{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T09:11:38Z","timestamp":1792141898810,"version":"4.4.0"},"posted":{"date-parts":[[2023,9,1]]},"group-title":"Neurology","reference-count":42,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,7,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Language dysfunction is increasingly recognized as a prevalent and early affected cognitive domain in individuals with MS.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>To establish a network-level model of language dysfunction in MS.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Cognitive data and 3T functional and structural brain MRI were acquired from 54 MS patients and 54 matched healthy controls (HCs). Functional summary measures (anteriority, segregation, betweenness, within-ness) of the extended language network (ELN) were calculated and structural imaging metrics were derived. Group differences in ELN connectivity were evaluated. Associations between ELN connectivity and language performance were assessed; in the MS group, an unsupervised learning approach was used to assess relationships between multimodal neuroimaging features derived from language-related areas and performance on language tasks.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    The MS group performed worse on semantic fluency and rapid automized naming tests (\n                    <jats:italic>p<\/jats:italic>\n                    &lt;0.005) compared to HC. Regarding ELN measures, the MS group exhibited higher within-ELN connectivity than HCs (\n                    <jats:italic>p<\/jats:italic>\n                    &lt;0.05). Principal component analysis (PCA) yielded a multimodal latent component that uniquely correlated with language performance (\n                    <jats:italic>p<\/jats:italic>\n                    &lt;0.05).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>We identified network-level functional and structural measures to potentially characterize language dysfunction in MS. Further studies leveraging these features may reveal mechanisms and predictors of language dysfunction specific to MS.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2023.08.30.23294843","type":"posted-content","created":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T16:20:27Z","timestamp":1693585227000},"source":"Crossref","is-referenced-by-count":4,"title":["Characterizing the Extended Language Network in Individuals with Multiple Sclerosis"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6880-9740","authenticated-orcid":false,"given":"Alexander S.","family":"Ratzan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1349-4252","authenticated-orcid":false,"given":"Leila","family":"Simani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jordan 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