A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis
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 M...
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doaj-6dc2e2fd21794494b271915d7e66d1442020-11-25T01:15:28ZengMDPI AGSymmetry2073-89942019-08-0111897910.3390/sym11080979sym11080979A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy DiagnosisAbdul Basit0Saqib Ali Khan1Waqas Tariq Toor2Naeem Maroof3Muhammad Saadi4Atif Ali Khan5Department of Computer Engineering, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan 64200, PakistanDepartment of Computer Engineering, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan 64200, PakistanDepartment of Electrical Engineering, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan 64200, PakistanDepartment of Computer Engineering, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan 64200, PakistanDepartment of Electrical Engineering, University of Central Punjab, Lahore 54000, PakistanDepartment of Medicine, and Institute of Genomics and Systems Biology, Knapp Center for Biomedical Discovery, University of Chicago, Chicago, IL 60637, USAEpilepsy 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.https://www.mdpi.com/2073-8994/11/8/979epilepsyfunctional magnetic resonance imagingdissimilarity of activityfunctional connectivity analysisBOLD signalsupport vector machine |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Abdul Basit Saqib Ali Khan Waqas Tariq Toor Naeem Maroof Muhammad Saadi Atif Ali Khan |
spellingShingle |
Abdul Basit Saqib Ali Khan Waqas Tariq Toor Naeem Maroof Muhammad Saadi Atif Ali Khan A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis Symmetry epilepsy functional magnetic resonance imaging dissimilarity of activity functional connectivity analysis BOLD signal support vector machine |
author_facet |
Abdul Basit Saqib Ali Khan Waqas Tariq Toor Naeem Maroof Muhammad Saadi Atif Ali Khan |
author_sort |
Abdul Basit |
title |
A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis |
title_short |
A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis |
title_full |
A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis |
title_fullStr |
A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis |
title_full_unstemmed |
A Novel Dissimilarity of Activity Biomarker and Functional Connectivity Analysis for the Epilepsy Diagnosis |
title_sort |
novel dissimilarity of activity biomarker and functional connectivity analysis for the epilepsy diagnosis |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2019-08-01 |
description |
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. |
topic |
epilepsy functional magnetic resonance imaging dissimilarity of activity functional connectivity analysis BOLD signal support vector machine |
url |
https://www.mdpi.com/2073-8994/11/8/979 |
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