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Record W4390198863 · doi:10.1002/alz.081845

Tracing the spatial extent of cortical tau over time in Alzheimer’s disease

2023· article· en· W4390198863 on OpenAlexaff
Frédéric St‐Onge, Alexa Pichet Binette, Marianne Chapleau, John C.S. Breitner, Sylvia Villeneuve

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill Genome CentreMcGill University
Fundersnot available
KeywordsTemporal lobeNeuroimagingNeocortexDementiaPathologicalPsychologyAlzheimer's diseaseTau pathologyAlzheimer's Disease Neuroimaging InitiativeStandardized uptake valueNeuroscienceCognitive declineDiseaseMedicineInternal medicinePathologyPositron emission tomographyEpilepsy

Abstract

fetched live from OpenAlex

Abstract Background Neuropathological studies defined the paradigm that tau—the pathological hallmark of Alzheimer’s disease (AD)—spreads homogenously across individuals from the medial temporal lobe to the neocortex. This staging system is referred to as Braak stages. However, neuroimaging studies have since highlighted significant inter‐regional and inter‐individual differences in tau spreading patterns. We investigated the spatial extent of cortical tau over time in the amnestic AD spectrum. Method We included 195 amyloid‐positive ADNI participants that had at least two tau‐PET scans (90 cognitively unimpaired [CU], 66 with mild cognitive impairment [MCI], 39 with AD dementia; average follow‐up 2.6 years). We derived regional thresholds of tau positivity for 64 cortical regions and the amygdalae using Gaussian mixture modeling and applied them to all tau scans. The same approach was used to obtain thresholds for composite regions corresponding to the Braak stages. For each region, we calculated the rate of change in the amount (i.e., standardized uptake value ratio [SUVR] change) of tau pathology and the annual change in the positivity status (i.e., spatial extent change). These rates were compared between diagnostic groups using linear mixed models. Result Across diagnostic groups, change in tau positivity followed Braak staging; over 90% of participants positive at follow‐up either progressed or were already positive on a previous Braak stage at baseline (Fig‐1AI) and greater SUVR change was observed in temporal regions (Fig‐1B). However, across the brain, there was heterogeneity between individual patterns of tau positivity progression (Fig‐1AII). This heterogeneity was most striking in MCI patients, where many regions progressed from low to high levels of tau (Fig‐1C). Regional SUVR change did not differ between participants with AD or with MCI (Fig‐2A), but participants with MCI showed the fastest change in regional positivity compared to participants with AD and CU participants. (Fig‐2B) Conclusion While tau propagation follows Braak staging, inter‐individual differences can be found by considering a fine‐grained approach of regional progression to positivity. Furthermore, participants with MCI have the highest number of regions becoming abnormal annually. Our research pushes the importance of considering the entire brain when studying change in tau pathology, particularly before AD diagnosis.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.327
Teacher spread0.293 · 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
Published2023
Admission routes1
Has abstractyes

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