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Record W2921783885 · doi:10.1093/brain/awz039

Cerebral perfusion changes in presymptomatic genetic frontotemporal dementia: a GENFI study

2019· article· en· W2921783885 on OpenAlexafffund
Henk Mutsaerts, Saira Saeed Mirza, Jan Petr, David L. Thomas, David M. Cash, Martina Bocchetta, Enrico De Vita, Arron W.S. Metcalfe, Zahra Shirzadi, Andrew D. Robertson, Maria Carmela Tartaglia, Sara Mitchell, Sandra E. Black, Morris Freedman, David F. Tang‐Wai, Ron Keren, Ekaterina Rogaeva, John C. van Swieten, Robert Laforce, Fabrizio Tagliavini, Barbara Borroni, Daniela Galimberti, James B. Rowe, Caroline Graff, Giovanni B. Frisoni, Elizabeth Finger, Sandro Sorbi, Alexandre de Mendonça, Jonathan D. Rohrer, Bradley J. MacIntosh, Mario Masellis, Christin Andersson, Silvana Archetti, Andrea Arighi, Luisa Benussi, Maura Cosseddu, Katrina M. Dick, Carlos Ferreira, Chiara Fenoglio, Nick C. Fox, Stefano Gazzina, Roberta Ghidoni, Marina Grisoli, Lize C. Jiskoot, Simon Mead, Lieke Meeter, Rick van Minkelen, Benedetta Nacmias, Linn Öijerstedt, Sébastien Ourselin, Alessandro Padovani, Jessica Panman, Cristina Polito, Rosa Rademakers, Giacomina Rossi, Martin N. Rossor, Elio Scarpini, Håkan Thonberg, Pietro Tiraboschi, Jason D. Warren

Bibliographic record

VenueBrain · 2019
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsWestern UniversityUniversité LavalBaycrest HospitalOccupational Cancer Research CentreHealth Sciences CentreUniversity Health NetworkUniversity of TorontoSunnybrook Health Science Centre
FundersMedical Research CouncilCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchMinistero della SaluteNational Institute on Handicapped ResearchWeston Brain InstituteUniversity College London
KeywordsFrontotemporal dementiaCerebral blood flowC9orf72DementiaPsychologyNeuroscienceMedicinePathologyCardiologyDisease

Abstract

fetched live from OpenAlex

Genetic forms of frontotemporal dementia are most commonly due to mutations in three genes, C9orf72, GRN or MAPT, with presymptomatic carriers from families representing those at risk. While cerebral blood flow shows differences between frontotemporal dementia and other forms of dementia, there is limited evidence of its utility in presymptomatic stages of frontotemporal dementia. This study aimed to delineate the cerebral blood flow signature of presymptomatic, genetic frontotemporal dementia using a voxel-based approach. In the multicentre GENetic Frontotemporal dementia Initiative (GENFI) study, we investigated cross-sectional differences in arterial spin labelling MRI-based cerebral blood flow between presymptomatic C9orf72, GRN or MAPT mutation carriers (n = 107) and non-carriers (n = 113), using general linear mixed-effects models and voxel-based analyses. Cerebral blood flow within regions of interest derived from this model was then explored to identify differences between individual gene carrier groups and to estimate a timeframe for the expression of these differences. The voxel-based analysis revealed a significant inverse association between cerebral blood flow and the expected age of symptom onset in carriers, but not non-carriers. Regions included the bilateral insulae/orbitofrontal cortices, anterior cingulate/paracingulate gyri, and inferior parietal cortices, as well as the left middle temporal gyrus. For all bilateral regions, associations were greater on the right side. After correction for partial volume effects in a region of interest analysis, the results were found to be largely driven by the C9orf72 genetic subgroup. These cerebral blood flow differences first appeared approximately 12.5 years before the expected symptom onset determined on an individual basis. Cerebral blood flow was lower in presymptomatic mutation carriers closer to and beyond their expected age of symptom onset in key frontotemporal dementia signature regions. These results suggest that arterial spin labelling MRI may be a promising non-invasive imaging biomarker for the presymptomatic stages of genetic 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 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 categoriesInsufficient payload (model declined to judge)
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 score0.998

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)0.0030.000

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 head0.013
GPT teacher head0.297
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 teacher head, not a consensus.

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

Citations61
Published2019
Admission routes2
Has abstractyes

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