{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T01:51:43Z","timestamp":1781920303147,"version":"3.54.5"},"reference-count":68,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["physoc.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Journal of Physiology"],"published-print":{"date-parts":[[2026,5]]},"abstract":"\n Abstract<\/jats:title>\n \n Cerebral blood flow is stabilized through dynamic adjustments across both macro\u2010 and microvascular compartments. While dynamic cerebral autoregulation (dCA) quantifies upstream pressure\u2013flow coupling, downstream microvascular responses are less well characterized and may represent a distinct but functionally linked process. This study tested whether a two\u2010component cascade model, treating dCA and an empirically derived index of microvascular function (MF) as sequential stages can represent the integrated regulation of cortical oxygenation. Data from 41 healthy adults (20\u201345\u00a0years) were analysed. Beat\u2010to\u2010beat mean arterial pressure (MAP), middle cerebral artery flow velocity (CBFV) and cortical oxyhaemoglobin (O\n 2<\/jats:sub>\n Hb) were recorded during supine spontaneous oscillations and forced oscillations at 0.05\u00a0Hz using repeated sit\u2013stand manoeuvres. Transfer function analysis quantified frequency\u2010domain gain, phase and coherence for MAP\u2192CBFV (dCA), CBFV\u2192O\n 2<\/jats:sub>\n Hb (MF) and MAP\u2192O\n 2<\/jats:sub>\n Hb (total pathway). The cascade model was computed as the product of dCA and MF transfer functions. The cascade model derived indices showed strong correlations with total pathway gain, phase and coherence measures during both spontaneous and forced oscillations, with improved linear coupling under forced oscillations. These results support the applicability of a two\u2010component cascade model for integrated cerebrovascular regulation and suggest that serial interactions between macrovascular and microvascular regulatory mechanisms jointly shape the frequency\u2010dependent propagation of arterial pressure to brain\u2010tissue oxygenation dynamics.\n \n \n image<\/jats:alt-text>\n <\/jats:graphic>\n <\/jats:boxed-text>\n <\/jats:p>\n <\/jats:sec>\n \n Key points<\/jats:title>\n \n \n \n Cerebral blood flow is stabilized by coordinated regulation across large arteries and microvessels, but their dynamic interaction has not been experimentally modelled.<\/jats:p>\n <\/jats:list-item>\n \n We examined whether a two\u2010component cascade model linking upstream dynamic cerebral autoregulation and downstream microvascular function can explain how blood pressure oscillations influence cortical oxygenation.<\/jats:p>\n <\/jats:list-item>\n \n In 41\u00a0healthy adults, we recorded beat\u2010to\u2010beat blood pressure, middle cerebral artery blood flow velocity and near\u2010infrared spectroscopy\u2010derived oxygenation during rest and sit\u2013stand manoeuvres, and analysed them using transfer function analysis.<\/jats:p>\n <\/jats:list-item>\n \n \n Gains, phases and coherences derived from the cascade model closely matched those from the direct blood pressure\u2013O\n 2<\/jats:sub>\n Hb relationship, particularly under forced oscillations during sit\u2013stand, demonstrating the model's physiological relevance.\n <\/jats:p>\n <\/jats:list-item>\n \n The cascade model provides a mechanistic framework to separate and quantify large\u2010 and small\u2010vessel contributions to cerebral blood flow regulation, with potential application in future studies of ageing and cerebrovascular disease.<\/jats:p>\n <\/jats:list-item>\n <\/jats:list>\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1113\/jp290519","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T01:33:35Z","timestamp":1776908015000},"page":"3763-3785","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A cascade model of dynamic cerebral autoregulation"],"prefix":"10.1113","volume":"604","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5218-2414","authenticated-orcid":false,"given":"Takuya","family":"Kurazumi","sequence":"first","affiliation":[{"name":"Institute for Exercise and Environmental Medicine Texas Health Presbyterian Hospital Dallas Dallas TX USA"},{"name":"Department of Neurology University of Texas Southwestern Medical Center Dallas TX USA"},{"name":"Department of Social Medicine, Division of Hygiene Nihon University School of Medicine Tokyo Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0073-5915","authenticated-orcid":false,"given":"Kartavya","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Neurology University of Texas Southwestern Medical Center Dallas TX USA"},{"name":"Department of Neurology Mayo Clinic Rochester MN USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4277-6727","authenticated-orcid":false,"given":"Ricardo R. 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