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Record W3112132370 · doi:10.1002/alz.041821

Trajectory of apathy, cognition and neural correlates in the decades before symptoms in frontotemporal dementia

2020· article· en· W3112132370 on OpenAlexaff
Maura Malpetti, Rogier Kievit, P. Simon Jones, Timothy Rittman, Kamen A. Tsvetanov, John C. van Swieten, Barbara Borroni, Daniela Galimberti, Raquel Sánchez‐Valle, Robert Laforce, Fermín Moreno, Matthis Synofzik, Caroline Graff, Mario Masellis, Maria Carmela Tartaglia, Rik Vandenberghe, Elizabeth Finger, Fabrizio Tagliavini, Alexandre de Mendonça, Isabel Santana, Christopher Butler, Simon Ducharme, Alexander Gerhard, Adrian Danek, Johannes Levin, Markus Otto, Giovanni B. Frisoni, Roberta Ghidoni, Carolin Heller, Emily Todd, Martina Bocchetta, Rhian S. Convery, Georgia Peakman, Katrina Moore, Jonathan D. Rohrer, James B. Rowe

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversity of TorontoWestern UniversitySunnybrook Health Science CentreUniversité Laval
Fundersnot available
KeywordsApathyFrontotemporal dementiaPsychologyCognitionDementiaCognitive declinePsychiatryLongitudinal studyInternal medicineMedicineClinical psychologyDiseasePathology

Abstract

fetched live from OpenAlex

Abstract Background In frontotemporal dementia (FTD), apathy is reported to negatively impact the prognosis and survival of patients. The study of genetic FTD in its pre‐symptomatic period permits the investigation of markers in the early stages of disease progression. Therefore, in pre‐symptomatic gene carriers in the multicentre Genetic FTD Initiative (GENFI), we examined longitudinal apathy changes in association with cognitive decline over time, and baseline measures of atrophy. Method Six hundred participants from Data Freeze 4 (2019) were included: 304 pre‐symptomatic mutation carriers, and 296 family members without mutations. Clinical assessment and a structural MRI scan were undertaken at baseline, and annually for at least 2‐years. Latent Growth Curve modelling (LGCM) was used to assess: 1) longitudinal changes in apathy, as measured by the apathy subscale of the Revised Cambridge Behavioural Inventory; 2) the relationship with longitudinal changes in executive function; 3) the association with baseline regional grey matter volumes. The time to the expected year of symptom onset was included as a covariate. Result Univariate LGCM identified a significant linear increase in apathy scores for pre‐symptomatic carriers (estimate= 0.51, se=0.18, z scores= 2.88, p=0.004), but not in non‐carriers (estimate= 0.08, se=0.08, z scores=1.04, p=0.30). An equality constrained comparison suggested a significant group difference in the progression of apathy (i.e. the slope) ((∆ χ2=10.14; p=0.0015). Pre‐symptomatic carriers also had a significant decline in executive functions (est=‐0.07, se=0.03, z scores=‐2.49, p=0.017), which was predicted by baseline apathy (standard estimate=‐0.40, p=0.008). In pre‐symptomatic carriers only, the annual rate of change in apathy was significantly associated with brain volume in frontal lobe (standard estimate=‐0.47, p=0.008) and cingulate cortex (standard estimate=‐0.29, p=0.05) at the baseline. Conclusion Apathy progresses significantly in pre‐symptomatic FTD and predicts a sub‐clinical deterioration of executive performance in gene carriers. Apathy changes over 2 years are associated with volume in the frontal lobe and cingulate gyrus measured at the baseline. We suggest that apathy may be a modifiable factor to protect function and cognition in those with, or at risk of, frontotemporal dementia.

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)0.004
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.027
GPT teacher head0.289
Teacher spread0.262 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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