The purpose of this study was to identify the specific metabolic brain pattern characteristic for Parkinson's disease (PD): Parkinson's disease-related pattern (PDRP), using network analysis of [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images in a cohort of Slovenian PD patients. Twenty PD patients (age 70.1±7.8years, Movement Disorder Society Unified Parkinson's Disease Motor Rating Scale (MDS-UPDRS-III) 38.3±12.2; disease duration 4.3±4.1years) and 20 age-matched normal controls (NCs) underwent FDG-PET brain imaging. An automatic voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was applied to these scans for PDRP-Slovenia identification. The pattern was characterized by relative hypermetabolism in pallidum, putamen, thalamus, brain stem, and cerebellum associated with hypometabolism in sensorimotor cortex, posterior parietal, occipital, and frontal cortices. The expression of PDRP-Slovenia discriminated PD patients from NCs (p<0.0001) and correlated positively with patients' clinical score (MDS-UPDRS-III, p=0.03). Additionally, its topography agrees well with the original PDRP (p<0.001) identified in American cohort of PD patients. We validated the PDRP-Slovenia expression on additional FDG-PET scans of 20 PD patients, 20 NCs, and 25 patients with atypical parkinsonism (AP). We confirmed that the expression of PDRP-Slovenia manifests good diagnostic accuracy with specificity and sensitivity of 85-90% at optimal pattern expression cutoff for discrimination of PD patients and NCs and is not expressed in AP. PDRP-Slovenia proves to be a robust and reproducible functional imaging biomarker independent of patient population. It accurately differentiates PD patients from NCs and AP and correlates well with the clinical measure of PD progression.

The effect of 18F-FDG-PET image reconstruction algorithms on the expression of characteristic metabolic brain network in Parkinson’s disease

This study aimed to investigate the metabolic brain network and its relationship with depression symptoms using 18F-fluorodeoxyglucose positron emission tomography data in 78 pre-chemotherapy cancer patients with depression and 80 matched healthy subjects. Functional and structural imbalance or disruption of brain networks frequently occur following chemotherapy in cancer patients. However, few studies have focused on the topological organization of the metabolic brain network in cancer with depression, especially those without chemotherapy. The nodal and global parameters of the metabolic brain network were computed for cancer patients and healthy subjects. Significant decreases in metabolism were found in the frontal and temporal gyri in cancer patients compared with healthy subjects. Negative correlations between depression and metabolism were found predominantly in the inferior frontal and cuneus regions, whereas positive correlations were observed in several regions, primarily including the insula, hippocampus, amygdala, and middle temporal gyri. Furthermore, a higher clustering efficiency, longer path length, and fewer hubs were found in cancer patients compared with healthy subjects. The topological organization of the whole-brain metabolic networks may be disrupted in cancer. Finally, the present findings may provide a new avenue for exploring the neurobiological mechanism, which plays a key role in lessening the depression effects in pre-chemotherapy cancer patients.

1838 Objectives In this study, we aimed to first cross-validate the reproducibility of disease-specific brain networks and metabolic activity associated with progressive supranuclear palsy (PSP) by comparing clinically-confirmed patients in the USA and China. Methods The US cohort of 10 patients with PSP and 10 age-matched healthy controls was scanned on a GE Advance PET camera. The Chinese cohort of the same sample size was scanned on a Siemens Biogragh PET/CT system in China. We first identified a PSP-related pattern (PSPRP) in each cohort and computed network scores prospectively in the other cohort. We then localized brain regions with common or different metabolic abnormalities in the same data using statistical parametric mapping analysis. Results Both cohorts produced analogous PSPRPs characterized by metabolic decreases in the middle prefrontal cortex/cingulate, ventrolateral prefrontal cortex, striatum, thalamus and midbrain, covarying with metabolic increases in the hippocampus and parieto-temporal regions. PSPRP scores were similarly elevated (P < 0.0001; post-hoc tests following one-way analysis of variance across the two cohorts) in the patients relative to the controls in the derivation cohort in the USA and in the validation cohort in China or vice versa. PSPRP scores of each pattern correlated strongly (r 蠅 0.96; P < 0.001) in the two corresponding cohorts from the USA and China, respectively. Moreover, the two cohorts of patients shared a large number of overlapping areas with regional metabolic abnormalities (P < 0.0001) in the same cortical and subcortical regions as in both PSPRPs. Conclusions This dual-center collaborative study demonstrated the high comparability and reproducibility of PSP-related metabolic activities at network and regional levels across patient populations, PET instruments and analytical techniques. These results suggest that metabolic brain network activity may serve as a reliable and objective marker of PSP and is worthy of wide applications in clinical diagnosis and translational research.

Neuropsychiatric symptoms (NPSs) often occur in early-age-of-onset Alzheimer's disease (EOAD) and cluster into sub-syndromes (SSy). The aim of this study was to investigate the association between 18 F-FDG-PET regional and connectivity-based brain metabolic dysfunctions and neuropsychiatric SSy. NPSs were assessed in 27 EOAD using the Neuropsychiatric Inventory and further clustered into four SSy (apathetic, hyperactivity, affective, and psychotic SSy). Eighty-five percent of EOAD showed at least one NPS. Voxel-wise correlations between SSy scores and brain glucose metabolism (assessed with 18 F-FDG positron emission tomography) were studied. Interregional correlation analysis was used to explore metabolic connectivity in the salience (aSN) and default mode networks (DMN) in a larger sample of EOAD (N = 51) and Healthy Controls (N = 57). The apathetic, hyperactivity, and affective SSy were highly prevalent (>60%) as compared to the psychotic SSy (33%). The hyperactivity SSy scores were associated with increase of glucose metabolism in frontal and limbic structures, implicated in behavioral control. A comparable positive correlation with part of the same network was found for the affective SSy scores. On the other hand, the apathetic SSy scores were negatively correlated with metabolism in the bilateral orbitofrontal and dorsolateral frontal cortex known to be involved in motivation and decision-making processes. Consistent with these SSy regional correlations with brain metabolic dysfunction, the connectivity analysis showed increases in the aSN and decreases in the DMN. Behavioral abnormalities in EOAD are associated with specific dysfunctional changes in brain metabolic activity, in particular in the aSN that seems to play a crucial role in NPSs in EOAD. Hum Brain Mapp 37:4234-4247, 2016. © 2016 Wiley Periodicals, Inc.

Objective: Early-onset Alzheimer's disease (EAD) shows distinct features from late-onset Alzheimer's disease (LAD). To explore the characteristics of EAD, clinical, neuropsychological, and functional imaging studies have been conducted. However, differences between EAD and LAD are not clear, especially in terms of brain connectivity and networks. In this study, we investigated the differences in metabolic connectivity between EAD and LAD by adopting graph theory measures.Methods: We analyzed 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) images to investigate the distinct features of metabolic connectivity between EAD and LAD. Using metabolic connectivity and graph theory analysis, metabolic network differences between LAD and EAD were explored.Results: Results showed the decreased connectivity centered in the cingulate gyri and occipital regions in EAD, whereas decreased connectivity in the occipital and temporal regions as well as increased connectivity in the supplementary motor area were observed in LAD when compared with age-matched control groups. Global efficiency and clustering coefficients were decreased in EAD but not in LAD. EAD showed progressive network deterioration as a function of disease severity and clinical dementia rating (CDR) scores, mainly in terms of connectivity between the cingulate gyri and occipital regions. Global efficiency and clustering coefficients were also decreased along with disease severity.Conclusion: These results indicate that EAD and LAD have distinguished features in terms of metabolic connectivity, with EAD demonstrating more extensive and progressive deterioration.

Previous studies showed the involvement of brain regions associated with both spatial learning and associative learning in spatial memory extinction, although the specific role of the dorsal and ventral hippocampus and the extended hippocampal system including the mammillary body in the process is still controversial. The present study aimed to identify the involvement of the dorsal and ventral hippocampus, together with cortical regions, the amygdaloid nuclei, and the mammillary bodies in the extinction of a spatial memory task. To address these issues, quantitative cytochrome c oxidase histochemistry was applied as a metabolic brain mapping method. Rats were trained in a reference memory task using the Morris water maze, followed by an extinction procedure of the previously acquired memory task. Results show that rats learned successfully the spatial memory task as shown by the progressive decrease in measured latencies to reach the escape platform and the results obtained in the probe test. Spatial memory was subsequently extinguished as shown by the descending preference for the previously reinforced location. A control naïve group was added to ensure that brain metabolic changes were specifically related with performance in the spatial memory extinction task. Extinction of the original spatial learning task significantly modified the metabolic activity in the dorsal and ventral hippocampus, the amygdala and the mammillary bodies. Moreover, the ventral hippocampus, the lateral mammillary body and the retrosplenial cortex were differentially recruited in the spatial memory extinction task, as shown by group differences in brain metabolic networks. These findings provide new insights on the brain regions and functional brain networks underlying spatial memory, and specifically spatial memory extinction. © 2016 Wiley Periodicals, Inc.

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. 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. 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.

パーキンソン病における脳代謝ネットワークパターン

Characteristic metabolic brain pattern in Slovenian Parkinson's disease patients

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. [(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. 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. 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.

Cognitive training has reported to improve cognitive performance in Mild Cognitive Impairment (MCI) as well as in older healthy subjects. 18F-FDG-PET is widely used in the diagnoses of dementia for its ability to identify early metabolic changes. This study was aimed to assess the effect of cognitive stimulation on brain metabolic network and clinical cognitive performance. Thirty aMCI subjects were enrolled in the study and allocated in two groups matched for cognitive profile, sex and schooling and then randomly assigned to the training arm or to the placebo arm. All subjects underwent neuropsychological assessment and PET imaging before and after intervention. We found significant association between brain metabolism and cognitive stimulation in treated aMCI subjects. Brain metabolic changes included Brodmann areas reported to be involved in working memory and attentive processes as well as executive functions. Our study shows that metabolic changes occur earlier than possible clinical changes related to the intervention. 18F-FDG-PET could provide a useful biomarker of response to identify a population of aMCI suitable to respond to treatment, according to most recent data on default network mode and its adaptivity to external stimuli.

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.

Many studies have demonstrated the small-worldness of the human brain, and have revealed a sexual dimorphism in brain network properties. However, little is known about the gender effects on the topological organization of the brain metabolic covariance networks. To investigate the small-worldness and the gender differences in the topological architectures of human brain metabolic networks. FDG-PET data of 400 healthy right-handed subjects (200 women and 200 age-matched men) were involved in the present study. Metabolic networks of each gender were constructed by calculating the covariance of regional cerebral glucose metabolism (rCMglc) across subjects on the basis of AAL parcellation. Gender differences of network and nodal properties were investigated by using the graph theoretical approaches. Moreover, the gender-related difference of rCMglc in each brain region was tested for investigating the relationships between the hub regions and the brain regions showing significant gender-related differences in rCMglc. We found prominent small-world properties in the domain of metabolic networks in each gender. No significant gender difference in the global characteristics was found. Gender differences of nodal characteristic were observed in a few brain regions. We also found bilateral and lateralized distributions of network hubs in the females and males. Furthermore, we first reported that some hubs of a gender located in the brain regions showing weaker rCMglc in this gender than the other gender. The present study demonstrated that small-worldness was existed in metabolic networks, and revealed gender differences of organizational patterns in metabolic network. These results maybe provided insights into the understanding of the metabolic substrates underlying individual differences in cognition and behaviors.

Levodopa (L-dopa) has been at the forefront of antiparkinsonian therapy for a half century. Recent advances in functional brain imaging have contributed substantially to the understanding of the effects of L-dopa and other dopaminergic treatment on the activity of abnormal motor and cognitive brain circuits in Parkinson's disease patients. Progress has also been made in understanding the functional pathology of dyskinesias, a common side effect of l-dopa treatment, at both regional and network levels. Here, we review these studies, focusing mainly on the new mechanistic insights provided by metabolic brain imaging and network analysis.

Corticobasal degeneration is an uncommon parkinsonian variant condition that is diagnosed mainly on clinical examination. To facilitate the differential diagnosis of this disorder, we used metabolic brain imaging to characterize a specific network that can be used to discriminate corticobasal degeneration from other atypical parkinsonian syndromes. Ten non-demented patients (eight females/two males; age 73.9 ± 5.7 years) underwent metabolic brain imaging with (18)F-fluorodeoxyglucose positron emission tomography for atypical parkinsonism. These individuals were diagnosed clinically with probable corticobasal degeneration. This diagnosis was confirmed in the three subjects who additionally underwent post-mortem examination. Ten age-matched healthy subjects (five females/five males; age 71.7 ± 6.7 years) served as controls for the imaging studies. Spatial covariance analysis was applied to scan data from the combined group to identify a significant corticobasal degeneration-related metabolic pattern that discriminated (P < 0.001) the patients from the healthy control group. This pattern was characterized by bilateral, asymmetric metabolic reductions involving frontal and parietal cortex, thalamus, and caudate nucleus. These pattern-related changes were greater in magnitude in the cerebral hemisphere opposite the more clinically affected body side. The presence of this corticobasal degeneration-related metabolic topography was confirmed in two independent testing sets of patient and control scans, with elevated pattern expression (P < 0.001) in both disease groups relative to corresponding normal values. We next determined whether prospectively computed expression values for this pattern accurately discriminated corticobasal degeneration from multiple system atrophy and progressive supranuclear palsy (the two most common atypical parkinsonian syndromes) on a single case basis. Based upon this measure, corticobasal degeneration was successfully distinguished from multiple system atrophy (P < 0.001) but not progressive supranuclear palsy, presumably because of the overlap (∼ 24%) that existed between the corticobasal degeneration- and the progressive supranuclear palsy-related metabolic topographies. Nonetheless, excellent discrimination between these disease entities was achieved by computing hemispheric asymmetry scores for the corticobasal degeneration-related pattern on a prospective single scan basis. Indeed, a logistic algorithm based on the asymmetry scores combined with separately computed expression values for a previously validated progressive supranuclear palsy-related pattern provided excellent specificity (corticobasal degeneration: 92.7%; progressive supranuclear palsy: 94.1%) in classifying 58 testing subjects. In conclusion, corticobasal degeneration is associated with a reproducible disease-related metabolic covariance pattern that may help to distinguish this disorder from other atypical parkinsonian syndromes.

Combined PET and MR imaging (PET/MR imaging) has progressed tremendously in recent years. The focus of current research has shifted from technologic challenges to the application of this new multimodal imaging technology in the areas of oncology, cardiology, neurology, and infectious diseases. This article reviews studies in preclinical and clinical translation. The common theme of these initial results is the complementary nature of combined PET/MR imaging that often provides additional insights into biologic systems that were not clearly feasible with just one modality alone. However, in vivo findings require ex vivo validation. Combined PET/MR imaging also triggers a multitude of new developments in image analysis that are aimed at merging and using multimodal information that ranges from better tumor characterization to analysis of metabolic brain networks. The combination of connectomics information that maps brain networks derived from multiparametric MR data with metabolic information from PET can even lead to the formation of a new research field that we would call cometomics that would map functional and metabolic brain networks. These new methodologic developments also call for more multidisciplinarity in the field of molecular imaging, in which close interaction and training among clinicians and a variety of scientists is needed.

Over the past two decades, the development of functional imaging methods has greatly promoted our understanding on the changes of neurons following neurodegenerative disorders, such as Parkinson's disease (PD). The application of a spatial covariance analysis on 18F-FDG PET imaging has led to the identification of a distinctive disease-related metabolic pattern. This pattern has proven to be useful in clinical diagnosis, disease progression monitoring as well as assessment of the neuronal changes before and after clinical treatment. It may potentially serve as an objective biomarker on disease progression monitoring, assessment, histological and functional evaluation of related diseases. PD is one of the most common neurodegenerative disorders in the elderly. It is characterized by progressive loss of dopamine neurons in the substantia nigra pars compacta. Throughout the course of disease, the most obvious symptoms are movement-related, such as resting tremor, muscle rigidity, hypokinesia and postural instability (Worth, 2013). Currently, a definite diagnosis of PD is made by clinical evaluation with at least 2 years of follow-up (Hughes et al., 2002; Bhidayasiri and Reichmann, 2013), due to the overlap of motor symptoms between early PD and atypical parkinsonism including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). However, this classic diagnostic criterion does not benefit the early diagnosis of disease. The prognostic outcome and treatment option are substantially different between PD and atypical parkinsonism. Thus it is critical to develop biomarkers for earlier and more accurate diagnosis of PD. Generally, appropriate diagnostic biomarker for PD ought to cover several key characteristics: (i) minimal invasiveness to detect the biomarker in easily accessible body tissue or fluids, (ii) excellent sensitivity to explore the patients with PD, (iii) high specificity to prevent false-positive results in PD-free individuals, and (iv) robustness against potential affecting factors. A PD-related spatial covariance pattern (PDRP) with quantifiable expression on 18F-FDG PET imaging has been gradually detected using a spatial covariance method during the last two decades and it has been demonstrated to be the right diagnostic biomarker for PD (Eidelberg et al., 1994). PDRP has proven not only to be effective in early discrimination of PD from atypical parkinsonian disorders, but also to be able to assess the disease progression and treatment response. Thus it is considered as a multifunctional biomarker. In this review, we aim to provide an overview of the development in pattern-based biomarker for PD.

BackgroundGender differences of the human brain are an important issue in neuroscience research. In recent years, an increasing amount of evidence has been gathered from noninvasive neuroimaging studies supporting a sexual dimorphism of the human brain. However, there is a lack of imaging studies on gender differences of brain metabolic networks based on a large population sample.Materials and MethodsFDG PET data of 400 right-handed, healthy subjects, including 200 females (age: 25∼45 years, mean age±SD: 40.9±3.9 years) and 200 age-matched males were obtained and analyzed in the present study. We first investigated the regional differences of brain glucose metabolism between genders using a voxel-based two-sample t-test analysis. Subsequently, we investigated the gender differences of the metabolic networks. Sixteen metabolic covariance networks using seed-based correlation were analyzed. Seven regions showing significant regional metabolic differences between genders, and nine regions conventionally used in the resting-state network studies were selected as regions-of-interest. Permutation tests were used for comparing within- and between-network connectivity between genders.ResultsCompared with the males, females showed higher metabolism in the posterior part and lower metabolism in the anterior part of the brain. Moreover, there were widely distributed patterns of the metabolic networks in the human brain. In addition, significant gender differences within and between brain glucose metabolic networks were revealed in the present study.ConclusionThis study provides solid data that reveal gender differences in regional brain glucose metabolism and brain glucose metabolic networks. These observations might contribute to the better understanding of the gender differences in human brain functions, and suggest that gender should be included as a covariate when designing experiments and explaining results of brain glucose metabolic networks in the control and experimental individuals or patients.

Anorexia nervosa (AN), a disorder of unknown etiology, has the highest mortality rate of any psychiatric disorder. Drawing the brain metabolic pattern of AN may help to target the core biological and psychological features of the disorder and to perfect the diagnosis and recovery criteria. In this study, we used 18F-FDG PET to show brain metabolic network for AN. Glucose metabolism in 6 AN patients and 12 age-matched healthy controls was studied using 18F-FDG PET. SPM2 was used to compare brain metabolism in AN patients with that in healthy controls. Four of 6 AN patients took deep brain stimulation (DBS) targeted in nucleus accumbens (NAcc). About 3 to 6 months after the surgery, the 4 AN patients took another 18F-FDG PET scan to assess the change in brain glucose metabolism. The SPM (statistical parametric mapping ) analysis showed hypermetabolism in the frontal lobe (bilateral, BA10, BA11, BA47), the limbic lobe (bilateral, hippocampus, and amygdala), lentiform nucleus (bilateral), left insula (BA13), and left subcallosal gyrus (BA25). It also showed hypometabolism in the parietal lobe (bilateral, BA7, BA40). The hypermetabolism in frontal lobe, hippocampus, and lentiform nucleus decreased after NAcc-DBS. The changes in brain glucose metabolism illustrated the brain metabolic pattern in AN patients. Furthermore, the pattern can be modulated by NAcc-DBS, which confirmed specificity of the pattern. The regions with altered metabolism could interconnect to form a network and integrate information related to appetite. Our study may provide information for targeting the potential candidate brain regions for understanding the pathophysiology of AN and assessing the effects of existing and future treatment approaches.