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AimTo evaluate aspects of cognition impacted by individuals with and without normal tension glaucoma.MethodsFifty normal tension glaucoma (NTG) and 50 control patients ≥50y of age were recruited from the UCSF Department of Ophthalmology. Demographic data and glaucoma parameters were extracted from electronic medical records for both groups. Tests of executive function [Executive Abilities: Measures and Instruments for Neurobehavioral Evaluation and Research (EXAMINER)] and learning and memory [California Verbal Learning Test-Second Edition (CVLT-II)] were administered to both NTG and controls. Race, handedness, best-corrected visual acuity, maximum intraocular pressure, optic nerve cup-to-disc ratio, visual field and optic nerve optical coherence tomography parameters, and a measure of general health (Charlson Comorbidity Index) were compared between NTG and controls as well as within NTG subgroups. Multivariate linear regression was used to compare group performances on the EXAMINER battery and CVLT-II while controlling for age, sex, and years of education.ResultsNTG and controls were comparable with respect to age, sex, race, education, handedness, and the Charlson Comorbidity Index (P>0.05 for all). Performance on the EXAMINER composite score and the CVLT-II did not differ between NTG and controls (P>0.05 for both).ConclusionThis is the first prospective study in which the cognitive function of subject with NTG were evaluated using a comprehensive, computerized neurocognitive battery. Subjects with NTG do not perform worse than unaffected controls on tests of executive function, learning, and memory. Results do not support the hypothesis that individuals with NTG are at higher risk for cognitive dysfunction and/or dementia.

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]

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: 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]

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]

Background: Previous reports on APOE ε4 allele distribution in different populations have been inconclusive. The Subjective Cognitive Decline-Questionnaire 9 (SCD-Q9) was developed to identify those at risk of objective cognitive impairment [OCI; including mild cognitive impairment (MCI) and dementia groups), but its association with APOE ε4 and discriminatory powers for SCD with subtle cognitive decline (SCDs) and OCI in memory clinics are unclear. Objectives: To investigate demographic distribution of APOE ε4, its association with SCD-Q9 scores, and its ability to discriminate SCDs and OCI groups from normal control (NC). Methods: A total of 632 participants were recruited (NC = 243, SCDs = 298, OCI = 91). APOE ε4 allele distribution and association with SCD-Q9 scores were calculated and the effects on cognitive impairment were analyzed. Receiver operating characteristic (ROC) analysis was applied to identify discriminatory powers for NC, SCDs, and OCI. Results: Total APOE ε4 frequency was 13.1%. This did not vary by demography but was higher in patients with OCI. The SCD-Q9 scores were higher in APOE ε4 carriers than non-carriers in the OCI group. The area under the curve (AUC) for discriminating from OCI using APOE ε4 were 0.587 and 0.575, using SCD-Q9 scores were 0.738 and 0.571 for NC and SCDs groups, respectively. When we combined APOE ε4 and SCD-Q9 scores into the model, the AUC increased to 0.747 for discriminating OCI from NC. However, when OCI group was split into MCI and dementia groups, only total SCD-Q9 score was the independent affecting factor of MCI. Conclusion: This study demonstrated that the distribution of APOE ε4 alleles did not vary with different demographic characteristics in a large-scale cohort from a memory clinic. APOE ε4 alleles may be associated with scores of SCD-Q9 reflecting the degree of cognitive complaints but their additional contribution to SCD-Q9 scores is marginal in discriminating between NC, SCDs, and OCI. [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]

Alzheimer's disease (AD) has a long preclinical stage that can last for decades prior to progressing toward amnestic mild cognitive impairment (aMCI) and/or dementia. Subjective cognitive decline (SCD) is characterized by self-experienced memory decline without any evidence of objective cognitive decline and is regarded as the later stage of preclinical AD. It has been reported that the changes in structural covariance patterns are affected by AD pathology in the patients with AD and aMCI within the specific large-scale brain networks. However, the changes in structural covariance patterns including normal control (NC), SCD, aMCI, and AD are still poorly understood. In this study, we recruited 42 NCs, 35 individuals with SCD, 43 patients with aMCI, and 41 patients with AD. Gray matter (GM) volumes were extracted from 10 readily identifiable regions of interest involved in high-order cognitive function and AD-related dysfunctional structures. The volume values were used to predict the regional densities in the whole brain by using voxel-based statistical and multiple linear regression models. Decreased structural covariance and weakened connectivity strength were observed in individuals with SCD compared with NCs. Structural covariance networks (SCNs) seeding from the default mode network (DMN), salience network, subfields of the hippocampus, and cholinergic basal forebrain showed increased structural covariance at the early stage of AD (referring to aMCI) and decreased structural covariance at the dementia stage (referring to AD). Moreover, the SCN seeding from the executive control network (ECN) showed a linearly increased extent of the structural covariance during the early and dementia stages. The results suggest that changes in structural covariance patterns as the order of NC-SCD-aMCI-AD are divergent and dynamic, and support the structural disconnection hypothesis in individuals with SCD. [ABSTRACT FROM AUTHOR]

Objective: Individuals with subjective cognitive decline (SCD) or amnestic mild cognitive impairment (aMCI) represent important targets for the early detection and intervention of Alzheimer's disease (AD). In this study, we employed a multi-kernel support vector machine (SVM) to examine whether white matter (WM) structural networks can be used for screening SCD and aMCI. Methods: A total of 138 right-handed participants [51 normal controls (NC), 36 SCD, 51 aMCI] underwent MRI brain scans. For each participant, three types of WM networks with different edge weights were constructed with diffusion MRI data: fiber number-weighted networks, mean fractional anisotropy-weighted networks, and mean diffusivity (MD)-weighted networks. By employing a multiple-kernel SVM, we seek to integrate information from three weighted networks to improve classification performance. The accuracy of classification between each pair of groups was evaluated via leave-one-out cross-validation. Results: For the discrimination between SCD and NC, an area under the curve (AUC) value of 0.89 was obtained, with an accuracy of 83.9%. Further analysis revealed that the methods using three types of WM networks outperformed other methods using single WM network. Moreover, we found that most of discriminative features were from MD-weighted networks, which distributed among frontal lobes. Similar classification performance was also reported in the differentiation between subjects with aMCI and NCs (accuracy = 83.3%). Between SCD and aMCI, an AUC value of 0.72 was obtained, with an accuracy of 72.4%, sensitivity of 74.5% and specificity of 69.4%. The highest accuracy was achieved with features only selected from MD-weighted networks. Conclusion: White matter structural network features help machine learning algorithms accurately identify individuals with SCD and aMCI from NCs. Our findings have significant implications for the development of potential brain imaging markers for the early detection of AD. [ABSTRACT FROM AUTHOR]

The mild cognitive impairment (MCI) stage plays an essential role in preventing the progression of older adults to Alzheimer's disease. In this study, neurofeedback training (NFT) is applied to improve MCI brain cognitive function. To assess the improvement effect, a novel algorithm called Weighted Multiple Multiscale Entropy (WMMSE) is proposed to extract and analyze the electroencephalogram (EEG) features of patients with MCI. To overcome the information loss problem of traditional multiscale entropy (MSE), WMMSE fully considered the correlation of the sequence and the contribution of each sequence to the total entropy. The experimental group composed of 39 patients with MCI was subjected to NFT for 10 days during two sessions. The control group included 21 patients with MCI without any intervention. The Lempel-Ziv complexity (LZC) was used for primary assessment, and WMMSE was used to accurately analyze the effect of NFT. The results show that the WMMSE values of F4, C3, C4, O1, and T5 channels post-NFT are higher compared with pre-NFT and significant differences (P < 0.05). Moreover, the cognitive subscale of the Montreal Cognitive Assessment (MoCA) results shows that the post-NFT score is higher than the pre-NFT in the vast majority of the patients with MCI and significant differences (P < 0.05). When compared with the control group, the WMMSE values of the experimental group increased in each channel. Therefore, the NFT intervention method contributes to brain cognitive functional recovery, and WMMSE can be used as a biomarker to evaluate the state of MCI brain cognitive function. [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]

Objective: To define resilience metrics for cognitive decline based on plasma and cerebrospinal fluid (CSF) amyloid-β (Aβ) and examine the demographic, genetic, and neuroimaging factors associated with interindividual differences among metrics of resilience and to demonstrate the ability of such metrics to predict the diagnostic conversion to mild cognitive impairment (MCI). Methods: In this study, cognitively normal (CN) participants with Aβ-positive were included from the Sino Longitudinal Study on Cognitive Decline (SILCODE, n = 100) and Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 144). Using a latent variable model of data, metrics of resilience [brain resilience (BR), cognitive resilience (CR), and global resilience (GR)] were defined based on the plasma Aβ and CSF Aβ. Linear regression analyses were applied to investigate the association between characteristics of individuals (age, sex, educational level, genetic, and neuroimaging factors) and their resilience. The plausibility of these metrics was tested using linear mixed-effects models and Cox regression models in longitudinal analyses. We also compared the effectiveness of these metrics with conventional metrics in predicting the clinical progression. Results: Although individuals in the ADNI cohort were older (74.68 [5.65] vs. 65.38 [4.66], p < 0.001) and had higher educational levels (16.3 [2.6] vs. 12.6 [2.8], p < 0.001) than those in the SILCODE cohort, similar loadings between resilience and its indicators were found within both models. BR and GR were mainly associated with age, women, and brain volume in both cohorts. Prediction models showed that higher CR and GR were related to better cognitive performance, and specifically, all types of resilience to CSF Aβ could predict longitudinal cognitive decline. Conclusion: Different phenotypes of resilience depending on cognition and brain volumes were associated with different factors. Such comprehensive resilience provided insight into the mechanisms of susceptibility for Alzheimer's disease (AD) at the individual level, and interindividual differences in resilience had the potential to predict the disease progression in CN people. [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: Visual rating scales for medial temporal lobe atrophy (MTA) and posterior atrophy (PA) have been reported to be useful for Alzheimer's disease diagnosis in routine clinical practice.Objective: To investigate the efficacy of combined MTA and PA visual rating scales to discriminate amnestic mild cognitive impairment (aMCI) patients from healthy controls.Methods: This study included T1-weighted MRI images from two different cohorts. In the first cohort, we recruited 73 patients with aMCI and 48 group-matched cognitively normal controls for training and validation. Visual assessments of MTA and PA were carried out for each participant. Global gray matter volume and density were estimated using voxel-based morphometry analysis as the objective reference. We investigated the discriminative power of a single visual rating scale and the combination of the MTA and PA rating scales for identifying aMCI. The second cohort, consisting of 33 aMCI patients and 45 controls, was used to verify the reliability of the visual assessments.Results: Compared with the single visual rating scale, the combination of the MTA and PA exhibited the best discriminative power, with an AUC of 0.818±0.041, which was similar to the diagnostic accuracy of the gray matter volumetric measures. The discriminative power of the combined MTA and PA was verified in the second cohort (AUC 0.824±0.058).Conclusion: The combined MTA and PA rating scales demonstrated practical diagnostic value for distinguishing aMCI patients from controls, suggesting its potential to serve as a convenient and reproducible method to assess the degree of atrophy in clinical settings. [ABSTRACT FROM AUTHOR]

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]

Minimal hepatic encephalopathy (MHE), which shows mild cognitive impairment, is a subtle complication of cirrhosis that has been shown to affect daily functioning and quality of life. However, until 2014, relevant guidelines do not give much attention to the diagnosis and treatment of MHE, resulting in patients being ignored and denied the benefits of treatment. In this review, we summarize recent cognition-based research about (1) alteration of nerve cells, including astrocytes, microglial cells and neurons, in mild cognitive impairment in MHE; (2) comparison of methods in detecting cognitive impairment in MHE; and (3) comparison of methods for therapy of cognitive impairment in MHE. We hope to provide information about diagnosis and treatment of cognitive impairment in patients with MHE. [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]

Since late stage dementia, including Alzheimer's disease (AD), cannot be reversed by any available drugs, there is increasing research interest in the preclinical stage of AD, i.e., subjective cognitive decline (SCD). SCD is characterized by self-perceptive cognitive decline but is difficult to detect using objective tests. At SCD stage, the cognitive deficits can be more easily reversed compared to that of mild cognitive impairment (MCI) and AD only if accurate diagnosis of SCD and early intervention can be developed. In this paper, we review the recent progress of SCD research including current assessment tools, biomarkers, neuroimaging, intervention and expected prognosis, and the potential relevance to traumatic brain injury (TBI)-induced cognitive deficits. [ABSTRACT FROM AUTHOR]

Background: Detecting subtle changes in visual attention from electroencephalography (EEG) and the perspective of eye movement in mild cognitive impairment (MCI) patients can be of great significance in screening early Alzheimer's disease (AD) in a large population at primary care.Objective: We proposed an automatic, non-invasive, and quick MCI detection approach based on multimodal physiological signals for clinical decision-marking.Methods: The proposed model recruited 152 patients with MCI and 184 healthy elderly controls (HC) who underwent EEG and eye movement signal recording under a visual stimuli task, as well as other neuropsychological assessments. Forty features were extracted from EEG and eye movement signals by linear and nonlinear analysis. The features related to MCI were selected by logistic regression analysis. To evaluate the efficacy of this MCI detection approach, we applied the same procedures to achieve the Clinical model, EEG model, Eye movement model, EEG+ Clinical model, Eye movement+ Clinical model, and Combined model, and compared the classification accuracy between the MCI and HC groups with the above six models.Results: After the penalization of logistic regression analysis, five features from EEG and eye movement features exhibited significant differences (p < 0.05). In the classification experiment, the combined model resulted in the best accuracy. The average accuracy for the Clinical/EEG/Eye movement/EEG+ Clinical/Eye movement+ Clinical/Combined model was 68.69%, 61.79%, 73.13%, 69.46%, 75.61%, and 81.51%, respectively.Conclusion: These results suggest that the proposed MCI detection tool has the potential to screen MCI patients from HCs and may be a powerful tool for personalized precision MCI screening in the large-scale population under primary care condition. [ABSTRACT FROM AUTHOR]

Subjective cognitive decline (SCD) is a risk factor for Alzheimer's disease (AD). Urinary Alzheimer-associated neuronal thread protein (AD7c-NTP) has been identified as a biomarker for AD. It was hypothesized that if urinary AD7c-NTP were also elevated in SCD, as it is in prodromal AD (mild cognitive impairment stage), it could be a convenient and efficient clinical biomarker for the early diagnosis of SCD. SCD is often accompanied by a depressive state (DS), and the impact of DS on urinary AD7c-NTP levels remains unknown. A total of 297 right-handed Chinese Han subjects were recruited, including 98 subjects with SCD, 92 patients with DS, and 107 well-matched cognitively normal controls (NC). The levels of AD7c-NTP in urine samples were measured using an enzyme-linked immunosorbent assay AD7c-NTP kit. Our results demonstrated that urinary AD7c-NTP levels in the SCD group (0.7561±0.5657 ng/mL) were not significantly higher than in either the DS (0.7527±0.5607 ng/mL) or NC (0.7214±0.5077 ng/mL) groups. Furthermore, urinary AD7c-NTP levels were not correlated with Hamilton Depression Rating Scale and Hamilton Anxiety Scale scores, but they were negatively associated with Mini-Mental State Examination scores (r = -0.222, p = 0.033) and Montreal Cognitive Assessment-Basic scores (r = -0.207, p = 0.048). Urinary AD7c-NTP level is not elevated in SCD and is unaffected by DS. Urinary AD7c-NTP may therefore not be a good potential biomarker for SCD and DS, although it may become elevated with more severe cognitive decline. [ABSTRACT FROM AUTHOR]

Background: Since subjective cognitive decline (SCD) was standardized in 2014, many studies have investigated its features. However, the risk of SCD (plus) progressing to AD is much higher, and yet there have been few studies reporting the risk factors and neuropsychological assessment characteristics of SCD (plus). Objective: To characterize SCD (plus) by comparing it with normal control (NC), amnesic mild cognitive impairment (aMCI), and Alzheimer Disease (AD) regarding their demographics, lifestyle, family history of dementia, multimorbidity and the neuropsychological assessments. Methods: A total of 135 participants were recruited, including 23 NC, 30 SCD (plus), 45 aMCI and 37 AD. Descriptive statistics were provided. A logistic regression model was used to analyze the affecting factors of SCD (plu s), and finally the Receiver Operating Characteristic (ROC) analysis was applied to distinguish between SCD (plus) and NC. Results: (1) SCD (plus) group was younger than both the aMCI group and AD group. It consisted of more participants with mental work and higher body mass index (BMI) than the AD group. (2) Scores of Auditory Verbal Learning Test - Immediate recall (AVLT-IR) and AVLT-Long delayed recall (AVLT-LR) decreased in the following order: NC→SCD (plus)→aMCI→AD. (3) The Area Under Curve (AUC) for discriminating SCD (plus) and NC group was from 0.673 to 0.838. Conclusion: Aging is an important risk factor of both NC progressing to SCD (plus), and SCD (plus) progressing to aMCI or AD. In addition to aging, lower education level and lower BMI were significantly associated with greater odds of SCD (plus) progressing to aMCI or AD patients, whereas mental work was a protective factor of SCD (plus) progressing to AD. Finally, AVLT is a sensitive indicator of the cognitive decline and impairment in SCD (plus) in relative to normal controls. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated a close relationship between regional cerebral blood flow (rCBF) and resting state functional connectivity changes in normal healthy people. However, little is known about the parameter changes in the most vulnerable regions in Alzheimer's disease (AD). Forty AD patients and 30 healthy controls participated in this study. The data of resting-state perfusion and functional magnetic resonance imaging (fMRI) was collected. By using voxel-wise arterial spin labeling (ASL) perfusion, we identified several regions of altered rCBF in AD patients. Then, by using resting state fMRI analysis, including amplitude low frequency fluctuation (ALFF) and seed-based functional connectivity, we investigated the changes of functional activity and connectivity among the identified rCBF regions. We extracted cognition-related parameters and searched for a sensitive biomarker to differentiate the AD patients from the normal controls (NC). Compared with controls, AD patients showed special disruptions in rCBF, which were mainly located in the left posterior cingulate cortex (PCC), the left and right dorsolateral prefrontal cortex (DLPFC), the left inferior parietal lobule (IPL), the right middle temporal gyrus (MTG), the left middle occipital gyrus (MOG), and the left precuneus (PCu). ALFF was performed based on the seven regions identified by the ASL method, and AD patients presented significantly decreased ALFF in the left PCC, left IPL, right MTG, left MOG, and left PCu and increased ALFF in the bilateral DLPFC. We constituted the network based on the seven regions and found that there was decreased connectivity among the identified regions in the AD patients, which predicted a disruption in the default mode network (DMN), executive control network (ECN) and visual network (VN). Furthermore, these abnormal parameters are closely associated with cognitive performances in AD patients. We combined the rCBF and ALFF value of PCC/PCu as a biomarker to differentiate the two groups and reached a sensitivity of 85.3% and a specificity of 88.5%. Our findings suggested that there was disrupted rCBF, functional activity and connectivity in specific cognition-related regions in Alzheimer's disease, which can be used as a valuable imaging biomarker for the diagnosis of AD. [ABSTRACT FROM AUTHOR]

Purpose/aim: We investigated the regularity changes of the retinal nerve fiber layer (RNFL) and macular ganglion cell complex (mGCC) of the amnestic mild cognitive impairment (aMCI) patients in this prospective cohort study. Materials and methods: Twenty-four aMCI patients and 30 health controls, who are more than 60 years old, were recruited into the study. The RNFL and the mGCC average thickness were measured with Fourier-domain optical coherence tomography (FD-OCT). Results: Compared with that in the controls, the intraocular pressure (IOP) was significantly lower in the aMCI patients. A significant decrease in RNFL thickness in superior temporal, temporal upper (TU), and temporal upper and lower (TL) (TU+TL) quadrants was found in the aMCI patients than in the controls. The average thickness of the mGCC was also significantly thinner in the aMCI patients than in the controls. Conclusions: Retinal degeneration in the aMCI patients detected by OCT together with lower IOP may indicate disease pathological progression. [ABSTRACT FROM AUTHOR]

Aims: To investigate the white matter (WM) integrity and hippocampal functional connectivity (FC) in type 2 diabetes mellitus (T2DM) patients without mild cognitive impairment (MCI) by using diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. Methods: Twelve T2DM patients without MCI and 24 age, sex and education matched healthy controls (HC) were recruited. DTI and rs-fMRI data were subsequently acquired on a 3.0T MR scanner. Tract-based spatial statistics (TBSS) combining region of interests (ROIs) analysis was used to investigate the alterations of DTI metrics (fractional anisotropy (FA), mean diffusivity (MD), λ1 and λ23) and FC measurement was performed to calculate hippocampal FC with other brain regions. Cognitive function was evaluated by using Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Brain volumes were also evaluated among these participants. Results: There were no difference of MMSE and MoCA scores between two groups. Neither whole brain nor regional brain volume decrease was revealed in T2DM patients without MCI. DTI analysis revealed extensive WM disruptions, especially in the body of corpus callosum (CC). Significant decreases of hippocampal FC with certain brain structures were revealed, especially with the bilateral frontal cortex. Furthermore, the decreased FA in left posterior thalamic radiation (PTR) and increased MD in the splenium of CC were closely related with the decreased hippocampal FC to caudate nucleus and frontal cortex. Conclusions: T2DM patients without MCI showed extensive WM disruptions and abnormal hippocampal FC. Moreover, the WM disruptions and abnormal hippocampal FC were closely associated. Highlights - T2DM patients without MCI demonstrated no obvious brain volume decrease. - Extensive white matter disruptions, especially within the body of corpus callosum, were revealed with DTI analysis among the T2DM patients. - Despite no MCI in T2DM patients, decreased functional connectivity between hippocampal region and some critical brain regions were detected. - The alterations in hippocampal functional connectivity were closely associated with those of the white matter structures in T2DM patients. This trial was registered to ClinicalTrials.gov (NCT02420470, https://www.clinical trials.gov/). [ABSTRACT FROM AUTHOR]

• Surrogate data generation method IAAFT solves the issue of EEG data shortage. • BiLSTM obtains better classification result than other traditional methods. • The SampEn extraction method retaining the high temporal resolution of EEG. The early diagnosis of mild cognitive impairment (MCI) is a essential prevention of further development of MCI into Alzheimer's disease (AD). Electroencephalogram (EEG) has many advantages compared to other methods in the analysis of AD in an early stage, but there are some limitations of EEG such as small size of datasets caused by difficulty in clinical data collection and too many other interfering signals are contained. Recent years, deep learning (DL) have overcome these limitations relatively. In this study, a novel model which aims to classify MCI and healthy control (HC) was constructed based on iterative amplitude adjusted Fourier transform (IAAFT) and bidirectional long short-term memory (BiLSTM). IAAFT is used to overcome the problems caused by small datasets; sample entropy (SampEn) feature extraction is used to further reduce computational time and obtain better classification results; BiLSTM for better capture of EEG temporal connections. The performance of the model was evaluated on a clinical dataset containing 10 MCI and 10 HC. Compared with the traditional EEG classification method, the result shows that BiLSTM is more suitable for the EEG classification task, and the classification accuracy is significantly improved by data augmentation. After performing 10-fold cross-validation and 10-fold data augmentation, the model achieved a maximum classification accuracy of 97.20 ± 1.74 %. The results indicate that the model can be used to diagnose MCI patients with the EEG small datasets. Meanwhile, The data augmentation used in this study has a high reference value for other resting-state EEG classification tasks. [ABSTRACT FROM AUTHOR]

Individuals diagnosed with mild cognitive impairment (MCI) are at high risk of transition to Alzheimer’s disease (AD). However, little is known about functional characteristics of the conversion from MCI to AD. Resting-state functional magnetic resonance imaging was performed in 25 AD patients, 31 MCI patients, and 42 well-matched normal controls at baseline. Twenty-one of the 31 MCI patients converted to AD at approximately 24 months of follow-up. Functional connectivity strength (FCS) and seed-based functional connectivity analyses were used to assess the functional differences among the groups. Compared to controls, subjects with MCI and AD showed decreased FCS in the default-mode network and the occipital cortex. Importantly, the FCS of the left angular gyrus and middle occipital gyrus was significantly lower in MCI-converters as compared with MCI-nonconverters. Significantly decreased functional connectivity was found in MCI-converters compared to nonconverters between the left angular gyrus and bilateral inferior parietal lobules, dorsolateral prefrontal and lateral temporal cortices, and the left middle occipital gyrus and right middle occipital gyri. We demonstrated gradual but progressive functional changes during a median 2-year interval in patients converting from MCI to AD, which might serve as early indicators for the dysfunction and progression in the early stage of AD. [ABSTRACT FROM AUTHOR]

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

Aims Neuroimaging studies have demonstrated both structural and functional abnormalities in widespread brain regions in patients with subcortical vascular mild cognitive impairment (sv MCI). However, whether and how these changes alter functional brain network organization remains largely unknown. Methods We recruited 21 patients with sv MCI and 26 healthy control ( HC) subjects who underwent resting-state functional magnetic resonance imaging scans. Graph theory-based network analyses were used to investigate alterations in the topological organization of functional brain networks. Results Compared with the HC individuals, the patients with sv MCI showed disrupted global network topology with significantly increased path length and modularity. Modular structure was also impaired in the sv MCI patients with a notable rearrangement of the executive control module, where the parietal regions were split out and grouped as a separate module. The sv MCI patients also revealed deficits in the intra- and/or intermodule connectivity of several brain regions. Specifically, the within-module degree was decreased in the middle cingulate gyrus while it was increased in the left anterior insula, medial prefrontal cortex and cuneus. Additionally, increased intermodule connectivity was observed in the inferior and superior parietal gyrus, which was associated with worse cognitive performance in the sv MCI patients. Conclusion Together, our results indicate that sv MCI patients exhibit dysregulation of the topological organization of functional brain networks, which has important implications for understanding the pathophysiological mechanism of sv MCI. [ABSTRACT FROM AUTHOR]

Increased levels of Alzheimer-associated neuronal thread protein (AD7c-NTP) are often detected in urine in the early course of Alzheimer’s disease (AD), which makes it a promising biomarker for AD. However, whether the concentration of urinary AD7c-NTP is increased in patients with mild cognitive impairment (MCI) remains unclear. The aim of this study was to explore the value of urinary AD7c-NTP to assist in the diagnosis of cognitive impairment by comparing differences in urinary AD7c-NTP among normal controls, MCI patients and AD patients. One hundred and seventy patients from the Xuan wu Hospital, Capital Medical University were divided into three groups according to their clinical diagnosis: an AD group (n = 45), an MCI group (n = 60) and a normal group (n = 65). The Mini Mental State Examination and the Montreal Cognitive Assessment scale were used to screen for the diagnosis of AD and MCI, and patients met the diagnostic criteria of the National Institute of Neurological Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association. The level of urinary AD7c-NTP was determined using the enzyme-linked immunosorbent assay method. The urinary levels of AD7c-NTP in the AD group (median 2.14 [range 0.49–6.39] ng/ml) and the MCI group (median 1.57 [range 0.4–4.15] ng/ml) were significantly higher than those of the normal group (median 0.53 [range 0.04–2.07] ng/ml). To our knowledge our study is the first to show that the level of urinary AD7c-NTP in MCI patients is higher than in healthy people, which suggests that the level of urinary AD7c-NTP may be an important biomarker for early diagnosis of MCI. [ABSTRACT FROM AUTHOR]

Compared to normal aging adults, individuals with amnestic mild cognitive impairment (aMCI) have significantly increased risk for progressing into Alzheimer’s disease (AD). Autopsy studies found that most of the brains of aMCI cases showed anatomical features associated with AD pathology. The recent development of non-invasive neuroimaging technique, such as diffusion tensor imaging (DTI), makes it possible to investigate the microstructures of the cerebral white matter in vivo. We hypothesized that disrupted white matter (WM) integrity existed in aMCI. So we used DTI technique, by measuring fractional anisotropy (FA) and mean diffusivity (MD), to test the brain structures involved in patients with aMCI. DTI scans were collected from 40 patients with aMCI, and 28 normal controls (NC). Tract-based spatial statistics (TBSS) analyses of whole-brain FA and MD images in each individual and group comparisons were carried out. Compared to NC, aMCI patients showed significant FA reduction bilaterally, in the association and projection fibers of frontal, parietal, and temporal lobes, corpus callosum, bilateral corona radiation, right posterior thalamic radiation and right sagittal stratum. aMCI patients also showed significantly increased MD widespreadly in the association and projection fibers of frontal, parietal and temporal lobes, and corpus callosum. Assessment of the WM integrity of the frontal, parietal, temporal lobes, and corpus callosum by using DTI measures may aid early diagnosis of aMCI. [ABSTRACT FROM AUTHOR]

Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer's disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility. [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]

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]

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 To investigate the subtle functional connectivity alterations of aMCI based on AAL atlas with 1024 regions (AAL_1024 atlas). Materials and methods Functional MRI images of 32 aMCI patients (Male/Female: 15/17, Ages: 66.8 ± 8.36 y) and 35 normal controls (Male/Female:13/22, Ages: 62.4 ± 8.14 y) were obtained in this study. Firstly, functional connectivity networks were constructed by Pearson’s Correlation based on the subtle AAL_1024 atlas. Then, local and global network parameters were calculated from the thresholding functional connectivity matrices. Finally, multiple-comparison analysis was performed on these parameters to find the functional network alterations of aMCI. And furtherly, a couple of classifiers were adopted to identify the aMCI by using the network parameters. Results More subtle local brain functional alterations were detected by using AAL_1024 atlas. And the predominate nodes including hippocampus, inferior temporal gyrus, inferior parietal gyrus were identified which was not detected by AAL_90 atlas. The identification of aMCI from normal controls were significantly improved with the highest accuracy (98.51%), sensitivity (100%) and specificity (97.14%) compared to those (88.06%, 84.38% and 91.43% for the highest accuracy, sensitivity and specificity respectively) obtained by using AAL_90 atlas. Conclusion More subtle functional connectivity alterations of aMCI could be found based on AAL_1024 atlas than those based on AAL_90 atlas. Besides, the identification of aMCI could also be improved. [ABSTRACT FROM AUTHOR]

Amnestic mild cognitive impairment (aMCI) is accompanied by the accelerated cognitive decline and rapid brain degeneration with aging. However, the age-related alterations of the topological organization of the brain connectome in aMCI patients remained largely unknown. In this study, we constructed the brain structural connectome in 51 aMCI patients and 51 healthy controls by diffusion magnetic resonance imaging and deterministic tractography. The different age-related alteration patterns of the global and regional network metrics between aMCI patients and healthy controls were assessed by a linear regression model. Compared with healthy controls, significantly decreased global and local network efficiency in aMCI patients were found. When correlating network efficiency with age, we observed a significant decline in network efficiency with aging in the aMCI patients, while not in the healthy controls. The age-related decreases of nodal efficiency in aMCI patients were mainly distributed in the key regions of the default-mode network, such as precuneus, anterior cingulate gyrus, and parahippocampal gyrus. In addition, age-related decreases in the connection strength of the edges between peripheral nodes were observed in aMCI patients. Moreover, the decreased regional efficiency of the parahippocampal gyrus was correlated with impaired memory performances in patients. The present study suggests an age-related disruption of the topological organization of the brain structural connectome in aMCI patients, which may provide evidence for different neural mechanisms underlying aging in aMCI and may serve as a potential imaging marker for the early diagnosis of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer’s disease (AD). Non-invasive diagnostic methods are desirable to identify MCI for early therapeutic interventions. In this study, we proposed a support vector machine (SVM)-based method to discriminate between MCI patients and normal controls (NCs) using multi-level characteristics of magnetic resonance imaging (MRI). This method adopted a radial basis function (RBF) as the kernel function, and a grid search method to optimize the two parameters of SVM. The calculated characteristics, i.e., the Hurst exponent (HE), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and gray matter density (GMD), were adopted as the classification features. A leave-one-out cross-validation (LOOCV) was used to evaluate the classification performance of the method. Applying the proposed method to the experimental data from 29 MCI patients and 33 healthy subjects, we achieved a classification accuracy of up to 96.77%, with a sensitivity of 93.10% and a specificity of 100%, and the area under the curve (AUC) yielded up to 0.97. Furthermore, the most discriminative features for classification were found to predominantly involve default-mode regions, such as hippocampus (HIP), parahippocampal gyrus (PHG), posterior cingulate gyrus (PCG) and middle frontal gyrus (MFG), and subcortical regions such as lentiform nucleus (LN) and amygdala (AMYG). Therefore, our method is promising in distinguishing MCI patients from NCs and may be useful for the diagnosis of MCI. [ABSTRACT FROM AUTHOR]

Background: Alzheimer''s disease disrupts the topological architecture of whole-brain connectivity (i.e., the connectome); however, whether this disruption is present in amnestic mild cognitive impairment (aMCI), the prodromal stage of Alzheimer''s disease, remains largely unknown. Methods: We employed resting-state functional magnetic resonance imaging and graph theory approaches to systematically investigate the topological organization of the functional connectome of 37 patients with aMCI and 47 healthy control subjects. Frequency-dependent brain networks were derived from wavelet-based correlations of both high- and low-resolution parcellation units. Results: In the frequency interval .031–.063 Hz, the aMCI patients showed an overall decreased functional connectivity of their brain connectome compared with control subjects. Further graph theory analyses of this frequency band revealed an increased path length of the connectome in the aMCI group. Moreover, the disease targeted several key nodes predominantly in the default-mode regions and key links primarily in the intramodule connections within the default-mode network and the intermodule connections among different functional systems. Intriguingly, the topological aberrations correlated with the patients'' memory performance and differentiated individuals with aMCI from healthy elderly individuals with a sensitivity of 86.5% and a specificity of 85.1%. Finally, we demonstrated a high reproducibility of our findings across different large-scale parcellation schemes and validated the test-retest reliability of our network-based approaches. Conclusions: This study demonstrates a disruption of whole-brain topological organization of the functional connectome in aMCI. Our finding provides novel insights into the pathophysiological mechanism of aMCI and highlights the potential for using connectome-based metrics as a disease biomarker. [Copyright &y& Elsevier]