Structural, functional and neurochemical imaging mapping of non-motor symptoms in Parkinson’s disease

Clicks: 1
ID: 325253
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #53 of 113 articles by views in Brain communications

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Non-motor symptoms, including rapid eye movement sleep behavior disorder (RBD), depression, and anxiety, are common and often co-occurring in patients with Parkinson’s disease (PD). This study aimed to investigate their potential shared neurobiological substrates by integrating structural, functional, and neurochemical imaging data. We analyzed data from 638 Parkinson’s disease patients from the Parkinson’s Progression Markers Initiative (PPMI), with available 3T T1-weighted MRI scans. Rapid eye movement sleep behavior disorder, depression and anxiety severity were assessed using validated clinical scales (RBD Screening Questionnaire score, Geriatric Depression Scale and State-Trait Anxiety Inventory). Voxel-based morphometry (VBM) multivariate regression analyses were performed to identify grey matter (GM) volume loss associated with each clinical symptom. All analyses were rigorously controlled for a comprehensive set of potential confounders, including age, sex, education, disease duration, motor severity and cognitive dysfunction, thereby minimizing confounding effects related to other aspects of the disease. Coordinate-based network mapping was then applied using a large normative resting-state functional connectome (n=1000), to characterise symptom-specific functional networks based on brain areas functionally connected to the voxel-based morphometry-derived clusters. Finally, spatial correlations between these networks and normative neurotransmitter density maps from PET data were assessed. Voxel-based morphometry analyses revealed distinct patterns of grey matter atrophy across the three symptoms (pFWE<0.05), overlapping in the left middle temporal and right middle frontal gyri. The coordinate-based functional network mapping approach demonstrated that the grey matter atrophy pattern associated with each symptom (pFWE<10-6) converged onto brain networks involving several cortical regions and overlapping across symptoms, and with the greatest spatial affinity, among canonical large-scale networks, with the Dorsal and Ventral Attention networks (DAN and VAN). All three symptom-related networks showed significant alignment with the noradrenaline transporters (NAT) spatial distribution (pFDR<0.05). Overall, this study proposes a novel conceptual and methodological framework integrating well-established and validated techniques to identify the neuroanatomical bases of specific diseases or symptoms, potentially of interest for future research. Our neuroimaging findings in the large PPMI cohort of early Parkinson’s disease patients demonstrate that the brain networks associated with RBD, depression, and anxiety non-motor symptoms were largely overlapping, involved the attention networks and were spatially aligned with the noradrenergic system, suggesting that these symptoms may have shared neurobiological substrates.
Reference Key
openalex_W7203675443 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chiara Camastra, Aldo Quattrone, Aldo Quattrone, Andrea Quattrone, Andrea Quattrone
Journal Brain communications
Year 2026
DOI
10.1093/braincomms/fcag316
URL
https://doi.org/10.1093/braincomms/fcag316
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.