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With the advent of an aging society, Alzheimer's disease (AD) has gradually become a major ailment affecting the elderly. AD is a neurodegenerative disorder associated with cognitive impairments. In AD patients, brain network connections are disrupted, and their topological properties are also affected, leading to the disintegration of anatomical and functional connections. Anatomical connections can be tracked and evaluated using structural magnetic imaging (MRI) and diffusion tensor imaging ( DTI), while functional connections are detected through functional MRI to assess their connectivity status. This review incorporates the findings of previous scholars and summarizes the current research of AD. It mainly discusses the imaging characteristics of large-scale brain network changes in AD patients, so as to provide researchers with scientific and objective imaging markers for AD prediction and early diagnosis, as well as future research. [ABSTRACT FROM AUTHOR]

As a neurodegenerative disease, Alzheimer's disease (AD) seriously affects the health of older people. Changes in synapses occur first over the course of the disease, perhaps even before the formation of Aβ plaques. Histone deacetylase (HDAC) mediates the damage of Aβ oligomers to dendritic spines. Therefore, we examined the relationship between HDAC activity and synaptic defects using an HDAC inhibitor (HDACI), BG45, in the human neuroblastoma SH-SY5Y cell line with stable overexpression of Swedish mutant APP (APPsw) and in APP/PS1 transgenic mice during this study. The cells were treated with 15 μM BG45 and the APP/PS1 mice were treated with 30 mg/kg BG45. We detected the levels of synapse-related proteins, HDACs, tau phosphorylation, and amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors using Western blotting and immunohistochemistry. We also measured the expression of cytoskeletal proteins in the cell model. The mRNA levels of the glutamate ion receptor alginate subunit 2 (GRIK2), sodium voltage-gated channel beta subunit (SCN3B), synaptophysin (SYP), Grm2 (the gene encoding glutamate receptor subunit 2 (GluR2)), Grid2IP, glutamate receptor interacting protein 1 (GRIP1), and GRIP2 were detected to explore the effects of the HDACI on regulating the expression of synaptic proteins and AMPA receptors. According to our studies, the expressions of HDAC1, HDAC2, and HDAC3 were increased, which were accompanied by the downregulation of the synapse-related proteins SYP, postsynaptic dendritic protein (PSD-95), and spinophilin as early as 24 h after transfection with the APPsw gene. BG45 upregulated the expression of synapse-related proteins and repaired cytoskeletal damage. In vivo, BG45 alleviated the apoptosis-mediated loss of hippocampal neurons, upregulated synapse-related proteins, reduced Aβ deposition and phosphorylation of tau, and increased the levels of the synapse-related genes GRIK2, SCN3B, SYP, Grm2, and Grid2IP. BG45 increased the expression of the AMPA receptor subunits GluA1, GluA2, and GluA3 on APPsw-transfected cells and increased GRIP1 and GRIP2 expression and AMPA receptor phosphorylation in vivo. Based on these results, HDACs are involved in the early process of synaptic defects in AD models, and BG45 may rescue synaptic damage and the loss of hippocampal neurons by specifically inhibiting HDAC1, HDAC2, and HDAC3, thereby modulating AMPA receptor transduction, increasing synapse-related gene expression, and finally enhancing the function of excitatory synapses. BG45 may be considered a potential drug for the treatment of early AD in further studies. [ABSTRACT FROM AUTHOR]

Background: Given the varying vulnerability of the rostral and caudal regions of the hippocampus to neuropathology in the Alzheimer's disease (AD) continuum, accurately assessing structural changes in these subregions is crucial for early AD detection. The development of reliable and robust automatic segmentation methods for hippocampal subregions (HS) is of utmost importance. Objective: Our aim is to propose and validate a HS segmentation model that is both training‐free and highly generalizable. This method should exhibit comparable accuracy and efficiency to state‐of‐the‐art techniques. The segmented HS can serve as a biomarker for studying the progression of AD. Methods: We utilized the functional magnetic resonance imaging of the Brain's Integrated Registration and Segmentation Tool (FIRST) to segment the entire hippocampus. By intersecting the segmentation results with the Brainnetome (BN) atlas, we obtained coarse segmentation of the four HS regions. This coarse segmentation was then employed as a shape prior term in the lattice Boltzmann (LB) model, as well as for initializing contours. Additionally, image gradients and local gray levels were integrated into the external force terms of the LB model to refine the coarse segmentation results. We assessed the segmentation accuracy of the model using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated the potential of the segmentation results as AD biomarkers on both the ADNI and Xuanwu datasets. Results: The median Dice similarity coefficients (DSC) for the left caudal, right caudal, left rostral, and right rostral hippocampus were 0.87, 0.88, 0.88, and 0.89, respectively. The proportion of segmentation results with a DSC exceeding 0.8 was 77%, 78%, 77%, and 94% for the respective regions. In terms of volume, the correlation coefficients between the segmentation results of the four HS regions and the gold standard were 0.95, 0.93, 0.96, and 0.96, respectively. Regarding asymmetry, the correlation coefficient between the segmentation result's right caudal minus left caudal and the corresponding gold standard was 0.91, while for right rostral minus left rostral, it was 0.93. Over time, we observed a decline in the volumes of the four HS regions and the total hippocampal volume of mild cognitive impairment (MCI) converters. Analysis of inter‐group differences revealed that, except for the right rostral region in the ADNI dataset, the p‐values for the four HS regions in the normal controls (NC), MCI, and AD groups from both datasets were all below 0.05. The right caudal hippocampal volume demonstrated correlation coefficients of 0.47 and 0.43 with the mini‐mental state examination (MMSE) and Montreal cognitive assessment (MoCA), respectively. Similarly, the left rostral hippocampal volume showed correlation coefficients of 0.50 and 0.58 with MMSE and MoCA, respectively. Conclusions: Our framework allows for direct application to different brain magnetic resonance (MR) datasets without the need for training. It eliminates the requirement for complex image preprocessing steps while achieving segmentation accuracy comparable to deep learning (DL) methods even with small sample sizes. Compared to traditional active contour models (ACM) and atlas‐based methods, our approach exhibits significant speed advantages. The segmented HS regions hold promise as potential biomarkers for studying the progression of AD. [ABSTRACT FROM AUTHOR]

Background: A minimum spanning tree (MST) is a unique efficient network comprising the necessary connections needed to connect all regions in a network while retaining the lowest possible cost of connection weight.Objective: This study aimed to utilize functional near-infrared spectroscopy (fNIRS) to analyze brain activity in different regions and then construct MST-based regions to characterize the brain topologies of participants with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC).Methods: A 46 channel fNIRS setup was used on all participants, with correlation being calculated for each channel pair. An MST was constructed from the resulting correlation matrix, from which graph theory measures were calculated. The average number of connections within a lobe in the left versus right hemisphere was calculated to identify which lobes displayed and abnormal amount of connectivity.Results: Compared to those in the MCI group, the AD group showed a less integrated network structure, with a higher characteristic path length, but lower leaf fraction, maximum degree, and degree divergence. The AD group also showed a higher number of connections in the frontal lobe within the left hemisphere and a lower number between hemispheric frontal lobes as compared to MCI.Conclusion: These results indicate a deviation in network structure and connectivity within patient groups that is consistent with the theory of dysconnectivity for AD. Additionally, the AD group showed strong correlations between the Hamilton depression rating scale and different graph metrics, suggesting a link between network organization and the recurrence of depression in AD. [ABSTRACT FROM AUTHOR]

Elevation of soluble wild-type (WT) tau occurs in synaptic compartments in Alzheimer's disease. We addressed whether tau elevation affects synaptic transmission at the calyx of Held in slices from mice brainstem. Whole-cell loading of WT human tau (h-tau) in presynaptic terminals at 10-20 µM caused microtubule (MT) assembly and activity-dependent rundown of excitatory neurotransmission. Capacitance measurements revealed that the primary target of WT h-tau is vesicle endocytosis. Blocking MT assembly using nocodazole prevented tau-induced impairments of endocytosis and neurotransmission. Immunofluorescence imaging analyses revealed that MT assembly by WT h-tau loading was associated with an increased MT-bound fraction of the endocytic protein dynamin. A synthetic dodecapeptide corresponding to dynamin 1-pleckstrin-homology domain inhibited MT-dynamin interaction and rescued tau-induced impairments of endocytosis and neurotransmission. We conclude that elevation of presynaptic WT tau induces de novo assembly of MTs, thereby sequestering free dynamins. As a result, endocytosis and subsequent vesicle replenishment are impaired, causing activity-dependent rundown of neurotransmission. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to explore the utility of hippocampal radiomics using multiparametric simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) for early diagnosis of Alzheimer's disease (AD). Methods: A total of 53 healthy control (HC) participants, 55 patients with amnestic mild cognitive impairment (aMCI), and 51 patients with AD were included in this study. All participants accepted simultaneous PET/MRI scans, including 18F‐fluorodeoxyglucose (18F‐FDG) PET, 3D arterial spin labeling (ASL), and high‐resolution T1‐weighted imaging (3D T1WI). Radiomics features were extracted from the hippocampus region on those three modal images. Logistic regression models were trained to classify AD and HC, AD and aMCI, aMCI and HC respectively. The diagnostic performance and radiomics score (Rad‐Score) of logistic regression models were evaluated from 5‐fold cross‐validation. Results: The hippocampal radiomics features demonstrated favorable diagnostic performance, with the multimodal classifier outperforming the single‐modal classifier in the binary classification of HC, aMCI, and AD. Using the multimodal classifier, we achieved an area under the receiver operating characteristic curve (AUC) of 0.98 and accuracy of 96.7% for classifying AD from HC, and an AUC of 0.86 and accuracy of 80.6% for classifying aMCI from HC. The value of Rad‐Score differed significantly between the AD and HC (p < 0.001), aMCI and HC (p < 0.001) groups. Decision curve analysis showed superior clinical benefits of multimodal classifiers compared to neuropsychological tests. Conclusion: Multiparametric hippocampal radiomics using PET/MRI aids in the identification of early AD, and may provide a potential biomarker for clinical applications. [ABSTRACT FROM AUTHOR]

Objective: To explore the association between the apolipoprotein E (APOE) genotype and objectively assessed cognitive function. Methods: In this cross-sectional study, 537 participants underwent a neuropsychological assessment for cognitive function and blood testing for APOE genotype. Based on cognitive test results, participants were stratified into two cohorts: Cognitively Unimpaired participants (CU) and Cognitively Impaired participants (CI). The CI group was further divided into Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). Furthermore, we conducted age stratification, categorizing participants into three age groups: age 1: <65 years, age 2: 65–75 years, and age 3: >75 years. We assessed the disparities in cognitive function associated with ε4 carrier status across different age brackets. Plasma amyloid-β levels were measured in a cohort of 294 participants to investigate potential interactions involving ε4 carrier status, diagnosis, sex, or plasma markers. Results: The APOE genotypic distribution among the 537 participants was characterized as follows: ε2/ε2 (5 participants), ε2/ε3 (67), ε2/ε4 (13), ε3/ε3 (330), ε3/ε4 (113), and ε4/ε4 (9). Allele frequencies were: ε3 at 78.21%, ε4 at 13.41%, and ε2 at 8.38%. Notably, the ε4 carrier frequency was markedly elevated in the AD group at 81.8% when compared to MCI at 32.8% and CU at 21.3% (p < 0.05). Within the Cognitively Unimpaired (CU) cohort, the sole discernible contrast between ε4+ and ε4− emerged in STT-B (p < 0.05). Within the CI group, ε4 carriers showed statistically poorer scores as compared to non-ε4 carriers in several cognitive tests (p < 0.05). Age stratification result revealed that, among ε4 carriers, cognitive function scores within the age 3 group were significantly inferior to those of age 1 and age 2 groups (p < 0.05). Plasma amyloid-β detection was applied to the 294 participants. We tested plasma amyloid-β (Aβ42) and plasma amyloid-β (Aβ40) levels and calculated the Aβ42/Aβ40 ratio. We found that among female ε4 carriers, both Aβ42 and the Aβ42/Aβ40 ratio were notably lower than their male counterparts (p < 0.05). Conclusions: The ε3/ε3 was the most prevalent among participants, succeeded by ε3/ε4 and ε2/ε3. The least prevalent were ε2/ε4, ε4/ε4, and ε2/ε2 genotypes. The ε3 was predominant, followed by the ε4 and ε2. Individuals with the ε4 allele exhibited significant cognitive impairment, with an especially high prevalence in AD group at 81.8%. The study unveils a pronounced correlation between the ε4 allele and cognitive deficits, implying its potential role in the advancement and severity of cognitive disorders, notably Alzheimer's disease. Cognitive function declines with age in individuals carrying the ε4, and women are more affected by ε4. [ABSTRACT FROM AUTHOR]

Background: Sleep disorders are prevalent among patients with Alzheimer's disease (AD), and the APOE ε4 genotype is a key genetic risk factor for sporadic AD. However, the combined effect of the genotype and sleep disorders on cognitive decline remains uncertain. Methods: A total of 972 participants were drawn from the SILCODE cohort, comprising 655 without the ε4 allele (APOE−) and 317 with ε4 allele (APOE+). Data were collected, including neuropsychological assessments, sleep measurements, plasma biomarkers, and PET imaging. A Sleep Composite Index (SCI) was created, categorizing participants into high risk (Sleep+) and low risk (Sleep−). Results: Significant predictions of dementia risk associated with plasma p‐tau181, neurofilament light chain (NfL), and SCI. Individuals with both Sleep+ and APOE+ had a higher risk of dementia compared to those with Sleep‐. The Sleep+/APOE+ group had higher plasma NfL levels than the Sleep−/APOE− group. Similar trends emerged in plasma NfL levels among the Aβ PET‐positive subgroup. Plasma NfL levels explained 23% of the relationship between SCI and cognitive impairment. Conclusion: Our study highlights sleep disorder was associated with cognitive decline, with plasma NfL playing a partial mediating role. These findings explain how sleep disorders affect cognitive function and emphasize the importance of healthy sleep for older adults. [ABSTRACT FROM AUTHOR]

Background: Developing biomarkers for early stage AD patients is crucial. Glucose metabolism measured by 18F-FDG PET is the most common biomarker for evaluating cellular energy metabolism to diagnose AD. Arterial spin labeling (ASL) MRI can potentially provide comparable diagnostic information to 18F-FDG PET in patients with neurodegenerative disorders. However, the conclusions about the diagnostic performance of AD are still controversial between 18F-FDG PET and ASL. This study aims to compare quantitative cerebral blood flow (CBF) and glucose metabolism measured by 18F-FDG PET diagnostic values in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) using integrated PET/MR. Results: Analyses revealed overlapping between decreased regional rCBF and 18F-FDG PET SUVR in patients with AD compared with NC participants in the bilateral parietotemporal regions, frontal cortex, and cingulate cortex. Compared with NC participants, patients with aMCI exclusively demonstrated lower 18F-FDG PET SUVR in the bilateral temporal cortex, insula cortex, and inferior frontal cortex. Comparison of the rCBF in patients with aMCI and NC participants revealed no significant difference (P > 0.05). The ROC analysis of rCBF in the meta-ROI could diagnose patients with AD (AUC, 0.87) but not aMCI (AUC, 0.61). The specificity of diagnosing aMCI has been improved to 75.56% when combining rCBF and 18F-FDG PET SUVR. Conclusion: ASL could detect similar aberrant patterns of abnormalities compared to 18F-FDG PET in patients with AD compared with NC participants but not in aMCI. The diagnostic efficiency of 18F-FDG-PET for AD and aMCI patients remained higher to ASL. Our findings support that applying 18F-FDG PET may be preferable for diagnosing AD and aMCI. Key points: Hypometabolic brain areas are more widespread in patients with AD and aMCI than hypoperfused brain areas. 18F-FDG PET provides superior diagnostic performance over ASL for patients with AD and aMCI. After comprehensive consideration, PET alone is recommended for the diagnosis of AD and aMCI patients. [ABSTRACT FROM AUTHOR]

Both plasma biomarkers and brain network topology have shown great potential in the early diagnosis of Alzheimer's disease (AD). However, the specific associations between plasma AD biomarkers, structural network topology, and cognition across the AD continuum have yet to be fully elucidated. This retrospective study evaluated participants from the Sino Longitudinal Study of Cognitive Decline cohort between September 2009 and October 2022 with available blood samples or 3.0‐T MRI brain scans. Plasma biomarker levels were measured using the Single Molecule Array platform, including β‐amyloid (Aβ), phosphorylated tau181 (p‐tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The topological structure of brain white matter was assessed using network efficiency. Trend analyses were carried out to evaluate the alterations of the plasma markers and network efficiency with AD progression. Correlation and mediation analyses were conducted to further explore the relationships among plasma markers, network efficiency, and cognitive performance across the AD continuum. Among the plasma markers, GFAP emerged as the most sensitive marker (linear trend: t = 11.164, p = 3.59 × 10−24; quadratic trend: t = 7.708, p = 2.25 × 10−13; adjusted R2 = 0.475), followed by NfL (linear trend: t = 6.542, p = 2.9 × 10−10; quadratic trend: t = 3.896, p = 1.22 × 10−4; adjusted R2 = 0.330), p‐tau181 (linear trend: t = 8.452, p = 1.61 × 10−15; quadratic trend: t = 6.316, p = 1.05 × 10−9; adjusted R2 = 0.346) and Aβ42/Aβ40 (linear trend: t = −3.257, p = 1.27 × 10−3; quadratic trend: t = −1.662, p = 9.76 × 10−2; adjusted R2 = 0.101). Local efficiency decreased in brain regions across the frontal and temporal cortex and striatum. The principal component of local efficiency within these regions was correlated with GFAP (Pearson's R = −0.61, p = 6.3 × 10−7), NfL (R = −0.57, p = 6.4 × 10−6), and p‐tau181 (R = −0.48, p = 2.0 × 10−4). Moreover, network efficiency mediated the relationship between general cognition and GFAP (ab = −0.224, 95% confidence interval [CI] = [−0.417 to −0.029], p =.0196 for MMSE; ab = −0.198, 95% CI = [−0.42 to −0.003], p =.0438 for MOCA) or NfL (ab = −0.224, 95% CI = [−0.417 to −0.029], p =.0196 for MMSE; ab = −0.198, 95% CI = [−0.42 to −0.003], p =.0438 for MOCA). Our findings suggest that network efficiency mediates the association between plasma biomarkers, specifically GFAP and NfL, and cognitive performance in the context of AD progression, thus highlighting the potential utility of network‐plasma approaches for early detection, monitoring, and intervention strategies in the management of AD. [ABSTRACT FROM AUTHOR]

Aims: We evaluated whether Subjective Cognitive Decline (SCD) subtypes could be empirically derived within the Sino Longitudinal Study on Cognitive Decline (SILCODE) SCD cohort and examined associated neuroimaging markers, biomarkers, and clinical outcomes. Methods: A cluster analysis was performed on eight neuropsychological test scores from 124 SCD SILCODE participants and 57 normal control (NC) subjects. Structural and functional neuroimaging indices were used to evaluate the SCD subgroups. Results: Four subtypes emerged: (1) dysexecutive/mixed SCD (n = 23), (2) neuropsychiatric SCD (n = 24), (3) amnestic SCD (n = 22), and (4) cluster‐derived normal (n = 55) who exhibited normal performance in neuropsychological tests. Compared with the NC group, each subgroup showed distinct patterns in gray matter (GM) volume and the amplitude of low‐frequency fluctuations (ALFF). Lower fractional anisotropy (FA) values were only found in the neuropsychiatric SCD group relative to NC. Conclusion: The identification of empirically derived SCD subtypes demonstrates the presence of heterogeneity in SCD neuropsychological profiles. The cluster‐derived normal group may represent the majority of SCD individuals who do not show progressive cognitive decline; the dysexecutive/mixed SCD and amnestic SCD might represent high‐risk groups with progressing cognitive decline; and finally, the neuropsychiatric SCD may represent a new topic in SCD research. [ABSTRACT FROM AUTHOR]

Apolipoprotein E-ε4 (APOE-ε4) carriers had elevated cerebrospinal fluid (CSF) presynaptic protein growth-associated protein-43 (GAP-43), but the underlying mechanism is not fully understood. We investigated how the APOE-ε4 genotype affects the baseline and longitudinal changes in CSF GAP-43 and their associations with β-amyloid positron emission tomography (Aβ PET), CSF phosphorylated tau 181 (p-Tau 181), neurodegeneration, and cognitive decline. Compared to APOE-ε4 non-carriers, APOE-ε4 carriers had higher baseline levels and faster rates of increases in Aβ PET, CSF p-Tau 181 , and CSF GAP-43. Both higher baseline levels and faster rates of increase in CSF GAP-43 were associated with greater baseline Aβ PET and CSF p-Tau 181 , which fully mediated the APOE-ε4 effect on CSF GAP-43 elevations. Independent of Aβ PET and CSF p-Tau 181 , APOE-ε4 carriage was associated with exacerbated GAP-43-related longitudinal hippocampal atrophy and cognitive decline, especially in Aβ+ participants (GAP-43 × time × APOE-ε4). These findings suggest that the APOE-ε4 effect on GAP-43-related presynaptic dysfunction is mediated by primary Alzheimer's pathologies and independently correlates to hippocampal atrophy and cognitive decline in the future. [ABSTRACT FROM AUTHOR]

Abnormal glucose metabolism and hemodynamic changes in the brain are closely related to cognitive function, providing complementary information from distinct biochemical and physiological processes. However, it remains unclear how to effectively integrate these two modalities across distinct brain regions. In this study, we developed a connectome‐based sparse coupling method for hybrid PET/MRI imaging, which could effectively extract imaging markers of Alzheimer's disease (AD) in the early stage. The FDG‐PET and resting‐state fMRI data of 56 healthy controls (HC), 54 subjective cognitive decline (SCD), and 27 cognitive impairment (CI) participants due to AD were obtained from SILCODE project (NCT03370744). For each participant, the metabolic connectome (MC) was constructed by Kullback–Leibler divergence similarity estimation, and the functional connectome (FC) was constructed by Pearson correlation. Subsequently, we measured the coupling strength between MC and FC at various sparse levels, assessed its stability, and explored the abnormal coupling strength along the AD continuum. Results showed that the sparse MC–FC coupling index was stable in each brain network and consistent across subjects. It was more normally distributed than other traditional indexes and captured more SCD‐related brain areas, especially in the limbic and default mode networks. Compared to other traditional indices, this index demonstrated best classification performance. The AUC values reached 0.748 (SCD/HC) and 0.992 (CI/HC). Notably, we found a significant correlation between abnormal coupling strength and neuropsychological scales (p <.05). This study provides a clinically relevant tool for hybrid PET/MRI imaging, allowing for exploring imaging markers in early stage of AD and better understanding the pathophysiology along the AD continuum. [ABSTRACT FROM AUTHOR]

Hippocampal morphological change is one of the main hallmarks of Alzheimer's disease (AD). However, whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment (MCI) to AD dementia and whether these features provide any neurobiological foundation remains unclear. The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging (MRI) markers for AD. Multivariate classifier-based support vector machine (SVM) analysis provided individual-level predictions for distinguishing AD patients (n = 261) from normal controls (NCs; n = 231) with an accuracy of 88.21% and intersite cross-validation. Further analyses of a large, independent the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 1228) reinforced these findings. In MCI groups, a systemic analysis demonstrated that the identified features were significantly associated with clinical features (e.g., apolipoprotein E (APOE) genotype, polygenic risk scores, cerebrospinal fluid (CSF) Aβ, CSF Tau), and longitudinal changes in cognition ability; more importantly, the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up. These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI, and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus. The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI. [ABSTRACT FROM AUTHOR]

Precision medicine for Alzheimer's disease (AD) necessitates the development of personalized, reproducible, and neuroscientifically interpretable biomarkers, yet despite remarkable advances, few such biomarkers are available. Also, a comprehensive evaluation of the neurobiological basis and generalizability of the end‐to‐end machine learning system should be given the highest priority. For this reason, a deep learning model (3D attention network, 3DAN) that can simultaneously capture candidate imaging biomarkers with an attention mechanism module and advance the diagnosis of AD based on structural magnetic resonance imaging is proposed. The generalizability and reproducibility are evaluated using cross‐validation on in‐house, multicenter (n = 716), and public (n = 1116) databases with an accuracy up to 92%. Significant associations between the classification output and clinical characteristics of AD and mild cognitive impairment (MCI, a middle stage of dementia) groups provide solid neurobiological support for the 3DAN model. The effectiveness of the 3DAN model is further validated by its good performance in predicting the MCI subjects who progress to AD with an accuracy of 72%. Collectively, the findings highlight the potential for structural brain imaging to provide a generalizable, and neuroscientifically interpretable imaging biomarker that can support clinicians in the early diagnosis of AD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disorder with significant heterogeneity. Different AD phenotypes may be associated with specific brain network changes. Uncovering disease heterogeneity by using functional networks could provide insights into precise diagnoses. We investigated the subtypes of AD using nonnegative matrix factorization clustering on the previously identified 216 resting-state functional connectivities that differed between AD and normal control subjects. We conducted the analysis using a discovery dataset (n = 809) and a validated dataset (n = 291). Next, we grouped individuals with mild cognitive impairment according to the model obtained in the AD groups. Finally, the clinical measures and brain structural characteristics were compared among the subtypes to assess their relationship with differences in the functional network. Individuals with AD were clustered into 4 subtypes reproducibly, which included those with 1) diffuse and mild functional connectivity disruption (subtype 1), 2) predominantly decreased connectivity in the default mode network accompanied by an increase in the prefrontal circuit (subtype 2), 3) predominantly decreased connectivity in the anterior cingulate cortex accompanied by an increase in prefrontal cortex connectivity (subtype 3), and 4) predominantly decreased connectivity in the basal ganglia accompanied by an increase in prefrontal cortex connectivity (subtype 4). In addition to these differences in functional connectivity, differences between the AD subtypes were found in cognition, structural measures, and cognitive decline patterns. These comprehensive results offer new insights that may advance precision medicine for AD and facilitate strategies for future clinical trials. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a common neurodegenerative disease. Abundant evidence demonstrates that oxidative stress may be not only an early event in this disease, but also a key factor in the pathogenesis of AD. Ginkgo biloba extract (EGb) has a strong ability to scavenge oxygen free radicals and supply hydrogen. The present study aims to investigate the effects of EGb on Neuro 2A cells transfected with Swedish mutant APP (APPsw). Stably transfected Neuro 2A cell lines expressing human wild-type APP (APP695), APPsw, or empty vector(neo) pEGFP-N2 were treated with 100 μg/ml EGb for 0, 2, 4, 6, 8, and 10 h. Oxidative stress was assessed by measuring free radicals and the activities of antioxidant enzymes. Our studies showed that EGb treatment reduced the production of reactive oxygen species (ROS) and the levels of malondialdehyde (MDA) significantly while total superoxide dismutase (T-SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) activities were enhanced in Neuro 2A cells overexpressing APPsw. Meanwhile, Aβ levels in these cells were also reduced compared to the levels in untreated cells and control cells (empty vector(neo) pEGFP-N2). These findings suggest that EGb can reduce oxidative stress by decreasing free radical and enhancing antioxidant status, further leading to reduced Aβ aggregation; EGb might be a potential therapeutic agent for Alzheimer's disease (AD). [ABSTRACT FROM AUTHOR]

Simple Summary: Objective biomarkers for the diagnosis of subjective cognitive decline (SCD) are critical for intervention in the Alzheimer's disease (AD) disease process. Previous research has shown that functional magnetic resonance imaging (fMRI) is a significant tool for detecting abnormal interregional functional connectivity (FC) in SCD populations. However, traditional FC calculations do not reveal the high individual variation in the SCD population. We proposed a new framework for individual proportion loss of functional connectivity strength (IPLFCS) based on FC to identify SCD biomarkers, and further explore the relationship between biomarkers and amyloid deposition as well as neuropsychological performance. The study findings indicated that IPLFCS in the left middle temporal gyrus (LMTG) was identified as a potential biomarker that correlated with cortical amyloid deposition and cognitive performance. High individual variation in the subjective cognitive decline (SCD) population makes functional connectivity (FC) biomarkers unstable. This study proposed a novel individual FC index, named individual proportion loss of functional connectivity strength (IPLFCS), and explored potential biomarkers for SCD using this new index. We proposed an IPLFCS analysis framework and compared it with traditional FC in Chinese and Western cohorts. Post hoc tests were used to determine biomarkers. Pearson's correlation analysis was used to investigate the correlation between neuropsychological scores or cortical amyloid deposits and IPLFCS biomarkers. Receiver operating characteristic curves were utilized to evaluate the ability of potential biomarkers to distinguish between groups. IPLFCS of the left middle temporal gyrus (LMTG) was identified as a potential biomarker. The IPLFC was correlated with the traditional FC (r = 0.956, p < 0.001; r = 0.946, p < 0.001) and cortical amyloid deposition (r = −0.245, p = 0.029; r = −0.185, p = 0.048) in both cohorts. Furthermore, the IPLFCS decreased across the Alzheimer's disease (AD) continuum. Its diagnostic efficiency was superior to that of existing fMRI biomarkers. These findings suggest that IPLFCS of the LMTG could be a potential biomarker of SCD. [ABSTRACT FROM AUTHOR]

Background: Several studies have examined NCAPH2 methylation in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD), but little is known of NCAPH2 methylation in subjective cognitive decline (SCD). Objective: To examine whether methylation of peripheral NCAPH2 are differentially changed at various phases of AD, and whether it could serve as a diagnostic biomarker for SCD. Methods: A total of 40 AD patients, 52 aMCI patients, 148 SCD patients, and 193 cognitively normal controls (NCs) were recruited in the current case-control study. Besides, 54 cognitively normal individuals have received amyloid positron emission tomography (amyloid PET) scans. Using bisulfite pyrosequencing method, we measured blood DNA methylation in the NCAPH2 gene promoter. Results: The main outcomes were: 1) For SCD, there was no significant difference between SCD and NC regarding NCAPH2 methylation; 2) For aMCI, NCAPH2 methylation at CpG2 were significantly lower in aMCI compared with NC and SCD in the entire population and male subgroup; 3) For AD, NCAPH2 methylation at CpG1 were significantly lower in AD compared with NC among females; 4) A relationship with apolipoprotein E (APOE) ɛ4 status was shown. Receiver operating characteristic (ROC) analysis by combining NCAPH2 methylation, age, education, and APOEɛ4 status could distinguish between patients with aMCI (area under the curve (AUC): 0.742) and AD (AUC: 0.873) from NCs. Conclusion: NCAPH2 methylation levels were altered at the aMCI and AD stage and may be convenient and cost-effective biomarkers of AD and aMCI. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Subjective cognitive decline (SCD) complaints as the early manifestation of mild cognitive impairment (MCI) may be harbingers of objective cognitive decline. SCD-questinnaire9 (SCD-Q9) is developed to investigate the early sign for MCI. However, few studies have reported its power for discriminating MCI from healthy controls (HCs). Therefore, this study aims to investigate the discrimination power of SCD-Q9 as a brief screening tool for early detection of SCD in MCI. Methods: 84 HCs and 205 people with MCI were recruited. Their demographic information and scores of SCD-Q9 were compared. A binary logistic regression model was used to analyze the potential affecting factors of MCI, and the Receiver Operating Characteristic analysis was applied to test the discrimination powers of those factors, including SCD-Q9. Results: (1) Single and total scores of SCD-Q9 were all lower in the MCI group than those in the HC group. (2) Ageing, lower education and higher total scores of SCD-Q9 were associated with MCI. (3) Area Under the Curves (AUC) of SCD-Q9 for discriminating MCI from HC group was 0.815 and when integrating age and education, the AUC improved slightly and reached 0.839. Additionally, the sensitivity and specificity were 68.8% and 85.7%, respectively when a cut-off value of 3 was applied. Conclusions: SCD-Q9 may be able to detect the subjective cognitive decline in MCI early, but it may be used together with other screening questionnaires to improve its sensitivity and further verification of its power is needed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline‐questionnaire 9 (SCD‐Q9) was developed to detect SCD complaints at risk of mild cognitive impairment (MCI). However, our previous findings indicated that its coverage might be insufficient. To test this hypothesis, we recently translated SCD‐Q21. Objective: To examine the reliability and validity of this translated SCD‐Q21 and to explore its effectiveness for discriminating MCI from controls. Methods: Item analysis was performed to understand its item discrimination and homogeneity. The Cronbach's α and Spearman‐Brown's split‐half coefficients were calculated to test its reliability. The Kaiser–Meyer–Olkin (KMO) value, Bartlett's sphericity test, and exploratory factor analysis (EFA) were used to examine its construct validity. The content validity was evaluated using five‐grade Likert scale. Finally, the SCD‐Q21 scores in MCI and controls were compared. Results: The difference of each item between the extreme groups was significant. The Cronbach's α coefficient was.913 and Spearman‐Brown's split‐half coefficient was.894. When performing holding one‐out approach, the Cronbach's α coefficient ranged from.906 to.914. The KMO value was.929 and the difference of Bartlett's Sphericity test was significant. All experts scored 5 points when assessing its content. Finally, a significant difference of score was found between MCI and NC groups. Conclusions: The reliability and validity of the SCD‐Q21 are good, which may pave a way for its application in a wider Chinese‐speaking population. [ABSTRACT FROM AUTHOR]

Purpose: [18F]-D3FSP is a new β-amyloid (Aβ) PET imaging tracer designed to decrease nonspecific signals in the brain by reducing the formation of the N-demethylated product. However, its optimal reference region for calculating the standardized uptake value ratio (SUVR) and its relation to the well-established biomarkers of Alzheimer’s disease (AD) are still unclear.We recruited 203 participants from the Greater Bay Area Healthy Aging Brain Study (GHABS) to undergo [18F]-D3FSP Aβ PET imaging. We analyzed plasma Aβ42/Aβ40, p-Tau181, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) using the Simoa platform. We compared the standardized uptake value (SUV) of five reference regions (cerebellum, cerebellum cortex, brainstem/PONs, white matter, composite of the four regions above) and AD typical cortical region (COMPOSITE) SUVR among different clinical groups. The association of D3FSP SUVR with plasma biomarkers, imaging biomarkers, and cognition was also investigated.Brainstem/PONs SUV showed the lowest fluctuation across diagnostic groups, and COMPOSITE D3FSP SUVR had an enormous effect distinguishing cognitively impaired (CI) individuals from cognitively unimpaired (CU) individuals. COMPOSITE SUVR (Referred to brainstem/PONs) was positively correlated with p-Tau181 (p < 0.001), GFAP (p < 0.001), NfL (p = 0.014) in plasma and temporal-metaROI tau deposition (p < 0.001), and negatively related to plasma Aβ42/Aβ40 (p < 0.001), temporal-metaROI cortical thickness (p < 0.01), residual hippocampal volume (p < 0.001) and cognition (p < 0.001). The voxel-wise analysis replicated these findings.This study suggests brainstem/PONs as an optimal reference region for calculating D3FSP SUVR to quantify cortical Aβ plaques in the brain. [18F]-D3FSP could distinguish CI from CU and strongly correlates with well-established plasma biomarkers, tau PET, neurodegeneration, and cognitive decline. However, future head-to-head comparisons of [18F]-D3FSP PET images with other validated Aβ PET tracers or postmortem results are crucial.Methods: [18F]-D3FSP is a new β-amyloid (Aβ) PET imaging tracer designed to decrease nonspecific signals in the brain by reducing the formation of the N-demethylated product. However, its optimal reference region for calculating the standardized uptake value ratio (SUVR) and its relation to the well-established biomarkers of Alzheimer’s disease (AD) are still unclear.We recruited 203 participants from the Greater Bay Area Healthy Aging Brain Study (GHABS) to undergo [18F]-D3FSP Aβ PET imaging. We analyzed plasma Aβ42/Aβ40, p-Tau181, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) using the Simoa platform. We compared the standardized uptake value (SUV) of five reference regions (cerebellum, cerebellum cortex, brainstem/PONs, white matter, composite of the four regions above) and AD typical cortical region (COMPOSITE) SUVR among different clinical groups. The association of D3FSP SUVR with plasma biomarkers, imaging biomarkers, and cognition was also investigated.Brainstem/PONs SUV showed the lowest fluctuation across diagnostic groups, and COMPOSITE D3FSP SUVR had an enormous effect distinguishing cognitively impaired (CI) individuals from cognitively unimpaired (CU) individuals. COMPOSITE SUVR (Referred to brainstem/PONs) was positively correlated with p-Tau181 (p < 0.001), GFAP (p < 0.001), NfL (p = 0.014) in plasma and temporal-metaROI tau deposition (p < 0.001), and negatively related to plasma Aβ42/Aβ40 (p < 0.001), temporal-metaROI cortical thickness (p < 0.01), residual hippocampal volume (p < 0.001) and cognition (p < 0.001). The voxel-wise analysis replicated these findings.This study suggests brainstem/PONs as an optimal reference region for calculating D3FSP SUVR to quantify cortical Aβ plaques in the brain. [18F]-D3FSP could distinguish CI from CU and strongly correlates with well-established plasma biomarkers, tau PET, neurodegeneration, and cognitive decline. However, future head-to-head comparisons of [18F]-D3FSP PET images with other validated Aβ PET tracers or postmortem results are crucial.Results: [18F]-D3FSP is a new β-amyloid (Aβ) PET imaging tracer designed to decrease nonspecific signals in the brain by reducing the formation of the N-demethylated product. However, its optimal reference region for calculating the standardized uptake value ratio (SUVR) and its relation to the well-established biomarkers of Alzheimer’s disease (AD) are still unclear.We recruited 203 participants from the Greater Bay Area Healthy Aging Brain Study (GHABS) to undergo [18F]-D3FSP Aβ PET imaging. We analyzed plasma Aβ42/Aβ40, p-Tau181, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) using the Simoa platform. We compared the standardized uptake value (SUV) of five reference regions (cerebellum, cerebellum cortex, brainstem/PONs, white matter, composite of the four regions above) and AD typical cortical region (COMPOSITE) SUVR among different clinical groups. The association of D3FSP SUVR with plasma biomarkers, imaging biomarkers, and cognition was also investigated.Brainstem/PONs SUV showed the lowest fluctuation across diagnostic groups, and COMPOSITE D3FSP SUVR had an enormous effect distinguishing cognitively impaired (CI) individuals from cognitively unimpaired (CU) individuals. COMPOSITE SUVR (Referred to brainstem/PONs) was positively correlated with p-Tau181 (p < 0.001), GFAP (p < 0.001), NfL (p = 0.014) in plasma and temporal-metaROI tau deposition (p < 0.001), and negatively related to plasma Aβ42/Aβ40 (p < 0.001), temporal-metaROI cortical thickness (p < 0.01), residual hippocampal volume (p < 0.001) and cognition (p < 0.001). The voxel-wise analysis replicated these findings.This study suggests brainstem/PONs as an optimal reference region for calculating D3FSP SUVR to quantify cortical Aβ plaques in the brain. [18F]-D3FSP could distinguish CI from CU and strongly correlates with well-established plasma biomarkers, tau PET, neurodegeneration, and cognitive decline. However, future head-to-head comparisons of [18F]-D3FSP PET images with other validated Aβ PET tracers or postmortem results are crucial.Conclusion: [18F]-D3FSP is a new β-amyloid (Aβ) PET imaging tracer designed to decrease nonspecific signals in the brain by reducing the formation of the N-demethylated product. However, its optimal reference region for calculating the standardized uptake value ratio (SUVR) and its relation to the well-established biomarkers of Alzheimer’s disease (AD) are still unclear.We recruited 203 participants from the Greater Bay Area Healthy Aging Brain Study (GHABS) to undergo [18F]-D3FSP Aβ PET imaging. We analyzed plasma Aβ42/Aβ40, p-Tau181, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) using the Simoa platform. We compared the standardized uptake value (SUV) of five reference regions (cerebellum, cerebellum cortex, brainstem/PONs, white matter, composite of the four regions above) and AD typical cortical region (COMPOSITE) SUVR among different clinical groups. The association of D3FSP SUVR with plasma biomarkers, imaging biomarkers, and cognition was also investigated.Brainstem/PONs SUV showed the lowest fluctuation across diagnostic groups, and COMPOSITE D3FSP SUVR had an enormous effect distinguishing cognitively impaired (CI) individuals from cognitively unimpaired (CU) individuals. COMPOSITE SUVR (Referred to brainstem/PONs) was positively correlated with p-Tau181 (p < 0.001), GFAP (p < 0.001), NfL (p = 0.014) in plasma and temporal-metaROI tau deposition (p < 0.001), and negatively related to plasma Aβ42/Aβ40 (p < 0.001), temporal-metaROI cortical thickness (p < 0.01), residual hippocampal volume (p < 0.001) and cognition (p < 0.001). The voxel-wise analysis replicated these findings.This study suggests brainstem/PONs as an optimal reference region for calculating D3FSP SUVR to quantify cortical Aβ plaques in the brain. [18F]-D3FSP could distinguish CI from CU and strongly correlates with well-established plasma biomarkers, tau PET, neurodegeneration, and cognitive decline. However, future head-to-head comparisons of [18F]-D3FSP PET images with other validated Aβ PET tracers or postmortem results are crucial. [ABSTRACT FROM AUTHOR]

Background: The dynamic functional connectivity (dFC) has been used successfully to investigate the dysfunction of Alzheimer's disease (AD) patients. The reconfiguration intensity of nodal dFC, which means the degree of alteration between FCs at different time scales, could provide additional information for understanding the reconfiguration of brain connectivity. Results: In this paper, we introduced a feature named time distance nodal connectivity diversity (tdNCD), and then evaluated the network reconfiguration intensity in every specific brain region in AD using a large multicenter dataset (N = 809 from 7 independent sites). Our results showed that the dysfunction involved in three subnetworks in AD, including the default mode network (DMN), the subcortical network (SCN), and the cerebellum network (CBN). The nodal tdNCD inside the DMN increased in AD compared to normal controls, and the nodal dynamic FC of the SCN and the CBN decreased in AD. Additionally, the classification analysis showed that the classification performance was better when combined tdNCD and FC to classify AD from normal control (ACC = 81%, SEN = 83.4%, SPE = 80.6%, and F1-score = 79.4%) than that only using FC (ACC = 78.2%, SEN = 76.2%, SPE = 76.5%, and F1-score = 77.5%) with a leave-one-site-out cross-validation. Besides, the performance of the three classes classification was improved from 50% (only using FC) to 53.3% (combined FC and tdNCD) (macro F1-score accuracy from 46.8 to 48.9%). More importantly, the classification model showed significant clinically predictive correlations (two classes classification: R = −0.38, P < 0.001; three classes classification: R = −0.404, P < 0.001). More importantly, several commonly used machine learning models confirmed that the tdNCD would provide additional information for classifying AD from normal controls. Conclusions: The present study demonstrated dynamic reconfiguration of nodal FC abnormities in AD. The tdNCD highlights the potential for further understanding core mechanisms of brain dysfunction in AD. Evaluating the tdNCD FC provides a promising way to understand AD processes better and investigate novel diagnostic brain imaging biomarkers for AD. [ABSTRACT FROM AUTHOR]

Background: Sleep appears to be a sensitive biomarker that facilitates early detection and effective intervention for Alzheimer's disease, while subjective cognitive decline (SCD) is a risk factor for Alzheimer's disease. Prefrontal cortex atrophy is associated with both sleep disruption and cognitive decline. Transcranial brain photobiomodulation (PBM) therapy can enhance frontal cortex oxygen consumption, increasing frontal cortex mediated memory function.Objective: This study aimed to test whether PBM therapy targeting the frontal cortex could improve sleep and cognitive function in SCD.Methods: Fifty-eight SCDs were divided into the PBM group (N = 32) in which real light therapy was administered and a sham light therapy group (N = 26). All the participants received either real light or sham light therapy for 6 days consecutively, while the sleep data were recorded. The n-back task was employed to measure each participant's working memory.Results: We found no differences in sleep efficiency change (F = 211, p = 0.279), REM stage percent change (F = 420, p = 0.91), and wake-up time (F = 212, p = 0.277) between the two groups. The sleep efficiency and REM were improved within the true light group on the fifth day. The true light group perform better than the control group in the n-back test, the accuracy was higher in the 2-back test (88.6% versus 79.6%, p = 0.001), and the reaction time in 1-back was shorter (544.80±202.00 versus 592.87±222.05, p = 0.003).Conclusion: After five days of PBM therapy targeting the prefrontal cortex, sleep efficiency and N-back cognitive performance were improved on the fifth day. [ABSTRACT FROM AUTHOR]

Background: Predicting the risk of disease progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has important clinical significance. This study aimed to provide a personalized MCI-to-AD conversion prediction via radiomics-based predictive modelling (RPM) with multicenter 18F-fluorodeoxyglucose positron emission tomography (FDG PET) data. Method: FDG PET and neuropsychological data of 884 subjects were collected from Huashan Hospital, Xuanwu Hospital, and from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. First, 34,400 radiomic features were extracted from the 80 regions of interest (ROIs) for all PET images. These features were then concatenated for feature selection, and an RPM model was constructed and validated on the ADNI dataset. In addition, we used clinical data and the routine semiquantification index (standard uptake value ratio, SUVR) to establish clinical and SUVR Cox models for further comparison. FDG images from local hospitals were used to explore RPM performance in a separate cohort of individuals with healthy controls and different cognitive levels (a complete AD continuum). Finally, correlation analysis was conducted between the radiomic biomarkers and neuropsychological assessments. Results: The experimental results showed that the predictive performance of the RPM Cox model was better than that of other Cox models. In the validation dataset, Harrell's consistency coefficient of the RPM model was 0.703 ± 0.002, while those of the clinical and SUVR models were 0.632 ± 0.006 and 0.683 ± 0.009, respectively. Moreover, most crucial imaging biomarkers were significantly different at different cognitive stages and significantly correlated with cognitive disease severity. Conclusion: The preliminary results demonstrated that the developed RPM approach has the potential to monitor progression in high-risk populations with AD. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD) is considered as the first symptomatic manifestation of Alzheimer's disease (AD), which is also affected by different cultural backgrounds. Establishing cross-cultural prediction models of SCD is challenging.Objective: To establish prediction models of SCD available for both the Chinese and European populations.Methods: In this project, 330 SCD from China and 380 SCD from Germany are intended to be recruited. For all participants, standardized assessments, including clinical, neuropsychological, apolipoprotein E (APOE) genotype, blood, and multi-parameter magnetic resonance imaging (MRI) at baseline will be conducted. Participants will voluntarily undergo amyloid positron emission tomography (PET) and are classified into amyloid-β (Aβ) positive SCD (SCD+) and Aβ negative SCD (SCD-). First, baseline data of all SCD individuals between the two cohorts will be compared. Then, key features associated with brain amyloidosis will be extracted in SCD+ individuals, and the diagnosis model will be established using the radiomics method. Finally, the follow-up visits will be conducted every 12 months and the primary outcome is the conversion to mild cognitive impairment or dementia. After a 4-year follow-up, we will extract factors associated with the conversion risk of SCD using Cox regression analysis.Results: At present, 141 SCD from China and 338 SCD from Germany have been recruited. Initial analysis showed significant differences in demographic information, neuropsychological tests, and regional brain atrophy in SCD compared with controls in both cohorts.Conclusion: This project may be of great value for future implications of SCD studies in different cultural backgrounds.Trial Registration: ClinicalTrials.gov, NCT04696315. Registered 3 January 2021. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disease that influences elderly populations, with no effective method for its treatment so far. To improve its diagnosis and treatment, changes of small molecule metabolite during AD should be elucidated. Kai-Xin-San (KXS) is an herbal formulae that has been widely used to treat mental disorders, especially amnesia and depression in China. Experimental AD was induced in rats by an intraperitoneal injection of d -galactose ( d -gal) and administered intragastrically with aluminum chloride (AlCl 3 ) simultaneously for 105 days. Morris water maze task as a behavior test was used for testing the effects of KXS on AD model and pathological changes to the brain were assessed by hematoxylin-eosin staining and immunohistochemistry. The levels of Bcl-2 and ChAT in hippocampus were evaluated by western-blot. Furthermore, metabolite profiling of AD was performed through ultra-performance liquid chromatography/electrospray ionization quadruple time-of- flight-high-definition mass spectrometry (UPLC/ESI-Q-TOF/HDMS) combined with pattern recognition approaches and pathway analysis. d -gal and AlCl 3 -treated caused a decline in spatial learning and memory, hippocampal histopathological abnormalities and increased Aβ1-40 levels in the brain cortex and hippocampus along with decreased Bcl-2 and ChAT expression in the hippocampus. KXS significantly improved the cognitive impairment induced by d -gal and AlCl 3 , attenuated hippocampal histopathological abnormalities, reduced Aβ1-40 levels and increased Bcl-2 and ChAT expression in the hippocampus. A total of 48 metabolites were considered as potential biomarkers of AD, and 36 metabolites may correlate with the regulation of KXS treatment on AD. Changes in AD metabolic profiling were close to normal states through regulating multiple perturbed pathways after KXS treatment. This study has revealed the potential biomarkers and metabolic networks of AD, illuminated the biochemistry mechanism of AD and the metabolic pathways influenced by KXS. [ABSTRACT FROM AUTHOR]

Background: Plasma amyloid-β (Aβ) may facilitate identification of individuals with brain amyloidosis. Gut microbial dysbiosis in Alzheimer's disease (AD) is increasingly being recognized. However, knowledge about alterations of gut microbiota in preclinical AD, as well as whether the combination of plasma Aβ and gut microbiota could identify preclinical AD, remains largely unknown. Methods: This study recruited 34 Aβ-negative cognitively normal (CN−) participants, 32 Aβ-positive cognitively normal (CN+) participants, and 22 patients with cognitive impairment (CI), including 11 patients with mild cognitive impairment (MCI) and 11 AD dementia patients. All participants underwent neuropsychological assessments and fecal microbiota analysis through 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. Meso Scale Discovery (MSD) kits were used to quantify the plasma Aβ40, Aβ42, and Aβ42/Aβ40 in CN− and CN+ participants. Using Spearman's correlation analysis, the associations of global standard uptake value rate (SUVR) with altered gut microbiota and plasma Aβ markers were separately evaluated. Furthermore, the discriminative power of the combination of gut microbiota and plasma Aβ markers for identifying CN+ individuals was investigated. Results: Compared with the CN− group, the CN+ group showed significantly reduced plasma Aβ42 (p = 0.011) and Aβ42/Aβ40 (p = 0.003). The relative abundance of phylum Bacteroidetes was significantly enriched, whereas phylum Firmicutes and class Deltaproteobacteria were significantly decreased in CN+ individuals in comparison with that in CN− individuals. Particularly, the relative abundance of phylum Firmicutes and its corresponding SCFA-producing bacteria exhibited a progressive decline tendency from CN− to CN+ and CI. Besides, the global brain Aβ burden was negatively associated with the plasma Aβ42/Aβ40 (r = −0.298, p = 0.015), family Desulfovibrionaceae (r = −0.331, p = 0.007), genus Bilophila (r = −0.247, p = 0.046), and genus Faecalibacterium (r = −0.291, p = 0.018) for all CN participants. Finally, the combination of plasma Aβ markers, altered gut microbiota, and cognitive performance reached a relatively good discriminative power in identifying individuals with CN+ from CN− (AUC = 0.869, 95% CI 0.782 ~ 0.955). Conclusions: This study provided the evidence that the gut microbial composition was altered in preclinical AD. The combination of plasma Aβ and gut microbiota may serve as a non-invasive, cost-effective diagnostic tool for early AD screening. Targeting the gut microbiota may be a novel therapeutic strategy for AD. Trial registration: This study has been registered in ClinicalTrials.gov (NCT03370744, https://www.clinicaltrials.gov) in November 15, 2017. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD), an at-risk condition of Alzheimer's disease (AD), can involve various cognitive domains, such as memory, language, planning, and attention.Objective: We aim to explore the difference in amyloid load between the single memory domain SCD (sd-SCD) and the multidomain SCD (md-SCD) and assess the relationship of amyloid pathology with quantitative SCD scores and objective cognition.Methods: A total of 63 SCD participants from the SILCODE study underwent the clinical evaluation, neuropsychological assessment, and 18F-florbetapir PET scan. Global amyloid standard uptake value ratio (SUVr) was calculated. Additionally, regional amyloid SUVr was quantified in 12 brain regions of interests. A nonparametric rank ANCOVA was used to compare the global and regional amyloid SUVr between the md-SCD (n = 34) and sd-SCD (n = 29) groups. A multiple linear regression analysis was conducted to test the relationship of amyloid SUVr with quantitative SCD scores and objective cognition.Results: Compared with individuals with sd-SCD, individuals with md-SCD had increased global amyloid SUVr (F = 5.033, p = 0.029) and regional amyloid SUVr in the left middle temporal gyrus (F = 12.309, p = 0.001; Bonferroni corrected), after controlling for the effects of age, sex, and education. When pooling all SCD participants together, the increased global amyloid SUVr was related with higher SCD-plus sum scores and lower Auditory Verbal Learning Test-delayed recall scores.Conclusion: According to our findings, individuals with md-SCD showed higher amyloid accumulation than individuals with sd-SCD, suggesting that md-SCD may experience a more advanced stage of SCD. Additionally, increased global amyloid load was predictive of a poorer episodic memory function in SCD individuals. [ABSTRACT FROM AUTHOR]

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 (18F‐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. [ABSTRACT FROM AUTHOR]

Introduction: Subjective cognitive decline (SCD) represents a cognitively normal state but at an increased risk for developing Alzheimer's disease (AD). Recognizing the glucose metabolic biomarkers of SCD could facilitate the location of areas with metabolic changes at an ultra-early stage. The objective of this study was to explore glucose metabolic biomarkers of SCD at the region of interest (ROI) level. Methods: This study was based on cohorts from two tertiary medical centers, and it was part of the SILCODE project (NCT03370744). Twenty-six normal control (NC) cases and 32 SCD cases were in cohort 1; 36 NCs, 23 cases of SCD, 32 cases of amnestic mild cognitive impairment (aMCIs), 32 cases of AD dementia (ADDs), and 22 cases of dementia with Lewy bodies (DLBs) were in cohort 2. Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) imaging and magnetic resonance imaging (MRI), and subjects from cohort 1 additionally underwent amyloid-PET scanning. The ROI analysis was based on the Anatomical Automatic Labeling (AAL) template; multiple permutation tests and repeated cross-validations were conducted to determine the metabolic differences between NC and SCD cases. In addition, receiver operating characteristic curves were used to evaluate the capabilities of potential glucose metabolic biomarkers in distinguishing different groups. Pearson correlation analysis was also performed to explore the correlation between glucose metabolic biomarkers and neuropsychological scales or amyloid deposition. Results: Only the right middle temporal gyrus (RMTG) passed the methodological verification, and its metabolic levels were correlated with the degrees of complaints (R = − 0.239, p = 0.009), depression (R = − 0.200, p = 0.030), and abilities of delayed memory (R = 0.207, p = 0.025), and were weakly correlated with cortical amyloid deposition (R = − 0.246, p = 0.066). Furthermore, RMTG metabolism gradually decreased across the cognitive continuum, and its diagnostic efficiency was comparable (NC vs. ADD, aMCI, or DLB) or even superior (NC vs. SCD) to that of the metabolism of the posterior cingulate cortex or precuneus. Conclusions: These findings suggest that the hypometabolism of RMTG could be a typical feature of SCD, and the large-scale hypometabolism in patients with symptomatic stages of AD may start from the RMTG, which gradually progresses starting in the preclinical stage. The specificity of identifying SCD from the perspective of self-perceived symptoms is likely to be increased by the detection of RMTG metabolism. [ABSTRACT FROM AUTHOR]

Background and purpose: The aim of this study was to discover the associations between HMOX‐1 and Alzheimer's disease (AD). Methods: A total of 500 AD patients and 500 healthy controls were recruited in this study. Polymer chain reaction was used. Results: There was a statistically significant difference between AD patients and controls in both the dominant and recessive models of HMOX‐1 rs2071746 after adjustment for age, gender and education (dominant model: p = 0.047, odds ratio [OR] 1.34, 95% confidence interval [CI] 1.00–1.78, adjusted; recessive model: p = 0.049, OR 1.34, 95% CI 1.00–1.80, adjusted). There was also a trend for an association between the dominant model and late‐onset AD after adjustment for age, gender and education (dominant model: p = 0.084, OR 1.37, 95% CI 0.96–1.95, adjusted). Conclusions: We found an association between the dominant and recessive models of HMOX1 rs2071746 and AD. [ABSTRACT FROM AUTHOR]

• Anticholinergic drug use is an independent risk factor for all-cause dementia and Alzheimer's disease. • Heavier anticholinergic drug exposure is associated with a higher risk of dementia. • Different categories of anticholinergic drugs exert distinct effects on incident dementia. • It is important to manage and prescribe anticholinergic drugs to help optimize early prevention strategies for dementia. Dementia is one of the greatest global challenges for public health; however, the relationship between anticholinergic drugs and dementia remains unclear. The aim of the present study was to perform a systematic review and meta-analysis of the predictive roles of anticholinergic drugs in dementia risk. After pooling fourteen longitudinal and case-control studies with a total of 1,564,181 subjects, anticholinergic drug use was associated with an increased risk of all-cause dementia and Alzheimer's disease. Both low and high anticholinergic drug burdens were associated with dementia. Moreover, there was a dose-dependent relationship between anticholinergic drugs and risk of dementia. With respect to the categories of anticholinergic drugs, antiparkinson, urological drugs, and antidepressants increased the risk for dementia; however, cardiovascular and gastrointestinal drugs played potentially protective roles. These findings underscore the importance of anticholinergic drugs as a potential modifiable risk factor for dementia and provide treatment priorities to optimize dementia prevention. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD) is the earliest symptomatic manifestation of preclinical Alzheimer's disease (AD). Gut microbiota may serve as a susceptibility factor for AD. Altered gut microbiota has been reported in patients with mild cognitive impairment (MCI) and AD dementia. However, whether gut microbial compositions changed in SCD remains largely unknown.Objective: To characterize the gut microbiota in SCD.Methods: In this study, a total of 105 participants including 38 normal controls (NC), 53 individuals with SCD, and 14 patients with cognitive impairment (CI) were recruited. Gut microbiota of all participants isolated from fecal samples were investigated using 16S ribosomal RNA (rRNA) Illumina Miseq sequencing technique. The gut microbial compositions were compared among the three groups, and the association between altered gut microbiota and cognitive performance was analyzed. To validate the alteration of gut microbiota in SCD, we conducted amyloid positron emission tomography (PET) in selected participants and further compared the gut microbiota among subgroups.Results: The abundance of phylum Firmicutes, class Clostridia, order Clostridiales, family Ruminococcaceae, and genus Faecalibacterium showed a trend toward a progressive decline from NC to SCD and CI. Specifically, the abundance of the anti-inflammatory genus Faecalibacterium was significantly decreased in SCD compared with NC. In addition, altered bacterial taxa among the three groups were associated with cognitive performance. The findings were validated in SCD participants with positive amyloid evidence.Conclusion: The composition of gut microbiota is altered in individuals with SCD. This preliminary study will provide novel insights into the pathophysiological mechanism of AD. [ABSTRACT FROM AUTHOR]

Introduction: Subjective cognitive decline (SCD) represents a cognitively normal state but at an increased risk for developing Alzheimer's disease (AD). Recognizing the glucose metabolic biomarkers of SCD could facilitate the location of areas with metabolic changes at an ultra-early stage. The objective of this study was to explore glucose metabolic biomarkers of SCD at the region of interest (ROI) level. Methods: This study was based on cohorts from two tertiary medical centers, and it was part of the SILCODE project (NCT03370744). Twenty-six normal control (NC) cases and 32 SCD cases were in cohort 1; 36 NCs, 23 cases of SCD, 32 cases of amnestic mild cognitive impairment (aMCIs), 32 cases of AD dementia (ADDs), and 22 cases of dementia with Lewy bodies (DLBs) were in cohort 2. Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) imaging and magnetic resonance imaging (MRI), and subjects from cohort 1 additionally underwent amyloid-PET scanning. The ROI analysis was based on the Anatomical Automatic Labeling (AAL) template; multiple permutation tests and repeated cross-validations were conducted to determine the metabolic differences between NC and SCD cases. In addition, receiver operating characteristic curves were used to evaluate the capabilities of potential glucose metabolic biomarkers in distinguishing different groups. Pearson correlation analysis was also performed to explore the correlation between glucose metabolic biomarkers and neuropsychological scales or amyloid deposition. Results: Only the right middle temporal gyrus (RMTG) passed the methodological verification, and its metabolic levels were correlated with the degrees of complaints (R = − 0.239, p = 0.009), depression (R = − 0.200, p = 0.030), and abilities of delayed memory (R = 0.207, p = 0.025), and were weakly correlated with cortical amyloid deposition (R = − 0.246, p = 0.066). Furthermore, RMTG metabolism gradually decreased across the cognitive continuum, and its diagnostic efficiency was comparable (NC vs. ADD, aMCI, or DLB) or even superior (NC vs. SCD) to that of the metabolism of the posterior cingulate cortex or precuneus. Conclusions: These findings suggest that the hypometabolism of RMTG could be a typical feature of SCD, and the large-scale hypometabolism in patients with symptomatic stages of AD may start from the RMTG, which gradually progresses starting in the preclinical stage. The specificity of identifying SCD from the perspective of self-perceived symptoms is likely to be increased by the detection of RMTG metabolism. [ABSTRACT FROM AUTHOR]

It was reported that acupuncture could treat Alzheimer's disease (AD) with the potential mechanisms remaining unclear. The aim of the study is to explore the effect of the combination stimulus of Hegu (LI4) and Taichong (LR3) on the resting-state brain networks in AD, beyond the default network (DMN). Twenty-eight subjects including 14 AD patients and 14 healthy controls (HCs) matched by age, gender, and educational level were recruited in this study. After the baseline resting-state MRI scans, the manual acupuncture stimulation was performed for 3 minutes, and then, another 10 minutes of resting-state fMRI scans was acquired. In addition to the DMN, five other resting-state networks were identified by independent component analysis (ICA), including left frontal parietal network (lFPN), right frontal parietal network (rFPN), visual network (VN), sensorimotor network (SMN), and auditory network (AN). And the impaired connectivity in the lFPN, rFPN, SMN, and VN was found in AD patients compared with those in HCs. After acupuncture, significantly decreased connectivity in the right middle frontal gyrus (MFG) of rFPN (P = 0.007) was identified in AD patients. However, reduced connectivity in the right inferior frontal gyrus (IFG) (P = 0.047) and left superior frontal gyrus (SFG) (P = 0.041) of lFPN and some regions of the SMN (the left inferior parietal lobula (P = 0.004), left postcentral gyrus (PoCG) (P = 0.001), right PoCG (P = 0.032), and right MFG (P = 0.010)) and the right MOG of VN (P = 0.003) was indicated in HCs. In addition, after controlling for the effect of acupuncture on HCs, the functional connectivity of the right cerebellum crus I, left IFG, and left angular gyrus (AG) of lFPN showed to be decreased, while the left MFG of IFPN and the right lingual gyrus of VN increased in AD patients. These findings might have some reference values for the interpretation of the combination stimulus of Hegu (LI4) and Taichong (LR3) in AD patients, which could deepen our understanding of the potential mechanisms of acupuncture on AD. [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: Education plays a potential important effect on the prevalence and incidence of dementia. However, most of the evidence based on convenience sampling.Objective: To explore effects of education on cognition in individuals with subjective cognitive decline (SCD) and cognitive impairment (CI) from a population-based study.Methods: We examined the effect of education on cognition among individuals with SCD (n = 451) and CI (n = 280) from a population-based study. A series of neuropsychological tests of memory, executive, language, and general cognitive function were used to assess the participants.Results: Multiple regression analyses revealed that education has a positive effect on cognition in both SCD and CI group in the population-based research. Further stratification study showed that the beneficial effect of education remains in the SCD group regardless of the education level, especially in the SCD participants with a low education level. However, that effect of education exists in the CI group with a low education level and disappears in the high education level.Conclusion: These results from a population-based sample suggest that high educational attainment may delay cognitive decline in the individuals with SCD regardless of high or low educational level, and high education only predicts cognition in those in the low educational level in CI group. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD) is considered the earliest symptomatic manifestation of preclinical Alzheimer's disease (AD). Currently, given the lack of effective and curable pharmacological treatments for AD, non-pharmacological interventions (NPIs) for individuals with SCD may provide a valuable opportunity for the secondary prevention of AD.Objective: This systematic review and meta-analysis, conducted in accordance with the PRISMA guidelines, aimed to investigate the benefits of current NPIs in the population with SCD.Methods: The online electronic databases, including MEDLINE, Cochrane Central Register of Controlled Trials, EMBASE, PsycInfo, and CINAHL, were searched to identify randomized controlled trials of NPIs for SCD. Intervention strategies were psychological and health-related education interventions, mind-body therapy, lifestyle modification, cognitive training, and multidomain interventions. Outcomes included subjective memory, objective memory, global cognitive function, psychological well-being, and mood. Study quality was determined using the criteria of the Cochrane collaboration's tool. The Hedges' g of change was analyzed.Results: Eighteen studies were included in this review and meta-analysis. Overall, psychological and health-related education interventions exhibited a medium effect on objective memory function (Hedges' g = 0.53, p = 0.01). Cognitive training led to a small effect on objective memory, which was marginal statistically (Hedges' g = 0.19, p = 0.05). In addition, cognitive training also significantly improved subjective memory performance (Hedges' g = 0.49, p = 0.0003) and psychological well-being (Hedges' g = 0.27, p = 0.03).Conclusion: Overall, the psychological intervention and cognitive training may be beneficial to cognitive function and psychological well-being. NPIs may be effectively implemented in older adults with SCD. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study is to investigate the hippocampal neurodegeneration and its associated aberrant functions in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients using simultaneous PET/MRI. Methods: Forty-two cognitively normal controls (NC), 38 MCI, and 22 AD patients were enrolled in this study. All subjects underwent 18F-FDG PET/functional MRI (fMRI) and high-resolution T1-weighted MRI scans on a hybrid GE Signa PET/MRI scanner. Neurodegeneration in hippocampus and its subregions was quantified by regional gray matter volume and 18F-FDG standardized uptake value ratio (SUVR) relative to cerebellum. An iterative reblurred Van Cittert iteration method was used for voxelwise partial volume correction on 18F-FDG PET images. Regional gray matter volume was estimated from voxel-based morphometric analysis with MRI. fMRI data were analyzed after slice time correction and head motion correction using statistical parametric mapping (SPM12) with DPARSF toolbox. The regions of interest including hippocampus, cornu ammonis (CA1), CA2/3/dentate gyrus (DG), and subiculum were defined in the standard MNI space. Results: Patient groups had reduced SUVR, gray matter volume, and functional connectivity compared to NC in CA1, CA2/3/DG, and subiculum (AD < MCI < NC). There was a linear correlation between the left CA2/3DG gray matter volume and 18F-FDG SUVR in AD patients (P < 0.001, r = 0.737). Significant correlation was also found between left CA2/3/DG-superior medial frontal gyrus functional connectivity and left CA2/3/DG hypometabolism in patients with AD. The functional connectivity of right CA1-precuneus in patients with MCI and right subiculum-superior frontal gyrus in patients with AD was positively correlated with mini mental status examination scores (P < 0.05). Conclusion: Our findings demonstrate that the associations existed at subregional hippocampal level between the functional connectivity measured by fMRI and neurodegeneration measured by structural MRI and 18F-FDG PET. Our results may provide a basis for precision neuroimaging of hippocampus in AD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD‐associated functional brain alterations using one of the world's largest resting‐state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta‐analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default‐mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus (p <.05, Bonferroni corrected). Moreover, these functional alterations were significantly correlated with the degree of cognitive impairment (AD and MCI groups) and amyloid‐β burden. Machine learning models were trained to recognize key fMRI features to predict individual diagnostic status and clinical score. Leave‐one‐site‐out cross‐validation established that diagnostic status (mean area under the receiver operating characteristic curve: 0.85) and clinical score (mean correlation coefficient between predicted and actual Mini‐Mental State Examination scores: 0.56, p <.0001) could be predicted with high accuracy. Collectively, our findings highlight the potential for a reproducible and generalizable functional brain imaging biomarker to aid the early diagnosis of AD and track its progression. [ABSTRACT FROM AUTHOR]

Background: Cognitive reserve (CR) and brain reserve (BR) could offer protective effects on cognition in the early stage of Alzheimer's disease (AD). However, the effects of CR or BR on cognition in individuals with subjective cognitive decline (SCD) are not clear.Objective: To explore the effects of CR and BR on cognition in subjects with SCD.Methods: We included 149 subjects from the Sino Longitudinal Study on Cognitive Decline (SILCODE) study. Education was used as a proxy for CR, and head circumference was used as a proxy for BR. Multiple linear regression models were conducted to examine the effects of CR and BR on cognitive scores. Furthermore, we assessed differences in effects between APOEɛ4 carriers with SCD (n = 35) and APOEɛ4 non-carriers with SCD (n = 114) and linear trends among 4 reserve levels (low BR/CR, high BR/low CR, low BR/high CR, and high BR/high CR).Results: Both CR and BR had independent positive effects on multiple cognitive measures in SCD participants, and the effects of CR were greater than those of BR. CR has positive effects on cognitive measures in both APOEɛ4 carriers and non-carriers with SCD. However, the positive effects of BR on cognitive measures were observed in APOEɛ4 non-carriers with SCD but not in APOEɛ4 carriers with SCD. Furthermore, there was a linear trend toward better cognitive performance on all cognitive measures in the BR+/CR+ group, followed by the BR-/CR+, BR+/CR-, and BR-/CR-groups.Conclusion: This study suggests that both CR and BR have the potential to delay or slow cognitive decline in individuals with SCD. [ABSTRACT FROM AUTHOR]

Background: Clinical research has demonstrated that brain reserve (BR) could exert positive effects on cognition for patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the effects of BR on cognition in individuals with subjective cognitive decline (SCD) are not clear.Objective: To examine cross-sectional effects of BR on cognition in SCD populations.Methods: One hundred forty-nine subjects were studied from the Sino Longitudinal Study on Cognitive Decline (SILCODE) study. Head circumference was used as a proxy of BR. Cognition was assessed across four domains (memory, executive, language, and general cognitive functions). Multiple linear regression models were conducted to examine effects of BR on cognitive scores. Furthermore, we addressed the question that whether the degree of self-perception of cognitive decline modified the effect of BR on cognitive performance in SCD subjects.Results: We found a positive effect of BR on language cognition in subjects with SCD. Furthermore, the positive effect of BR on language cognition survived in SCD participants with a low degree of self-perception of cognitive decline while disappeared in SCD participants with a high degree of self-perception of cognitive decline.Conclusion: This study suggests that BR has the potential to delay or slow down cognitive decline in SCD individuals, especially for mild SCD. [ABSTRACT FROM AUTHOR]

A growing awareness about brain health and Alzheimer's disease in the general population is leading to an increasing number of cognitively unimpaired individuals, who are concerned that they have reduced cognitive function, to approach the medical system for help. The term subjective cognitive decline (SCD) was conceived in 2014 to describe this condition. Epidemiological data provide evidence that the risk for mild cognitive impairment and dementia is increased in individuals with SCD. However, the majority of individuals with SCD will not show progressive cognitive decline. An individually tailored diagnostic process might be reasonable to identify or exclude underlying medical conditions in an individual with SCD who actively seeks medical help. An increasing number of studies are investigating the link between SCD and the very early stages of Alzheimer's disease and other neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD), the main cause of dementia, is characterized by the aggregation of amyloid-β (Aβ). This pathophysiological process starts many years to decades before the onset of clinical symptoms. Cerebrospinal fluid analysis and amyloid positron emission tomography scans are two standard methods to measure brain Aβ, but their invasive nature and expense limit their usage as screening tools. Therefore, peripheral Aβ studies have grown exponentially during the past few years. In this review, we discuss recent studies on plasma Aβ and its potential as a reliable biomarker of AD. [ABSTRACT FROM AUTHOR]

As an enrichment strategy supplemented by the diagnostic framework of subjective cognitive decline (SCD), SCD plus identifies features that may increase the likelihood of including future-Alzheimer's disease (AD) patients. This study aimed to identify the shared and distinct atrophy patterns between patients specified by SCD plus and amnestic mild cognitive impairment (aMCI, a prodromal stage of AD) and to investigate the extent that automated brain magnetic resonance imaging (MRI) volumetry can differentiate patients with SCD from normal control (NC) participants and patients with aMCI. We acquired structural MRI brain scans from 44 patients with aMCI, 40 patients with SCD (who met the major criteria of SCD plus), and 48 NC participants. Automatic brain segmentation was performed to quantify the volumetric measures of cognitive-relevant areas. These volumetric measures were compared across the 3 groups with analysis of variance. In addition, we performed support vector machine analyses using volumetric measures of single regions or multiple regions to further evaluate the sensitivity of automated brain volumetry in differentiating a specific group from another. The atrophy patterns in patients with aMCI and SCD were similar. Using the regional volumetric measures, we achieved high performance in differentiating aMCI and SCD from NCs (average classification accuracy [ACC] > 90%). However, the performance was not ideal when differentiating aMCI from SCD (ACC < 63%). In conclusion, patients with SCD specified by SCD plus presented similar atrophy patterns as patients with aMCI, which was distinguishable from NC participants. Future studies should aim to associate the atrophy patterns of SCD with possible conversion to aMCI or AD in a longitudinal design. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline (SCD) refers to the self-perception of cognitive decline among cognitively normal elderly individuals. SCD related worry confers a higher risk of developing cognitive decline. However, the clinical characteristics of SCD patients with worry are not clear.Objective: To explore the clinical characteristics of SCD patients with worry.Methods: A cross-sectional study was carried out, with 270 consecutive participants of the Sino Longitudinal Study on Cognitive Decline (SILCODE) study. Participants were classified as normal controls (n = 36), SCD patients without worry (n = 91), or SCD patients with worry (n = 143) and were comprehensively compared on 1) their self-perception of cognitive decline, 2) multiple cognitive domains, 3) neuropsychiatric symptoms, and 4) sleep status.Results: SCD patients with worry had significantly more self-perception of cognitive decline (p < 0.001); increased depression (p < 0.001) and anxiety (p < 0.001); decreased sleep quality (p < 0.001), sleep latency (p < 0.05), sleep time (p < 0.01), and sleep efficiency (p < 0.05); more sleep disorders (p < 0.05) and daytime dysfunction (p < 0.05); and a higher global score on the Pittsburgh Sleep Quality Index (p < 0.001) than normal controls. Although there was a significant increase only in self-perception of cognitive decline (p < 0.001), anxiety (p < 0.001), and Pittsburgh Sleep Quality Index scores (p < 0.05), the severity of the increase in those without worry was between that in SCD patients with worry and normal controls.Conclusion: Our findings show that participants who had SCD with worry showed distinct clinical characteristics compared with normal controls and SCD patients without worry, which could be useful for understanding the higher risk in SCD patients with worry of subsequently developing Alzheimer's disease. [ABSTRACT FROM AUTHOR]