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Record W4205155552 · doi:10.1002/alz.050928

Detecting clinical progression from abnormal regional brain volumes at baseline in genetic frontotemporal dementia: A GENFI study

2021· article· en· W4205155552 on OpenAlexaff
Martina Bocchetta, Emily Todd, Jennifer Nicholas, Georgia Peakman, David M. Cash, Rhian S. Convery, Lucy L. Russell, David L. Thomas, Juan Eugenio Iglesias, John C. van Swieten, Lize C. Jiskoot, Harro Seelaar, Barbara Borroni, Daniela Galimberti, Raquel Sánchez‐Valle, Robert Laforce, Fermín Moreno, Matthis Synofzik, Caroline Graff, Mario Masellis, Maria Carmela Tartaglia, James B. Rowe, Rik Vandenberghe, Elizabeth Finger, Fabrizio Tagliavini, Alexandre de Mendonça, Isabel Santana, Christopher Butler, Simon Ducharme, Alexander Gerhard, Adrian Danek, Johannes Levin, Markus Otto, Sandro Sorbi, Isabelle Le Ber, Florence Pasquier, Jonathan D. Rohrer

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMcGill UniversityWestern UniversitySunnybrook Health Science CentreOccupational Cancer Research CentreUniversité LavalUniversity of TorontoHôpital de l'Enfant-Jésus
Fundersnot available
KeywordsFrontotemporal dementiaC9orf72Clinical Dementia RatingMedicineInternal medicinePsychologyNuclear medicineDementiaDisease

Abstract

fetched live from OpenAlex

Abstract Background Genetic frontotemporal dementia is highly heterogeneous, with different progression patterns seen between individuals. Using in vivo MR images from the Genetic FTD Initiative (GENFI), we aimed to identify clinical progression in genetic mutation carriers from their brain volumes at baseline. Method Cortical and subcortical volumes of interest (VOIs) were generated using automated parcellation methods on volumetric 3T T1‐weighted MRI scans for 480 carriers (198 GRN, 202 C9orf72, 80 MAPT). W‐scores for 79 VOIs were computed from a linear regression model carried out on 298 non‐carrier cognitively normal controls adjusting for the effect of age, sex, total intracranial volume and scanner type. Carriers were divided into three disease stages based on their global CDR® plus NACC FTLD score (CDR‐GS): asymptomatic (0), possibly or mildly symptomatic (0.5) and fully symptomatic (1 or more). Cut‐off points for each VOI were derived from Youden indices estimated with ROC curves to distinguish between CDR‐GS=0 and CDR‐GS≥1 within each gene. CDR‐GS=0.5 carriers (30 GRN, 32 C9orf72, 13 MAPT) were classified as ‘normal’ or ‘abnormal’ based on these cut‐off points. We compared the CDR® plus NACC FTLD sum‐of‐boxes scores (CDR‐SOB) at one year follow‐up in these two groups. Result Compared to those with normal baseline volumes, C9orf72 expansion carriers at CDR‐GS=0.5 showed significantly higher CDR‐SOB scores at follow‐up if they had abnormal volumes in the total frontal (+5 points), orbitofrontal (+3), dorsolateral prefrontal (+6), or anterior cingulate (+4) cortices, the basal‐paralaminar amygdala region (+2), CA1 region of the hippocampus (+4), total hippocampus (+6), cerebellar lobule VIIIb (+4), or lateral ventricles (+8). GRN mutation carriers showed significantly higher CDR‐SOB scores if their volumes were abnormal in the frontal (+10), parietal (+14), insula (+8), orbitofrontal (+12), or medial parietal (+7) cortices, or the total hippocampus (+10). MAPT mutation carriers with abnormal volumes in the lobule VI and dentate nucleus had 1 point higher CDR‐SOB scores at follow‐up. Conclusion Abnormal baseline volumes in specific VOI within each of the genetic groups were related to worse CDR‐SOB scores over time. Future studies including longer follow‐up intervals and other longitudinal biomarkers are needed to explore this further.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.079
GPT teacher head0.378
Teacher spread0.298 · 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
Published2021
Admission routes1
Has abstractyes

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