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Non-motor symptoms, including rapid eye movement sleep behaviour disorder (RBD), depression and anxiety, are common and often co-occurring in patients with Parkinson's disease. This study aimed to investigate their potential shared neurobiological substrates by integrating structural, functional and neurochemical imaging data. We analysed data from 638 Parkinson's disease patients from the Parkinson's Progression Markers Initiative (PPMI), with available 3T T1-weighted MRI scans. RBD, 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 characterize symptom-specific functional networks based on brain areas functionally connected to the VBM-derived clusters. Finally, spatial correlations between these networks and normative neurotransmitter density maps from PET data were assessed. VBM analyses revealed distinct patterns of GM 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 GM 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. 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.