Aging is linked to declines in the autonomic nervous system [which controls things like heart rate and breathing], reduced coordination between brain activity and blood flow, and weaker blood vessel responses. These changes may play a role in memory loss and neurodegenerative diseases. To better understand this, we studied how aging affects the way the brain integrates signals from the heart, lungs, and blood flow.

Using two independent brain imaging [resting-state fMRI] datasets with heart and breathing measurements from younger and older adults, we found that older adults showed reduced connections between heart rate, breathing patterns, carbon dioxide levels, and the brain’s oxygenation signal [BOLD signal]. These reductions were most noticeable in brain regions that help regulate automatic body functions, such as the orbitofrontal cortex, anterior cingulate cortex, insula, basal ganglia, and white matter. Younger adults showed stronger heart rate–brain signal coupling in white matter and faster brain responses to breathing and carbon dioxide changes in gray matter.

We also tested whether heart rate variability biofeedback [HRV-BF]—a non-invasive breathing-based training that improves natural heart rate rhythms—could affect these brain-body connections. In older adults, HRV-BF shifted heart rate–brain coupling patterns to look more like those of younger adults.

These results suggest that HRV-BF may help counteract age-related declines in brain and blood vessel function. Overall, this study shows how closely linked body rhythms are to brain health and highlights a potential strategy to support brain function and preserve cognitive health as we age.

Fig 1 – Schematic for the model to determine the physiological component of the BOLD signal. [A] After detrending and normalizing heart rate and respiratory variation, the signals are convolved with CRF and RRF basis functions. For every voxel, a general linear model is used to find beta weights for each of the cardiac and respiration regressors to minimize the error from the original BOLD signal. [B] Example of an original BOLD signal [normalized to percent signal change] and the corresponding physiological component.

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This study aimed to explore how sex and the presence of a specific gene, apolipoprotein E (APOE), affect the brain’s white matter structure in relation to Alzheimer’s disease (AD). To do so, the researchers used data from nine major long-term aging studies, which included information from 4,741 participants, with an average age of 73, and 9,671 brain scans. The focus was on analyzing differences in white matter structure based on sex and whether participants carried the APOE ε4 gene, which is known to increase the risk of Alzheimer’s disease.��

The results showed clear differences in white matter structure between sexes and between those with and without the APOE ε4 gene. Women generally had lower brain tissue integrity in certain areas, and those who carried the APOE ε4 gene had more free water in their brain tissue, which can indicate changes in white matter. These findings point to significant disparities in brain structure related to both sex and the presence of the APOE ε4 gene, which may help explain some of the variations seen in Alzheimer’s disease.��

In conclusion, these results highlight the important role that sex and the APOE ε4 gene play in the health of white matter in the brain. While the findings provide valuable insights into how these factors contribute to AD, more research is needed to fully understand the reasons behind these differences and how they might influence the development of Alzheimer’s disease.��

FIGURE 1��

Forty-eight white matter tractography templates were used in the present study, and can be grouped into TC (A), association (B), projection (C), and limbic tracts (D). IFG, inferior frontal gyrus; IFOF, inferior fronto-occipital fasciculus; ILF, inferior longitudinal fasciculus; IPL, inferior parietal lobe; M1, primary motor cortex; PMd, dorsal premotor; PMv, ventral premotor; S1, primary somatosensory cortex; SLF, superior longitudinal fasciculus; SLF-TP, temporoparietal superior longitudinal fasciculus; SMA, supplementary motor area; SPL, superior parietal lobe; TC, transcallosal; UF, uncinate fasciculus.��

Alzheimer’s disease (AD) is characterized by the buildup of certain proteins in the brain and damage to brain cells, which can lead to memory and thinking problems. This study aimed to explore whether blood tests measuring these proteins could predict changes in brain network activity over time in people who do not yet show signs of cognitive decline. Researchers looked at a group of participants from the Baltimore Longitudinal Study of Aging, measuring levels of proteins related to AD (amyloid-β, tau), brain cell injury, and other factors like astrogliosis (a type of brain cell response to damage). They then compared these blood markers to brain scans to see how changes in brain connectivity were related to these biomarkers over several years.��

The study followed 490 participants (average age 65) over an average of 4 years. They found that higher levels of certain proteins (Aβ42/40, GFAP, and NfL) were linked to faster changes in brain connectivity in several brain networks, particularly in people with higher amyloid levels. These changes in brain function were more pronounced in those with abnormal amyloid buildup in the brain. In contrast, no significant changes were seen in people without amyloid buildup. Additionally, the study found that brain network activity at the beginning of the study could predict later changes in cognitive abilities, such as memory and verbal skills.��

In conclusion, for cognitively healthy individuals, certain blood markers can predict future changes in brain network activity, especially in those with higher amyloid levels. These changes in brain function could potentially contribute to future cognitive decline.��

This scoping review systematically examines the relationship between extreme weather events (EWEs) and adverse health outcomes in short-stay patients undergoing post-acute care (PAC) and long-stay residents in nursing homes (NHs). The review included 10 retrospective cohort studies, primarily focused on hurricanes in the Southern United States, with one study addressing flood risk in North Dakota. The studies mainly investigated the effects of hurricanes on long-stay NH residents and their associations with increased 30- and 90-day mortality rates, unplanned hospitalizations, and changes in cognitive impairment.

The findings showed a consistent association between hurricane exposure and elevated mortality risks within 30 and 90 days, especially for long-stay NH residents and those in hospice care. Short-stay PAC patients also experienced higher hospitalization rates following hurricane exposure. The review highlights the need for future research to evaluate the impact of other types of EWEs beyond hurricanes, across broader geographic areas, and with a focus on longer-term health outcomes, associated costs, and potential disparities affecting vulnerable NH populations as climate-related EWEs become more frequent and severe.

This study investigates the variability in diffusion tensor imaging (DTI) data, particularly when data are merged from multiple sites, which is crucial for large-scale analyses. DTI measures can be affected by spatially varying and correlated noise, making it important to understand how different factors—like physiology, subject behavior, and scanner interaction—impact the reliability of the results. The researchers focused on characterizing the sources of variance in DTI metrics in different brain regions to improve the accuracy of future analyses.

Using data from 1,035 subjects, aged 22 to 103, in the Baltimore Longitudinal Study of Aging, the study analyzed how DTI variance changes over time and across multiple factors. Each subject had up to 12 longitudinal DTI scans, and the authors examined how factors such as age, scan interval, motion, sex, and session order affected DTI variance in different regions of the brain. They found that the impact of these factors was complex and varied across regions. For example, the time between scans was associated with increased variance in some areas (like the cuneus and occipital gyrus) but decreased variance in others (such as the caudate nucleus). Additionally, males showed higher variability in specific regions, and head motion had a mixed impact on DTI variance across different regions.

The findings highlight the need for researchers to consider the variability in DTI metrics when analyzing data and planning studies. By accounting for these regional variations in variability, researchers can improve the accuracy and reliability of DTI-based analyses, especially in large, multi-site studies. This work also emphasizes the importance of including variance estimates in data sharing to enhance the quality of future research.

A study from the Baltimore Longitudinal Study of Aging investigated the link between skeletal muscle mitochondrial function and its effects on cognitive and mobility outcomes in older adults. Analyzing data from 596 participants, the research found that better mitochondrial function in muscles, indicated by faster post-exercise recovery rates, was associated with improved cognitive functions, particularly in psychomotor speed, and enhanced mobility, including walking speed. Additionally, muscle fat infiltration and certain inflammation markers were found to mediate these relationships, suggesting that both fat in muscles and inflammation could play roles in how muscle health affects cognitive and physical functions. These findings point to potential interventions targeting mitochondrial function to improve both cognitive and mobility outcomes in aging populations.

A study examining cognitive decline in aging has highlighted the significant role of changes in the brain’s white matter, particularly in how it functions in relation to blood oxygen levels. Utilizing functional magnetic resonance imaging (fMRI), researchers analyzed the blood oxygenation level-dependent (BOLD) signals within white matter to explore their relationship with cognitive deterioration as people age. The study involved breaking down white matter into functional hubs using independent component analysis, a method that helps map out different areas based on their activity patterns during rest. The team then used these data to create a network graph, enabling them to measure and understand how different white matter regions contribute to brain function. Their findings indicate that changes in these BOLD signals in specific white matter areas can mediate the effects of aging on cognitive abilities, suggesting that the functional integrity of white matter is crucial for maintaining cognitive health in older adults. This research opens up new possibilities for targeting cognitive decline interventions more effectively.

A study involving participants from the Baltimore Longitudinal Study of Aging examined how physical activity levels at different stages of life affect health outcomes in later years. Researchers tracked participants’ exercise habits from adolescence through their current age and found that past physical activity levels were closely linked to their activity in the same decade as measured by questionnaires and devices like accelerometers. They developed a pattern of life course physical activity (LCPA) by ranking participants based on their reported exercise intensity over the years. This pattern showed that lifetime physical activity consistently correlates with various health markers such as cardiovascular fitness (peak VO2), blood sugar levels, muscle mass and density, body fat, walking speed, and overall physical performance, even after adjusting for current exercise habits. The findings highlight the lasting impact of maintaining regular physical activity throughout life, beyond just the benefits seen from recent exercise.

A study from the ������ý���� Memory and Aging Project explored how enlarged perivascular spaces (ePVS) in the brain, which are associated with vascular health, interact with genetic factors—specifically, the presence of the apolipoprotein (APOE)-ε4 allele, a genetic variant linked to Alzheimer’s disease risk—on cognitive functions. Analyzing brain MRI scans and cognitive performance data from 326 participants, researchers found that having more ePVS was associated with worse cognitive performance in naming and executive functions, particularly among those carrying the APOE-ε4 allele. Interestingly, while the impact of ePVS on cognition was more pronounced in APOE-ε4 carriers initially, over time, the negative effects of ePVS on cognition were stronger in non-carriers. This suggests that APOE-ε4 carriers might experience earlier cognitive decline influenced by other factors related to their genetic risk, overshadowing the impact of ePVS as they age.