Automatic Detection of Focal Cortical Dysplasia Type II in MRI: Is the Application of Surface-Based Morphometry and Machine Learning Promising?

Background and ObjectivesFocal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.MethodsMagnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.ResultsNeural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.ConclusionAnalyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.

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Deep Convolutional Neural Network Based Interictal-Preictal Electroencephalography Prediction: Application to Focal Cortical Dysplasia Type-II

Frontiers in Neurology ◽

10.3389/fneur.2020.594679 ◽

2020 ◽

Vol 11 ◽

Author(s):

Yoon Gi Chung ◽

Yonghoon Jeon ◽

Sun Ah Choi ◽

Anna Cho ◽

Hunmin Kim ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Focal Cortical Dysplasia ◽

Cortical Dysplasia ◽

Deep Convolutional Neural Network ◽

Type Ii

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Surgical outcome and prognostic factors in epilepsy patients with MR-negative focal cortical dysplasia

PLoS ONE ◽

10.1371/journal.pone.0249929 ◽

2021 ◽

Vol 16 (4) ◽

pp. e0249929

Author(s):

Min Jae Seong ◽

Su Jung Choi ◽

Eun Yeon Joo ◽

Young-Min Shon ◽

Dae-Won Seo ◽

...

Keyword(s):

Prognostic Factors ◽

Surgical Outcomes ◽

Focal Cortical Dysplasia ◽

Cortical Dysplasia ◽

Type I ◽

Epileptiform Discharges ◽

Interictal Epileptiform Discharges ◽

Neuroimaging Data ◽

Magnetic Resonance Imaging Mri ◽

Drug Resistant Epilepsy

Objective Focal cortical dysplasia (FCD) represents a heterogeneous group of disorders of the cortical formation and is one of the most common causes of epilepsy. Magnetic resonance imaging (MRI) is the modality of choice for detecting structural lesions, and the surgical prognosis in patients with MR lesions is favorable. However, the surgical prognosis of patients with MR-negative FCD is unknown. We aimed to evaluate the long-term surgical outcomes and prognostic factors in MR-negative FCD patients through comprehensive presurgical data. Methods We retrospectively reviewed data from 719 drug-resistant epilepsy patients who underwent resective surgery and selected cases in which surgical specimens were pathologically confirmed as FCD Type I or II. If the epileptogenic focus and surgical specimens were obtained from brain areas with a normal MRI appearance, they were classified as MR-negative FCD. Surgical outcomes were evaluated at 2 and 5 years, and clinical, neurophysiological, and neuroimaging data of MR-negative FCD were compared to those of MR-positive FCD. Results Finally, 47 MR-negative and 34 MR-positive FCD patients were enrolled in the study. The seizure-free rate after surgery (Engel classification I) at postoperative 2 year was 59.5% and 64.7% in the MR-negative and positive FCD groups, respectively (p = 0.81). This rate decreased to 57.5% and 44.4% in the MR-negative and positive FCD groups (p = 0.43) at postoperative 5 years. MR-negative FCD showed a higher proportion of FCD type I (87.2% vs. 50.0%, p = 0.001) than MR-positive FCD. Unilobar cerebral perfusion distribution (odds ratio, OR 5.41) and concordance of interictal epileptiform discharges (OR 5.10) were significantly associated with good surgical outcomes in MR-negative FCD. Conclusion In this study, MR-negative and positive FCD patients had a comparable surgical prognosis, suggesting that comprehensive presurgical evaluations, including multimodal neuroimaging studies, are crucial for obtaining excellent surgical outcomes even in epilepsy patients with MR-negative FCD.

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Transcriptomic profiling of high- and low-spiking regions reveals novel epileptogenic mechanisms in focal cortical dysplasia type II patients

Molecular Brain ◽

10.1186/s13041-021-00832-4 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Arpna Srivastava ◽

Krishan Kumar ◽

Jyotirmoy Banerjee ◽

Manjari Tripathi ◽

Vivek Dubey ◽

...

Keyword(s):

Rna Sequencing ◽

Focal Cortical Dysplasia ◽

Phospholipid Metabolism ◽

Cortical Dysplasia ◽

Type Ii ◽

Tissue Samples ◽

Significant Enrichment ◽

Gene Expression Studies ◽

Oligodendrocyte Development ◽

High Throughput Gene Expression

AbstractFocal cortical dysplasia (FCD) is a malformation of the cerebral cortex with poorly-defined epileptogenic zones (EZs), and poor surgical outcome in FCD is associated with inaccurate localization of the EZ. Hence, identifying novel epileptogenic markers to aid in the localization of EZ in patients with FCD is very much needed. High-throughput gene expression studies of FCD samples have the potential to uncover molecular changes underlying the epileptogenic process and identify novel markers for delineating the EZ. For this purpose, we, for the first time performed RNA sequencing of surgically resected paired tissue samples obtained from electrocorticographically graded high (MAX) and low spiking (MIN) regions of FCD type II patients and autopsy controls. We identified significant changes in the MAX samples of the FCD type II patients when compared to non-epileptic controls, but not in the case of MIN samples. We found significant enrichment for myelination, oligodendrocyte development and differentiation, neuronal and axon ensheathment, phospholipid metabolism, cell adhesion and cytoskeleton, semaphorins, and ion channels in the MAX region. Through the integration of both MAX vs non-epileptic control and MAX vs MIN RNA sequencing (RNA Seq) data, PLP1, PLLP, UGT8, KLK6, SOX10, MOG, MAG, MOBP, ANLN, ERMN, SPP1, CLDN11, TNC, GPR37, SLC12A2, ABCA2, ABCA8, ASPA, P2RX7, CERS2, MAP4K4, TF, CTGF, Semaphorins, Opalin, FGFs, CALB2, and TNC were identified as potential key regulators of multiple pathways related to FCD type II pathology. We have identified novel epileptogenic marker elements that may contribute to epileptogenicity in patients with FCD and could be possible markers for the localization of EZ.

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Genome-wide DNA Methylation and RNAseq Analyses Identify Aberrant Signalling Pathways in Focal Cortical Dysplasia (FCD) Type II

Scientific Reports ◽

10.1038/s41598-018-35892-5 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 7

Author(s):

Aparna Banerjee Dixit ◽

Devina Sharma ◽

Manjari Tripathi ◽

Arpna Srivastava ◽

Debasmita Paul ◽

...

Keyword(s):

Dna Methylation ◽

Focal Cortical Dysplasia ◽

Cortical Dysplasia ◽

Signalling Pathways ◽

Type Ii ◽

Genome Wide

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Scalp High Frequency Oscillations Associated with Rhythmic Spikes Activity in Focal Cortical Dysplasia Type II

Journal of the Japan Epilepsy Society ◽

10.3805/jjes.33.672 ◽

2016 ◽

Vol 33 (3) ◽

pp. 672-682

Author(s):

Azusa Tabata ◽

Keiko Hara ◽

Motoki Inaji ◽

Natsumi Tamada ◽

Reina Kawanami ◽

...

Keyword(s):

High Frequency ◽

Focal Cortical Dysplasia ◽

Cortical Dysplasia ◽

Type Ii ◽

High Frequency Oscillations

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Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning

Frontiers in Neurology ◽

10.3389/fneur.2020.548305 ◽

2020 ◽

Vol 11 ◽

Author(s):

Yi Guo ◽

Yushan Liu ◽

Wenjie Ming ◽

Zhongjin Wang ◽

Junming Zhu ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Focal Cortical Dysplasia ◽

Cortical Dysplasia ◽

Machine Learning Algorithms ◽

Classification Model ◽

Supervised Machine Learning ◽

Seizure Onset ◽

Glioneuronal Tumors ◽

Patients With Epilepsy

Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy.Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study.Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature “age at seizure onset” with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD.Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.

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