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Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns

Lerch, Ondrej ; Ferreira, Daniel ; Stomrud, Erik LU orcid ; van Westen, Danielle LU orcid ; Tideman, Pontus LU ; Palmqvist, Sebastian LU orcid ; Mattsson-Carlgren, Niklas LU orcid ; Hort, Jakub ; Hansson, Oskar LU orcid and Westman, Eric (2024) In Alzheimer's Research and Therapy 16(1).
Abstract

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the ‘severity index’ generated using a standard classification model trained on patients with AD dementia versus a new model... (More)

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the ‘severity index’ generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI). Methods: We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk “disease-like” or low-risk “CN-like”. Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data. Results: In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57). Conclusion: When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.

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author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Alzheimer’s disease, Atrophy patterns, Multivariate analysis, Structural MRI, Subjective cognitive decline
in
Alzheimer's Research and Therapy
volume
16
issue
1
article number
153
publisher
BioMed Central (BMC)
external identifiers
  • pmid:38970077
  • scopus:85197708892
ISSN
1758-9193
DOI
10.1186/s13195-024-01517-5
language
English
LU publication?
yes
additional info
Publisher Copyright: © The Author(s) 2024.
id
bc08f7c1-3224-40f9-9f93-5cbcbcf82012
date added to LUP
2024-08-29 14:58:09
date last changed
2025-07-05 10:44:04
@article{bc08f7c1-3224-40f9-9f93-5cbcbcf82012,
  abstract     = {{<p>Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the ‘severity index’ generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI). Methods: We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk “disease-like” or low-risk “CN-like”. Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data. Results: In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57). Conclusion: When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.</p>}},
  author       = {{Lerch, Ondrej and Ferreira, Daniel and Stomrud, Erik and van Westen, Danielle and Tideman, Pontus and Palmqvist, Sebastian and Mattsson-Carlgren, Niklas and Hort, Jakub and Hansson, Oskar and Westman, Eric}},
  issn         = {{1758-9193}},
  keywords     = {{Alzheimer’s disease; Atrophy patterns; Multivariate analysis; Structural MRI; Subjective cognitive decline}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{BioMed Central (BMC)}},
  series       = {{Alzheimer's Research and Therapy}},
  title        = {{Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns}},
  url          = {{http://dx.doi.org/10.1186/s13195-024-01517-5}},
  doi          = {{10.1186/s13195-024-01517-5}},
  volume       = {{16}},
  year         = {{2024}},
}