Discovering Hidden Links: Harnessing Similarity Network Fusion to Reveal Common Clusters in Healthy Aging, Mild Cognitive Impairment, and Dementia
Bibliographic record
Abstract
Abstract 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. Therefore, using data from 515 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) and a multivariate clustering approach, Similarity Network Fusion (SNF), 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. We identified four data-driven groups that overlap diagnostic classifications and feature more similar neural and cognitive profiles. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".