Vestibular contribution to path integration deficits in 'at-genetic-risk' for Alzheimer's disease
Path integration changes may precede a clinical presentation of Alzheimer's disease by several years. Studies to date have focused on how spatial cell changes affect path integration in preclinical AD. However, vestibular input is also critical for intact path integration. Here, we developed th...
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| Veröffentlicht in: | PloS one 2023-01, Vol.18 (1), p.e0278239-e0278239 |
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| creator | Coughlan, Gillian Plumb, William Zhukovsky, Peter Aung, Min Hane Hornberger, Michael |
| description | Path integration changes may precede a clinical presentation of Alzheimer's disease by several years. Studies to date have focused on how spatial cell changes affect path integration in preclinical AD. However, vestibular input is also critical for intact path integration. Here, we developed the vestibular rotation task that requires individuals to manually point an iPad device in the direction of their starting point following rotational movement, without any visual cues. Vestibular features were derived from the sensor data using feature selection. Machine learning models illustrate that the vestibular features accurately classified Apolipoprotein E ε3ε4 carriers and ε3ε3 carrier controls (mean age 62.7 years), with 65% to 79% accuracy depending on task trial. All machine learning models produced a similar classification accuracy. Our results demonstrate the cross-sectional role of the vestibular system in Alzheimer's disease risk carriers. Future investigations should examine if vestibular functions explain individual phenotypic heterogeneity in path integration among Alzheimer's disease risk carriers. |
| doi_str_mv | 10.1371/journal.pone.0278239 |
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Studies to date have focused on how spatial cell changes affect path integration in preclinical AD. However, vestibular input is also critical for intact path integration. Here, we developed the vestibular rotation task that requires individuals to manually point an iPad device in the direction of their starting point following rotational movement, without any visual cues. Vestibular features were derived from the sensor data using feature selection. Machine learning models illustrate that the vestibular features accurately classified Apolipoprotein E ε3ε4 carriers and ε3ε3 carrier controls (mean age 62.7 years), with 65% to 79% accuracy depending on task trial. All machine learning models produced a similar classification accuracy. Our results demonstrate the cross-sectional role of the vestibular system in Alzheimer's disease risk carriers. Future investigations should examine if vestibular functions explain individual phenotypic heterogeneity in path integration among Alzheimer's disease risk carriers.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0278239</identifier><identifier>PMID: 36595510</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Alzheimer Disease - genetics ; Alzheimer's disease ; Alzheimers disease ; Apolipoprotein E ; Biology and Life Sciences ; Classification ; Computer and Information Sciences ; Cross-Sectional Studies ; Cues ; Diagnosis ; Engineering and Technology ; Evaluation ; Genetic aspects ; Genetic screening ; Health risks ; Heterogeneity ; Humans ; Integration ; Learning algorithms ; Machine learning ; Medicine and Health Sciences ; Middle age ; Middle Aged ; Neurodegenerative diseases ; Physical Sciences ; Research and Analysis Methods ; Risk ; Rotation ; Social Sciences ; Support vector machines ; Vestibular system ; Vestibule, Labyrinth ; Visual stimuli</subject><ispartof>PloS one, 2023-01, Vol.18 (1), p.e0278239-e0278239</ispartof><rights>Copyright: © 2023 Coughlan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Coughlan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Coughlan et al 2023 Coughlan et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-3c4ece6cfb18d631c71611af80e47b214a98623f73076f0388b5eb7a0efbbfe63</citedby><cites>FETCH-LOGICAL-c692t-3c4ece6cfb18d631c71611af80e47b214a98623f73076f0388b5eb7a0efbbfe63</cites><orcidid>0000-0002-2214-3788</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810179/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810179/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2101,2927,23865,27923,27924,53790,53792,79471,79472</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36595510$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Vann, Seralynne</contributor><creatorcontrib>Coughlan, Gillian</creatorcontrib><creatorcontrib>Plumb, William</creatorcontrib><creatorcontrib>Zhukovsky, Peter</creatorcontrib><creatorcontrib>Aung, Min Hane</creatorcontrib><creatorcontrib>Hornberger, Michael</creatorcontrib><title>Vestibular contribution to path integration deficits in 'at-genetic-risk' for Alzheimer's disease</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Path integration changes may precede a clinical presentation of Alzheimer's disease by several years. Studies to date have focused on how spatial cell changes affect path integration in preclinical AD. However, vestibular input is also critical for intact path integration. Here, we developed the vestibular rotation task that requires individuals to manually point an iPad device in the direction of their starting point following rotational movement, without any visual cues. Vestibular features were derived from the sensor data using feature selection. Machine learning models illustrate that the vestibular features accurately classified Apolipoprotein E ε3ε4 carriers and ε3ε3 carrier controls (mean age 62.7 years), with 65% to 79% accuracy depending on task trial. All machine learning models produced a similar classification accuracy. Our results demonstrate the cross-sectional role of the vestibular system in Alzheimer's disease risk carriers. Future investigations should examine if vestibular functions explain individual phenotypic heterogeneity in path integration among Alzheimer's disease risk carriers.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Alzheimer Disease - genetics</subject><subject>Alzheimer's disease</subject><subject>Alzheimers disease</subject><subject>Apolipoprotein E</subject><subject>Biology and Life Sciences</subject><subject>Classification</subject><subject>Computer and Information Sciences</subject><subject>Cross-Sectional Studies</subject><subject>Cues</subject><subject>Diagnosis</subject><subject>Engineering and Technology</subject><subject>Evaluation</subject><subject>Genetic aspects</subject><subject>Genetic screening</subject><subject>Health risks</subject><subject>Heterogeneity</subject><subject>Humans</subject><subject>Integration</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Medicine and Health 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'at-genetic-risk' for Alzheimer's disease</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-01-03</date><risdate>2023</risdate><volume>18</volume><issue>1</issue><spage>e0278239</spage><epage>e0278239</epage><pages>e0278239-e0278239</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Path integration changes may precede a clinical presentation of Alzheimer's disease by several years. Studies to date have focused on how spatial cell changes affect path integration in preclinical AD. However, vestibular input is also critical for intact path integration. Here, we developed the vestibular rotation task that requires individuals to manually point an iPad device in the direction of their starting point following rotational movement, without any visual cues. Vestibular features were derived from the sensor data using feature selection. Machine learning models illustrate that the vestibular features accurately classified Apolipoprotein E ε3ε4 carriers and ε3ε3 carrier controls (mean age 62.7 years), with 65% to 79% accuracy depending on task trial. All machine learning models produced a similar classification accuracy. Our results demonstrate the cross-sectional role of the vestibular system in Alzheimer's disease risk carriers. Future investigations should examine if vestibular functions explain individual phenotypic heterogeneity in path integration among Alzheimer's disease risk carriers.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36595510</pmid><doi>10.1371/journal.pone.0278239</doi><tpages>e0278239</tpages><orcidid>https://orcid.org/0000-0002-2214-3788</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Algorithms Alzheimer Disease - genetics Alzheimer's disease Alzheimers disease Apolipoprotein E Biology and Life Sciences Classification Computer and Information Sciences Cross-Sectional Studies Cues Diagnosis Engineering and Technology Evaluation Genetic aspects Genetic screening Health risks Heterogeneity Humans Integration Learning algorithms Machine learning Medicine and Health Sciences Middle age Middle Aged Neurodegenerative diseases Physical Sciences Research and Analysis Methods Risk Rotation Social Sciences Support vector machines Vestibular system Vestibule, Labyrinth Visual stimuli |
| title | Vestibular contribution to path integration deficits in 'at-genetic-risk' for Alzheimer's disease |
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