{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T20:57:23Z","timestamp":1773867443506,"version":"3.50.1"},"reference-count":63,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:00:00Z","timestamp":1772928000000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>\n                      Fatigue affects between 36.5% and 78% of people with multiple sclerosis (pwMS) and significantly impairs daily life. The neurobiological mechanisms underlying fatigue remain poorly understood and information about the time\u2010varying communication between brain regions and the networks they form may offer new insights into the complex pathology of MS\u2010related fatigue. Brain regions continuously reconfigure how they communicate within distinct networks (i.e., time\u2010varying reconfigurations) and aberrant time\u2010varying reconfigurations may contribute to the perception of fatigue in pwMS. This study aimed to explore if and how time\u2010varying reconfigurations are associated with fatigue in pwMS. In this cross\u2010sectional study, 155 pwMS (62% female; age\u2009=\u200939\u2009\u00b1\u200910\u2009years; disease duration\u2009=\u200910\u2009\u00b1\u20098\u2009years; median EDSS\u2009=\u20091.0\u2009\u00b1\u20092.0) and 48 healthy controls (HC) (71% female; age\u2009=\u200933\u2009\u00b1\u200910\u2009years) underwent clinical, neuropsychological, and (resting\u2010state functional) MRI assessments. Fatigue was evaluated with the \u201cFatigue Scale for Motor and Cognitive Function\u201d, comprising total, motor, and cognitive fatigue scores. Time\u2010varying connectivity was derived using a sliding\u2010window approach, with data\u2010driven assignment of brain regions to one of eight resting\u2010state networks for each window. Promiscuity (dispersion of reconfigurations), flexibility (frequency of reconfigurations), cohesion (joint reconfigurations), and disjointedness (independent reconfigurations) described the time\u2010varying reconfigurations of the whole brain and its networks. Among pwMS, 57% reported experiencing at least mild total fatigue (motor: 60%, cognitive: 57%). Higher total fatigue was correlated with greater global promiscuity (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.21,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.032) and disjointedness (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.24,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.008). Similarly, higher motor fatigue was associated with greater global promiscuity (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.25,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.008), flexibility (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.21,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.032), and disjointedness (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.28,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). The associations with disjointedness remained significant even after controlling for demographics, clinical measures, and structural brain damage, such as lesion load and atrophy (total fatigue: adj.\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.23,\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u2009=\u20090.17,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.033; motor fatigue: adj.\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.38,\n                      <jats:italic>\u03b2<\/jats:italic>\n                      \u2009=\u20090.16,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.026). Network\u2010level analyses in pwMS revealed that higher total (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.25,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.016) and motor (\n                      <jats:italic>r<\/jats:italic>\n                      \u2009=\u20090.25,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.016) fatigue were associated with greater limbic network promiscuity. No significant correlations were found for cognitive fatigue in pwMS, or for total, motor, and cognitive fatigue in HC (all\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&gt;\u20090.05). Elevated levels of fatigue, particularly motor fatigue, in pwMS were linked to more unstable network reconfigurations, particularly of regions in the limbic network, possibly reflecting dysfunctional reward processing. More frequent dispersion requires more energy and may therefore contribute to increased fatigue.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration<\/jats:title>\n                    <jats:p>\n                      This project was pre\u2010registered at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/clinicaltrials.gov\">ClinicalTrials.gov<\/jats:ext-link>\n                      (registration number: NCT04892134)\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hbm.70480","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T06:37:11Z","timestamp":1773038231000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Time\u2010Varying Brain Functional Reconfiguration Patterns Associated With Fatigue in Multiple Sclerosis"],"prefix":"10.1002","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2839-4000","authenticated-orcid":false,"given":"Stefanie","family":"Hechenberger","sequence":"first","affiliation":[{"name":"Department of Neurology, Research Unit for Neuroplasticity and Repair Medical University of Graz  Graz Austria"},{"name":"Department of Neurology Medical University of Graz  Graz Austria"},{"name":"Clinical Department of Oncology, University Medical Center of Internal Medicine Medical University of Graz  Graz Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2884-2827","authenticated-orcid":false,"given":"Tommy A. 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