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Record W4390903620 · doi:10.1093/brain/awae011

Clinicopathological correlates in the frontotemporal lobar degeneration–motor neuron disease spectrum

2024· article· en· W4390903620 on OpenAlexfundno aff
Álvaro Carbayo, Sergi Borrego‐Écija, Janina Turón‐Sans, Elena Cortés‐Vicente, Laura Molina‐Porcel, Jordi Gascón‐Bayarri, M.Á. Rubio, Mónica Povedano, Josep Gámez, Javier Sotoca, Raúl Juntas‐Morales, Míriam Almendrote, Marta Marquié, Raquel Sánchez‐Valle, Ignacio Illán‐Gala, Oriol Dols‐Icardo, Sara Rubio‐Guerra, Sara Bernal, Marta Caballero‐Ávila, Ana Vesperinas, Ellen Gelpí, Ricard Rojas‐García

Bibliographic record

VenueBrain · 2024
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsnot available
FundersEuropean Social FundEuropean Regional Development FundAlzheimer’s SocietyGlobal Brain Health InstituteAlzheimer's SocietyInstituto de Salud Carlos IIIAlzheimer SocietyAlzheimer's Association
KeywordsFrontotemporal lobar degenerationMotor neuronAmyotrophic lateral sclerosisNeuroscienceMedicineDiseaseDegeneration (medical)Frontotemporal dementiaAudiologyPsychologyPathologyDementia

Abstract

fetched live from OpenAlex

Amyotrophic lateral sclerosis (ALS) is a devastating motor neuron disease (MND) that shares a common clinical, genetic and pathologic spectrum with frontotemporal dementia (FTD). It is highly heterogeneous in its presentation and features. Up to 50% of patients with MND develop cognitive-behavioural symptoms during the course of the disease, meeting criteria for FTD in 10%-15% of cases. In the absence of a precise biomarker, neuropathology is still a valuable tool to understand disease nosology, reach a definite diagnostic confirmation and help define specific subgroups of patients with common phenotypic, genetic and biomarker profiles. However, few neuropathological series have been published, and the frequency of frontotemporal lobar degeneration (FTLD) in MND is difficult to estimate. In this work we describe a large clinicopathological series of MND patients, analysing the frequency of concurrent FTLD changes and trying to define specific subgroups of patients based on their clinical, genetic and pathological characteristics. We performed an observational, retrospective, multicentre case study. We included all cases meeting neuropathological criteria for MND from the Neurological Tissue Bank of the FRCB-IDIBAPS-Hospital Clínic Barcelona Biobank between 1994 and 2022, regardless of their last clinical diagnosis. While brain donation is encouraged in all patients, it is performed in very few, and representativeness of the cohort might not be precise for all patients with MND. We retrospectively reviewed clinical and neuropathological data and describe the main clinical, genetic and pathogenic features, comparing neuropathologic groups between MND with and without FTLD changes and aiming to define specific subgroups. We included brain samples from 124 patients, 44 of whom (35.5%) had FTLD neuropathologic features (i.e. FTLD-MND). Pathologic TDP-43 aggregates were present in 93.6% of the cohort and were more extensive (higher Brettschneider stage) in those with concurrent FTLD (P < 0.001). Motor symptom onset was more frequent in the bulbar region in FTLD-MND cases than in those with isolated MND (P = 0.023), with no differences in survival. We observed a better clinicopathological correlation in the MND group than in the FTLD-MND group (93.8% versus 61.4%; P < 0.001). Pathogenic genetic variants were more common in the FTLD-MND group, especially C9orf72. We describe a frequency of FTLD of 35.5% in our series of neuropathologically confirmed cases of MND. The FTLD-MND spectrum is highly heterogeneous in all aspects, especially in patients with FTLD, in whom it is particularly difficult to define specific subgroups. In the absence of definite biomarkers, neuropathology remains a valuable tool for a definite diagnosis, increasing our knowledge in disease nosology.

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)0.001
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 score0.268
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.045
GPT teacher head0.339
Teacher spread0.294 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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