Unraveling gender-specific structural brain differences in drug-resistant epilepsy using advanced deep learning techniques

Abstract

Factors of age, gender, and psychiatric comorbidities in epileptic patients, particularly those with drug-resistant epilepsy (DRE), have not received sufficient attention in clinical practice and research, current research in this domain focus primarily on seizure management. Consequently, a detailed investigation of these differences to understand how each gender's brain changes as a prognosis for epilepsy remains lacking. Furthermore, no previous studies delved into the use of 3D structural MRI (sMRI)-based deep neural networks to predict the biological gender (BG) of epileptic patients. To address this gap and gain insights into the structural aspects of epileptic patients' brains, this study proposed various approaches employing sMRI-based deep neural networks for predicting the BG of epileptic patients. Additionally, it will introduce an innovative preprocessing pipeline, the 3D brain pipeline, and compare it with the standard voxel-based morphometry (VBM) pipeline. the results concluded that there are obvious structural brain differences between genders in epileptic patients, which can be effectively predicted by deep learning approaches, despite the variations that could be raised from age and development of epilepsy. The results also showed that the standard VBM pipeline performs novel 3D brain pipeline, achieving higher metrics, including accuracy (0.961) and AUC (0.97). These findings underscore the significance of considering gender-specific brain changes in epilepsy research and clinical practices, where patients should be treated separately based on their gender.

Keywords

• Biological gender
• Gender
• Gender prediction
• Epilepsy
• Deep learning
• MRI