Rapid eye movement sleep behaviour disorder has been evaluated using Parkinson's disease-related metabolic network. It is unknown whether this disorder is itself associated with a unique metabolic network. 18F-fluorodeoxyglucose positron emission tomography was performed in 21 patients (age 65.0±5.6 years) with idiopathic rapid eye movement sleep behaviour disorder and 21 age/gender-matched healthy control subjects (age 62.5±7.5 years) to identify a disease-related pattern and examine its evolution in 21 hemi-parkinsonian patients (age 62.6±5.0 years) and 16 moderate parkinsonian patients (age 56.9±12.2 years). We identified a rapid eye movement sleep behaviour disorder-related metabolic network characterized by increased activity in pons, thalamus, medial frontal and sensorimotor areas, hippocampus, supramarginal and inferior temporal gyri, and posterior cerebellum, with decreased activity in occipital and superior temporal regions. Compared to the healthy control subjects, network expressions were elevated (P<0.0001) in the patients with this disorder and in the parkinsonian cohorts but decreased with disease progression. Parkinson's disease-related network activity was also elevated (P<0.0001) in the patients with rapid eye movement sleep behaviour disorder but lower than in the hemi-parkinsonian cohort. Abnormal metabolic networks may provide markers of idiopathic rapid eye movement sleep behaviour disorder to identify those at higher risk to develop neurodegenerative parkinsonism.

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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 [18F]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.

Predicting progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell’s C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

Correction to: Journal of Cerebral Blood Flow and Metabolism advance online publication, 29 July 2015; doi:10.1038/jcbfm.2015.173. Following the online publication of this article, the authors noted that the order of the appearance of affiliations and the information of the correspondence were placed incorrectly. The affiliations of the authors and the order of the correspondence have been reordered as follows: Jingjie Ge2,4, Ping Wu2,4, Shichun Peng3, Huan Yu1, Huiwei Zhang2, Yihui Guan2, David Eidelberg3, Chuantao Zuo2, Yilong Ma3,5, Jian Wang1,5 1Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China; 2PET Center, Huashan Hospital, Fudan University, Shanghai, China and 3Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York, USA. Correspondence: Dr J Wang, Department of Neurology, Huashan Hospital, Fudan University, No. 12 Middle Wulumuqi Road, Jing'an District, Shanghai 200040, China or Dr Y Ma, Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA. 4These authors contributed equally to this work. 5These authors shared senior authorship. E-mail: wangjian336@hotmail.com or yma@nshs.edu

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Background: Alzheimer’s disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18F-fluorodeoxy-glucose positron emission tomography (18F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. Methods: In this study, 18F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student’s t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach’s alpha coefficient for radiomic feature stability analyses. Pearson’s correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer’s disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. Results: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. Conclusion: The research in this paper proved that the novel approach based on high-order radiomic features extracted from 18F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.

The therapeutic benefits of bilateral capsulotomy for the treatment of refractory obsessive compulsive disorder (OCD) are probably attributed to interruption of the cortico-striato-thalamo-cortical circuitry. We evaluated resting brain metabolism and treatment response in OCD patients using positron emission tomography (PET) imaging. [(18)F]-fluoro-deoxy-glucose PET was performed in eight OCD patients precapsulotomy and postcapsulotomy. We determined metabolic differences between preoperative images in patients and those in eight age-matched healthy volunteers, and postoperative changes and clinical correlations in the patients. The OCD patients showed widespread metabolic increases in normalized glucose metabolism in the bilateral orbitofrontal cortex and inferior frontal gyrus, cingulate gyrus, and bilateral pons/cerebellum, and metabolic decreases bilaterally in the precentral and lingual gyri. Bilateral capsulotomy resulted in significant metabolic decreases bilaterally in the prefrontal cortical regions, especially in the dorsal anterior cingulate cortex (ACC) and in the medial dorsal thalamus and caudate nucleus. In contrast, metabolism increased bilaterally in the precentral and lingual gyri. Clinical improvement in patients correlated with metabolic changes in the bilateral dorsal ACC and in the right middle occipital gyrus after capsulotomy. This study underscores the importance of the internal capsule in modulating ventral prefrontal and dorsal anterior cingulate neuronal activity in the neurosurgical management of OCD patients.