech Republic), Milan BRÁZDIL (203 Czech Republic, belonging to the institution) and Ivan REKTOR (203 Czech Republic, guarantor, belonging to the institution).
Edition Human Brain mapping, Hoboken, WILEY-BLACKWELL, 2021, 1065-9471.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 30103 Neurosciences
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 5.399
RIV identification code RIV/00216224:14740/21:00120107
Organization unit Central European Institute of Technology
Doi http://dx.doi.org/10.1002/hbm.25413
UT WoS 000633522300001
Keywords in English data fusion; neuroimaging; nonlesional epilepsy; seizure onset zone
Tags 14110127, CF MAFIL, podil, rivok
Tags International impact, Reviewed
Changed by Changed by: Mgr. Pavla Foltynová, Ph.D., učo 106624. Changed: 9/2/2022 13:07.
Abstract
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.
Links
LM2018129, research and development projectName: Národní infrastruktura pro biologické a medicínské zobrazování Czech-BioImaging
Investor: Ministry of Education, Youth and Sports of the CR
NV17-32292A, research and development projectName: Detekce léze u nelezionální epilepsie s využitím multimodálního zobrazování
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