Shichun Peng

Metabolic resting-state brain networks in health and disease

Significance We present an innovative approach to evaluate default mode network (DMN) activity in individual subjects using metabolic imaging. After characterizing a distinct set of metabolic resting state networks (RSNs) in healthy subjects, network activity was tracked over time in patients with neurodegenerative disorders, such as Parkinson’s disease and Alzheimer’s disease. We found that the dominant normal metabolic RSN, which corresponded to the DMN, is preserved in early-stage Parkinson’s disease patients. Although significant DMN reductions developed later, these changes were reversible in part by dopamine treatment. This finding contrasts with Alzheimer’s disease, in which DMN loss is rapid and continuous, beginning before clinical diagnosis. Metabolic imaging can provide a versatile, quantitative means of assessing brain disease at the network level. The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson’s disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the “DMN-like” dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer’s disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer’s disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.

Abnormal metabolic brain networks in a nonhuman primate model of parkinsonism.

Parkinsons disease (PD) is associated with a characteristic regional metabolic covariance pattern that is modulated by treatment. To determine whether a homologous metabolic pattern is also present in nonhuman primate models of parkinsonism, 11 adult macaque monkeys with parkinsonism secondary to chronic systemic 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and 12 age-matched healthy animals were scanned with [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET). A subgroup comprising five parkinsonian and six control animals was used to identify a parkinsonism-related pattern (PRP). For validation, analogous topographies were derived from other subsets of parkinsonian and control animals. The PRP topography was characterized by metabolic increases in putamen/pallidum, thalamus, pons, and sensorimotor cortex, as well as reductions in the posterior parietal-occipital region. Pattern expression was significantly elevated in parkinsonian relative to healthy animals (P < 0.00001). Parkinsonism-related topographies identified in the other derivation sets were very similar, with significant pairwise correlations of region weights (r > 0.88; P < 0.0001) and subject scores (r > 0.74; P < 0.01). Moreover, pattern expression in parkinsonian animals correlated with motor ratings (r > 0.71; P < 0.05). Thus, homologous parkinsonism-related metabolic networks are demonstrable in PD patients and in monkeys with experimental parkinsonism. Network quantification may provide a useful biomarker for the evaluation of new therapeutic agents in preclinical models of PD.

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

The scaled subprofile model (SSM)(1-4) is a multivariate PCA-based algorithm that identifies major sources of variation in patient and control group brain image data while rejecting lesser components (Figure 1). Applied directly to voxel-by-voxel covariance data of steady-state multimodality images, an entire group image set can be reduced to a few significant linearly independent covariance patterns and corresponding subject scores. Each pattern, termed a group invariant subprofile (GIS), is an orthogonal principal component that represents a spatially distributed network of functionally interrelated brain regions. Large global mean scalar effects that can obscure smaller network-specific contributions are removed by the inherent logarithmic conversion and mean centering of the data(2,5,6). Subjects express each of these patterns to a variable degree represented by a simple scalar score that can correlate with independent clinical or psychometric descriptors(7,8). Using logistic regression analysis of subject scores (i.e. pattern expression values), linear coefficients can be derived to combine multiple principal components into single disease-related spatial covariance patterns, i.e. composite networks with improved discrimination of patients from healthy control subjects(5,6). Cross-validation within the derivation set can be performed using bootstrap resampling techniques(9). Forward validation is easily confirmed by direct score evaluation of the derived patterns in prospective datasets(10). Once validated, disease-related patterns can be used to score individual patients with respect to a fixed reference sample, often the set of healthy subjects that was used (with the disease group) in the original pattern derivation(11). These standardized values can in turn be used to assist in differential diagnosis(12,13) and to assess disease progression and treatment effects at the network level(7,14-16). We present an example of the application of this methodology to FDG PET data of Parkinsons Disease patients and normal controls using our in-house software to derive a characteristic covariance pattern biomarker of disease.

Dopamine cell transplantation in Parkinson's disease: challenge and perspective

BACKGROUND Functional imaging provides a valuable adjunct to clinical evaluation for assessing the efficacy of cell-based restorative therapies in Parkinsons disease (PD). SOURCES OF DATA In this article, we review the latest advances on the use of positron emission tomography (PET) imaging in evaluating the surgical outcome of embryonic dopamine (DA) cell transplantation in PD patients. AREAS OF AGREEMENT These studies suggest long-term cell survival and clinical benefit following striatal transplantation of fetal nigral tissue in PD patients and in models of experimental parkinsonism. AREAS OF CONTROVERSY Adverse events subsequent to transplantation have also been noted and attributed to a variety of causes. GROWING POINTS Optimal outcomes of DA cell transplantation therapies are dependent on tissue composition and phenotype of DA neurons in the graft. AREAS TIMELY FOR DEVELOPING RESEARCH Given continued progress in DA neuron production from stem cells in recent years, transplantation of neural stem cells may be the next to enter clinical trials in patients. CONCLUSION The existing data from studies of embryonic DA transplantation for advanced PD have provided valuable insights for the design of new cell-based therapies for the treatment of this and related neurodegenerative disorders.

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 Parkinsons 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.

Modulation of abnormal metabolic brain networks by experimental therapies in a nonhuman primate model of Parkinson’s disease: an application to human retinal pigment epithelial (hRPE) cell implantation

Abnormal covariance pattern of regional metabolism associated with Parkinson disease (PD) is modulated by dopaminergic pharmacotherapy. Using high-resolution 18F-FDG PET and network analysis, we previously derived and validated a parkinsonism-related metabolic pattern (PRP) in nonhuman primate models of PD. It is currently not known whether this network is modulated by experimental therapeutics. In this study, we examined changes in network activity by striatal implantation of human levodopa-producing retinal pigment epithelial (hRPE) cells in parkinsonian macaques and evaluated the reproducibility of network activity in a small test–retest study. Methods: 18F-FDG PET scans were acquired in 8 healthy macaques and 8 macaques with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)–induced bilateral nigrostriatal dopaminergic lesions after unilateral putaminal implantation of hRPE cells or sham surgery. PRP activity was measured prospectively in all animals and in a subset of test–retest animals using a network quantification approach. Network activity and regional metabolic values were compared on a hemispheric basis between animal groups and treatment conditions. Results: All individual macaques showed clinical improvement after hRPE cell implantation compared with the sham surgery. PRP activity was elevated in the untreated MPTP hemispheres relative to those of the normal controls (P < 0.00005) but was reduced (P < 0.05) in the hRPE-implanted hemispheres. The modulation observed in network activity was supported by concurrent local and remote changes in regional glucose metabolism. PRP activity remained unchanged in the untreated MPTP hemispheres versus the sham-operated hemispheres. PRP activity was also stable (P ≥ 0.29) and correlated (R2 ≥ 0.926; P < 0.00005) in the test–retest hemispheres. These findings were highly reproducible across several PRP topographies generated in multiple cohorts of parkinsonian and healthy macaques. Conclusion: We have demonstrated long-term therapeutic effects of hRPE cell implantation in nonhuman primate models of PD. The implantation of such levodopa-producing cells can concurrently decrease the elevated metabolic network activity in parkinsonian brains on an individual basis. These results parallel the analogous findings reported in patients with PD undergoing levodopa therapy and other symptomatic interventions. With further validation in large samples, 18F-FDG PET imaging with network analysis may provide a viable biomarker for assessing treatment response in animal models of PD after experimental therapies.

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

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.

Dopamine: PET Imaging and Parkinson Disease

This article discusses the current use of PET imaging in the evaluation of dopamine function in Parkinson disease (PD). The article reviews the major radioligands targeting dopaminergic systems in patients with parkinsonian disorders. The primary objective is to show the novel clinical applications of molecular imaging in the diagnosis and assessment of motor and nonmotor symptoms in PD.

Increased putamen hypercapnic vasoreactivity in levodopa-induced dyskinesia

In a rodent model of Parkinsons 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.

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 Parkinsons 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 18F‐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.