Cargando…
Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes
BACKGROUND: Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer’s disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajec...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
IOS Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9277685/ https://www.ncbi.nlm.nih.gov/pubmed/35570482 http://dx.doi.org/10.3233/JAD-215289 |
_version_ | 1784746035563724800 |
---|---|
author | Drouin, Shannon M. McFall, G. Peggy Potvin, Olivier Bellec, Pierre Masellis, Mario Duchesne, Simon Dixon, Roger A. |
author_facet | Drouin, Shannon M. McFall, G. Peggy Potvin, Olivier Bellec, Pierre Masellis, Mario Duchesne, Simon Dixon, Roger A. |
author_sort | Drouin, Shannon M. |
collection | PubMed |
description | BACKGROUND: Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer’s disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses. OBJECTIVE: To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers. METHODS: We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class. RESULTS: For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ(1–42). Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ(1–40), higher depressive symptomology, and lower body mass index. CONCLUSION: Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities. |
format | Online Article Text |
id | pubmed-9277685 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | IOS Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92776852022-07-25 Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes Drouin, Shannon M. McFall, G. Peggy Potvin, Olivier Bellec, Pierre Masellis, Mario Duchesne, Simon Dixon, Roger A. J Alzheimers Dis Research Article BACKGROUND: Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer’s disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses. OBJECTIVE: To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers. METHODS: We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class. RESULTS: For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ(1–42). Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ(1–40), higher depressive symptomology, and lower body mass index. CONCLUSION: Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities. IOS Press 2022-06-28 /pmc/articles/PMC9277685/ /pubmed/35570482 http://dx.doi.org/10.3233/JAD-215289 Text en © 2022 – The authors. Published by IOS Press https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) License (https://creativecommons.org/licenses/by-nc/4.0/) , which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Drouin, Shannon M. McFall, G. Peggy Potvin, Olivier Bellec, Pierre Masellis, Mario Duchesne, Simon Dixon, Roger A. Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes |
title | Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes |
title_full | Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes |
title_fullStr | Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes |
title_full_unstemmed | Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes |
title_short | Data-Driven Analyses of Longitudinal Hippocampal Imaging Trajectories: Discrimination and Biomarker Prediction of Change Classes |
title_sort | data-driven analyses of longitudinal hippocampal imaging trajectories: discrimination and biomarker prediction of change classes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9277685/ https://www.ncbi.nlm.nih.gov/pubmed/35570482 http://dx.doi.org/10.3233/JAD-215289 |
work_keys_str_mv | AT drouinshannonm datadrivenanalysesoflongitudinalhippocampalimagingtrajectoriesdiscriminationandbiomarkerpredictionofchangeclasses AT mcfallgpeggy datadrivenanalysesoflongitudinalhippocampalimagingtrajectoriesdiscriminationandbiomarkerpredictionofchangeclasses AT potvinolivier datadrivenanalysesoflongitudinalhippocampalimagingtrajectoriesdiscriminationandbiomarkerpredictionofchangeclasses AT bellecpierre datadrivenanalysesoflongitudinalhippocampalimagingtrajectoriesdiscriminationandbiomarkerpredictionofchangeclasses AT masellismario datadrivenanalysesoflongitudinalhippocampalimagingtrajectoriesdiscriminationandbiomarkerpredictionofchangeclasses AT duchesnesimon datadrivenanalysesoflongitudinalhippocampalimagingtrajectoriesdiscriminationandbiomarkerpredictionofchangeclasses AT dixonrogera datadrivenanalysesoflongitudinalhippocampalimagingtrajectoriesdiscriminationandbiomarkerpredictionofchangeclasses AT datadrivenanalysesoflongitudinalhippocampalimagingtrajectoriesdiscriminationandbiomarkerpredictionofchangeclasses |