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Record W2045217295 · doi:10.1002/hbm.21107

Network interactions explain effective encoding in the context of medial temporal damage in MCI

2010· article· en· W2045217295 on OpenAlexafffund
Andrea B. Protzner, Jennifer Mandzia, Sandra E. Black, Mary Pat McAndrews

Bibliographic record

VenueHuman Brain Mapping · 2010
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of TorontoSunnybrook Health Science CentreHealth Sciences CentreUniversity Health Network
FundersCanadian Institutes of Health ResearchAlzheimer SocietyJames S. McDonnell Foundation
KeywordsNeurosciencePsychologyTemporal lobeContext (archaeology)HumEpisodic memoryRecognition memoryEncoding (memory)CognitionBiologyEpilepsy

Abstract

fetched live from OpenAlex

Selective dysfunction in the medial temporal lobe (MTL) in amnestic mild cognitive impairment (MCI) results in a relatively circumscribed impairment in episodic memory. Previously, we found that activation extent in MTL during encoding correlated with subsequent recognition (hit rate) in controls but not in MCI patients (Mandzia et al. [2009]: Neurobiol Aging 30:717-730). Here, we examined whether functional connectivity amongst MTL and cortical regions might better explain differences in subsequent recognition success. Participants underwent fMRI scanning during picture encoding, and multivariate analysis was used to characterize the relationship between network activations and recognition. Both patients and controls activated a canonical MTL encoding network. However, this network correlated with hit rate only for controls. In MCI patients, recognition variability was best explained by the engagement of an additional network including BA 20. We propose that this pattern represents functional reorganization caused by reduced efficiency in the MTL network. Our findings suggest that understanding brain-behavior relationships in neurological disorders requires examination of large-scale networks, even when dysfunction is relatively focal as in MCI.

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)0.002
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.054
GPT teacher head0.301
Teacher spread0.247 · 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.

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

Citations14
Published2010
Admission routes2
Has abstractyes

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