SMAS: Structural MRI-based AD Score using Bayesian supervised VAE Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.1016/j.compbiomed.2025.110829
This study introduces the Structural MRI-based Alzheimer's Disease Score (SMAS), a novel index intended to quantify Alzheimer's Disease (AD)-related morphometric patterns using a deep learning Bayesian-supervised Variational Autoencoder (Bayesian-SVAE). The SMAS index was constructed using baseline structural MRI data from the DELCODE study and evaluated longitudinally in two independent cohorts: DELCODE (n=415) and ADNI (n=190). Our findings indicate that SMAS has strong associations with cognitive performance (DELCODE: r=-0.83; ADNI: r=-0.62), age (DELCODE: r=0.50; ADNI: r=0.28), hippocampal volume (DELCODE: r=-0.44; ADNI: r=-0.66), and total gray matter volume (DELCODE: r=-0.42; ADNI: r=-0.47), suggesting its potential as a biomarker for AD-related brain atrophy. Moreover, our longitudinal studies indicated that SMAS may be useful for the early identification and tracking of AD. The model demonstrated significant predictive accuracy in distinguishing cognitively healthy individuals from those with AD (DELCODE: AUC=0.971 at baseline, 0.833 at 36 months; ADNI: AUC=0.817 at baseline, improving to 0.903 at 24 months). Notably, over 36 months, the SMAS index outperformed existing measures such as SPARE-AD and hippocampal volume. The relevance map analysis revealed significant morphological changes in key AD-related brain regions, including the hippocampus, posterior cingulate cortex, precuneus, and lateral parietal cortex, highlighting that SMAS is a sensitive and interpretable biomarker of brain atrophy, suitable for early AD detection and longitudinal monitoring of disease progression.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.compbiomed.2025.110829
- OA Status
- gold
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413270267
Raw OpenAlex JSON
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https://openalex.org/W4413270267Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.compbiomed.2025.110829Digital Object Identifier
- Title
-
SMAS: Structural MRI-based AD Score using Bayesian supervised VAEWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-08-18Full publication date if available
- Authors
-
Aditya Nemali, José Bernal, Renat Yakupov, Deeksha Singh, Martin Dyrba, Enise I. Incesoy, Soumya Mukherjee, Oliver Peters, Ersin Ersözlü, Julian Hellmann‐Regen, L. Preis, Josef Priller, Eike Jakob Spruth, Slawek Altenstein, Andrea Lohse, Anja Schneider, Klaus Fließbach, Okka Kimmich, Jens Wiltfang, Niels Hansen, Björn H. Schott, Ayoob Rostamzadeh, Wenzel Glanz, Michaela Butryn, Katharina Büerger, Daniel Janowitz, Michael Ewers, Robert Perneczky, Boris‐Stephan Rauchmann, Stefan Teipel, Ingo Kilimann, Doreen Göerß, Christoph Laske, Sebastian Sodenkamp, Annika Spottke, Marie Coenjaerts, Frederic Brosseron, Falk Lüsebrink, Peter Dechent, Klaus Scheffler, Stefan Hetzer, Luca Kleineidam, Mitchell Stark, F. Jessen, Emrah Düzel, Gabriel ZieglerList of authors in order
- Landing page
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https://doi.org/10.1016/j.compbiomed.2025.110829Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
-
https://resolver.sub.uni-goettingen.de/purl?gro-2/151078Direct OA link when available
- Concepts
-
Precuneus, Posterior cingulate, Atrophy, Neuroimaging, Alzheimer's Disease Neuroimaging Initiative, Biomarker, Psychology, Voxel-based morphometry, Imaging biomarker, Neuropsychology, Internal medicine, Medicine, Neuroscience, Magnetic resonance imaging, Alzheimer's disease, Cognition, Disease, White matter, Radiology, Biology, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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66Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sensitive | 196 |
| abstract_inverted_index.AD-related | 97, 177 |
| abstract_inverted_index.Structural | 4 |
| abstract_inverted_index.introduces | 2 |
| abstract_inverted_index.monitoring | 210 |
| abstract_inverted_index.precuneus, | 186 |
| abstract_inverted_index.predictive | 122 |
| abstract_inverted_index.structural | 36 |
| abstract_inverted_index.suggesting | 90 |
| abstract_inverted_index.Alzheimer's | 6, 16 |
| abstract_inverted_index.Autoencoder | 27 |
| abstract_inverted_index.Variational | 26 |
| abstract_inverted_index.cognitively | 126 |
| abstract_inverted_index.constructed | 33 |
| abstract_inverted_index.hippocampal | 75, 165 |
| abstract_inverted_index.independent | 48 |
| abstract_inverted_index.individuals | 128 |
| abstract_inverted_index.performance | 65 |
| abstract_inverted_index.significant | 121, 172 |
| abstract_inverted_index.(AD)-related | 18 |
| abstract_inverted_index.associations | 62 |
| abstract_inverted_index.demonstrated | 120 |
| abstract_inverted_index.highlighting | 191 |
| abstract_inverted_index.hippocampus, | 182 |
| abstract_inverted_index.longitudinal | 102, 209 |
| abstract_inverted_index.morphometric | 19 |
| abstract_inverted_index.outperformed | 158 |
| abstract_inverted_index.progression. | 213 |
| abstract_inverted_index.interpretable | 198 |
| abstract_inverted_index.morphological | 173 |
| abstract_inverted_index.distinguishing | 125 |
| abstract_inverted_index.identification | 113 |
| abstract_inverted_index.longitudinally | 45 |
| abstract_inverted_index.(Bayesian-SVAE). | 28 |
| abstract_inverted_index.Bayesian-supervised | 25 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 46 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.42957049 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |