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Record W7119512736 · doi:10.1002/alz70856_106914

Data‐Driven Characterization of Heterogeneous Brain Atrophy and White Matter Hyperintensity Progression in Frontotemporal Dementia

2025· article· en· W7119512736 on OpenAlexaff
Amelie Metz, Maxime Montembeault, Yashar Zeighami, Mahsa Dadar

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsFrontotemporal dementiaPrimary progressive aphasiaAtrophyFrontotemporal lobar degenerationSemantic dementiaHyperintensityDementiaAphasiaNeuroimaging

Abstract

fetched live from OpenAlex

Abstract Background Frontotemporal Dementia (FTD) encompasses a spectrum of neurodegenerative disorders with a diverse range of clinical presentations and overlapping phenotypes, highlighting its heterogeneity. This study applied disease progression modeling to identify novel, data‐driven subtypes of brain atrophy patterns and White Matter Hyperintensity (WMH) burden, as well as their progression, in the FTD spectrum. Methods Our analysis included 56 individuals with behavioral variant Frontotemporal Dementia (bvFTD), 33 with semantic variant Primary Progressive Aphasia (svPPA), and 24 with non‐fluent variant Primary Progressive Aphasia (nfvPPA) from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) cohort. We quantified frontal, temporal, and subcortical brain atrophy and ventricular expansion using Deformation‐Based Morphometry (Metz et al. 2025) and derived frontotemporal lobar WMH volumes based on FLAIR (Dadar et al. 2021). To identify subtypes of participants with distinct brain atrophy and WMH patterns, we employed the Subtype and Stage Inference (SuStaIn) method (Young et al., 2018). Additionally, we examined the differences in trajectories of cognitive decline and brain measures between subtypes using linear mixed‐effects models. Results SuStaIn identified three distinct disease progression subtypes within the FTD spectrum. Subtype zero ( n = 15) showed no increased brain atrophy and WMH burden compared to healthy controls. In the first subtype ( n = 45), brain atrophy progressed from temporal to subcortical regions, followed by ventricular expansion and eventually increased WMH load in the bilateral frontal lobe. In the second subtype ( n = 53), frontal lobe WMH involvement preceded ventricular expansion and frontal lobe atrophy (Figure 1). The atrophy‐first subtype was associated with greater cognitive impairment, particularly in language‐related tasks such as confrontational naming and verbal fluency. In contrast, the WMH‐first subtype exhibited higher behavioral impairment, along with a more rapid decline, as reflected in the Clinical Dementia Rating (all p < 0.05, Figure 2). While SuStaIn did not fully distinguish all clinical variants, it effectively differentiated most individuals with svPPA and bvFTD, aligning with the symptom profiles observed in these subtypes (Figure 3). Conclusion Our findings suggest distinct disease progression trajectories along the FTD continuum that can be identified in vivo, with subtypes differing in whether WMH burden or brain atrophy precedes the other.

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)0.004
metaresearch head score (Gemma)0.008
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 score0.008
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.317
Teacher spread0.292 · 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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