Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning

Abstract

Epilepsy is a neurological disorder characterized by abnormal brain activity. Epileptic patients suffer from unpredictable seizures, which may cause a loss of awareness. Seizures are considered drug resistant if treatment does not affect success. This leads practitioners to calculate the cortical thickness to measure the distance between the brain’s white and grey matter surfaces at various locations to perform a surgical intervention. In this study, we introduce using machine learning as an approach to classify extracted measurements from T1-weighted magnetic resonance imaging. Data were collected from the epilepsy unit at King Abdulaziz University Hospital. We applied two trials to classify the extracted measurements from T1-weighted MRI for drug-resistant epilepsy and healthy control subjects. The preprocessing sequence on T1-weighted MRI images was performed using C++ through BrainSuite’s pipeline. The first trial was performed on seven different combinations of four commonly selected measurements. The best performance was achieved in Exp6 and Exp7, with 80.00% accuracy, 83.00% recall score, and 83.88% precision. It is noticeable that grey matter volume and white matter volume measurements are more significant than the cortical thickness measurement. The second trial applied four different machine learning classifiers after applying 10-fold cross-validation and principal component analysis on all extracted measurements as in the first trial based on the mentioned previous works. The K-nearest neighbours model outperformed the other machine learning classifiers with 97.11% accuracy, 75.00% recall score, and 75.00% precision.

Morphometrics of the preserved post-surgical hemisphere in pediatric drug-resistant epilepsy

“…Here, we compared postsurgical morphometry of the preserved hemisphere of children with DRE and non-neurological controls on (i) gross volumes of the lateral ventricles (LV), gray matter (GM), and white matter (WM); (ii) CxT, CV, and cortical surface area (CSA) of 34 regions; and (iii) volume of nine subcortical structures. CxT, CV, and CSA are commonly utilized in studying DRE 12 and have distinct genetic profiles and lifespan trajectories. [13][14][15] CxT linearly decreases with age; 16 CSA and CV follow a curvilinear trajectory with CSA peaking before CV.…”

“…Here, we compared the postsurgical morphometry of the preserved hemisphere of 32 children with DRE and 51 age-matched typically developing controls on (i) gross anatomical measures of the lateral ventricles (LV), grey matter (GM), and white matter (WM); (ii) CxT (the average distance between the white and pial surfaces), CV, and cortical surface area (CSA) separately for 34 cortical parcels; and (iii) volume of nine subcortical structures. CxT, CV, and CSA in particular are commonly utilised in studying DRE; 19 have distinct genetic profiles, life span trajectories, and associations with disease; [20][21][22] and are differentially associated with cognitive development and neurodevelopmental disorders. [23][24][25] Whereas CxT development follows a linear decrease with age, 26 CSA and CV follow a curvilinear trajectory with CV peaking earlier than CSA.…”

Memristive Neural Networks for Predicting Seizure Activity

“…In recent years, machine learning has proved to be a very effective tool for studying epilepsy. This can be explained by the fact that machine learning algorithms allow one to analyze a large amounts of data on brain activity [ 10 - 14 ] and medical images [ 15 , 16 ], which, in its turn, helps better understand the nature of epileptic seizures, detect the regions of their origin and propagation, and develop the most effective plan of drug therapy taking into account individual patient’s characteristics [ 17 , 18 ]. At the same time, it should be noted that the efficiency of deep ANN training depends directly on the quality of data used for training.…”