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Record W7116891477 · doi:10.1002/alz70861_108074

Identifying multimodal amyloid, tau, and neurodegeneration (ATN) subtypes in Alzheimer’s Disease across datasets and imaging markers

2025· article· en· W7116891477 on OpenAlexaff
Katrina Carver, Andrew Clappison, Min Su Kang, Nesrine Rahmouni, Jenna Stevenson, Walter Swardfager, JoAnne McLaurin, Dr Bojana Stefanovic, Richard H. Swartz, Sean M. Nestor, Jennifer S Rabin, Serge Gauthier, Jean‐Paul Soucy, Jean Chen, Mario Masellis, Sandra E. Black, Pedro Rosa‐Neto, Julie Ottoy, Maged Goubran

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMontreal Neurological Institute and HospitalArtificial Intelligence in Medicine (Canada)McGill UniversitySunnybrook HospitalBaycrest HospitalOntario Brain InstituteSunnybrook Health Science Centre
Fundersnot available
KeywordsDiseaseNeurodegenerationNeuroimagingClinical trialBiomarker

Abstract

fetched live from OpenAlex

BACKGROUND: Alzheimer's Disease (AD) is heterogeneous in pathology and clinical presentation. A comprehensive study on the spatio-temporal progression of multimodal imaging biomarkers in AD is currently lacking. Here, we identified multimodal subtypes of amyloid, tau, and neurodegeneration (ATN) progression in two independent datasets across the AD spectrum. METHOD: We employed cross-sectional datasets from the ADNI (376 CN, 95 Aβ+ MCI, and 55 Aβ+ Dementia) and TRIAD (133 CN, 45 Aβ+ MCI, and 31 Aβ+ AD) studies (Table 1). We applied the Subtype and Stage Inference (SuStaIn) algorithm, which accounts for phenotypical and temporal heterogeneity, to identify subtypes (Figure 1). The following multimodal imaging markers were used in SuStaIn: 18F-AZD4694 amyloid-SUVR or 18F-Florbetapir and 18F-Florbetaben Centiloids, 18F-MK6240 or 18F-Flortaucipir tau-SUVR, and T1-weighted MRI volumes. Each marker was evaluated in four meta-ROIs: medial temporal (MTL), lateral temporal, frontal, and parieto-occipital. We studied the disease progression patterns of each generated multimodal subtype and its demographic features using omnibus and post-hoc statistics. RESULT: Both cohorts identified three subtypes: "early-tau typical ATN", "late-tau typical ATN", and "tau-first" (Figure 2). The "early-tau typical ATN" subtype showed initial increases in frontal and neocortical Aβ, followed by MTL and neocortical tau, and eventually widespread atrophy. The "late-tau typical ATN" subtype, on the other hand, showed the earliest increases in entorhinal-hippocampal Aβ, followed by severe widespread Aβ, with frontal being the latest, and eventually neocortical tau with atrophy. Finally, the "tau-first" subtype's progression was similar to the "early-tau typical ATN" subtype with an additional early tau wave, potentially indicating primary age-related tauopathy (PART) pathology that may or may not transition into AD. We observed a statistically significant dependent relationship between subtype membership and AT status (p _adj < 0.05). The "tau-first" subtype was significantly younger than other subtypes (p _adj < 0.05). CONCLUSION: Our findings replicated the previously reported typical amyloid-first (ATN) subtype and tau-first subtype, but demonstrated that the typical amyloid-first subtype in fact consists of an early- and a late-tau subtype. These insights into the progression patterns of A, T, and N imaging biomarkers across datasets and radioligands may inform trials and understanding the heterogeneity of pathology trajectories.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.340
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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