In vivo classification of neuropathological disease stages in Parkinson's disease by diffusion tensor imaging.
In vivo classification of neuropathological disease stages in Parkinson's disease by diffusion tensor imaging.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
DE · Author affiliation · country only
Department of Neurology, University of Ulm, Ulm, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany; Department of Nuclear Medicine, University Hospital Ulm, Ulm, Germany.Location evidence
Munich, DE · Author affiliation
Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany; Department of Nuclear Medicine, University Hospital Ulm, Ulm, Germany; Department of Diagnostic and Interventional Neuroradiology, School of Medicine and Health, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany; TUM-Neuroimaging Center, TUM Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.Location evidence
A plain-language reading has not been prepared for this paper yet.
Original abstract
BACKGROUND: A neuropathological staging system for Parkinson's disease (PD) proposes that PD may disseminate in a sequential regional pattern in the brain during six disease stages. Diffusion tensor imaging (DTI) can identify PD-associated patterns of brain alterations at the group level. OBJECTIVE: (1) To develop a framework for a hypothesis-guided DTI-based approach targeted to automatically analyze in vivo the fiber tracts that are typically involved at each neuropathological stage of PD, and (2) to apply this framework to a large sample of MRI datasets of patients with PD in comparison with controls. METHODS: A tract of interest (TOI)-based fiber tracking approach was used to analyze tracts that become involved during the course of PD, representative of the six neuropathological stages. The TOI-based technique of tractwise fractional anisotropy statistics (TFAS) was applied to calculate fractional anisotropy (FA) alterations for the investigated tracts. RESULTS: 206 DTI datasets were analysed (i.e., 102 DTI scans from patients with PD at different clinical and cognitive stages and 104 DTI scans from age- and sex-matched healthy controls). The TOI-related analyses showed differences between PD patients and controls for the tracts corresponding to each neuropathological stage, i.e., in the olfactory tract, the initiation states in the medulla-oblongata, the catecholaminergic tract, the corticopontine tract, the inferior longitudinal fascicle, and the superior longitudinal fascicle. Based on these differences, a sequential pattern classification could be performed into stages with sequentially increased tract alterations, indicating a more advanced neurodegenerative process. CONCLUSIONS: The herein used TOI-based framework could enable tracking disease stages in PD in vivo, as an approach to the propagation concept of neuropathological stages in PD. In future applications, this framework may be used not only for individual clinical work-up purposes, but also may enlarge the spectrum of potential non-invasive surrogate markers as a neuroimaging-based read-out for PD studies at the group level within a clinical context.