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.

Background: Mounting evidence suggests that sex differences exist in cognitive reserve (CR) for cognitively unimpaired (CU) elderly individuals. Global left frontal connectivity (gLFC connectivity) is a reliable neural substrate of CR. Objective: The purpose of this study was to explore sex differences in gLFC connectivity among CU elderly individuals. Methods: One hundred thirteen normal controls (NCs) (women = 66) and 132 individuals with subjective cognitive decline (SCD) (women = 92) were recruited from the Sino Longitudinal Study on Cognitive Decline (SILCODE) (data 1). Among them, 88 subjects underwent amyloid-β (Aβ) imaging, including 32 Aβ+ and 56 Aβ–subjects. Forty-six subjects underwent another rs-fMRI examination (data 2) to validate the repeatability of the calculation of gLFC connectivity, which was determined through seed-based functional connectivity between the LFC and voxels throughout the whole brain. Independent-sample t-tests were used to evaluate the sex differences in gLFC connectivity across different subgroups (NC versus SCD, Aβ+ versus Aβ–). Partial correlation analysis was used to calculate the correlations between gLFC connectivity and cognitive assessments. Results: Women exhibited lower gLFC connectivity in both the NC (p = 0.001) and SCD (p = 0.020) subgroups than men. Women also exhibited lower gLFC connectivity in both the Aβ–(p = 0.006) and Aβ+ (p = 0.025) groups. However, the significant difference disappeared in the Aβ+ group when considering the covariates of age, education, total intracranial volume, and APOE4-carrying status. In addition, gLFC connectivity values were negatively correlated with Geriatric Depression Scale scores in the SCD group (r = –0.176, p = 0.047). Conclusion: Women showed lower gLFC connectivity among CU elderly individuals.

Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current individual brain atrophy detection models are deficient. Here, we proposed a framework called generative adversarial network constrained multiple loss autoencoder (GANCMLAE) for precisely depicting individual atrophy patterns. The GANCMLAE model was trained using normal controls (NCs) from the Alzheimer's Disease Neuroimaging Initiative cohort, and the Xuanwu cohort was employed to validate the robustness of the model. The potential of the model for identifying different atrophy patterns of MCI subtypes was also assessed. Furthermore, the clinical application potential of the GANCMLAE model was investigated. The results showed that the model can achieve good image reconstruction performance on the structural similarity index measure (0.929 ± 0.003), peak signal‐to‐noise ratio (31.04 ± 0.09), and mean squared error (0.0014 ± 0.0001) with less latent loss in the Xuanwu cohort. The individual atrophy patterns extracted from this model are more precise in reflecting the clinical symptoms of MCI subtypes. The individual atrophy patterns exhibit a better discriminative power in identifying patients with AD and MCI from NCs than those of the t ‐test model, with areas under the receiver operating characteristic curve of 0.867 (95%: 0.837–0.897) and 0.752 (95%: 0.71–0.790), respectively. Similar findings are also reported in the AD and MCI subgroups. In conclusion, the GANCMLAE model can serve as an effective tool for individualised atrophy detection.

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Background: There has been no report on convergent local abnormalities of multiple functional brain imaging modalities including β-amyloid (Aβ) deposition, glucose metabolism, and resting-state functional magnetic resonance imaging (RS-fMRI) activities for participants with subjective cognitive decline (SCD). Methods: Fifty participants with SCD and 15 normal controls (NC) were scanned with both [18F]-florbetapir positron emission tomography (PET) and [18F]-fluorodeoxyglucose PET, each PET sacn accompanied with simultaneous RS-fMRI. Voxel-wise metrics were analyzed, including Aβ deposition, glucose metabolism, and three local metrics for RS-fMRI, i.e., amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC). Results: The SCD group showed increased Aβ deposition and increased glucose metabolism (P < 0.05, corrected), as well as decreased ALFF, ReHo, and DC (P < 0.05, uncorrected) in the same area of the left dorsal precuneus (dPCu). The dPCu showed negative resting state functional connectivity (RSFC) with the default mode network (DMN). Regarding global Aβ deposition positivity, the Aβ deposition in the dPCu showed a gradient change, i.e., SCD+ > SCD- > NC-. Further, both SCD+ and SCD- showed increased glucose metabolism and decreased RS-fMRI metrics in the dPCu. Conclusions: The convergent abnormal activities in the dPCu of SCD indicate that the dPCu is an early vulnerable region. The anti-RSFC of the dPCu with DMN supports that the earliest symptoms might be more related to other cognitive functions (e.g., unfocused attention) than episodic memory. (Funded by the National Key Research and Development Program of China and others; ClinicalTrials.gov number, NCT03370744.)