Estimating Alzheimer's Disease Progression Score Using Machine Learning on FreeSurfer‐Derived MRI Gray Matter Volumes
Background The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes variability in cognitive symptoms, diagnostic test results, and progression rates. This study aims to enhance the understanding of AD progre...
Saved in:
| Published in: | Alzheimer's & dementia Vol. 20; no. S2 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Hoboken
John Wiley and Sons Inc
01.12.2024
|
| Subjects: | |
| ISSN: | 1552-5260, 1552-5279 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Background
The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes variability in cognitive symptoms, diagnostic test results, and progression rates. This study aims to enhance the understanding of AD progression by integrating neuroimaging metrics with demographic data using a novel machine learning technique.
Method
We used supervised Variational Autoencoders (VAEs), a generative AI method, to analyze high‐dimensional neuroimaging data for AD progression, incorporating age and gender as covariates. We used two non‐overlapping datasets: 257 samples from ADNI3 and 676 from ADNI2, analyzing 68 cortical and 48 subcortical grey matter volumes extracted from their MRI 3D T1 images. The VAE model aimed to minimize reconstruction error (MSE), Kullback‐Leibler divergence, and classification error, and to estimate latent variables for each subject. A Disease Progression Score (DPS) was calculated by encoding the extracted imaging features into a latent space and projecting test data onto a trajectory curve. The study used stratified sampling for robustness and assessed the model's performance using the area under the ROC curve (AUC), and correlated the mean DPS with cognitive assessment scores by applying Kendall’s Tau.
Result
The VAE model demonstrated excellent discriminative power in classifying AD progression stages, with ROC AUC values near 1, particularly when using all 116 features. Cortical volumes were more predictive than subcortical volumes. The mean DPS showed a statistically significant correlation with cognitive assessments (p<0.01), with Kendall's Tau values of 0.66 for CDR‐SB, ‐0.45 for MMSE, and ‐0.49 for MoCA, indicating its validity as a quantitative biomarker for cognitive decline in AD.
Conclusion
Supervised VAEs can effectively model the progression of Alzheimer’s disease brain atrophy on MRI and could potentially serve as imaging biomarkers in clinical trials. The strong correlation between the DPS and cognitive assessments highlights the potential of supervised VAEs to provide a quantifiable measure of AD severity, which would be useful for clinical assessments and for objectively monitoring disease progression. The study's methodology and findings contribute to computational neuroscience and offer a foundation for future research in early detection and personalized treatment strategies for Alzheimer's disease. |
|---|---|
| AbstractList | Background
The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes variability in cognitive symptoms, diagnostic test results, and progression rates. This study aims to enhance the understanding of AD progression by integrating neuroimaging metrics with demographic data using a novel machine learning technique.
Method
We used supervised Variational Autoencoders (VAEs), a generative AI method, to analyze high‐dimensional neuroimaging data for AD progression, incorporating age and gender as covariates. We used two non‐overlapping datasets: 257 samples from ADNI3 and 676 from ADNI2, analyzing 68 cortical and 48 subcortical grey matter volumes extracted from their MRI 3D T1 images. The VAE model aimed to minimize reconstruction error (MSE), Kullback‐Leibler divergence, and classification error, and to estimate latent variables for each subject. A Disease Progression Score (DPS) was calculated by encoding the extracted imaging features into a latent space and projecting test data onto a trajectory curve. The study used stratified sampling for robustness and assessed the model's performance using the area under the ROC curve (AUC), and correlated the mean DPS with cognitive assessment scores by applying Kendall’s Tau.
Result
The VAE model demonstrated excellent discriminative power in classifying AD progression stages, with ROC AUC values near 1, particularly when using all 116 features. Cortical volumes were more predictive than subcortical volumes. The mean DPS showed a statistically significant correlation with cognitive assessments (p<0.01), with Kendall's Tau values of 0.66 for CDR‐SB, ‐0.45 for MMSE, and ‐0.49 for MoCA, indicating its validity as a quantitative biomarker for cognitive decline in AD.
Conclusion
Supervised VAEs can effectively model the progression of Alzheimer’s disease brain atrophy on MRI and could potentially serve as imaging biomarkers in clinical trials. The strong correlation between the DPS and cognitive assessments highlights the potential of supervised VAEs to provide a quantifiable measure of AD severity, which would be useful for clinical assessments and for objectively monitoring disease progression. The study's methodology and findings contribute to computational neuroscience and offer a foundation for future research in early detection and personalized treatment strategies for Alzheimer's disease. |
| Author | Haynor, David R. Sung, Junhyoun Chan, Kwun Chuen Gary Shibata, Dean Shui, Lan |
| AuthorAffiliation | 2 National Alzheimer's Coordinating Center, University of Washington, Seattle, WA USA 3 The University of Texas MD Anderson Cancer Center, Houston, TX USA 1 University of Washington, Seattle, WA USA |
| AuthorAffiliation_xml | – name: 2 National Alzheimer's Coordinating Center, University of Washington, Seattle, WA USA – name: 1 University of Washington, Seattle, WA USA – name: 3 The University of Texas MD Anderson Cancer Center, Houston, TX USA |
| Author_xml | – sequence: 1 givenname: Junhyoun surname: Sung fullname: Sung, Junhyoun email: jsung13@uw.edu organization: University of Washington, Seattle, WA – sequence: 2 givenname: Dean surname: Shibata fullname: Shibata, Dean organization: National Alzheimer's Coordinating Center, University of Washington, Seattle, WA – sequence: 3 givenname: Kwun Chuen Gary surname: Chan fullname: Chan, Kwun Chuen Gary organization: National Alzheimer's Coordinating Center, University of Washington, Seattle, WA – sequence: 4 givenname: Lan surname: Shui fullname: Shui, Lan organization: The University of Texas MD Anderson Cancer Center, Houston, TX – sequence: 5 givenname: David R. surname: Haynor fullname: Haynor, David R. organization: University of Washington, Seattle, WA |
| BookMark | eNp9kM1Kw0AUhQepYFvd-ASzE4TUmfzMJCsp_bOQotjqws0wSW7akfyUmbSlXfkIPqNPYkpKwY2re7nnOwfu6aBWURaA0C0lPUqI_SCzQ4_4nDLnArWp59mWZ_Ogdd4ZuUIdYz4JcYlPvTbajUylclmpYon72WEFKgd9Z_BQGZAG8IsulxqMUWWB53GpAb-ZIzuT8UoVgEOQujgean2sAeYbnYL--foeglZbSPDsdYonWu5rR1WBxu9ltsnBXKPLVGYGbk6zixbj0WLwZIXPk-mgH1oxZYFjsdRjju9y7kgnAeLYKZecJQAsiQLiA4kCCPwUEkiIRyAilHkph0RyN3Zt2-mixyZ2vYlySGIoKi0zsdb1z3ovSqnEX6VQK7Est4JSTj1uszrhvkmIdWmMhvRspkQcOxd156LpvIZpA-9UBvt_SNEPP06eX8AGicw |
| ContentType | Journal Article |
| Copyright | 2024 The Alzheimer's Association. published by Wiley Periodicals LLC on behalf of Alzheimer's Association. |
| Copyright_xml | – notice: 2024 The Alzheimer's Association. published by Wiley Periodicals LLC on behalf of Alzheimer's Association. |
| DBID | 24P AAYXX CITATION 5PM |
| DOI | 10.1002/alz.087163 |
| DatabaseName | Wiley Online Library Open Access CrossRef PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: 24P name: Wiley-Blackwell Open Access Collection url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| DocumentTitleAlternate | BIOMARKERS |
| EISSN | 1552-5279 |
| EndPage | n/a |
| ExternalDocumentID | PMC11715726 10_1002_alz_087163 ALZ087163 |
| Genre | abstract |
| GroupedDBID | --- --K --M .~1 0R~ 1B1 1OC 1~. 1~5 24P 33P 4.4 457 4G. 53G 5VS 7-5 71M 7RV 7X7 8FI 8FJ 8P~ AAEDT AAIKJ AAKOC AALRI AAMMB AANLZ AAOAW AAXLA AAXUO AAYCA AAYWO ABBQC ABCQJ ABCUV ABIVO ABJNI ABMAC ABMZM ABUWG ABWVN ACCMX ACCZN ACGFS ACGOF ACPOU ACRPL ACVFH ACXQS ADBBV ADBTR ADCNI ADEZE ADHUB ADKYN ADMUD ADNMO ADPDF ADVLN ADZMN AEFGJ AEIGN AEKER AENEX AEUPX AEUYR AEVXI AFKRA AFPUW AFTJW AFWVQ AGHFR AGHNM AGUBO AGWIK AGXDD AGYEJ AIDQK AIDYY AIGII AITUG AIURR AJRQY AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS ALUQN AMRAJ AMYDB ANZVX AZQEC BENPR BFHJK BLXMC C45 CCPQU DCZOG EBS EJD EMOBN EO8 EO9 EP2 EP3 F5P FDB FEDTE FIRID FNPLU FYUFA G-Q GBLVA HMCUK HVGLF HX~ HZ~ IHE J1W K9- LATKE LEEKS M0R M41 MO0 MOBAO N9A NAPCQ O-L O9- OAUVE OVD OVEED OZT P-8 P-9 P2P PC. PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PSYQQ Q38 QTD RIG ROL RPM RPZ SDF SDG SEL SES SSZ SUPJJ TEORI UKHRP ~G- 9DU AAYXX AFFHD CITATION EFLBG ~HD 5PM |
| ID | FETCH-LOGICAL-c1693-6f56384773a3de032f7a76dee6db908e0b9e98feded050eb0165f7eda74c4223 |
| IEDL.DBID | 24P |
| ISSN | 1552-5260 |
| IngestDate | Tue Nov 25 09:07:26 EST 2025 Sat Nov 29 07:11:44 EST 2025 Mon Aug 11 05:48:04 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | S2 |
| Language | English |
| License | Attribution This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c1693-6f56384773a3de032f7a76dee6db908e0b9e98feded050eb0165f7eda74c4223 |
| OpenAccessLink | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Falz.087163 |
| PageCount | 3 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_11715726 crossref_primary_10_1002_alz_087163 wiley_primary_10_1002_alz_087163_ALZ087163 |
| PublicationCentury | 2000 |
| PublicationDate | December 2024 |
| PublicationDateYYYYMMDD | 2024-12-01 |
| PublicationDate_xml | – month: 12 year: 2024 text: December 2024 |
| PublicationDecade | 2020 |
| PublicationPlace | Hoboken |
| PublicationPlace_xml | – name: Hoboken |
| PublicationTitle | Alzheimer's & dementia |
| PublicationYear | 2024 |
| Publisher | John Wiley and Sons Inc |
| Publisher_xml | – name: John Wiley and Sons Inc |
| SSID | ssj0040815 |
| Score | 2.4099395 |
| Snippet | Background
The early diagnosis and monitoring of Alzheimer's disease (AD) presents a significant challenge due to its heterogeneous nature, which includes... |
| SourceID | pubmedcentral crossref wiley |
| SourceType | Open Access Repository Index Database Publisher |
| SubjectTerms | Biomarkers |
| Title | Estimating Alzheimer's Disease Progression Score Using Machine Learning on FreeSurfer‐Derived MRI Gray Matter Volumes |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Falz.087163 https://pubmed.ncbi.nlm.nih.gov/PMC11715726 |
| Volume | 20 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Consumer Health Database (ProQuest) customDbUrl: eissn: 1552-5279 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0040815 issn: 1552-5260 databaseCode: M0R dateStart: 20240101 isFulltext: true titleUrlDefault: https://search.proquest.com/familyhealth providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 1552-5279 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0040815 issn: 1552-5260 databaseCode: 7RV dateStart: 20240101 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1552-5279 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0040815 issn: 1552-5260 databaseCode: BENPR dateStart: 20240101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Health & Medical customDbUrl: eissn: 1552-5279 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0040815 issn: 1552-5260 databaseCode: 7X7 dateStart: 20240101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1552-5279 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0040815 issn: 1552-5260 databaseCode: PIMPY dateStart: 20240101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVWIB databaseName: Wiley-Blackwell Open Access Collection customDbUrl: eissn: 1552-5279 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0040815 issn: 1552-5260 databaseCode: 24P dateStart: 20240101 isFulltext: true titleUrlDefault: https://authorservices.wiley.com/open-science/open-access/browse-journals.html providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpZ3JSsQwGICD28GLCyqOGwEFQai2Sds04GVQRwVHBhURLyVN_uiAVum4oCcfwWf0ScwyMzoeBPFSCklL-femf74itJYkghQKaJBqKYKYyyTIZMQDqYWWQEzC1E7TR-z4OLu44K0htN3bC-P5EP0FN-sZLl5bBxdFZ-sLGipuXjdDW-7TYTQaRZRZmyZxqxeHY5PsEkdLTezrVhr24aRk6-vagXT0sy3ye7nq8k1j8n9POoUmunUmrnvDmEZDUM6g5z3j0LZELa9w_eb1Gtq3UK138K7_SoNbtlnLgzrwqeVbYtdRgJuu4xJwF8Z6hc14owI4faw0VB9v77vGjp9A4ebJId6vxAtuOmwnPnexrzOLzhp7ZzsHQffPC4G0cBajuMT4ZcwYFVRBSIlmgqUKIFUFDzMICw4806BAhUkIhd0TpRkowWIZm4JjDo2UdyXMIxwZG6Cx5CAoj4uMC5lKpXgEWgAtJKmh1Z7883vP18g9SZnkRm65l1sNZQOq6U-1cOzBkbJ97SDZUcSihJG0hjacbn65e14_uvRnC3-ZvIjGialwfG_LEhp5qB5hGY3Jp4d2p1pxtmiOzfDkE-uP6B8 |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpZ1ba9swFMdF1w7Wl7VlG83Wi2CFwcCrLcuW9RjapilLQljCKHsxsnSUBFpvOG1G89SP0M_YT1Jdcmn2MBh7M0g2RuccnWPpr58ROkoSQQoFcZBqKQLKZRJkMuKB1EJLICZhamfpFut0sstL3p1pc-xZGM-HWCy42chw87UNcLsgfbykhoqr6ZfQ1vvxC7RBTVqyij5Cu_OJmJpslzhcamK_t9JwQSclx8t7V_LRn7rI5_WqSziNrf981W30elZp4rp3jR20BuUb9PvMhLQtUssBrl9NhzC6hurTGJ_6fRrctXItj-rAPUu4xE5TgNtOcwl4hmMdYNPeqAB6t5WG6vH-4dR48gQUbn-7wOeVuMNtB-7E393sN36L-o2z_kkzmP17IZAWz2JMl5jIpIzFIlYQxkQzwVIFkKqChxmEBQeeaVCgwiSEwp6K0gyUYFRSU3K8Q-vlzxJ2EY6MF8RUchAxp0XGhUylUjwCLSAuJKmhj3MD5L88YSP3LGWSm3HL_bjVULZim0VXi8debSlHQ4fJjiIWJYykNfTZGecvT8_rrR_-6v2_dD5Er5r9ditvXXS-fkCbxNQ7Xumyh9ZvqlvYRy_l5GY0rg6cYz4BLU_rEw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpZ3bStxAGMeH1krxRiu2uFrtgIIgpCaTmUzmcnFdFXeXRUWkN2Ey840uaJSsB_Sqj9Bn7JM4h3V1vRBK7wIzCWG-Y5L__ILQOmOSlBrSKDNKRlQoFuUqEZEy0iggtmAab-kO7_Xy01PRH2lz3F6YwIcYv3BzkeHztQtwuNZm64UaKi8ef8au308_ok-UcR-XhPafEzG11Y55XCpzz1tZPKaTkq2Xcyfq0Vtd5Ot-1Rec9tx_3uoXNDvqNHEzuMY8-gDVArrfsSHtmtTqDDcvHs9hcAn1xhC3wnca3HdyrYDqwEeOcIm9pgB3veYS8AjHeobteLsGOLqtDdR_f_9pWU--A427h_t4t5YPuOvBnfjEZ7_hV3Tc3jne3otG_16IlMOzWNMxG5mU81SmGuKUGC55pgEyXYo4h7gUIHIDGnTMYijdrijDQUtOFbUtxzc0VV1VsIhwYr0gpUqATAUtcyFVprQWCRgJaalIA609G6C4DoSNIrCUSWHXrQjr1kD5hG3GUx0ee3KkGpx7THaS8IRxkjXQpjfOO1cvmp1f4WjpXyb_QJ_7rXbR2e8dLKMZYtudIHT5jqZu6ltYQdPq7mYwrFe9Xz4B2fLqjg |
| 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=Estimating+Alzheimer%27s+Disease+Progression+Score+Using+Machine+Learning+on+FreeSurfer%E2%80%90Derived+MRI+Gray+Matter+Volumes&rft.jtitle=Alzheimer%27s+%26+dementia&rft.au=Sung%2C+Junhyoun&rft.au=Shibata%2C+Dean&rft.au=Chan%2C+Kwun+Chuen+Gary&rft.au=Shui%2C+Lan&rft.date=2024-12-01&rft.issn=1552-5260&rft.eissn=1552-5279&rft.volume=20&rft.issue=S2&rft_id=info:doi/10.1002%2Falz.087163&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_alz_087163 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1552-5260&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1552-5260&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1552-5260&client=summon |