Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission.

PURPOSE: Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, multiple system atrophy) at individual patient level via principal component analysis (PCA). METHODS: FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level. RESULTS: Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912). CONCLUSION: Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset.

Metabolic Brain Changes Can Predict the Underlying Pathology in Neurodegenerative Brain Disorders: A Case Report of Sporadic Creutzfeldt-Jakob Disease with Concomitant Parkinson's Disease.

The co-occurrence of multiple proteinopathies is being increasingly recognized in neurodegenerative disorders and poses a challenge in differential diagnosis and patient selection for clinical trials. Changes in brain metabolism captured by positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) allow us to differentiate between different neurodegenerative disorders either by visual exploration or by studying disease-specific metabolic networks in individual patients. However, the impact of multiple proteinopathies on brain metabolism and metabolic networks remains unknown due to the absence of pathological studies. In this case study, we present a 67-year-old patient with rapidly progressing dementia clinically diagnosed with probable sporadic Creutzfeldt-Jakob disease (sCJD). However, in addition to the expected pronounced cortical and subcortical hypometabolism characteristic of sCJD, the brain FDG PET revealed an intriguing finding of unexpected relative hypermetabolism in the bilateral putamina, raising suspicions of coexisting Parkinson's disease (PD). Additional investigation of disease-specific metabolic brain networks revealed elevated expression of both CJD-related pattern (CJDRP) and PD-related pattern (PDRP) networks. The patient eventually developed akinetic mutism and passed away seven weeks after symptom onset. Neuropathological examination confirmed neuropathological changes consistent with sCJD and the presence of Lewy bodies confirming PD pathology. Additionally, hyperphosphorylated tau and TDP-43 pathology were observed, a combination of four proteinopathies that had not been previously reported. Overall, this case provides valuable insights into the complex interplay of neurodegenerative pathologies and their impact on metabolic brain changes, emphasizing the role of metabolic brain imaging in evaluating potential presence of multiple proteinopathies.

Infusion Therapies in the Treatment of Parkinson's Disease.

Oral levodopa is the gold-standard therapy for treating Parkinson's disease (PD) but after a few years of treatment the therapeutic window narrows, and patients often experience various treatment-related complications. Patients in this advanced PD stage may benefit from alternative therapy, such as continuous intrajejunal delivery of levodopa-carbidopa intestinal gel (LCIG; or carbidopa-levodopa enteral suspension), continuous intrajejunal delivery of levodopa-carbidopa-entacapone intestinal gel, or continuous subcutaneous apomorphine infusion. Consideration and initiation of infusion therapies in advanced PD are suggested before the onset of major disability. The present review summarizes clinical evidence for infusion therapy in advanced PD management, discusses available screening tools for advanced PD, and provides considerations around optimal use of infusion therapy.

Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer's disease-related pattern.

BACKGROUND: Alzheimer's disease-related pattern (ADRP) is a metabolic brain biomarker of Alzheimer's disease (AD). While ADRP is being introduced into research, the effect of the size of the identification cohort and the effect of the resolution of identification and validation images on ADRP's performance need to be clarified. METHODS: 240 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography images [120 AD/120 cognitive normals (CN)] were selected from the Alzheimer's disease neuroimaging initiative database. A total of 200 images (100 AD/100 CN) were used to identify different versions of ADRP using a scaled subprofile model/principal component analysis. For this purpose, five identification groups were randomly selected 25 times. The identification groups differed in the number of images (20 AD/20 CN, 30 AD/30 CN, 40 AD/40 CN, 60 AD/60 CN, and 80 AD/80 CN) and image resolutions (6, 8, 10, 12, 15 and 20 mm). A total of 750 ADRPs were identified and validated through the area under the curve (AUC) values on the remaining 20 AD/20 CN with six different image resolutions. RESULTS: ADRP's performance for the differentiation between AD patients and CN demonstrated only a marginal average AUC increase, when the number of subjects in the identification group increases (AUC increase for about 0.03 from 20 AD/20 CN to 80 AD/80 CN). However, the average of the lowest five AUC values increased with the increasing number of participants (AUC increase for about 0.07 from 20 AD/20 CN to 30 AD/30 CN and for an additional 0.02 from 30 AD/30 CN to 40 AD/40 CN). The resolution of the identification images affects ADRP's diagnostic performance only marginally in the range from 8 to 15 mm. ADRP's performance stayed optimal even when applied to validation images of resolution differing from the identification images. CONCLUSIONS: While small (20 AD/20 CN images) identification cohorts may be adequate in a favorable selection of cases, larger cohorts (at least 30 AD/30 CN images) shall be preferred to overcome possible/random biological differences and improve ADRP's diagnostic performance. ADRP's performance stays stable even when applied to the validation images with a resolution different than the resolution of the identification ones.

Investigation of Paraoxonase-1 Genotype and Enzyme-Kinetic Parameters in the Context of Cognitive Impairment in Parkinson's Disease.

Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD), which often progresses to PD dementia. PD patients with and without dementia may differ in certain biochemical parameters, which could thus be used as biomarkers for PD dementia. The enzyme paraoxonase 1 (PON1) has previously been investigated as a potential biomarker in the context of other types of dementia. In a cohort of PD patients, we compared a group of 89 patients with cognitive impairment with a group of 118 patients with normal cognition. We determined the kinetic parameters Km and Vmax for PON1 for the reaction with dihydrocoumarin and the genotype of four single nucleotide polymorphisms in PON1. We found that no genotype or kinetic parameter correlated significantly with cognitive impairment in PD patients. However, we observed associations between PON1 rs662 and PON1 Km (p < 10-10), between PON1 rs662 and PON1 Vmax (p = 9.33 × 10-7), and between PON1 rs705379 and PON1 Vmax (p = 2.21 × 10-10). The present study is novel in three main aspects. (1) It is the first study to investigate associations between the PON1 genotype and enzyme kinetics in a large number of subjects. (2) It is the first study to report kinetic parameters of PON1 in a large number of subjects and to use time-concentration progress curves instead of initial velocities to determine Km and Vmax in a clinical context. (3) It is also the first study to calculate enzyme-kinetic parameters in a clinical context with a new algorithm for data point removal from progress curves, dubbed iFIT. Although our results suggest that in the context of PD, there is no clinically useful correlation between cognitive status on the one hand and PON1 genetic and enzyme-kinetic parameters on the other hand, this should not discourage future investigation into PON1's potential associations with other types of dementia.

Sporadic Creutzfeldt-Jakob disease is associated with reorganization of metabolic connectivity in a pathological brain network.

BACKGROUND AND PURPOSE: Although sporadic Creutzfeldt-Jakob disease (sCJD) is a rare cause of dementia, it is critical to understand its functional networks as the prion protein spread throughout the brain may share similar mechanisms with other more common neurodegenerative disorders. In this study, the metabolic brain network associated with sCJD was investigated and its internal network organization was explored. METHODS: We explored 2-[18 F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) brain scans of 29 sCJD patients, 56 normal controls (NCs) and 46 other dementia patients from two independent centers. sCJD-related pattern (CJDRP) was identified in a cohort of 16 pathologically proven sCJD patients and 16 age-matched NCs using scaled subprofile modeling/principal component analysis and was prospectively validated in an independent cohort of 13 sCJD patients and 20 NCs. The pattern's specificity was tested on other dementia patients and its clinical relevance by clinical correlations. The pattern's internal organization was further studied using graph theory methods. RESULTS: The CJDRP was characterized by relative hypometabolism in the bilateral caudate, thalami, middle and superior frontal gyri, parietal lobe and posterior cingulum in association with relative hypermetabolism in the hippocampi, parahippocampal gyri and cerebellum. The pattern's expression significantly discriminated sCJD from NCs and other dementia patients (p < 0.005; receiver operating characteristic analysis CJD vs. NCs area under the curve [AUC] 0.90-0.96, sCJD vs. Alzheimer's disease AUC 0.78, sCJD vs. behavioral variant of frontotemporal dementia AUC 0.84). The pattern's expression significantly correlated with cognitive, functional decline and disease duration. The metabolic connectivity analysis revealed inefficient information transfer with specific network reorganization. CONCLUSIONS: The CJDRP is a robust metabolic biomarker of sCJD. Due to its excellent clinical correlations it has the potential to monitor disease in emerging disease-modifying trials.

Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia.

Behavioral variant of frontotemporal dementia (bvFTD) is common among young-onset dementia patients. While bvFTD-specific multivariate metabolic brain pattern (bFDRP) has been identified previously, little is known about its temporal evolution, internal structure, effect of atrophy, and its relationship with nonspecific resting-state networks such as default mode network (DMN). In this multicenter study, we explored FDG-PET brain scans of 111 bvFTD, 26 Alzheimer's disease, 16 Creutzfeldt-Jakob's disease, 24 semantic variant primary progressive aphasia (PPA), 18 nonfluent variant PPA and 77 healthy control subjects (HC) from Slovenia, USA, and Germany. bFDRP was identified in a cohort of 20 bvFTD patients and age-matched HC using scaled subprofile model/principle component analysis and validated in three independent cohorts. It was characterized by hypometabolism in frontal cortex, insula, anterior/middle cingulate, caudate, thalamus, and temporal poles. Its expression in bvFTD patients was significantly higher compared to HC and other dementia syndromes (p < .0004), correlated with cognitive decline (p = .0001), and increased over time in longitudinal cohort (p = .0007). Analysis of internal network organization by graph-theory methods revealed prominent network disruption in bvFTD patients. We have further found a specific atrophy-related pattern grossly corresponding to bFDRP; however, its contribution to the metabolic pattern was minimal. Finally, despite the overlap between bFDRP and FDG-PET-derived DMN, we demonstrated a predominant role of the specific bFDRP. Taken together, we validated the bFDRP network as a diagnostic/prognostic biomarker specific for bvFTD, provided a unique insight into its highly reproducible internal structure, and proved that bFDRP is unaffected by structural atrophy and independent of normal resting state networks loss.

Cortical Brain Perfusion and Cognitive Event Related Potentials in Patients with Psychomotor Retardation in Late Onset Depression.

BACKGROUND: Late onset depression is characterised by pronounced cognitive impairment, more somatic complaints and psychomotor retardation. Psychomotor slowing may be due to impairment in either motor or cognitive domain. Electrophysiology may be particularly convenient as a tool in studies of psychomotor retardation, as it can separate central cognitive processing from the motor processing. SUBJECTS AND METHODS: In this study we compared event related potentials (ERP) in the two groups of patients with late onset depression and psychomotor slowing as measured by reaction time (RT): a group of patients with lower RT was compared to a group with a higher RT. Twenty patients with late onset depression were included in the study after they had reached remission. Four weeks after reaching remission patients were reevaluated clinically using Hamilton Depression Rating Scale, Mini Mental State Examination, and with a computer version of the Stroop task. ERP, accuracy and RTs were simultaneously recorded. Both groups of patients aditionaly underwent a perfusion SPECT imaging. RESULTS: There were no differences between the short and long RT groups of patients in amplitudes of the late positive Stroop related potentials. The group of patients with longer RTs showed significant hyperperfusion in precentral gyrus, parietal regions, cuneus and hypoperfusion within insular, frontal, temporal and limbic (parahyppocampal gyrus and anterior cingulate) cortices, as well as cerebellum. CONCLUSION: We found no ERP differences between the two groups suggesting that although patients may differ on psychomotor retardation measured as RT, their cognitive abilities may be quite similar. Perfusion SPECT imaging however revealed a significant difference between them. This may be due to a process of compensation and applying different strategies to cope with cognitive impairment in the two groups.

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones.

Automated differential diagnosis of dementia syndromes using FDG PET and machine learning.

Background: Metabolic brain imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive diagnostic and differential diagnostic tool for neurodegenerative dementias. In the clinic, scans are usually visually interpreted. However, computer-aided approaches can improve diagnostic accuracy. We aimed to build two machine learning classifiers, based on two sets of FDG PET-derived features, for differential diagnosis of common dementia syndromes. Methods: We analyzed FDG PET scans from three dementia cohorts [63 dementia due to Alzheimer's disease (AD), 79 dementia with Lewy bodies (DLB) and 23 frontotemporal dementia (FTD)], and 41 normal controls (NCs). Patients' clinical diagnosis at follow-up (25 ± 20 months after scanning) or cerebrospinal fluid biomarkers for Alzheimer's disease was considered a gold standard. FDG PET scans were first visually evaluated. Scans were pre-processed, and two sets of features extracted: (1) the expressions of previously identified metabolic brain patterns, and (2) the mean uptake value in 95 regions of interest (ROIs). Two multi-class support vector machine (SVM) classifiers were tested and their diagnostic performance assessed and compared to visual reading. Class-specific regional feature importance was assessed with Shapley Additive Explanations. Results: Pattern- and ROI-based classifier achieved higher overall accuracy than expert readers (78% and 80% respectively, vs. 71%). Both SVM classifiers performed similarly to one another and to expert readers in AD (F1 = 0.74, 0.78, and 0.78) and DLB (F1 = 0.81, 0.81, and 0.78). SVM classifiers outperformed expert readers in FTD (F1 = 0.87, 0.83, and 0.63), but not in NC (F1 = 0.71, 0.75, and 0.92). Visualization of the SVM model showed bilateral temporal cortices and cerebellum to be the most important features for AD; occipital cortices, hippocampi and parahippocampi, amygdala, and middle temporal lobes for DLB; bilateral frontal cortices, middle and anterior cingulum for FTD; and bilateral angular gyri, pons, and vermis for NC. Conclusion: Multi-class SVM classifiers based on the expression of characteristic metabolic brain patterns or ROI glucose uptake, performed better than experts in the differential diagnosis of common dementias using FDG PET scans. Experts performed better in the recognition of normal scans and a combined approach may yield optimal results in the clinical setting.

Stereotyped Relationship Between Motor and Cognitive Metabolic Networks in Parkinson's Disease.

BACKGROUND: Idiopathic Parkinson's disease (iPD) is associated with two distinct brain networks, PD-related pattern (PDRP) and PD-related cognitive pattern (PDCP), which correlate respectively with motor and cognitive symptoms. The relationship between the two networks in individual patients is unclear. OBJECTIVE: To determine whether a consistent relationship exists between these networks, we measured the difference between PDRP and PDCP expression, termed delta, on an individual basis in independent populations of patients with iPD (n = 356), patients with idiopathic REM sleep behavioral disorder (iRBD) (n = 21), patients with genotypic PD (gPD) carrying GBA1 variants (n = 12) or the LRRK2-G2019S mutation (n = 14), patients with atypical parkinsonian syndromes (n = 238), and healthy control subjects (n = 95) from the United States, Slovenia, India, and South Korea. METHODS: We used [18 F]-fluorodeoxyglucose positron emission tomography and resting-state fMRI to quantify delta and to compare the measure across samples; changes in delta over time were likewise assessed in longitudinal patient samples. Lastly, we evaluated delta in prodromal individuals with iRBD and subjects with gPD. RESULTS: Delta was abnormally elevated in each of the four iPD samples (P < 0.05), as well as in the at-risk iRBD group (P < 0.05), with increasing values over time (P < 0.001). PDRP predominance was also present in gPD, with higher values in patients with GBA1 variants compared with the less aggressive LRRK2-G2019S mutation (P = 0.005). This trend was not observed in patients with atypical parkinsonian syndromes, who were accurately discriminated from iPD based on PDRP expression and delta (area under the curve = 0.85; P < 0.0001). CONCLUSIONS: PDRP predominance, quantified by delta, assays the spread of dysfunction from motor to cognitive networks in patients with PD. Delta may therefore aid in differential diagnosis and in tracking disease progression in individual patients. © 2022 International Parkinson and Movement Disorder Society.

Development of a deep learning network for Alzheimer's disease classification with evaluation of imaging modality and longitudinal data.

Objective. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer's disease (AD), there remains a clear clinical need for reliable tools to extract diagnoses from neuroimages. Significant work has been done to develop deep learning (DL) networks using neuroimaging for AD diagnosis. However, no particular model has emerged as optimal. Due to a lack of direct comparisons and evaluations on independent data, there is no consensus on which modality is best for diagnostic models or whether longitudinal information enhances performance. The purpose of this work was (1) to develop a generalizable DL model to distinguish neuroimaging scans of AD patients from controls and (2) to evaluate the influence of imaging modality and longitudinal data on performance.Approach. We trained a 2-class convolutional neural network (CNN) with and without a cascaded recurrent neural network (RNN). We used datasets of 772 (NAD = 364,Ncontrol= 408) 3D18F-FDG PET scans and 780 (NAD = 280,Ncontrol= 500) T1-weighted volumetric-3D MR images (containing 131 and 144 patients with multiple timepoints) from the Alzheimer's Disease Neuroimaging Initiative, plus an independent set of 104 (NAD = 63,NNC = 41)18F-FDG PET scans (one per patient) for validation.Main Results. ROC analysis showed that PET-trained models outperformed MRI-trained, achieving maximum AUC with the CNN + RNN model of 0.93 ± 0.08, with accuracy 82.5 ± 8.9%. Adding longitudinal information offered significant improvement to performance on18F-FDG PET, but not on T1-MRI. CNN model validation with an independent18F-FDG PET dataset achieved AUC of 0.99. Layer-wise relevance propagation heatmaps added CNN interpretability.Significance. The development of a high-performing tool for AD diagnosis, with the direct evaluation of key influences, reveals the advantage of using18F-FDG PET and longitudinal data over MRI and single timepoint analysis. This has significant implications for the potential of neuroimaging for future research on AD diagnosis and clinical management of suspected AD patients.

Adverse effects of levodopa/carbidopa intrajejunal gel treatment: A single-center long-term follow-up study.

OBJECTIVES: Levodopa/carbidopa intrajejunal gel (LCIG) is an effective therapeutic strategy to overcome levodopa-induced motor complications in advanced Parkinson's disease (PD). However, it requires invasive percutaneous endoscopic gastrojejunostomy (PEG-J) and may be associated with serious adverse effects (AE). In this study, we aimed to evaluate long-term AEs related to LCIG treatment in a large homogenous cohort of advanced PD patients. METHODS: One hundred three consecutive PD patients were regularly monitored for LCIG-related, PEG-J-related, and device-related AEs up to 14 years. Incidence of AEs was studied in time applying a time-to-event analysis and Cox proportional hazard model with age, disease duration, gender, and recurrent AE as covariates. Health-related quality of life (HRQoL) was estimated at each visit and compared to HRQoL before the LCIG treatment. RESULTS: Among 296 AEs noted, 48.8% were LCIG-related, 32.4% PEG-J-related, and 19.6% device-related. While most of the studied AEs steadily accumulated throughout the follow-up period, 24.3% of the patients (95% CI 10.1%-36.3%) experienced PEG-J-related AE already within the first days after the PEG-J insertion. Cox model revealed that older patients had higher probability of psychosis, PEG-J- and device-related AEs (p < .05, p < .05, and p = .02) and suggested increased recurrence risk in those with early PEG-J and device-related AEs. Despite relatively high incidence of AEs, HRQoL significantly increased in the follow-up period (p < .0001). CONCLUSION: AEs related to LCIG treatment are common. Therefore, careful patient selection and monitoring throughout the treatment is recommended, especially in those with early side effects. Nevertheless, LCIG significantly improves HRQoL in advanced PD patients on a long term.

Identification and validation of Alzheimer's disease-related metabolic brain pattern in biomarker confirmed Alzheimer's dementia patients.

Metabolic brain biomarkers have been incorporated in various diagnostic guidelines of neurodegenerative diseases, recently. To improve their diagnostic accuracy a biologically and clinically homogeneous sample is needed for their identification. Alzheimer's disease-related pattern (ADRP) has been identified previously in cohorts of clinically diagnosed patients with dementia due to Alzheimer's disease (AD), meaning that its diagnostic accuracy might have been reduced due to common clinical misdiagnosis. In our study, we aimed to identify ADRP in a cohort of AD patients with CSF confirmed diagnosis, validate it in large out-of-sample cohorts and explore its relationship with patients' clinical status. For identification we analyzed 2-[18F]FDG PET brain scans of 20 AD patients and 20 normal controls (NCs). For validation, 2-[18F]FDG PET scans from 261 individuals with AD, behavioral variant of frontotemporal dementia, mild cognitive impairment and NC were analyzed. We identified an ADRP that is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. ADRP expression significantly differentiated AD from NC (AUC = 0.95) and other dementia types (AUC = 0.76-0.85) and its expression correlated with clinical measures of global cognition and neuropsychological indices in all cohorts.

Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography.

PURPOSE: Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value. METHODS: A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[18F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored. RESULTS: The newly identified DLBRP shared topographic similarities with ADRP (R2 = 24%) and PDRP (R2 = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time. CONCLUSION: DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.

Genetic Variability of the Vitamin D Receptor Affects Susceptibility to Parkinson's Disease and Dopaminergic Treatment Adverse Events.

Vitamin D is a lipid-soluble molecule and an important transcriptional regulator in many tissues and organs, including the brain. Its role has been demonstrated also in Parkinson's disease (PD) pathogenesis. Vitamin D receptor (VDR) is responsible for the initiation of vitamin D signaling cascade. The aim of this study was to assess the associations of VDR genetic variability with PD risk and different PD-related phenotypes. We genotyped 231 well characterized PD patients and 161 healthy blood donors for six VDR single nucleotide polymorphisms, namely rs739837, rs4516035, rs11568820, rs731236, rs2228570, and rs1544410. We observed that VDR rs2228570 is associated with PD risk (p < 0.001). Additionally, we observed associations of specific VDR genotypes with adverse events of dopaminergic treatment. VDR rs1544410 (GG vs. GA + AA: p = 0.005; GG vs. GA: p = 0.009) was associated with the occurrence of visual hallucinations and VDR rs739837 (TT vs. GG: p = 0.036), rs731236 (TT vs. TC + CC: p = 0.011; TT vs. TC: p = 0.028; TT vs. CC: p = 0.035), and rs1544410 (GG vs. GA: p = 0.014) with the occurrence of orthostatic hypotension. We believe that the reported study may support personalized approach to PD treatment, especially in terms of monitoring vitamin D level and vitamin D supplementation in patients with high risk VDR genotypes.

Abnormal metabolic covariance patterns associated with multiple system atrophy and progressive supranuclear palsy.

PURPOSE: Differentiation between neurodegenerative parkinsonisms, whose early clinical presentation is similar, may be improved with metabolic brain imaging. In this study we applied a specific network analysis to 2-[18F]FDG PET brain scans to identify the characteristic metabolic patterns for multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) in a new European cohort. We also developed a new tool to recognize and estimate patients' metabolic brain heterogeneity. METHODS: 20 MSA-P patients, 20 PSP patients and 20 healthy controls (HC) underwent 2-[18F]FDG PET brain imaging. The scaled subprofile model/principal component analysis was applied to identify MSA/PSP-related patterns; MSARP and PSPRP. Additional, 56 MSA, 45 PSP, 116 PD and 61 HC subjects were analyzed for validation. We innovatively applied heat-map analysis to extract and graphically display the pattern's regional sub-scores in individual subjects. RESULTS: MSARP was characterized by hypometabolism in cerebellum and putamen, and PSPRP by hypometabolism in medial prefrontal cortices, nucleus caudatus, frontal cortices and mesencephalon. Patterns' expression discriminated between MSA/PSP patients and HCs as well as between different parkinsonian cohorts (p < 0.001). Both patterns were sensitive and specific (AUC for MSARP/PSPRP: 0.96/0.99). Heat-map analysis showed differences within MSA/PSP subjects and HCs consistent with clinical presentation. CONCLUSIONS: Replication and validation of MSARP and PSPRP confirms robustness of these metabolic biomarkers and supports its application in clinical and research practice. Heat-map analysis gives us an insight into the contribution of various pattern's regions to patterns' expression in individual subjects, which improves our insight into the heterogeneity of studied syndromes.

Continuous Dopaminergic Stimulation Improves Cortical Maladaptive Changes in Advanced Parkinson's Disease.

BACKGROUND: With the progression of Parkinson's disease (PD), pulsatile treatment with oral levodopa causes maladaptive changes within basal ganglia-thalamo-cortical circuits, which are clinically expressed as motor fluctuations and dyskinesias. At the level of the motor cortex, these changes may be detected using transcranial magnetic stimulation (TMS), as abnormal corticospinal and intracortical excitability and absent response to plasticity protocols. OBJECTIVE: We investigated the effect of continuous dopaminergic stimulation on cortical maladaptive changes related to oral levodopa treatment. METHODS: Twenty patients with advanced PD were tested using TMS within 1 week before and again 6 months after the introduction of levodopa-carbidopa intestinal gel. We measured resting and active motor thresholds, input/output curve, short interval intracortical inhibition curve, cortical silent period, and response to intermittent theta burst stimulation. Patients were clinically assessed with Part III and Part IV of the Movement Disorders Society Unified Parkinson's Disease Rating Scale. RESULTS: Six months after the introduction of levodopa-carbidopa intestinal gel, motor fluctuations scores (P = 0.001) and dyskinesias scores (P < 0.001) were reduced. Resting and active motor threshold (P = 0.012 and P = 0.015) and x-intercept of input/output curve (P = 0.005) were also decreased, while short-interval intracortical inhibition and response to intermittent theta bust stimulation were improved (P = 0.026 and P = 0.031, respectively). Changes in these parameters correlated with clinical improvement. CONCLUSIONS: In patients with advanced PD, switching from intermittent to continuous levodopa delivery increased corticospinal excitability and improved deficient intracortical inhibition and abnormal motor cortex plasticity, along with amelioration of motor fluctuations and dyskinesias. Continuous dopaminergic stimulation ameliorates maladaptive changes inflicted by chronic pulsatile dopaminergic stimulation. © 2022 International Parkinson and Movement Disorder Society.

Switching and Combining Device-Aided Therapies in Advanced Parkinson's Disease: A Double Centre Retrospective Study.

BACKGROUND: Device-aided therapies (DAT), such as continuous subcutaneous apomorphine infusion (CSAI), levodopa-carbidopa intestinal gel infusion (LCIG), and deep brain stimulation of the subthalamic nucleus (STN-DBS), have markedly changed the treatment landscape of advanced Parkinson's disease (aPD). In some patients, it is necessary to switch or combine DATs for various reasons. The aim of this retrospective study was to explore the frequency and reasons for switching between or combining DATs in two movement disorders centres in Slovenia and Israel. METHODS: We collected and analysed demographic and clinical data from aPD patients who switched between or combined DATs. Motor and non-motor reasons, adverse events for switching/combining, and their frequency were examined, as was the effect of DAT using the Global Improvement subscale of the Clinical Global Impression Scale, Movement Disorders Society Unified Parkinson's Disease Rating Scale part III, Mini Mental State Examination, and Parkinson's Disease Questionnaire 39. Descriptive statistics and non-parametric tests were used to analyse the data. RESULTS: Of 505 aPD patients treated with DATs at both centres between January 2009 and June 2021, we identified in a total of 30 patients (6%) who either switched DAT (n = 24: 7 LCIG-to-STN-DBS, 1 LCIG-to-CSAI, 5 CSAI-to STN-DBS, 8 CSAI-to-LCIG, 1 STN-DBS-to-LCIG, 1 LCIG-to-CSAI-to-STN-DBS, and 1 STN-DBS-to-CSAI-to-LCIG) or combined DATs (n = 6:5 STN-DBS+LCIG and 1 STN-DBS+CSAI-to-STN-DBS+LCIG). In most of these patients, an inadequate control of motor symptoms was the main reason for switching or combining DATs, but non-motor reasons (related to the disease and/or DAT) were also identified. CONCLUSIONS: Switching between and combining DATs is uncommon, but in some patients brings substantial clinical improvement and should be considered in those who have either inadequate symptom control on DAT treatment or have developed DAT-related complications.

Personality Changes After Subthalamic Nucleus Stimulation in Parkinson's Disease.

BACKGROUND: While deep brain stimulation of the subthalamic nucleus (STN-DBS) significantly improves motor deficits in patients with Parkinson's disease (PD), it is still unclear whether it affects personality functioning. OBJECTIVE: The objective of the present study was to examine personality changes in patients with PD after STN-DBS from the perspectives of both the patients and caregivers. Moreover, by assessing the premorbid personalities of the patients, we tried to determine individual vulnerability to STN-DBS-induced personality changes. METHODS: In total, 27 patients and their caregivers participated in our retrospective observational study. They were asked to assess the patients' personality changes with the Iowa Scale of Personality Changes (ISPC) and the patients' premorbid personalities with the Big Five Inventory (BFI). RESULTS: Caregivers reported significant personality changes in the ISPC domains of Executive Disturbance (p = 0.01) and Disturbed Social Behavior (p = 0.02). Most of the ISPC domains were positively correlated with Conscientiousness, while Executive Disturbance was negatively correlated with Neuroticism of the BFI scale. CONCLUSION: Our results show that executive and social functioning are the two most vulnerable domains in patients with PD after STN-DBS, especially in those patients who score higher for neuroticism and lower for conscientiousness on the BFI scale. The results of our study may provide movement disorder specialists with better counseling options and better selection of DBS candidates. Caregivers' perspective might contribute significantly in understanding postoperative personality changes.