Biomarker Changes during 20 Years Preceding Alzheimer's Disease.

BACKGROUND: Biomarker changes that occur in the period between normal cognition and the diagnosis of sporadic Alzheimer's disease have not been extensively investigated in longitudinal studies. METHODS: We conducted a multicenter, nested case-control study of Alzheimer's disease biomarkers in cognitively normal participants who were enrolled in the China Cognition and Aging Study from January 2000 through December 2020. A subgroup of these participants underwent testing of cerebrospinal fluid (CSF), cognitive assessments, and brain imaging at 2-year-to-3-year intervals. A total of 648 participants in whom Alzheimer's disease developed were matched with 648 participants who had normal cognition, and the temporal trajectories of CSF biochemical marker concentrations, cognitive testing, and imaging were analyzed in the two groups. RESULTS: The median follow-up was 19.9 years (interquartile range, 19.5 to 20.2). CSF and imaging biomarkers in the Alzheimer's disease group diverged from those in the cognitively normal group at the following estimated number of years before diagnosis: amyloid-beta (Aß)42, 18 years; the ratio of Aß42 to Aß40, 14 years; phosphorylated tau 181, 11 years; total tau, 10 years; neurofilament light chain, 9 years; hippocampal volume, 8 years; and cognitive decline, 6 years. As cognitive impairment progressed, the changes in CSF biomarker levels in the Alzheimer's disease group initially accelerated and then slowed. CONCLUSIONS: In this study involving Chinese participants during the 20 years preceding clinical diagnosis of sporadic Alzheimer's disease, we observed the time courses of CSF biomarkers, the times before diagnosis at which they diverged from the biomarkers from a matched group of participants who remained cognitively normal, and the temporal order in which the biomarkers became abnormal. (Funded by the Key Project of the National Natural Science Foundation of China and others; ClinicalTrials.gov number, NCT03653156.).

A mediation analysis of the role of total free fatty acids on pertinence of gut microbiota composition and cognitive function in late life depression.

BACKGROUND: Extensive evidence demonstrates correlations among gut microbiota, lipid metabolism and cognitive function. However, there is still a lack of researches in the field of late-life depression (LLD). This research targeted at investigating the relationship among gut microbiota, lipid metabolism indexes, such as total free fatty acids (FFAs), and cognitive functions in LLD. METHODS: Twenty-nine LLD patients from the Cognitive Outcome Cohort Study of Depression in Elderly were included. Cognitive functions were estimated through the Chinese version of Montreal Cognitive Assessment (MoCA). Blood samples were collected to evaluate serum lipid metabolism parameters. Fecal samples were evaluated for gut microbiota determination via 16S rRNA sequencing. Spearman correlation, linear regression and mediation analysis were utilized to explore relationship among gut microbiota, lipid metabolism and cognitive function in LLD patients. RESULTS: Spearman correlation analysis revealed significant correlations among Akkermansia abundance, total Free Fatty Acids (FFAs) and MoCA scores (P < 0.05). Multiple regression indicated Akkermansia and total FFAs significantly predicted MoCA scores (P < 0.05). Mediation analysis demonstrated that the correlation between decreased Akkermansia relative abundance and cognitive decline in LLD patients was partially mediated by total FFAs (Bootstrap 95%CI: 0.023-0.557), accounting for 43.0% of the relative effect. CONCLUSION: These findings suggested a significant relationship between cognitive functions in LLD and Akkermansia, as well as total FFAs. Total FFAs partially mediated the relationship between Akkermansia and cognitive functions. These results contributed to understanding the gut microbial-host lipid metabolism axis in the cognitive function of LLD.

Childhood parental companionship, self-esteem and prosocial behavior in college students: A correlation analysis.

BACKGROUND: We aimed to evaluate the correlation of Childhood parental companionship, self-esteem and prosocial behavior in college students. METHODS: We conducted a survey to assess the childhood parental companionship, self-esteem and prosocial behavior in our college from November 1, 2021 to December 15, 2021. The parental companionship status questionnaire, Self-Esteem Scale (SES) and prosocial behavior questionnaire were used for survey. Pearson linear correlation analysis was used for evaluating the correlation of childhood parental companionship, self-esteem and prosocial behavior in college students. The Bootstrap method was used to test the potentially mediating effect. RESULTS: A total of 2186 college students were included. The average total companionship score was (60.52 ± 5.17), the average self-esteem scale score was (27.15 ± 8.56), the prosocial behavior questionnaire score was (61.19 ± 15.04). Pearson correlation analysis indicated that childhood parental companionship was positively correlated with self-esteem (r = 0.679) and prosocial behavior(r = 0.679) in included college students (all P < 0.05). Self-esteem had mediating effect on parental companionship and prosocial behavior of included college students, its mediating effect was -0.445, accounting for 77.92 % of the total effect. CONCLUSIONS: Childhood parental companionship is positively correlated with self-esteem and prosocial behavior, and self-esteem play a mediating role in the parental companionship and prosocial behavior of college students.

Prediction of Alzheimer's disease using multi-variants from a Chinese genome-wide association study.

Previous genome-wide association studies have identified dozens of susceptibility loci for sporadic Alzheimer's disease, but few of these loci have been validated in longitudinal cohorts. Establishing predictive models of Alzheimer's disease based on these novel variants is clinically important for verifying whether they have pathological functions and provide a useful tool for screening of disease risk. In the current study, we performed a two-stage genome-wide association study of 3913 patients with Alzheimer's disease and 7593 controls and identified four novel variants (rs3777215, rs6859823, rs234434, and rs2255835; Pcombined = 3.07 × 10-19, 2.49 × 10-23, 1.35 × 10-67, and 4.81 × 10-9, respectively) as well as nine variants in the apolipoprotein E region with genome-wide significance (P < 5.0 × 10-8). Literature mining suggested that these novel single nucleotide polymorphisms are related to amyloid precursor protein transport and metabolism, antioxidation, and neurogenesis. Based on their possible roles in the development of Alzheimer's disease, we used different combinations of these variants and the apolipoprotein E status and successively built 11 predictive models. The predictive models include relatively few single nucleotide polymorphisms useful for clinical practice, in which the maximum number was 13 and the minimum was only four. These predictive models were all significant and their peak of area under the curve reached 0.73 both in the first and second stages. Finally, these models were validated using a separate longitudinal cohort of 5474 individuals. The results showed that individuals carrying risk variants included in the models had a shorter latency and higher incidence of Alzheimer's disease, suggesting that our models can predict Alzheimer's disease onset in a population with genetic susceptibility. The effectiveness of the models for predicting Alzheimer's disease onset confirmed the contributions of these identified variants to disease pathogenesis. In conclusion, this is the first study to validate genome-wide association study-based predictive models for evaluating the risk of Alzheimer's disease onset in a large Chinese population. The clinical application of these models will be beneficial for individuals harbouring these risk variants, and particularly for young individuals seeking genetic consultation.

Radiotherapy-related quality of life in patients with head and neck cancers: a meta-analysis.

OBJECTIVES: To compare effects of intensity-modulated radiotherapy (IMRT) with those of conventional radiotherapy on quality of life (QoL) and severity of xerostomia in patients with head and neck cancer. MATERIAL AND METHODS: PubMed, Cochrane, and Embase databases were searched to July 1, 2019, to identify relevant studies, using the following terms: radiotherapy, head and neck cancer, quality of life, cognition, xerostomia, two-/three-dimensional conformal radiation therapy, IMRT, conformal proton beam radiation therapy, stereotactic radiosurgery, and volumetric modulated arc therapy. The outcomes of interest were QoL measured by global health status; emotional, social, and cognitive function; and severity of xerostomia. RESULTS: Seven studies with a total of 761 patients (n = 369 with IMRT; n = 392 with conventional RT) were included in this study. Median patient age was 18-65 years. IMRT group patients had better global health status (pooled standardized mean difference [SMD] = 0.80, 95% CI 0.26 to 1.35, P = 0.004) and cognitive function (pooled SMD = 0.30, 95% CI 0.06 to 0.54, P = 0.013) than the conventional RT group. Patients receiving IMRT also had significantly lower scores for xerostomia than those receiving conventional RT (pooled SMD = - 0.60, 95% CI - 0.97 to - 0.24, P = 0.001). No differences were found in emotional function (P = 0.531) and social function (P = 0.348) between the two groups. CONCLUSION: IMRT significantly improves QoL and reduces the severity of xerostomia in patients with head and neck cancer. Results of this study provide clinicians with guidelines for decisions on the use of IMRT versus conventional RT.

Psychiatric diagnoses and their influencing factors in patients complaining of sleep problems: A study of a psychiatric consultation-liaison service.

Objective This study aimed to identify misdiagnosed or undiagnosed psychiatric disorders and the factors associated with these disorders in patients with sleep problems who are referred to a consultation-liaison service. Method Records of all inpatients receiving a consultation from the Psychiatry Department between January and December 2016 were retrospectively reviewed. Psychiatric diagnoses were analyzed using descriptive statistics, and the factors associated with the risk of these disorders in patients with sleep problems were determined by multiple logistic regression analysis. Results Of the 331 referral patients whose referral reason was simply having trouble in sleeping, only 97 patients were diagnosed with primary sleep disorder after consultation. The recognition rate of psychiatric disorders in inpatients with sleep problems among nonpsychiatric physicians was 29.3%. Anxiety (107, 45.7%) was the most common psychiatric diagnosis in patients with sleep problems followed by organic mental disorder (83, 35.5%), depression (37, 15.8%), and other mental disorders (8, 3.4%). Multiple logistic regression analysis revealed that a course >1 month (OR = 3.656, 95% CI = 2.171-6.156, p = 0.000) and sleep-wake rhythm disturbances (OR = 25.008, 95% CI = 5.826-107.341, p = 0.000) were associated with increased risks of psychiatric disorders. Conclusions The study showed that recognition rate of psychiatric disorders in inpatients with sleep problems was very low. A course >1 month and sleep-wake rhythm disturbances were associated with increased risks of disorders and could be used as indicators by nonpsychiatric physicians to improve diagnoses.

Gut Microbiota Dysbiosis and Inflammation Dysfunction in Late-Life Depression: An Observational Cross-Sectional Analysis.

Purpose: There are some challenges to diagnosis in the context of similar diagnostic criteria for late-life depression (LLD) and adult depression due to cognitive impairment and other clinical manifestations. The association between gut microbiota and inflammation remains unclear in LLD. We analyzed gut microbiota characteristics and serum inflammatory cytokines in individuals with LLD to explore the combined role of these two factors in potential biomarkers of LLD. Methods: This was an observational cross-sectional study. Fecal samples and peripheral blood from 29 patients and 33 sex- and age-matched healthy controls (HCs) were collected to detect gut microbiota and 12 inflammatory factors. We analyzed differences in diversity and composition of gut microbiota and evaluated relations among gut microbiota, inflammatory factors, and neuropsychological scales. We extracted potential biomarkers using receiver-operating characteristic curve analysis to predict LLD utilizing the combination of the microbiota and inflammatory cytokines. Results: Elevated systemic inflammatory cytokine levels and gut microbiota dysbiosis were found in LLD patients. Relative abundance of Verrucomicrobia at the phylum level and Megamonas, Citrobacter, and Akkermansia at the genus level among LLD patients was lower than HCs. Abundance of Coprococcus, Lachnobacterium, Oscillospira, and Sutterella was higher in LLD patients. Notably, IL6, IFNγ, Verrucomicrobia, and Akkermansia levels were correlated with depression severity. Our study identified IL6, Akkermansia, and Sutterella as predictors of LLD, and their combination achieved an area under the curve of 0.962 in distinguishing LLD patients from HCs. Conclusion: This research offers evidence of changes within gut microbiota and systemic inflammation in LLD. These findings possibly help elucidate functions of gut microbiota and systemic inflammation in LLD development and offer fresh ideas on biomarkers for clinical practise in the context of LLD.

Frequency-dependent alterations in functional connectivity in patients with Alzheimer's Disease spectrum disorders.

Background: In the spectrum of Alzheimer's Disease (AD) and related disorders, the resting-state functional magnetic resonance imaging (rs-fMRI) signals within the cerebral cortex may exhibit distinct characteristics across various frequency ranges. Nevertheless, this hypothesis has not yet been substantiated within the broader context of whole-brain functional connectivity. This study aims to explore potential modifications in degree centrality (DC) and voxel-mirrored homotopic connectivity (VMHC) among individuals with amnestic mild cognitive impairment (aMCI) and AD, while assessing whether these alterations differ across distinct frequency bands. Methods: This investigation encompassed a total of 53 AD patients, 40 aMCI patients, and 40 healthy controls (HCs). DC and VMHC values were computed within three distinct frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz), and slow-5 (0.01-0.027 Hz) for the three respective groups. To discern differences among these groups, ANOVA and subsequent post hoc two-sample t-tests were employed. Cognitive function assessment utilized the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA). Pearson correlation analysis was applied to investigate the associations between MMSE and MoCA scores with DC and VMHC. Results: Significant variations in degree centrality (DC) were observed among different groups across diverse frequency bands. The most notable differences were identified in the bilateral caudate nucleus (CN), bilateral medial superior frontal gyrus (mSFG), bilateral Lobule VIII of the cerebellar hemisphere (Lobule VIII), left precuneus (PCu), right Lobule VI of the cerebellar hemisphere (Lobule VI), and right Lobule IV and V of the cerebellar hemisphere (Lobule IV, V). Likewise, disparities in voxel-mirrored homotopic connectivity (VMHC) among groups were predominantly localized to the posterior cingulate gyrus (PCG) and Crus II of the cerebellar hemisphere (Crus II). Across the three frequency bands, the brain regions exhibiting significant differences in various parameters were most abundant in the slow-5 frequency band. Conclusion: This study enhances our understanding of the pathological and physiological mechanisms associated with AD continuum. Moreover, it underscores the importance of researchers considering various frequency bands in their investigations of brain function.

Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118.

The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD.

A review of current evidence for mild behavioral impairment as an early potential novel marker of Alzheimer's disease.

Mild behavioral impairment (MBI) is a neurobehavioral syndrome that occurs in the absence of cognitive impairment later in life (≥50 years of age). MBI is widespread in the pre-dementia stage and is closely associated with the progression of cognitive impairment, reflecting the neurobehavioral axis of pre-dementia risk states and complementing the traditional neurocognitive axis. Despite being the most common type of dementia, Alzheimer's disease (AD) does not yet have an effective treatment; therefore, early recognition and intervention are crucial. The Mild Behavioral Impairment Checklist is an effective tool for identifying MBI cases and helps identify people at risk of developing dementia. However, because the concept of MBI is still quite new, the overall understanding of it is relatively insufficient, especially in AD. Therefore, this review examines the current evidence from cognitive function, neuroimaging, and neuropathology that suggests the potential use of MBI as a risk indicator in preclinical AD.

Static and dynamic functional connectivity combined with the triple network model in amnestic mild cognitive impairment and Alzheimer's disease.

Background: Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) are characterized by abnormal functional connectivity (FC) of default-mode network (DMN), salience network (SN), and central executive network (CEN). Static FC (sFC) and dynamic FC (dFC) combined with triple network model can better study the dynamic and static changes of brain networks, and improve its potential diagnostic value in the diagnosis of AD spectrum disorders. Methods: Differences in sFC values and dFC variability patterns among the three brain networks of the three groups (53 AD patients, 40 aMCI patients, and 40 NCs) were computed by ANOVA using Gaussian Random Field theory (GRF) correction. The correlation between FC values (sFC values and dFC variability) in the three networks and cognitive scores (MMSE and MoCA) in AD and aMCI groups was analyzed separately. Results: Within the DMN network, there were significant differences of sFC values in right/left medial superior frontal gyrus and dFC variability in left opercular part inferior frontal gyrus and right dorsolateral superior frontal gyrus among the three groups. Within the CEN network, there were significant differences of sFC values in left superior parietal gyrus. Within the SN network, there were significant differences of dFC variability in right Cerebelum_7b and left opercular part inferior frontal gyrus. In addition, there was a significant negative correlation between FC values (sFC values of CEN and dFC variability of SN) and MMSE and MoCA scores. Conclusion: It suggests that sFC, dFC combined with triple network model can be considered as potential biomarkers for AD and aMCI.

Machine learning classifiers and associations of cognitive performance with hippocampal subfields in amnestic mild cognitive impairment.

Background: Neuroimaging studies have demonstrated alterations in hippocampal volume and hippocampal subfields among individuals with amnestic mild cognitive impairment (aMCI). However, research on using hippocampal subfield volume modeling to differentiate aMCI from normal controls (NCs) is limited, and the relationship between hippocampal volume and overall cognitive scores remains unclear. Methods: We enrolled 50 subjects with aMCI and 44 NCs for this study. Initially, a univariate general linear model was employed to analyze differences in the volumes of hippocampal subfields. Subsequently, two sets of dimensionality reduction methods and four machine learning techniques were applied to distinguish aMCI from NCs based on hippocampal subfield volumes. Finally, we assessed the correlation between the relative volumes of hippocampal subfields and cognitive test variables (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA)). Results: Significant volume differences were observed in several hippocampal subfields, notably in the left hippocampus. Specifically, the volumes of the hippocampal tail, subiculum, CA1, presubiculum, molecular layer, GC-ML-DG, CA3, CA4, and fimbria differed significantly between the two groups. The highest area under the curve (AUC) values for left and right hippocampal machine learning classifiers were 0.678 and 0.701, respectively. Moreover, the volumes of the left subiculum, left molecular layer, right subiculum, right CA1, right molecular layer, right GC-ML-DG, and right CA4 exhibited the strongest and most consistent correlations with MoCA scores. Conclusion: Hippocampal subfield volume may serve as a predictive marker for aMCI. These findings underscore the sensitivity of hippocampal subfield volume to overall cognitive performance.

A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study.

Background: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems. Methods: Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD. Findings: IBRAIN accurately differentiated individuals with AD from NCs (AUC = 0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson's disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) (AUC = 0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio (HR) = 6.52 [95% CI: 4.42∼9.62], p < 1 × 10-16), which offers similar powerful performance with Cerebrospinal Fluid (CSF) Aß (HR = 3.78 [95% CI: 2.63∼5.43], p = 2.13 × 10-14) and CSF Tau (HR = 3.77 [95% CI: 2.64∼5.39], p = 9.53 × 10-15) based on the COX and Log-rank test. Notably, the IBRAIN shows comparable sensitivity (beta = -0.70, p < 1 × 10-16) in capturing longitudinal changes in individuals with conversion to AD than CSF Aß (beta = -0.26, p = 4.40 × 10-9) and CSF Tau (beta = 0.12, p = 1.02 × 10-5). Interpretation: Our findings suggested that IBRAIN is a biologically relevant, specific, and sensitive neuroimaging biomarker that can serve as a clinical measure to uncover prodromal AD progression. It has strong potential for application in future clinical practice and treatment trials. Funding: Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, Beijing Natural Science Funds, the Fundamental Research Funds for the CentralUniversity, and the Startup Funds for Talents at Beijing Normal University.

Association between healthy lifestyle and memory decline in older adults: 10 year, population based, prospective cohort study.

OBJECTIVE: To identify an optimal lifestyle profile to protect against memory loss in older individuals. DESIGN: Population based, prospective cohort study. SETTING: Participants from areas representative of the north, south, and west of China. PARTICIPANTS: Individuals aged 60 years or older who had normal cognition and underwent apolipoprotein E (APOE) genotyping at baseline in 2009. MAIN OUTCOME MEASURES: Participants were followed up until death, discontinuation, or 26 December 2019. Six healthy lifestyle factors were assessed: a healthy diet (adherence to the recommended intake of at least 7 of 12 eligible food items), regular physical exercise (≥150 min of moderate intensity or ≥75 min of vigorous intensity, per week), active social contact (≥twice per week), active cognitive activity (≥twice per week), never or previously smoked, and never drinking alcohol. Participants were categorised into the favourable group if they had four to six healthy lifestyle factors, into the average group for two to three factors, and into the unfavourable group for zero to one factor. Memory function was assessed using the World Health Organization/University of California-Los Angeles Auditory Verbal Learning Test, and global cognition was assessed via the Mini-Mental State Examination. Linear mixed models were used to explore the impact of lifestyle factors on memory in the study sample. RESULTS: 29 072 participants were included (mean age of 72.23 years; 48.54% (n=14 113) were women; and 20.43% (n=5939) were APOE ε4 carriers). Over the 10 year follow-up period (2009-19), participants in the favourable group had slower memory decline than those in the unfavourable group (by 0.028 points/year, 95% confidence interval 0.023 to 0.032, P<0.001). APOE ε4 carriers with favourable (0.027, 95% confidence interval 0.023 to 0.031) and average (0.014, 0.010 to 0.019) lifestyles exhibited a slower memory decline than those with unfavourable lifestyles. Among people who were not carriers of APOE ε4, similar results were observed among participants in the favourable (0.029 points/year, 95% confidence interval 0.019 to 0.039) and average (0.019, 0.011 to 0.027) groups compared with those in the unfavourable group. APOE ε4 status and lifestyle profiles did not show a significant interaction effect on memory decline (P=0.52). CONCLUSION: A healthy lifestyle is associated with slower memory decline, even in the presence of the APOE ε4 allele. This study might offer important information to protect older adults against memory decline. TRIAL REGISTRATION: ClinicalTrials.gov NCT03653156.

Application of Diffusion Tensor Imaging Based on Automatic Fiber Quantification in Alzheimer's Disease.

BACKGROUND: Neuroimaging suggests that white matter microstructure is severely affected in Alzheimer's disease (AD) progression. However, whether alterations in white matter microstructure are confined to specific regions and whether they can be used as potential biomarkers to distinguish normal control (NC) from AD are unknown. METHODS: In this cross-sectional study, 33 cases of AD and 25 cases of NC were recruited for automatic fiber quantification (AFQ). A total of 20 fiber bundles were equally divided into 100 segments for quantitative assessment of fractional anisotropy (FA), mean diffusivity (MD), volume and curvature. In order to further evaluate the diagnostic value, the maximum redundancy minimum (mRMR) and LASSO algorithms were used to select features, calculate the Radscore of each subject, establish logistic regression models, and draw ROC curves, respectively, to assess the predictive power of four different models. RESULTS: There was a significant increase in the MD values in AD patients compared with healthy subjects. The differences were mainly located in the left cingulum hippocampus (HCC), left uncinate fasciculus (UF) and superior longitudinal fasciculus (SLF). The point-wise level of 20 fiber bundles was used as a classification feature, and the MD index exhibited the best performance to distinguish NC from AD. CONCLUSION: These findings contribute to the understanding of the pathogenesis of AD and suggest that abnormal white matter based on DTI-based AFQ analysis is helpful to explore the pathogenesis of AD.

Altered dynamic intrinsic brain activity of the default mode network in Alzheimer's disease: A resting-state fMRI study.

Objective: Static regional homogeneity (ReHo) based on the resting-state functional magnetic resonance imaging (rs-fMRI) has been used to study intrinsic brain activity (IBA) in Alzheimer's disease (AD). However, few studies have examined dynamic ReHo (dReHo) in AD. In this study, we used rs-fMRI and dReHo to investigate the alterations in dynamic IBA in patients with AD to uncover dynamic imaging markers of AD. Method: In total, 111 patients with AD, 29 patients with mild cognitive impairment (MCI), and 73 healthy controls (HCs) were recruited for this study ultimately. After the rs-fMRI scan, we calculated the dReHo values using the sliding window method. ANOVA and post hoc two-sample t-tests were used to detect the differences among the three groups. We used the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA) to evaluate the cognitive function of the subjects. The associations between the MMSE score, MoCA score, and dReHo were assessed by the Pearson correlation analysis. Results: Significant dReHo variability in the right middle frontal gyrus (MFG) and right posterior cingulate gyrus (PCG) was detected in the three groups through ANOVA. In post hoc analysis, the AD group exhibited significantly greater dReHo variability in the right MFG than the MCI group. Compared with the HC group, the AD group exhibited significantly increased dReHo variability in the right PCG. Furthermore, dReHo variability in the right PCG was significantly negatively correlated with the MMSE and MoCA scores of patients with AD. Conclusion: Disrupted dynamic IBA in the DMN might be an important characteristic of AD and could be a potential biomarker for the diagnosis or prognosis of AD.

Textural features reflecting local activity of the hippocampus improve the diagnosis of Alzheimer's disease and amnestic mild cognitive impairment: A radiomics study based on functional magnetic resonance imaging.

Background: Textural features of the hippocampus in structural magnetic resonance imaging (sMRI) images can serve as potential diagnostic biomarkers for Alzheimer's disease (AD), while exhibiting a relatively poor discriminant performance in detecting early AD, such as amnestic mild cognitive impairment (aMCI). In contrast to sMRI, functional magnetic resonance imaging (fMRI) can identify brain functional abnormalities in the early stages of cerebral disorders. However, whether the textural features reflecting local functional activity in the hippocampus can improve the diagnostic performance for AD and aMCI remains unclear. In this study, we combined the textural features of the amplitude of low frequency fluctuation (ALFF) in the slow-5 frequency band and structural images in the hippocampus to investigate their diagnostic performance for AD and aMCI using multimodal radiomics technique. Methods: Totally, 84 AD, 50 aMCI, and 44 normal controls (NCs) were included in the current study. After feature extraction and feature selection, the radiomics models incorporating sMRI images, ALFF values and their combinations in the bilateral hippocampus were established for the diagnosis of AD and aMCI. The effectiveness of these models was evaluated by receiver operating characteristic (ROC) analysis. The radiomics models were further validated using the external data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results: The results of ROC analysis showed that the radiomics models based on structural images in the hippocampus had a better diagnostic performance for AD compared with the models using ALFF, while the ALFF-based model exhibited better discriminant performance for aMCI than the models with structural images. The radiomics models based on the combinations of structural images and ALFF were found to exhibit the highest accuracy for distinguishing AD from NCs and aMCI from NCs. Conclusion: In this study, we found that the textural features reflecting local functional activity could improve the diagnostic performance of traditional structural models for both AD and aMCI. These findings may deepen our understanding of the pathogenesis of AD, contributing to the early diagnosis of AD.

Changes in cognitive function and related brain regions in chronic benzene poisoning: a case report.

To discuss the changes in cognitive function and related brain regions in patients with chronic benzene poisoning. Few studies have explored the damage to cognitive function that occurs in benzene toxic encephalopathy. It is important to identify early in the course of disease whether cognitive dysfunction is caused by benzene poisoning so that disease prognosis and appropriate treatment can be determined. We reported on the chronic benzene poisoning of a 41-year-old Han Chinese woman. The patient had graduated from primary school, and she had a cheerful and diligent personality. She had performed painting work for more than five years, and her primary work involved painting swivel chairs. The primary reasons she attended the psychiatric clinic were loss of appetite, she had experienced fatigue for more than 2 months, and she had had memory loss for a month. These symptoms seriously impacted the patient's daily life and ability to work. The patient's husband expressed concern that she could not recognize acquaintances, could not find her way home, and had lost approximately 5 kg per month over two months. We analyzed changes in this chronic benzene poisoning patient's cognitive function with cognitive function assessments and magnetic resonance imaging (MRI). Measurements were taken on presentation to hospital, during the patient's hospitalization, and three months following discharge. Long-term exposure to benzene can damage the central nervous system. However, it is difficult to recognize when cognitive impairment is caused by chronic benzene poisoning, as it rarely presents with a decline in cognitive function as the primary clinical manifestation. Atypical symptoms, such as decreased immune function and gastrointestinal issues, may be the first symptoms to appear, and these atypical symptoms are difficult to detect in the early stages of disease. Regular screening of high-risk groups is required to significantly reduce the incidence of systemic damage caused by benzene poisoning.

Low-dose clozapine-related seizure: A case report and literature review.

BACKGROUND: Treatment-resistant schizophrenia is a severe form of schizophrenia characterized by poor response to at least two antipsychotic drugs and is typically treated with clozapine. However, clozapine lowers the epileptic threshold, leading to seizures, which are severe side effects of antipsychotics that result in multiple complications. Clozapine-related seizures are generally considered to be dose-dependent and especially rare in the low-dose (150-300 mg/d) clozapine treated population. Due to clinical rarity, little is known about its clinical characteristics and treatment. CASE SUMMARY: A 62-year-old Chinese man with a 40-year history of treatment-resistant schizophrenia presented to the Emergency Department with symptoms of myoclonus, consciousness disturbance and vomiting after taking 125 mg clozapine. Upon admission, the patient had a suddenly generalized tonic-clonic seizure lasting for about half a minute with persistent disturbance of consciousness, fever, cough and bloody sputum, which was considered to be low-dose clozapine-related seizure. After antiepileptic and multiple anti-infection treatments, the patient was discharged without epileptic or psychotic symptoms. CONCLUSION: Our aim is to highlight the early prevention and optimal treatment of clozapine-related seizure through case analysis and literature review.

Comprehensive classification models based on amygdala radiomic features for Alzheimer's disease and mild cognitive impairment.

The amygdala is an important part of the medial temporal lobe and plays a pivotal role in the emotional and cognitive function. The aim of this study was to build and validate comprehensive classification models based on amygdala radiomic features for Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI). For the amygdala, 3360 radiomic features were extracted from 97 AD patients, 53 aMCI patients and 45 normal controls (NCs) on the three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) images. We used maximum relevance and minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) to select the features. Multivariable logistic regression analysis was performed to build three classification models (AD-NC group, AD-aMCI group, and aMCI-NC group). Finally, internal validation was assessed. After two steps of feature selection, there were 5 radiomic features remained in the AD-NC group, 16 features remained in the AD-aMCI group and the aMCI-NC group, respectively. The proposed logistic classification analysis based on amygdala radiomic features achieves an accuracy of 0.90 and an area under the ROC curve (AUC) of 0.93 for AD vs. NC classification, an accuracy of 0.81 and an AUC of 0.84 for AD vs. aMCI classification, and an accuracy of 0.75 and an AUC of 0.80 for aMCI vs. NC classification. Amygdala radiomic features might be early biomarkers for detecting microstructural brain tissue changes during the AD and aMCI course. Logistic classification analysis demonstrated the promising classification performances for clinical applications among AD, aMCI and NC groups.