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Unsupervised machine learning reveals lesional variability in focal cortical dysplasia at mesoscopic scale

Authors
 Hyo M Lee  ;  Ravnoor S Gill  ;  Fatemeh Fadaie  ;  Kyoo H Cho  ;  Marie C Guiot  ;  Seok-Jun Hong  ;  Neda Bernasconi  ;  Andrea Bernasconi 
Citation
 NEUROIMAGE-CLINICAL, Vol.28 : 102438, 2020-09 
Journal Title
NEUROIMAGE-CLINICAL
Issue Date
2020-09
MeSH
Epilepsy* ; Humans ; Magnetic Resonance Imaging ; Malformations of Cortical Development* / diagnostic imaging ; Malformations of Cortical Development, Group I* / diagnostic imaging ; Unsupervised Machine Learning
Keywords
Epilepsy ; Cortical dysplasia ; MRI
Abstract
Objective: Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation and a prevalent cause of surgically amenable epilepsy. While cellular and molecular biology data suggest that FCD lesional characteristics lie along a spectrum, this notion remains to be verified in vivo. We tested the hypothesis that machine learning applied to MRI captures FCD lesional variability at a mesoscopic scale. Methods: We studied 46 patients with histologically verified FCD Type II and 35 age- and sex-matched healthy controls. We applied consensus clustering, an unsupervised learning technique that identifies stable clusters based on bootstrap-aggregation, to 3 T multicontrast MRI (T1-weighted MRI and FLAIR) features of FCD normalized with respect to distributions in controls. Results: Lesions were parcellated into four classes with distinct structural profiles variably expressed within and across patients: Class-1 with isolated white matter (WM) damage; Class-2 combining grey matter (GM) and WM alterations; Class-3 with isolated GM damage; Class-4 with GM-WM interface anomalies. Class membership was replicated in two independent datasets. Classes with GM anomalies impacted local function (resting-state fMRI derived ALFF), while those with abnormal WM affected large-scale connectivity (assessed by degree centrality). Overall, MRI classes reflected typical histopathological FCD characteristics: Class-1 was associated with severe WM gliosis and interface blurring, Class-2 with severe GM dyslamination and moderate WM gliosis, Class-3 with moderate GM gliosis, Class-4 with mild interface blurring. A detection algorithm trained on class-informed data outperformed a class-naive paradigm. Significance: Machine learning applied to widely available MRI contrasts uncovers FCD Type II variability at a mesoscopic scale and identifies tissue classes with distinct structural dimensions, functional and histopathological profiles. Integrating in vivo staging of FCD traits with automated lesion detection is likely to inform the development of novel personalized treatments.
Files in This Item:
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DOI
10.1016/j.nicl.2020.102438
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
Yonsei Authors
Cho, Kyoo Ho(조규호) ORCID logo https://orcid.org/0000-0003-2402-7198
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/190015
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