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Record W2137540206 · doi:10.3389/fneur.2013.00131

Disentangling Hippocampal Shape Anomalies in Epilepsy

2013· article· en· W2137540206 on OpenAlexafffund
Hosung Kim, Tommaso Mansi, Neda Bernasconi

Bibliographic record

VenueFrontiers in Neurology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health Research
KeywordsEpilepsyHippocampal formationNeurosciencePsychologyCognitive psychology

Abstract

fetched live from OpenAlex

Drug-resistant temporal lobe epilepsy (TLE) and epileptic syndromes related to malformations of cortical development (MCD) are associated with complex hippocampal morphology. The contribution of volume and position to the overall hippocampal shape in these conditions has not been studied. We propose a surface-based framework to localize volume changes through measurement of Jacobian determinants, and quantify fine-scale position and curvature through a medial axis model. We applied our methodology to T1-weighted 3D volumetric MRI of 88 patients with TLE and 78 patients with MCD, including focal cortical dysplasia (FCD, n = 29), heterotopia (HET, n = 40), and polymicrogyria (PMG, n = 19). Patients were compared to 46 age- and sex-matched healthy controls. Surface-based analysis of volume in TLE revealed severe ipsilateral atrophy mainly along the rostro-caudal extent of the hippocampal CA1 subfield. In MCD, patterns of volume changes included bilateral CA1 atrophy in HET and FCD, and left dentate hypertrophy in all three groups. The analysis of curvature revealed medial bending of the posterior hippocampus in TLE, whereas in MCD there was a supero-medial shift of the hippocampal body. Albeit hippocampal shape anomalies in TLE and MCD result from a combination of volume and positional changes, their nature and distribution suggest different pathogenic mechanisms.

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)0.000
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 score0.029
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.004
GPT teacher head0.190
Teacher spread0.185 · 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

Citations37
Published2013
Admission routes2
Has abstractyes

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