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The asymmetric pattern of β-amyloid plaque distribution across Alzheimer's disease clinical progression stages remains unclear. In this study, 66 participants with normal cognition, 59 with subjective cognitive decline, 12 with mild cognitive impairment, and 11 with Alzheimer's disease dementia were included in the Sino Longitudinal Study on Cognitive Decline (SILCODE) cohort. A regional asymmetry index, denoting the left-right asymmetry of β-amyloid plaques, was derived for each region based on the Anatomical Automatic Labeling atlas. The level of β-amyloid plaques in each region was compared among different clinical stages of Alzheimer's disease using the analysis of variance. An additional correlation analysis examined the relationship between each region of interest's cognitive performance scores and asymmetry index values. We found that β-amyloid appears to be lateralized in different stages of Alzheimer's disease. In addition, there is a significant correlation between β-amyloid asymmetry in various brain regions and cognition. The observed Aβ lateralization could potentially be utilized as a neuroimaging biomarker throughout AD progression., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Previous studies have found that Alzheimer's disease (AD)-related plasma markers are associated with amyloid beta (Aβ) deposition, but the change of this association in different Aβ pathological stages remains unclear., Methods: Data were obtained from the SILCODE. According to the standardized uptake value ratio (SUVR) and Aβ stage classification, correlation analysis was performed among plasma biomarkers, and voxel/SUVR values in the regions of interest (ROI) and clinical scale information, respectively. Mediation analysis was used to study the possible pathways., Results: The proportion of cognitively normal (CN) and subjective cognitive decline (SCD) was the highest in stages A0 to 1, while in stages A2 to 4, the proportion of mild cognitive impairment (MCI) and AD increased. Plasma phosphorylated tau (p-tau)181 and glial fibrillary acidic protein (GFAP) levels were significantly lower in stage A0 compared to the later phases. Two pathways demonstrated fully mediated effects: positron emission tomography (PET) SUVR-plasma p-tau181-Mini-Mental State Examination (MMSE) and PET SUVR-plasma GFAP-MMSE., Discussion: This study demonstrated the role of plasma biomarkers in the early stage of AD, especially in SCD, from both the clinical diagnosis and Aβ stage dimensions., Highlights: Plasma ptau181 and GFAP level serve as indicators of early Alzheimer's disease and the pathologic Aβ staging classification. A possible ceiling effect of GFAP was observed in the mid-to-late stages of the AD course. This study confirms the role of AD plasma markers in promoting Aβ deposition at an early stage, particularly in females with subjective cognitive decline(SCD). The overlapping brain regions of plasma p-tau181, GFAP, and neurofilament light for Aβ deposition in the brain in early AD were distributed across various regions, including the posterior cingulate gyrus, rectus gyrus, and inferior temporal gyrus., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Aims: The patient being minimally conscious state (MCS) may benefit from wake-up interventions aimed at improving quality of life and have a higher probability of recovering higher level of consciousness compared to patients with the unresponsive wakefulness syndrome (UWS). However, differentiation of the MCS and UWS poses challenge in clinical practice. This study aimed to explore glucose metabolic pattern (GMP) obtained from 18 F-labeled-fluorodeoxyglucose positron emission tomography ( 18 F-FDG-PET) in distinguishing between UWS and MCS., Methods: Fifty-seven patients with disorders of consciousness (21 cases of UWS and 36 cases of MCS) who had undergone repeated standardized Coma Recovery Scale-Revised (CRS-R) evaluations were enrolled in this prospective study. 18 F-FDG-PET was carried out in all patients and healthy controls (HCs). Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate GMPs. The expression score of whole-brain GMP was obtained, and its diagnostic accuracy was compared with the standardized uptake value ratio (SUVR). The diagnostic efficiency was validated by one-year later clinical outcomes., Results: UWS-MCS GMP exhibited hypometabolism in the frontal-parietal cortex, along with hypermetabolism in the unilateral lentiform nucleus, putamen, and anterior cingulate gyrus. The UWS-MCS-GMP expression score was significantly higher in UWS compared to MCS patients (0.90 ± 0.85 vs. 0 ± 0.93, p < 0.001). UWS-MCS-GMP expression score achieved an area under the curve (AUC) of 0.77 to distinguish MCS from UWS, surpassing that of SUVR based on the frontoparietal cortex (AUC = 0.623). UWS-MCS-GMP expression score was significantly correlated with the CRS-R score (r = -0.45, p = 0.004) and accurately predicted the one-year outcome in 73.7% of patients., Conclusion: UWS and MCS exhibit specific glucose metabolism patterns, the UWS-MCS-GMP expression score significantly distinguishes MCS from UWS, making SSM/PCA a potential diagnostic methods in clinical practice for individual patients., (© 2024 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

The discovery of preclinical Alzheimer's disease (preAD) provides a wide time window for the early intervention of AD. The coupling relationships between glucose and oxygen metabolisms from hybrid PET/MRI can provide complementary information on the brain's physiological state for preAD. In this study, we purpose to explore the change of coupling relationship among 27 normal controls (NCs), 20 preADs, and 15 cognitive impairments (CIs). For each subject, we calculated the Spearman partial correlation between the fractional amplitude of low-frequency fluctuations (fALFF) and the regional homogeneity (ReHo) from functional image (fMRI), and the standard uptake value ratio (SUVR) from [18F] fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET), in the whole-brain and default mode network (DMN) as a novel potential biomarker. The diagnostic performance of this biomarker was evaluated by the receiver operating characteristic analysis. Significant Spearman correlations between the FDG SUVR and the fALFF/ReHo were found in 98% of subjects. For the DMN-based biomarker, there was a significant decreasing trend for the preAD and CI groups compared to the NC group, whereas no significant difference in preAD based on whole-brain. The correlation ρ value for the FDG SUVR/ReHo showed the highest area under curve of the preAD classification (0.787). The results imply the coupling relationship changed during the preAD stage in the DMN area., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown., Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry., Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied., Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p < 0.001). The above results appeared in both the whole brain and DMN., Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

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 18 F-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 18 F-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 18 F-FDG uptake and the disease duration in the iRBD and PD groups, respectively. We also explored relationships between 18 F-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 18 F-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., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Identifying whether patients with mild cognitive impairment (MCI) are converting to Alzheimer's disease (AD) is clinically important, but there are still controversies and doubts. We aimed to develop a novel connectome approach which could accurately and precisely predict whether MCI patients are converted to AD using 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET). In our study, FDG-PET images were acquired from 84 patients with MCI who converted to AD within 48 months and 109 patients with MCI without conversion within 48 months from the Alzheimer's Disease Neuroimaging Initiative database. The experimental results showed that the classification performance about whether an MCI patient would convert to AD were 92.1%, 87.1%, 94.4% and 0.95 (Accuracy, Sensitivity, Specificity and AUC). The abnormality of functional connection was located at Middle frontal gyrus, Posterior cingulate gyrus, Precentral gyrus, Precuneus and Temporal lobe. These finding showed the brain connectome as a practical approach for developing predictive neuroimaging biomarker.

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.

Objectives: 18 F-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 18 F-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.

The prevailing β-amyloid (Aβ)-cascade hypothesis is the most classical Alzheimer's disease (AD) pathogenesis. In this hypothesis, excessive Aβ 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 Aβ plaques in vivo. Thus, it is possible to investigate the difference of Aβ 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 Aβ 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 Aβ 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., (Copyright © 2016 Elsevier B.V. All rights reserved.)

One medical challenge in studying the amyloid-β (Aβ) peptide mechanism for Alzheimer's disease (AD) is exploring the law of beta toxic oligomers' diffusion in human brains in vivo. One beneficial means of solving this problem is brain network analysis based on graph theory. In this study, the characteristics of Aβ functional brain networks of Healthy Control (HC), Mild Cognitive Impairment (MCI), and AD groups were compared by applying graph theoretical analyses to Carbon 11-labeled Pittsburgh compound B positron emission tomography (11C PiB-PET) data. 120 groups of PiB-PET images from the ADNI database were analyzed. The results showed that the small-world property of MCI and AD were lost as compared to HC. Furthermore, the local clustering of networks was higher in both MCI and AD as compared to HC, whereas the path length was similar among the three groups. The results also showed that there could be four potential Aβ toxic oligomer seeds: Frontal&#95;Sup&#95;Medial&#95;L, Parietal&#95;Inf&#95;L, Frontal&#95;Med&#95;Orb&#95;R, and Parietal&#95;Inf&#95;R. These four seeds are corresponding to Regions of Interests referred by physicians to clinically diagnose AD.

Alzheimer's disease (AD) is a common health problem for elderly populations. Positron emission tomography-computed tomography (PET-CT)11C-PiB for beta-P (amyloid-β peptide, β-AP) imaging is an advanced method to diagnose AD in early stage. However, in practice radiologists lack a standardized value to semi-quantify β-AP. This paper proposes such a standardized value: SVβ-AP. This standardized value measures the mean ratio between the dimension of β-AP areas in PET and CT images. A computer aided diagnosis approach is also proposed to achieve SVβ-AP. A simulation experiment was carried out to pre-test the technical feasibility of the CAD approach and SVβ-AP. The experiment results showed that it is technically feasible.

The lattice Boltzmann (LB) method is a mesoscopic method based on kinetic theory and statistical mechanics. The main advantage of the LB method is parallel computation, which increases the speed of calculation. In the past decade, LB methods have gradually been introduced for image processing, e.g., image segmentation. However, a major shortcoming of existing LB methods is that they can only be applied to the processing of medical images with intensity homogeneity. In practice, however, many medical images possess intensity inhomogeneity. In this study, we developed a novel LB method to integrate edge and region information for medical image segmentation. In contrast to other segmentation methods, we added edge information as a relaxing factor and used region information as a source term. The proposed method facilitates the segmentation of medical images with intensity inhomogeneity and it still allows parallel computation. Preliminary tests of the proposed method are presented in this paper.

Exploring individual hallmarks of brain ageing is important. Here, we propose the age-related glucose metabolism pattern (ARGMP) as a potential index to characterize brain ageing in cognitively normal (CN) elderly people. We collected 18F-fluorodeoxyglucose (18F-FDG) PET brain images from two independent cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI, N = 127) and the Xuanwu Hospital of Capital Medical University, Beijing, China (N = 84). During follow-up (mean 80.60 months), 23 participants in the ADNI cohort converted to cognitive impairment. ARGMPs were identified using the scaled subprofile model/principal component analysis method, and cross-validations were conducted in both independent cohorts. A survival analysis was further conducted to calculate the predictive effect of conversion risk by using ARGMPs. The results showed that ARGMPs were characterized by hypometabolism with increasing age primarily in the bilateral medial superior frontal gyrus, anterior cingulate and paracingulate gyri, caudate nucleus, and left supplementary motor area and hypermetabolism in part of the left inferior cerebellum. The expression network scores of ARGMPs were significantly associated with chronological age (R = 0.808, p

One medical challenge in studying the amyloid-β (Aβ) peptide mechanism for Alzheimer's disease (AD) is exploring the law of beta toxic oligomers' diffusion in human brains in vivo. One beneficial means of solving this problem is brain network analysis based on graph theory. In this study, the characteristics of Aβ functional brain networks of Healthy Control (HC), Mild Cognitive Impairment (MCI), and AD groups were compared by applying graph theoretical analyses to Carbon 11-labeled Pittsburgh compound B positron emission tomography (11C PiB-PET) data. 120 groups of PiB-PET images from the ADNI database were analyzed. The results showed that the small-world property of MCI and AD were lost as compared to HC. Furthermore, the local clustering of networks was higher in both MCI and AD as compared to HC, whereas the path length was similar among the three groups. The results also showed that there could be four potential Aβ toxic oligomer seeds: Frontal_Sup_Medial_L, Parietal_Inf_L, Frontal_Med_Orb_R, and Parietal_Inf_R. These four seeds are corresponding to Regions of Interests referred by physicians to clinically diagnose AD.