CSF Biomarkers and Its Associations with 18F-AV133 Cerebral VMAT2 Binding in Parkinson's Disease-A Preliminary Report

Unified spatial normalization method of brain PET images using adaptive probabilistic brain atlas

PURPOSE

A unique advantage of the brain positron emission tomography (PET) imaging is the ability to image different biological processes with different radiotracers. However, the diversity of the brain PET image patterns also makes their spatial normalization challenging. Since structural MR images are not always available in the clinical practice, this study proposed a PET-only spatial normalization method based on adaptive probabilistic brain atlas.

METHODS

The proposed method (atlas-based method) consists of two parts, an adaptive probabilistic brain atlas generation algorithm, and a probabilistic framework for registering PET image to the generated atlas. To validate this method, the results of MRI-based method and template-based method (a widely used PET-only method) were treated as the gold standard and control, respectively. A total of 286 brain PET images, including seven radiotracers (FDG, PIB, FBB, AV-45, AV-1451, AV-133, [¹⁸F]altanserin) and four groups of subjects (Alzheimer disease, Parkinson disease, frontotemporal dementia, and healthy control), were spatially normalized using the three methods. The results were then quantitatively compared by using correlation analysis, meta region of interest (meta-ROI) standardized uptake value ratio (SUVR) analysis, and statistical parametric mapping (SPM) analysis.

RESULTS

The Pearson correlation coefficient between the images computed by atlas-based method and the gold standard was 0.908 ± 0.005. The relative error of meta-ROI SUVR computed by atlas-based method was 2.12 ± 0.18%. Compared with template-based method, atlas-based method was also more consistent with the gold standard in SPM analysis.

CONCLUSION

The proposed method provides a unified approach to spatially normalize brain PET images of different radiotracers accurately without MR images. A free MATLAB toolbox for this method has been provided.

Self-paced learning and privileged information based KRR classification algorithm for diagnosis of Parkinson's disease

Computer aided diagnosis (CAD) methods for Parkinson's disease (PD) can assist clinicians in diagnosis and treatment. Magnetic resonance imaging (MRI) based CAD methods can help reveal structural changes in brain. Classifier is a key component in CAD system, which directly affects the classification performance. Privileged information (PI) can assist to train the classifier by providing additional information, which makes test samples have less error and improves the classification accuracy. In this paper, we proposed a PI based kernel ridge regression plus (KRR+) in diagnosis of PD. Specifically, morphometric features and brain network features are extracted from MRI. Then, empirical kernel mapping feature expression method is used to make the data separable in high-dimensional space. Besides, we introduce self-paced learning that can adaptively select the sample in training of the model, which can further improve the classification performance. The experimental results show that the proposed method is effective for PD diagnosis, its performance is superior to existing classification model. This method is helpful to assist clinicians to find out possible neuroimaging biomarkers in the diagnosis of PD.

Mesenchymal stem cells in Parkinson's disease: Motor and nonmotor symptoms in the early posttransplant period

BACKGROUND

Treatment of patients with Parkinson disease (PD) using autologous mesenchymal stem cells (MSCs) is a promising method to influence the pathogenesis of the disease. The aim of this study was to assess the immediate results of the introduction of MSCs on the effectiveness of motor and nonmotor symptoms in patients with PD.

METHODS

MSCs were transplanted to 12 patients with PD through intravenous and tandem (intranasal + intravenous) injections. Effectiveness of the therapy was evaluated 1 and 3 months posttransplantation. Neurological examination of the intensity of motor symptoms was carried out in the morning after a 12 or 24 h break in taking antiparkinsonian drugs, then 1 h after they were taken. The intensity of motor symptoms was assessed with the help of Section III of the Unified PD Rating Scale of the International Society for Movement Disorders (UPDRS). The intensity of nonmotor symptoms was assessed with the help of the following scales: Hamilton Depression Rating Scale, the Pittsburgh Sleep Quality Index, the Epworth Sleepiness Scale, Nonmotor Symptoms Scale, and the 39-item Parkinson's Disease Questionnaire.

RESULTS

We found a statistically significant decrease in the severity of motor and nonmotor symptoms in the study group in the posttransplant period.

CONCLUSION

Positive results allow us to consider MSCs transplantation as a disease-modifying therapeutic strategy in PD. However, this method of PD treatment is not a fully understood process, which requires additional studies and a longer follow-up period to monitor the patients' condition posttransplantation.

Constipation Increases Disability and Decreases Dopamine Levels in the Nigrostriatal System through Gastric Inflammatory Factors in Parkinson's Disease

OBJECTIVE

Recent studies suggest that not only is constipation a clinical marker of premotor phase in Parkinson's Disease (PD), but is also correlated with the duration and severity. Some reports indicated that inflammatory from gut dysbiosis might be involved in the pathogenesis of PD, but the correlation between them remains poorly understood. This study aims to investigate how the presence of constipation affects the dopamine level of nigrostriatal system and whether gastrointestinal (GI) inflammation is involved in the brain-gut axis.

METHODS

Clinical materials, serum inflammatory factors, and datum of dopamine level including 84 cases and 83 controls, were collected consecutively and randomly from November 1, 2017 to October 31, 2018. Dopamine levels of nigrostriatal system were detected by [18F]-DTBZ radiotracer (18F-AV-133). Data analysis was conducted by variance, covariance analysis, bicorrelation, partial correlation, chi-square analysis and logistic regression.

RESULTS

The mean age of cases was older than that of controls, and male predominance was also observed (P<0.05). The mean scores of Hoehn-Yahr and unified Parkinson's disease rating scale Ⅲ (UPDRS-Ⅲ) were of significantly different duration between two groups (P<0.05). The total dose of levodopa was not different between two groups (P>0.05). The dopamine levels of putamen and caudate nucleus, especially in the dorsal part of putamen, were significantly decreased in cases than that in controls (P<0.05). There were significant differences of complement 3 (C3) and complement 4 (C4) between cases and controls (P<0.05). Dopamine levels in putamen and caudate nucleus were negatively correlated with serum concentrations of immunoglobulin A (IgA), immunoglobulin G (IgG) and C3 in cases (P<0.05). But we did not observe similar negative correlations in controls (P>0.05).

CONCLUSION

The presence of constipation may increase the severity of motor symptoms and decrease dopamine levels of nigrostriatal system in PD. Inflammatory factors may be involved in the brain-gut axis of PD.

The interactions of dopamine and oxidative damage in the striatum of patients with neurodegenerative diseases

The striatum with a number of dopamine containing neurons, receiving projections from the substantia nigra and ventral tegmental area; plays a critical role in neurodegenerative diseases of motor and memory function. Additionally, oxidative damage to nucleic acid may be vital in the development of age-associated neurodegeneration. The metabolism of dopamine is recognized as one of the sources of reactive oxygen species through the Fenton mechanism. The proposed interactions of oxidative insults and dopamine in the striatum during the progression of diseases are the hypotheses of most interest to our study. This study investigated the possibility of significant interactions between these molecules that are involved in the late-stage of Alzheimer's disease (AD), Parkinson disease (PD), Parkinson disease dementia, dementia with Lewy bodies, and controls using ELISA assays, autoradiography, and mRNA in situ hybridization assay. Interestingly, lower DNA/RNA oxidative adducts levels in the caudate and putamen of diseased brains were observed with the exception of an increased DNA oxidative product in the caudate of AD brains. Similar changes were found for dopamine concentration and vesicular monoamine transporter 2 densities. We also found that downstream pre-synaptic dopamine D1 Receptor binding correlated with dopamine loss in Lewy body disease groups, and RNA damage and β-site APP cleaving enzyme 1 in the caudate of AD. This is the first demonstration of region-specific alterations of DNA/RNA oxidative damage which cannot be viewed in isolation, but rather in connection with the interrelationship between different neuronal events; chiefly DNA oxidative adducts and density of vesicular monoamine transporter 2 densities in AD and PD patients.

The effect of ApoE ε4 on longitudinal brain region-specific glucose metabolism in patients with mild cognitive impairment: a FDG-PET study

While the ApoE ε4 allele is a known risk factor for mild cognitive impairment (MCI) and Alzheimer's disease, brain region specific effects remain elusive. In this study, we investigate whether the ApoE ε4 allele exhibits brain region specific effects in longitudinal glucose uptake among patients with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed FDG PET images, MRIs, and demographic information were downloaded from the ADNI database. An iterative reblurred Van Cittertiteration method was used for partial volume correction (PVC) on all PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. Longitudinal changes in ROI FDG standardized uptake value ratio (SUVR) relative to cerebellum in 24 ApoE ε4 carriers and 24 age-matched ApoE ε4 non-carriers were measured for up to 84-months (median 72 months, SD = 11.2 months) and compared using a generalized linear mixed effects model controlling for gender, education, baseline age, and follow-up period. Additionally, voxelwise analysis was performed by implementing a paired t-test comparing matched baseline and 72 month FDG SUVR images in ApoE carriers and non-carriers separately. Results with PVC were compared with ones from non-PVC based analysis. After applying PVC, the superior fontal, parietal, lateral temporal, medial temporal, caudate, thalamus, and post-cingulate, and amygdala regions had greater longitudinal decreases in FDG uptake in ApoE ε4 carriers with MCI compared to non-carriers with MCI. Similar forebrain and limbic clusters were found through voxelwise analysis. Compared to the PVC based analysis, fewer significant ApoE-associated regions and clusters were found in the non-PVC based PET analysis. Our findings suggest that the ApoE ε4 genotype is associated with a longitudinal decline in glucose uptake in 8 forebrain and limbic brain regions in the context of MCI. In conclusion, this 84-months longitudinal FDG PET study demonstrates a novel ApoE ε4-associated brain-region specific glucose metabolism pattern in patients with MCI. Partial volume correction improved FDG PET quantification.

Data-Driven Subtyping of Parkinson's Disease Using Longitudinal Clinical Records: A Cohort Study

Parkinson's disease (PD) is associated with diverse clinical manifestations including motor and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the heterogeneity of PD to define subtypes and their progression rates using an automated deep learning algorithm on the top of longitudinal clinical records. This study utilizes the data collected from the Parkinson's Progression Markers Initiative (PPMI), which is a longitudinal cohort study of patients with newly diagnosed Parkinson's disease. Clinical information including motor and non-motor assessments, biospecimen examinations, and neuroimaging results were used for identification of PD subtypes. A deep learning algorithm, Long-Short Term Memory (LSTM), was used to represent each patient as a multi-dimensional time series for subtype identification. Both visualization and statistical analysis were performed for analyzing the obtained PD subtypes. As a result, 466 patients with idiopathic PD were investigated and three subtypes were identified. Subtype I (Mild Baseline, Moderate Motor Progression) is comprised of 43.1% of the participants, with average age 58.79 ± 9.53 years, and was characterized by moderate functional decay in motor ability but stable cognitive ability. Subtype II (Moderate Baseline, Mild Progression) is comprised of 22.9% of the participants, with average age 61.93 ± 6.56 years, and was characterized by mild functional decay in both motor and non-motor symptoms. Subtype III (Severe Baseline, Rapid Progression) is comprised 33.9% of the patients, with average age 65.32 ± 8.86 years, and was characterized by rapid progression of both motor and non-motor symptoms. These subtypes suggest that when comprehensive clinical and biomarker data are incorporated into a deep learning algorithm, the disease progression rates do not necessarily associate with baseline severities, and the progression rate of non-motor symptoms is not necessarily correlated with the progression rate of motor symptoms.

Dopaminergic pathway and primary visual cortex are involved in the freezing of gait in Parkinson's disease: a PET-CT study

Background: Freezing of gait (FOG) could be partly alleviated by dopaminergic drugs but the mechanism still needs to be elucidated. The purpose of this study is to explore the mechanisms of FOG by vesicular monoamine transporter VMAT2 distribution with the ¹⁸F-AV133 tracer and 18-fludeoxyglucose positron emission tomography-computed tomography (¹⁸F-FDG PET-CT). Methods: Clinical material and PET-CT data were collected from 20 patients with FOG and 147 patients without FOG from November 1, 2017 to October 31, 2018. Brain scans of all participants were acquired over an approximately 20-min period, 120 min after injection of approximately 250 MBq ¹⁸F-AV133. The mean uptake ratios of different regions were identified by NeuroQ software of ¹⁸F-FDG PET-CT. Data analysis included variance, chi-square analysis, covariance analysis, and logistic regression. Results: Our data showed that patients with FOG were provided with greater doses of dopaminergic drugs (p<0.05). The frequency of FOG was 11.98% and increased as Parkinson's disease progressed. FOG was more common in the elderly and strongly associated with the duration. Cognitive impairments were obvious, assessed by Mini-Mental State Examination and Montreal Cognitive Assessment (p<0.05). The VMAT2 distribution with ¹⁸F-AV133 was decreased significantly in the caudate nucleus and lentiform nucleus while the metabolism of these areas was elevated, determined by ¹⁸F-FDG PET-CT (p<0.05). The metabolism of the primary visual cortex decreased obviously in patients with FOG compared with those without FOG (p<0.05). Conclusion: FOG mainly occurred in the advanced stage, and was strongly associated with the duration and larger dose of dopaminergic drugs. The dopamine level of the nigrostriatal system decreased significantly and the uptake ratios of the primary visual cortex dropped obviously in the FOG group compared with the non-FOG group. Our study suggests that both the dopaminergic pathway and the primary visual cortex are involved in the pathogenesis of FOG.

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease

Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few studies have been conducted in recent years on predictive or disease progression modeling of PD using clinical and biomarkers data. Neuroimaging, as another important information source for neurodegenerative disease, has also arisen considerable interests from the PD community. In this paper, we propose a deep learning method based on Graph Convolutional Networks (GCN) for fusing multiple modalities of brain images in relationship prediction which is useful for distinguishing PD cases from controls. On Parkinson's Progression Markers Initiative (PPMI) cohort, our approach achieved 0.9537±0.0587 AUC, compared with 0.6443±0.0223 AUC achieved by traditional approaches such as PCA.

α-Synuclein species as potential cerebrospinal fluid biomarkers for dementia with lewy bodies

Background: The objective of this study was to investigate the discriminating value of a range of CSF α‐synuclein species for dementia with Lewy bodies compared with Alzheimer's disease, PD, and cognitively normal controls.

Methods: We applied our recently published enzyme‐linked immunosorbent assays to measure the CSF levels of total α‐synuclein, oligomeric α‐synuclein, and phosphorylated α‐synuclein in dementia with Lewy bodies (n = 42), Alzheimer's disease (n = 39), PD (n = 46), and controls (n = 78). General linear models corrected for age and sex were performed to assess differences in α‐synuclein levels between groups. We used backward‐elimination logistic regression analysis to investigate the combined discriminating value of the different CSF α‐synuclein species and Alzheimer's disease biomarkers.

Results: CSF levels of total α‐synuclein were lower in dementia with Lewy bodies and PD compared with Alzheimer's disease as well as controls (P < 0.001). In contrast, CSF levels of oligomeric α‐synuclein were higher in dementia with Lewy bodies and PD compared with Alzheimer's disease (P < 0.05) and controls (P < 0.001). No group differences were found for phosphorylated α‐synuclein. In dementia with Lewy bodies and PD, CSF total α‐synuclein levels positively correlated with tau and phosphorylated tau (both r > 0.40, P < 0.01), but not with amyloid‐β_1‐42. The optimal combination to differentiate dementia with Lewy bodies from controls consisted of amyloid‐β_1‐42, tau, total α‐synuclein, oligomeric α‐synuclein, age, and sex (AUC, 0.90). To differentiate dementia with Lewy bodies from Alzheimer's disease, the combination of tau and oligomeric α‐synuclein resulted in an AUC of 0.83. CSF α‐synuclein species do not contribute to the differentiation of dementia with Lewy bodies from PD.

Conclusions: CSF α‐synuclein species could be useful as part of a biomarker panel for dementia with Lewy bodies. Evaluating both oligomeric α‐synuclein and total α‐synuclein in CSF helps in the diagnosis of dementia with Lewy bodies. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Effect of rs3910105 in the Synuclein Gene on Dopamine Transporter Availability in Healthy Subjects

PURPOSE

The present study investigated associations between dopamine transporter (DAT) availability and α-synuclein levels in cerebrospinal fluid, as well as synuclein gene (SNCA) transcripts, and the effect of single nucleotide polymorphism of SNCA on DAT availability in healthy subjects.

MATERIALS AND METHODS

The study population comprised healthy controls who underwent ¹²³I-FP-CIT single-photon emission computed tomography screening. Five SNCA probes were used to target the boundaries of exon 3 and exon 4 (SNCA-E3E4), transcripts with a long 3'UTR region (SNCA-3UTR-1, SNCA-3UTR-2), transcripts that skip exon 5 (SNCA-E4E6), and the rare short transcript isoforms that comprise exons 1-4 (SNCA-007).

RESULTS

In total, 123 healthy subjects (male 75, female 48) were included in this study. DAT availability in the caudate nucleus (p=0.0661) and putamen (p=0.0739) tended to differ according to rs3910105 genotype. In post-hoc analysis, DAT availability in the putamen was lower in subjects of TT genotype than those of CC/CT (p=0.0317). DAT availability in the caudate nucleus also showed a trend similar to that in the putamen (p=0.0597). Subjects of CT genotype with rs3910105 showed negative correlations with DAT availability in the putamen with SNCA-E3E4 (p=0.037, rho=-0.277), and SNCA-E4E6 (p=0.042, rho=-0.270), but not those of CC/TT genotypes.

CONCLUSION

This is the first study to investigate the association of rs3910105 in SNCA with DAT availability. rs3910105 had an effect on DAT availability, and the correlation between DAT availability and SNCA transcripts were significant in CT genotypes of rs3910105.

Update on Molecular Imaging in Parkinson's Disease

Advances in radionuclide tracers have allowed for more accurate imaging that reflects the actions of numerous neurotransmitters, energy metabolism utilization, inflammation, and pathological protein accumulation. All of these achievements in molecular brain imaging have broadened our understanding of brain function in Parkinson's disease (PD). The implementation of molecular imaging has supported more accurate PD diagnosis as well as assessment of therapeutic outcome and disease progression. Moreover, molecular imaging is well suited for the detection of preclinical or prodromal PD cases. Despite these advances, future frontiers of research in this area will focus on using multi-modalities combining positron emission tomography and magnetic resonance imaging along with causal modeling with complex algorithms.

Discovery, validation and optimization of cerebrospinal fluid biomarkers for use in Parkinson's disease

INTRODUCTION

Parkinson's disease (PD) is a complex and phenotypically heterogeneous neurodegenerative disease, for which the diagnosis is mainly based on clinical parameters (even if neuroimaging plays a role in diagnostic assessment); as a consequence, misdiagnosis is common, especially in early stages. Thus, there is an urgent need of having available biomarkers in order to achieve an early and accurate diagnosis. Since molecular changes in the brain are reliably and timely reflected in cerebrospinal fluid (CSF), CSF represents an ideal source for biomarkers of different pathophysiological processes characterizing the disease since its early phases. Areas covered: The aim of this review is to provide an update on the role, development and validation of most studied CSF biomarkers showing a role in the diagnosis and/or prognosis of PD. Oligomeric alpha-synuclein, DJ-1, lysosomal enzymes (namely, glucocerebrosidase) show consistent evidence as potential diagnostic biomarkers of PD. Neurofilament light chain may also have a significant role in differentiating PD from other parkinsonisms. Amyloid beta peptide 1-42 has consistently shown a prognostic value in terms of development of cognitive impairment and dementia in PD patients. Expert commentary: CSF biomarkers represent a very promising approach to early and differential diagnosis of PD. The biomarkers available so far need preanalytical and analytical validation in order to have these CSF biomarkers ready for clinical use.