Date Published: May 31, 2019
Publisher: Public Library of Science
Author(s): Tuva R. Hope, Per Selnes, Irena Rektorová, Lubomira Anderkova, Nela Nemcova-Elfmarkova, Zuzana Balážová, Anders Dale, Atle Bjørnerud, Tormod Fladby, Pew-Thian Yap.
To meet the need for Parkinson’s disease biomarkers and evidence for amount and distribution of pathological changes, MRI diffusion tensor imaging (DTI) has been explored in a number of previous studies. However, conflicting results warrant further investigations. As tissue microstructure, particularly of the grey matter, is heterogeneous, a more precise diffusion model may benefit tissue characterization. The purpose of this study was to analyze the diffusion-based imaging technique restriction spectrum imaging (RSI) and DTI, and their ability to detect microstructural changes within brain regions associated with motor function in Parkinson’s disease. Diffusion weighted (DW) MR images of a total of 100 individuals, (46 Parkinson’s disease patients and 54 healthy controls) were collected using b-values of 0–4000s/mm2. Output diffusion-based maps were estimated based on the RSI-model combining the full set of DW-images (Cellular Index (CI), Neurite Density (ND)) and DTI-model combining b = 0 and b = 1000 s/mm2 (fractional anisotropy (FA), Axial-, Mean- and Radial diffusivity (AD, MD, RD)). All parametric maps were analyzed in a voxel-wise group analysis, with focus on typical brain regions associated with Parkinson’s disease pathology. CI, ND and DTI diffusivity metrics (AD, MD, RD) demonstrated the ability to differentiate between groups, with strongest performance within the thalamus, prone to pathology in Parkinson’s disease. Our results indicate that RSI may improve the predictive power of diffusion-based MRI, and provide additional information when combined with the standard diffusivity measurements. In the absence of major atrophy, diffusion techniques may reveal microstructural pathology. Our results suggest that protocols for MRI diffusion imaging may be adapted to more sensitive detection of pathology at different sites of the central nervous system.
Parkinson’s disease (PD) is a slowly progressing neurodegenerative disorder, affecting both motor function and cognition. Accurate biomarkers for PD diagnosis and progression are scarce. PD is primarily diagnosed clinically after the pathology has reached an advanced stage, and confirmed post-mortem by loss of dopamine neurons in the substantia nigra and the presence of intracellular aggregates of α-synuclein called Lewy bodies . Formation of Lewy bodies, loss of neurites, loss of dopamine-containing neurons and gliosis in substantia nigra is an early phenomenon in PD, whereas cortical pathology is thought to precede major subcortical affection on Dementia with Lewy Bodies [2, 3]. Lewy body pathology spreads from the brainstem and subcortical structures, to related areas with eventual allo- and neocortical involvement . α-synuclein deposition is considered the core pathological substrate of PD. In addition to accumulation in Lewy-bodies, a major deposition site of central nervous system (CNS) oligomeric α-synuclein is in the presynapse, accompanied by transsynaptic dendritic spine loss [5, 6], suggesting widespread pathology. MRI studies have indicated that slight grey matter loss is involved in PD cognitive decline . In PD with normal cognition, there is slight volume decrease in the frontoparietal regions and also increase in the midbrain and cerebellum. There is also a second pattern with medial temporal lobe atrophy more closely coupled to cognitive decline , and we recently described cortical metabolic changes associated with biomarker changes in temporo-occipital and frontal regions .
We found that that both DTI-derived and RSI-derived metrics are sensitive to group effects with significant clusters (p < 0.05) in the thalamus, the hippocampus and the amygdala manifested as increased CI, MD, RD and AD and decreased ND in the PD group. CI was also significantly increased in the brainstem of the PD group. The AUC was largest using CI (0.66–0.69) with a larger group difference (~ 8%) compared to the remaining metrics (~ 0.55, ~ 3%), suggesting that CI may be more sensitive to pathological changes in the PD group. In this study, we investigated two diffusion-based MRI techniques and their sensitivity to microstructural changes within brain regions associated with motor function in PD. Group differences between a PD patient group and an age-matched HC group have been analyzed using both standard DTI-derived metrics (assuming Gaussian diffusion), and diffusion metrics describing the restricted non-Gaussian diffusion fraction derived from the RSI model. In conclusion according to our analyses, both DTI and RSI are sensitive to PD pathology. Changes measured using the two models reflect different aspects of neurodegeneration and should be considered complimentary techniques. Adapting protocols for more sensitive detection of PD pathology may improve detection and understanding of pathology. Source: http://doi.org/10.1371/journal.pone.0217922