MétaCan
Menu
Back to cohort
Record W4409493229 · doi:10.1093/brain/awaf132

Cognitive and neuropsychiatric profiles distinguish atypical parkinsonian syndromes

2025· article· en· W4409493229 on OpenAlexaboutno aff
Agustín Querejeta, James B. Rowe, Tanja Zerenner, Alistair Church, Riona Fumi, Alyssa Constantini, Edwin Jabbari, Marte Theilmann Jensen, Alexander Gerhard, Nicola Pavese, Christopher Kobylecki, P. Nigel Leigh, Ivan Koychev, Huw R. Morris, Sanjay Manohar

Bibliographic record

VenueBrain · 2025
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersCambridge Centre for Parkinson-PlusUniversity College London Hospitals Biomedical Research CentreMedical Research CouncilProgressive Supranuclear Palsy AssociationUniversity of OxfordParkinson's UKCurePSPNational Institute for Health and Care ResearchDepartment of Health and Social CareNIHR Cambridge Biomedical Research CentreWellcome Trust
KeywordsApathyProgressive supranuclear palsyPsychologyDementiaCognitionCorticobasal degenerationExecutive dysfunctionNeuropathologyFrontotemporal dementiaDepression (economics)Cognitive declinePsychiatryImpulsivityExecutive functionsAtrophyDiseaseNeuropsychologyMedicinePathology

Abstract

fetched live from OpenAlex

Atypical parkinsonian syndromes are distinguished from Parkinson's disease (PD) by additional neurological signs and characteristic underlying neuropathology. However, they can be diagnostically challenging, rapidly progressive and are often diagnosed late in disease course. Their different demographic features and prognoses are well studied, but the accompanying cognitive and psychiatric features may also facilitate diagnosis. Progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) may cause cognitive and behavioural manifestations that overlap with frontotemporal dementia, including non-fluent aphasia, apathy and impulsivity. Clinical diagnostic criteria have limited sensitivity, with pathologically confirmed PSP often having presented an initial clinical syndrome other than PSP-Richardson's syndrome. Here, we integrate cross-sectional multicentre baseline data from the PROSPECT-M-UK and Oxford Discovery cohorts. This allowed us to compare cognitive and psychiatric features across a total of 1138 people with PSP, CBS, multiple-system atrophy (MSA) and idiopathic PD. Data from the different cohorts were harmonized and compared using multiple linear regression. There were five key results: (i) different syndromes showed distinctive cognitive profiles, using readily applicable 'bedside' screening tools. Frontal executive dysfunction was most evident in PSP, visuospatial deficits in CBS, with milder deficits in memory and executive function in MSA, as compared with PD; (ii) the most prevalent neuropsychiatric features were depression and anxiety in CBS, apathy in PSP, with sleep disturbances common in PD. As expected, apathy correlated positively with impulsivity across all disorders. Neuropsychiatric features were generally better at discriminating between atypical parkinsonian syndromes than were the cognitive domains; (iii) both cognitive function and motor severity declined with disease duration, and motor function predicted cognition in PSP, CBS and PD but not in MSA, suggesting that in MSA cognitive and motor dysfunction are decoupled; (iv) plasma neurofilament light chain (NFL) levels, measured in a subset of patients, correlated with cognitive deficits in PSP, but not motor deficits; (v) cognitive deficits contributed to the impairment in activities of daily living after controlling for motor severity, with every two points on the Montreal Cognitive Assessment worsening the Schwab and England score by one point. In anticipation of future neuroprotective therapies, we present a classifier to improve diagnostic accuracy for atypical parkinsonian syndromes in vivo. Longitudinal cohort studies with resources for neuropathological gold standard diagnosis remain important to validate better diagnostic tools for people with PSP, CBD, MSA and atypical parkinsonism.

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 score0.190
Threshold uncertainty score0.469

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)0.0000.000

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 head0.015
GPT teacher head0.292
Teacher spread0.277 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBrainSame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207