Frontiers in Aging Neuroscience最新文献

Age-related hearing loss (ARHL) is one of the most common causes of disability in older adults. It is also frequently associated with neurological and neurodegenerative disorders, including dementia, as well as with stress, anxiety, depression, and social isolation. These observations suggest that ARHL should be considered not merely as a sensory dysfunction, but rather as a complex disease involving extra-auditory domains. Namely, identifying shared pathogenic determinants between hearing loss and neurodegenerative diseases remains a significant challenge. Increasing research in this field has highlighted common molecular mechanisms underlying age-related hearing and cognitive vulnerability, as well as potential overlapping neuronal networks involved in both cognitive and auditory neurodegeneration. In this review, we first outline the clinical features, risk factors, and molecular pathways involved in ARHL. We then examine the molecular mechanisms underlying ARHL at both peripheral (cochlea) and central level (auditory cortex), and subsequently discuss the cognitive comorbidities of ARHL, with a particular focus on cognitive impairment and affective disorders. From a translational point of view, exploring the extra-auditory consequences of ARHL will be crucial, as it will enable the identification of risk factors for both auditory and cognitive vulnerability and support the development of effective therapeutic interventions.

[This corrects the article DOI: 10.3389/fnagi.2025.1684331.].

Introduction: Post-stroke cognitive impairment (PSCI) is a prevalent and disabling consequence of stroke, yet objective tools for its early identification are lacking. This study aimed to develop and validate an interpretable machine learning (ML) model based on electroencephalography (EEG) to support the early detection of PSCI.

Methods: We conducted a study involving 174 participants, including stroke patients with and without cognitive impairment and age-matched healthy controls. Resting-state EEG was acquired from all subjects, and multidimensional features, including power spectral ratios and microstate parameters, were extracted. Feature selection was performed using LASSO regression, random forest, and the Boruta algorithm. Five machine learning models were evaluated and compared based on their area under the curve (AUC), accuracy, Brier score, calibration plots, and decision curve analysis. Model interpretability was explained using SHAP (Shapley Additive Explanations). The final validated model was deployed as an interactive web-based application.

Results: Seven EEG features were identified as most predictive of PSCI: the delta-plus-theta to alpha-plus-beta ratio (DTABR) in frontal, central, and global regions; the mean microstate duration of classes A and B (A-MMD, B-MMD); the mean frequency of microstate D (D-MFO); and the mean coverage of microstate A (A-MC). The random forest model demonstrated the highest performance (AUC = 0.91, accuracy = 0.83, specificity = 0.88, Brier score = 0.12), alongside satisfactory calibration and a positive net clinical benefit. The model was further validated on an independent external cohort (n = 42), showing robust predictive performance (AUC = 0.97, accuracy = 0.90). An accessible web tool was created for individualized risk prediction (https://eeg-predict.streamlit.app/).

Discussion: The findings suggest that an interpretable EEG-based ML model can provide accurate early screening of PSCI. Integration of this approach into clinical workflows may support personalized rehabilitation strategies and optimize post-stroke care. Future studies are warranted to validate the model in larger, multicenter cohorts.

Introduction: Age-related changes in brain signal complexity are associated with cognitive decline and reduced neural adaptivity in older adults. Exergaming offers a promising prophylactic intervention combining physical and cognitive training. The aim of the present study was to assess how exergaming alters the temporal trajectory of brain signal complexity at rest and during gameplay in older adults.

Methods: Twenty-eight healthy older adults participated in a 4-week exergaming intervention. Electroencephalography was recorded using 64 electrodes at rest (pre- and post-intervention) and during exergaming (pre-, mid-, and post-intervention). Brain signal complexity was quantified using multiscale entropy across 64 time scales on preprocessed signals.

Results: Post-intervention resting-state analysis revealed significant reductions at fine and increases at coarse scales in frontal, central, and posterior entropy. During gameplay, entropy declined widespread by mid-intervention, particularly at coarse scales over frontal, central and temporal regions. From mid- to post-intervention, the decline narrowed leaving a net pre-to-post reduction concentrated at coarse scales in these regions.

Discussion: Resting-state changes indicated a shift toward a younger brain profile, characterized by a transition from age-related increases in local processing to enhanced distributed processing, which may potentially mitigate the rise in neural modularity associated with aging. During gameplay, brain signal complexity decreased in week 2, followed by a modest change by week 4, consistent with the framework in which complexity initially streamlines and then adjusts toward a task-specific optimum. These findings suggest that exergaming can beneficially modulate brain complexity in older adults, offering the potential to reduce age-related neural decline and support healthy brain aging.

Aging is associated with changes in sensorimotor control that contribute to functional decline, mobility limitations, and increased fall risk. Traditional motor assessments often rely on subjective measures, highlighting the need for objective, quantitative tools. We developed three robot-based tasks using the vBOT planar manipulandum to evaluate sensorimotor performance in healthy young (<35 years) and older (>60 years) adults. These tasks uniquely combined bimanual control and altered dynamic conditions to assess age-related differences. The first task required bimanual coordination to control a virtual 2D arm over 400 center-out and return trials, targeting de novo motor learning. The second task involved unimanual reaching with the dominant hand, consisting of 200 trials in a null-field condition followed by 200 trials with object-like dynamic forces. The third task similarly began with 200 null-field trials and then introduced a viscous force field in the final 200 trials, with fast movements rewarded to encourage peak performance. This task also enabled comparison between dominant and non-dominant arms. All tasks detected age-related performance differences, with the viscous resistance task proving most sensitive to declines in movement speed, force generation, and response onset time. Scoring mechanisms that encouraged brisk performance amplified these effects. Across tasks, older adults generally moved more slowly, took longer to complete tasks, exerted lower peak forces, and had longer response onset times. However, some older participants performed comparably to younger individuals. In the third task, dominant arm performance consistently exceeded that of the non-dominant arm. These results demonstrate that robot-based tasks can sensitively quantify age-related sensorimotor decline and may offer valuable metrics for clinical assessment and monitoring.

Background: Psychosis, including delusions and hallucinations, is a significant neuropsychiatric symptom in Alzheimer's disease (AD) associated with poor prognosis. The relationship between psychosis and AD pathology remains controversial. This study investigates the role of AD pathology in mediating the association between psychosis and cognitive impairment.

Methods: Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We included individuals with a Clinical Dementia Rating (CDR) score of 0.5 or higher. Among a total of 833 individuals, 96 individuals with psychosis were matched to 192 individuals without psychosis using propensity scores based on age, sex, education level, and follow-up duration. Baseline cognitive performance was assessed using composite memory scores (ADNI-MEM) and executive function scores (ADNI-EF). AD pathology was measured using baseline cerebralspinal fluid (CSF) levels of β-amyloid_1-42 (Aβ_1-42), hyperphosphorylated-tau₁₈₁ (p-tau₁₈₁), and total tau. Logistic regression was performed to evaluate the association of psychosis with baseline cognitive performance and CSF biomarkers. Mediation analysis was conducted to assess whether AD biomarkers mediate the relationship between cognitive impairment and psychosis.

Results: Psychosis was significantly associated with worse ADNI MEM score (β = -0.622, p = 0.013) and worse ADNI EF score (β = -0.516, p = 0.003), and lower CSF Aβ_1-42 levels (β = -0.009, p = 0.007). No significant associations were found with p-tau₁₈₁ or total tau levels. Mediation analysis revealed that low CSF Aβ_1-42 levels mediated the relationship between cognitive impairment and psychosis.

Conclusion: These findings suggest that amyloid pathology may mediate the effect of baseline cognitive impairment on psychosis during disease in AD, highlighting a potential pathological link between cognitive decline and psychotic symptoms.

Introduction: Perioperative neurocognitive disorder (PND) describes a range of cognitive impairments associated with surgery and anaesthesia, often driven by neuroinflammation. This study explored a novel adult mouse model, in which preoperative subclinical infection, induced by low-dose lipopolysaccharide (LPS) in combination with surgery, led to cognitive dysfunction in adult mice.

Methods: Adult male C57BL/6J mice were treated with 0.75 mg/kg LPS two hours before undergoing tibial fracture fixation or appendicectomy. Spontaneous activity and anxiety-like behaviours were tested by open field test. Cognitive outcomes were evaluated using the novel object recognition test and morris water maze. Inflammatory markers and synaptic proteins in the hippocampus were analysed through ELISA, RT-qPCR, and Western blot, while proteomics provided deeper insights into molecular changes.

Results: We found that preoperative LPS sensitised the immune system, leading to heightened neuroinflammation and microglial activation after surgery. This was accompanied by memory and learning impairments. Key synaptic proteins, including PSD-95, GAP-43, SYN and mature BDNF, were significantly reduced, indicating disrupted synaptic function. Proteomics revealed changes in pathways related to immune responses, synaptic organisation, and energy metabolism, providing a potential molecular basis for these cognitive deficits.

Discussion: This study provided a practical adult mouse model for PND, demonstrating that low-dose LPS followed by surgery induced an inflammatory response, leading to postoperative impairments in learning and memory.

Purpose: To explore neurodynamic bases underlying subjective cognitive decline (SCD) based on edge-centric functional network.

Methods: 211 SCD patients and 210 healthy controls (HC) were recruited from the Alzheimer's Disease Neuroimaging Initiative. Edge time series (ETS) were obtained based on resting-state functional magnetic resonance data. The top 10% co-fluctuation signals of all time points in ETS were extracted to construct the high-amplitude frame networks, and the co-fluctuation signals from the remaining time points were used to construct the low-amplitude frame networks. In both network states, the graph theory and network-based statistics (NBS) analyses were used to compare SCD and HC. The correlation of the imaging indicators with cognitive scores and apolipoprotein E (APOE) ε4 genes was performed by Spearman correlation analysis.

Results: SCD exhibited lower peak amplitude and longer trough-to-trough duration (TTD) compared to HC. In both network states, the normalized clustering coefficient, normalized characteristic path length, small-worldness, and global efficiency of SCD were significantly reduced, and the altered nodal centralities of SCD predominantly exhibited a decreasing trend. However, the high-amplitude frame network identified more altered brain regions compared to the low-amplitude frame network. Furthermore, a SCD-related subnetwork was found in the high-amplitude frame network, which was composed of 11 brain regions and 13 edges. TTD was positively related to the number of APOE ε4 genes; the normalized characteristic path length, the betweenness centrality of right postcentral gyrus, and the connection between bilateral angular gyrus were correlated with cognitive scores.

Conclusion: Our findings demonstrate that the edge-centric network framework reveals details of brain network alterations in SCD through different perspectives, and these alterations hold potential as novel biomarkers for SCD.

Alzheimer's disease (AD), characterized by progressive cognitive decline, memory impairment and behavioral disturbances, is the most common form of dementia, and no disease-modifying treatments are available to halt or slow its progression. Amyloid-beta (Aβ) is suggested to play a pivotal role in the pathogenesis of AD, and enhancing the clearance of Aβ from the brain has emerged as a major research direction. As the primary receptor for Aβ clearance at the blood-brain barrier (BBB), low-density lipoprotein receptor-related protein 1 (LRP1) plays a crucial role in regulating Aβ transport and metabolism. Understanding the mechanisms through which LRP1 functions, as well as the factors that influence its activity is essential for enhancing Aβ clearance from the brain and developing targeted therapeutic strategies for Alzheimer's disease. In this review, we introduce the transport of Aβ across the BBB, followed by a discussion of the basic structure and function of LRP1 and its role in AD progression. Then, we summarize factors affecting LRP1 function and current advances in LRP1-targeted therapies. Finally, we explore the potential of LRP1 as a therapeutic target for AD. So, LRP1 may be a central modulator of Aβ dynamics and a clinically actionable target for treatment of Alzheimer's disease.