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Record W1975947480 · doi:10.1109/tmi.2012.2230640

Novel Surface-Smoothing Based Local Gyrification Index

2012· article· en· W1975947480 on OpenAlexafffund
Evgeniy Lebed, Claudia Jacova, Lei Wang, Mirza Faisal Beg

Bibliographic record

VenueIEEE Transactions on Medical Imaging · 2012
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersCanadian Institutes of Health Research
KeywordsGyrificationDementiaNeuroscienceTemporal lobeAtrophyArtificial intelligenceComputer sciencePsychologyMedicineDiseasePathologyCerebral cortex

Abstract

fetched live from OpenAlex

The quantication of cortical surface folding is important in identifying and classifying many neurodegenerative diseases. Much work has been done to identify regional and global brain folding, and in this paper we review some of these methods, as well as propose a new method that has advantages over the existing state of art. Using our novel proposed method, we mapped the local gyrification index on the cortical surface for subjects with mild Alzheimer's dementia (n=20) , very mild dementia (n=23) and age-matched healthy subjects (n=52). In our experiments we find a consistent pattern of gyrification changes in the dementia subjects, with regions generally affected early on in the progression of Alzheimer pathology, including medial temporal lobe, and cingulate gyrus, having decreased gyrification. At the same time we observe increased gyrification in dementia subjects, in frontal, anterior temporal and posteriorly located regions. We speculate that in neurodegenerative diseases including Alzheimer Disease, the folding of the entire cortical mantle undergoes dynamic changes as regional atrophy begins and expands, with both decreases and increases in gyrification.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.285
Teacher spread0.248 · 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 designBench or experimental
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

Citations31
Published2012
Admission routes2
Has abstractyes

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