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Record W2949849150 · doi:10.3389/fneur.2019.00486

Quantitative 18F-AV1451 Brain Tau PET Imaging in Cognitively Normal Older Adults, Mild Cognitive Impairment, and Alzheimer's Disease Patients

2019· article· en· W2949849150 on OpenAlexfundno aff
Qian Zhao, Min Liu, Lingxia Ha, Yun Zhou

Bibliographic record

VenueFrontiers in Neurology · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on AgingNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchKey Research and Development Program of NingxiaNational Institutes of HealthH. Lundbeck A/SServierU.S. Department of DefenseEli Lilly and CompanyNatural Science Foundation of Ningxia ProvinceNingxia Medical UniversityNational Natural Science Foundation of ChinaEisaiGenentechIXICONorthern California Institute for Research and EducationPfizerBiogenBioClinicaUniversity of Southern CaliforniaNovartis Pharmaceuticals CorporationBristol-Myers SquibbF. Hoffmann-La RocheAlzheimer's Drug Discovery FoundationMeso Scale DiagnosticsAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsStandardized uptake valueNeuroimagingPositron emission tomographyStatistical parametric mappingWhite matterBinding potentialPsychologyNuclear medicineCognitive impairmentPartial volumeAlzheimer's Disease Neuroimaging InitiativeTau pathologyAlzheimer's diseaseNeuroscienceMagnetic resonance imagingMedicineInternal medicineCognitionDiseaseRadiology

Abstract

fetched live from OpenAlex

Recent tau Positron Emission Tomography (PET) allows assessment of regional neurofibrillary tangles deposition in human brain. 18F-AV1451 is characterized by high selectivity for pathologic tau aggregates. The objectives of the study are 1) to quantitatively characterize regional brain tau deposition measured by 18F-AV1451 PET in cognitively normal older adults (CN), mild cognitive impairment (MCI) and AD participants; 2) to evaluate the correlations between CSF biomarkers or Mini-Mental State Examination (MMSE) and standardized uptake value ratio (SUVR); and 3) to evaluate the partial volume effects on 18F-AV1451 brain uptake. Methods: The study included total 115 participants (CN=49, MCI=58 and AD=8) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Preprocessed 18F-AV1451 PET images, structural MRIs, and demographic and clinical assessments were downloaded from the ADNI database. Voxelwise partial volume correction (PVC) on PET images was introduced. PVC and non-PVC SUVR images were compared. The correlation between SUVR and MMSE, CSF total tau (t-tau) and phosphorylated tau (p-tau) were also assessed. Results: 18F-AV1451 prominently specific binding was found in the amygdala, entorhinal cortex, parahippocampus, fusiform, posterior cingulate, temporal, parietal and frontal brain regions. Most SUVRs showed significantly higher uptake of 18F-AV1451 in AD than MCI and CN participants. SUVRs of small regions like amygdala, entorhinal cortex and parahippocampus were statistically improved by PVC in groups (p0.05). Declined MMSE score was observed with increasing 18F-AV1451 binding in amygdala, entorhinal cortex, parahippocampus and fusiform. CSF p-tau was positively correlated with 18F-AV1451 deposition. Conclusion: The typical deposition of 18F-AV1451 tau PET imaging in AD brain was found in amygdala, entorhinal cortex, fusiform and parahippocampus, and they were strongly associated with cognitive impairment and CSF biomarkers. 18F-AV-1451 PET imaging could not differentiate the MCI from CN population. More tau deposition related to decreased MMSE score and increased CSF p-tau level . PVC improved the results of tau deposition and correlation studies in small brain regions and suggested to be routinely used in 18F-AV1451 tau PET quantification.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.278
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2019
Admission routes1
Has abstractyes

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