Gait disturbances are important clinical features of cerebral small vessel disease (CSVD) that increase the risk of falls and disability. Brain structural alterations and gait disturbances in CSVD patients have been well demonstrated. However, intrinsic resting cerebral function patterns in CSVD patients with gait disorders remain largely unknown. Fifty-eight CSVD patients were enrolled in our studies and categorized into the gait disorder group (CSVD-GD, n = 29) and no-gait disorder group (CSVD-NGD, n = 29) based on a gait examination. Gait was quantitatively assessed with the Timed Up and Go test and the intelligent device for energy expenditure and activity (IDEEA). Functional MRI and fractional amplitude of low-frequency fluctuation (fALFF) analyses were employed to explore local intrinsic neural oscillation alterations. Functional connectivity based on fALFF results was calculated to detect the potential changes in remote connectivity. Compared with the CSVD-NGD group, the CSVD-GD group showed decreased fALFF in regions mainly located in the sensorimotor network and frontoparietal network, such as the left supplementary motor area (SMA.L) and the left superior parietal gyrus, and increased fALFF in the right inferior frontal gyrus (orbital part), the left caudate, and the left precuneus. Moreover, the CSVD-GD patients exhibited lower connectivity between the SMA.L and temporal lobe, which was related to gait speed. The fALFF value of the SMA.L was associated with cadence. This study highlights the regional and network interaction abnormalities of the SMA in CSVD patients with gait disturbances. These findings could provide further insight into the neural mechanisms of gait disturbances in CSVD.

Background Blood biomarkers that can be used for preclinical Alzheimer’s disease (AD) diagnosis would enable trial enrollment at a time when the disease is potentially reversible. Here, we investigated plasma neuronal-derived extracellular vesicle (nEV) cargo in patients along the Alzheimer’s continuum, focusing on cognitively normal controls (NCs) with high brain β-amyloid (Aβ) loads (Aβ+). Methods The study was based on the Sino Longitudinal Study on Cognitive Decline project. We enrolled 246 participants, including 156 NCs, 45 amnestic mild cognitive impairment (aMCI) patients, and 45 AD dementia (ADD) patients. Brain Aβ loads were determined using positron emission tomography. NCs were classified into 84 Aβ− NCs and 72 Aβ+ NCs. Baseline plasma nEVs were isolated by immunoprecipitation with an anti-CD171 antibody. After verification, their cargos, including Aβ, tau phosphorylated at threonine 181, and neurofilament light, were quantified using a single-molecule array. Concentrations of these cargos were compared among the groups, and their receiver operating characteristic (ROC) curves were constructed. A subset of participants underwent follow-up cognitive assessment and magnetic resonance imaging. The relationships of nEV cargo levels with amyloid deposition, longitudinal changes in cognition, and brain regional volume were explored using correlation analysis. Additionally, 458 subjects in the project had previously undergone plasma Aβ quantification. Results Only nEV Aβ was included in the subsequent analysis. We focused on Aβ42 in the current study. After normalization of nEVs, the levels of Aβ42 were found to increase gradually across the cognitive continuum, with the lowest in the Aβ− NC group, an increase in the Aβ+ NC group, a further increase in the aMCI group, and the highest in the ADD group, contributing to their diagnoses (Aβ− NCs vs. Aβ+ NCs, area under the ROC curve values of 0.663; vs. aMCI, 0.857; vs. ADD, 0.957). Furthermore, nEV Aβ42 was significantly correlated with amyloid deposition, as well as longitudinal changes in cognition and entorhinal volume. There were no differences in plasma Aβ levels among NCs, aMCI, and ADD individuals. Conclusions Our findings suggest the potential use of plasma nEV Aβ42 levels in diagnosing AD-induced cognitive impairment and Aβ+ NCs. This biomarker reflects cortical amyloid deposition and predicts cognitive decline and entorhinal atrophy.

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Alzheimer's disease (AD) is a neurodegenerative disease that gradually impairs cognitive functions. Recently, there has been a conceptual shift toward AD to view the disease as a continuum. Since AD is currently incurable, effective intervention to delay or prevent pathological cognitive decline may best target the early stages of symptomatic disease, such as subjective cognitive decline (SCD), in which cognitive function remains relatively intact. Diagnostic methods for identifying AD, such as cerebrospinal fluid biomarkers and positron emission tomography, are invasive and expensive. Therefore, it is imperative to develop blood biomarkers that are sensitive, less invasive, easier to access, and more cost effective for AD diagnosis. This review aimed to summarize the current data on whether individuals with SCD differ reliably and effectively in subjective and objective performances compared to cognitively normal elderly individuals, and to find one or more convenient and accessible blood biomarkers so that researchers can identify SCD patients with preclinical AD in the population as soon as possible. Owing to the heterogeneity and complicated pathogenesis of AD, it is difficult to make reliable diagnoses using only a single blood marker. This review provides an overview of the progress achieved to date with the use of SCD blood biomarkers in patients with preclinical AD, highlighting the key areas of application and current challenges.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.