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Neuroimaging Phenotyping and Assessment of Structural‐Metabolic‐Electrophysiological Alterations in the Temporal Neocortex of Focal Cortical Dysplasia IIIa
Author(s) -
Mo Jiajie,
Wei Wei,
Liu Zhenyu,
Zhang Jianguo,
Ma Yanshan,
Sang Lin,
Hu Wenhan,
Zhang Chao,
Wang Yao,
Wang Xiu,
Liu Chang,
Zhao Baotian,
Gao Dongmei,
Tian Jie,
Zhang Kai
Publication year - 2021
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27615
Subject(s) - neocortex , fluid attenuated inversion recovery , neuroimaging , temporal lobe , cortical dysplasia , hyperintensity , medicine , positron emission tomography , epilepsy , magnetic resonance imaging , pathology , neuroscience , nuclear medicine , radiology , psychology
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 T 2 ‐weighted fluid‐attenuated inversion recovery (FLAIR), synthetic T 1 ‐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 ( P uncorrected < 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