Cerebral Metabolic Patterns In Neurodegeneration

Neurodegenerative disorders comprise a group of conditions, including Parkinson’s and Alzheimer’s disease, in which neurons in specific brain regions deteriorate. Different neurodegenerative diseases exhibit distinct patterns of brain damage. Despite this, they often present with similar features in the beginning, and a ‘typical’ clinical picture can take years to develop. Establishing a diagnosis early in the disease course can be challenging, even for experts. Brain imaging with 18F-FDG PET may offer a solution, as 18F-FDG PET can be used to identify areas with disrupted glucose metabolism caused by the underlying disease process. In this thesis, several distinguishable disease-related patterns were identified in 18F-FDG PET brain scans using a mathematical model called spatial covariance analysis. This model determines metabolic brain patterns by identifying areas of relative metabolic differences between healthy controls and patients. These patterns can be used to quantify the degree of disease activity in the brain scans of new patients. We investigated whether this method would be useful at the early stages of neurodegenerative disease. We studied patients with rapid eye movement sleep behavior disorder (RBD), an early stage of Parkinson’s disease. The Parkinson’s disease-related brain pattern was already present in the scans of RBD patients, even though these patients did not yet manifest typical parkinsonian motor symptoms. We also found that mildly cognitively impaired subjects expressed the Alzheimer’s disease-related brain pattern years before developing full-blown dementia. 18F-FDG PET is a widely-available modality which is valuable for the study of neurodegenerative disorders, especially when combined with advanced analytical techniques

Circuit imaging biomarkers in preclinical and prodromal Parkinson's disease

Abstract Parkinson’s disease (PD) commences several years before the onset of motor features. Pathophysiological understanding of the pre-clinical or early prodromal stages of PD are essential for the development of new therapeutic strategies. Two categories of patients are ideal to study the early disease stages. Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents a well-known prodromal stage of PD in which pathology is presumed to have reached the lower brainstem. The majority of patients with iRBD will develop manifest PD within years to decades. Another category encompasses non-manifest mutation carriers, i.e. subjects without symptoms, but with a known mutation or genetic variant which gives an increased risk of developing PD. The speed of progression from preclinical or prodromal to full clinical stages varies among patients and cannot be reliably predicted on the individual level. Clinical trials will require inclusion of patients with a predictable conversion within a limited time window. Biomarkers are necessary that can confirm pre-motor PD status and can provide information regarding lead time and speed of progression. Neuroimaging changes occur early in the disease process and may provide such a biomarker. Studies have focused on radiotracer imaging of the dopaminergic nigrostriatal system, which can be assessed with dopamine transporter (DAT) single photon emission computed tomography (SPECT). Loss of DAT binding represents an effect of irreversible structural damage to the nigrostriatal system. This marker can be used to monitor disease progression and identify individuals at specific risk for phenoconversion. However, it is known that changes in neuronal activity precede structural changes. Functional neuro-imaging techniques, such as 18F-2-fluoro-2-deoxy-D-glucose Positron Emission Tomography (18F-FDG PET) and functional magnetic resonance imaging (fMRI), can be used to model the effects of disease on brain networks when combined with advanced analytical methods. Because these changes occur early in the disease process, functional imaging studies are of particular interest in prodromal PD diagnosis. In addition, fMRI and 18F-FDG PET may be able to predict a specific future phenotype in prodromal cohorts, which is not possible with DAT SPECT. The goal of the current review is to discuss the network-level brain changes in pre-motor PD

Occipital hypometabolism is a risk factor for conversion to Parkinson’s disease in isolated REM sleep behaviour disorder

Purpose: Isolated REM sleep behaviour disorder (iRBD) patients are at high risk of developing clinical syndromes of the α-synuclein spectrum. Progression markers are needed to determine the neurodegenerative changes and to predict their conversion. Brain imaging with 18F-FDG PET in iRBD is promising, but longitudinal studies are scarce. We investigated the regional brain changes in iRBD over time, related to phenoconversion.Methods: Twenty iRBD patients underwent two consecutive 18F-FDG PET brain scans and clinical assessments (3.7 ± 0.6 years apart). Seventeen patients also underwent 123I-MIBG and 123I-FP-CIT SPECT scans at baseline. Four subjects phenoconverted to Parkinson’s disease (PD) during follow-up. 18F-FDG PET scans were compared to controls with a voxel-wise single-subject procedure. The relationship between regional brain changes in metabolism and PD-related pattern scores (PDRP) was investigated.Results: Individual hypometabolism t-maps revealed three scenarios: (1) normal 18F-FDG PET scans at baseline and follow-up (N = 10); (2) normal scans at baseline but occipital or occipito-parietal hypometabolism at follow-up (N = 4); (3) occipital hypometabolism at baseline and follow-up (N = 6). All patients in the last group had pathological 123I-MIBG and 123I-FP-CIT SPECT. iRBD converters (N = 4) showed occipital hypometabolism at baseline (third scenario). At the group level, hypometabolism in the frontal and occipito-parietal regions and hypermetabolism in the cerebellum and limbic regions were progressive over time. PDRP z-scores increased over time (0.54 ± 0.36 per year). PDRP expression was driven by occipital hypometabolism and cerebellar hypermetabolism.Conclusions: Our results suggest that occipital hypometabolism at baseline in iRBD implies a short-term conversion to PD. This might help in stratification strategies for disease-modifying trials.</p

Four-YearFollow-upof [F-18]Fluorodeoxyglucose Positron Emission Tomography-Based Parkinson's Disease-Related Pattern Expression in 20 Patients With Isolated Rapid Eye Movement Sleep Behavior Disorder Shows Prodromal Progression

Background: Isolated rapid eye movement sleep behavior disorder is known to be prodromal for alpha-synucleinopathies, such as Parkinson's disease (PD) and dementia with Lewy bodies. The [18F]fluorodeoxyglucose-positron emission tomography (PET)–based PD-related brain pattern can be used to monitor disease progression. Objective: We longitudinally investigated PD-related brain pattern expression changes in 20 subjects with isolated rapid eye movement sleep behavior disorder to investigate whether this may be a suitable technique to study prodromal PD progression in these patients and to identify potential phenoconverters. Methods: Subjects underwent two [18F]fluorodeoxyglucose-PET brain scans ~3.7 years apart, along with baseline and repeated motor, cognitive, and olfactory testing within roughly the same time frame. Results: At baseline, 8 of 20 (40%) subjects significantly expressed the PD-related brain pattern (with z scores above the receiver operating characteristic–determined threshold). At follow-up, six additional subjects exhibited significant PD-related brain pattern expression (70% in total). PD-related brain pattern expression increased in all subjects (P = 0.00008). Four subjects (20%), all with significant baseline PD-related brain pattern expression, phenoconverted to clinical PD. Conclusions: Suprathreshold PD-related brain pattern expression and greater score rate of change may signify greater shorter-term risk for phenoconversion. Our results support the use of serial PD-related brain pattern expression measurements as a prodromal PD progression biomarker in patients with isolated rapid eye movement sleep behavior disorder

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder

Background and Objectives 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson’s disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer’s disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. Methods We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. Results The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman’s rho =0.62, P=0.004). Conclusion In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages

The metabolic pattern of idiopathic REM sleep behavior disorder reflects early-stage Parkinson's disease

Rationale: Idiopathic REM sleep behavior disorder (iRBD) is considered a prodromal stage of Parkinson's disease (PD) and other Lewy-body disorders. Spatial covariance analysis of [18F]-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG-PET) data has disclosed a specific brain pattern of altered glucose metabolism in PD. In this study, we identify the metabolic pattern underlying iRBD and compare it to the known PD pattern. To understand the relevance of the iRBD pattern to disease progression, we study the expression of the iRBD pattern in de novo PD patients.Methods:The iRBD-related pattern was identified in18F-FDG-PET scans of 21 patients with polysomnographically-confirmed iRBD and 19 controls using spatial covariance analysis. Expression of the iRBD-related pattern was subsequently computed in18F-FDG-PET scans of 44 controls and 38 de novo, treatment-naïve PD patients. Of these 38 PD patients, 24 had probable RBD according to the Mayo Sleep Questionnaire. Neuropsychological evaluation showed mild cognitive impairment in 20 PD patients (PD-MCI), of whom sixteen also had concomitant RBD and roughly half (11/20) had bilateral motor symptoms.Results:The iRBD-related pattern was characterized by relative hypermetabolism in cerebellum, brainstem, thalamus, sensorimotor cortex, and hippocampus, and by relative hypometabolism in middle cingulate, temporal, occipital and parietal cortices. This topography partially overlapped with the PD-related pattern (PDRP). The iRBD-related pattern was significantly expressed in PD patients compared to controls (P<0.0001). iRBD-related pattern expression was not significantly different between PD patients with and without probable RBD, or between PD patients with unilateral or bilateral parkinsonism. iRBD-related pattern expression was higher in PD-MCI patients, compared to PD patients with preserved cognition (P= 0.001). Subject scores on the iRBD-related pattern were highly correlated to subject scores on the PDRP (r=0.94, P<0.0001).Conclusion:In conclusion, our results show that the iRBDRP is an early manifestation of the PDRP. Expression of both PDRP and iRBDRP was higher in patients with a more severe form of PD (PD-MCI), which indicates that expression of the two patterns increases with disease severity

Rapid Eye Movement Sleep Behavior Disorder:Abnormal Cardiac Image and Progressive Abnormal Metabolic Brain Pattern

BACKGROUND: Isolated rapid eye movement sleep behavior disorder (iRBD) is prodromal for α-synucleinopathies. OBJECTIVE: The aim of this study was to determine whether pathological cardiac [123 I]meta-iodobenzylguanidine scintigraphy ([123 I]MIBG) is associated with progression of [18 F]fluorodeoxyglucose-positron emission tomography-based Parkinson's disease (PD)-related brain pattern (PDRP) expression in iRBD. METHODS: Seventeen subjects with iRBD underwent [18 F]fluorodeoxyglucose-positron emission tomography brain imaging twice ~3.6 years apart. In addition, [123 I]MIBG and [123 I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane single-photon emission computed tomography ([123 I]FP-CIT-SPECT) at baseline were performed. Olfactory, cognitive, and motor functions were tested annually. RESULTS: Twelve of 17 subjects had pathological [123 I]MIBG. At baseline, 6 of 12 of these expressed the PDRP (suprathreshold PDRP z score). At follow-up, 12 of 17 subjects had suprathreshold PDRP z scores, associated with pathological [123 I]MIBG in 92% and with pathological [123 I]FP-CIT-SPECT in 75%. Subjects with pathological [123 I]MIBG had higher PDRP z score change per year (P = 0.027). Three subjects phenoconverted to PD; all had pathological [123 I]MIBG and [123 I]FP-CIT-SPECT, suprathreshold baseline PDRP z scores, and hyposmia. CONCLUSIONS: Pathological [123 I]MIBG was associated with progressive and suprathreshold PDRP z scores at follow-up. Abnormal [123 I]MIBG likely identifies iRBD as prodromal PD earlier than pathological [123 I]FP-CIT-SPECT. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

A resting-state fMRI pattern of spinocerebellar ataxia type 3 and comparison with F-18-FDG PET

Spinocerebellar ataxia type 3 (SCA3) is a rare genetic neurodegenerative disease. The neurobiological basis of SCA3 is still poorly understood, and up until now resting-state fMRI (rs-fMRI) has not been used to study this disease. In the current study we investigated (multi-echo) rs-fMRI data from patients with genetically confirmed SCA3 (n = 17) and matched healthy subjects (n = 16). Using independent component analysis (ICA) and subsequent regression with bootstrap resampling, we identified a pattern of differences between patients and healthy subjects, which we coined the fMRI SCA3 related pattern (fSCA3-RP) comprising cerebellum, anterior striatum and various cortical regions. Individual fSCA3-RP scores were highly correlated with a previously published F-18-FDG PET pattern found in the same sample (rho = 0.78, P = 0.0003). Also, a high correlation was found with the Scale for Assessment and Rating of Ataxia scores (r = 0.63, P = 0.007). No correlations were found with neuropsychological test scores, nor with levels of grey matter atrophy. Compared with the F-18-FDG PET pattern, the fSCA3-RP included a more extensive contribution of the mediofrontal cortex, putatively representing changes in default network activity. This rs-fMRI identification of additional regions is proposed to reflect a consequence of the nature of the BOLD technique, enabling measurement of dynamic network activity, compared to the more static F-18-FDG PET methodology. Altogether, our findings shed new light on the neural substrate of SCA3, and encourage further validation of the fSCA3-RP to assess its potential contribution as imaging biomarker for future research and clinical use

Validation of the REM behaviour disorder phenoconversion-related pattern in an independent cohort

Background: A brain glucose metabolism pattern related to phenoconversion in patients with idiopathic/isolated REM sleep behaviour disorder (iRBDconvRP) was recently identified. However, the validation of the iRBDconvRP in an external, independent group of iRBD patients is needed to verify the reproducibility of such pattern, so to increase its importance in clinical and research settings. The aim of this work was to validate the iRBDconvRP in an independent group of iRBD patients. Methods: Forty iRBD patients (70 ± 5.59 years, 19 females) underwent brain [18F]FDG-PET in Seoul National University. Thirteen patients phenoconverted at follow-up (7 Parkinson disease, 5 Dementia with Lewy bodies, 1 Multiple system atrophy; follow-up time 35 ± 20.56 months) and 27 patients were still free from parkinsonism/dementia after 62 ± 29.49 months from baseline. We applied the previously identified iRBDconvRP to validate its phenoconversion prediction power. Results: The iRBDconvRP significantly discriminated converters from non-converters iRBD patients (p = 0.016; Area under the Curve 0.74, Sensitivity 0.69, Specificity 0.78), and it significantly predicted phenoconversion (Hazard ratio 4.26, C.I.95%: 1.18–15.39). Conclusions: The iRBDconvRP confirmed its robustness in predicting phenoconversion in an independent group of iRBD patients, suggesting its potential role as a stratification biomarker for disease-modifying trials.</p

The cerebral metabolic topography of spinocerebellar ataxia type 3

Introduction: We aimed to uncover the pattern of network-level changes in neuronal function in Spinocerebellar ataxia type 3 (SCA3). Methods: 17 genetically-confirmed SCA3 patients and 16 controls underwent structural MRI and static resting-state [F-18]-Fluoro-deoxyglucose Positron Emission Tomography (FDG-PET) imaging. A SCA3-related pattern (SCA3-RP) was identified using a multivariate method (scaled subprofile model and principal component analysis (SSM PCA)). Participants were evaluated with the Scale for Assessment and Rating of Ataxia (SARA) and with neuropsychological examination including tests for language, executive dysfunction, memory, and information processing speed. The relationships between SCA3-RP expression and clinical scores were explored. Voxel based morphology (VBM) was applied on MRI-T1 images to assess possible correlations between FDG reduction and grey matter atrophy. Results: The SCA3-RP disclosed relative hypometabolism of the cerebellum, caudate nucleus and posterior parietal cortex, and relatively increased metabolism in somatosensory areas and the limbic system. This topography, which was not explained by regional atrophy, correlated significantly with ataxia (SARA) scores (rho = 0.72; P = 0.001). SCA3 patients showed significant deficits in executive function and information processing speed, but only letter fluency correlated with SCA3-RP expression (rho = 0.51; P = 0.04, uncorrected for multiple comparisons). Conclusion: The SCA3 metabolic profile reflects network-level alterations which are primarily associated with the motor features of the disease. Striatum decreases additional to cerebellar hypometabolism underscores an intrinsic extrapyramidal involvement in SCA3. Cerebellar-posterior parietal hypometabolism together with anterior parietal (sensory) cortex hypermetabolism may reflect a shift from impaired feedforward to compensatory feedback processing in higher-order motor control. The demonstrated SCA3-RP provides basic insight in cerebral network changes in this disease