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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/95461A25-6793-44B2-86C6-10CB0B937482 | - |
| Identifier | https://doi.org/10.3390/brainsci10110777 | - |
| Identifier | https://www.mdpi.com/2076-3425/10/11/777 | - |
| Language | en | - |
| Extent | 18 pages | en |
| Title | Quantitative identification of functional connectivity disturbances in neuropsychiatric lupus based on resting-state fMRI: a robust machine learning approach | en |
| Creator | Simos Nikolaos-Ioannis | en |
| Creator | Σιμος Νικολαος-Ιωαννης | el |
| Creator | Dimitriadis Stavros | en |
| Creator | Kavroulakis Eleftherios | en |
| Creator | Manikis Georgios | en |
| Creator | Μανικης Γεωργιος | el |
| Creator | Bertsias George | en |
| Creator | Simos Panagiotis | en |
| Creator | Maris Thomas G. | en |
| Creator | Papadaki Efrosini | en |
| Publisher | MDPI | en |
| Content Summary | 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. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2021-04-21 | - |
| Date of Publication | 2020 | - |
| Subject | Neuropsychiatric systemic lupus erythematosus | en |
| Subject | Resting-state functional MRI (rs-fMRI) | en |
| Subject | Graph theory | en |
| Subject | Functional connectivity | en |
| Subject | Surrogate data | en |
| Subject | Machine learning | en |
| Subject | Visuomotor ability | en |
| Subject | Mental flexibility | en |
| Bibliographic Citation | N. J. Simos, S. I. Dimitriadis, E. Kavroulakis, G. C. Manikis, G. Bertsias, P. Simos, T. G. Maris, and E. Papadaki, “Quantitative identification of functional connectivity disturbances in neuropsychiatric lupus based on resting-state fMRI: a robust machine learning approach,” Brain Sci., vol. 10, no. 11, Oct. 2020. doi: 10.3390/brainsci10110777 | en |
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