Parameters from site classification to harmonize MRI clinical studies: Application to a multi-site Parkinson's disease dataset

Multi-site MRI datasets are crucial for big data research. However, neuroimaging studies must face the batch effect. Here, we propose an approach that uses the predictive probabilities provided by Gaussian processes (GPs) to harmonize clinical-based studies. A multi-site dataset of 216 Parkinson's disease (PD) patients and 87 healthy subjects (HS) was used. We performed a site GP classification using MRI data. The outcomes estimated from this classification, redefined like Weighted HARMonization PArameters (WHARMPA), were used as regressors in two different clinical studies: A PD versus HS machine learning classification using GP, and a VBM comparison (FWE-p < .05, k = 100). Same studies were also conducted using conventional Boolean site covariates, and without information about site belonging. The results from site GP classification provided high scores, balanced accuracy (BAC) was 98.39% for grey matter images. PD versus HS classification performed better when the WHARMPA were used to harmonize (BAC = 78.60%; AUC = 0.90) than when using the Boolean site information (BAC = 56.31%; AUC = 0.71) and without it (BAC = 57.22%; AUC = 0.73). The VBM analysis harmonized using WHARMPA provided larger and more statistically robust clusters in regions previously reported in PD than when the Boolean site covariates or no corrections were added to the model. In conclusion, WHARMPA might encode global site-effects quantitatively and allow the harmonization of data. This method is user-friendly and provides a powerful solution, without complex implementations, to clean the analyses by removing variability associated with the differences between sites

Abstracts from the 8th International Conference on cGMP Generators, Effectors and Therapeutic Implications

This work was supported by a restricted research grant of Bayer AG

Glucose-6-Phosphatase-Dehydrogenase activity as modulative association between Parkinson's disease and periodontitis.

UNLABELLED The association between periodontitis (PD) and Parkinson's disease (PK) is discussed due to the inflammatory component of neurodegenerative processes. PK severity and affected areas were determined using the following neuropsychological tests: Unified Parkinson's Disease Rating Score (UPDRS) and Hoehn and Yahr; non-motoric symptoms by Non-Motor Symptoms Scale (NMSS), and cognitive involvement by Mini-Mental State Examination (MMSE). Neuroinflammation and the resulting Glucose-6-Phosphatase-Dehydrogenase (G6PD) dysfunction are part of the pathophysiology of PK. This study aimed to evaluate these associations in periodontal inflammation. Clinical data and saliva-, serum-, and RNA-biobank samples of 50 well-characterized diametric patients with PK and five age- and sex-matched neurologically healthy participants were analyzed for G6PD function, periodontal pathogens (Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, Prevotella intermedia, Campylobacter rectus, Fusobacterium nucleatum, and Filifactor alocis), monocyte chemoattractant protein (MCP) 1, and interleukin (IL) 1-beta. Regression analysis was used to identify associations between clinical and behavioral data, and t-tests were used to compare health and disease. Compared with PK, no pathogens and lower inflammatory markers (p < 0.001) were detectible in healthy saliva and serum, PK-severity/UPDRS interrelated with the occurrence of Prevotella intermedia in serum as well as IL1-beta levels in serum and saliva (p = 0.006, 0.019, 0.034), Hoehn and Yahr correlated with Porphyromonas gingivalis, Prevotella intermedia, RNA IL1-beta regulation, serum, and saliva IL1-beta levels, with p-values of 0.038, 0.011, 0.008, <0.001, and 0.010, while MMSE was associated with Aggregatibacter actinomycetemcomitans, Fusobacterium nucleatum, serum MCP 1 levels, RNA IL1-beta regulation and G6PD serum activity (p = 0.036, 0.003, 0.045, <0.001, and 0.021). Cognitive and motor skills seem to be important as representative tests are associated with periodontal pathogens and oral/general inflammation, wherein G6PD-saliva dysfunction might be involved. CLINICAL TRIAL REGISTRATION https://www.bfarm.de/DE/Das-BfArM/Aufgaben/Deutsches-Register-Klinischer-Studien/_node.html, identifier DRKS00005388

The default mode network and cognition in Parkinson's disease: A multimodal resting‐state network approach

Involvement of the default mode network (DMN) in cognitive symptoms of Parkinson's disease (PD) has been reported by resting-state functional MRI (rsfMRI) studies. However, the relation to metabolic measures obtained by [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) is largely unknown. We applied multimodal resting-state network analysis to clarify the association between intrinsic metabolic and functional connectivity abnormalities within the DMN and their significance for cognitive symptoms in PD. PD patients were classified into normal cognition (n = 36) and mild cognitive impairment (MCI; n = 12). The DMN was identified by applying an independent component analysis to FDG-PET and rsfMRI data of a matched subset (16 controls and 16 PD patients) of the total cohort. Besides metabolic activity, metabolic and functional connectivity within the DMN were compared between the patients' groups and healthy controls (n = 16). Glucose metabolism was significantly reduced in all DMN nodes in both patient groups compared to controls, with the lowest uptake in PD-MCI (p < .05). Increased metabolic and functional connectivity along fronto-parietal connections was identified in PD-MCI patients compared to controls and unimpaired patients. Functional connectivity negatively correlated with cognitive composite z-scores in patients (r = -.43, p = .005). The current study clarifies the commonalities of metabolic and hemodynamic measures of brain network activity and their individual significance for cognitive symptoms in PD, highlighting the added value of multimodal resting-state network approaches for identifying prospective biomarkers.Keywords: Parkinson's disease; [18F]-FDG-PET; default mode network; metabolic covariance; mild cognitive impairment; resting-state fMRI

Network degeneration in Parkinson's disease: multimodal imaging of nigro-striato-cortical dysfunction

The spreading hypothesis of neurodegeneration assumes an expansion of neural pathologies along existing neural pathways. Multimodal neuroimaging studies have demonstrated distinct topographic patterns of cerebral pathologies in neurodegeneration. For Parkinson's disease the hypothesis so far rests largely on histopathological evidence of alpha-synuclein spreading in a characteristic pattern and progressive nigrostriatal dopamine depletion. Functional consequences of nigrostriatal dysfunction on cortical activity remain to be elucidated. Our goal was to investigate multimodal imaging correlates of degenerative processes in Parkinson's disease by assessing dopamine depletion and its potential effect on striatocortical connectivity networks and cortical metabolism in relation to parkinsonian symptoms. We combined F-18-DOPA-PET, F-18-fluorodeoxyglucose (FDG)-PET and resting state functional MRI to multimodally characterize network alterations in Parkinson's disease. Forty-two patients with mild-to-moderate stage Parkinson's disease and 14 age-matched healthy control subjects underwent a multimodal imaging protocol and comprehensive clinical examination. A voxel-wise group comparison of F-18-DOPA uptake identified the exact location and extent of putaminal dopamine depletion in patients. Resulting dusters were defined as seeds for a seed-to-voxel functional connectivity analysis. F-18-FDG metabolism was compared between groups at a whole-brain level and uptake values were extracted from regions with reduced putaminal connectivity. To unravel associations between dopaminergic activity, striatocortical connectivity, glucose metabolism and symptom severity, correlations between normalized uptake values, seed-to-cluster beta-values and clinical parameters were tested while controlling for age and dopaminergic medication. Aside from cortical hypometabolism, F-18-FDG-PET data for the first time revealed a hypometabolic midbrain cluster in patients with Parkinson's disease that comprised caudal parts of the bilateral substantia nigra pars compacta. Putaminal dopamine synthesis capacity was significantly reduced in the bilateral posterior putamen and correlated with ipsilateral nigral F-18-FUG uptake. Resting state functional MRI data indicated significantly reduced functional connectivity between the dopamine depleted putaminal seed and cortical areas primarily belonging to the sensorimotor network in patients with Parkinson's disease. In the inferior parietal cortex, hypoamnectivity in patients was significantly correlated with lower metabolism (left P= 0.021, right P = 0.018). Of note, unilateral network alterations quantified with different modalities corresponded with contralateral motor impairments. In conclusion, our results support the hypothesis that degeneration of nigrostriatal fibres functionally impairs distinct striatocortical connections, disturbing the efficient interplay between motor processing areas and impairing motor control in patients with Parkinson's disease. The present study is the first to reveal trimodal evidence for network-dependent degeneration in Parkinson's disease by outlining the impact of functional nigrostriatal pathway impairment on striatocortical functional connectivity networks and cortical metabolism

Data_Sheet_1_Random forest analysis of midbrain hypometabolism using [18F]-FDG PET identifies Parkinson's disease at the subject-level.pdf

Parkinson's disease (PD) is currently diagnosed largely on the basis of expert judgement with neuroimaging serving only as a supportive tool. In a recent study, we identified a hypometabolic midbrain cluster, which includes parts of the substantia nigra, as the best differentiating metabolic feature for PD-patients based on group comparison of [18F]-fluorodeoxyglucose ([18F]-FDG) PET scans. Longitudinal analyses confirmed progressive metabolic changes in this region and, an independent study showed great potential of nigral metabolism for diagnostic workup of parkinsonian syndromes. In this study, we applied a machine learning approach to evaluate midbrain metabolism measured by [18F]-FDG PET as a diagnostic marker for PD. In total, 51 mid-stage PD-patients and 16 healthy control subjects underwent high-resolution [18F]-FDG PET. Normalized tracer update values of the midbrain cluster identified by between-group comparison were extracted voxel-wise from individuals' scans. Extracted uptake values were subjected to a random forest feature classification algorithm. An adapted leave-one-out cross validation approach was applied for testing robustness of the model for differentiating between patients and controls. Performance of the model across all runs was evaluated by calculating sensitivity, specificity and model accuracy for the validation data set and the percentage of correctly categorized subjects for test data sets. The random forest feature classification of voxel-based uptake values from the midbrain cluster identified patients in the validation data set with an average sensitivity of 0.91 (Min: 0.82, Max: 0.94). For all 67 runs, in which each of the individuals was treated once as test data set, the test data set was correctly categorized by our model. The applied feature importance extraction consistently identified a subset of voxels within the midbrain cluster with highest importance across all runs which spatially converged with the left substantia nigra. Our data suggest midbrain metabolism measured by [18F]-FDG PET as a promising diagnostic imaging tool for PD. Given its close relationship to PD pathophysiology and very high discriminatory accuracy, this approach could help to objectify PD diagnosis and enable more accurate classification in relation to clinical trials, which could also be applicable to patients with prodromal disease.</p

Longitudinal trimodal imaging of midbrain-associated network degeneration in Parkinson’s disease

The prevailing network perspective of Parkinson's disease (PD) emerges not least from the ascending neuropathology traceable in histological studies. However, whether longitudinal in vivo correlates of network degeneration in PD can be observed remains unresolved. Here, we applied a trimodal imaging protocol combining 18F-fluorodeoxyglucose (FDG)- and 18F-fluoro-L-Dopa- (FDOPA)-PET with resting-state functional MRI to assess longitudinal changes in midbrain metabolism, striatal dopamine depletion and striatocortical dysconnectivity in 17 well-characterized PD patients. Whole-brain (un)paired-t-tests with focus on midbrain or striatum were performed between visits and in relation to 14 healthy controls (HC) in PET modalities. Resulting clusters of FDOPA-PET comparisons provided volumes for seed-based functional connectivity (FC) analyses between visits and in relation to HC. FDG metabolism in the left midbrain decreased compared to baseline along with caudatal FDOPA-uptake. This caudate cluster exhibited a longitudinal FC decrease to sensorimotor and frontal areas. Compared to healthy subjects, dopamine-depleted putamina indicated stronger decline in striatocortical FC at follow-up with respect to baseline. Increasing nigrostriatal deficits and striatocortical decoupling were associated with deterioration in motor scores between visits in repeated-measures correlations. In summary, our results demonstrate the feasibility of in-vivo tracking of progressive network degeneration using a multimodal imaging approach. Specifically, our data suggest advancing striatal and widespread striatocortical dysfunction via an anterior-posterior gradient originating from a hypometabolic midbrain cluster within a well-characterized and only mild to moderately affected PD cohort during a relatively short period

Dopaminergic pathways and resting-state functional connectivity in Parkinson’s disease with freezing of gait

Freezing of gait is a common phenomenon of advanced Parkinson’s disease. Besides locomotor function per se, a role of cognitive deficits has been suggested. Limited evidence of associated dopaminergic deficits points to caudatal denervation. Further, altered functional connectivity within resting-state networks with importance for cognitive functions has been described in freezers. A potential pathophysiological link between both imaging findings has not yet been addressed. The current study sought to investigate the association between dopaminergic pathway dysintegrity and functional dysconnectivity in relation to FOG severity and cognitive performance in a well-characterized PD cohort undergoing high-resolution 6-[18F]fluoro-L-Dopa PET and functional MRI. The freezing of gait questionnaire was applied to categorize patients (n = 59) into freezers and non-freezers. A voxel-wise group comparison of 6-[18F]fluoro-L-Dopa PET scans with focus on striatum was performed between both well-matched and neuropsychologically characterized patient groups. Seed-to-voxel resting-state functional connectivity maps of the resulting dopamine depleted structures and dopaminergic midbrain regions were created and compared between both groups. For a direct between-group comparison of dopaminergic pathway integrity, a molecular connectivity approach was conducted on 6-[18F]fluoro-L-Dopa scans. With respect to striatal regions, freezers showed significant dopaminergic deficits in the left caudate nucleus, which exhibited altered functional connectivity with regions of the visual network. Regarding midbrain structures, the bilateral ventral tegmental area showed altered functional coupling to regions of the default mode network. An explorative examination of the integrity of dopaminergic pathways by molecular connectivity analysis revealed freezing-associated impairments in mesolimbic and mesocortical pathways. This study represents the first characterization of a link between dopaminergic pathway dysintegrity and altered functional connectivity in Parkinson’s disease with freezing of gait and hints at a specific involvement of striatocortical and mesocorticolimbic pathways in freezers

GBAVariants in Parkinson's Disease: Clinical, Metabolomic, and Multimodal Neuroimaging Phenotypes

Background Alterations in theGBAgene (NM_000157.3) are the most important genetic risk factor for Parkinson's disease (PD). BiallelicGBAmutations cause the lysosomal storage disorder Gaucher's disease. TheGBAvariants p.E365K and p.T408M are associated with PD but not with Gaucher's disease. The pathophysiological role of these variants needs to be further explored. Objective This study analyzed clinical, neuropsychological, metabolic, and neuroimaging phenotypes of patients with PD carrying theGBAvariants p.E365K and p.T408M. Methods GBAwas sequenced in 56 patients with mid-stage PD. Carriers ofGBAvariants were compared with noncarriers regarding clinical history and symptoms, neuropsychological features, metabolomics, and multimodal neuroimaging. Blood plasma gas chromatography coupled to mass spectrometry, 6-[F-18]fluoro-L-Dopa positron emission tomography (PET), [F-18]fluorodeoxyglucose PET, and resting-state functional magnetic resonance imaging were performed. Results Sequence analysis detected 13 heterozygousGBAvariant carriers (7 with p.E365K, 6 with p.T408M). One patient carried aGBAmutation (p.N409S) and was excluded. Clinical history and symptoms were not significantly different between groups. Global cognitive performance was lower in variant carriers. Metabolomic group differences were suggestive of more severe PD-related alterations in carriers versus noncarriers. Both PET scans showed signs of a more advanced disease; [F-18]fluorodeoxyglucose PET and functional magnetic resonance imaging showed similarities with Lewy body dementia and PD dementia in carriers. Conclusions This is the first study to comprehensively assess (neuro-)biological phenotypes ofGBAvariants in PD. Metabolomics and neuroimaging detected more significant group differences than clinical and behavioral evaluation. These alterations could be promising to monitor effects of disease-modifying treatments targeting glucocerebrosidase metabolism. (c) 2020 The Authors.Movement Disorderspublished by Wiley Periodicals LLC. on behalf of International Parkinson and Movement Disorder Society