Volume changes of the limbic system and their link to age-related memory decline in healthy adults.

Modulation of fronto-parietal and autonomic activity underpins preserved working memory in middle-aged adults

Cognitive aging is a heterogeneous process, often diverging from chronological age. However, it remains unclear when early signs of cognitive decline emerge and which neurophysiological markers distinguish age-related cognitive alteration from preserved performances. This study aimed to identify early indicators of cognitive versus chronological aging. In a group of middle-aged adults (MA, n = 60), we distinguished high performers (MA_HP; n = 24; mean age ± SEM 54.2 ± 0.6-year-old) from low performers (MA_LP; n = 24; mean age ± SEM 54.3 ± 0.7-year-old) considering their scores in a visuo-spatial working memory (VSWM) task. Both MA groups were compared to young adults (YA, n = 24; mean age ± SEM = 22.7 ± 0.5-year-old). Electroencephalography (EEG) and pupillometry were recorded while participants performed the WM task. Our results showed that aging is accompanied by reduced pupil dilation peak. Yet, MA_HP exhibited prompt pupil dilation, comparable to YA, while MA_LP showed delayed and blunted pupil responses. Regarding EEG engagement index (EI), both MA groups exhibited significantly higher frontal EI than YA, while parietal EI was only increased in MA_HP compared to YA. Considering event related spectrum perturbation, aging was characterized by a loss of fronto-central and parietal theta event related depression (ERD) during the task. MA_HP distinguished themselves by parietal alpha and beta ERD during encoding and frontal theta, alpha and beta ERD during retention. Collectively, our data suggest that high WM performance in midlife depends on the flexible modulation of autonomic and cortical resources, potentially facilitating the attentional mobilization necessary for cognitive coping. These neurophysiological signatures allow clearer distinction between a chronological aging profile and a successful cognitive aging profile.

The hippocampus and cortical memory networks have an inflection point in middle childhood

Connections within a network that supports semantic memory matures early, but connectivity between this network and hippocampus does not mature until at least 7-years of age, identifies 7-years as a developmental inflection point in both hippocampal signaling and hippocampal-cortical memory interactions that may support multiple memory systems.

Stage-dependent effects of cognitive reserve on memory and brain structural integrity across the spectrum from healthy aging to Alzheimer’s disease

Introduction Cognitive reserve (CR) has been proposed as a key factor explaining inter-individual variability in cognitive performance despite comparable neuropathology. However, its role across the Alzheimer’s disease (AD) continuum remains unclear. This study investigates stage-dependent effects of CR on the relationship between memory performance and brain structural network integrity across healthy subjects (HS), individuals with subjective cognitive decline (SCD), and patients with amnestic mild cognitive impairment (a-MCI), and AD dementia. Materials and methods A total of 209 participants underwent a comprehensive neuropsychological assessment and 3T MRI. Source-based morphometry identified three grey matter structural covariance networks, involving orbitofrontal-temporal-insular regions (OTIN), precuneus-posterior cingulate cortex (PreCiN), and cingulate-hippocampal regions (CHiN). A composite memory score was derived using factor analysis. Regression and moderation models examined the predictive and moderating effects of CR (operationalized as years of education) and network integrity on cognitive performance within each group. Results OTIN and PreCiN showed progressive structural vulnerability along the AD continuum, whereas CHiN showed no significant between-group differences. Across the sample, OTIN and PreCiN integrity significantly predicted cognitive performance. In HS, CR was positively associated with memory performance independently of structural network integrity, suggesting an additive protective role of cognitive reserve in healthy aging. In the SCD group, CR was not directly associated with memory, and only limited effects emerged, indicating early alterations in reserve-related processes. In a-MCI patients, the significant interaction between CR and OTIN integrity suggested patterns consistent with compensatory mechanisms, with higher reserve supporting memory despite structural decline. In AD patients, CR and its interaction with structural networks no longer predicted cognitive outcomes, suggesting a possible exhaustion of reserve capacity. Conclusion These findings support a stage-dependent model of CR, characterized by an additive protective role in healthy aging, patterns consistent with compensatory recruitment in early cognitive decline, and a possible loss of reserve effectiveness beyond a critical neuropathological threshold. Distinct network vulnerabilities and stage-specific CR effects highlight potential windows for reserve-enhancing interventions across the AD continuum.

Individual differences in hearing decline predict region-specific brain and cognitive changes in healthy aging men

Background Cognitive decline in aging is heterogeneous and accelerates after approximately 60 years of age. Hearing decline is a common, potentially modifiable risk factor that may contribute to individual differences in cognitive aging and brain structural change. In addition to its cognitive consequences, hearing loss can impair communication and contribute to reduced social participation, loneliness, and poorer quality of life. However, the extent to which longitudinal changes in hearing predict cognitive performance and regional brain structure remains unclear. Methods We assessed 51 healthy males from a longitudinal birth cohort extensively evaluated with cognitive testing and MRI across the lifespan. At ages 70 and 71 (recruited in 2024), participants completed four-frequency pure-tone audiometry (4PTA), audible contrast threshold (ACT), speech-in-noise tests (HINT), cognitive assessments, and structural MRI scans. All participants had a prior visit at age 63 ± 1 year (recruited in 2015–2018) with the same cognitive test battery. Previously, machine learning algorithm models were used to identify cognitive decline, and participants were clustered into four cognitive trajectories. These predefined clusters were used to examine whether hearing measures (4PTA, ACT, HINT) predicted cognitive outcomes and brain volume. Findings The combination of PTA and HINT together showed the strongest contribution to classifying previously defined cognitive trajectory groups (p = 0.022). An XGBoost model with SHapley Additive exPlanations (SHAP) values identified bilaterally combined temporal lobe volume as key MRI feature influencing ACT, whereas cerebellum and fourth ventricle were key MRI features influencing HINT. Interpretation These findings highlight complex hearing–cognition–brain interactions and support integrating diverse auditory measures into cognitive aging research.

Resting state brain activity and association with transfer of cognitive training gains

Background Normal aging is accompanied by cognitive decline, structural and functional brain changes. Cognitive training is a potentially effective intervention for cognitive improvement. Transfer of training gains to untrained tasks is the ultimate goal of cognitive training. However, the neural mechanisms underlying successful transfer remain underinvestigated. Objective To examine the predictive role of resting-state functional connectivity in the transfer of training gains. Methods We analyzed resting-state fMRI and cognitive data of 181 healthy older adults (mean age: 68 years) who underwent a 4-week cognitive training at three study sites. The control group consisted of 54 older adults. Participants underwent neuropsychological assessments before and directly after the training, as well as 12 weeks after. We used aggregate scores representing working memory, memory and executive functions to assess transfer effects. Baseline resting-state fMRI was used to investigate functional connectivity. We used a seed-based and an independent component analysis approach to examine brain network activity. Results The majority of our participants transferred cognitive training gains successfully over a three-month period. Baseline resting-state functional connectivity within the default mode network and the central executive network did not predict transfer of training gains. Conclusions Baseline resting-state functional connectivity of large-scale networks does not appear to predict who will benefit from cognitive training in healthy older adults. These findings contribute to a better understanding of the functional brain mechanisms underlying transfer of training gains and highlight the need for larger, multi-modal neuroimaging studies to identify reliable neural predictors of cognitive training outcomes.

Different hypothalamic regions in late-onset Alzheimer's disease and aging: involvement and links with cognitive and non-cognitive features.