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Record W4390194897 · doi:10.1002/alz.080390

Associations between brain TSPO and [<sup>18</sup>F]FDG‐PET signals in neurodegenerative disorders

2023· article· en· W4390194897 on OpenAlexaff
Luiza Santos Machado, Pedro Vidor, Christian Limberger, Lavínia Perquim de Carvalho, Leonardo Machado, Guilherme Da Silva Carvalho, Andréia Silva da Rocha, Bruna Bellaver, Carolina Soares, Pâmela C.L. Ferreira, Tharick A. Pascoal, Pedro Rosa‐Neto, Andréa Lessa Benedet, Kaj Blennow, Henrik Zetterberg, Nicholas J. Ashton, Eduardo R. Zimmer

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsTranslocator proteinProgressive supranuclear palsyCorticobasal degenerationFrontotemporal dementiaNeurodegenerationPrimary progressive aphasiaMedicineDementiaNeurosciencePositron emission tomographyFrontotemporal lobar degenerationPsychologyDiseasePathologyNeuroinflammation

Abstract

fetched live from OpenAlex

Abstract Background Recent evidence indicates that activated microglial cells contribute to the brain FDG‐PET signal changes in neurodegenerative disorders. Radiopharmaceuticals targeting the 18‐kDa translocator protein (TSPO) have been used to measure microglial activation. The combination of FDG‐PET and TSPO‐PET allows for assessing, in‐vivo and non‐invasively, whether they associate in brain regions vulnerable to neurodegeneration. So far, multiple studies have used FDG‐PET and TSPO‐PET concomitantly, but to our knowledge, no systematic evaluation has been conducted to assess whether they are associated. We aimed to systematically summarize the current data on brain TSPO‐PET and FDG‐PET signals in individuals with neurodegenerative disorders. Method Studies performing TSPO‐ and FDG‐PET in neurodegenerative disorders were searched in PubMed and Web of Science. Included articles must have performed PET images concomitantly with both tracers in healthy controls and individuals presenting with neurodegenerative disorders. This review complied with PRISMA (2020) guidelines and was registered at PROSPERO (CRD42022354523). Result A total of 272 articles were found after a search on PubMed and Web of Science, with eight meeting the inclusion criteria. We included clinical studies with individuals presenting Alzheimer’s Disease (AD), Parkinson’s Disease (PD), corticobasal syndrome and progressive supranuclear palsy, behavioral variant frontotemporal dementia, and progressive nonfluent aphasia. In AD, five studies presented negative associations, and one presented a positive association between TSPO and FDG signals in brain regions vulnerable to AD. In PD (3 studies) and the other related neurodegenerative disorders (2 studies) evaluated, we found a negative correlation between FDG‐PET and TSPO‐PET signals in vulnerable regions. Conclusion All studies presented associations between FDG‐ and TSPO‐PET signals. These findings suggest that microglia, indexed by TSPO binding, negatively impacts brain glucose metabolism indexed by FDG‐PET. Further studies are needed to address associations between TSPO‐PET and FDG‐PET in the asymptomatic and early stages of neurodegenerative disorders.

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)0.003
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0140.016
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.049
GPT teacher head0.338
Teacher spread0.289 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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