Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach

Brain Sciences

ThomasG. Maris¹  GeorgiosC. Manikis¹  Efrosini Papadaki¹  Panagiotis Simos¹  NicholasJohn Simos¹  Eleftherios Kavroulakis²  George Bertsias³  StavrosI. Dimitriadis⁴ 

[1] 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;Department of Rheumatology, Clinical Immunology and Allergy, Medical School, University of Crete, University Hospital of Heraklion, 71003 Heraklion, Greece;Integrative Neuroimaging Lab, 55133 Thessaloniki, Greece;

关键词: neuropsychiatric systemic lupus erythematosus;    rs-fMRI;    graph theory;    functional connectivity;    surrogate data;    machine learning;   

DOI  :  10.3390/brainsci10110777

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown