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Record W4408989290 · doi:10.1101/2025.03.28.25324785

Neurodevelopmental effects of genetic frontotemporal dementia mutations revealed by total intracranial volume differences

2025· preprint· en· W4408989290 on OpenAlexafffund
Isis So, Arabella Bouzigues, Lucy L. Russell, Phoebe H. Foster, Eve Ferry‐Bolder, John C. van Swieten, Lize C. Jiskoot, Harro Seelaar, Raquel Sánchez‐Valle, Robert Laforce, Caroline Graff, Daniela Galimberti, Rik Vandenberghe, Alexandre de Mendonça, Pietro Tiraboschi, Isabel Santana, Alexander Gerhard, Johannes Levin, Sandro Sorbi, Markus Otto, Florence Pasquier, Simon Ducharme, Christopher Butler, Isabelle Le Ber, Maria Carmela Tartaglia, Mario Masellis, James B. Rowe, Matthis Synofzik, Fermín Moreno, Barbara Borroni, Tyler Kolander, Carly Mester, Danielle Brushaber, Kejal Kantarci, Hilary W. Heuer, Leah K. Forsberg, Jonathan D. Rohrer, Bradley F. Boeve, Adam L. Boxer, Howard J. Rosen, Elizabeth Finger

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA regulation and disease
Canadian institutionsOccupational Cancer Research CentreDouglas Mental Health University InstituteUniversité LavalParkwood InstituteSunnybrook Health Science CentreLawson Health Research InstituteWestern University
FundersCanadian Institutes of Health ResearchAlzheimer NederlandStichting DioraphteInstituto de Salud Carlos IIIZonMw
KeywordsFrontotemporal dementiaBrain sizeDementiaVolume (thermodynamics)NeuroscienceMutationMedicinePsychologyGeneticsPsychiatryInternal medicineBiologyGeneMagnetic resonance imagingRadiologyDisease

Abstract

fetched live from OpenAlex

ABSTRACT Background and Objectives Converging evidence hints at neurodevelopmental effects in people at risk of genetic frontotemporal dementia (FTD), including associations between FTD-causing mutations and neurodevelopmental disorders, and differences in young adult mutation carriers compared to familial non-mutation carriers in total intracranial volume (TIV) and cognition. We aimed to investigate TIV and educational attainment differences between adult mutation carriers and familial non-mutation carriers, as measures of the structural and functional neurodevelopmental effects of the FTD-causing genetic mutations. Methods This cross-sectional cohort study was facilitated through the FTD Prevention Initiative (FPI). Participants, aged 18 to 86 years, were pathogenic mutation carriers of GRN , MAPT , or C9orf72 , or familial non-carriers. ANCOVAs were computed per gene to compare outcome means for the main effect of group by carrier status, while controlling for birth decade, sex, and visit site (to account for unique scanners and educational systems). Pearson’s correlations were used to examine associations between TIV and education. Results Nine-hundred two mutation carriers (mean±SD; age=50.0±13.2 years, sex=55% female, n ( GRN )=298, n( MAPT )=187, n ( C9orf72 )=417) were compared to 532 familial non-carriers (age=48.0±12.9 years, sex=58% female, n ( GRN )=201, n( MAPT )=114), n ( C9orf72 )=217). Consistent with prior findings in young adults, GRN carriers showed larger TIV compared to familial non-carriers (95% confidence interval [CI]=1431994-1457123, p =0.049, η 2 p =0.008). Larger TIV correlated with higher years of education in GRN carriers (95% CI=0.01-0.24, r (295)=0.12, p =0.03) and GRN non-carriers (95% CI=0.08-0.34, r(198)=0.21, p =0.002). MAPT carriers demonstrated smaller TIV than non-carriers (95% CI=1417819-1450628, p =0.039, η 2 p =0.02). Models with C9orf72 and education as outcome variables did not reveal significant differences. Discussion In support of the neurodevelopmental hypothesis of FTD, GRN and MAPT mutations are linked to likely structural neurodevelopmental changes in TIV, some of which correlate to years of education. These findings motivate further research to identify mechanisms by which FTD mutations influence neurodevelopment and ascertain their suitability as targets for interventions.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.005
GPT teacher head0.227
Teacher spread0.222 · 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

Labeled directly by 2 models reading the full record.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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