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Alzheimer’s disease (AD), the most common cause of dementia, is a neurodegenerative disease that seriously threatens human health and life quality. The main pathological features of AD include the widespread deposition of amyloid-beta and neurofibrillary tangles in the brain. So far, the pathogenesis of AD remains elusive, and no radical treatment has been developed. In recent years, mounting evidence has shown that there is a bidirectional interaction between the gut and brain, known as the brain–gut axis, and that the intestinal microbiota are closely related to the occurrence and development of neurodegenerative diseases. In this review, we will summarize the laboratory and clinical evidence of the correlation between intestinal flora and AD, discuss its possible role in the pathogenesis, and prospect its applications in the diagnosis and treatment of AD.

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) has become a popular approach for studying cerebral hemodynamics in a range of disorders and has recently been included as part of the Human Connectome Project-Aging (HCP-A). Due to the high spatial resolution and multiple post-labeling delays, ASL data from HCP-A holds promise for localization of hemodynamic signals not only in gray matter but also in white matter. However, gleaning information about white matter hemodynamics with ASL is challenging due in part to longer blood arrival times in white matter compared to gray matter. In this work, we present an analytical approach for deriving measures of cerebral blood flow (CBF) and arterial transit times (ATT) from the ASL data from HCP-A and report on gray and white matter hemodynamics in a large cohort (n = 234) of typically aging adults (age 36–90 years). Pseudo-continuous ASL data were acquired with labeling duration = 1500 ms and five post-labeling delays = 200 ms, 700 ms, 1200, 1700 ms, and 2200 ms. ATT values were first calculated on a voxel-wise basis through normalized cross-correlation analysis of the acquired signal time course in that voxel and an expected time course based on an acquisition-specific Bloch simulation. CBF values were calculated using a two-compartment model and with age-appropriate blood water longitudinal relaxation times. Using this approach, we found that white matter CBF reduces (ρ = 0.39) and white matter ATT elongates (ρ = 0.42) with increasing age (p < 0.001). In addition, CBF is lower and ATTs are longer in white matter compared to gray matter across the adult lifespan (Wilcoxon signed-rank tests; p < 0.001). We also found sex differences with females exhibiting shorter white matter ATTs than males, independently of age (Wilcoxon rank-sum test; p < 0.001). Finally, we have shown that CBF and ATT values are spatially heterogeneous, with significant differences in cortical versus subcortical gray matter and juxtacortical versus periventricular white matter. These results serve as a characterization of normative physiology across the human lifespan against which hemodynamic impairment due to cerebrovascular or neurodegenerative diseases could be compared in future studies.

Parkinson’s disease (PD) is a movement disorder characterized by the early loss of nigrostriatal dopaminergic pathways producing significant network changes impacting motor coordination. Recently three motor stages of PD have been proposed (a silent period when nigrostriatal loss begins, a prodromal motor period with subtle focal manifestations, and clinical PD) with evidence that motor cortex abnormalities occur to produce clinical PD[8]. We directly assess structural changes in the primary motor cortex and corticospinal tract using parallel analyses of longitudinal clinical and cross-sectional pathological cohorts thought to represent different stages of PD. 18F-FP-CIT positron emission tomography and subtle motor features identified patients with idiopathic rapid-eye-movement sleep behaviour disorder (n = 8) that developed prodromal motor signs of PD. Longitudinal diffusion tensor imaging before and after the development of prodromal motor PD showed higher fractional anisotropy in motor cortex and corticospinal tract compared to controls, indicating adaptive structural changes in motor networks in concert with nigrostriatal dopamine loss. Histological analyses of the white matter underlying the motor cortex showed progressive disorientation of axons with segmental replacement of neurofilaments with α-synuclein, enlargement of myelinating oligodendrocytes and increased density of their precursors. There was no loss of neurons in the motor cortex in early or late pathologically confirmed motor PD compared to controls, although there were early cortical increases in neuronal neurofilament light chain and myelin proteins in association with α-synuclein accumulation. Our results collectively provide evidence of a direct impact of PD on primary motor cortex and its output pathways that begins in the prodromal motor stage of PD with structural changes confirmed in early PD. These adaptive structural changes become considerable as the disease advances potentially contributing to motor PD.

Synaptic loss strongly correlates with cognitive impairment in Alzheimer’s disease (AD), yet the mechanism linking its origin and pattern remain unclear. Given that connected brain regions share molecular and synaptic features, and pathological tau, a key driver of synaptic degeneration, propagates through brain networks, we hypothesize that network architecture may influence synaptic loss in AD. By combining synaptic vesicle glycoprotein 2 A (SV2A) PET in 91 AD patients and 54 controls with normative connectome data, we show strongly connected regions exhibit similar levels of synaptic loss, and synaptic loss in one region is associated with connectivity-weighted synaptic loss in connected regions. Regions strongly connected to the epicenter show greater and faster synaptic loss. Plasma p-tau181 levels correlate with network-constrained synaptic loss, and post-mortem data confirm reduced SV2A expression in tau-rich areas. These findings support that synaptic vulnerability in AD is partially constrained by network topology and is modulated by phosphorylated tau. Synaptic loss contributes to cognitive decline in Alzheimer’s disease, but its spatial distribution remains poorly understood. Here, the authors show that synaptic loss is partially shaped by network connectivity and modulated by phosphorylated tau.

Background Progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by clinical features and a combination of biomarkers may increase the predictive power. In the present study, we investigated whether the combination of olfactory function and plasma neuronal-derived exosome (NDE) Aβ 1–42 can best predict progression to AD dementia. Methods 87 MCI patients were enrolled and received the cognitive assessment at 2-year and 3-year follow-up to reevaluate cognition. In the meanwhile, 80 healthy controls and 88 AD dementia patients were enrolled at baseline as well to evaluate the diagnose value in cross-section. Olfactory function was evaluated with the sniffin sticks (SS-16) and Aβ 1–42 levels in NDEs were determined by ELISA. Logistic regression was performed to evaluate the risk factors for cognitive decline in MCI at 2-year and 3-year revisits. Results In the cross cohort, lower SS-16 scores and higher Aβ 1–42 levels in NDEs were found in MCI and AD dementia compared to healthy controls. For the longitudinal set, 8 MCI individuals developed AD dementia within 2 years, and 16 MCI individuals developed AD dementia within 3 years. The two parameter-combination of SS-16 scores and Aβ 1–42 level in NDEs showed better prediction in the conversion of MCI to AD dementia at 2-year and 3-year revisit. Moreover, after a 3-year follow-up, SS-16 scores also significantly predicted the conversion to AD dementia, where lower scores were associated with a 10-fold increased risk of developing AD dementia ( p  = 0.006). Similarly, higher Aβ 1–42 levels in NDEs in patients with MCI increased the risk of developing AD dementia by 8.5-fold ( p  = 0.002). Conclusion A combination of two biomarkers of NDEs (Aβ 1–42 ) and SS-16 predicted the conversion of MCI to AD dementia more accurately in combination. These findings have critical implications for understanding the pathophysiology of AD dementia and for developing preventative treatments for cognitive decline.

Background Various plasma phosphorylated tau species have been shown to be associated with amyloid-β (Aβ) PET and Tau PET in Alzheimer’s disease (AD), but whether APOE ε4 affects the interaction between glial fibrillary acidic protein (GFAP) and phosphorylated tau (pTau), and whether a three-way interaction exists among APOE ε4, GFAP, and pTau that influences AD progression remain unclear. Methods The study included 563 participants from the Chinese Preclinical Alzheimer’s Disease Study (CPAS) and 243 from Alzheimer’s Disease Neuroimaging Initiative (ADNI), all of whom underwent Aβ PET, magnetic resonance imaging (MRI), neuropsychological assessments, and plasma biomarker analyses (GFAP, pTau181, pTau231, pTau217), with subsets undergoing Tau PET. The longitudinal data of 101 participants from ADNI were additionally included. We employed linear regression models with interaction terms to examine how APOE ε4 status and plasma GFAP levels modulate the relationships between plasma pTau biomarkers and AD pathology cross-sectionally and longitudinally. Results Plasma GFAP and pTau biomarkers (pTau181, pTau231, pTau217) are significantly elevated in Aβ-positive individuals, with stronger Aβ–pTau associations observed in APOE ε4 carriers (CPAS: β = 0.26, p  = 0.003 for pTau231; ADNI: β = 0.45, p  < 0.001 for pTau181). Across two cohorts, plasma GFAP levels significantly strengthened the associations between pTau biomarkers and Tau PET. Furthermore, subsequent analyses revealed that this modulatory effect of GFAP on the links between pTau and PET-derived pathological changes was more pronounced in APOE ε4 non-carriers, whereas in APOE ε4 carriers, a significant interaction between GFAP and pTau was only observed in specific Braak stage-specific regions within the CPAS cohort. In longitudinal analyses, we also observed stronger pTau181-associated longitudinal tau accumulation in individuals with high GFAP levels (Braak III-IV). Conclusion We demonstrate that APOE ε4 status critically modulates the relationship between pTau and Aβ pathology, whereas plasma GFAP primarily influences pTau–tau pathology associations, particularly in individuals without APOE ε4 allele. These findings underscore the role of reactive astrogliosis in tau propagation and support the utility of plasma biomarkers for AD diagnosis and prognosis.

Background and Objective: Alzheimer's disease (AD) has been shown to affect vision in human patients and animal models. This study was conducted to explore ocular abnormalities in the primary visual pathway and their relationship with hippocampal atrophy in patients with AD and mild cognitive impairment (MCI). The aim of this study was to investigate the potential value of ocular examinations as a biomarker during the AD progression. Methods: Patients with MCI ( n = 23) or AD ( n = 17) and age-matched cognitively normal controls (NC; n = 19) were enrolled. Pattern visual-evoked potentials (PVEP), flash electroretinogram (FERG) recordings and optical coherence tomography (OCT) were performed for all participants. Hippocampal volumes were measured by 3T magnetic resonance imaging. Cognitive function was assessed by Mini Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog). Pearson correlation was employed to analyze the potential associations between ocular abnormalities and hippocampal volumes. Hierarchical regression models were conducted to determine associations between cognitive performances and ocular abnormalities as well as hippocampal volumes after adjusting for confounding factors including age, sex, cognitive reserve, and APOE4 status. Results: PVEP amplitude of P100 waveform was significantly decreased in AD patients compared to MCI and normal individuals. In FERG test, delayed latencies of rod response, rod cone response and 3.0 flicker time were found in cognitively impaired groups, indicating dysfunctions of both the rod and cone systems in the disease progression. OCT test revealed reduced macular retinal nerve fiber layer (m-RNFL) thickness in MCI and AD patients, which significantly correlated with brain structure of hippocampus particularly vulnerable during the progression of AD. Interestingly, P100 amplitude showed a significant association with hippocampal volumes even after adjusting confounding factors including age, sex, and cognitive reserve. Hierarchical regression analysis further demonstrated that m-RNFL thickness, as well as hippocampal volumes, significantly associated with ADAS-cog scores. Conclusion: P100 amplitude and m-RNFL thickness showed significant correlations with brain structure involved in AD-related neurodegeneration, and therefore proved to be potential indicators of brain imaging pathologies.

With the sustained growth of the economy and significant changes in social demographics, the issue of elderly-related diseases has increasingly drawn attention, particularly. Alzheimer’s disease (AD), as a representative disease of neurodegenerative diseases, has become a major challenge, affecting the health and quality of life of the elderly population severely. In recent years, the incidence, prevalence and mortality rates of AD have increased in China, imposing substantial economic burdens on families, society and the entire healthcare system. To proactively address this challenge and respond to the national ‘Healthy China Action’ initiative, leading experts from authoritative institutions jointly authored the China Alzheimer Report 2025. Building on previous editions, this report updates epidemiological data on AD in China, thoroughly analyses the latest economic burdens of the disease and comprehensively evaluates the current status of AD diagnosis and treatment services, as well as the allocation of public health resources in our country. Its release reflects China’s progress in AD research and prevention, underscores societal concern for elderly health and aims to provide scientific guidance and data support for AD prevention, diagnosis and treatment. It also facilitates academic exchanges and cooperation, enhancing public awareness and promoting active participation in elderly healthcare, towards achieving ‘healthy ageing’ in China.

Background Alzheimer's disease (AD) exhibits heterogeneity in pathological changes involving β‐amyloid (Aβ), tau, neural macrostructure and microstructure. We aimed to evaluate juxtacortical free water fraction (FWF), its correlation with neuropathological severity, and its spatiotemporal pattern using MRI and PET in AD. Method This prospective study included 198 AD participants with Aβ PET positivity (mean age, 70.6 years ± 7.7 [SD]; 120 women) and 161 cognitively normal participants (CN) (mean age, 66.1 years ± 8.6; 103 women). Diffusion MRI was used to measure FWF and diffusion tensor image analysis along the perivascular space (DTI‐ALPS) index. Cortical thickness and hippocampus volume were automatically segmented using T1‐weighted sequences. The 18F‐Florbetapir and 18F‐MK‐6240 were used to visualize Aβ and tau accumulation separately. The machine‐learning algorithm Subtype and Stage Inference (SuStaIn) was employed to model the spatiotemporal patterns of juxtacortical FWF change. Result When compared to CN, we observed an extensive increase of FWF in AD (all p < 0.05), which correlated with Aβ and tau deposition, as well as cortical thinning (all p < 0.05, Figure 1A and 1B). Using SuStaIn, we identified two distinct spatiotemporal trajectories of FWF changes (Figure 2A and 2B). The Orbitofrontal‐first subtype had smaller left hippocampus volume (p = 0.02) and lower DTI‐ALPS index (p < 0.001) compared to the Precuneus‐first subtype (Figure 3A). There were significant correlations between SuStaIn stages and amyloid and tau deposition in the cortex, as well as cortical thickness (all p <0.05). The significant negative correlation between SuStaIn stages and DTI‐ALPS index was only found in the Precuneus‐first subtype (r = ‐0.24, p = 0.024). Increased SuStaIn stage was associated with worse cognitive performance assessed by Mini‐Mental State Examination (MMSE) in both subtypes (all p < 0.01, Figure 3B). Figure 3C displays inferred trajectories of FWF, amyloid and tau deposition in the cortex across SuStaIn stages in the two different subtypes. Visually comparing the biomarkers showed that the amyloid deposition in the cortex was found to reach a plateau early than FWF and tau. Conclusion Juxtacortical FWF change exhibited diverse spatiotemporal patterns during AD progression, which emerged as a coexisting pathophysiological feature alongside amyloid deposition in the pathogenesis of AD.

Alzheimer's disease (AD) exhibits heterogeneity in pathological changes involving β-amyloid (Aβ), tau, neural macrostructure and microstructure. We aimed to evaluate juxtacortical free water fraction (FWF), its correlation with neuropathological severity, and its spatiotemporal pattern using MRI and PET in AD. This prospective study included 198 AD participants with Aβ PET positivity (mean age, 70.6 years ± 7.7 [SD]; 120 women) and 161 cognitively normal participants (CN) (mean age, 66.1 years ± 8.6; 103 women). Diffusion MRI was used to measure FWF and diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. Cortical thickness and hippocampus volume were automatically segmented using T1-weighted sequences. The F-Florbetapir and F-MK-6240 were used to visualize Aβ and tau accumulation separately. The machine-learning algorithm Subtype and Stage Inference (SuStaIn) was employed to model the spatiotemporal patterns of juxtacortical FWF change. When compared to CN, we observed an extensive increase of FWF in AD (all p < 0.05), which correlated with Aβ and tau deposition, as well as cortical thinning (all p < 0.05, Figure 1A and 1B). Using SuStaIn, we identified two distinct spatiotemporal trajectories of FWF changes (Figure 2A and 2B). The Orbitofrontal-first subtype had smaller left hippocampus volume (p = 0.02) and lower DTI-ALPS index (p < 0.001) compared to the Precuneus-first subtype (Figure 3A). There were significant correlations between SuStaIn stages and amyloid and tau deposition in the cortex, as well as cortical thickness (all p <0.05). The significant negative correlation between SuStaIn stages and DTI-ALPS index was only found in the Precuneus-first subtype (r = -0.24, p = 0.024). Increased SuStaIn stage was associated with worse cognitive performance assessed by Mini-Mental State Examination (MMSE) in both subtypes (all p < 0.01, Figure 3B). Figure 3C displays inferred trajectories of FWF, amyloid and tau deposition in the cortex across SuStaIn stages in the two different subtypes. Visually comparing the biomarkers showed that the amyloid deposition in the cortex was found to reach a plateau early than FWF and tau. Juxtacortical FWF change exhibited diverse spatiotemporal patterns during AD progression, which emerged as a coexisting pathophysiological feature alongside amyloid deposition in the pathogenesis of AD.