{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T09:16:12Z","timestamp":1788599772599,"version":"build-2803163510"},"reference-count":64,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2023,11,21]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Nonlinear dynamical measures, such as fractal dimension (FD), entropy, and Lempel-Ziv complexity (LZC), have been extensively investigated individually for detecting information content in magnetoencephalograms (MEGs) from patients with Alzheimer\u2019s disease (AD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>To compare systematically the performance of twenty conventional and recently introduced nonlinear dynamical measures in studying AD versus mild cognitive impairment (MCI) and healthy control (HC) subjects using MEG.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We compared twenty nonlinear measures to distinguish MEG recordings from 36 AD (mean age\u200a=\u200a74.06\u00b16.95 years), 18 MCI (mean age\u200a=\u200a74.89\u00b15.57 years), and 26 HC subjects (mean age\u200a=\u200a71.77\u00b16.38 years) in different brain regions and also evaluated the effect of the length of MEG epochs on their performance. We also studied the correlation between these measures and cognitive performance based on the Mini-Mental State Examination (MMSE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The results obtained by LZC, zero-crossing rate (ZCR), FD, and dispersion entropy (DispEn) measures showed significant differences among the three groups. There was no significant difference between HC and MCI. The highest Hedge\u2019s g effect sizes for HC versus AD and MCI versus AD were respectively obtained by Higuchi\u2019s FD (HFD) and fuzzy DispEn (FuzDispEn) in the whole brain and was most prominent in left lateral. The results obtained by HFD and FuzDispEn had a significant correlation with the MMSE scores. DispEn-based techniques, LZC, and ZCR, compared with HFD, were less sensitive to epoch length in distinguishing HC form AD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>FuzDispEn was the most consistent technique to distinguish MEG dynamical patterns in AD compared with HC and MCI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3233\/jad-230544","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T11:38:40Z","timestamp":1700221120000},"page":"1151-1162","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Assessment and Comparison of Nonlinear Measures in Resting-State Magnetoencephalograms in Alzheimer\u2019s Disease and Mild Cognitive Impairment"],"prefix":"10.1177","volume":"96","author":[{"given":"Hamed","family":"Azami","sequence":"first","affiliation":[{"name":"Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada"},{"name":"Department of Psychiatry, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elham","family":"Daftarifard","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutics, Mazandaran University of Medical Sciences, Sari, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Humeau-Heurtier","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Angers, LARIS, SFR MathSTIC, Angers, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Fernandez","sequence":"additional","affiliation":[{"name":"Department of Legal Medicine, Psychiatry and Pathology, Complutense University of Madrid, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Abasolo","sequence":"additional","affiliation":[{"name":"Centre for Biomedical Engineering, School of Mechanical Engineering Sciences, University of Surrey, Guildford, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarek K.","family":"Rajji","sequence":"additional","affiliation":[{"name":"Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada"},{"name":"Department of Psychiatry, University of Toronto, Toronto, ON, Canada"},{"name":"Toronto Dementia Research Alliance, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"ref001","doi-asserted-by":"publisher","DOI":"10.1037\/a0029857"},{"key":"ref002","unstructured":"WimoA, PrinceM (2010) World Alzheimer Report 2010: The Global Economic Impact of Dementia. 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