Does adding MRI and CSF-based biomarkers improve cognitive status classification based on cognitive performance questionnaires?
Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns abo...
Saved in:
| Published in | PloS one Vol. 18; no. 5; p. e0285220 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
08.05.2023
Public Library of Science (PLoS) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1932-6203 1932-6203 |
| DOI | 10.1371/journal.pone.0285220 |
Cover
| Abstract | Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown.
Data come from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence.
Our analysis showed a slight improvement in variance explained (pseudo-R2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence.
MRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection. |
|---|---|
| AbstractList | Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown.
Data come from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence.
Our analysis showed a slight improvement in variance explained (pseudo-R2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence.
MRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection. Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown.BACKGROUNDCognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown.Data come from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence.METHODSData come from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence.Our analysis showed a slight improvement in variance explained (pseudo-R2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence.RESULTSOur analysis showed a slight improvement in variance explained (pseudo-R2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence.MRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection.CONCLUSIONMRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection. Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown. Data come from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence. Our analysis showed a slight improvement in variance explained (pseudo-R.sup.2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R.sup.2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence. MRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection. Background Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown. Methods Data come from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence. Results Our analysis showed a slight improvement in variance explained (pseudo-R2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence. Conclusion MRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection. Background Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown. Methods Data come from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence. Results Our analysis showed a slight improvement in variance explained (pseudo-R.sup.2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R.sup.2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence. Conclusion MRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection. Background Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown. Methods Data come from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence. Results Our analysis showed a slight improvement in variance explained (pseudo-R 2 ) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R 2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence. Conclusion MRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection. BackgroundCognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been widely used in population-based studies, providing insight into the population dynamics of dementia. However, researchers have raised concerns about the accuracy of cognitive assessments. MRI and CSF biomarkers may provide improved classification, but the potential improvement in classification in population-based studies is relatively unknown.MethodsData come from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We examined whether the addition of MRI and CSF biomarkers improved cognitive status classification based on cognitive status questionnaires (MMSE). We estimated several multinomial logistic regression models with different combinations of MMSE and CSF/MRI biomarkers. Based on these models, we also predicted prevalence of each cognitive status category using a model with MMSE only and a model with MMSE + MRI + CSF measures and compared them to diagnosed prevalence.ResultsOur analysis showed a slight improvement in variance explained (pseudo-R2) between the model with MMSE only and the model including MMSE and MRI/CSF biomarkers; the pseudo-R2 increased from .401 to .445. Additionally, in evaluating differences in predicted prevalence for each cognitive status, we found a small improvement in the predicted prevalence of cognitively normal individuals between the MMSE only model and the model with MMSE and CSF/MRI biomarkers (3.1% improvement). We found no improvement in the correct prediction of dementia prevalence.ConclusionMRI and CSF biomarkers, while important for understanding dementia pathology in clinical research, were not found to substantially improve cognitive status classification based on cognitive status performance, which may limit adoption in population-based surveys due to costs, training, and invasiveness associated with their collection. |
| Audience | Academic |
| Author | Farina, Mateo P. Crimmins, Eileen M. Saenz, Joseph |
| AuthorAffiliation | 2 Human Development and Family Sciences, University of Texas at Austin, Austin, Texas, United States of America Niigata University, JAPAN 1 School of Gerontology, University of Southern California, Los Angeles, California, United States of America 3 Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, Arizona, United States of America |
| AuthorAffiliation_xml | – name: Niigata University, JAPAN – name: 1 School of Gerontology, University of Southern California, Los Angeles, California, United States of America – name: 3 Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, Arizona, United States of America – name: 2 Human Development and Family Sciences, University of Texas at Austin, Austin, Texas, United States of America |
| Author_xml | – sequence: 1 givenname: Mateo P. orcidid: 0000-0002-0765-6550 surname: Farina fullname: Farina, Mateo P. – sequence: 2 givenname: Joseph surname: Saenz fullname: Saenz, Joseph – sequence: 3 givenname: Eileen M. surname: Crimmins fullname: Crimmins, Eileen M. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37155663$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNk11v0zAUhiM0xD7gHyCIhITgoiVOYsfhZpoKg0pDkzbg1jqxT1oX1y52MtgVfx33Y6OdJjElUo7s531zPuzDZM86i0nynGRDUlTk3cz13oIZLuLyMMs5zfPsUXJA6iIfsDwr9rbi_eQwhFmW0YIz9iTZj3pKGSsOkj8fHIYUlNJ2kn65GKdgVTq6PB00EFCljXZz8D_Qh1TPF95dYSrdxOpOxyh00PUhlQZC0K2W0Gln07UwBv_ABfrW-TlYienPHsOSs6A9huOnyeMWTMBnm-9R8u3049fR58HZ-afx6ORsICtadwOQRBHEpqxplVctFDkvm6KgiAWRlaRc1lnFSNwolSxzBaRSqBpJC4ACalkcJS_Xvgvjgtj0LoicE0I4LxmPxHhNKAczsfA6Fn4tHGixWnB-IsB3WhoUpGlUxeu8pCymEV-mMGbDeUaRKYrRi669eruA619gzK0hycRyfDcpiOX4xGZ8UXe8ybJv5qgk2s6D2Ulmd8fqqZi4q-hKGCs5iw5vNg7erVot5jpINAYsun5dMGU1z5boqzvo_W3ZUBOIlWvbuvhjuTQVJ1XJaUZKWkZqeA8VH4VzLWOJrY7rO4K3O4LIdPi7m0AfghhfXjycPf--y77eYqcIppsGZ_rlkQu74IvtVt_2-OZqROD9GpDeheCxFVJ3qyMeS9Pmf4Ms74gfNP-_EUk4Vg |
| CitedBy_id | crossref_primary_10_1111_ene_16235 crossref_primary_10_3233_JAD_240206 crossref_primary_10_1002_pst_2413 |
| Cites_doi | 10.1212/WNL.0000000000005549 10.2174/157488408784293723 10.3233/JAD-160248 10.1002/ana.21610 10.1038/aps.2017.28 10.1016/j.neuint.2009.08.006 10.1186/s13195-021-00805-8 10.1136/jnnp-2014-307662 10.1371/journal.pone.0090814 10.1186/s40478-019-0743-1 10.1371/journal.pone.0127396 10.1073/pnas.0904532106 10.1097/WAD.0b013e3182163b62 10.1016/S1474-4422(08)70162-5 10.1097/EDE.0000000000000945 10.1016/j.bionps.2019.100005 10.3233/JAD-180548 10.1093/brain/awn146 10.1007/978-3-319-44775-9_3 10.1002/alz.12283 10.1001/archneur.64.7.1023 10.1016/j.neurobiolaging.2019.08.009 10.1097/EDE.0000000000001219 10.1515/CCLM.2011.086 10.1016/B978-0-12-374105-9.00186-6 10.1371/journal.pone.0138866 10.3233/JAD-2010-1221 |
| ContentType | Journal Article |
| Copyright | Copyright: © 2023 Farina 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. COPYRIGHT 2023 Public Library of Science 2023 Farina 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. 2023 Farina et al 2023 Farina et al 2023 Farina 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. |
| Copyright_xml | – notice: Copyright: © 2023 Farina 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. – notice: COPYRIGHT 2023 Public Library of Science – notice: 2023 Farina 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. – notice: 2023 Farina et al 2023 Farina et al – notice: 2023 Farina 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. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM IOV ISR 3V. 7QG 7QL 7QO 7RV 7SN 7SS 7T5 7TG 7TM 7U9 7X2 7X7 7XB 88E 8AO 8C1 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AEUYN AFKRA ARAPS ATCPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU D1I DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. KB. KB0 KL. L6V LK8 M0K M0S M1P M7N M7P M7S NAPCQ P5Z P62 P64 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PTHSS PYCSY RC3 7X8 5PM ADTOC UNPAY DOA |
| DOI | 10.1371/journal.pone.0285220 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Opposing Viewpoints Gale In Context: Science ProQuest Central (Corporate) Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Biotechnology Research Abstracts Nursing & Allied Health Database Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Meteorological & Geoastrophysical Abstracts Nucleic Acids Abstracts Virology and AIDS Abstracts Agricultural Science Collection Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Public Health Database Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection Agricultural & Environmental Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Materials Science Collection ProQuest Central Korea Engineering Research Database Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Materials Science Database Nursing & Allied Health Database (Alumni Edition) Meteorological & Geoastrophysical Abstracts - Academic ProQuest Engineering Collection Biological Sciences Agriculture Science Database ProQuest Health & Medical Collection Medical Database Algology Mycology and Protozoology Abstracts (Microbiology C) Biological Science Database Engineering Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts Environmental Science Database Materials Science Collection ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition Engineering Collection Environmental Science Collection Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Meteorological & Geoastrophysical Abstracts Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database Virology and AIDS Abstracts ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Environmental Science Collection Entomology Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Environmental Science Database ProQuest Nursing & Allied Health Source (Alumni) Engineering Research Database ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) Materials Science Collection ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) Agricultural & Environmental Science Collection AIDS and Cancer Research Abstracts Materials Science Database ProQuest Materials Science Collection ProQuest Public Health ProQuest Nursing & Allied Health Source ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Animal Behavior Abstracts Materials Science & Engineering Collection Immunology Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic Agricultural Science Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 5 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| DocumentTitleAlternate | MRI and CSF-based biomarkers improve cognitive status classification based on cognitive performance |
| EISSN | 1932-6203 |
| ExternalDocumentID | 2811188468 oai_doaj_org_article_1bbd789245684b84b6deb338805e6d5e 10.1371/journal.pone.0285220 PMC10166486 A748501454 37155663 10_1371_journal_pone_0285220 |
| Genre | Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
| GeographicLocations | United States |
| GeographicLocations_xml | – name: United States |
| GrantInformation_xml | – fundername: NIA NIH HHS grantid: T32 AG000037 – fundername: NIA NIH HHS grantid: R01 AG060110 – fundername: NIA NIH HHS grantid: R00 AG058799 – fundername: NIA NIH HHS grantid: P30 AG017265 – fundername: NIA NIH HHS grantid: K99 AG076964 – fundername: NIA NIH HHS grantid: U01 AG024904 – fundername: NIA NIH HHS grantid: P30 AG043073 – fundername: ; grantid: T32AG000037; P30 AG043073; P30 AG17265; K99AG076964; R00 AG058799 |
| GroupedDBID | --- 123 29O 2WC 53G 5VS 7RV 7X2 7X7 7XC 88E 8AO 8C1 8CJ 8FE 8FG 8FH 8FI 8FJ A8Z AAFWJ AAUCC AAWOE AAYXX ABDBF ABIVO ABJCF ABUWG ACGFO ACIHN ACIWK ACPRK ACUHS ADBBV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHMBA ALMA_UNASSIGNED_HOLDINGS AOIJS APEBS ARAPS ATCPS BAWUL BBNVY BCNDV BENPR BGLVJ BHPHI BKEYQ BPHCQ BVXVI BWKFM CCPQU CITATION CS3 D1I D1J D1K DIK DU5 E3Z EAP EAS EBD EMOBN ESTFP ESX EX3 F5P FPL FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO IEA IGS IHR IHW INH INR IOV IPY ISE ISR ITC K6- KB. KQ8 L6V LK5 LK8 M0K M1P M48 M7P M7R M7S M~E NAPCQ O5R O5S OK1 OVT P2P P62 PATMY PDBOC PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PUEGO PV9 PYCSY RNS RPM RZL SV3 TR2 UKHRP WOQ WOW ~02 ~KM ADRAZ ALIPV BBORY CGR CUY CVF ECM EIF IPNFZ NPM RIG 3V. 7QG 7QL 7QO 7SN 7SS 7T5 7TG 7TM 7U9 7XB 8FD 8FK AZQEC C1K DWQXO FR3 GNUQQ H94 K9. KL. M7N P64 PKEHL PQEST PQUKI RC3 7X8 5PM ADTOC UNPAY AAPBV ABPTK N95 |
| ID | FETCH-LOGICAL-c759t-ac1d1eeb495727fa3284b335ee31c7c58c90761fa34dc42da17dedbc53aa3a9c3 |
| IEDL.DBID | M48 |
| ISSN | 1932-6203 |
| IngestDate | Sun Jul 02 11:04:09 EDT 2023 Fri Oct 03 12:38:30 EDT 2025 Sun Oct 26 04:16:43 EDT 2025 Tue Sep 30 17:14:12 EDT 2025 Thu Oct 02 11:48:34 EDT 2025 Tue Oct 07 07:44:41 EDT 2025 Mon Oct 20 22:00:29 EDT 2025 Mon Oct 20 16:27:07 EDT 2025 Thu Oct 16 16:16:08 EDT 2025 Thu Oct 16 16:09:12 EDT 2025 Thu May 22 21:20:05 EDT 2025 Thu Apr 03 07:03:59 EDT 2025 Thu Apr 24 22:58:36 EDT 2025 Wed Oct 01 02:50:26 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | Copyright: © 2023 Farina 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. 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. cc-by Creative Commons Attribution License |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c759t-ac1d1eeb495727fa3284b335ee31c7c58c90761fa34dc42da17dedbc53aa3a9c3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. |
| ORCID | 0000-0002-0765-6550 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1371/journal.pone.0285220 |
| PMID | 37155663 |
| PQID | 2811188468 |
| PQPubID | 1436336 |
| PageCount | e0285220 |
| ParticipantIDs | plos_journals_2811188468 doaj_primary_oai_doaj_org_article_1bbd789245684b84b6deb338805e6d5e unpaywall_primary_10_1371_journal_pone_0285220 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10166486 proquest_miscellaneous_2811569806 proquest_journals_2811188468 gale_infotracmisc_A748501454 gale_infotracacademiconefile_A748501454 gale_incontextgauss_ISR_A748501454 gale_incontextgauss_IOV_A748501454 gale_healthsolutions_A748501454 pubmed_primary_37155663 crossref_citationtrail_10_1371_journal_pone_0285220 crossref_primary_10_1371_journal_pone_0285220 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2023-05-08 |
| PublicationDateYYYYMMDD | 2023-05-08 |
| PublicationDate_xml | – month: 05 year: 2023 text: 2023-05-08 day: 08 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Francisco – name: San Francisco, CA USA |
| PublicationTitle | PloS one |
| PublicationTitleAlternate | PLoS One |
| PublicationYear | 2023 |
| Publisher | Public Library of Science Public Library of Science (PLoS) |
| Publisher_xml | – name: Public Library of Science – name: Public Library of Science (PLoS) |
| References | OL Lopez (pone.0285220.ref011) 2018; 90 CS Latimer (pone.0285220.ref022) 2019; 7 SV Frankfort (pone.0285220.ref026) 2008; 3 Chen G fang (pone.0285220.ref021) 2017; 38 IO Korolev (pone.0285220.ref007) 2016; 11 HL Sun (pone.0285220.ref029) 2018; 2018 BA Bernard (pone.0285220.ref018) 2010 JM Hale (pone.0285220.ref003) 2020; 31 R Khoury (pone.0285220.ref005) 2019; 1 C Clark (pone.0285220.ref025) 2021; 13 RP Friedrich (pone.0285220.ref015) 2010; 107 LB Zahodne (pone.0285220.ref031) 2019; 84 JA Sonnen (pone.0285220.ref006) 2008; 7 LG Apostolova (pone.0285220.ref012) 2012; 26 LM Shaw (pone.0285220.ref019) 2009; 65 A Harten (pone.0285220.ref017) 2011; 49 AJ Mitchell (pone.0285220.ref020) 2017 H Funato (pone.0285220.ref016) 1998; 152 MP Murphy (pone.0285220.ref023) 2010; 19 A Vijayakumar (pone.0285220.ref014) 2012 L Tariciotti (pone.0285220.ref024) 2018; 65 F Blanc (pone.0285220.ref009) 2015; 10 L Frings (pone.0285220.ref010) 2014; 9 LA van de Pol (pone.0285220.ref008) 2007; 64 N Le Bastard (pone.0285220.ref028) 2009; 55 J Simrén (pone.0285220.ref027) 2021; 17 J Feeney (pone.0285220.ref001) 2016; 53 SM Nestor (pone.0285220.ref013) 2008; 131 RM Ahmed (pone.0285220.ref004) 2014; 85 KZ Gianattasio (pone.0285220.ref002) 2019; 30 EClipSE Collaborative Members (pone.0285220.ref030) 2010; 133 |
| References_xml | – volume: 90 start-page: e1920 issue: 21 year: 2018 ident: pone.0285220.ref011 article-title: Amyloid deposition and brain structure as long-term predictors of MCI, dementia, and mortality publication-title: Neurology doi: 10.1212/WNL.0000000000005549 – volume: 3 start-page: 123 issue: 2 year: 2008 ident: pone.0285220.ref026 article-title: Amyloid Beta Protein and Tau in Cerebrospinal Fluid and Plasma as Biomarkers for Dementia: A Review of Recent Literature. publication-title: Curr Clin Pharmacol doi: 10.2174/157488408784293723 – volume: 133 start-page: 2210 issue: Pt 8 year: 2010 ident: pone.0285220.ref030 article-title: Education, the brain and dementia: neuroprotection or compensation? publication-title: Brain J Neurol – volume: 53 start-page: 1107 issue: 3 year: 2016 ident: pone.0285220.ref001 article-title: Measurement Error, Reliability, and Minimum Detectable Change in the Mini-Mental State Examination, Montreal Cognitive Assessment, and Color Trails Test among Community Living Middle-Aged and Older Adults publication-title: J Alzheimers Dis JAD doi: 10.3233/JAD-160248 – volume: 65 start-page: 403 issue: 4 year: 2009 ident: pone.0285220.ref019 article-title: Cerebrospinal Fluid Biomarker Signature in Alzheimer’s Disease Neuroimaging Initiative Subjects publication-title: Ann Neurol doi: 10.1002/ana.21610 – volume: 38 start-page: 1205 issue: 9 year: 2017 ident: pone.0285220.ref021 article-title: Amyloid beta: structure, biology and structure-based therapeutic development. publication-title: Acta Pharmacol Sin. doi: 10.1038/aps.2017.28 – volume: 55 start-page: 820 issue: 8 year: 2009 ident: pone.0285220.ref028 article-title: No correlation between time-linked plasma and CSF Aβ levels publication-title: Neurochem Int doi: 10.1016/j.neuint.2009.08.006 – volume: 13 start-page: 65 issue: 1 year: 2021 ident: pone.0285220.ref025 article-title: Plasma neurofilament light and phosphorylated tau 181 as biomarkers of Alzheimer’s disease pathology and clinical disease progression. publication-title: Alzheimers Res Ther. doi: 10.1186/s13195-021-00805-8 – volume: 85 start-page: 1426 issue: 12 year: 2014 ident: pone.0285220.ref004 article-title: Biomarkers in dementia: clinical utility and new directions publication-title: J Neurol Neurosurg Psychiatry doi: 10.1136/jnnp-2014-307662 – volume: 152 start-page: 983 issue: 4 year: 1998 ident: pone.0285220.ref016 article-title: Astrocytes containing amyloid beta-protein (Abeta)-positive granules are associated with Abeta40-positive diffuse plaques in the aged human brain. publication-title: Am J Pathol – volume: 9 start-page: e90814 issue: 3 year: 2014 ident: pone.0285220.ref010 article-title: Longitudinal Grey and White Matter Changes in Frontotemporal Dementia and Alzheimer’s Disease. publication-title: PLOS ONE. doi: 10.1371/journal.pone.0090814 – volume: 7 start-page: 91 issue: 1 year: 2019 ident: pone.0285220.ref022 article-title: Resistance and resilience to Alzheimer’s disease pathology are associated with reduced cortical pTau and absence of limbic-predominant age-related TDP-43 encephalopathy in a community-based cohort publication-title: Acta Neuropathol Commun doi: 10.1186/s40478-019-0743-1 – volume: 10 start-page: e0127396 issue: 6 year: 2015 ident: pone.0285220.ref009 article-title: Cortical Thickness in Dementia with Lewy Bodies and Alzheimer’s Disease: A Comparison of Prodromal and Dementia Stages. publication-title: PLOS ONE. doi: 10.1371/journal.pone.0127396 – volume: 107 start-page: 1942 issue: 5 year: 2010 ident: pone.0285220.ref015 article-title: Mechanism of amyloid plaque formation suggests an intracellular basis of Aβ pathogenicity publication-title: Proc Natl Acad Sci doi: 10.1073/pnas.0904532106 – volume: 26 start-page: 17 issue: 1 year: 2012 ident: pone.0285220.ref012 article-title: Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment and Alzheimer’s disease. publication-title: Alzheimer Dis Assoc Disord. doi: 10.1097/WAD.0b013e3182163b62 – volume: 7 start-page: 704 issue: 8 year: 2008 ident: pone.0285220.ref006 article-title: Biomarkers for cognitive impairment and dementia in elderly people publication-title: Lancet Neurol doi: 10.1016/S1474-4422(08)70162-5 – volume: 30 start-page: 291 issue: 2 year: 2019 ident: pone.0285220.ref002 article-title: Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study. publication-title: Epidemiol Camb Mass. doi: 10.1097/EDE.0000000000000945 – volume: 1 start-page: 100005 year: 2019 ident: pone.0285220.ref005 article-title: Diagnostic biomarkers of Alzheimer’s disease: A state-of-the-art review. publication-title: Biomark Neuropsychiatry. doi: 10.1016/j.bionps.2019.100005 – volume: 65 start-page: 1417 issue: 4 year: 2018 ident: pone.0285220.ref024 article-title: Clinical Experience with Cerebrospinal Fluid Aβ42, Total and Phosphorylated Tau in the Evaluation of 1,016 Individuals for Suspected Dementia publication-title: J Alzheimers Dis JAD doi: 10.3233/JAD-180548 – volume: 131 start-page: 2443 issue: 9 year: 2008 ident: pone.0285220.ref013 article-title: Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s disease neuroimaging initiative database. publication-title: Brain doi: 10.1093/brain/awn146 – start-page: 37 volume-title: Cognitive Screening Instruments year: 2017 ident: pone.0285220.ref020 doi: 10.1007/978-3-319-44775-9_3 – volume: 17 start-page: 1145 issue: 7 year: 2021 ident: pone.0285220.ref027 article-title: The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer’s disease. publication-title: Alzheimers Dement doi: 10.1002/alz.12283 – volume: 64 start-page: 1023 issue: 7 year: 2007 ident: pone.0285220.ref008 article-title: Magnetic Resonance Imaging Predictors of Cognition in Mild Cognitive Impairment publication-title: Arch Neurol doi: 10.1001/archneur.64.7.1023 – volume: 84 start-page: 109 year: 2019 ident: pone.0285220.ref031 article-title: The role of education in a vascular pathway to episodic memory: brain maintenance or cognitive reserve publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2019.08.009 – volume: 31 start-page: 745 issue: 5 year: 2020 ident: pone.0285220.ref003 article-title: Trends in the Risk of Cognitive Impairment in the United States, 1996–2014. publication-title: Epidemiol Camb Mass. doi: 10.1097/EDE.0000000000001219 – volume: 49 start-page: 353 year: 2011 ident: pone.0285220.ref017 article-title: Tau and p-tau as CSF biomarkers in dementia: A meta-analysis publication-title: Clin Chem Lab Med CCLM FESCC doi: 10.1515/CCLM.2011.086 – start-page: 187 volume-title: Encyclopedia of Movement Disorders year: 2010 ident: pone.0285220.ref018 doi: 10.1016/B978-0-12-374105-9.00186-6 – volume: 2018 start-page: e7302045 year: 2018 ident: pone.0285220.ref029 article-title: The Correlations of Plasma and Cerebrospinal Fluid Amyloid-Beta Levels with Platelet Count in Patients with Alzheimer’s Disease publication-title: BioMed Res Int – volume: 11 start-page: e0138866 issue: 2 year: 2016 ident: pone.0285220.ref007 article-title: Predicting Progression from Mild Cognitive Impairment to Alzheimer’s Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. publication-title: PLOS ONE. doi: 10.1371/journal.pone.0138866 – year: 2012 ident: pone.0285220.ref014 article-title: Comparison of Hippocampal Volume in Dementia Subtypes. publication-title: ISRN Radiol – volume: 19 start-page: 311 issue: 1 year: 2010 ident: pone.0285220.ref023 article-title: Alzheimer’s Disease and the β-Amyloid Peptide publication-title: J Alzheimers Dis JAD doi: 10.3233/JAD-2010-1221 |
| SSID | ssj0053866 |
| Score | 2.4495587 |
| Snippet | Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has been... Background Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has... BackgroundCognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has... Background Cognitive status classification (e.g. dementia, cognitive impairment without dementia, and normal) based on cognitive performance questionnaires has... |
| SourceID | plos doaj unpaywall pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e0285220 |
| SubjectTerms | Activities of daily living Algorithms Alzheimer Disease - diagnostic imaging Alzheimer Disease - pathology Alzheimer's disease Alzheimers disease Amyloid beta-Peptides Analysis Biological markers Biology and Life Sciences Biomarkers Brain research Cerebrospinal fluid Classification Cognition Cognitive ability Cognitive Dysfunction - diagnostic imaging Cognitive Dysfunction - pathology Data collection Dementia Dementia disorders Diagnosis Disease Progression Evaluation Humans Invasiveness Magnetic Resonance Imaging Medical imaging Medicine and Health Sciences Memory Modelling Neurodegenerative diseases Neuroimaging Older people Polls & surveys Population dynamics Population studies Population-based studies Questionnaires Regression analysis Regression models Research and Analysis Methods Scanners tau Proteins |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Na9RAFB9kL3oR61dTq44iqIds8zGTTE5Sq0srVKG10luYzEctLEloNogn_3Xfm8ymDRbag7CHJfNLYN73zLz3hpA3ELKmXMZpaCLJQlZpFVbK8pAlibVCgxJarHc-_Jrtn7Avp_z0ylVfmBM2tAceCLcTV5XORYHnc4JV8Ms0rP-whQk3meYGrW8kivViarDBoMVZ5gvl0jze8XyZt01t5uBRIeiIJo7I9esfrfKsXTbddSHnv5mTd_u6lb9_yeXyiltaPCD3fTxJd4d5bJA7pn5INrzGdvSdbyv9_hH586mBB5g_VJ_Rw6MDKmtN944XIToyTbEOH1N1Ljp67jYaDB1TiyiWHfUdVRhqY26RYycdXoQ_l8D2sg6BukkBrpZgVrsPj8nJ4vP3vf3QX78QqpwXq1CqWMfGVLCEgiDHyhQ8GVCeG5PGKldcqAI3QWCAacUSLeNcG10pnkqZykKlT8isBoJvEsoTK2NgfBHZijGZyFzlrEosz23GhGYBSde8KJXvTY5XZCxLd-CWwxplIGeJHCw9BwMSjm-1Q2-OG_Afkc0jFjtruwcgb6WXt_ImeQvISxSScihTHe1DuZsz4c5oYTKvHQK7a9SYvnMm-64rD779uAXo-GgCeutBtgFyKOlLJmBO2LVrgtyeIMFGqMnwJor0mipdmQjwcQJiTwFvrsX8-uFX4zB-FFPyatP0A4ZnhYiygDwdtGKkLHCAwzohDYiY6MuE9NOR-vyna26Ou0kgE_DR-ahat-Lu1v_g7jNyL4Eo1mW0im0yW1305jlEnavqhTMwfwGYboMe priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjR1db9Mw0BrdA7wgxtcKAwxCAh7SNbGdjwc0bWPThrSCOob2Fjm2UyZVSWhaIZ7469w5TkrEBJPyEMXnSPd9tu_OhLyGkJUJ6TPPjCX3eKaVl6lceDwI8jzWoIQ51jufTcKTC_7xUlxukElbC4Npla1NtIZalwr3yHeDGLQyBm8Z71XfPbw1Ck9X2ys0pLtaQb-3LcZukc0AO2MNyObB0eTztLXNoN1h6AroWOTvOn6NqrIwI_C0EIyMew7K9vHvrPWgmpf1daHo3xmVt1dFJX_-kPP5H-7q-B656-JMut8IxhbZMMV9suU0uaZvXbvpdw_Irw8lfMC8omJGz6anVBaaHp4fe-jgNMX6fEzhWdT0ym5AGNqlHFEsR1rVVGEIjjlHls20mQgva8BqXZ9ALVIAV0gwt_XeQ3JxfPTl8MRz1zJ4KhLJ0pPK174xGSytIPjJJQMPlzEmjGG-ipSIVYKbIzDAteKBln6kjc6UYFIymSj2iAwKIPg2oSLIpQ8CkYzzjHMZyEhFPAtyEeUhjzUfEtbyIlWuZzlenTFP7UFcBGuXhpwpcjB1HBwSr5tVNT07_gN_gGzuYLHjtv1QLmapU-DUzzIdxQmeEwO68ITaANZg_oQJtTBD8gKFJG3KVzu7ke5HPLZnt4DMKwuBXTcKTOuZyVVdp6efvt4A6HzaA3rjgPISyKGkK6UAnLCbVw9ypwcJtkP1hrdRpFuq1Olay2BmK-bXD7_shvGnmKpXmHLVwIgwicfhkDxutKKjLHBAwPqBDUnc05ce6fsjxdU32_Qcd5lAJuCno061bsTdJ_9G5Cm5E0DcanNY4x0yWC5W5hnEmcvsuTMevwGyd4FP priority: 102 providerName: ProQuest – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELbK9gAXoLy6UMAgxOOQdBPbeZzQUli1SC2opVU5oMixnVKxyq6ajRAc4K8z4zgpgSLKAWkPUfzZWo_HM2PPI4Q8ApOVCRkwz4wk93iulZerQng8DIsi0bAJC8x33t6JNvf560NxuEQ-tLkwjoJwRpzOKuvJx4dZadYdJdexXlHjPfUDFgdtD38OIB-0JRgUo8e24hDejC0wAekCWY4EmOoDsry_83b8vvE0h14UjphLp_vTSD11Zav6d7J7gP_sLMP09_jKi3U5l18-y-n0J-U1uUK-tdNuYlY--fUi99XXXypC_je6XCWXndlLx80oK2TJlNfIihMsFX3qql8_u06-v5zBCwxzKo_o9u4WlaWmG3sTD_WtplguACOKTip6bO9DDO0ioChmR9UVVXgiwBAoy3W06QgPp8D5aboEtVQFXClB-lfPb5D9yat3G5ue-0qEp2KRLjypAh0Yk8NJD2yxQjJQuDljwhgWqFiJRKV4VwMNXCseahnE2uhcCSYlk6liN8mgBBqtEirCQgbAn-moyDmXoYxVzPOwEHER8UTzIWEtM2TKlVDHL3lMM-sXjOEo1ZAzQ6JnjuhD4nW95k0Jkb_gXyCfdVgsAG5fwKpnbrWzIM91nKTotobpwi_SBmYN0liYSAszJPeRS7Mmm7YTY9k45ol1JcNkHloEFgEpMcroSNZVlW29OTgHaG-3B3riQMUMyKGky-yAOSFT9pBrPSSIMtVrXkWubqlSZWECqjgBEzmBnu0-O7v5QdeMg2LkYGlmdYMRUZqMoiG51WzLjrKwAgKOM2xIkt6G7ZG-31Ief7Q12PHSC3gCBvW7vX2u1b39rx3ukEshGNY2yDZZI4PFSW3ugiG8yO85cfYD5ue6ag priority: 102 providerName: Unpaywall |
| Title | Does adding MRI and CSF-based biomarkers improve cognitive status classification based on cognitive performance questionnaires? |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/37155663 https://www.proquest.com/docview/2811188468 https://www.proquest.com/docview/2811569806 https://pubmed.ncbi.nlm.nih.gov/PMC10166486 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0285220&type=printable https://doaj.org/article/1bbd789245684b84b6deb338805e6d5e http://dx.doi.org/10.1371/journal.pone.0285220 |
| UnpaywallVersion | publishedVersion |
| Volume | 18 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVFSB databaseName: Free Full-Text Journals in Chemistry customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: HH5 dateStart: 20060101 isFulltext: true titleUrlDefault: http://abc-chemistry.org/ providerName: ABC ChemistRy – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: KQ8 dateStart: 20060101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: KQ8 dateStart: 20061001 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: DOA dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVEBS databaseName: Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: ABDBF dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: EBSCOhost Food Science Source customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: A8Z dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/login.aspx?authtype=ip,uid&profile=ehost&defaultdb=fsr providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: DIK dateStart: 20060101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: GX1 dateStart: 20060101 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M~E dateStart: 20060101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: RPM dateStart: 20060101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 7X7 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: BENPR dateStart: 20061201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Technology Collection customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 8FG dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/technologycollection1 providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 1932-6203 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: 8C1 dateStart: 20061201 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVFZP databaseName: Scholars Portal Journals: Open Access customDbUrl: eissn: 1932-6203 dateEnd: 20250930 omitProxy: true ssIdentifier: ssj0053866 issn: 1932-6203 databaseCode: M48 dateStart: 20061201 isFulltext: true titleUrlDefault: http://journals.scholarsportal.info providerName: Scholars Portal |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjR1db9Mw0Nq6B3hBjK8VRjEI8fGQqknsfDygqSsrG1LL1FFUniLHdsqkKsmaVrAn_jp3TpoRMWBSFVX22dLd-Xxn-z4IeQkmq8uF7Vq6J5jFYiWtWCbcYo6TJIECIUww3nk09o6n7OOMz7bI5qG9ImBx7dEO60lNl4vuj4vLAxD4d6Zqg29vBnXzLNVd0JdgUvRe5RcWlpbCJ9iqzsY22QH1FWJ9hxGrnxpA4D2viqn722QNnWVS-9cbeCtfZMV11umfTpa31mkuLr-LxeI3DTa8S-5Upiftl2tll2zp9B7ZrYS7oG-qDNRv75Of7zNoQFejdE5HkxMqUkUHZ0MLdZ6iGLKPXj3Lgp6bOwlNay8kihFK64JKtMrRDclwnpYD4c8VYH4VskANUgCXCtiBi4MHZDo8-jw4tqpKDZb0ebiyhLSVrXUMpy2whxLhgtKLXZdr7drSlzyQId6XQAdTkjlK2L7SKpbcFcIVoXQfklYKBN8jlDuJsGGNhL0kZkw4wpc-i52E-4nHAsXaxN3wIpJVGnOsprGIzNucD8eZkpwRcjCqONgmVj0qL9N4_Af-ENlcw2ISbtOQLedRJdORHcfKD0J8OgZ04ecpDVjDjsi1p7huk2e4SKIyorXeSqK-zwLznAvIvDAQmIgjRU-fuVgXRXTy6csNgM4mDaDXFVCSATmkqKIrACdM8NWA3G9AwnYiG917uKQ3VCkiJwB1GICZGsDIzTK_vvt53Y2TovdeqrN1CcO9MOh5bfKolIqassABDkcKt02Chrw0SN_sSc-_mTzoePEEawIm7daidSPuPv43Ik_IbQdMWePWGuyT1mq51k_B9FzFHbLtz3z4BgMbv8MPHbJzeDQ-nXTMZU7HbC3QNh2f9r_-AlR-jvE |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LbxMxELZKOJQLorwaKNQgEHDYNLu293FAVWmJGtoUqS_ltnhtb6gU7S7ZRFVP_CN-IzP7Cisq6KVSDtF6bMmemW_G9syYkDfgsjIhbWaZvuQWj7SyIhULiztOHPsalDDGfOfRkbt_xr-MxXiF_KpzYTCsssbEAqh1qvCMfMvxQSt9sJb-dvbDwlej8Ha1fkKjFIsDc3UJW7b843AP-PvWcQafT3f3repVAUt5IphbUtnaNiaCnQHY7lgyAOiIMWEMs5WnhK8C3NtDA9eKO1ranjY6UoJJyWSgGIx7h9zlDLAE9McbNxs8wA7XrdLzmGdvVdLQy9LE9MCOg6vTb5m_4pWAxhZ0smmaX-fo_h2vubpIMnl1KafTP4zh4AG5X3mxdKcUuzWyYpKHZK3CiZy-r4pZf3hEfu6l8AGjlpIJHR0PqUw03T0ZWGg-NcXsfwwQmuX0ojjeMLQJaKKY7LTIqUIHHyOaCiGiZUf4syTMltkPtJgU0CUSwDzffkzOboU9T0gngQVfJ1Q4sbRB3IJ-HHEuHekpj0dOLLzY5b7mXcJqXoSqqoiOD3NMw-Kaz4OdUbmcIXIwrDjYJVbTKysrgvyH_hOyuaHFet7Fh3Q2CSt4CO0o0p4f4C00TBd-rjYwawBXYVwtTJdsopCEZXJsg0rhjsf94mYYJvO6oMCaHgkGDU3kIs_D4dfzGxCdHLeI3lVEcQrLoWSVqAFzwlphLcqNFiUgk2o1r6NI16uSh0sdhp61mF_f_KppxkExEDAx6aKkEW7g990ueVpqRbOywAEBuxPWJX5LX1pL325JLr4XJdXxDAtkAgbtNap1I-4--_dENsnq_unoMDwcHh08J_cc8JCLaFl_g3Tms4V5AR7tPHpZwAgl324bt34DaDC5Mg |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELdGkYAXxPhaYTCDQMBD2iaO8_GAprFSrYwNtDHUt-DYTplUJaFpNe2J_4u_jjvno0RMsJdJfajisyX77n53tu_OhLwAl5VxYTNLD4RrubGSViwTbrmOkySBAiVMMN_54NDbO3E_TPhkjfyqc2EwrLLGRAPUKpN4Rt53AtDKAKxl0E-qsIjPw9F2_sPCF6TwprV-TqMUkX19fgbbt-LteAi8fuk4o_dfdves6oUBS_o8XFhC2srWOoZdAtjxRDAA65gxrjWzpS95IEPc50ODq6TrKGH7SqtYciYEE6FkMO41ct1nLMRwQn_SbPYARzyvStVjvt2vJKOXZ6nugU0Ht2fQMoXmxYDGLnTyWVZc5PT-Hbt5c5nm4vxMzGZ_GMbRHXK78mjpTimC62RNp3fJeoUZBX1dFbZ-c4_8HGbwASOY0ik9OBpTkSq6ezyy0JQqipUAMFhoXtBTc9ShaRPcRDHxaVlQic4-RjcZgaJlR_izIsxXmRDUTAroUgHAXmzfJydXwp4HpJPCgm8Qyp1E2CB64SCJXVc4wpe-GzsJ9xPPDZTbJazmRSSr6uj4SMcsMld-PuySyuWMkINRxcEusZpeeVkd5D_075DNDS3W9jYfsvk0qqAisuNY-UGIN9IwXfh5SsOsAWi59hTXXbKFQhKVibINQkU7vhuYW2KYzHNDgfU9UtSUqVgWRTT-9PUSRMdHLaJXFVGSwXJIUSVtwJywbliLcrNFCSglW80bKNL1qhTRSp-hZy3mFzc_a5pxUAwKTHW2LGm4FwYDr0sellrRrCxwgMNOhXVJ0NKX1tK3W9LT76a8Op5ngUzAoL1GtS7F3Uf_nsgWuQGIFX0cH-4_JrcccJZN4GywSTqL-VI_Aed2ET81KELJt6uGrd8xE711 |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwELbK9gAXoLy6UMAgxOOQdBPbeZzQUli1SC2opVU5oMixnVKxyq6ajRAc4K8z4zgpgSLKAWkPUfzZWo_HM2PPI4Q8ApOVCRkwz4wk93iulZerQng8DIsi0bAJC8x33t6JNvf560NxuEQ-tLkwjoJwRpzOKuvJx4dZadYdJdexXlHjPfUDFgdtD38OIB-0JRgUo8e24hDejC0wAekCWY4EmOoDsry_83b8vvE0h14UjphLp_vTSD11Zav6d7J7gP_sLMP09_jKi3U5l18-y-n0J-U1uUK-tdNuYlY--fUi99XXXypC_je6XCWXndlLx80oK2TJlNfIihMsFX3qql8_u06-v5zBCwxzKo_o9u4WlaWmG3sTD_WtplguACOKTip6bO9DDO0ioChmR9UVVXgiwBAoy3W06QgPp8D5aboEtVQFXClB-lfPb5D9yat3G5ue-0qEp2KRLjypAh0Yk8NJD2yxQjJQuDljwhgWqFiJRKV4VwMNXCseahnE2uhcCSYlk6liN8mgBBqtEirCQgbAn-moyDmXoYxVzPOwEHER8UTzIWEtM2TKlVDHL3lMM-sXjOEo1ZAzQ6JnjuhD4nW95k0Jkb_gXyCfdVgsAG5fwKpnbrWzIM91nKTotobpwi_SBmYN0liYSAszJPeRS7Mmm7YTY9k45ol1JcNkHloEFgEpMcroSNZVlW29OTgHaG-3B3riQMUMyKGky-yAOSFT9pBrPSSIMtVrXkWubqlSZWECqjgBEzmBnu0-O7v5QdeMg2LkYGlmdYMRUZqMoiG51WzLjrKwAgKOM2xIkt6G7ZG-31Ief7Q12PHSC3gCBvW7vX2u1b39rx3ukEshGNY2yDZZI4PFSW3ugiG8yO85cfYD5ue6ag |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Does+adding+MRI+and+CSF-based+biomarkers+improve+cognitive+status+classification+based+on+cognitive+performance+questionnaires%3F&rft.jtitle=PloS+one&rft.au=Farina%2C+Mateo+P&rft.au=Saenz%2C+Joseph&rft.au=Crimmins%2C+Eileen+M&rft.date=2023-05-08&rft.pub=Public+Library+of+Science&rft.eissn=1932-6203&rft.volume=18&rft.issue=5&rft.spage=e0285220&rft_id=info:doi/10.1371%2Fjournal.pone.0285220&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1932-6203&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1932-6203&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1932-6203&client=summon |