{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T07:19:42Z","timestamp":1791271182576,"version":"4.1.0"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Hum. Neurosci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Despite using beta oscillations within the subthalamic nucleus as a biomarker of akinesia or rigidity in Parkinson\u2019s disease, a specific biomarker for freezing of gait (FOG) remains unclear. Recently, scalp phase-amplitude coupling (PAC) measured through scalp electroencephalography (EEG) has emerged as a promising tool for analyzing brain function. In this study, we examined whether PAC could be a biomarker for FOG.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We enrolled 11 patients with Parkinson\u2019s disease and recorded scalp EEG in preparation for and during gait while simultaneously assessing motor function, including FOG. We investigated changes in cortical PAC during walking with and without FOG and examined its correlation with the postural instability and gait difficulty (PIGD) score.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Patient characteristics were as follows: mean age 59.1\u2009\u00b1\u20096.9\u2009years, disease duration 13.9\u2009\u00b1\u20094.1\u2009years, and seven men. Four trials were excluded from the analysis owing to artifacts. In the trials without FOG (<jats:italic>n<\/jats:italic>\u2009=\u200918), beta-gamma PAC in the sensorimotor area decreased during gait preparation (<jats:italic>p<\/jats:italic>\u2009=\u20090.011; linear mixed-effects model), which was not the case in trials with FOG (<jats:italic>n<\/jats:italic>\u2009=\u20096) (<jats:italic>p<\/jats:italic>\u2009=\u20090.64; linear mixed-effects model). Using a support vector machine, machine learning of PAC during preparation for walking predicted the presence of FOG with an accuracy of 71.2%. Conversely, PAC increased during walking in trials with FOG (<jats:italic>p<\/jats:italic>\u2009=\u20090.0042; linear mixed-effects model), and PAC 20\u2009s after the start of walking was positively correlated with the PIGD score (correlation coefficient\u2009=\u20090.406, <jats:italic>p<\/jats:italic>\u2009=\u20090.032; Pearson\u2019s rank correlation).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Beta-gamma PAC in the sensorimotor area during preparation for walking differs depending on the emergence of FOG. As gait symptoms worsened, beta-gamma PAC in the sensorimotor area during walking gradually increased. Cortical PAC may be a biomarker for FOG in Parkinson\u2019s disease and may lead to the development of strategies to prevent falls in the future.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnhum.2024.1495272","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T11:54:54Z","timestamp":1731498894000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Beta-gamma phase-amplitude coupling of scalp electroencephalography during walking preparation in Parkinson\u2019s disease differs depending on the freezing of gait"],"prefix":"10.3389","volume":"18","author":[{"given":"Yuki","family":"Kimoto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoki","family":"Tani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takuto","family":"Emura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takahiro","family":"Matsuhashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takuto","family":"Yamamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuya","family":"Fujita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoru","family":"Oshino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koichi","family":"Hosomi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui Ming","family":"Khoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shimpei","family":"Miura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takahiro","family":"Fujinaga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takufumi","family":"Yanagisawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haruhiko","family":"Kishima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"108811","DOI":"10.1016\/j.jneumeth.2020.108811","article-title":"A novel method for calculating beta band burst durations in Parkinson's disease using a physiological baseline","volume":"343","author":"Anderson","year":"2020","journal-title":"J. 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