Feature selection; Functional connectivity; Machine learning; Nested cross validation; Neuropsychiatric systemic lupus erythematosus-npsle; Recursive feature elimination; Resting-state fmri; Restingstate networks; Supervised learning; Support vector machines; Nested cross validations; Resting state; Systemic lupus erythematosus; Signal Processing; Media Technology; Radiology, Nuclear Medicine and Imaging; Instrumentation
Abstract :
[en] In this study we explored the robustness of machine learning algorithms for the classification of Neuropsychiatric systemic lupus erythematosus (NPSLE) patients and healthy controls using resting-state fMRI functional connectivity matrices. NPSLE, which is driven by systemic autoimmune inflammation in the context of lupus, involves a wide range of focal and diffuse central and peripheral nervous system symptoms and poses significant diagnostic challenges. Machine learning applications on clinical data may enhance the existing workflow for NPSLE classification as there is no established method of applying neuroimaging data to the diagnosis of NPSLE. Feature selection methods were applied prior to the classification process in order to perform the classification process on a lower dimension feature space. The Connectivity Matrix used consisted of pairwise regional functional associations of the fMRI signals (ROI to ROI correlations) within each of three predetermined brain networks in 41 NPSLE patients and 31 healthy control subjects. Support Vector Machines (SVM) was utilized in the final model. Results were evaluated using a nested cross validation methodology to prevent overfitting, and enhance generalization. Regions of Interest (ROI's) that contributed most in the final model were: Right Inferior Temporal, Thalamus, Left Angular Gyrus, Right Precuneus, Left Primary Motor Cortex, SMA, Left and Right Primary Motor Cortex. With a final F1 score of up to 77%, the results demonstrate the potential for the future implementation of similar methods in the diagnosis of NPSLE.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Simos, Nikolaos-Ioannis ; Université de Liège - ULiège > Département des sciences cliniques > Neurologie ; Department of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece ; Computational Bio-Medicine Laboratory, Foundation for Research and Technology-Hellas, Heraklion, Greece
Kavroulakis, E.; Department of Radiology Medical School, University of Crete, Heraklion, Greece
Manikis, G.C.; Foundation for Research and Technology-Hellas, Computational Bio-Medicine Laboratory, Heraklion, Greece
Bertsias, G.; Department of Rheumatology Medical School, University of Crete, Heraklion, Greece ; Foundation for Research and Technology-Hellas, Insitute of Molecular Biology and Biotechnology, Heraklion, Greece
Papadaki, E.; Department of Radiology Medical School, University of Crete, Heraklion, Greece
Marias, K.; Department of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
Language :
English
Title :
Machine learning classification of neuropsychiatric systemic lupus erythematosus patients using resting-state fmri functional connectivity
Publication date :
December 2019
Event name :
2019 IEEE International Conference on Imaging Systems and Techniques (IST)
Event place :
Abu Dhabi, United Arab Emirates
Event date :
08-12-2019 => 10-12-2019
Main work title :
IST 2019 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
Publisher :
Institute of Electrical and Electronics Engineers Inc.
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