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Record W4205137218 · doi:10.1002/alz.056513

MRI correlates of neuropsychiatric symptom progression in pre‐dementia <i>GRN</i> and <i>C9orf72</i> mutation carriers

2021· article· en· W4205137218 on OpenAlexaff
Hyunwoo Lee, Atri Chatterjee, Karteek Popuri, Mirza Faisal Beg, Ian R. Mackenzie, Dana Wittenberg, Rosa Rademakers, Ging‐Yuek Robin Hsiung

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsC9orf72Frontotemporal dementiaDementiaWhite matterGrey matterPsychologyOncologyInternal medicineMutationFrontotemporal lobar degenerationMedicineMagnetic resonance imagingGeneticsBiologyDiseaseRadiology

Abstract

fetched live from OpenAlex

Abstract Background Frontotemporal dementia (FTD) patients with mutations in the C9orf72 or the GRN genes frequently exhibit neuropsychiatric symptoms (NPS), which may precede the onset of dementia. We hypothesized that the burden and progression of NPS in pre‐dementia mutation carriers are associated with structural abnormalities detectable in brain MRI. Method We analyzed data from 42 participants before onset of dementia (11 C9orf72+ mutation carriers, 8 GRN+ mutation carriers, and 23 non‐carrier family members (NCs); mean±SD age: 46y±11, 50y±9, and 53y±8 respectively) recruited through the University of British Columbia FTD Study. Participants were assessed longitudinally for NPS with Neuropsychiatric Inventory (NPI), Frontal Behavioural Inventory (FBI) and Iowa Scale of Personality Change (ISPC) (mean follow‐up: 8.4y). Participants also underwent 1.5T MRI at the first and third annual visits (mean interval: 28±8m). If a participant developed FTD during the follow‐up (N=2), we only analyzed observations collected before the conversion. We processed T1‐weighted MRI to calculate annualized volumetric changes in the grey‐matter, the white‐matter and the hypointense white‐matter signal‐abnormalities (WMSA), within the entire brain and lobar region‐of‐interests. We applied a random‐slope model to the NPI, FBI and ISPC scores to estimate subject‐specific slopes of their progression. Then, we applied a linear model to evaluate the association between the estimated slopes and the MRI measures, among the genetic groups. Covariates included sex, age, baseline assessment scores, baseline WMSA and total white‐matter volumes. Result There were non‐significant baseline group differences in the MRI or NPS measures. Overall, a higher rate of frontal lobar WMSA accumulation was associated with a higher rate of NPI score increase. However, GRN+ carriers had a faster progression of NPS compared to NCs, and the higher NPI slope was associated with a higher rate of frontal lobar white‐matter volume loss. The FBI and ISPC slopes were not significantly associated with the MRI measures. Conclusion Prior to the onset of FTD, the emergence of NPS in mutation carriers may be correlated with the degree of frontal lobar white‐matter lesions. In GRN+ carriers, the progression of NPS, as represented by NPI score increases, may also be associated with frontal white‐matter volume loss.

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)0.000
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.294
Teacher spread0.284 · 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
Published2021
Admission routes1
Has abstractyes

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