Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach.
[en] Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients.
Disciplines :
Neurosciences & behavior
Author, co-author :
Simos, Nikolaos-Ioannis ; Université de Liège - ULiège > Département des sciences cliniques > Neurologie ; Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece ; Department of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece
Dimitriadis, Stavros I ; Integrative Neuroimaging Lab, 55133 Thessaloniki, Greece ; 1st Department of Neurology, G.H. "AHEPA", School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece ; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF24 4HQ, UK ; Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Research Institute School of Medicine, & MRC Centre for Neuropsychiatric Genetics and Genomics, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF14 4EP, UK
Kavroulakis, Eleftherios; Department of Radiology, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece
Manikis, Georgios C ; Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece
Bertsias, George ; Department of Rheumatology, Clinical Immunology and Allergy, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece ; Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece
Simos, Panagiotis; Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece ; Department of Psychiatry, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece
Maris, Thomas G; Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece ; Department of Radiology, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece
Papadaki, Efrosini ; Computational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece ; Department of Radiology, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece
Language :
English
Title :
Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach.
Funding: The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers” (Project Number: 1220).
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