{"doi":"10.1002/ana.26902","title":"Presynaptic Dopaminergic Imaging Characterizes Patients with <scp>REM</scp> Sleep Behavior Disorder Due to Synucleinopathy","abstract":"OBJECTIVE: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). METHODS: In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography-confirmed RBD who eventually phenoconverted to overt alpha-synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non-phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. RESULTS: Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD-converters from DLB-converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z-score cutoff of -1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of -1.0 at the most affected hemisphere putamen/caudate ratio characterized PD-converters. INTERPRETATION: Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease-modifying trials. 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[majority verdict 'partial' (4/5 passes agreed)]","gain":0.0,"priority":"essential","scored":false},{"key":"f_discovery_metadata","dimension":"F","label":"Description of the dataset as an object","action":"Add a 'Data Records' section: itemise every file in the deposit and every variable or sample it holds, with counts and units. Describe the dataset as an object in its own right, not as a by-product of the findings — this is what makes it discoverable to someone who is not looking for your paper.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No itemised inventory of the dataset exists; the data are not described in a section, table, or list.","gain":0.0,"priority":"essential","scored":false},{"key":"a_access_conditions_stated","dimension":"A","label":"Access level labelled","action":"State the access level in words, using the standard vocabulary: 'These data are open access' / 'These data are controlled access'. A reader — and a harvester — should not have to infer the access level from the presence of a download link.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No access-level label is applied to the data; the only statement is 'available upon reasonable request', which does not label the access level. [majority verdict 'no' (3/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"i_community_standard_vocabulary","dimension":"I","label":"Community standard / vocabulary","action":"Adopt and NAME your domain's data standard — the minimum-information checklist, metadata schema, or ontology your community uses (MIAME/MINSEQE, ISA-Tab, BIDS, an OBO ontology, HL7 FHIR/OMOP) — and say which one you followed. A reporting checklist standardises your paper; it does nothing for your data. In clinical / human-subjects, describe the data with OMOP CDM, CDISC SDTM or HL7 FHIR.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No data or metadata community standard (e.g., MIAME, ontology) is named. [majority verdict 'no' (3/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"r_documentation_codebook","dimension":"R","label":"Documentation / codebook","action":"Ship a README and a data dictionary IN the deposit — every file, every variable, its units, its allowed values, its missing-value codes. It is the cheapest single thing that makes a dataset usable by someone who was not in the lab, and a table buried in the article does not travel with the data.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No documentation object (README, data dictionary, codebook) is mentioned as accompanying the data. [majority verdict 'no' (4/5 passes agreed)]","gain":0.0,"priority":"important","scored":false},{"key":"a_controlled_access_for_sensitive","dimension":"A","label":"Gatekeeper for sensitive data","action":"Route sensitive data through an institutional gatekeeper — deposit in a controlled- access repository (dbGaP, EGA) with a Data Access Committee and a published DUA — rather than through the corresponding author's inbox. An author-gated dataset dies with the author's email address, and 'on reasonable request' has been shown repeatedly not to yield data. For sensitive/human clinical / human-subjects data, use a controlled-access repository such as dbGaP or EGA.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"Anonymized data used in this study are available upon reasonable request from the corresponding author (D.A.).","why":"The gatekeeper is a natural person (the corresponding author), not an institutional committee or repository. [majority verdict 'partial' (4/5 passes agreed)]","gain":0.0,"priority":"useful","scored":false},{"key":"i_qualified_references","dimension":"I","label":"Identifiers for the resources the data depend on","action":"Cite by identifier every resource the data depend on — the source datasets' accessions, the reference build (GRCh38 / GCA_000001405.28), the cohort application number, the code DOI — and register those relations on the dataset record (IsDerivedFrom, IsSupplementTo). A name is not a link: it cannot be resolved, versioned, or followed by a machine.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"we used a normal dataset based on 118 healthy volunteers ... belonging to the PPMI database (more details can be found at https://www.ppmi-info.org)","why":"The paper provides a URL for the PPMI database, an external resource identifier. [downgraded to 'no' — no verifiable quote from the paper] [majority verdict 'no' (4/5 passes agreed)]","gain":0.0,"priority":"useful","scored":false},{"key":"a_timeline_retention","dimension":"A","label":"Availability timing & retention","action":"State when the data become available AND how long they will be retained — cite the repository's preservation policy. NIH DMS Element 4 asks for both; most papers give neither.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No temporal commitment is stated regarding when the data become available or how long they persist.","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T11:22:54.757066Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}