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Record W3176508287 · doi:10.1212/wnl.0000000000012410

Characterizing the Clinical Features and Atrophy Patterns of <i>MAPT</i> -Related Frontotemporal Dementia With Disease Progression Modeling

2021· article· en· W3176508287 on OpenAlexafffund
Alexandra L. Young, Martina Bocchetta, Lucy L. Russell, Rhian S. Convery, Georgia Peakman, Emily Todd, David M. Cash, Caroline Greaves, John C. van Swieten, Lize C. Jiskoot, Harro Seelaar, Fermín Moreno, Raquel Sánchez‐Valle, Barbara Borroni, Robert Laforce, Mario Masellis, Maria Carmela Tartaglia, Caroline Graff, Daniela Galimberti, James B. Rowe, Elizabeth Finger, Matthis Synofzik, Rik Vandenberghe, Alexandre de Mendonça, Fabrizio Tagliavini, Isabel Santana, Simon Ducharme, Christopher Butler, Alexander Gerhard, Johannes Levin, Adrian Danek, Markus Otto, Sandro Sorbi, Steven Williams, Daniel C. Alexander, Jonathan D. Rohrer, Martin N. Rossor, Nick C. Fox, Jason D. Warren, Ione Woollacott, Rachelle Shafei, Carolin Heller, Imogen J. Swift, Katrina Moore, Rita Guerreiro, José Brás, David L. Thomas, Jennifer Nicholas, Simon Mead, Lieke Meeter, Jessica Panman, Janne M. Papma, Jackie M. Poos, Rick van Minkelen, Yolande A.L. Pijnenburg, Myriam Barandiarán, Begoña Indakoetxea, Alazne Gabilondo, Mikel Tainta, María de Arriba, Ana Gorostidi, Miren Zulaica, Jorge Villanúa, Zigor Díaz, Sergi Borrego‐Écija, Jaume Olives, Albert Lladó, Mircea Balasa, Anna Antonell, Núria Bargalló, Enrico Premi, Maura Cosseddu, Stefano Gazzina, Alessandro Padovani, Roberto Gasparotti, Silvana Archetti, Sandra E. Black, Sara Mitchell, Ekaterina Rogaeva, Morris Freedman, Ron Keren, David F. Tang‐Wai, Linn Öijerstedt, Christin Andersson, Vesna Jelić, Håkan Thonberg, Andrea Arighi, Chiara Fenoglio, Elio Scarpini, Giorgio Fumagalli, Thomas Cope, Carolyn Timberlake, Timothy Rittman, Christen Shoesmith, Rosa Rademakers, Carlo Wilke, H.‐O. Karnath, Benjamin Bender, Rose Bruffaerts, Philip Van Damme, Mathieu Vandenbulcke, Catarina B. Ferreira, Gabriel Miltenberger, Carolina Maruta, Ana Verdelho, Sònia Afonso, Ricardo Taipa, Paola Caroppo, Giuseppe Di Fede, Giorgio Giaccone, Sara Prioni, Veronica Redaelli, Giacomina Rossi, Pietro Tiraboschi, Diana Duro, Maria Rosário Almeida, Miguel Castelo‐Branco, Maria João Leitão, Miguel Tábuas‐Pereira, Beatriz Santiago, Serge Gauthier, Pedro Rosa‐Neto, Michele Veldsman, Paul Thompson, Catharina Prix, Tobias Hoegen, Elisabeth Wlasich Mag.rer.nat, Sandra Loosli, Sonja Schönecker, Elisa Semler Dr.hum.bio, Sarah Anderl‐Straub, Benedetta Nacmias, Camilla Ferrari, Cristina Polito, Gemma Lombardi, Valentina Bessi

Bibliographic record

VenueNeurology · 2021
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsHealth Sciences CentreMcGill University Health CentreMontreal Neurological Institute and HospitalSunnybrook Health Science CentreWestern University
FundersUK Dementia Research InstituteMinistero della SaluteNIHR Cambridge Biomedical Research CentreEuropean CommissionCanadian Institutes of Health ResearchGuarantors of BrainAstex PharmaceuticalsWolfson FoundationBrain Research TrustWellcome TrustAlzheimer's SocietyDeutsche ForschungsgemeinschaftBrain Research UKMenzies Centre for Australian Studies, King's College London, University of LondonEli Lilly and CompanyNational Institute for Health and Care ResearchEU Joint Programme – Neurodegenerative Disease ResearchMedical Research CouncilBiogenAssociation for Frontotemporal Degeneration
KeywordsFrontotemporal dementiaAtrophyTemporal lobeTemporal cortexFrontotemporal lobar degenerationPsychologyPathologyDementiaNeuroscienceMutationBiologyMedicineEpilepsyGeneticsDiseaseGene

Abstract

fetched live from OpenAlex

<h3>Background and Objective</h3> Mutations in the <i>MAPT</i> gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of <i>MAPT</i> mutations have grouped all different mutations together and shown an association with focal atrophy of the temporal lobe. The variability in atrophy patterns between each particular <i>MAPT</i> mutation is less well-characterized. We aimed to investigate whether there were distinct groups of <i>MAPT</i> mutation carriers based on their neuroanatomical signature. <h3>Methods</h3> We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning technique that identifies groups of individuals with distinct progression patterns, to characterize patterns of regional atrophy in <i>MAPT-</i>associated FTD within the Genetic FTD Initiative (GENFI) cohort study. <h3>Results</h3> Eighty-two <i>MAPT</i> mutation carriers were analyzed, the majority of whom had P301L, IVS10+16, or R406W mutations, along with 48 healthy noncarriers. SuStaIn identified 2 groups of <i>MAPT</i> mutation carriers with distinct atrophy patterns: a temporal subtype, in which atrophy was most prominent in the hippocampus, amygdala, temporal cortex, and insula; and a frontotemporal subtype, in which atrophy was more localized to the lateral temporal lobe and anterior insula, as well as the orbitofrontal and ventromedial prefrontal cortex and anterior cingulate. There was one-to-one mapping between IVS10+16 and R406W mutations and the temporal subtype and near one-to-one mapping between P301L mutations and the frontotemporal subtype. There were differences in clinical symptoms and neuropsychological test scores between subtypes: the temporal subtype was associated with amnestic symptoms, whereas the frontotemporal subtype was associated with executive dysfunction. <h3>Conclusion</h3> Our results demonstrate that different <i>MAPT</i> mutations give rise to distinct atrophy patterns and clinical phenotype, providing insights into the underlying disease biology and potential utility for patient stratification in therapeutic trials.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.267

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 integrity0.0000.001
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 head0.033
GPT teacher head0.326
Teacher spread0.293 · 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.

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

Citations66
Published2021
Admission routes2
Has abstractyes

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