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Discovering Hidden Links: Harnessing Similarity Network Fusion to Reveal Common Clusters in Healthy Aging, Mild Cognitive Impairment, and Dementia
Taeko Bourque, Peter Zhukovsky, Cassandra Morrison, John AE Anderson. Alzheimer s & Dementia, 2025-12-01
Topic: Risk, prevention and care in population studies
Abstract Background Cognitively Unimpaired (CU), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD) are clinical labels used to categorize degrees of cognitive impairment in the aging brain. Older adults experience brain changes associated with aging and cognitive decline at different ages and progress at varying rates, leading to heterogeneous patterns of cognitive decline. As a result, people within the same diagnostic category often exhibit significant differences in cognitive abilities and brain functions. The goal of the present study was to investigate how data‐driven categories map onto diagnostic categories. Method We combined brain imaging features (cortical thickness average, surface area, and volume) with cognitive measures (Alzheimer's Disease Assessment Scale‐Cognitive, Mini‐Mental State Exam, Montreal Cognitive Assessment, Clinical Dementia Rating) in older adults who had a clinical diagnosis of CU, MCI, or AD using Similarity Network Fusion (SNF), a multivariate clustering approach. SNF is a new computational method for data integration that leverages common and complementary information in different types of data. We used data from 515 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Result We identified four data‐driven groups spanning a gradient of cognitive and neural severity, with an average silhouette width of 0.55, indicating good cluster structure. Group 1 (87% diagnosed with dementia) showed the greatest impairment, while Group 4 (96% cognitively unimpaired) showed minimal impairment. Groups 2 and 3 captured transitional stages, including an “at‐risk” group with early neural and cognitive decline. The top contributing features included the right lingual surface area (NMI = 0.34) and MMSE scores (NMI = 0.052). Significant demographic differences were observed across clusters (e.g., age: F(3, 511) = 30.39, p < 0.001). Conclusion The current study provides evidence that more nuanced, data‐driven approaches can reveal commonalities in the etiology and underlying neurobiology of individuals across traditional categories of CU, MCI, or AD. These results may lead to more focal therapies and a better understanding of who is at risk for converting to dementia, allowing for earlier detection and treatment of cognitive decline.
Key findings
- Older adults experience brain changes associated with aging and cognitive decline at different ages and progress at varying rates, leading to heterogeneous patterns of cognitive decline.
- As a result, people within the same diagnostic category often exhibit significant differences in cognitive abilities and brain functions.
- Result We identified four data‐driven groups spanning a gradient of cognitive and neural severity, with an average silhouette width of 0.55, indicating good cluster structure.
- Significant demographic differences were observed across clusters (e.g., age: F(3, 511) = 30.39, p < 0.001).
- These results may lead to more focal therapies and a better understanding of who is at risk for converting to dementia, allowing for earlier detection and treatment of cognitive decline.
Why it matters
Using brain imaging and cognitive data from 515 people, a clustering method identified four data-driven groups that cut across the standard labels of cognitively unimpaired, mild cognitive impairment and Alzheimer's disease. This is a methodological study about how people are categorized, not about treating or preventing dementia.
Study details
- Participants515
- Evidence strengthLow
How this study scored 36/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 35 of the 96 points we could judge it on, which is 36 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s & Dementia) |
| Journal's citation record | 2 / 4 | Alzheimer s & Dementia: journal h-index 228 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 515 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 1 / 6 | Cited 0.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author John AE Anderson holds only 4 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 0 / 3 | Carleton University: institution h-index 546 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 35 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 35 of the 96 points we could judge it on is 36 out of 100. |
| Total | 36 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Bourque, T., Zhukovsky, P., Morrison, C., & Anderson, J. A. E. (2025). Discovering Hidden Links: Harnessing Similarity Network Fusion to Reveal Common Clusters in Healthy Aging, Mild Cognitive Impairment, and Dementia. Alzheimer s & Dementia. https://doi.org/10.1002/alz70856_105149
Vancouver
Bourque T, Zhukovsky P, Morrison C, Anderson JAE. Discovering Hidden Links: Harnessing Similarity Network Fusion to Reveal Common Clusters in Healthy Aging, Mild Cognitive Impairment, and Dementia. Alzheimer s & Dementia. 2025. doi:10.1002/alz70856_105149.
BibTeX
@article{bourque2025-be12e86b,
author = {Bourque, Taeko and Zhukovsky, Peter and Morrison, Cassandra and Anderson, John AE},
title = {{Discovering Hidden Links: Harnessing Similarity Network Fusion to Reveal Common Clusters in Healthy Aging, Mild Cognitive Impairment, and Dementia}},
journal = {Alzheimer s \& Dementia},
year = {2025},
doi = {10.1002/alz70856_105149},
url = {https://doi.org/10.1002/alz70856_105149}
}
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