Progress in blood biomarkers of subjective cognitive decline in preclinical Alzheimer’s disease

Alzheimer's disease(AD)is a neurodegenerative disease that gradually impairs cognitive functions.Recently,there has been a conceptual shift toward AD to view the disease as a continuum.Since AD is currently incurable,effective intervention to delay or prevent pathological cognitive decline may best target the early stages of symptomatic disease,such as subjective cognitive decline(SCD),in which cognitive function remains relatively intact.Diagnostic methods for identifying AD,such as cerebrospinal fluid biomarkers and positron emission tomography,are invasive and expensive.Therefore,it is imperative to develop blood biomarkers that are sensitive,less invasive,easier to access,and more cost effective for AD diagnosis.This review aimed to summarize the current data on whether individuals with SCD differ reliably and effectively in subjective and objective performances compared to cognitively normal elderly individuals,and to find one or more convenient and accessible blood biomarkers so that researchers can identify SCD patients with preclinical AD in the population as soon as possible.Owing to the heterogeneity and complicated pathogenesis of AD,it is difficult to make reliable diagnoses using only a single blood marker.This review provides an overview of the progress achieved to date with the use of SCD blood biomarkers in patients with preclinical AD,highlighting the key areas of application and current challenges.

The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline

Background Subjective cognitive decline(SCD)is a preclinical stage along the Alzheimer’s disease(AD)continuum.However,little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD.Methods Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls(HC).Pearson correlation analysis was computed to assess the relationships among network metrics,neuropsychological performance and pathological biomarkers.Finally,we used the multiple kernel learning-support vector machine(MKL-SVM)to differentiate the SCD and HC individuals.Results SCD individuals showed higher nodal topological properties(including nodal strength,nodal global efficiency and nodal local efficiency)associated with amyloid-βlevels and memory function than the HC,and these regions were mainly located in the default mode network(DMN).Moreover,increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus(PHG)and other DMN-related regions was found in SCD individuals compared with HC individuals.These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%.Conclusion The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD.The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.

Hippocampal resting-state functional connectivity with the mPFC and DLPFC moderates and mediates the association between education level and memory function in subjective cognitive decline

Objective:This study aims to determine the relationship between education level,memory function,and hippocampus functional and structural alterations in subjective cognitive decline(SCD).Methods:Seventy-five participants with SCD were divided into high education(HE)and low education(LE)level groups.A Wechsler Memory Scale-Chinese Revision test and functional and structural MRI were performed within 1 week after participant recruitment.The bilateral hippocampus resting-state functional connectivity(rsF C),gray matter volume(GMV)of brain regions identified by rsF C analysis,and moderating and mediating effects were assessed.Results:Compared with the LE group,HE individuals showed 1)higher memory quotient(MQ)and Digit Span subscore,2)decreased hippocampal rs FC with the right medial prefrontal cortex(mPFC)and dorsolateral prefrontal cortex(DLPFC),and 3)increased GMV in the right mP FC and DLPFC.The bilateral hippocampus-right DLPFCrs FC significantly associated with the MQ and the bilateral hippocampus-right m PFCrs FC with the Digit Span subscore in each group.The bilateral hippocampus-right DLPFCrsFC moderated the relationship between the education level and MQ.The bilateral hippocampus-right mP FC rsF C mediated the relationship between the education level and Digit Span subscore in all subjects.Conclusion:The hippocampal rsF C with the right mP FC and DLPFC contributes to the education level effect on memory function in SCD.

Computerized Cognitive Training in Healthy Older Adults:Baseline Cognitive Level and Subjective Cognitive Concerns Predict Training Outcome

Computerized cognitive training (CCT) can improve cognition in older adults. However, specific programs for this target group have rarely been developed, and predictors of training success are largely unclear. In a randomized controlled pilot trial, we examined effects of a six-week CCT, tailored to the cognitive profile of healthy older adults (EG, n = 21) as compared to a nonspecific CCT (CG, n = 18) matched in frequency and intensity. No interaction effects between Group and Time were found. However, within-group analysis showed significant gains in verbal and non-verbal memory, executive and visuospatial functions and subjective cognitive concerns (SCC) in the EG, while the CG only benefitted in non-verbal memory and set-shifting. Low cognitive performance and lower SCC at baseline were the most consistent predictors of cognitive gains in the EG. Thus, CCTs specifically tailored to older adults seem advantageous compared to non-specific CCT. Further, we conclude that SCC may be related to reduced cognitive plasticity.

Optimizing Alzheimer’s Disease Therapy Using a Neural Intelligent Agent-Based Platform

Alzheimer’s disease affects millions of persons every year. Negative emotions such as stress and frustration have a negative impact on memory function and Alzheimer's patients experience more negative emotions than healthy adults. Non-pharmacological treatment such as immersion in virtual environments could help Alzheimer patients by reducing their negative emotions, but it has restrictions and requirements. In this work, we present three virtual reality relaxing systems in which the patients are immersed in relaxing environments. We propose to use intelligent agents in order to adapt the relaxing environment to each participant and optimize its relaxation effect. The intelligent agents track the emotions of patients using electroencephalography as input in order to adapt the environments. We designed each system with different levels of intelligence in order to analyze the impact of the adaptation on the patients. Experiments were performed for each system on participants with subjective cognitive decline. Results show that these relaxing systems can reduce negative emotions and improve participants’ memory performance. The positive effects on affective state and memory persisted for a longer period of time and were generally more effective for the systems with more intelligence. We believe that the combination of a relaxing environment, virtual reality, intelligent agents for adapting the environment, and brain assessment is a promising method for helping Alzheimer’s patients.