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Record W4312086984 · doi:10.1002/alz.066680

Situating tau pathology and neuroinflammation along the principal gradients of brain organisation in Alzheimer’s disease

2022· article· en· W4312086984 on OpenAlexaff
Julie Ottoy, Min Su Kang, Yi‐Hsuan Yeh, Reinder Vos de Wael, Bo‐yong Park, Jonah Isen, Mary Agopian, Gleb Bezgin, Firoza Z Lussier, Sulantha Mathotaarachchi, Jenna Stevenson, Mira Chamoun, Nesrine Rahmouni, Robert Hopewell, Gassan Massarweh, Jean‐Paul Soucy, Serge Gauthier, Boris C. Bernhardt, Sandra E. Black, Pedro Rosa‐Neto, Maged Goubran

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsConnectomeNeuroscienceNeuroinflammationDiffusion MRICovarianceTau pathologyFunctional connectivityBiologyPattern recognition (psychology)PsychologyComputer scienceArtificial intelligenceDiseaseAlzheimer's diseaseMathematicsPathologyMedicineMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Abstract Background The spread of abnormal tau along connectivity‐based networks is a hallmark of Alzheimer’s disease (AD). In addition, activated microglia was shown to affect tau spread across networks. However, traditional network approaches using atlas‐defined regions suffer from assuming within‐region homogenous connectivity, as well as abrupt and linear connectivity changes between the regions; while, in fact, brain structure and function exist as complex overlapping axes of connectivity variation. Here, we studied smooth/continuous spatial transitions or ‘gradients’ of connectivity and their link to gradients of pathology in AD. This work provides novel insights into brain architecture driving pathology spread. Method We enrolled 220 individuals including 106 cognitively normal Aβ‐negative (A‐) and 37 A+, and 77 MCI/AD A+ (TRIAD). From diffusion‐weighted MRI and fMRI data, we estimated region‐to‐region fiber count and correlated timeseries, respectively, and averaged subject‐wise connectivity matrices to create structural and functional ‘template connectomes’. From 18F‐MK6240 and 11C‐PBR28 PET data, we extracted regional tau and neuroinflammation SUVR values, respectively, and created covariance matrices for each disease stage. Next, the template connectomes/covariance matrices were subjected to non‐linear dimensionality reduction to extract embedded components (‘gradients’). We correlated the primary gradients of functional or structural connectivity (G1FC/G1SC) with the primary and secondary gradients of tau or neuroinflammation (G1TAU/G2TAU; G1INFLAM/G2INFLAM). We employed three brain atlases for validation: high‐resolution customized Glasser‐atlas (1332 equally‐sized subregions), DKT, and Schaefer‐100. Result G1FC replicated the previously reported ‘uni‐to‐transmodal’ functional gradient (Margulies 2016) and correlated strongly with G1TAU in cognitively impaired A+ (Fig.1 A); illustrating a common topographic variation in tau spread and functional connectivity in symptomatic stages. Conversely, G1SC ran between medial‐temporal‐lobe (MTL)/posterior and frontal/anterior regions, and correlated strongly with both G2TAU (Fig.1 B) and G1INFLAM (Fig.1 C) in cognitively (un)impaired A+; illustrating a common topographic variation in tau/microglia and structural connectivity already present at early disease stages. Different brain atlases led to similar results. Conclusion We identified two gradients of continuous topographic variation in tau and neuroinflammation, potentially reflecting heterogeneity of AD‐subgroups. While microglial activation and early‐stage tau propagation may be largely dominated by structural organisation, later‐stage tau propagation may be more heterogeneously associated with variation in structural and particularly functional connectivity.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.042
GPT teacher head0.268
Teacher spread0.227 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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