{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T20:01:00Z","timestamp":1775160060636,"version":"3.50.1"},"posted":{"date-parts":[[2025,10,31]]},"group-title":"Neurology","reference-count":57,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,3,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Migraine is a prevalent and disabling neurological disorder, characterized by impaired regulation of migraine burden, sensory processing, and cognitive-emotional states. Brain entropy quantifies the complexity of neural dynamics, where reduced entropy may reflect diminished neural adaptability, but its assessment with fMRI in migraine remains limited. Here, we examined alterations in brain entropy and their associations with clinical burden, migraine phase, and symptomatology. Resting-state fMRI data were acquired from adults with episodic migraine, chronic migraine, and healthy controls. Following standard preprocessing, voxel-wise sample entropy was computed, and group differences were assessed using ANCOVA with age and sex as covariates. Associations with clinical burden and symptom measures were examined within affected regions. In chronic migraine, attack timing-related changes in entropy were further explored, and the Largest Lyapunov Exponent (LLE) was estimated to characterize chaotic dynamics underlying attack-related complexity changes. Migraine patients showed reduced entropy in visual, dorsal attention, and default mode network regions compared to controls, most pronounced in chronic migraine. Lower entropy correlated with greater headache frequency and longer illness duration. In chronic migraine, entropy relatively increased during attacks in multisensory integration regions and was associated with positive and elevated LLEs, indicating partially restored complexity with weakly chaotic dynamics. Patients experiencing phonophobia and nausea also exhibited increased entropy in multisensory integration and default mode network regions. Our findings demonstrate widespread reductions in brain entropy in migraine, reflecting impaired neural adaptability, whereas attacks may transiently restore complexity partially through weakly chaotic dynamics. These results advance understanding of migraine pathophysiology and highlight potential targets for therapeutic intervention.<\/jats:p>\n                <jats:sec>\n                  <jats:title>Highlights<\/jats:title>\n                  <jats:list list-type=\"simple\">\n                    <jats:list-item>\n                      <jats:p>- Migraine is associated with reduced brain entropy across visual, dorsal attention, and default mode network regions, correlating with clinical burden.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>- Reduced entropy reflects constrained neural adaptability within affected regions.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>- Migraine attacks transiently restore entropy, suggesting partial recovery of neural adaptability.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>- Positive and elevated largest Lyapunov exponents indicate a shift toward weakly chaotic dynamics during migraine attacks in multisensory integration regions.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>- Symptoms such as phonophobia and nausea are linked to increased entropy in multisensory integration and default mode regions.<\/jats:p>\n                    <\/jats:list-item>\n                  <\/jats:list>\n                <\/jats:sec>","DOI":"10.1101\/2025.10.29.25339059","type":"posted-content","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T21:05:43Z","timestamp":1761944743000},"source":"Crossref","is-referenced-by-count":0,"title":["Reduced brain entropy in migraine with partial restoration during attacks: a resting-state fMRI study"],"prefix":"10.64898","author":[{"given":"Majid","family":"Saberi","sequence":"first","affiliation":[{"name":"Headache and Orofacial Pain Effort (H.O.P.E.), Biologic and Materials Science & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dajung J","family":"Kim","sequence":"additional","affiliation":[{"name":"Headache and Orofacial Pain Effort (H.O.P.E.), Biologic and Materials Science & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, USA"},{"name":"School of Interdisciplinary Engineering, Changwon National University, Changwon, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Su","family":"Hu","sequence":"additional","affiliation":[{"name":"Headache and Orofacial Pain Effort (H.O.P.E.), Biologic and Materials Science & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandre F","family":"DaSilva","sequence":"additional","affiliation":[{"name":"Headache and Orofacial Pain Effort (H.O.P.E.), Biologic and Materials Science & Prosthodontics, University of Michigan School of Dentistry, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"issue":"1","key":"2026040212250744000_2025.10.29.25339059v2.1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s40122-021-00240-5","article-title":"Structural and functional brain changes in migraine","volume":"10","year":"2021","journal-title":"Pain and Therapy"},{"key":"2026040212250744000_2025.10.29.25339059v2.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2007.04.042"},{"key":"2026040212250744000_2025.10.29.25339059v2.3","doi-asserted-by":"crossref","first-page":"112757","DOI":"10.1016\/j.chaos.2022.112757","article-title":"A new algorithm for Largest Lyapunov Exponent determination for noisy chaotic signal studies with application to Electroencephalographic signals analysis for epilepsy and epileptic seizures detection. 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