(A) structural network study on prognostic heterogeneity and individual reserve variability in early Parkinson’s disease

Abstract

Parkinson’s disease (PD) is the second-most common progressive neurodegenerative disorder, with its prevalence increasing rapidly worldwide as the population ages. Characterized by both motor and non-motor symptoms, PD presents a heterogeneous array of symptoms and prognoses, emphasizing the need for early intervention resulting in optimal therapeutic outcomes. This study focuses on the use of Diffusion Tensor Imaging (DTI) to detect early-stage anatomical changes in PD, particularly in the context of its pathological evidence of retrograde axonal degeneration. Despite extensive research, the mechanisms underlying variability in long-term prognoses and individual disease severity in PD remain elusive, and no curative treatments are currently available. This study investigated white matter changes associated with heterogeneous long-term prognoses, specifically in Levodopa-induced dyskinesia (LID) and Parkinson’s disease dementia (PDD). Additionally, to explore white matter structural status related to variability in individual disease progression, I examined white matter structural networks linked to motor reserve in early, drug-naïve PD patients. In studies 1 and 2, drug-naïve PD patients were categorized into high and low-risk groups based on their risk of developing Levodopa-induced dyskinesia (LID) or Parkinson’s disease dementia (PDD) within five years of diagnosis, respectively. Employing DTI and advanced voxel-wise and network approaches, including degree-based and network-based statistics, this study identified distinct structural networks in white matter that are indicative of microstructural alterations associated with LID and PDD. Furthermore, the study explored the relationship between connectivity strength and the development of LID and conversion to PDD. In study 3, the motor reserve was estimated using Unified Parkinson’s Disease Rating Scale Part III (UPDRS-III) scores and dopamine transporter availability in the posterior putamen through a residual-based model. Threshold-free network-based statistics (TFNBS) analysis was then employed to delineate the structural brain network associated with motor reserve. I assessed the effect of the network connectivity strength on the longitudinal increase in medication dosage over a 3-year follow-up period. The study revealed that specific white matter structural networks in corticostriatal regions are predictive of LID onset in drug-naïve PD patients. Moreover, disrupted white matter connectivity in frontal and posterior cortical regions, correlating with frontal/executive dysfunction, was associated with early dementia conversion in PD with mild cognitive impairment (PD-MCI). These findings underscore the distinctive mechanisms behind the heterogeneous nature of PD and the individual variability in disease response). The study also identified a structural network associated with motor reserve, primarily located in the frontal region and cerebellum, where a stronger network (indicative of higher motor reserve) correlated with a slower increase in medication dosage over time. In conclusion, identifying white matter networks associated with specific long-term prognoses could be key in predicting development risks and beneficial in medication intervention. Moreover, individual reserve variability in motor symptoms may serve as a prognostic marker for disease progression. These insights could significantly enhance our understanding of PD, leading to novel, personalized treatment strategies targeting the disease at the initial onset.