Functional Connectivity Alterations Based on Hypometabolic Region May Predict Clinical Prognosis of Temporal Lobe Epilepsy: A Simultaneous 18F-FDG PET/fMRI Study.

(1) Background: Accurate localization of the epileptogenic zone and understanding the related functional connectivity (FC) alterations are critical for the prediction of clinical prognosis in patients with temporal lobe epilepsy (TLE). We aim to localize the hypometabolic region in TLE patients, compare the differences in FC alterations based on hypometabolic region and structural lesion, respectively, and explore their relationships with clinical prognosis. (2) Methods: Thirty-two TLE patients and 26 controls were recruited. Patients underwent 18F-FDG PET/MR scan, surgical treatment, and a 2−3-year follow-up. Visual assessment and voxel-wise analyses were performed to identify hypometabolic regions. ROI-based FC analyses were performed. Relationships between clinical prognosis and FC values were performed by using Pearson correlation analyses and receiver operating characteristic (ROC) analysis. (3) Results: Hypometabolic regions in TLE patients were found in the ipsilateral hippocampus, parahippocampal gyrus, and temporal lobe (p < 0.001). Functional alterations based on hypometabolic regions showed a more extensive whole-brain FC reduction. FC values of these regions negatively correlated with epilepsy duration (p < 0.05), and the ROC curve of them showed significant accuracy in predicting postsurgical outcome. (4) Conclusions: In TLE patients, FC related with hypometabolic region obtained by PET/fMRI may provide value in the prediction of disease progression and seizure-free outcome.

Transcriptomic and Functional Evidence Show Similarities between Human Amniotic Epithelial Stem Cells and Keratinocytes.

Amniotic epithelial stem cells (AESCs) are considered as potential alternatives to keratinocytes (KCs) in tissue-engineered skin substitutes used for treating skin damage. However, their clinical application is limited since similarities and distinctions between AESCs and KCs remain unclear. Herein, a transcriptomics analysis and functional evaluation were used to understand the commonalities and differences between AESCs and KCs. RNA-sequencing revealed that AESCs are involved in multiple epidermis-associated biological processes shared by KCs and show more similarity to early stage immature KCs than to adult KCs. However, AESCs were observed to be heterogeneous, and some possessed hybrid mesenchymal and epithelial features distinct from KCs. A functional evaluation revealed that AESCs can phagocytose melanosomes transported by melanocytes in both 2D and 3D co-culture systems similar to KCs, which may help reconstitute pigmented skin. The overexpression of TP63 and activation of NOTCH signaling could promote AESC stemness and improve their differentiation features, respectively, bridging the gap between AESCs and KCs. These changes induced the convergence of AESC cell fate with KCs. In future, modified reprogramming strategies, such as the use of small molecules, may facilitate the further modulation human AESCs for use in skin regeneration.

Characterizing the heterogeneous metabolic progression in idiopathic REM sleep behavior disorder.

OBJECTIVE: Idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of synucleinopathies such as Parkinson's disease (PD). Positron emission tomography (PET) with 18F-FDG reveals metabolic perturbations, which are scored by spatial covariance analysis. However, the resultant pattern scores do not capture the spatially heterogeneous trajectories of metabolic changes between individual brain regions. Assuming metabolic progression occurs as a continuum from the healthy control (HC) condition to iRBD and then PD, we investigated spatial dynamics of progressively perturbed glucose metabolism in a cross-sectional study. METHODS: 19 iRBD patients, 38 PD patients and 19 HC subjects underwent 18F-FDG PET. The images were spatially normalized, scaled to the global mean uptake, and automatically parcellated. We contrasted regional metabolism by group, and allocated the inferred progression to one of several possible trajectories. We further investigated the correlations between 18F-FDG uptake and the disease duration in the iRBD and PD groups, respectively. We also explored relationships between 18F-FDG uptake and the Unified Parkinson's Disease Rating Scale motor (UPDRS III) scores in the PD group. RESULTS: PD patients exhibited more extensive relative hyper- and hypo-metabolism than iRBD patients. We identified three dynamic metabolic trajectories, cross-sectional hypo- or hypermetabolism, cross-sectionally unchanged hypo- or hypermetabolism, cross-sectionally late hypo- or hypermetabolism, appearing only in the contrast of PD with iRBD. No correlation was found between relative 18F-FDG metabolism and disease duration in the iRBD group. Regional hyper- and hypo-metabolism in the PD patients correlated with disease duration or clinical UPDRS III scores. CONCLUSION: Cerebral metabolism changes heterogeneously in a continuum extending from HC to iRBD and PD groups in this preliminary study. The distinctive metabolic trajectories point towards a potential neuroimaging biomarker for conversion of iRBD to frank PD, which should be amenable to advanced pattern recognition analysis in future longitudinal studies.

Brain Functional and Structural Signatures in Parkinson's Disease.

The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson's disease (PD). 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal-occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.

[Wearable Device for Non-Invasive Continuously Blood Pressure Monitoring].

In aging society the development of non-invasive continuously blood pressure monitors which are suitable for homes, communities and nursing homes has a wide range of applications. This paper proposes a non-invasive continuously blood pressure monitoring based on wearable device which uses MSP430F5529 as the central processor. The design is divided into signal acquisition module, central control module, display module, power supply module and host computer module. The experimental results showed that DBP (375/390, 96.15%) and SBP estimation values (377/390, 96.67%) are in 95% confidence interval, which means our design passes Bland-Altman test with high accuracy and stability.

Disrupted network topology in patients with Lewy bodies dementia compared to Alzheimer's disease, Parkinson disease dementia and Health Control.

The clinical manifestation of Lewy body dementia (DLB) is distinct from Alzheimer's disease (AD), but overlap with Parkinson's disease dementia (PDD). However, little is known about different topology properties of abnormal brain networks associated with these neurodegenerative diseases. In order to study the difference of brain networks in various dementia subtypes, we used $^{\mathbf {18}}\text{F}$-Fluorodeoxyglucose positron emission tomography ($^{\mathbf {18}}\text{F}$-FDG PET) images and graph theory methods to investigate altered whole-brain intrinsic glucose metabolic functional networks in three Chinese dementia groups compared to healthy control (HC) group, including 22 AD patients, 18 PDD patients, 22 DLB patients and 22 HC subjects from Huashan Hospital, Shanghai, China. The experimental results disclosed that in the three dementia groups, compared to HC group, the small-world characteristics were lost. Additionally, compared with HC group, the clustering coefficients of three dementia groups were higher; the characteristic path lengths were longer. In terms of local efficiency and global efficiency, it was at the lowest level in DLB group. We also found differences about distributions of hub regions amongst the four groups. This finding could further help physicians to understand pathological mechanisms of different dementia.

Glucose Metabolic Brain Network Differences between Chinese Patients with Lewy Body Dementia and Healthy Control.

Dementia with Lewy bodies (DLB) is the second most common degenerative dementia of the central nervous system. The technique 18F-fluorodeoxyglucose positron emission tomography (18F FDG PET) was used to investigate brain metabolism patterns in DLB patients. Conventional statistical methods did not consider intern metabolism transforming connections between various brain regions; therefore, most physicians do not understand the underlying neuropathology of DLB patients. In this study, 18F FDG-PET images and graph-theoretical methods were used to investigate alterations in whole-brain intrinsic functional connectivity in a Chinese DLB group and healthy control (HC) group. This experimental study was performed on 22 DLB patients and 22 HC subjects in Huashan Hospital, Shanghai, China. Experimental results indicate that compared with the HC group, the DLB group has severely impaired small-world network. Compared to those of the HC group, the clustering coefficients of the DLB group were higher and characteristic path lengths were longer, and in terms of global efficiencies, those of the DLB group was also lower. Moreover, four significantly altered regions were observed in the DLB group: Inferior frontal gyrus, opercular part (IFG.R), olfactory cortex (OLF.R), hippocampus (HIP.R), and fusiform gyrus (FFG.L). Amongst them, in the DLB group, betweenness centrality became strong in OLF.R, HIP.R, and FFG.L, whereas betweenness centrality became weaker in IFG.R. Finally, IFGoperc.R was selected as a seed and a voxel-wise correlation analysis was performed. Compared to the HC group, the DLB group showed several regions of strengthened connection with IFGoperc.R; these regions were located in the prefrontal cortex and regions of weakened connection were located in the occipital cortex. The results of this paper may help physicians to better understand and characterize DLB patients.

Study of the Influence of Age in 18F-FDG PET Images Using a Data-Driven Approach and Its Evaluation in Alzheimer's Disease.

Objectives: 18F-FDG PET scan is one of the most frequently used neural imaging scans. However, the influence of age has proven to be the greatest interfering factor for many clinical dementia diagnoses when analyzing 18F-FDG PET images, since radiologists encounter difficulties when deciding whether the abnormalities in specific regions correlate with normal aging, disease, or both. In the present paper, the authors aimed to define specific brain regions and determine an age-correction mathematical model. Methods: A data-driven approach was used based on 255 healthy subjects. Results: The inferior frontal gyrus, the left medial part and the left medial orbital part of superior frontal gyrus, the right insula, the left anterior cingulate, the left median cingulate, and paracingulate gyri, and bilateral superior temporal gyri were found to have a strong negative correlation with age. For evaluation, an age-correction model was applied to 262 healthy subjects and 50 AD subjects selected from the ADNI database, and partial correlations between SUVR mean and three clinical results were carried out before and after age correction. Conclusion: All correlation coefficients were significantly improved after the age correction. The proposed model was effective in the age correction of both healthy and AD subjects.

Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease.

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.

Differences in Aß brain networks in Alzheimer's disease and healthy controls.

The prevailing ß-amyloid (Aß)-cascade hypothesis is the most classical Alzheimer's disease (AD) pathogenesis. In this hypothesis, excessive Aß plaque deposition in human brain is considered to be the cause of AD. Carbon 11-labeled Pittsburgh compound B Positron emission tomography (11C-PiB PET) is the latest technology to detect Aß plaques in vivo. Thus, it is possible to investigate the difference of Aß brain networks between AD patients and Health Controls (HC) by analyzing 11C-PiB PET images. In this study, a graph-theoretical method was employed to investigate the topological properties of Aß networks in 18 Chinese AD patients and 16 HC subjects from Huashan Hospital, Shanghai. The results showed that both groups demonstrated small-world property, and this property was more obvious in AD group. Additionally, the clustering coefficients and path lengths were significantly lower in AD group. The global efficiency was larger in AD than in HC. A direct comparison between with and without regression found that sex, age and weight had no significant effect on the Aß network. Moreover, three altered regions in AD group were identified, including left cuneus (CUN.L), right caudate nucleus (CAU.R) and left superior frontal gyrus (SFGdor. L). A voxel-wise correlation analysis showed that in AD patients, the regions of strengthened connection with CUN.L were mainly located in frontal cortex and parietal cortex, the regions of strengthen connection with CAU.R were mainly located in temporal cortex. Finally, a machine learning based analysis demonstrated that the three regions could be better biomarkers than the whole brain for AD classification.

A novel individual-level morphological brain networks constructing method and its evaluation in PET and MR images.

Mapping the human brain is one of the great scientific challenges of the 21st century. Brain network analysis is an effective technique based on graph theory that is widely used to investigate network patterns in the human brain. Currently, mapping an individual brain network using a single image has been a hotspot in the field of brain science; techniques, such as the Kullback-Leibler (KL) method, have applications in structural Magnetic Resonance (MR) imaging. However, maintaining an image's intensity, shape, texture and gradient information during feature extraction is very challenging. In this study, we propose a novel method for individual-level network construction based on the high-resolution Brainnetome Atlas, which shows 246 brain regions. Principal components (PCs) were obtained for each brain region using principal component analysis (PCA) for feature extraction. Individual brain networks were followed and used to construct the PC similarity measurement based on the mutual information (MI) method. To evaluate the robustness of the proposed method, three independent experiments were carried out. In the first, 34 healthy subjects underwent two Carbon 11-labeled Pittsburgh compound B Positron emission tomography (11C-PiB PET) scans; in the second, 32 healthy subjects underwent two structural MRI scans; and in the last, 10 Alzheimer's disease (AD) subjects and 10Healthy Control (HC) subjects underwent 11C-PiB PET scans. For each subject, network metrics including clustering coefficient, path length, small-world coefficient, efficiency and node betweenness centrality were calculated. The results suggested that both the individual PET and structural MRI networks exhibited a good small-word property, and the variances within subjects was also quite small in all metrics, The average value of Coefficient of variation (CV) map was 0.33 and 0.32 for PiB PET and MR images respectively, and intra-class correlation coefficients (ICC) range from approximately 0.4 to 0.7, indicating that the new method was well adapted to the subjects. The results of intra-class correlation coefficients from the test-retest experiment were consistent with previous research employing KL divergence, but with low computational complexity. Further, differences between AD subjects and HC subjects can be observed in network metrics. The method proposed herein provides a new perspective for investigating individual brain connectivity; it would enable neuroscientists to further understand the functions of the human brain.