Neuroimaging plays a crucial role in understanding Parkinson’s disease (PD). Numerous studies have utilized a variety of techniques like PET, SPECT, and MRI to study genetic forms of PD, focusing on genes like GBA1, LRRK2, and Parkin ( Imaging Markers in Genetic Forms of Parkinson’s Disease - PMC). Despite advancements, no imaging marker has been proven to be universally sensitive for the prediction and monitoring of the disease progression. Novel imaging methods sensitive to brain tissue composition are emerging, showing promising correlations with disease severity.
Structural MRI often shows minimal changes in early stages, with inconsistent patterns of brain atrophy emerging later. Functional imaging techniques like PET and SPECT, particularly those targeting the dopaminergic system (e.g., DAT-SPECT), can reveal characteristic patterns of reduced tracer uptake, but their sensitivity and specificity vary (Neuroimaging and fluid biomarkers in Parkinson’s disease in an era of targeted interventions | Nature Communications). Emerging techniques sensitive to brain tissue composition, such as neuromelanin-sensitive MRI and diffusion imaging, show promise in detecting subtle changes and correlating with disease severity. However, the heterogeneous nature of PD means that different imaging modalities may be more or less effective depending on the specific subtype and stage of the disease, emphasizing the need for a multimodal approach to accurately capture the full spectrum of PD-related brain changes.
The newly proposed biological staging model for Parkinson’s disease (PD), such as the Neuronal alpha-Synuclein Disease Integrated Staging System (NSD-ISS), offers a promising framework for advancing imaging studies in PD. This model, which integrates biomarkers like α-synuclein pathology (detected by Seed Amplification Assay) and dopaminergic dysfunction (assessed by neuroimaging), provides a biological foundation for disease classification that can be directly linked to imaging findings (https://www.thelancet.com/journals/laneur/article/PIIS1474-4422(23)00405-2/abstract). By aligning imaging studies with this staging system, researchers can potentially identify stage-specific neuroimaging markers, enhancing our understanding of disease progression and heterogeneity. This approach may enable the development of more sensitive and specific set of imaging biomarkers for early diagnosis, disease monitoring, and treatment response assessment. Furthermore, it could facilitate the design of targeted imaging studies that focus on specific biological stages of PD, potentially uncovering new insights into the underlying pathophysiology and regional brain changes associated with each stage of the disease.
Several large-scale imaging registries have been established for PD, providing rich datasets for advanced analysis. The Parkinson’s Progression Markers Initiative (PPMI) and the Parkinson’s Disease Progressive Neuroimaging Initiative (PDPNI) are two examples that collect longitudinal clinical, imaging, and biomarker data. These registries encompass various imaging modalities, including structural MRI, diffusion imaging, functional MRI, and PET scans targeting dopaminergic function and glucose metabolism.
By harnessing the power of advanced AI techniques such as deep learning and multivariate pattern analysis, we can unlock new insights from these multi-modal datasets. The real potential lies in aligning our analyses with the proposed biological staging model for PD. This approach could yield novel multi-parametric imaging markers that correspond to specific disease stages. Imagine the possibilities of integrating structural MRI features, PET-derived metabolic patterns, diffusion metrics, and α-synuclein pathology markers. Such comprehensive analysis could revolutionize our ability to diagnose PD earlier, monitor its progression more accurately, and predict treatment responses with greater precision. (Longitudinal Progression Markers of Parkinson’s Disease: Current View on Structural Imaging | Current Neurology and Neuroscience Reports, https://www.nature.com/articles/s41467-024-49949-9).
This direction of research holds immense promise for advancing our understanding and management of PD. I am keen to know what are your thoughts on this topic (@martab, @whiter, @LauraJonkman)? Also, it would be helpful to know how might we best utilize the needed resources for DCoPs interested in this research topic to push our field forward in the frame of Data Modality and Methodology Task Force thread (@hirotaka, @ehutchins)?
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