ReviewParkinsonism & related disorders2020
MRI biomarkers of motor and non-motor symptoms in Parkinson's disease.
Review in Parkinsonism & related disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it, 95 citations in OpenAlex.
- Genome-Wide Meta-Analysis of Parkinson's Disease Associated Genetic Loci and Validation of Therapeutic Targets.Brain and behavior · 2025Pooled it
- Identifing two distinct cortical progression subtypes of Parkinson's disease through multimodal neuroimaging.European journal of nuclear medicine and molecular imaging · 2026Article
- Preparing for Parkinson's disease prevention trials: Current progress and future directions.Journal of Parkinson's disease · 2026Review
- Prediction of Motor Symptom Progression of Parkinson's Disease Through Multimodal Imaging-Based Machine Learning.Journal of imaging informatics in medicine · 2026Article
- Prediction of depression development in Parkinson's disease patients: a radiomics-based machine learning study.BMC neurology · 2026Article
- Chemical exchange saturation transfer MRI for neurodegenerative diseases: An update on clinical and preclinical studies.Neural regeneration research · 2026Article
- Reduced striatal dopamine transmission as a transdiagnostic substrate of psychomotor retardation.Brain : a journal of neurology · 2026Review
- Selective amygdala subregional atrophy in Parkinson's disease: from subjective cognitive decline to mild cognitive impairment.Frontiers in aging neuroscience · 2026Article
- Advances in ocular motor and pupil biomarkers for neurological disorders.Brain communications · 2026Review
- Neuroimaging in advanced Parkinson's disease: insights into pathophysiology, biomarkers, and personalized therapies.Journal of neural transmission (Vienna, Austria : 1996) · 2025Review
- Review
- Classification of Parkinson's disease with and without dopaminergic deficiency based on non-motor symptoms and structural neuroimaging.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Characterizing the heterogeneity of neurodegenerative diseases through EEG normative modeling.NPJ Parkinson's disease · 2025Article
- A Multi-omics Framework Based on Machine Learning as a Predictor of Cognitive Impairment Progression in Early Parkinson's Disease.Neurology and therapy · 2025Article
- Cognitive impairment in Parkinson's disease and other parkinsonian syndromes.Journal of neural transmission (Vienna, Austria : 1996) · 2025Review
- Review of multimodal machine learning approaches in healthcare.An international journal on information fusion · 2025Article
- Identification of Depression Subtypes in Parkinson's Disease Patients via Structural MRI Whole-Brain Radiomics: An Unsupervised Machine Learning Study.CNS neuroscience & therapeutics · 2025Article
- Dopamine transporter availability based on white matter hyperintensity during early to mid-stage Parkinson's disease and multiple system atrophy: a case control study.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- The Spectrum of Cognitive Impairment in Atypical Parkinsonism Syndromes: A Comprehensive Review of Current Understanding and Research.Diseases (Basel, Switzerland) · 2025Review
- Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Parkinson's disease is a heterogeneous disorder with both motor and non-motor symptoms that contribute to functional impairment. To develop effective, disease modifying treatments for these symptoms, biomarkers are necessary to detect neuropathological changes early in the disease course and monitor changes over time. Advances in MRI scan sequences and analytical techniques present numerous promising metrics to detect changes within the nigrostriatal system, implicated in the cardinal motor symptoms of the disease, and detect broader dysfunction involved in the non-motor symptoms, such as cognitive impairment. There is emerging evidence that iron sensitive, neuromelanin sensitive, diffusion sensitive, and resting state functional magnetic imaging measures can capture changes within the nigrostriatal system. Iron, neuromelanin, and diffusion sensitive measures demonstrate high specificity and sensitivity in distinguishing Parkinson's disease relative to controls, with inconsistent results differentiating Parkinson's disease relative to atypical parkinsonian disorders. They may also serve as useful monitoring biomarkers, with each possibly detecting different aspects of the disease course (early nigrosome changes versus broader substantia nigra changes). Investigations of non-motor symptoms, such as cognitive impairment, require careful consideration of the nature of cognitive deficits to characterize regional and network specific impairment. While the early, executive dysfunction observed is consistent with nigrostriatal degeneration, the memory and visuospatial impairments, the harbingers of a dementia process reflect dopaminergic independent dysfunction involving broader regions of the brain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.