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Background: Parkinson's disease (PD) is a slowly progressive neurodegenerative disorder which affects widespread areas of the brainstem, basal ganglia and cerebral cortex. A number of proteins are known to accumulate in parkinsonian brains including ubiquitin and alpha-synuclein. Prion diseases are sporadic, genetic or infectious disorders with various clinical and histopathological features caused by prion proteins as infectious proteinaceous particles transmitting a misfolded protein configuration through brain tissue. The most important form is Creutzfeldt-Jakob disease which is associated with a self-propagating pathological precursor form of the prion protein that is physiologically widely distributed in the central nervous system. Discussion: It has recently been found that alpha-synuclein may behave similarly to the prion precursor and propagate between cells. The post-mortem proof of alpha-synuclein containing Lewy bodies in embryonic dopamine cells transplants in PD patient suggests that the misfolded protein might be transmitted from the diseased host to donor neurons reminiscent of prion behavior. The involvement of the basal ganglia and brainstem in the degenerative process are other congruencies between Parkinson's and Creutzfeldt-Jakob disease. However, a number of issues advise caution before categorizing Parkinson's disease as a prion disorder, because clinical appearance, brain imaging, cerebrospinal fluid and neuropathological findings exhibit fundamental differences between both disease entities. Most of all, infectiousness, a crucial hallmark of prion diseases, has never been observed in PD so far. Moreover, the cellular propagation of the prion protein has not been clearly defined and it is, therefore, difficult to assess the molecular similarities between the two disease entities. Summary: At the current state of knowledge, the molecular pathways of transmissible pathogenic proteins are not yet fully understood. Their exact involvement in the pathophysiology of prion disorders and neurodegenerative diseases has to be further investigated in order to elucidate a possible overlap between both disease categories that are currently regarded as distinct entities.
Longitudinal changes of cortical microstructure in Parkinson's disease assessed with T1 relaxometry
(2016)
Background: Histological evidence suggests that pathology in Parkinson's disease (PD) goes beyond nigrostriatal degeneration and also affects the cerebral cortex. Quantitative MRI (qMRI) techniques allow the assessment of changes in brain tissue composition. However, the development and pattern of disease-related cortical changes have not yet been demonstrated in PD with qMRI methods. The aim of this study was to investigate longitudinal cortical microstructural changes in PD with quantitative T1 relaxometry.
Methods: 13 patients with mild to moderate PD and 20 matched healthy subjects underwent high resolution T1 mapping at two time points with an interval of 6.4 years (healthy subjects: 6.5 years). Data from two healthy subjects had to be excluded due to MRI artifacts. Surface-based analysis of cortical T1 values was performed with the FreeSurfer toolbox.
Results: In PD patients, a widespread decrease of cortical T1 was detected during follow-up which affected large parts of the temporo-parietal and occipital cortices and also frontal areas. In contrast, age-related T1 decrease in the healthy control group was much less pronounced and only found in lateral frontal, parietal and temporal areas. Average cortical T1 values did not differ between the groups at baseline (p = 0.17), but were reduced in patients at follow-up (p = 0.0004). Annualized relative changes of cortical T1 were higher in patients vs. healthy subjects (patients: − 0.72 ± 0.64%/year; healthy subjects: − 0.17 ± 0.41%/year, p = 0.007).
Conclusions: In patients with PD, the development of widespread changes in cortical microstructure was observed as reflected by a reduction of cortical T1. The pattern of T1 decrease in PD patients exceeded the normal T1 decrease as found in physiological aging and showed considerable overlap with the pattern of cortical thinning demonstrated in previous PD studies. Therefore, cortical T1 might be a promising additional imaging marker for future longitudinal PD studies. The biological mechanisms underlying cortical T1 reductions remain to be further elucidated.
Mutations in the PINK1 gene cause autosomal recessive familial Parkinson’s disease (PD). The gene encodes a mitochondrial protein kinase that plays an important role in maintaining mitochondrial function and integrity. However, the pathophysiological link between mutation-related bioenergetic deficits and the degenerative process in dopaminergic neurons remains to be elucidated. We performed phosphorous (31P) and proton (1H) 3-T magnetic resonance spectroscopic imaging (MRSI) in 11 members of a German family with hereditary PD due to PINK1 mutations (PARK6) compared to 23 age-matched controls. All family members had prior 18-Fluorodopa (FDOPA) positron emission tomography (PET). The striatal FDOPA uptake was correlated with quantified metabolic brain mapping in MRSI. At group level, the heterozygous PINK1 mutation carriers did not show any MRSI abnormalities relative to controls. In contrast, homozygous individuals with manifest PD had putaminal GPC, PCr, HEP and β-ATP levels well above the 2SD range of controls. Across all subjects, the FDOPA Ki values correlated positively with MI (r = 0.879, p<0.001) and inversely with β-ATP (r = −0.784, p = 0.008) and GPC concentrations (r = −0.651, p = 0.030) in the putamen. Our combined imaging data suggest that the dopaminergic deficit in this family with PD due to PINK1 mutations relates to osmolyte dysregulation, while the delivery of high energy phosphates was preserved. Our results corroborate the hypothesis that PINK1 mutations result in reduced neuronal survival, most likely due to impaired cellular stress resistance.
No association between Parkinson disease and autoantibodies against NMDA-type glutamate receptors
(2019)
Background: IgG-class autoantibodies to N-Methyl-D-Aspartate (NMDA)-type glutamate receptors define a novel entity of autoimmune encephalitis. Studies examining the prevalence of NMDA IgA/IgM antibodies in patients with Parkinson disease with/without dementia produced conflicting results. We measured NMDA antibodies in a large, well phenotyped sample of Parkinson patients without and with cognitive impairment (n = 296) and controls (n = 295) free of neuropsychiatric disease. Detailed phenotyping and large numbers allowed statistically meaningful correlation of antibody status with diagnostic subgroups as well as quantitative indicators of disease severity and cognitive impairment.
Methods: NMDA antibodies were analysed in the serum of patients and controls using well established validated assays. We used anti-NMDA antibody positivity as the main independent variable and correlated it with disease status and phenotypic characteristics.
Results: The frequency of NMDA IgA/IgM antibodies was lower in Parkinson patients (13%) than in controls (22%) and higher than in previous studies in both groups. NMDA IgA/IgM antibodies were neither significantly associated with diagnostic subclasses of Parkinson disease according to cognitive impairment, nor with quantitative indicators of disease severity and cognitive impairment. A positive NMDA antibody status was positively correlated with age in controls but not in Parkinson patients.
Conclusion: It is unlikely albeit not impossible that NMDA antibodies play a significant role in the pathogenesis or progression of Parkinson disease e.g. to Parkinson disease with dementia, while NMDA IgG antibodies define a separate disease of its own.
Background: The nonmotor symptom spectrum of Parkinson’s disease (PD) includes progressive cognitive decline mainly in late stages of the disease. The aim of this study was to map the patterns of altered structural connectivity of patients with PD with different cognitive profiles ranging from cognitively unimpaired to PD-associated dementia.
Methods: Diffusion tensor imaging and neuropsychological data from the observational multicentre LANDSCAPE study were analyzed. A total of 134 patients with PD with normal cognitive function (56 PD-N), mild cognitive impairment (67 PD-MCI), and dementia (11 PD-D) as well as 72 healthy controls were subjected to whole-brain-based fractional anisotropy mapping and covariance analysis with cognitive performance measures.
Results: Structural data indicated subtle changes in the corpus callosum and thalamic radiation in PD-N, whereas severe white matter impairment was observed in both PD-MCI and PD-D patients including anterior and inferior fronto-occipital, uncinate, insular cortices, superior longitudinal fasciculi, corona radiata, and the body of the corpus callosum. These regional alterations were demonstrated for PD-MCI and were more pronounced in PD-D. The pattern of involved regions was significantly correlated with the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) total score.
Conclusions: The findings in PD-N suggest impaired cross-hemispherical white matter connectivity that can apparently be compensated for. More pronounced involvement of the corpus callosum as demonstrated for PD-MCI together with affection of fronto-parieto-temporal structural connectivity seems to lead to gradual disruption of cognition-related cortico-cortical networks and to be associated with the onset of overt cognitive deficits. The increase of regional white matter damage appears to be associated with the development of PD-associated dementia.
Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.