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Record W1899627281 · doi:10.1002/hipo.22395

Posterior compensatory network in cognitively intact elders with hippocampal atrophy

2014· article· en· W1899627281 on OpenAlexaff
Michael Valenzuela, Andrew Turner, Nicole A. Kochan, Wei Wen, Chao Suo, Harry Hallock, Perminder S. Sachdev, Michael Breakspear

Bibliographic record

VenueHippocampus · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersNational Health and Medical Research CouncilAustralian Rotary HealthJames S. McDonnell Foundation
KeywordsHippocampal formationAtrophyPsychologyNeuroscienceHippocampusWorking memoryCognitionSpatial memoryPopulationCognitive psychologyPosterior cortical atrophyPosterior parietal cortexMedicineDementiaPathologyDisease

Abstract

fetched live from OpenAlex

Functional compensation in late life is poorly understood but may be vital to understanding long-term cognitive trajectories. To study this we first established an empirically derived threshold to distinguish hippocampal atrophy in those with Mild Cognitive Impairment (MCI n = 34) from those with proficient cognition (PRO n = 22), using data from a population-based cohort. Next, to identify compensatory networks we compared cortical activity patterns during a graded spatial working memory (SWM) task in only cognitively proficient individuals, either with (PROATR ) or without hippocampal atrophy (PRONIL ). Multivariate Partial Least Squares analyses revealed that these groups engaged spatially distinct SWM-related networks. In those with hippocampal atrophy and under conditions of basic-SWM demand, expression of a posterior compensatory network (PCN) comprised calcarine and posterior parietal cortex strongly correlated with superior SWM performance (r = -0.96). In these individuals, basic level SWM response times were faster and no less accurate than in those with no hippocampal atrophy. Cognitively proficient older individuals with hippocampal atrophy may, therefore, uniquely engage posterior brain areas when performing simple spatial working memory tasks.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.126
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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 teacher head, 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

Citations10
Published2014
Admission routes1
Has abstractyes

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