MétaCan
Menu
Back to cohort
Record W4404679245 · doi:10.1016/j.isci.2024.111440

Functional resilience of the neural visual recognition system post-pediatric occipitotemporal resection

2024· article· en· W4404679245 on OpenAlexaff
Michael C. Granovetter, Anne Margarette S. Maallo, Shouyu Ling, Sophia Robert, Erez Freud, Christina Patterson, Marlene Behrmann

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsYork University
FundersAsian Pacific FundNational Institute of General Medical SciencesNational Eye InstituteNational Science FoundationAES CorporationEpilepsy SocietyUniversity of PittsburghResearch to Prevent BlindnessEye and Ear Foundation of PittsburghCarnegie Mellon UniversityNational Institutes of HealthAmerican Psychological FoundationAmerican Epilepsy Society
KeywordsResilience (materials science)NeuroscienceCognitive sciencePsychologyComputer science

Abstract

fetched live from OpenAlex

Neural representations for visual stimuli typically emerge with a bilateral distribution across occipitotemporal cortex (OTC). Pediatric patients undergoing unilateral OTC resection offer an opportunity to evaluate whether representations for visual stimulus individuation can sufficiently develop in a single OTC. Here, we assessed the non-resected hemisphere of patients with pediatric resection within ( n = 9) and outside ( n = 12) OTC, as well as healthy controls' two hemispheres ( n = 21). Using functional magnetic resonance imaging, we mapped category selectivity (CS), and representations for visual stimulus individuation (for faces, objects, and words) with repetition suppression (RS). There were no group differences in CS or RS. However, OTC resection patients' accuracy on face and object (but not word) recognition was lower than controls'. The neuroimaging results highlight neural resilience following damage to the contralateral homologue. Critically, however, a single OTC does not suffice for typical behavior, and, thereby, implicates the necessary contributions of bilateral OTC for visual recognition.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.217

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.068
GPT teacher head0.343
Teacher spread0.275 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueiScienceSame topicTraumatic Brain Injury ResearchFrench-language works237,207