Cognitive Normal Controls Research Articles

ASSOCIATION BETWEEN GUT MICROBIOTA, DIET, AND COGNITION IN ELDERLY PATIENTS WITH MILD COGNITIVE IMPAIRMENT

Abstract Objectives To compare the gut microbiota in individuals with mild cognitive impairment (MCI) and normal controls (NCs) and explore the association between dietary intake and various cognitive domains across identified enterotypes, providing a basis for dietary interventions based on the Microbiota-Gut-Brain Axis. Methods We analyzed fecal samples and 16S ribosomal RNA sequences for microbiota among 100 participants (NCs: 50, MCI: 50). Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), and Auditory Verbal Learning Test (AVLT). A semi-quantitative food frequency questionnaire was used to assess participants’ dietary intake, and differences in dietary habits and cognitive function among distinct enterotypes were analyzed. Results Participants with MCI exhibited lower bacterial richness. Linear Discriminant Analysis of Effect Sizes (LEfSe-LDA) revealed different major microbial genera between the groups, and a random forest model based on dominant bacterial genera could distinguish between MCI and NCs (AUC=0.743, 95%CI:0.646-0.840). Cluster analysis identified two enterotypes: Escherichia-Shigella (enterotype E) and Bacteroides (enterotype B). When grouped by enterotypes, similar dietary intake was associated with different cognitive domains: notably, egg intake positively correlated with MOCA and AVLT scores for enterotype E, but not for enterotype B. Conclusions: Significant differences exist in the gut microbiota of individuals with MCI compared to those with normal cognition, with dominant genera showing good predictive performance. The impact of different enterotypes on the relationship between dietary intake and cognitive function should be considered when making dietary recommendations in clinical practice.

Classifying Alzheimer's Disease Using a Finite Basis PhysicsNeural Network.

The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, interpretability, ethical integration, population variety, and picture standardization must be addressed using deep learning for the functional magnetic resonance imaging (MRI) classification of AD in order to guarantee a trustworthy and practical therapeutic application. In this manuscript Classifying AD using a finite basis physics neural network (CAD-FBPINN) is proposed. Initially, images are collected from AD Neuroimaging Initiative (ADNI) dataset. The images are fed to Pre-processing segment. During the preprocessing phase the reverse lognormal Kalman filter (RLKF) is used to enhance the input images. Then the preprocessed images are given to the feature extraction process. Feature extraction is done by Newton-time-extracting wavelet transform (NTEWT), which is used to extract the statistical features such as the mean, kurtosis, and skewness. Finally the features extracted are given to FBPINNs for Classifying AD such as early mild cognitive impairment (EMCI), AD, mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), normal control (NC), and subjective memory complaints (SMCs). In General, FBPINN does not express adapting optimization strategies to determine optimal factors to ensure correct AD classification. Hence, sea-horse optimization algorithm (SHOA) to optimize FBPINN, which accurately classifies AD. The proposed technique implemented in python and efficacy of the CAD-FBPINN technique is assessed with support of numerous performances like accuracy, precision, Recall, F1-score, specificity and negative predictive value (NPV) is analyzed. Proposed CAD-FBPINN method attain 30.53%, 23.34%, and 32.64% higher accuracy; 20.53%, 25.34%, and 29.64% higher precision; 20.53%, 25.34%, and 29.64% higher NP values analyzed with the existing for Classifying AD Stages through Brain Modifications using FBPINNs Optimized with sea-horse optimizer. Then, the effectiveness of the CAD-FBPINN technique is compared to other methods that are currently in use, such as AD diagnosis and classification using a convolution neural network algorithm (DC-AD-AlexNet), Predicting diagnosis 4 years before Alzheimer's disease incident (PDP-ADI-GCNN), and Using the DC-AD-AlexNet convolution neural network algorithm, diagnose and classify AD.

Contribution of alpha-synuclein pathology to cerebral glucose metabolism in patients with amnestic MCI.

The in vivo detection of mixed Alzheimer's disease (AD) and α-synuclein (αSyn) pathology is important for clinical management and prognostic stratification. We investigated the contribution of αSyn pathology, detected by cerebrospinal fluid (CSF) seed amplification assay (αSyn SAA), on [18F]-fluorodeoxyglucose positron emission tomography (FDG PET) pattern in subjects with amnestic mild cognitive impairment (aMCI). We included 562 aMCI participants and 204 cognitively normalcontrols (CN) with available αSyn SAA and cerebral metabolic rate for glucose utilization (rCMRgl) data. 24% of aMCI cases were positive (+) for CSF αSyn SAA. Compared to CN, both αSyn+ and negative (-) aMCI participants showed reductions in rCMRgl within AD typical regions. αSyn+ aMCI had lower rCMRgl within AD and dementia with Lewy bodies (DLB) typical regions compared to αSyn- aMCI, even after stratification according to the CSF AT(N) system. αSyn pathology contributes to a distinct FDG PET pattern in aMCI. αSyn pathology can be detected in vivo by CSF αSyn SAA. We investigated the FDG PET pattern in aMCI patients with CSF αSyn SAA positivity. αSyn+ aMCI showed a marked brain hypometabolism in AD and DLB typical regions.

Cholesterol esterification is hampered in alzheimer’s disease and cholesteryl esters composition is consequently altered

Introduction and Aim: Several epidemiological studies indicate a strong inverse association between the risk of developing Alzheimer’s disease (AD) and plasma HDL-C levels. The mechanism by which plasma HDL influence the pathogenesis and progression of AD is still unsolved and since cholesterol esterification is a crucial step in HDL metabolism it could be involved. The purpose of this study was to evaluate cholesterol esterification and HDL subclasses in plasma and cerebrospinal fluid (CSF) of Alzheimer’s Disease (AD) patients. Methods: The study enrolled 70 AD patients and 74 cognitively-normal controls comparable for age and sex. Lipids and lipoprotein profile, cholesterol esterification, and cholesterol efflux capacity (CEC) were evaluated in plasma and CSF using assays set for measurement in plasma, which were appropriately modified for CSF. Results: AD patients have normal plasma lipids, but significantly reduced unesterified cholesterol and unesterified/total cholesterol ratio. Lecithin:cholesterol acyltransferase (LCAT) activity and cholesterol esterification rate (CER), two measures of the efficiency of the esterification process, were reduced by 29% and 16%, respectively, in plasma of AD patients. Plasma HDL subclass distribution in AD patients was comparable to that of controls, but the content of small discoidal preβ-HDL particles was significantly reduced. In agreement with the reduced preβ-HDL particles, cholesterol efflux capacity mediated by the transporters ABCA1 and ABCG1 was reduced in AD patients’ plasma. The CSF unesterified to total cholesterol ratio was increased in AD patients, and CSF CER and CEC from astrocytes were significantly reduced in AD patients. In the AD group, a significant positive correlation was observed between plasma unesterified cholesterol and unesterified/total cholesterol ratio with Aβ1-42 CSF content. Conclusions: Taken together data indicate that cholesterol esterification is hampered in plasma and CSF of AD patients, and that plasma cholesterol esterification biomarkers (unesterified cholesterol and unesterified/total cholesterol ratio) are significantly associated to disease biomarkers (i.e., CSF Aβ1-42).

A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN.

This study aims to develop an optimally performing convolutional neural network to classify Alzheimer's disease into mild cognitive impairment, normal controls, or Alzheimer's disease classes using a magnetic resonance imaging dataset. To achieve this, we focused the study on addressing the challenge of image noise, which impacts the performance of deep learning models. The study introduced a scheme for enhancing images to improve the quality of the datasets. Specifically, an image enhancement algorithm based on histogram equalization and bilateral filtering techniques was deployed to reduce noise and enhance the quality of the images. Subsequently, a convolutional neural network model comprising four convolutional layers and two hidden layers was devised for classifying Alzheimer's disease into three (3) distinct categories, namely mild cognitive impairment, Alzheimer's disease, and normal controls. The model was trained and evaluated using a 10-fold cross-validation sampling approach with a learning rate of 0.001 and 200 training epochs at each instance. The proposed model yielded notable results, such as an accuracy of 93.45% and an area under the curve value of 0.99 when trained on the three classes. The model further showed superior results on binary classification compared with existing methods. The model recorded 94.39%, 94.92%, and 95.62% accuracies for Alzheimer's disease versus normal controls, Alzheimer's disease versus mild cognitive impairment, and mild cognitive impairment versus normal controls classes, respectively.

The Effects of PICALM rs3851179 and Age on Brain Atrophy and Cognition Along the Alzheimer's Disease Continuum.

Rs3851179, a variant of PICALM gene, and age are the risk factors of Alzheimer's disease (AD). AD is divided into early-onset AD (EOAD, < 65 years) and late-onset AD (LOAD, ≥ 65 years) by age. The purpose was to investigate the impact of different genotypes of PICALM rs3851179 on brain atrophy and cognitive decline across the AD continuum in different age groups. Four hundred seven cognitive normal (CN) controls, 362 mild cognitive impairment (MCI) patients, and 94 AD patients were enrolled to assess the interaction between AD continuum, age status, and PICALM on gray matter volume (GMV), global cognition, memory function, and executive function using full factorial ANCOVA (3 × 2 × 2). The interaction between AD continuum and PICALM significantly affected the GMV of the left putamen (PUT.L). rs3851179 A-allele carriers did not show a significant decrease in PUT.L GMV from CN to MCI to AD, while GG-allele carriers did. The interaction between AD continuum and age status was significant on GMV of the left angular gyrus (ANG.L) and right superior occipital gyrus (SOG.R). LOAD had higher GMV of ANG.L and SOG.R than EOAD. The interactive effects among AD continuum, age status, and PICALM were not significant on GMV but were significant on global cognition and executive function. The A-allele was found to have a protective effect on global cognition and executive function in EOAD, but not significantly so in LOAD. PICALM rs3851179 A-allele might alleviate the atrophy of PUT.L across the AD continuum than GG-allele. Age status did not affect the interaction between AD continuum and PICALM on brain atrophy. The ANG.L and SOG.R atrophied more severely in EOAD than in LOAD. Rs3851179 A-allele was protective for global cognition and executive function in EOAD.

Abnormalities of Rest-Activity and Light Exposure Rhythms Associated with Cognitive Function in Patients with Mild Cognitive Impairment (MCI).

We aimed to examine the difference in rest-activity rhythm (RAR) and light exposure rhythm (LER) between patients with mild cognitive impairment (MCI) and normal controls (NC), and to verify their relationships with cognitive functions. The neuropsychological battery was administered to participants above 50 years old. The MCI diagnosis was made according to Petersen's criteria. Ten patients with MCI (77.90 ± 6.95 years) and eight NC (74.75 ± 5.06 years) were studied. Actigraphy (Actiwatch 2; Philips Respironics) was recorded at home for 5 days. RAR and LER variables, including interdaily stability (IS), intradaily variability (IV) and relative amplitude, were calculated using nonparametric analyses. The associations between cognitive performance and RAR and LER variables were explored using generalized linear models. There were no significant differences in RAR or LER variables between MCI and NC. There was a significant main effect of RAR-IS on the Stroop Color and Word Test (SCWT), indicating a positive relationship between RAR stability and SCWT performance. There was a significant group by RAR-IS interaction on Trail Making Test-A, indicating a negative relationship in MCI compared to NC. There was a significant group by LER-IV interaction on the Boston Naming Test, indicating a positive relationship in MCI compared to NC. There was no disruption in RAR and LER in patients with MCI. Our study showed that circadian rhythm abnormality was associated with a decline in executive function. However, circadian rhythm abnormality was not associated with declines in processing speed and language function in patients with MCI, implying an altered pathophysiology compared to NC.

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome.

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

일상생활 수행능력 평가도구의 유효성 분석 연구

Objective: The study’s aim was to evaluate the validity of the Korean version of the University of California San Diego Performance- based Skills Assessment, Validation of Intermediate Measures (K-UPSA-2-VIM) in patients with dementia (D), mild cognitive impairment (MCI), and cognitive normal control group (CN).&#x0D; Methods: Study participants were 25 patients with D, 43 patients with MCI, and 111 controls with CN group, respectively. For cognitive assessment, Mini Mental State Examination, Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological battery, and Clinical Dementia Rating were used. For functional assessment, Barthel-Activities of Daily Living, Instrumental Activities of Daily Living, Dementia Screening questionnaire, and K-UPSA-2-VIM were used.&#x0D; Results: Statistically significant differences were observed in all subdomains and total score of the K-UPSA-2-VIM among three cognitive groups. K-UPSA-2-VIM demonstrated 75.7% of sensitivity and 65.1% of specificity, with an area under the curve (AUC) of 0.731 (95% confidence interval [CI]: 0.641-0.821, p&lt;0.001) in discriminating between CN and MCI groups. In discriminating between MCI and D groups, 76.7% of sensitivity and 64.0% of specificity, with an AUC of 0.706 (95% CI: 0.580-0.833, p=0.005) were demonstrated.&#x0D; Conclusion: The K-UPSA-2-VIM is useful to evaluate activities of daily living function in Korean patients with D and MCI.

Preliminary MRS study of critical values of relevant brain metabolites in elderly Chinese patients with post-stroke cognitive impairment

ObjectiveProton magnetic resonance spectroscopy (1H-MRS) was applied in this study to detect metabolite changes in the brain of post-stroke cognitive impairment (PSCI) and normal volunteers. The levels of N-acetylaspartate (NAA) and creatinine (Cr) and in the frontal lobe, hippocampus and cingulate gyrus were measured to distinguish patients with post-stroke cognitive impairment (PSCI) and normal control group (NC). The relationship between them and cognitive function was explored and a critical value of the metabolite ratio was predicted. This study may serve as a reference for the diagnosis of cognitive dysfunction after stroke. MethodsA total of 46 patients with PSCI (PSCI group, all patients are unilateral cerebral infarction or intracerebral haemorrhage) were screened by the Mini-Mental Status Examination (MMSE), and 35 healthy volunteers were selected as normal control group (NC group). The general information of gender, age, and education level was matched between the two groups. Two groups of subjects were examined using MRS and evaluated for cognitive function using the MMSE test and the Montreal Cognitive Assessment Scale (MoCA). The correlation between MRS and neurobehavioral scale (MMSE test and MoCA scale) was analysed, and the possible demarcation points of the brain metabolism of PSCI were evaluated. ResultThe MMSE and MoCA scores of patients with PSCI were lower significantly when compared with those of the NC group (P < 0.05). The NAA/Cr values of the bilateral hippocampus, bilateral frontal lobe and bilateral anterior and posterior cingulate gyrus in the PSCI group were lower than those in the NC group (P < 0.05). The NAA/Cr cut-off value for the right frontal lobe was 1.533, and the NAA/Cr sensitivity, specificity and Youden index for the right frontal lobe were 0.943, 0.935, and 0.878. ConclusionNAA/Cr values in the MRS bilateral frontal, bilateral hippocampus and bilateral anterior and posterior cingulate gyrus were reduced in the cognitively impaired post-stroke patients compared to the normal control group. MRS was also found to be correlated with the score of neurobehavioral scale (MMSE test and MoCA scale) and the combination of the two could evaluate cognitive dysfunction more comprehensively and objectively. NAA/Cr value of the right frontal lobe < 1.533 indicated that PSCI may occur. In accordance with this cut-off point, PSCI could be detected as early as possible and timely intervention could be carried out.

Eye movement dysfunction for screening mild cognitive impairment

AbstractBackgroundEarly screening of cognitive impairment is crucial to delay cognitive deterioration and daily living disability in the elderly. However, the complexity and high cost of existing strategies appear to be great limits to the early screening on a large scale. Eye movement test shows a huge advantage in the early identification of mild cognitive impairment (MCI) because of quick, non‐invasive and cost‐effective features.MethodThe study involved a total of 151 participants: 90 MCI patients and 50 cognitive normal controls (CN) . All participants underwent eye movement signals under four visual tasks, including direct gaze towards target, smooth pursuit tasks, saccading toward a jumping target and looking away a jumping target. Five features were extracted from eye movement signals by linear and nonlinear analysis, including saccade number in resting state, saccade number in smooth persuit tasks, saccade lantency, antisaccade lantency, antisaccade error rate. Discriminant features selection was performed using the logistic regression (LR) analysis. Receiver operator characteristic (ROC) analyses and the area under the curve (AUC) were employed to investigate classification ability.ResultAfter the penalization of LR analysis, there were two top features that exhibited significant differences between MCI and CN, which included saccade number in smooth pursuit tasks and antisaccade error rate (p&lt;0.05). ROC curves showed a favorable diagnostic accuracy of the two features in the discrimination between controls and MCI (AUC: 0.823, P&lt;0.01).ConclusionThese results suggest that eye movement test has the potential to screen patients with MCI from the elderly with cognitive normality. Therefore, eye movement test is a powerful tool for broad screening in community‐dwelling older adults.

Early changes of fecal short-chain fatty acid levels in patients with mild cognitive impairments.

To compare the fecal levels of short-chain fatty acids (SCFAs) in patients with mild cognitive impairment (MCI) and normal controls (NCs) and to examine whether fecal SCFAs could be used as the biomarker for the identification of patients with MCI. To examine the relationship between fecal SCFAs and amyloid-β (Aβ) deposition in the brain. A cohort of 32 MCI patients, 23 Parkinson's disease (PD) patients, and 27 NC were recruited in our study. Fecal levels of SCFAs were measured using chromatography and mass spectrometry. Disease duration, ApoE genotype, body mass index, constipation, and diabetes were evaluated. To assess cognitive impairment, we used the Mini-Mental Status Examination (MMSE). To assess brain atrophy, the degree of medial temporal atrophy (MTA score, Grade 0-4) was measured by structural MRI. Aβ positron emission tomography with 18 F-florbetapir (FBP) was performed in seven MCI patients at the time of stool sampling and in 28 MCI patients at an average of 12.3 ± 0.4 months from the time of stool sampling to detect and quantify Aβ deposition in the brain. Compared with NC, MCI patients had significantly lower fecal levels of acetic acid, butyric acid, and caproic acid. Among fecal SCFAs, acetic acid performed the best in discriminating MCI from NC, achieved an AUC of 0.752 (p = 0.001, 95% CI: 0.628-0.876), specificity of 66.7%, and sensitivity of 75%. By combining fecal levels of acetic acid, butyric acid, and caproic acid, the diagnostic specificity was significantly improved, reaching 88.9%. To better verify the diagnostic performance of SCFAs, we randomly assigned 60% of participants into training dataset and 40% into testing dataset. Only acetic acid showed significantly difference between these two groups in the training dataset. Based on the fecal levels of acetic acid, we achieved the ROC curve. Next, the ROC curve was evaluated in the independent test data and 61.5% (8 in 13) of patients with MCI, and 72.7% (8 in 11) of NC could be identified correctly. Subgroup analysis showed that reduced fecal SCFAs in MCI group were negatively associated with Aβ deposition in cognition-related brain regions. Reductions in fecal SCFAs were observed in patients with MCI compared with NC. Reduced fecal SCFAs were negatively associated with Aβ deposition in cognition-related brain regions in MCI group. Our findings suggest that gut metabolite SCFAs have the potential to serve as early diagnostic biomarkers for distinguishing patients with MCI from NC and could serve as potential targets for preventing AD.

Oscillatory markers of neuroHIV-related cognitive impairment and Alzheimer's disease during attentional interference processing.

People with HIV (PWH) frequently experience mild cognitive decline, which is typically attributed to HIV-associated neurocognitive disorder (HAND). However, such declines could also be a sign of early Alzheimer's disease (AD) in older PWH. Distinguishing these two pathologies in PWH is exceedingly difficult, as there is a major knowledge gap regarding their neural and neuropsychological bases. In the current study, we begin to address this knowledge gap by recording magnetoencephalography (MEG) during a flanker interference task in 31 biomarker-confirmed patients on the AD spectrum (ADS), 25 older participants with HAND, and 31 cognitively-normal controls. MEG data was examined in the time-frequency domain using a data-driven approach. Our results indicated that the clinical groups (ADS/HAND) performed significantly worse than controls on the task and exhibited aberrations in interference-related theta and alpha oscillations, some of which were disease-specific. Specifically, patients (ADS/HAND) exhibited weaker interference activity in frontoparietal and cingulate cortices compared to controls, while the ADS group exhibited stronger theta interference than those with HAND in frontoparietal, occipital, and temporal cortices. These results reveal overlapping and distinct patterns of neurophysiological alterations among those with ADS and HAND in attentional processing centers and suggest the existence of unique oscillatory markers of each condition.

Hypothalamic Functional Connectivity and Apathy in People with Alzheimer's Disease and Cognitively Normal Healthy Controls.

Earlier studies have described the neural markers of apathy in Alzheimer's disease (AD) and mild cognitive impairment (MCI), but few focused on the motivation circuits. Here, we targeted hypothalamus, a hub of the motivation circuit. To examine hypothalamic resting state functional connectivity (rsFC) in relation to apathy. We performed whole-brain regression of hypothalamic rsFC against Apathy Evaluation Scale (AES) total score and behavioral, cognitive, and emotional subscores in 29 patients with AD/MCI and 28 healthy controls (HC), controlling for age, sex, education, cognitive status, and depression. We evaluated the results at a corrected threshold and employed path analyses to assess possible interaction between hypothalamic rsFCs, apathy and depression/memory. Finally, we re-examined the findings in a subsample of amyloid-β-verified AD. AES total score correlated negatively with hypothalamic precuneus (PCu)/posterior cingulate cortex (PCC) and positively with left middle temporal gyrus (MTG) and supramarginal gyrus rsFCs. Behavioral subscore correlated negatively with hypothalamic PCu/PCC and positively with middle frontal gyrus rsFC. Cognitive subscore correlated positively with hypothalamic MTG rsFC. Emotional subscore correlated negatively with hypothalamic calcarine cortex rsFC. In path analyses, hypothalamic-PCu/PCC rsFC negatively modulated apathy and, in turn, depression. The model where hypothalamic MTG rsFC and memory independently modulated apathy also showed a good fit. The findings of diminished hypothalamic-PCu/PCC rsFC in relation to apathy and, in turn, depression were confirmed in amyloid-verified AD. The findings together support a role of altered hypothalamic connectivity in relation to apathy and depression, and modulation of apathy by memory dysfunction.

Impaired retinal capillary function in patients with clinical Alzheimer’s disease

AbstractBackgroundExtensive evidence indicates that vasculopathy, especially the level of microcirculation, contributes to neurodegeneration in AD. However, it is not easy to direct monitor cerebral microcirculation. The retinal microvasculature has been used as a surrogate to study cerebral vascular changes. Indeed, decreased retinal microvascular network densities were reported in patients with AD. The goal of this project is to determine the retinal capillary function (RCF, the efficiency of blood flow transferring in the capillary network) in patients with Alzheimer’s disease (AD).MethodTwenty patients (age 60‐84, mean ± SD: 72.8 ± 7.7 yrs) with AD and 14 age‐matched cognitive normal controls (CN, age 62‐81, mean ± SD: 68.6 ± 6.7 yrs) were recruited. There were no differences in vascular risk factors including smoking, hypertension, hyperlipidemia, type 2 diabetes, and cardiovascular disease between groups (Chi‐square test, all P &gt; 0.05). One eye of each subject in both groups was imaged. Retinal blood flow (RBF) was measured using a Retinal Function Imager and retinal capillary density (RCD, expressed as fractal dimension Dbox) was measured using optical coherence tomography angiography. RCF was defined as the ratio of RBF to RCD.ResultRCF was 1.62 ± 0.56 nl/s/Dbox (mean ± SD) in the AD group, which was significant lower than that (2.56 ± 0.25 nl/s/Dbox, P = 0.0001) in the CN group. The change of RCF in the AD group represented 28% lower than the CN group. RCF was significantly related to RBF in the AD group (r = 0.98, P &lt; 0.05) and the CN group (r = 0.65, P &lt; 0.05).ConclusionOur study is the first to demonstrate the impaired retinal capillary function in patients with AD. The alteration of RCF was mainly due to decreased retinal blood flow, which the capillary network can transfer. The RCF may be developed as a biomarker to study cerebral microcirculation in patients with AD.

Spatial Distribution of Rare Missense Variants Within Protein Structures is Associated with AD Risk

AbstractBackgroundDespite large genome‐wide association studies, only ∼30% of the heritability of Alzheimer’s disease is explained. The Alzheimer’s Disease Sequencing Project Whole Exome Sequencing (ADSP WES) has identified millions of genetic variants, over 97% of which are rare (MAF&lt;1%), with 23% appearing in only one person. These rare variants could provide valuable information about new and previously identified risk loci for AD. However, current analysis strategies do not have power to detect associations for such rare variants.MethodWe have developed a protein structure‐based approach that evaluates rare missense variants based on their spatial distribution in a known protein structure rather than on their allele frequency. We hypothesize that AD cases exhibit clustering of rare variants within a protein structure relative to cognitive normal controls. We applied our approach to the ADSP WES Discovery Dataset with 5,522 AD cases and 4,919 controls on 5,969 genes with known structures from the Protein Data Bank (PDB) and 17,450 genes with Alpha Fold2 predicted structures. Only rare variants(MAF&lt;0.05) were included in the analysis. We validated the identified genes within an independent dataset with multi‐ancestry individuals (ADSP WGS Replication) and a European‐ancestry dataset with 15,078 individuals (ADSP validation dataset).ResultWe identified three significant genes (TREM2, SORL1, and EXOC3L4) and one suggestive gene (CSF1R) with AD‐associated spatial clusterings from the ADSP WES data. For TREM2(PDB:6XDS; p‐value = 3.592E‐07) and SORL1(PDB:3WSY;p‐value = 6.701E‐05), two known AD genes, the spatial clusters are significant after excluding known AD risk variants, indicating the presence of additional low‐frequency risk variants within these genes. EXOC3L4(AlphaFold2:Q17RC7;p‐value = 2.504E‐05) is a novel AD risk gene that has a cluster of variants primarily shared by AD cases around the C terminal end of the Sec6 domain. This cluster replicated with significant associations in the ADSP WGS Replication and ADSP validation dataset.ConclusionOur result suggests multiple rare missense variants in TREM2, SORL1, EXOC3L4, and likely CSF1R are associated with AD risk by spatial clustering within the protein structure. These spatial clusterings have been replicated in two additional datasets. These spatial patterns may indicate potential functional regions in the protein structure associated with AD risk and are prime targets for further experimental validation.

Association of plasma brain-derived neurotrophic factor with Alzheimer's disease and its influencing factors in Chinese elderly population.

To explore the association of plasma brain-derived neurotrophic factor (BDNF) levels with Alzheimer's disease and its influencing factors. A total of 1,615 participants were included in the present study. Among all subjects, 660 were cognitive normal controls (CNCs), 571 were mild cognitive impairment (MCI) patients, and 384 were dementia with Alzheimer's type (DAT) patients. BDNF in blood samples collected from these subjects was analyzed via the Luminex assay. Additionally, DNA extraction and APOE4 genotyping were performed on leukocytes using a blood genotyping DNA extraction kit. All data were processed with SPSS 20.0 software. Analysis of variance (ANOVA) or analysis of covariance (ANCOVA) was used to compare differences among groups on plasma BDNF. Pearson and Spearman correlation analysis examined the correlation between BDNF and cognitive impairment, and linear regression analysis examined the comprehensive effects of diagnosis, gender, age, education, and sample source on BDNF. BDNF levels in DAT patients were higher than those in CNC and MCI patients (P < 0.01). BDNF levels were significantly correlated with CDR, MMSE, and clinical diagnosis (P < 0.001). Age, education, occupation, and sample source had significant effects on BDNF differences among the CNC, MCI, and DAT groups (P < 0.001). BDNF first decreased and then increased with cognitive impairment in the ApoE4-negative group (P < 0.05). Plasma BDNF levels decreased in the MCI stage and increased in the dementia stage and were affected by age, education, occupation, and sample source. Unless the effects of sample heterogeneity and methodological differences can be excluded, plasma BDNF is difficult to become a biomarker for the early screening and diagnosis of AD.

On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer’s Disease

Alzheimer’s disease (AD) is a global health issue that predominantly affects older people. It affects one’s daily activities by modifying neural networks in the brain. AD is categorized by the death of neurons, the creation of amyloid plaques, and the development of neurofibrillary tangles. In clinical settings, an early diagnosis of AD is critical to limit the problems associated with it and can be accomplished using neuroimaging modalities, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Deep learning (DL) techniques are widely used in computer vision and related disciplines for various tasks such as classification, segmentation, detection, etc. CNN is a sort of DL architecture, which is normally useful to categorize and extract data in the spatial and frequency domains for image-based applications. Batch normalization and dropout are commonly deployed elements of modern CNN architectures. Due to the internal covariance shift between batch normalization and dropout, the models perform sub-optimally under diverse scenarios. This study looks at the influence of disharmony between batch normalization and dropout techniques on the early diagnosis of AD. We looked at three different scenarios: (1) no dropout but batch normalization, (2) a single dropout layer in the network right before the softmax layer, and (3) a convolutional layer between a dropout layer and a batch normalization layer. We investigated three binaries: mild cognitive impairment (MCI) vs. normal control (NC), AD vs. NC, AD vs. MCI, one multiclass AD vs. NC vs. MCI classification problem using PET modality, as well as one binary AD vs. NC classification problem using MRI modality. In comparison to using a large value of dropout, our findings suggest that using little or none at all leads to better-performing designs.

P-tau231 as a Diagnostic Biomarker for Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.

Some previous studies have shown that cerebrospinal fluid (CSF) levels of p-tau231 were significantly higher in patients with Alzheimer's disease (AD) compared to that in patients with mild cognitive impairment (MCI) and normal control (NC), whereas some other studies did not. Due to contradictory results, we aimed to conduct a systematic review and meta-analysis study on previous investigations to examine the potential role of CSF p-tau231 as a biomarker of AD and MCI. PubMed, Scopus, and Web of Science were searched in March 2021 for studies on the CSF level of p-tau231 in AD, MCI, and NC. The statistical analysis was performed via standardized mean difference (SMD) methodology with a 95% confidence interval. A total of 10 studies including 1141 subjects were included. The present study showed that CSF level of p-tau231 was significantly higher in AD patients compared to that in MCI patients (SMD = 160.94 [11.11, 310.78], P = 0.04) and NC patients (SMD = 436.21 [164.88, 707.54], P < 0.00). Moreover, comparison of MCI and NC showed a significantly higher level of CSF p-tau231 in MCI compared to NC (SMD = 341.44 [59.73, 623.14], P = 0.02). P-tau231 showed to be a valuable biomarker of discrimination AD, MCI, and NC based on our findings. This meta-analysis showed that the CSF p-tau231 can reliably differentiate AD patients from MCI and NC patients. Furthermore, based on our findings the level of CSF p-tau231 was significantly higher in MCI compared to NC. Therefore, p-tau231 can be added to the list of potential biomarkers for the diagnosis of AD and MCI in further studies. However, further investigations are needed to confirm our findings.

Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning.

The combination and integration of multimodal imaging and clinical markers have introduced numerous classifiers to improve diagnostic accuracy in detecting and predicting AD; however, many studies cannot ensure the homogeneity of data sets and consistency of results. In our study, the XGBoost algorithm was used to classify mild cognitive impairment (MCI) and normal control (NC) populations through five rs-fMRI analysis datasets. Shapley Additive exPlanations (SHAP) is used to analyze the interpretability of the model. The highest accuracy for diagnosing MCI was 65.14% (using the mPerAF dataset). The characteristics of the left insula, right middle frontal gyrus, and right cuneus correlated positively with the output value using DC datasets. The characteristics of left cerebellum 6, right inferior frontal gyrus, opercular part, and vermis 6 correlated positively with the output value using fALFF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, left temporal pole, and middle temporal gyrus correlated positively with the output value using mPerAF datasets. The characteristics of the right middle temporal gyrus, left middle temporal gyrus, and left hippocampus correlated positively with the output value using PerAF datasets. The characteristics of left cerebellum 9, vermis 9, and right precentral gyrus, right amygdala, and left middle occipital gyrus correlated positively with the output value using Wavelet-ALFF datasets. We found that the XGBoost algorithm constructed from rs-fMRI data is effective for the diagnosis and classification of MCI. The accuracy rates obtained by different rs-fMRI data analysis methods are similar, but the important features are different and involve multiple brain regions, which suggests that MCI may have a negative impact on brain function.