Subjective cognitive decline (SCD) is a high‐risk yet less understood status before developing Alzheimer's disease (AD). This work included 76 SCD individuals with two (baseline and 7 years later) neuropsychological evaluations and a baseline T1‐weighted structural MRI. A machine learning‐based model was trained based on 198 baseline neuroimaging (morphometric) features and a battery of 25 clinical measurements to discriminate 24 progressive SCDs who converted to mild cognitive impairment (MCI) at follow‐up from 52 stable SCDs. The SCD progression was satisfactorily predicted with the combined features. A history of stroke, a low education level, a low baseline MoCA score, a shrunk left amygdala, and enlarged white matter at the banks of the right superior temporal sulcus were found to favor the progression. This is to date the largest retrospective study of SCD‐to‐MCI conversion with the longest follow‐up, suggesting predictable far‐future cognitive decline for the risky populations with baseline measures only. These findings provide valuable knowledge to the future neuropathological studies of AD in its prodromal phase.

Subjective cognitive decline (SCD) is considered the earliest preclinical stage of Alzheimer’s disease (AD) that precedes mild cognitive impairment (MCI). Effective and accurate diagnosis of SCD is crucial for early detection of and timely intervention in AD. In this study, brain functional connectome (i.e., functional connections and graph theory metrics) based on the resting-state functional magnetic resonance imaging (rs-fMRI) provided multiple information about brain networks and has been used to distinguish individuals with SCD from normal controls (NCs). The consensus connections and the discriminative nodal graph metrics selected by group least absolute shrinkage and selection operator (LASSO) mainly distributed in the prefrontal and frontal cortices and the subcortical regions corresponded to default mode network (DMN) and frontoparietal task control network. Nodal efficiency and nodal shortest path showed the most significant discriminative ability among the selected nodal graph metrics. Furthermore, the comparison results of topological attributes suggested that the brain network integration function was weakened and network segregation function was enhanced in SCD patients. Moreover, the combination of brain connectome information based on multiple kernel-support vector machine (MK-SVM) achieved the best classification performance with 83.33% accuracy, 90.00% sensitivity, and an area under the curve (AUC) of 0.927. The findings of this study provided a new perspective to combine machine learning methods with exploration of brain pathophysiological mechanisms in SCD and offered potential neuroimaging biomarkers for diagnosis of early-stage AD.

Background: Previous studies have linked internet use with several beneficial outcomes for brain health, but there is little data on this among older Chinese.Objective: The goal of this study was to explore the association between internet use and cognitive impairment and to explore the possible mechanisms by which internet use prevents cognitive decline.Methods: The current study consisted of two cohorts: one from the China Longitudinal Aging Study (CLAS), which included 610 older adults with mild cognitive impairment (MCI), 192 with dementia, and 2,218 healthy older adults; the second cohort included 39 healthy adults from the Shanghai brain health foundation (SHBHF2016001), who underwent T1 cranial magnetic resonance imaging at baseline, from which their volumes of the hippocampus, amygdala, and globus pallidus were calculated. Moreover, they were also followed up for 1 year. Through standardized questionnaires, detailed general demographic information and internet use information was obtained. The cognitive diagnosis of each participant was made by attending psychiatrists at baseline, and their overall cognitive function was assessed by the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA).Results: In cohort 1, after controlling age, gender, smoker, tea drinker, take exercise, diabetes, and hypertension, we found that internet use was associated with MCI (P = 0.015, OR = 0.478, 95% CI: 0.264~0.867), but not for dementia (P = 0.205, OR = 0.393, 95% CI: 0.093~1.665). In cohort 2, we found that the globus pallidus volume of internet users was significantly larger than that of non-users, and the MMSE change score was lower (P < 0.05). The results of partial correlation analysis (controlled for sex, age and education) showed that the change of MMSE value was positively correlated with the volume of left globus pallidus (r = 0.547, P = 0.004).Conclusions: Internet use might be a protective factor for mild cognitive impairment among the Chinese elderly, and it may prevent a decline in cognitive decline by affecting the volume of the globus pallidus.

Subjective cognitive decline (SCD) is considered the earliest stage of the clinical manifestations of the continuous progression of Alzheimer’s Disease (AD). Previous studies have suggested that multimodal brain networks play an important role in the early diagnosis and mechanisms underlying SCD. However, most of the previous studies focused on a single modality, and lacked correlation analysis between different modal biomarkers and brain regions. In order to further explore the specific characteristic of the multimodal brain networks in the stage of SCD, 22 individuals with SCD and 20 matched healthy controls (HCs) were recruited in the present study. We constructed the individual morphological, structural and functional brain networks based on 3D-T1 structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. A t-test was used to select the connections with significant difference, and a multi-kernel support vector machine (MK-SVM) was applied to combine the selected multimodal connections to distinguish SCD from HCs. Moreover, we further identified the consensus connections of brain networks as the most discriminative features to explore the pathological mechanisms and potential biomarkers associated with SCD. Our results shown that the combination of three modal connections using MK-SVM achieved the best classification performance, with an accuracy of 92.68%, sensitivity of 95.00%, and specificity of 90.48%. Furthermore, the consensus connections and hub nodes based on the morphological, structural, and functional networks identified in our study exhibited abnormal cortical-subcortical connections in individuals with SCD. In addition, the functional networks presented more discriminative connections and hubs in the cortical-subcortical regions, and were found to perform better in distinguishing SCD from HCs. Therefore, our findings highlight the role of the cortical-subcortical circuit in individuals with SCD from the perspective of a multimodal brain network, providing potential biomarkers for the diagnosis and prediction of the preclinical stage of AD.