{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:21:47Z","timestamp":1784139707592,"version":"3.55.0"},"reference-count":63,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:00:00Z","timestamp":1722384000000},"content-version":"vor","delay-in-days":212,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Edson Family Fund"},{"DOI":"10.13039\/100001605","name":"Epilepsy Foundation of America","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001605","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["UG3 NS123066"],"award-info":[{"award-number":["UG3 NS123066"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Benjamin A. Miller Family Fellowship"},{"name":"Mayo Clinic Neurology Artificial Intelligence Program"},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2105233"],"award-info":[{"award-number":["IIS-2105233"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer\u2019s disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10\u201320 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record \u00d7 channel \u00d7 frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer\u2019s disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Na\u00efve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer\u2019s Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta\/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59\u20130.91) and Alzheimer\u2019s disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer\u2019s subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.<\/jats:p>","DOI":"10.1093\/braincomms\/fcae227","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T01:57:32Z","timestamp":1722477452000},"source":"Crossref","is-referenced-by-count":15,"title":["Data-driven retrieval of population-level EEG features and their role in neurodegenerative diseases"],"prefix":"10.1093","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1992-2403","authenticated-orcid":false,"given":"Wentao","family":"Li","sequence":"first","affiliation":[{"name":"Department of Neurology, Mayo Clinic , Rochester, MN 55905 , USA"},{"name":"Department of Neurology, Kaiser Permanente Northern California , Sacramento, CA 95758 , 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H","family":"Brinkmann","sequence":"additional","affiliation":[{"name":"Department of Neurology, Mayo Clinic , Rochester, MN 55905 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Crepeau","sequence":"additional","affiliation":[{"name":"Department of Neurology, Mayo Clinic , Rochester, MN 55905 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Worrell","sequence":"additional","affiliation":[{"name":"Department of Neurology, Mayo Clinic , Rochester, MN 55905 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Winnie","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences, Mayo Clinic , Rochester, MN 55905 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter","family":"Kremers","sequence":"additional","affiliation":[{"name":"Department of Quantitative Health Sciences, Mayo Clinic , Rochester, MN 55905 , 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