Metabolic brain networks in translational neurology: concepts and applications

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson’s Disease in 3D Nuclear Imaging Data

Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) is a PET technique employed to obtain a representation of brain metabolic function. In this study we employed 3D CNNs in FDG-PET brain images with the purpose of discriminating patients diagnosed with Parkinson’s disease (PD) from controls. We employed Scaled Subprofile Modeling using Principal Component Analysis as a preprocessing step to focus on specific brain regions and limit the number of voxels that are used as input for the CNNs, thereby increasing the signal-to-noise ratio in our data. We performed hyperparameter optimization on three CNN architectures to estimate the classification accuracy of the networks on new data. The best performance that we obtained was [Formula: see text] and area under the receiver operating characteristic curve [Formula: see text] on the test set. We believe that, with larger datasets, PD patients could be reliably distinguished from controls by FDG-PET scans alone and that this technique could be applied to more clinically challenging tasks, like the differential diagnosis of neurological disorders with similar symptoms, such as PD, Progressive Supranuclear Palsy (PSP) and Multiple System Atrophy (MSA).

Postural instability and gait disorders after subthalamic nucleus deep brain stimulation in Parkinson's disease: a PET study

INTRODUCTION

Patients with Parkinson's disease sometimes report postural instability and gait disorders (PIGD) after subthalamic nucleus deep brain stimulation (STN-DBS). Whether this is the direct consequence of DBS or the result of natural disease progression is still subject to debate.

OBJECTIVE

To compare changes in brain metabolism during STN-DBS between patients with and without PIGD after surgery.

METHODS

We extracted consecutive patients from a database where all Rennes Hospital patients undergoing STN-DBS are registered, with regular prospective updates of their clinical data. Patients were divided into two groups (PIGD and No PIGD) according to changes after surgery, as measured with a composite score based on the selected Unified Parkinson's Disease Rating Scale items. All patients underwent positron emission tomography with ¹⁸[F]-fluorodeoxyglucose 3 months before and after surgery. We ran an ANOVA with two factors (group: PIGD vs. No PIGD; and phase: preoperative vs. postoperative) on SPM8 to compare changes in brain metabolism between the two groups.

RESULTS

Participants were 56 patients, including 10 in the PIGD group. The two groups had similar baseline (i.e., before surgery) characteristics. We found two clusters of increased metabolism in the PIGD group relative to the No PIGD group: dorsal midbrain/pons, including locomotor mesencephalic region and reticular pontine formation, and right motor cerebellum.

CONCLUSION

We found different metabolic changes during DBS-STN among patients with PIGD, concerning brain regions that are already known to be involved in gait disorders in Parkinson's disease, suggesting that DBS is responsible for the appearance of PIGD.

A 3D Deep Residual Convolutional Neural Network for Differential Diagnosis of Parkinsonian Syndromes on 18F-FDG PET Images

Idiopathic Parkinsons disease and atypical parkinsonian syndromes have similar symptoms at early disease stages, which makes the early differential diagnosis difficult. Positron emission tomography with ¹⁸F-FDG shows the ability to assess early neuronal dysfunction of neurodegenerative diseases and is well established for clinical use. In the past decades, machine learning methods have been widely used for the differential diagnosis of parkinsonism based on metabolic patterns. Unlike these conventional machine learning methods relying on hand-crafted features, the deep convolutional neural networks, which have achieved significant success in medical applications recently, have the advantage of learning salient feature representations automatically and effectively. This advantage may offer more appropriate invisible features extracted from data for the enhancement of the diagnosis accuracy. Therefore, this paper develops a 3D deep convolutional neural network on ¹⁸F-FDG PET images for the automated early diagnosis. Furthermore, we depicted in saliency maps the decision mechanism of the deep learning method to assist the physiological interpretation of deep learning performance. The proposed method was evaluated on a dataset with 920 patients. In addition to improving the accuracy in the differential diagnosis of parkinsonism compared to state-of-the-art approaches, the deep learning methods also discovered saliency features in a number of critical regions (e.g., midbrain), which are widely accepted as characteristic pathological regions for movement disorders but were ignored in the conventional analysis of FDG PET images.

Factors affecting the harmonization of disease-related metabolic brain pattern expression quantification in [18F]FDG-PET (PETMETPAT)

Introduction

The implementation of spatial-covariance [¹⁸F]fluorodeoxyglucose positron emission tomography–based disease-related metabolic brain patterns as biomarkers has been hampered by intercenter imaging differences. Within the scope of the JPND-PETMETPAT working group, we illustrate the impact of these differences on Parkinson's disease–related pattern (PDRP) expression scores.

Methods

Five healthy controls, 5 patients with idiopathic rapid eye movement sleep behavior disorder, and 5 patients with Parkinson's disease were scanned on one positron emission tomography/computed tomography system with multiple image reconstructions. In addition, one Hoffman 3D Brain Phantom was scanned on several positron emission tomography/computed tomography systems using various reconstructions. Effects of image contrast on PDRP scores were also examined.

Results

Human and phantom raw PDRP scores were systematically influenced by scanner and reconstruction effects. PDRP scores correlated inversely to image contrast. A Gaussian spatial filter reduced contrast while decreasing intercenter score differences.

Discussion

Image contrast should be considered in harmonization efforts. A Gaussian filter may reduce noise and intercenter effects without sacrificing sensitivity. Phantom measurements will be important for correcting PDRP score offsets.

Cortical thinning in patients with REM sleep behavior disorder is associated with clinical progression

The aim of this study is to determine whether structural MRI measures are associated with clinical impairment and progression to a Lewy body disease in patients with idiopathic REM sleep behavior disorder (iRBD). Twenty-seven patients with iRBD in addition to patients with de novo PD and healthy controls were included from the Parkinson's Progression Markers Initiative. Patients with iRBD were followed for up to 3 years. Clinical and MRI measures were compared across groups and the association between clinical features and structural MRI was assessed in iRBD patients. Cox regression analyses were applied to identify risk factors for progressing to a Lewy body disease in iRBD. Our results showed that, at baseline, iRBD patients showed parietal and occipital cortical thinning, compared to controls. They also showed worse motor and non-motor abilities, some of which correlated with motor, frontal or temporal cortical thinning. At follow-up, six (22%) iRBD patients were diagnosed with a Lewy body disorder. These patients showed cortical thinning in frontal, occipital and parietal areas compared to iRBD non-converters. Cortical thinning was a significant predictor for future development of a Lewy body disorder (HR: 0.784; 95% CI: 0.640-0.960; p = 0.02). We conclude that cortical thinning is associated with worse motor and non-motor abilities, and predicts conversion to a Lewy body disorder in iRBD, suggesting it could be used to select candidates for clinical trials to delay the onset of neurodegenerative disease.

Network imaging biomarkers: insights and clinical applications in Parkinson's disease

Parkinson's disease presents several practical challenges: it can be difficult to distinguish from atypical parkinsonian syndromes, clinical ratings can be insensitive as markers of disease progression, and its non-motor manifestations are not readily assessed in animal models. These challenges, along with others, are beginning to be addressed by innovative imaging methods to characterise Parkinson's disease-specific functional networks across the whole brain and measure their expression in each patient. These signatures can help improve differential diagnosis, guide selection of patients for clinical trials, and quantify treatment responses and placebo effects in individual patients. The primary Parkinson's disease-related metabolic pattern has been replicated in multiple patient populations and used as an outcome measure in clinical trials. It can also be used as a predictor of near-term phenoconversion in prodromal syndromes, such as rapid eye movement sleep behaviour disorder. Functional network imaging holds great promise for future clinical use in the management of neurodegenerative disorders.

Gene therapy reduces Parkinson’s disease symptoms by reorganizing functional brain connectivity

Gene therapy is emerging as a promising approach for treating neurological disorders, including Parkinson’s disease (PD). A phase 2 clinical trial showed that delivering glutamic acid decarboxylase (GAD) into the subthalamic nucleus (STN) of patients with PD had therapeutic effects. To determine the mechanism underlying this response, we analyzed metabolic imaging data from patients who received gene therapy and those randomized to sham surgery, all of whom had been scanned preoperatively and at 6 and 12 months after surgery. Those who receivedGADgene therapy developed a unique treatment-dependent polysynaptic brain circuit that we termed as theGAD–related pattern (GADRP), which reflected the formation of new polysynaptic functional pathways linking the STN to motor cortical regions. Patients in both the treatment group and the sham group expressed the previously reported placebo network (the sham surgery–related pattern or SSRP) when blinded to the treatment received. However, only the appearance of the GADRP correlated with clinical improvement in the gene therapy–treated subjects. Treatment-induced brain circuits can thus be useful in clinical trials for isolating true treatment responses and providing insight into their underlying biological mechanisms.

FDG-PET hypometabolism is more sensitive than MRI atrophy in Parkinson's disease: A whole-brain multimodal imaging meta-analysis

Recently, revised diagnostic criteria for Parkinson's disease (PD) were introduced (Postuma et al., 2015). Yet, except for well-established dopaminergic imaging, validated imaging biomarkers for PD are still missing, though they could improve diagnostic accuracy. We conducted systematic meta-analyses to identify PD-specific markers in whole-brain structural magnetic resonance imaging (MRI), [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET) and diffusion tensor imaging (DTI) studies. Overall, 74 studies were identified including 2323 patients and 1767 healthy controls. Studies were first grouped according to imaging modalities (MRI 50; PET 14; DTI 10) and then into subcohorts based on clinical phenotypes. To ensure reliable results, we combined established meta-analytical algorithms - anatomical likelihood estimation and seed-based D mapping - and cross-validated them in a conjunction analysis. Glucose hypometabolism was found using FDG-PET extensively in bilateral inferior parietal cortex and left caudate nucleus with both meta-analytic methods. This hypometabolism pattern was confirmed in subcohort analyses and related to cognitive deficits (inferior parietal cortex) and motor symptoms (caudate nucleus). Structural MRI showed only small focal gray matter atrophy in the middle occipital gyrus that was not confirmed in subcohort analyses. DTI revealed fractional anisotropy reductions in the cingulate bundle near the orbital and anterior cingulate gyri in PD. Our results suggest that FDG-PET reliably identifies consistent functional brain abnormalities in PD, whereas structural MRI and DTI show only focal alterations and rather inconsistent results. In conclusion, FDG-PET hypometabolism outperforms structural MRI in PD, although both imaging methods do not offer disease-specific imaging biomarkers for PD.

Hybrid PET-MRI Applications in Movement Disorders

Even before the success of combined positron emission tomography and computed tomography (PET/CT), the neuroimaging community was conceiving the idea to integrate the positron emission tomography (PET), with very high molecular quantitative data but low spatial resolution, and magnetic resonance imaging (MRI), with high spatial resolution. Several technical limitations have delayed the use of a hybrid scanner in neuroimaging studies, including the full integration of the PET detector ring within the MRI system, the optimization of data acquisition, and the implementation of reliable methods for PET attenuation, motion correction, and joint image reconstruction. To be valid and useful in clinical and research settings, this instrument should be able to simultaneously acquire PET and MRI, and generate quantitative parametric PET images comparable to PET-CT. While post hoc co-registration of combined PET and MRI data acquired separately became the most reliable technique for the generation of "fused" PET-MRI images, only hybrid PET-MRI approach allows merging these measurements naturally and correlating them in a temporal manner. Furthermore, hybrid PET-MRI represents the most accurate tool to investigate in vivo the interplay between molecular and functional aspects of brain pathophysiology. Hybrid PET-MRI technology is still in the early stages in the movement disorders field, due to the limited availability of scanners with integrated optimized methodological models. This technology is ideally suited to investigate interactions between resting-state functional/arterial spin labeling MRI and [¹⁸F]FDG PET glucose metabolism in the evaluation of the brain "hubs" particularly vulnerable to neurodegeneration, areas with a high degree of connectivity and associated with an efficient synaptic neurotransmission. In Parkinson's disease, hybrid PET-MRI is also the ideal instrument to deeper explore the relationship between resting-state functional MRI and dopamine release at [¹¹C]raclopride PET challenge, in the identification of early drug-naïve Parkinson's disease patients at higher risk of motor complications and in the evaluation of the efficacy of novel neuroprotective treatment able to restore at the same time the altered resting state and the release of dopamine. In this chapter, we discuss the key methodological aspects of hybrid PET-MRI; the evidence in movement disorders of the key resting-state functional and perfusion MRI; [¹⁸F]FDG PET and [¹¹C]raclopride PET challenge studies; the potential advantages of using hybrid PET-MRI to investigate the pathophysiology of movement disorders and neurodegenerative diseases. Future directions of hybrid PET-MRI will be discussed alongside with up-to-date technological innovations on hybrid systems.

Identifying mild-moderate Parkinson's disease using whole-brain functional connectivity

OBJECTIVE

Our study aims to extract significant disorder-associated patterns from whole brain functional connectivity to distinguish mild-moderate Parkinson's disease (PD) patients from controls.

METHODS

Resting-state fMRI data were measured from thirty-six PD individuals and thirty-five healthy controls. Multivariate pattern analysis was applied to investigate whole-brain functional connectivity patterns in individuals with 'mild-moderate' PD. Additionally, the relationship between the asymmetry of functional connectivity and the side of the initial symptoms was also analyzed.

RESULTS

In a leave-one-out cross-validation, we got the generalization rate of 80.28% for distinguishing PD patients from controls. The most discriminative functional connectivity was found in cortical networks that included the default mode, sensorimotor and attention networks. Compared to patients with the left side initially affected, an increased abnormal functional connectivity was found in patients in whom the right side was initially affected.

CONCLUSIONS

Our results indicated that discriminative functional connectivity is likely associated with disturbances of cortical networks involved in sensorimotor control and attention. The spatiotemporal patterns of motor asymmetry may be related to the lateralized dysfunction on the early stages of PD.

SIGNIFICANCE

This study identifies discriminative functional connectivity that is associated with disturbances of cortical networks. Our results demonstrated new evidence regarding the functional brain changes related to the unilateral motor symptoms of early PD.

A multivariate metabolic imaging marker for behavioral variant frontotemporal dementia

Introduction

The heterogeneity of behavioral variant frontotemporal dementia (bvFTD) calls for multivariate imaging biomarkers.

Methods

We studied a total of 148 dementia patients from the Feinstein Institute (Center-A: 25 bvFTD and 10 Alzheimer's disease), Technical University of Munich (Center-B: 44 bvFTD and 29 FTD language variants), and Alzheimer's Disease Neuroimaging Initiative (40 Alzheimer's disease subjects). To identify the covariance pattern of bvFTD (behavioral variant frontotemporal dementia–related pattern [bFDRP]), we applied principal component analysis to combined 18F-fluorodeoxyglucose–positron emission tomography scans from bvFTD and healthy subjects. The phenotypic specificity and clinical correlates of bFDRP expression were assessed in independent testing sets.

Results

The bFDRP was identified in Center-A data (24.1% of subject × voxel variance; P < .001), reproduced in Center-B data (P < .001), and independently validated using combined testing data (receiver operating characteristics–area under the curve = 0.97; P < .0001). The expression of bFDRP was specifically elevated in bvFTD patients (P < .001) and was significantly higher at more advanced disease stages (P = .035:duration; P < .01:severity).

Discussion

The bFDRP can be used as a quantitative imaging marker to gauge the underlying disease process and aid in the differential diagnosis of bvFTD.

European Association of Nuclear Medicine and European Academy of Neurology recommendations for the use of brain 18 F-fluorodeoxyglucose positron emission tomography in neurodegenerative cognitive impairment and dementia: Delphi consensus

BACKGROUND AND PURPOSE

Recommendations for using fluorodeoxyglucose positron emission tomography (FDG-PET) to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured.

METHODS

Twenty-one questions on diagnostic issues and on semi-automated analysis to assist visual reading were defined. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiver operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion.

RESULTS

Of the 1435 papers, 58 papers provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudo-dementia; differentiating between AD and DLB, FTLD or vascular dementia, between DLB and FTLD, and between Parkinson's disease and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in Parkinson's disease; using semi-automated assessment to assist visual reading. Panellists did not support FDG-PET use for pre-clinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis and Huntington disease diagnoses, and for amyotrophic lateral sclerosis or Huntington-disease-related cognitive decline.

CONCLUSIONS

Despite limited formal evidence, panellists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semi-automated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations.

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 [¹⁸F]‑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 (ρ = 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 (ρ = 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.

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A metabolic cerebral pattern could be identified in FDG-PET data of SCA3 patients, which was not explained by regional atrophy.

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Striatum decreases in the SCA3-pattern reflect extrapyramidal involvement.

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The SCA3-pattern reflects changes in higher-order motor control.

Reproducible network and regional topographies of abnormal glucose metabolism associated with progressive supranuclear palsy: Multivariate and univariate analyses in American and Chinese patient cohorts

Progressive supranuclear palsy (PSP) is a rare movement disorder and often difficult to distinguish clinically from Parkinson's disease (PD) and multiple system atrophy (MSA) in early phases. In this study, we report reproducible disease-related topographies of brain network and regional glucose metabolism associated with PSP in clinically-confirmed independent cohorts of PSP, MSA, and PD patients and healthy controls in the USA and China. Using ¹⁸ F-FDG PET images from PSP and healthy subjects, we applied spatial covariance analysis with bootstrapping to identify a PSP-related pattern (PSPRP) and estimate its reliability, and evaluated the ability of network scores for differential diagnosis. We also detected regional metabolic differences using statistical parametric mapping analysis. We produced a highly reliable PSPRP characterized by relative metabolic decreases in the middle prefrontal cortex/cingulate, ventrolateral prefrontal cortex, striatum, thalamus and midbrain, covarying with relative metabolic increases in the hippocampus, insula and parieto-temporal regions. PSPRP network scores correlated positively with PSP duration and accurately discriminated between healthy, PSP, MSA and PD groups in two separate cohorts of parkinsonian patients at both early and advanced stages. Moreover, PSP patients shared many overlapping areas with abnormal metabolism in the same cortical and subcortical regions as in the PSPRP. With rigorous cross-validation, this study demonstrated highly comparable and reproducible PSP-related metabolic topographies at network and regional levels across different patient populations and PET scanners. Metabolic brain network activity may serve as a reliable and objective marker of PSP, although cross-validation applying recent diagnostic criteria and classification is warranted.

Validation of abnormal glucose metabolism associated with Parkinson's disease in Chinese participants based on 18F-fluorodeoxyglucose positron emission tomography imaging

PURPOSE

We previously identified disease-related cerebral metabolic characteristics associated with Parkinson's disease (PD) in the Chinese population using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) imaging. The present study aims to assess data reproducibility and robustness of the metabolic activity characteristics across independent cohorts.

PATIENTS AND METHODS

Forty-eight patients with PD and 48 healthy controls from Chongqing district, in addition to 33 patients with PD and 33 healthy controls from Shanghai district were recruited. Each subject underwent brain 18F-FDG PET/CT imaging in a resting state. Based on the brain images, differences between the groups and PD-related cerebral metabolic activities were graphically and quantitatively evaluated.

RESULTS

Both PD patient cohorts exhibited analogous cerebral patterns characterized by metabolic increase in the putamen, globus pallidus, thalamus, pons, sensorimotor cortex and cerebellum, along with metabolic decrease in parieto-occipital areas. Additionally, the metabolic pattern was highly indicative of the disease, with a significant elevation in PD patients compared with healthy controls (p<0.001) in both the derivation (Shanghai) and validation (Chongqing) cohorts.

CONCLUSION

This dual-center study demonstrated the high comparability and reproducibility of PD-related cerebral metabolic activity patterns across independent Chinese cohorts and may serve as an objective diagnostic marker for the disease.

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.

Disruption of network for visual perception of natural motion in primary dystonia

In healthy subjects, brain activation in motor regions is greater during the visual perception of "natural" target motion, which complies with the two-thirds power law, than of "unnatural" motion, which does not. It is unknown whether motion perception is normally mediated by a specific network that can be altered in the setting of disease. We used block-design functional magnetic resonance imaging and covariance analysis to identify normal network topographies activated in response to "natural" versus "unnatural" motion. A visual motion perception-related pattern (VPRP) was identified in 12 healthy subjects, characterized by covarying activation responses in the inferior parietal lobule, frontal operculum, lateral occipitotemporal cortex, amygdala, and cerebellum (Crus I). Selective VPRP activation during "natural" motion was confirmed in 12 testing scans from healthy subjects. Consistent network activation was not seen, however, in 29 patients with dystonia, a neurodevelopmental disorder in which motion perception pathways may be involved. Using diffusion tractography, we evaluated the integrity of anatomical connections between the major VPRP nodes. Indeed, fiber counts in these pathways were substantially reduced in the dystonia subjects. In aggregate, the findings associate normal motion perception with a discrete brain network which can be disrupted under pathological conditions.

Altered Functional Brain Connectomes between Sporadic and Familial Parkinson's Patients

Familial Parkinson's disease (PD) is often caused by mutation of a certain gene, while sporadic PD is associated with variants of genes which can influence the susceptibility to PD. The goal of this study was to investigate the difference between the two forms of PD in terms of brain abnormalities using resting-state functional MRI and graph theory. Thirty-one familial PD patients and 36 sporadic PD patients underwent resting-state functional MRI scanning. Frequency-dependent functional connectivity was calculated for each subject using wavelet-based correlations of BOLD signal over 246 brain regions from Brainnetome Atlas. Graph theoretical analysis was then performed to analyze the topology of the functional network, and functional connectome differences were identified with a network-based statistical approach. Our results revealed a frequency-specific (0.016 and 0.031 Hz) connectome difference between familial and sporadic forms of PD, as indicated by an increase in assortativity and decrease in the nodal strength in the left medial amygdala of the familial PD group. In addition, the familial PD patients also showed a distinctive functional network between the left medial amygdala and regions related to retrieval of motion information. The present study indicates that the medial amygdala might be most vulnerable to both sporadic and familial PD. Our findings provide some new insights into disrupted resting-state functional connectomes between sporadic PD and familial PD.

Levodopa-induced abnormal involuntary movements correlate with altered permeability of the blood-brain-barrier in the basal ganglia

Chronic levodopa treatment leads to the appearance of dyskinesia in the majority of Parkinson’s disease patients. Neurovascular dysregulation in putaminal and pallidal regions is thought to be an underlying feature of this complication of treatment. We used microPET to study unilaterally lesioned 6-hydroxydopamine rats that developed levodopa-induced abnormal involuntary movements (AIMs) after three weeks of drug treatment. Animals were scanned with [¹⁵O]-labeled water and [¹⁸F]-fluorodeoxyglucose, to map regional cerebral blood flow and glucose metabolism, and with [¹¹C]-isoaminobutyric acid (AIB), to assess blood-brain-barrier (BBB) permeability, following separate injections of levodopa or saline. Multitracer scan data were acquired in each animal before initiating levodopa treatment, and again following the period of daily drug administration. Significant dissociation of vasomotor and metabolic levodopa responses was seen in the striatum/globus pallidus (GP) of the lesioned hemisphere. These changes were accompanied by nearby increases in [¹¹C]-AIB uptake in the ipsilateral GP, which correlated with AIMs scores. Histopathological analysis revealed high levels of microvascular nestin immunoreactivity in the same region. The findings demonstrate that regional flow-metabolism dissociation and increased BBB permeability are simultaneously induced by levodopa within areas of active microvascular remodeling, and that such changes correlate with the severity of dyskinesia.

Increased putamen hypercapnic vasoreactivity in levodopa-induced dyskinesia

In a rodent model of Parkinson's disease (PD), levodopa-induced involuntary movements have been linked to striatal angiogenesis - a process that is difficult to document in living human subjects. Angiogenesis can be accompanied by localized increases in cerebral blood flow (CBF) responses to hypercapnia. We therefore explored the possibility that, in the absence of levodopa, local hypercapnic CBF responses are abnormally increased in PD patients with levodopa-induced dyskinesias (LID) but not in their nondyskinetic (NLID) counterparts. We used H215O PET to scan 24 unmedicated PD subjects (12 LID and 12 NLID) and 12 matched healthy subjects in the rest state under normocapnic and hypercapnic conditions. Hypercapnic CBF responses were compared to corresponding levodopa responses from the same subjects. Group differences in hypercapnic vasoreactivity were significant only in the posterior putamen, with greater CBF responses in LID subjects compared with the other subjects. Hypercapnic and levodopa-mediated CBF responses measured in this region exhibited distinct associations with disease severity: the former correlated with off-state motor disability ratings but not symptom duration, whereas the latter correlated with symptom duration but not motor disability. These are the first in vivo human findings linking LID to microvascular changes in the basal ganglia.

Merging Clinical and Imaging Biomarkers to Tackle Parkinson's Disease

Background

In Parkinson's disease, biomarkers represent tools that are potentially suitable for either clinical or research settings and are useful in predicting onset, confirming diagnosis, detecting progression, and evaluating response to potential disease-modifying treatments. The range of available biomarkers in Parkinson's disease is fast expanding and includes an increasing amount of laboratory, clinical, and imaging data. Indeed, the latter 2 represent the cornerstones of the diagnostic criteria for Parkinson's disease recently proposed by the International Parkinson and Movement Disorders Society Task Force on the definition of Parkinson's disease.

Methods and Results

In this review, we describe current knowledge and emerging findings on clinical (with emphasis on nonmotor symptoms) and imaging biomarkers for Parkinson's disease, with a focus on prodromal, diagnostic, and middle/advanced phases.

Conclusion

An increasing body of evidence suggests that merging clinical and imaging biomarkers through disease stages may be the best, fastest track to tackle Parkinson's disease.

Molecular Imaging and Updated Diagnostic Criteria in Lewy Body Dementias

PURPOSE OF REVIEW

The aims of the study were to review recent advances in molecular imaging in the Lewy body dementias (LBD) and determine if these may support the clinical but contested temporal profile distinction between Parkinson disease (PD) with dementia (PDD) versus dementia with Lewy bodies (DLB).

RECENT FINDINGS

There do not appear to be major regional cerebral metabolic or neurotransmitter distinctions between PDD and DLB. However, recent studies highlight the relative discriminating roles of Alzheimer proteinopathies. PDD patients have lower cortical β-amyloid deposition than DLB. Preliminary tau PET studies suggest a gradient of increasing tau binding from cognitively normal PD (absent to lowest) to cognitively impaired PD (low) to DLB (intermediate) to Alzheimer disease (AD; highest). However, tau binding in DLB, including the medial temporal lobe, is substantially lower than in AD. Alzheimer-type proteinopathies appear to be more common in DLB compared to PDD with relative but no absolute differences. Given the spectrum of overlapping pathologies, future α-synuclein ligands are expected to have the best potential to distinguish the LBD from pure AD.

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 ¹⁸F‐fluorodeoxyglucose PET, dopamine transporter imaging, and olfactory testing. For reference, we included ¹⁸F‐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.

Neuroimaging in pre-motor Parkinson's disease

The process of neurodegeneration in Parkinson's disease begins long before the onset of clinical motor symptoms, resulting in substantial cell loss by the time a diagnosis can be made. The period between the onset of neurodegeneration and the development of motoric disease would be the ideal time to intervene with disease modifying therapies. This pre-motor phase can last many years, but the lack of a specific clinical phenotype means that objective biomarkers are needed to reliably detect prodromal disease. In recent years, recognition that patients with REM sleep behaviour disorder (RBD) are at particularly high risk of future parkinsonism has enabled the development of large prodromal cohorts in which to investigate novel biomarkers, and neuroimaging has generated some of the most promising results to date. Here we review investigations undertaken in RBD and other pre-clinical cohorts, including modalities that are well established in clinical Parkinson's as well as novel imaging methods. Techniques such as high resolution MRI of the substantia nigra and functional imaging of Parkinsonian brain networks have great potential to facilitate early diagnosis. Further longitudinal studies will establish their true value in quantifying prodromal neurodegeneration and predicting future Parkinson's.

Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations

Little is known of the precise relationship between the expression of disease-related metabolic patterns and nigrostriatal dopaminergic dysfunction in parkinsonism. We studied 51 subjects with Parkinson's disease (PD) (18 non-demented, 24 demented, and 9 dementia with Lewy bodies) and 127 with atypical parkinsonian syndromes (47 multiple system atrophy (MSA), 38 progressive supranuclear palsy (PSP), and 42 corticobasal syndrome (CBS)) with ¹⁸F-fluorodeoxyglucose PET to quantify the expression of previously validated disease-related patterns for PD, MSA, PSP, and CBS and ¹⁸F-fluoropropyl-β-CIT PET to quantify caudate and putamen dopamine transporter (DAT) binding. The patients in each group exhibited significant elevations in the expression of the corresponding disease-related pattern ( p < 0.001), relative to 16 healthy subjects. With the exception of cerebellar MSA (MSA-C), all groups displayed significant reductions in putamen DAT binding relative to healthy subjects ( p < 0.05). Correlations between the dopaminergic and metabolic measures were significant in PD and CBS but not in MSA and PSP. In all patient groups with the exception of MSA-C and CBS, pattern expression values and DAT binding correlated with disease duration and severity measures. The findings suggest that in these parkinsonian disorders, metabolic network expression and DAT binding provide complementary information regarding the underlying disease process.

Apomorphine pump in advanced Parkinson's disease: Effects on motor and nonmotor symptoms with brain metabolism correlations

INTRODUCTION

Patients with advanced Parkinson's disease (PD) and contraindications for subthalamic nucleus deep brain stimulation (DBS) could particularly benefit from subcutaneous infusion therapy with apomorphine. This original study was designed to evaluate the general efficacy of add-on apomorphine in motor and nonmotor symptoms in advanced PD, while characterizing the changes induced in brain glucose metabolism. The aim was to look at the underlying anatomical-functional pathways.

METHODS

12 patients with advanced PD were assessed before and after 6months of add-on apomorphine, using resting-state ¹⁸F-fluorodeoxyglucose positron emission tomography and exhaustive clinical assessments.

RESULTS

After 6months of therapy, oral treatment was significantly reduced. Both motor and nonmotor scores improved, with a beneficial effect on executive functions, quality of life and apathy. Significant metabolic changes were observed, with overall increases in the right fusiform gyrus and hippocampus, alongside a decrease in the left middle frontal gyrus. Consistent correlations between significant changes in clinical scores and metabolism were established.

CONCLUSION

Well tolerated, add-on apomorphine appears to be an interesting option for patients with fluctuations and contra-indications for DBS. Changes in brain metabolism, with beneficial effects on motor and nonmotor symptoms were observed after 6months. These preliminary results have to be confirmed by further studies.

Distinct brain networks underlie cognitive dysfunction in Parkinson and Alzheimer diseases

OBJECTIVE

To determine whether cognitive impairment in Parkinson disease (PD) and Alzheimer disease (AD) derives from the same network pathology.

METHODS

We analyzed 18F-fluorodeoxyglucose PET scans from 40 patients with AD and 40 age-matched healthy controls from the Alzheimer's Disease Neuroimaging Initiative and scanned an additional 10 patients with AD and 10 healthy controls at The Feinstein Institute for Medical Research to derive an AD-related metabolic pattern (ADRP) analogous to our previously established PD cognition-related pattern (PDCP) and PD motor-related pattern (PDRP). We computed individual subject expression values for ADRP and PDCP in 89 patients with PD and correlated summary scores for cognitive functioning with network expression. We also evaluated changes in ADRP and PDCP expression in a separate group of 15 patients with PD scanned serially over a 4-year period.

RESULTS

Analysis revealed a significant AD-related metabolic topography characterized by covarying metabolic reductions in the hippocampus, parahippocampal gyrus, and parietal and temporal association regions. Expression of ADRP, but not PDCP, was elevated in both AD groups and correlated with worse cognitive summary scores. Patients with PD showed slight ADRP expression, due to topographic overlap with the network underlying PD motor-related pattern degeneration, but only their PDCP expression values increased as cognitive function and executive performance declined. Longitudinal data in PD disclosed an analogous dissociation of network expression.

CONCLUSIONS

Cognitive dysfunction in PD is associated with a specific brain network that is largely spatially and functionally distinct from that seen in relation to AD.

Flow-metabolism dissociation in the pathogenesis of levodopa-induced dyskinesia

Levodopa-induced dyskinesia (LID) is the most common, disruptive complication of Parkinson's disease (PD) pharmacotherapy, yet despite decades of research, the changes in regional brain function underlying LID remain largely unknown. We previously found that the cerebral vasomotor and metabolic responses to levodopa are dissociated in PD subjects. Nonetheless, it is unclear whether levodopa-mediated dissociation is exaggerated in LID or distinguishes LID from non-LID subjects. To explore this possibility, we used dual-tracer positron emission tomography to quantify regional cerebral blood flow and metabolic activity in 28 PD subjects (14 LID, 14 non-LID), scanned before and during intravenous levodopa infusion. Levodopa-mediated dissociation was most prominent in the posterior putamen (P < 0.0001) and greater in LID than in non-LID and test-retest subjects. Strikingly, LID subjects also showed increased sensorimotor cortex (SMC) activity in the baseline, unmedicated state. Imaging data from an independent PD sample (106 subjects) linked these differences to loss of mesocortical dopamine terminals in advanced patients. In aggregate, the data suggest that LID results from an overactive vasomotor response to levodopa in the putamen on a background of disease-related increases in SMC activity. LID may thus be amenable to treatment that modulates the function of these 2 regions.

Reproducibility of a Parkinsonism-related metabolic brain network in non-human primates: A descriptive pilot study with FDG PET

BACKGROUND

We have previously defined a parkinsonism-related metabolic brain network in rhesus macaques using a high-resolution research positron emission tomography camera. This brief article reports a descriptive pilot study to assess the reproducibility of network activity and regional glucose metabolism in independent parkinsonian macaques using a clinical positron emission tomography/CT camera.

METHODS

[(18)F]fluorodeoxyglucose PET scans were acquired longitudinally over 3 months in three drug-naïve parkinsonian and three healthy control cynomolgus macaques. Group difference and test-retest stability in network activity and regional glucose metabolism were evaluated graphically, using all brain images from these macaques.

RESULTS

Comparing the parkinsonian macaques with the controls, network activity was elevated and remained stable over 3 months. Normalized glucose metabolism increased in putamen/globus pallidus and sensorimotor regions but decreased in posterior parietal cortices.

CONCLUSIONS

Parkinsonism-related network activity can be reliably quantified in different macaques with a clinical positron emission tomography/CT scanner and is reproducible over a period typically employed in preclinical intervention studies. This measure can be a useful biomarker of disease process or drug effects in primate models of Parkinson's disease.

Parkinson's disease-related network topographies characterized with resting state functional MRI

Spatial covariance mapping can be used to identify and measure the activity of disease-related functional brain networks. While this approach has been widely used in the analysis of cerebral blood flow and metabolic PET scans, it is not clear whether it can be reliably applied to resting state functional MRI (rs-fMRI) data. In this study, we present a novel method based on independent component analysis (ICA) to characterize specific network topographies associated with Parkinson's disease (PD). Using rs-fMRI data from PD and healthy subjects, we used ICA with bootstrap resampling to identify a PD-related pattern that reliably discriminated the two groups. This topography, termed rs-MRI PD-related pattern (fPDRP), was similar to previously characterized disease-related patterns identified using metabolic PET imaging. Following pattern identification, we validated the fPDRP by computing its expression in rs-fMRI testing data on a prospective case basis. Indeed, significant increases in fPDRP expression were found in separate sets of PD and control subjects. In addition to providing a similar degree of group separation as PET, fPDRP values correlated with motor disability and declined toward normal with levodopa administration. Finally, we used this approach in conjunction with neuropsychological performance measures to identify a separate PD cognition-related pattern in the patients. This pattern, termed rs-fMRI PD cognition-related pattern (fPDCP), was topographically similar to its PET-derived counterpart. Subject scores for the fPDCP correlated with executive function in both training and testing data. These findings suggest that ICA can be used in conjunction with bootstrap resampling to identify and validate stable disease-related network topographies in rs-fMRI. Hum Brain Mapp 38:617-630, 2017. © 2016 Wiley Periodicals, Inc.

Resting-state connectivity predicts levodopa-induced dyskinesias in Parkinson's disease

Background

Levodopa‐induced dyskinesias are a common side effect of dopaminergic therapy in PD, but their neural correlates remain poorly understood.

Objectives

This study examines whether dyskinesias are associated with abnormal dopaminergic modulation of resting‐state cortico‐striatal connectivity.

Methods

Twelve PD patients with peak‐of‐dose dyskinesias and 12 patients without dyskinesias were withdrawn from dopaminergic medication. All patients received a single dose of fast‐acting soluble levodopa and then underwent resting‐state functional magnetic resonance imaging before any dyskinesias emerged. Levodopa‐induced modulation of cortico‐striatal resting‐state connectivity was assessed between the putamen and the following 3 cortical regions of interest: supplementary motor area, primary sensorimotor cortex, and right inferior frontal gyrus. These functional connectivity measures were entered into a linear support vector classifier to predict whether an individual patient would develop dyskinesias after levodopa intake. Linear regression analysis was applied to test which connectivity measures would predict dyskinesia severity.

Results

Dopaminergic modulation of resting‐state connectivity between the putamen and primary sensorimotor cortex in the most affected hemisphere predicted whether patients would develop dyskinesias with a specificity of 100% and a sensitivity of 91% (P < .0001). Modulation of resting‐state connectivity between the supplementary motor area and putamen predicted interindividual differences in dyskinesia severity (R² = 0.627, P = .004). Resting‐state connectivity between the right inferior frontal gyrus and putamen neither predicted dyskinesia status nor dyskinesia severity.

Conclusions

The results corroborate the notion that altered dopaminergic modulation of cortico‐striatal connectivity plays a key role in the pathophysiology of dyskinesias in PD. © 2016 International Parkinson and Movement Disorder Society

Hybrid PET/MR Imaging and Brain Connectivity

In recent years, brain connectivity is gaining ever-increasing interest from the interdisciplinary research community. The study of brain connectivity is characterized by a multifaceted approach providing both structural and functional evidence of the relationship between cerebral regions at different scales. Although magnetic resonance (MR) is the most established imaging modality for investigating connectivity in vivo, the recent advent of hybrid positron emission tomography (PET)/MR scanners paved the way for more comprehensive investigation of brain organization and physiology. Due to the high sensitivity and biochemical specificity of radiotracers, combining MR with PET imaging may enrich our ability to investigate connectivity by introducing the concept of metabolic connectivity and cometomics and promoting new insights on the physiological and molecular bases underlying high-level neural organization. This review aims to describe and summarize the main methods of analysis of brain connectivity employed in MR imaging and nuclear medicine. Moreover, it will discuss practical aspects and state-of-the-art techniques for exploiting hybrid PET/MR imaging to investigate the relationship of physiological processes and brain connectivity.

Assessing cerebral glucose metabolism in patients with idiopathic rapid eye movement sleep behavior disorder

Idiopathic rapid eye movement sleep behavior disorder (RBD) is a risk marker for subsequent development of neurodegenerative parkinsonism. In this study, we aimed to investigate whether regional cerebral metabolism is altered in patients with RBD and whether regional metabolic activities are associated with clinical measurements in individual patients. Twenty-one patients with polysomnogram-confirmed RBD and 21 age-matched healthy controls were recruited to undertake positron emission tomography imaging with [(18)F]fluorodeoxyglucose. Differences in normalized regional metabolism and correlations between metabolic activity and clinical indices in RBD patients were evaluated on a voxel basis using statistic parametric mapping analysis. Compared with controls, patients with RBD showed increased metabolism in the hippocampus/parahippocampus, cingulate, supplementary motor area, and pons, but decreased metabolism in the occipital cortex/lingual gyrus (P<0.001). RBD duration correlated with metabolism positively in the anterior vermis (r=0.55, P=0.01), but negatively in the medial frontal gyrus (r=-0.59, P=0.005). In addition, chin electromyographic activity presented a positive metabolic correlation in the hippocampus/parahippocampus (r=0.48, P=0.02), but a negative metabolic correlation in the posterior cingulate (r=-0.61, P=0.002). This study has suggested that region-specific metabolic abnormalities exist in RBD patients and regional metabolic activities are associated with clinical measures such as RBD duration and chin electromyographic activity.

Automated Differential Diagnosis of Early Parkinsonism Using Metabolic Brain Networks: A Validation Study

The differentiation of idiopathic Parkinson disease (IPD) from multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), the most common atypical parkinsonian look-alike syndromes (APS), can be clinically challenging. In these disorders, diagnostic inaccuracy is more frequent early in the clinical course when signs and symptoms are mild. Diagnostic inaccuracy may be particularly relevant in trials of potential disease-modifying agents, which typically involve participants with early clinical manifestations. In an initial study, we developed a probabilistic algorithm to classify subjects with clinical parkinsonism but uncertain diagnosis based on the expression of metabolic covariance patterns for IPD, MSA, and PSP. Classifications based on this algorithm agreed closely with final clinical diagnosis. Nonetheless, blinded prospective validation is required before routine use of the algorithm can be considered.

METHODS

We used metabolic imaging to study an independent cohort of 129 parkinsonian subjects with uncertain diagnosis; 77 (60%) had symptoms for 2 y or less at the time of imaging. After imaging, subjects were followed by blinded movement disorders specialists for an average of 2.2 y before final diagnosis was made. When the algorithm was applied to the individual scan data, the probabilities of IPD, MSA, and PSP were computed and used to classify each of the subjects. The resulting image-based classifications were then compared with the final clinical diagnosis.

RESULTS

IPD subjects were distinguished from APS with 94% specificity and 96% positive predictive value (PPV) using the original 2-level logistic classification algorithm. The algorithm achieved 90% specificity and 85% PPV for MSA and 94% specificity and 94% PPV for PSP. The diagnostic accuracy was similarly high (specificity and PPV > 90%) for parkinsonian subjects with short symptom duration. In addition, 25 subjects were classified as level I indeterminate parkinsonism and 4 more subjects as level II indeterminate APS.

CONCLUSION

Automated pattern-based image classification can improve the diagnostic accuracy in patients with parkinsonism, even at early disease stages.

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.