Multiple object tracking (MOT) is a core function of dynamic visual attention that relies on the ability to simultaneously monitor several moving objects. Although MOT performance is known to decline with age, and to depend on efficient oculomotor strategies, how these processes interact across the adult lifespan and under degraded visual input remains poorly understood. Here, we examined age-related differences in MOT under normal and gaze-contingent viewing conditions simulating central and peripheral visual field loss. Sixty participants aged 20–80 years completed a MOT task while eye movements were recorded, enabling characterization of performance and oculomotor behavior across five viewing conditions. Behavioral results revealed a continuous decline in tracking performance across adulthood, indicating a graded rather than categorical effect of age. Performance was strongly reduced by visual-field restrictions, with the largest impairments under central vision occlusion. Eye-tracking analyses showed that better performance was associated with greater reliance on centroid-based gaze strategies, consistent with distributed monitoring of target configurations. Critically, older adults relied more on focal, target-based tracking under conditions simulating peripheral vision loss, and less on centroid-based strategies; this shift was associated with poorer performance. In contrast, oculomotor behavior during full-field viewing was largely preserved across age. Together, these findings suggest that age-related differences in multiple object tracking are associated with combined sensory, attentional, and oculomotor mechanisms. Beyond reduced performance, these differences are also associated with systematic changes in visual sampling strategies during dynamic tracking.
Resting-state electroencephalography is increasingly proposed as a scalable marker of healthy brain ageing, yet its interpretability depends on distinguishing stable individual characteristics from condition-dependent measurement effects. This secondary analysis of an open lifespan dataset examined periodic alpha activity, alpha-peak detectability, aperiodic spectral structure, short-term repeatability, and longitudinal change under eyes-open and eyes-closed conditions before and after an extended cognitive battery. Eye state strongly organised both whether a posterior alpha peak could be detected and the values of periodic and aperiodic measures. Age-related trajectories were prominent for the aperiodic exponent and full-sample alpha activity, whereas evidence that age modified post-battery change was weak. Across follow-up, detectable alpha frequency and the aperiodic exponent showed mean decreases, while alpha power was comparatively stable. Level measures demonstrated moderate-to-high repeatability and rank-order stability, but post-battery and difference-in-differences change scores were considerably less reliable. The findings position resting EEG as a condition-dependent ageing phenotype rather than a unitary baseline. They further show that peak detection, recording context, attrition, and the reliability of change must be modelled explicitly when resting EEG is used for lifespan inference or normative applications.
This study aims to identify distinct clinical and biological subtypes of Mild Cognitive Impairment (MCI) and their progression stages using a data-driven multimodal approach, aimed at improving the stratification and prediction of conversion to Alzheimer’s disease (AD). We applied the Subtype and Stage Inference (SuStaIn) algorithm to a multimodal dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to identify distinct MCI subtypes and their progression stages on 558 patients classified as MCI amyloid-positive. Our model incorporated T1-weighted structural MRI (measuring cortical, subcortical, and hippocampal volumes), CSF biomarkers (Aβ1–42 and p-TAU), APOE-ε4 status, and a comprehensive set of neuropsychological scores. We delimited four subtypes, which two exhibited higher risk for conversion to AD. The remaining two were characterized by prominent ventricular enlargement and a significant association with geriatric depression, respectively. Predictive performance was driven mainly by the class of input features: a multimodal model using individual biomarkers alone achieved the best discrimination (AUC 0.865; accuracy 0.810), and adding SuStaIn subtype and stage did not improve it. However, subtype and stage in isolation (two variables rather than sixteen) reached an AUC of 0.811 with the highest recall observed (0.867), statistically indistinguishable from the full model. Thus, applying SuStaIn to multidomain data successfully disentangles the phenotypic heterogeneity of MCI. By separating temporal disease progression (staging) from inherent trait differences (subtyping), this framework provides a more refined diagnostic tool for patient stratification and early intervention in the AD continuum.
