Jiajie Mo1,2, Wei Wei3,4, Zhenyu Liu3,5,6, Jianguo Zhang1,2, Yanshan Ma7, Lin Sang7, Wenhan Hu1,2, Chao Zhang1,2, Yao Wang1,2, Xiu Wang1,2, Chang Liu1,2, Baotian Zhao1,2, Dongmei Gao1,2, Jie Tian3, Kai Zhang1,2. 1. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. 2. Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China. 3. CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. 4. School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China. 5. CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China. 6. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China. 7. Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China.
Abstract
BACKGROUND: Focal cortical dysplasia IIIa (FCD IIIa) is a common histopathological finding in temporal lobe epilepsy. However, subtle alterations in the temporal neocortex of FCD IIIa renders presurgical diagnosis and definition of the resective range challenging. PURPOSE: To explore neuroimaging phenotyping and structural-metabolic-electrophysiological alterations in FCD IIIa. STUDY TYPE: Retrospective. SUBJECTS: One hundred and sixty-seven subjects aged 4-39 years, including 64 FCD IIIa patients, 89 healthy controls and 14 FCD I patients as disease controls. FIELD STRENGTH/SEQUENCE: 3 T, fast-spin-echo T2 -weighted fluid-attenuated inversion recovery (FLAIR), synthetic T1 -weighted magnetization prepared rapid acquisition gradient echo (MPRAGE). ASSESSMENT: Surface-based linear model was applied to reveal neuroimaging phenotyping in FCD IIIa and assess its relationship with clinical variables. Logistic regression was implemented to identify FCD IIIa patients. Epileptogenicity mapping (EM) was conducted to explore the structural-metabolic-electrophysiological alterations in temporal neocortex of FCD IIIa. STATISTICAL TESTS: Student's t-test was applied to determine the significance of paired differences. Calibration curves were plotted to assess the goodness-of-fit (GOF) of the models, combined with the Hosmer-Lemeshow test. RESULTS: FCD IIIa exhibited widespread hyperintensities in temporal neocortex, and these alterations correlated with disease duration (Puncorrected < 0.01). Machine learning model accurately identified 84.4% of FCD IIIa patients, 92.1% of healthy controls and 92.9% of FCD I patients. Cross-modality analysis showed a significant negative correlation between FLAIR hyperintensity and positron emission tomography hypometabolism P < 0.01). Furthermore, epileptogenic cortices were located predominantly in brain regions with FLAIR hyperintensity and hypometabolism. DATA CONCLUSION: FCD IIIa exhibited widespread temporal neocortex FLAIR hyperintensity. Automated machine learning of neuroimaging patterns is conducive for accurate identification of FCD IIIa. The degree and distribution of morphological alterations related to the extent of metabolic and epileptogenic abnormalities, lending support to its potential value for reduction of the radiative and invasive approaches during presurgical workup. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.
BACKGROUND: Focal cortical dysplasia IIIa (FCD IIIa) is a common histopathological finding in temporal lobe epilepsy. However, subtle alterations in the temporal neocortex of FCD IIIa renders presurgical diagnosis and definition of the resective range challenging. PURPOSE: To explore neuroimaging phenotyping and structural-metabolic-electrophysiological alterations in FCD IIIa. STUDY TYPE: Retrospective. SUBJECTS: One hundred and sixty-seven subjects aged 4-39 years, including 64 FCD IIIa patients, 89 healthy controls and 14 FCD Ipatients as disease controls. FIELD STRENGTH/SEQUENCE: 3 T, fast-spin-echo T2 -weighted fluid-attenuated inversion recovery (FLAIR), synthetic T1 -weighted magnetization prepared rapid acquisition gradient echo (MPRAGE). ASSESSMENT: Surface-based linear model was applied to reveal neuroimaging phenotyping in FCD IIIa and assess its relationship with clinical variables. Logistic regression was implemented to identify FCD IIIa patients. Epileptogenicity mapping (EM) was conducted to explore the structural-metabolic-electrophysiological alterations in temporal neocortex of FCD IIIa. STATISTICAL TESTS: Student's t-test was applied to determine the significance of paired differences. Calibration curves were plotted to assess the goodness-of-fit (GOF) of the models, combined with the Hosmer-Lemeshow test. RESULTS: FCD IIIa exhibited widespread hyperintensities in temporal neocortex, and these alterations correlated with disease duration (Puncorrected < 0.01). Machine learning model accurately identified 84.4% of FCD IIIa patients, 92.1% of healthy controls and 92.9% of FCD Ipatients. Cross-modality analysis showed a significant negative correlation between FLAIR hyperintensity and positron emission tomography hypometabolism P < 0.01). Furthermore, epileptogenic cortices were located predominantly in brain regions with FLAIR hyperintensity and hypometabolism. DATA CONCLUSION: FCD IIIa exhibited widespread temporal neocortex FLAIR hyperintensity. Automated machine learning of neuroimaging patterns is conducive for accurate identification of FCD IIIa. The degree and distribution of morphological alterations related to the extent of metabolic and epileptogenic abnormalities, lending support to its potential value for reduction of the radiative and invasive approaches during presurgical workup. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.