FDG PET, dopamine transporter SPECT, and olfaction: Combining biomarkers in REM sleep behavior disorder.

BACKGROUND: Idiopathic REM sleep behavior disorder is a prodromal stage of Parkinson's disease and dementia with Lewy bodies. Hyposmia, reduced dopamine transporter binding, and expression of the brain metabolic PD-related pattern were each associated with increased risk of conversion to PD. The objective of this study was to study the relationship between the PD-related pattern, dopamine transporter binding, and olfaction in idiopathic REM sleep behavior disorder. METHODS: In this cross-sectional study, 21 idiopathic REM sleep behavior disorder subjects underwent 18 F-fluorodeoxyglucose PET, dopamine transporter imaging, and olfactory testing. For reference, we included 18 F-fluorodeoxyglucose PET data of 19 controls, 20 PD patients, and 22 patients with dementia with Lewy bodies. PD-related pattern expression z-scores were computed from all PET scans. RESULTS: PD-related pattern expression was higher in idiopathic REM sleep behavior disorder subjects compared with controls (P = 0.048), but lower compared with PD (P = 0.001) and dementia with Lewy bodies (P < 0.0001). PD-related pattern expression was higher in idiopathic REM sleep behavior disorder subjects with hyposmia and in subjects with an abnormal dopamine transporter scan (P < 0.05, uncorrected). CONCLUSION: PD-related pattern expression, dopamine transporter binding, and olfaction may provide complementary information for predicting phenoconversion. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

The Dutch Parelsnoer Institute--Neurodegenerative diseases; methods, design and baseline results.

BACKGROUND: The Parelsnoer Institute is a collaboration between 8 Dutch University Medical Centers in which clinical data and biomaterials from patients suffering from chronic diseases (so called "Pearls") are collected according to harmonized protocols. The Pearl Neurodegenerative Diseases focuses on the role of biomarkers in the early diagnosis, differential diagnosis and in monitoring the course of neurodegenerative diseases, in particular Alzheimer's disease. The objective of this paper is to describe the design and methods of the Pearl Neurodegenerative Diseases, as well as baseline descriptive variables, including their biomarker profile. METHODS: The Pearl Neurodegenerative Diseases is a 3-year follow-up study of patients referred to a memory clinic with cognitive complaints. At baseline, all patients are subjected to a standardized examination, including clinical data and biobank materials, e.g. blood samples, MRI and cerebrospinal fluid. At present, in total more than 1000 patients have been included, of which cerebrospinal fluid and DNA samples are available of 211 and 661 patients, respectively. First descriptives of a subsample of the data (n = 665) shows that patients are diagnosed with dementia (45%), mild cognitive impairment (31%), and subjective memory complaints (24%). DISCUSSION: The Pearl Neurodegenerative Diseases is an ongoing large network collecting clinical data and biomaterials of more than 1000 patients with cognitive impairments. The project has started with data analyses of the baseline characteristics and biomarkers, which will be the starting point of future specific research questions that can be answered by this unique dataset.

Anterior temporal atrophy and posterior progression in patients with Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is characterized by specific motor and nonmotor impairments. This suggests that PD is characterized by disease-specific regional cortical atrophy. Given the change of symptoms over time, a concurrent increase in regional atrophy may further be assumed to reflect the dynamic process of disease progression. METHODS: In this study we retrospectively collected T1-weighted MRI scans from previous studies performed in our center, enabling the comparison of gray matter atrophy in 77 PD patients with 87 controls using voxel-based morphometry (VBM). This large VBM analysis provided the opportunity to investigate cortical atrophy in relation with disease progression. RESULTS: We found significant PD-related reductions of gray matter density bilaterally in the anterior temporal cortex, the left inferior frontal and left extrastriate visual cortex, independent from normal aging. The anterior temporal cortex did not show major progression, whereas particularly the posterior parts of the lateral temporal cortex and adjacent extrastriate visual cortex occurred at a later stage of disease. CONCLUSIONS: Temporal pole atrophy as an early sign of PD is consistent with the PD pathology classification of Braak. The initial anterior temporal atrophy with spread to occipitotemporal and posterior parietal regions may subserve 'emotion-based' sensorimotor transformations and deficits in the visual domain, respectively, which may be regarded as premotor symptoms.

Validation of parkinsonian disease-related metabolic brain patterns.

BACKGROUND: The objective of this study was to validate disease-related metabolic brain patterns for Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy. METHODS: The study included 20 patients with Parkinson's disease, 21 with multiple system atrophy, and 17 with progressive supranuclear palsy, all of whom had undergone a clinically motivated [18F]-fluoro-deoxyglucose positron emission tomography scan at an early stage of their disease. At a follow-up time after the scan of 2-4 years, a clinical diagnosis was made according to established clinical research criteria. Patient groups were compared with 18 healthy controls using a multivariate covariance image analysis technique called scaled subprofile model/principal component analysis. RESULTS: Disease-related metabolic brain patterns for these parkinsonian disorders were identified. Validation showed that these patterns were highly discriminative of the 3 disorders. CONCLUSIONS: Early diagnosis of parkinsonian disorders is feasible when the expression of disease-related metabolic brain patterns is quantified at a single-subject level.

Regional cortical grey matter loss in Parkinson's disease without dementia is independent from visual hallucinations.

In our previous functional magnetic resonance imaging study, Parkinson's disease (PD) patients with visual hallucinations (VH) showed reduced activations in ventral/lateral visual association cortices preceding image recognition, compared with both PD patients without VH and healthy controls. The primary aim of the current study was to investigate whether functional deficits are associated with grey matter volume changes. In addition, possible grey matter differences between all PD patients and healthy controls were assessed. By using 3-Tesla magnetic resonance imaging (MRI) and voxel-based morphometry (VBM), we found no differences between PD patients with (n = 11) and without VH (n = 13). However, grey matter decreases of the bilateral prefrontal and parietal cortex, left anterior superior temporal, and left middle occipital gyrus were found in the total group of PD patients, compared with controls (n = 14). This indicates that previously demonstrated functional deficits in PD patients with VH are not associated with grey matter loss. The strong left parietal reduction in both nondemented patient groups was hemisphere specific and independent of the side of PD symptoms.

Typical cerebral metabolic patterns in neurodegenerative brain diseases.

The differential diagnosis of neurodegenerative brain diseases on clinical grounds is difficult, especially at an early disease stage. Several studies have found specific regional differences of brain metabolism applying [(18)F]-fluoro-deoxyglucose positron emission tomography (FDG-PET), suggesting that this method can assist in early differential diagnosis of neurodegenerative brain diseases.We have studied patients who had an FDG-PET scan on clinical grounds at an early disease stage and included those with a retrospectively confirmed diagnosis according to strictly defined clinical research criteria. Ninety-six patients could be included of which 20 patients with Parkinson's disease (PD), 21 multiple system atrophy (MSA), 17 progressive supranuclear palsy (PSP), 10 corticobasal degeneration (CBD), 6 dementia with Lewy bodies (DLB), 15 Alzheimer's disease (AD), and 7 frontotemporal dementia (FTD). FDG PET images of each patient group were analyzed and compared to18 healthy controls using Statistical Parametric Mapping (SPM5).Disease-specific patterns of relatively decreased metabolic activity were found in PD (contralateral parietooccipital and frontal regions), MSA (bilateral putamen and cerebellar hemispheres), PSP (prefrontal cortex and caudate nucleus, thalamus, and mesencephalon), CBD (contralateral cortical regions), DLB (occipital and parietotemporal regions), AD (parietotemporal regions), and FTD (frontotemporal regions).The integrated method addressing a spectrum of various neurodegenerative brain diseases provided means to discriminate patient groups also at early disease stages. Clinical follow-up enabled appropriate patient inclusion. This implies that an early diagnosis in individual patients can be made by comparing each subject's metabolic findings with a complete database of specific disease related patterns.

The EEG response to pyridoxine-IV neither identifies nor excludes pyridoxine-dependent epilepsy.

PURPOSE: Pyridoxine-dependent epilepsy (PDE) is characterized by therapy-resistant seizures (TRS) responding to intravenous (IV) pyridoxine. PDE can be identified by increased urinary alpha-aminoadipic semialdehyde (α-AASA) concentrations and mutations in the ALDH7A1 (antiquitin) gene. Prompt recognition of PDE is important for treatment and prognosis of seizures. We aimed to determine whether immediate electroencephalography (EEG) alterations by pyridoxine-IV can identify PDE in neonates with TRS. METHODS: In 10 neonates with TRS, we compared online EEG alterations by pyridoxine-IV between PDE (n = 6) and non-PDE (n = 4). EEG segments were visually and digitally analyzed for average background amplitude and total power and relative power (background activity magnitude per frequency band and contribution of the frequency band to the spectrum). RESULTS: In 3 of 10 neonates with TRS (2 of 6 PDE and 1 of 4 non-PDE neonates), pyridoxine-IV caused flattening of the EEG amplitude and attenuation of epileptic activity. Quantitative EEG alterations by pyridoxine-IV consisted of (1) decreased central amplitude, p < 0.05 [PDE: median -30% (range -78% to -3%); non-PDE: -20% (range -45% to -12%)]; (2) unaltered relative power; (3) decreased total power, p < 0.05 [PDE: -31% (-77% to -1%); -27% (-73% to -13%); -35% (-56% to -8%) and non-PDE: -16% (-43% to -5%); -28% (-29% to -17%); -26% (-54% to -8%), in delta-, theta- and beta-frequency bands, respectively]; and (4) similar EEG responses in PDE and non-PDE. DISCUSSION: In neonates with TRS, pyridoxine-IV induces nonspecific EEG responses that neither identify nor exclude PDE. These data suggest that neonates with TRS should receive pyridoxine until PDE is fully excluded by metabolic and/or DNA analysis.

Structural Network Analysis Using Diffusion MRI Tractography in Parkinson's Disease and Correlations With Motor Impairment.

Functional impairment of spatially distributed brain regions in Parkinson's disease (PD) suggests changes in integrative and segregative network characteristics, for which novel analysis methods are available. To assess underlying structural network differences between PD patients and controls, we employed MRI T1 gray matter segmentation and diffusion MRI tractography to construct connectivity matrices to compare patients and controls with data originating from two different centers. In the Dutch dataset (Data-NL), 14 PD patients, and 15 healthy controls were analyzed, while 19 patients and 18 controls were included in the Canadian dataset (Data-CA). All subjects underwent T1 and diffusion-weighted MRI. Patients were assessed with Part 3 of the Unified Parkinson's Disease Rating Scale (UPDRS). T1 images were segmented using FreeSurfer, while tractography was performed using ExploreDTI. The regions of interest from the FreeSurfer segmentation were combined with the white matter streamline sets resulting from the tractography, to construct connectivity matrices. From these matrices, both global and local efficiencies were calculated, which were compared between the PD and control groups and related to the UPDRS motor scores. The connectivity matrices showed consistent patterns among the four groups, without significant differences between PD patients and control subjects, either in Data-NL or in Data-CA. In Data-NL, however, global and local efficiencies correlated negatively with UPDRS scores at both the whole-brain and the nodal levels [false discovery rate (FDR) 0.05]. At the nodal level, particularly, the posterior parietal cortex showed a negative correlation between UPDRS and local efficiency, while global efficiency correlated negatively with the UPDRS in the sensorimotor cortex. The spatial patterns of negative correlations between UPDRS and parameters for network efficiency seen in Data-NL suggest subtle structural differences in PD that were below sensitivity thresholds in Data-CA. These correlations are in line with previously described functional differences. The methodological approaches to detect such differences are discussed.

Metabolic Imaging in Parkinson Disease.

This review focuses on recent human 18F-FDG PET studies in Parkinson disease. First, an overview is given of the current analytic approaches to metabolic brain imaging data. Next, we discuss how 18F-FDG PET studies have advanced understanding of the relation between distinct brain regions and associated symptoms in Parkinson disease, including cognitive decline. In addition, the value of 18F-FDG PET studies in differential diagnosis, identifying prodromal patients, and the evaluation of treatment effects are reviewed. Finally, anticipated developments in the field are addressed.

Abnormal metabolic pattern associated with cognitive impairment in Parkinson's disease: a validation study.

Cognitive deficits in Parkinson's disease (PD) have been associated with a specific metabolic covariance pattern. Although the expression of this PD cognition-related pattern (PDCP) correlates with neuropsychological performance, it is not known whether the PDCP topography is reproducible across PD populations. We therefore sought to identify a PDCP topography in a new sample comprised of 19 Dutch PD subjects. Network analysis of metabolic scans from these individuals revealed a significant PDCP that resembled the original network topography. Expression values for the new PDCP correlated (P=0.001) with executive dysfunction on the Frontal Assessment Battery (FAB). Subject scores for the new PDCP correlated (P<0.001) with corresponding values for the original pattern, which also correlated (P<0.005) with FAB scores in this patient group. For further validation, subject scores for the new PDCP were computed in an independent group of 86 American PD patients. In this cohort, subject scores for the new and original PDCP topographies were closely correlated (P<0.001); significant correlations between pattern expression and cognitive performance (P<0.05) were observed for both PDCP topographies. These findings suggest that the PDCP is a replicable imaging marker of PD cognitive dysfunction.

The Alzheimer's disease-related glucose metabolic brain pattern.

PURPOSE: [(18)F]fluorodeoxyglucose (FDG) PET imaging of the brain can be used to assist in the differential diagnosis of dementia. Group differences in glucose uptake between patients with dementia and controls are well-known. However, a multivariate analysis technique called scaled subprofile model, principal component analysis (SSM/PCA) aiming at identifying diagnostic neural networks in diseases, have been applied less frequently. We validated an Alzheimer's Disease-related (AD) glucose metabolic brain pattern using the SSM/PCA analysis and applied it prospectively in an independent confirmation cohort. METHODS: We used FDG-PET scans of 18 healthy controls and 15 AD patients (identification cohort) to identify an AD-related glucose metabolic covariance pattern. In the confirmation cohort (n=15), we investigated the ability to discriminate between probable AD and non-probable AD (possible AD, mild cognitive impairment (MCI) or subjective complaints). RESULTS: The AD-related metabolic covariance pattern was characterized by relatively decreased metabolism in the temporoparietal regions and relatively increased metabolism in the subcortical white matter, cerebellum and sensorimotor cortex. Receiver-operating characteristic (ROC) curves showed at a cut-off value of z=1.23, a sensitivity of 93% and a specificity of 94% for correct AD classification. In the confirmation cohort, subjects with clinically probable AD diagnosis showed a high expression of the AD-related pattern whereas in subjects with a non-probable AD diagnosis a low expression was found. CONCLUSION: The Alzheimer's disease-related cerebral glucose metabolic covariance pattern identified by SSM/PCA analysis was highly sensitive and specific for Alzheimer's disease. This method is expected to be helpful in the early diagnosis of Alzheimer's disease in clinical practice.

Parkinson's disease-related perfusion and glucose metabolic brain patterns identified with PCASL-MRI and FDG-PET imaging.

INTRODUCTION: Under normal conditions, the spatial distribution of resting cerebral blood flow and cerebral metabolic rate of glucose are closely related. A relatively new magnetic resonance (MR) technique, pseudo-continuous arterial spin labeling (PCASL), can be used to measure regional brain perfusion. We identified a Parkinson's disease (PD)-related perfusion and metabolic covariance pattern in the same patients using PCASL and FDG-PET imaging and assessed (dis)similarities in the disease-related pattern between perfusion and metabolism in PD patients. METHODS: Nineteen PD patients and seventeen healthy controls underwent [(18)F]-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging. Of 14 PD patients and all healthy controls PCASL-MRI could be obtained. Data were analyzed using scaled subprofile model/principal component analysis (SSM/PCA). RESULTS: Unique Parkinson's disease-related perfusion and metabolic covariance patterns were identified using PCASL and FDG-PET in the same patients. The PD-related metabolic covariance brain pattern is in high accordance with previously reports. Also our disease-related perfusion pattern is comparable to the earlier described perfusion pattern. The most marked difference between our perfusion and metabolic patterns is the larger perfusion decrease in cortical regions including the insula. CONCLUSION: We identified PD-related perfusion and metabolic brain patterns using PCASL and FDG-PET in the same patients which were comparable with results of existing research. In this respect, PCASL appears to be a promising addition in the early diagnosis of individual parkinsonian patients.

Metabolic network as a progression biomarker of premanifest Huntington's disease.

BACKGROUND: The evaluation of effective disease-modifying therapies for neurodegenerative disorders relies on objective and accurate measures of progression in at-risk individuals. Here we used a computational approach to identify a functional brain network associated with the progression of preclinical Huntington's disease (HD). METHODS: Twelve premanifest HD mutation carriers were scanned with [18F]-fluorodeoxyglucose PET to measure cerebral metabolic activity at baseline and again at 1.5, 4, and 7 years. At each time point, the subjects were also scanned with [11C]-raclopride PET and structural MRI to measure concurrent declines in caudate/putamen D2 neuroreceptor binding and tissue volume. The rate of metabolic network progression in this cohort was compared with the corresponding estimate obtained in a separate group of 21 premanifest HD carriers who were scanned twice over a 2-year period. RESULTS: In the original premanifest cohort, network analysis disclosed a significant spatial covariance pattern characterized by progressive changes in striato-thalamic and cortical metabolic activity. In these subjects, network activity increased linearly over 7 years and was not influenced by intercurrent phenoconversion. The rate of network progression was nearly identical when measured in the validation sample. Network activity progressed at approximately twice the rate of single region measurements from the same subjects. CONCLUSION: Metabolic network measurements provide a sensitive means of quantitatively evaluating disease progression in premanifest individuals. This approach may be incorporated into clinical trials to assess disease-modifying agents. TRIAL REGISTRATION: Registration is not required for observational studies. FUNDING: NIH (National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering) and CHDI Foundation Inc.