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Record W4247294527 · doi:10.1016/j.jalz.2017.06.2373

[IC‐P‐100]: MOVING AWAY FROM SINGLE AD‐SIGNATURE ROI: ASSESSING THE RELATIONSHIP BETWEEN WHOLE‐BRAIN GRAY MATTER PATTERN, AD PATHOLOGY, AND COGNITION IN HEALTHY ELDERLY AT RISK OF AD

2017· article· en· W4247294527 on OpenAlexaff
Alexa Pichet Binette, Étienne Vachon‐Presseau, Renaud La Joie, Judes Poirier, Pedro Rosa‐Neto, D. Louis Collins, John C.S. Breitner, Sylvia Villeneuve

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldNeuroscience
TopicBrain Tumor Detection and Classification
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsAtrophyPsychologyCognitionCorrelationDementiaNeurodegenerationHippocampal formationNeuroscienceMedicinePathologyAudiologyDiseaseMathematics

Abstract

fetched live from OpenAlex

Hippocampal volume (HV) is often used to indicate neurodegeneration in Alzheimer's disease (AD) (Sperling et al., 2011). Hippocampal atrophy is not specific to AD, however (Bartsch & Wulff, 2015), and we investigated whether whole-brain (wb) gray matter (GM) pattern is better related to CSF biomarkers of AD or cognition than single metrics such as HV. We obtained structural MR images from 296 members of the PREVENT-AD cohort of cognitively normal adults with a parental or multiple-sibling history of AD dementia (mean age=64). We compared these with reference groups of 198 young individuals from the Cambridge dataset and 270 from the Human Connectome Project (both aged ≈35), as well as cognitively impaired ADNI participants (50 with early MCI, 65 with late MCI, and 72 with AD dementia (mean age ≈74)). We derived 24 GM regions of interest (ROI) from an independent components analysis across all groups. Next we assessed the mean GM density in all ROI separately for each reference group. Then, for each PREVENT-AD participant, we correlated the ROI–specific GM density pattern vs. that among the reference groups (Figure 1). We evaluated HV using patch-based segmentation (Coupé et al., 2011). Finally, we used general linear models to examine if the wbGM pattern similarity of each PREVENT-AD participant with the corresponding pattern of the reference groups, the HV, or individual ROIs, was associated with CSF Ab42 and p-tau levels, as well as cognitive domain scores on the RBANS. The wbGM pattern metrics, but not individual ROIs GM or HV, were associated with CSF biomarkers and cognition. A wbGM pattern similar to that of the young groups was related to better immediate memory, language and attention (all p ≤0.03), while a pattern similar to the cognitively impaired groups was associated with increased amyloid burden (p<0.05, Table 1). In healthy elderly with a family history of AD, wbGM pattern appears more potent as a predictor of cognitive performance and amyloid burden than HV or individual ROI metrics. Evaluation of whole-brain structural changes may therefore hold particular interest for early identification of AD in those at risk. Mean GM density per ROI in each group

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)0.002
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.078
GPT teacher head0.319
Teacher spread0.242 · 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
Published2017
Admission routes1
Has abstractyes

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