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Record W2909304848 · doi:10.3389/fnins.2018.00993

The Evaluation of Dynamic FDG-PET for Detecting Epileptic Foci and Analyzing Reduced Glucose Phosphorylation in Refractory Epilepsy

2019· article· en· W2909304848 on OpenAlexaboutno aff
Yongxiang Tang, Jeih‐San Liow, Zhimin Zhang, Jian Li, Tingting Long, Yulai Li, Beisha Tang, Shuo Hu

Bibliographic record

VenueFrontiers in Neuroscience · 2019
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsnot available
FundersXiangya Hospital, Central South UniversityNational Natural Science Foundation of China
KeywordsEpilepsyPositron emission tomographyNuclear medicineFluorodeoxyglucoseRefractory (planetary science)Kinetic energyBinding potentialChemistryNeuroscienceMedicinePhysicsPsychology

Abstract

fetched live from OpenAlex

Aims: Static fluorodeoxyglucose (FDG)-positron emission tomographic (PET) imaging plays an important role in the localization of epileptic foci. Dynamic FDG PET allows calculation of kinetic parameters. The aim of this study was to investigate whether kinetic parameters have potential for identifying epileptic foci, and to assess the correlation of parameters asymmetry indexes (ASYM) between dynamic and static FDG PET for understanding the pathophysiology of hypometabolism within intractable epilepsy. Methods: Seventeen patients who had refractory epilepsy correctly localized by static FDG PET with good outcome after foci resection were included. Eight controls were also studied. We performed dynamic and static FDG PET scan before operation. Images of both scans were coregistered to the montreal neurological institute space, regional time activity curves and activity concentration (AC) were obtained by applying the automated anatomical labeling template to the two spatially normalized images respectively. Kinetic parameters were obtained using a two-tissue non-reversible compartmental model with an image-derived input function. AC from the static scan was used. Side-to-side ASYM of both static AC and kinetic parameters were calculated and analyzed in the hypometabolic epileptogenic regions and non-epileptogenic regions. Results: Higher values of ASYM from both kinetic parameters and static AC were found in the patients compared to the controls from epileptogenic regions. In the non-epileptogenic regions, no ASYM differences were seen between patients and controls for all parameters. In patients, static AC showed larger ASYM than influx (K1) and efflux (k2) of capillaries, but there were no statistical differences of ASYM between net metabolic flux (Ki) or the phosphorylation (k3) and static AC. ASYM of static AC positively correlated with ASYM of k3. Conclusion: Dynamic FDG PET can provide equally effective in detecting the epileptic foci compared to static FDG PET in this small cohort. In addition, compared to capillary influx, the hypometabolism of epileptic foci may be related to reduced glucose phosphorylation.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.244

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 studies0.0000.000
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 head0.022
GPT teacher head0.335
Teacher spread0.313 · 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 designSimulation or modeling
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

Citations28
Published2019
Admission routes1
Has abstractyes

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