{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T01:39:57Z","timestamp":1773279597204,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Background: Disability progression in multiple sclerosis (MS) is increasingly recognized as a consequence of large-scale brain network disruption rather than isolated regional damage. Although diffusion tensor imaging (DTI) is the reference method for assessing structural connectivity, its limited availability restricts widespread clinical application. There is therefore a critical need for alternative approaches capable of capturing network-level alterations using routinely acquired MRI data. Objective: This study aimed to determine whether synthetic structural connectivity matrices derived from standard regional volumetric MRI can capture clinically meaningful network alterations in MS and predict subsequent functional progression, particularly upper limb decline. Methods: Regional brain volumetry was obtained from routine T1-weighted MRI using an automated, clinically approved volumetric pipeline. Synthetic structural connectivity matrices were generated by integrating principles of structural covariance, distance-dependent connectivity, and disease-specific vulnerability patterns. Graph-theoretical network metrics were extracted to characterize global and regional topology. Machine learning models including logistic regression, support vector machines, random forests, and gradient boosting were trained to predict clinical progression defined by worsening on the 9-Hole Peg Test. Dimensionality reduction was performed using principal component analysis, and model performance was evaluated using balanced accuracy, AUC-ROC, and resampling-based validation. Feature importance analyses were conducted to identify network vulnerability patterns. Results: Synthetic connectivity networks exhibited biologically plausible properties, including preserved but attenuated small-world organization. Global efficiency showed a strong inverse correlation with disability severity (EDSS). Patients with clinical progression demonstrated marked reductions in network integration and segregation, alongside increased characteristic path length. Machine learning models achieved robust prediction of upper limb functional decline, with ensemble-based methods performing best (balanced accuracy &gt; 80%, AUC-ROC up to 0.85). A limited subset of connections accounted for a disproportionate share of predictive power, predominantly involving frontoparietal associative networks, thalamocortical pathways, and inter-hemispheric connections. In a longitudinal subset, network-level alterations preceded measurable clinical deterioration by several months. Conclusions: Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS.<\/jats:p>","DOI":"10.3390\/life16030459","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T10:43:27Z","timestamp":1773225807000},"page":"459","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical Prediction of Functional Decline in Multiple Sclerosis Using Volumetry-Based Synthetic Brain Networks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2079-2383","authenticated-orcid":false,"given":"Alin","family":"Ciubotaru","sequence":"first","affiliation":[{"name":"Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0008-9696","authenticated-orcid":false,"given":"Alexandra","family":"Ma\u0219taleru","sequence":"additional","affiliation":[{"name":"Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4495-4004","authenticated-orcid":false,"given":"Thomas Gabriel","family":"Schreiner","sequence":"additional","affiliation":[{"name":"Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"},{"name":"Neurology Clinic, \u201cN. Oblu\u201d Clinical Emergency Hospital, 700309 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristiana","family":"Filip","sequence":"additional","affiliation":[{"name":"Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7386-0859","authenticated-orcid":false,"given":"Roxana","family":"Covali","sequence":"additional","affiliation":[{"name":"Department of Radiology, Biomedical Engineering Faculty, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laura","family":"Riscanu","sequence":"additional","affiliation":[{"name":"Department of Morphofunctional Sciences, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert-Valentin","family":"Bilcu","sequence":"additional","affiliation":[{"name":"Doctoral School, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1544-1325","authenticated-orcid":false,"given":"Laura-Elena","family":"Cucu","sequence":"additional","affiliation":[{"name":"Doctoral School, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sofia Alexandra","family":"Socolov-Mihaita","sequence":"additional","affiliation":[{"name":"Doctoral School, Carol Davila University of Medicine and Pharmacy, 020021 Bucharest, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8719-8268","authenticated-orcid":false,"given":"Diana","family":"L\u0103c\u0103tu\u0219u","sequence":"additional","affiliation":[{"name":"Department of Pharmacological Phisics, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florina","family":"Crivoi","sequence":"additional","affiliation":[{"name":"Department of Pharmacological Phisics, Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Albert","family":"Vamanu","sequence":"additional","affiliation":[{"name":"Basic and Clinical Neuroscience Department, Institute of Psychiatry, Psychology and Neuroscience, King\u2019s College London, London SE5 8AF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioana","family":"Martu","sequence":"additional","affiliation":[{"name":"Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucia Corina","family":"Dima-Cozma","sequence":"additional","affiliation":[{"name":"Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3768-6232","authenticated-orcid":false,"given":"Romica Sebastian","family":"Cozma","sequence":"additional","affiliation":[{"name":"Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oana-Roxana","family":"Bitere-Popa","sequence":"additional","affiliation":[{"name":"Grigore T. Popa University of Medicine and Pharmacy, 700115 Iasi, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1016\/S0140-6736(18)30481-1","article-title":"Multiple sclerosis","volume":"391","author":"Thompson","year":"2018","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/S1474-4422(12)70003-0","article-title":"Association between pathological and MRI findings in multiple sclerosis","volume":"11","author":"Filippi","year":"2012","journal-title":"Lancet Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e221512","DOI":"10.1148\/radiol.221512","article-title":"Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice","volume":"307","author":"Rocca","year":"2023","journal-title":"Radiology"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/S0006-3495(94)80775-1","article-title":"MR diffusion tensor spectroscopy and imaging","volume":"66","author":"Basser","year":"1994","journal-title":"Biophys. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.neuroimage.2012.06.081","article-title":"White matter integrity, fiber count, and other fallacies: The do\u2019s and don\u2019ts of diffusion MRI","volume":"73","author":"Jones","year":"2013","journal-title":"NeuroImage"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1093\/brain\/awz144","article-title":"Assessment of lesions on magnetic resonance imaging in multiple sclerosis: Practical guidelines","volume":"142","author":"Filippi","year":"2019","journal-title":"Brain"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s40263-014-0140-z","article-title":"Clinical relevance of brain volume measures in multiple sclerosis","volume":"28","author":"Airas","year":"2014","journal-title":"CNS Drugs"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ciubotaru, A., Smihor, M.I., Grosu, C., Alexa, D., Covali, R., Anic\u0103i, R.C., P\u0103v\u0103leanu, I., Cucu, A.I., Bobu, A.M., and Ghiciuc, C.M. (2025). Neurodegenerative Biomarkers in Multiple Sclerosis: At the Interface Between Research and Clinical Practice. Diagnostics, 15.","DOI":"10.3390\/diagnostics15091178"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1038\/nrn3465","article-title":"Imaging structural co-variance between human brain regions","volume":"14","author":"Giedd","year":"2013","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.neuroimage.2011.08.017","article-title":"Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex","volume":"59","author":"Gong","year":"2012","journal-title":"NeuroImage"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ciubotaru, A., Covali, R., Grosu, C., Alexa, D., Riscanu, L., Robert-Valentin, B., Popa, R., Sargu, G.D., Popa, C., and Filip, C. (2025). Integrated Biomarker-Volumetric Profiling Defines Neurodegenerative Subtypes and Predicts Neuroaxonal Injury in Multiple Sclerosis Based on Bayesian and Machine Learning Analyses. Biomedicines, 14.","DOI":"10.20944\/preprints202512.1111.v1"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2382","DOI":"10.1093\/brain\/awu132","article-title":"The hubs of the human connectome are implicated in brain disorders","volume":"137","author":"Crossley","year":"2014","journal-title":"Brain"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1038\/nrn3801","article-title":"Modern network science of neurological disorders","volume":"15","author":"Stam","year":"2014","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1177\/1352458517699875","article-title":"Functional network connectivity abnormalities in multiple sclerosis","volume":"24","author":"Rocca","year":"2018","journal-title":"Mult. Scler."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5799","DOI":"10.1002\/hbm.22586","article-title":"Functional correlates of cognitive dysfunction in multiple sclerosis","volume":"35","author":"Rocca","year":"2014","journal-title":"Hum. Brain Mapp."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1212\/WNL.0000000000001285","article-title":"Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis","volume":"84","author":"Schoonheim","year":"2015","journal-title":"Neurology"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Llufriu, S., Blanco, Y., Martinez-Heras, E., Casanova-Molla, J., Gabilondo, I., Sepulveda, M., Falcon, C., Berenguer, J., Bargallo, N., and Villoslada, P. (2012). Influence of corpus callosum damage on cognition and physical disability in multiple sclerosis. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0037167"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tiu, V.E., Enache, I., Panea, C.A., Tiu, C., and Popescu, B.O. (2022). Predictive MRI Biomarkers in MS-A Critical Review. Medicina, 58.","DOI":"10.3390\/medicina58030377"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kocevar, G., Stamile, C., Hannoun, S., Cotton, F., Vukusic, S., Durand-Dubief, F., and Sappey-Marinier, D. (2016). Graph theory-based brain connectivity for automatic classification of multiple sclerosis clinical courses. Front. Neurosci., 10.","DOI":"10.3389\/fnins.2016.00478"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1093\/brain\/awz355","article-title":"Long-range connections are more severely damaged and relevant for cognition in multiple sclerosis","volume":"143","author":"Meijer","year":"2020","journal-title":"Brain"},{"key":"ref_21","first-page":"2605","article-title":"Predicting cognitive decline in multiple sclerosis: A 5-year follow-up study","volume":"141","author":"Eijlers","year":"2018","journal-title":"Brain"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Anic\u0103i, R.C., Ciubotaru, A., Grosu, C., Alexa, D., Covali, R., P\u0103v\u0103leanu, I., Cucu, A.I., Bobu, A.M., Ghiciuc, C.M., and Leon, M.M. (2025). The Role of MRI Lesions in Identifying Secondary Progressive Multiple Sclerosis: A Comprehensive Review. J. Clin. Med., 14.","DOI":"10.3390\/jcm14124114"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Grosu, C., Ignat, E.B., Alexa, D., Ciubotaru, A., Leon, M.M., Ma\u0219taleru, A., Popescu, G., Cump\u0103t, C.M., Cucu, L.E., and Smihor, M.I. (2025). The Role of Nutrition and Physical Activity in Modulating Disease Progression and Quality of Life in Multiple Sclerosis. Nutrients, 17.","DOI":"10.3390\/nu17162713"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106558","DOI":"10.1016\/j.msard.2025.106558","article-title":"Refining outcome measures in multiple sclerosis clinical trials: A call to action","volume":"100","year":"2025","journal-title":"Mult. Scler. Relat. Disord."}],"container-title":["Life"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-1729\/16\/3\/459\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T11:02:13Z","timestamp":1773226933000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-1729\/16\/3\/459"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,11]]},"references-count":24,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["life16030459"],"URL":"https:\/\/doi.org\/10.3390\/life16030459","relation":{},"ISSN":["2075-1729"],"issn-type":[{"value":"2075-1729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,11]]}}}