Abstract
Epilepsy is a central nervous system disorder that results in asymmetries of brain regional activation and connectivity patterns. The detection of these abnormalities is oftentimes challenging and requires identification of robust bio-markers that are representative of disease activity. Functional Magnetic Resonance Imaging (fMRI) is one of the several methods that can be used to detect such bio-markers. fMRI has a high spatial resolution which makes it a suitable candidate for designing computational methods for computer-aided biomarker discovery. In this paper, we present a computational framework for analyzing fMRI data consisting of 100 epileptic and 80 healthy patients, with an overall goal to produce a novel bio-marker that is predictive of epilepsy. The proposed method is primarily based on Dissimilarity of Activity (DoA) analysis. We demonstrate that the bio-marker presented in this study can be used to capture asymmetries in activities by detecting any abnormalities in Blood Oxygenated Level Dependent (BOLD) signal. In order to represent all asymmetries (of connectivity and activation patterns), we used functional connectivity analysis (FCA) in conjunction with DoA to find underlying connectivity patterns of the regions. Subsequently, these biomarkers were used to train a Support Vector Machine (SVM) classifier that was able to distinguish between healthy and epileptic patients with 87.8% accuracy. These results demonstrate the applicability of computer-aided methods in complex disease diagnosis by simply utilizing the existing data. With the advent of all modern sensing and imaging techniques, the use of intelligent algorithms and advanced computational methods are increasingly becoming the future of computer-aided diagnosis.
Highlights
The human brain is a complex network consisting of anatomical regions that act as nodes.The connections between these nodes are called edges
The first was functional connectivity that has been previously used by Zhang et al [22] and the second one was a novel biomarker that captured dissimilarity of activity (DoA) between bilaterally homologous regions of the brain
We made used of Functional Magnetic Resonance Imaging (fMRI) data of 180 age matched individuals to produce biomarkers that are predictive of epilepsy
Summary
The human brain is a complex network consisting of anatomical regions that act as nodes The connections between these nodes are called edges. The connectivity and activity patterns among the regions are task specific and they can be disturbed by the presence of neuro-biological disease [2]. In the case of a neurobiological disease such as epilepsy, a relatively decreased activity is usually observed in the neo-cortical and mesial temporal regions [3]. Exploring and distinguishing such patterns are of great significance for the adequate diagnosis and treatment of serval neurological conditions. This study presents a computational framework and demonstrate the use of a novel bio-marker that can be used to distinguish between normal and epileptic patients
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
