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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. [ABSTRACT FROM AUTHOR]

Background: Individuals with subjective cognitive decline (SCD), defined by self-reported memory complaints but normal performance in objective neuropsychological tests, may be at higher risk of worsening or more frequent memory loss until conversion to Alzheimer's disease (AD) or related dementia. Asymmetry in two hemispheres is a cardinal character of human brain's structure and function, and altered brain asymmetry has also been connected with AD.Objective: This study aimed to determine whether the asymmetry of subcortical structures in individuals with SCD and amnestic mild cognitive impairment (aMCI) and AD patients are altered compared with normal controls (NC).Methods: We investigated neuroanatomical alterations in 35 SCD, 43 aMCI, and 41 AD subjects compared with 42 NC, focusing on asymmetrical changes in subcortical structures based on structural magnetic resonance images (sMRI). General linear model was conducted to test group differences, and partial correlation was used to model the interaction between asymmetry measurements and cognitive tests.Results: Individuals with SCD (lateral ventricle and cerebellum-WM), aMCI patients (lateral ventricle, pallidum, hippocampus, amygdala, accumbens, and ventral DC), and AD patients (lateral-ventricle, cerebellum-cortical pallidum, thalamus, hippocampus, amygdala, accumbens, and ventral DC) exhibited significant altered neuroanatomical asymmetries of volume, surface area, and shape compared with NC. Significant associations between shape asymmetry and neuropsychological examinations were found in the hippocampus and accumbens.Conclusion: Altered neuroanatomical asymmetries of subcortical structures were significantly detected in SCD individuals and aMCI patients as well AD patients, and these specific asymmetry alterations are potential to be used as neuroimaging markers and for monitoring disease progression. [ABSTRACT FROM AUTHOR]

Background: Visual rating scales for medial temporal lobe atrophy (MTA) and posterior atrophy (PA) have been reported to be useful for Alzheimer's disease diagnosis in routine clinical practice.Objective: To investigate the efficacy of combined MTA and PA visual rating scales to discriminate amnestic mild cognitive impairment (aMCI) patients from healthy controls.Methods: This study included T1-weighted MRI images from two different cohorts. In the first cohort, we recruited 73 patients with aMCI and 48 group-matched cognitively normal controls for training and validation. Visual assessments of MTA and PA were carried out for each participant. Global gray matter volume and density were estimated using voxel-based morphometry analysis as the objective reference. We investigated the discriminative power of a single visual rating scale and the combination of the MTA and PA rating scales for identifying aMCI. The second cohort, consisting of 33 aMCI patients and 45 controls, was used to verify the reliability of the visual assessments.Results: Compared with the single visual rating scale, the combination of the MTA and PA exhibited the best discriminative power, with an AUC of 0.818±0.041, which was similar to the diagnostic accuracy of the gray matter volumetric measures. The discriminative power of the combined MTA and PA was verified in the second cohort (AUC 0.824±0.058).Conclusion: The combined MTA and PA rating scales demonstrated practical diagnostic value for distinguishing aMCI patients from controls, suggesting its potential to serve as a convenient and reproducible method to assess the degree of atrophy in clinical settings. [ABSTRACT FROM AUTHOR]

Identifying amnestic mild cognitive impairment (aMCI) is of great clinical importance because aMCI is a putative prodromal stage of Alzheimer's disease. The present study aimed to explore the feasibility of accurately identifying aMCI with a magnetic resonance imaging (MRI) biomarker. We integrated measures of both gray matter (GM) abnormalities derived from structural MRI and white matter (WM) alterations acquired from diffusion tensor imaging at the voxel level across the entire brain. In particular, multi-modal brain features, including GM volume, WM fractional anisotropy, and mean diffusivity, were extracted from a relatively large sample of 64 Han Chinese aMCI patients and 64 matched controls. Then, support vector machine classifiers for GM volume, FA, and MD were fused to distinguish the aMCI patients from the controls. The fused classifier was evaluated with the leave-one-out and the 10-fold cross-validations, and the classifier had an accuracy of 83.59% and an area under the curve of 0.862. The most discriminative regions of GM were mainly located in the medial temporal lobe, temporal lobe, precuneus, cingulate gyrus, parietal lobe, and frontal lobe, whereas the most discriminative regions of WM were mainly located in the corpus callosum, cingulum, corona radiata, frontal lobe, and parietal lobe. Our findings suggest that aMCI is characterized by a distributed pattern of GM abnormalities and WM alterations that represent discriminative power and reflect relevant pathological changes in the brain, and these changes further highlight the advantage of multi-modal feature integration for identifying aMCI. [ABSTRACT FROM AUTHOR]