The brain undergoes major structural and functional changes from childhood through adolescence. Research suggests that neurodevelopment happens in a hierarchical way, meaning different brain regions and networks mature at different rates. However, less is known about how the brainā€™s intrinsic spatiotemporal propagationsā€”patterns showing how activity spreads across the brain over timeā€”develop during this period. This study examined how these activity patterns change from childhood to early adulthood.

Using a recently developed method that measures time-lagged dynamic propagations, the researchers analyzed how brain activity travels along three major axes of brain organization: the sensoryā€“association (S-A) axis, which connects basic sensory regions to higher-order thinking areas; the task-positive to default network (TP-D) axis, which reflects shifts between attention-focused networks and the default mode network active during rest and internal thought; and the somatomotorā€“visual (SM-V) axis, which links movement and visual processing regions. The results showed that these propagation patterns gradually become more adult-like over development. As children mature, they spend more time in S-A and TP-D propagation states, while the occurrence of SM-V propagation states decreases.

Importantly, top-down propagations along the S-A axisā€”meaning activity flowing from higher-order cognitive regions to sensory regionsā€”increased with age and were better predictors of cognitive performance than bottom-up propagations, which flow from sensory areas upward. These findings were replicated in two independent datasets, the Human Connectome Project Development cohort and the Nathan Kline Institute Rockland Sample, supporting the robustness and generalizability of the results. Overall, the study provides new insight into how large-scale functional brain dynamics develop during youth and how these changes support cognitive abilities.

Fig. 1: Spatiotemporal propagation patterns and their neurodevelopmental change from children to early adulthood.

AĢżThe first three propagation patterns derived from the reference cohort (HCP-A), represent group-level reference propagation patterns. Each row displays a full propagation cycle for the recurring spatiotemporal patterns: sensorimotor to association (S-A), task-positive to default mode networks (TP-D), and somatomotor to visual networks (SM-V). The patterns are depicted through their temporal phase cycle, ranging from 0 to 2Ļ€.ĢżBĢżExplained variance ratios of the first six propagation patterns from CPCA. The light blue line represents the youth cohort (HCP-D) and the dark line represents the reference adult cohort (HCP-A).ĢżCĢżBetween-cohort similarity matrix showing the pairwise Pearsonā€™s correlation of the propagation patterns across youth (HCP-D) and adult (HCP-A) propagation patterns. We also confirmed cross-cohort similarity using HCP Young Adult cohort (Nā€‰=ā€‰892, age 21-35, Figure.Ģż)].ĢżDĢżReliability of propagation patterns, assessed by the discriminability for HCP-D and HCP-A cohorts.ĢżEĢżAge-related similarity of propagation patterns to adult reference. Dots represent the spatial correlations of the propagation pattern between individuals in the youth cohort and the group-level adult reference. The regression line illustrates the developmental trend across age. Age effect was assessed using a Spearman correlation, withĢżpĢżvalues adjusted for multiple comparisons using the false-discovery-rate (FDR) correction. Significant age-related increases were observed for the S-A (pFDR <0.001), TP-D (pFDR <0.001) and SM-V (pFDR = 0.002) propagation patterns. Statistical significance is denoted by asterisks (*: pFDR <0.05).ĢżFĢżAge prediction using the first three dynamic patterns. A combination of the first three dominant propagation patterns in the PLSR model predicts age with a Spearmanā€™s correlation Ļ of 0.80 and a mean absolute error (MAE) of 1.87 years.

In brain imaging research, small changes in functional MRI (fMRI) signals caused by breathing and heart rate have often been dismissed as ā€œnoise.ā€ However, these fluctuations actually contain important information about how blood vessels in the brain and the bodyā€™s autonomic functions (like heart and breathing control) work.

In this study, we used these physiological signals to examine how brain function changes with age, using data from the large Lifespan Human Connectome Project Aging study. We found that as people get older, their fMRI signals show slower responses linked to breathing, faster responses linked to the heartbeat, and stronger connections between brain and heart signals. Importantly, these changes become especially noticeable after the age of 60, suggesting that declining vascular health and changes in autonomic function play a key role in aging.

We also tested whether these fMRI patterns might be influenced by age-related changes in brain structure, blood flow, or alertness during scans. Overall, our findings highlight major age effects in fMRI signals tied to heart and breathing activity. This work shows that resting-state fMRI can be used not only to study brain connectivity but also to reveal new markers of brain physiology that could help track vascular and autonomic changes with aging.

Fig. 1: Age effects on the spatiotemporal patterns of RV-coupled fMRI dynamics.

aĢżThe cross correlation between the global cortical fMRI signal and RV (positive lag values suggest that fMRI signals lag RV) for different age groups, with shade denoting the standard errors across subjects.ĢżbĢżIntra-cortical distributions of region-specific fMRI signal lag relative to RV (based on the Schaefer 300-parcel atlas). Regions exhibiting statistically significant between-group temporal lag differences were displayed at the bottom (ā€œAging IIā€‰>ā€‰Iā€, FDRā€‰<ā€‰0.05).ĢżcĢżTissue-type specific temporal lags relative to RV for each age group. Error bars indicate the standard errors of RV-fMRI temporal lags across subjects, and the shaded gray area highlights ROIs that exhibited statistically significant between-group differences (FDRā€‰<ā€‰0.05). ROI labels are shown at the bottom.

]]> /valiant/2025/08/25/the-physiological-component-of-the-bold-signal-impact-of-age-and-heart-rate-variability-biofeedback-training/ Mon, 25 Aug 2025 14:44:59 +0000 /valiant/?p=4966 Song, Richard, Min, Jungwon, Wang, Shiyu, Goodale, Sarah E., Rogge-Obando, Kimberly K., Yang, Ruoqi, Yoo, Hyunjoo, Nashiro, Kaoru, Chen, Jingyuan E., & Mather, Mara M. [2025]. ā€œā€ Imaging Neuroscience, 3, IMAG.a.99.

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 brainā€™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 rateā€“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]ā€”a non-invasive breathing-based training that improves natural heart rate rhythmsā€”could affect these brain-body connections. In older adults, HRV-BF shifted heart rateā€“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.

Understanding how the human brain changes throughout lifeā€”from infancy to old ageā€”is essential for learning about childhood development, aging, and brain disorders. In this study, we aimed to provide detailed information about the brainā€™s white matter pathways by examining their tiny structures (microstructure), larger organization (macrostructure), and the shape of the brainā€™s outer layer (cortex) connected to these pathways. We analyzed four large, high-quality datasets that included 2,789 brain scans from people aged 0 to 100 years, using advanced imaging techniques. We found that different features of white matter pathways develop and decline at different times and rates, depending on the brain area and pathway type. We also discovered connections between various features that could help explain biological changes at different life stages. Additionally, the patterns of change with age were unique for each feature, and the way white matter changes during development is strongly linked to how it changes during aging. Overall, this study provides important baseline data about white matter pathways in the human brain, which can help future research on normal brain development as well as brain diseases.

Fig 1

Microstructural, macrostructural, and cortical features associated with each of 63 white matter bundles.

Brain scans can be used to estimate a person’s “brain age,” which may be older or younger than their actual age. A larger difference between brain age and actual age can provide early warning signs of neurodegenerative diseases like Alzheimerā€™s, potentially allowing for earlier diagnosis and prevention. One type of brain scan, called diffusion MRI (dMRI), is especially useful for this because it can detect very subtle changes in the brainā€™s structure that may happen before more obvious signs appear. However, dMRI captures both large-scale (macrostructural) and small-scale (microstructural) features of the brain, and itā€™s unclear whether current models for estimating brain age from dMRI are focusing on the small-scale changes that matter most for early detection.

To better isolate the microstructural information, this study developed a new approach that reduces the influence of macrostructural features by aligning all brain scans to a common reference template. The method was tested using imaging data from 13,398 people across 12 different datasets. The researchers compared this new microstructure-focused dMRI brain age model to several other models based on T1-weighted MRI, a common type of scan that primarily captures macrostructural features.

They found that the dMRI-based brain age and T1-based brain age showed different patterns depending on the stage of disease. For people who were transitioning from normal cognitive function to mild cognitive impairment (MCI), the dMRI brain age appeared older than the T1-based brain age. In contrast, for those already diagnosed with Alzheimerā€™s disease, the dMRI brain age appeared younger. Models based on T1-weighted MRI generally performed better at identifying who had Alzheimerā€™s, but the dMRI-based brain age may be more helpful in identifying early, subtle changes that happen before symptoms begin.

Fig 1

Brain age estimation frameworks have proven effective for using affinely aligned brain images to identify common patterns of aging, with deviations from these patterns likely indicating presence of abnormal neuropathologic processes. A common theme of existing brain age estimation methods has been using T1w MRI, denoted as ā€œGM ageā€ in the first row. Among them, there have been many innovations in network design, such as DeepBrainNet (DBN) (Bashyam et al., 2020) and the 3D convolutional neural network of TSAN (Cheng et al., 2021). T1w MRI lacks detail in white matter (WM). Here, we take the two most commonly used modalities for characterizing WM microstructure, fractional anisotropy (FA), and mean diffusivity (MD), and we evaluate brain age estimation in two contexts. First, we examine the direct substitution of FA and MD for T1w image, which we denote as ā€œWM age affineā€ in the second row. A substantial amount of macrostructural differences is still present in WM age affine, notably ventricle enlargement. To isolate the microstructural changes, we apply non-rigid (deformable) registration into template space to mitigate the macrostructural changes and produce the ā€œWM age nonrigidā€ in the third row. We explore the relative timing of changes in these brain age variants and their relative explainability in the context of mild cognitive impairment. Throughout the paper, we adhere to a consistent color scheme when visualizing results from different brain age estimates within the same plot to facilitate easier visual inspection. Specifically, we use red to represent GM ages, blue for WM age nonrigid, and purple for WM age affine.

Understanding how the brain changes as we ageā€”especially in conditions like Alzheimerā€™s diseaseā€”is an important area of research. One part of the brain that hasnā€™t been studied as much is the white matter, which acts like a network of ā€œwiresā€ connecting different brain areas. This large study looked at how the structure of white matter relates to thinking and memory skills in older adults, including those with Alzheimerā€™s.

Researchers analyzed data from 9 separate studies, including nearly 4,500 adults aged 50 and older. Participants had brain scans using a technique called diffusion MRI (dMRI), along with memory and thinking tests over time. Most participants were cognitively healthy, while some had mild memory problems or Alzheimerā€™s dementia. The study focused on a specific feature of white matter called “free water” (FW), which can indicate damage or degeneration.

They found that higher levels of FW in white matterā€”especially in regions connected to memory like the cingulum and fornixā€”were strongly linked to worse memory and faster cognitive decline. These changes were even more noticeable in people who had other signs of Alzheimerā€™s, such as brain shrinkage, a genetic risk factor called APOE Īµ4, or positive tests for amyloid buildup (a hallmark of Alzheimerā€™s).

Overall, the study shows that changes in white matterā€”particularly FWā€”are an important piece of the puzzle in understanding memory loss and aging. These findings suggest that future brain studies should pay close attention to FW and highlight the importance of brain regions like the cingulum and fornix in Alzheimerā€™s-related decline.

Figure 1. ĢżCohort Characteristics and Data Harmonization

A, Participants were drawn from 9 well-established cohorts, including 3213 cognitively unimpaired (CU) individuals, 972 with mild cognitive impairment (MCI), and 282 with Alzheimer disease (AD) at baseline. B, The study also incorporated longitudinal data across 9208 cognitive sessions, spanning up to 13 years of follow-up. Longitudinal ComBat harmonization was applied to all imaging features to account for variability across imaging batches. C and D, Associations are shown between cingulum free-water (FW) and memory performance, using both raw (C) and harmonized (D) FW data, with points and lines color coded by imaging batch. Harmonized data were used across all analyses. ADNI indicates Alzheimerā€™s Disease Neuroimaging Initiative; BIOCARD, Biomarkers of Cognitive Decline Among Normal Adults; BLSA, Baltimore Longitudinal Study of Aging; MAP, Rush Memory and Aging Project; MARS, Minority Aging Research Study; NACC, National Alzheimerā€™s Coordinating Center; ROS, Religious Orders Study; VMAP, Vanderbilt Memory and Aging Project; WRAP, Wisconsin Registry for Alzheimerā€™s Prevention.

Ģż

This study looked at how certain combinations of genes, known as APOE and TOMM40-‘523 haplotypes, are linked to the health of brain connections in areas important for memory and thinking. Researchers focused on two racial groupsā€”non-Hispanic Black and White adultsā€”to see if the relationships between these genes and brain structure were different across groups.Ģż

They used brain scans to measure the quality of white matter, which helps different parts of the brain communicate. The researchers then compared people with different genetic combinations within each racial group.Ģż

In Black individuals who had a specific version of the APOE gene (called Īµ4+) and carried one copy of a gene variation called ā€˜523-S, the white matter in memory-related brain areas appeared healthier compared to those without that ā€˜523-S variation. In contrast, for White individuals with a different APOE version (Īµ3/Īµ3), having two copies of the ā€˜523-S variation was linked to signs of less healthy white matter.Ģż

Overall, the study suggests that this particular gene variation (‘523-S) might affect brain aging differently depending on both genetic background and race. For some, it may increase risk, while for others it could have a protective effect.Ģż

FIGURE 2Ģż

Residualized beta estimates of ā€˜523-S copy number and WMM metricsacross limbic system tracts in non-Hispanic White Īµ3/Īµ3 participants, adjusted by age, sex, education, and clinical diagnosis.Ģżpāˆ’valuesĢż<Ģż0.05 are shown withĢż*,Ģżpāˆ’valuesĢż<Ģż0.01 are shown withĢż**. WMM, white matter microstructure.

Ģż

Cognitive and mobility decline together (dual decline) is more strongly linked to the risk of developing dementia than cognitive decline alone. However, itā€™s still unclear whether this condition is related to certain patterns of brain shrinkage, damage to white matter (the tissue that connects brain regions), or other brain changes. Ģż

In the Baltimore Longitudinal Study of Aging, we studied participants with and without dual decline to compare changes in brain images, white matter damage, and specific biomarkers over up to 13 years. We looked at brain atrophy, white matter issues, and proteins in the blood that are linked to brain health, including markers for neurodegeneration like GFAP, NfL, amyloid beta, and tau. Ģż

Our results showed that people with dual decline experienced faster shrinkage in brain regions related to memory, movement, and language. Those with only mobility problems had brain shrinkage in one specific area, while those with memory decline had a different pattern. Dual decline also showed damage to white matter in several brain areas and a greater decline in the amyloid beta ratio, which is a sign of Alzheimerā€™s disease. Additionally, there were higher levels of proteins linked to brain damage in the blood. Ģż

In conclusion, individuals with dual decline may be at greater risk for brain shrinkage, white matter damage, and specific changes in biomarkers that could lead to dementia. This highlights the importance of dual decline as a potential signal of underlying brain and blood changes associated with dementia.Ģż

FIGURE 1Ģż

Flow chart of sample selection criteria. AD, Alzheimer’s disease; AĪ²42/40, amyloid beta 42/40 ratio; DTI, diffusion tensor imaging; GFAP, glial fibrillary acidic protein; MRI, magnetic resonance imaging; NfL, neurofilament light chain; pTau181, phosphorylated tau.Ģż

Ģż

Background: Late-life depression (LLD) is linked to changes in the brain, like smaller brain volumes, and faster brain aging compared to healthy people. However, there are few studies looking at what might predict whether LLD will come back. In this study, we used a machine learning model to measure “brain age” and looked at whether it could predict the chances of LLD returning.ĢżMethods: We followed 102 people with LLD and 43 healthy individuals for two years. People with LLD who had recently recovered were included in the study. They had brain scans at the start, and we checked how their depression progressed over time. Some participants relapsed while others stayed well. We used a brain age model to see if the brain age could predict who was more likely to relapse.ĢżResults: We found no major differences in brain age between the healthy group and people with LLD, whether they were in remission or had relapsed. Brain age didn’t predict how soon someone would relapse.ĢżConclusions: Surprisingly, brain age wasn’t different between healthy individuals and those who had recovered from LLD, and it wasn’t helpful in predicting if the depression would return. This result is different from some previous studies, which found that brain age was different in older adults, but it matches research showing that structural brain changes don’t always predict who will relapse.

FigureĢż1.Ģż(A)ĢżThe association between age and brain age in the healthy control participant (HC) and late-life depression (LLD) groups had high correlation and low mean absolute error (MAE).Ģż(B)ĢżBrain age residual and brain age were not significantly different between sites (ĢģĆĄ“«Ć½¹ŁĶų [VUMC], University of Pittsburgh [PITT], University of Illinois Chicago [UIC]). Brain age residual is the value from regressing age onto brain age (BA) (i.e., BA āˆ¼ Ī² interceptĢż+ ageĢżĆ— Ī²Ģż+ residual).Ģż

Imaging findings that are inconsistent with expected results for specific chronological age ranges can serve as early indicators of neurological disorders and increased mortality risk. Estimating chronological age from structural MRI data, and identifying deviations from expected results, has become crucial for developing biomarkers sensitive to such deviations. Alongside structural analysis, diffusion tensor imaging (DTI) has shown effectiveness in detecting age-related microstructural changes in brain white matter, making it a promising modality for brain age prediction.

Although early studies have attempted to use DTI for age estimation, it remains unclear whether the success of these predictions is due to the unique microstructural and diffusivity features of DTI or the macrostructural features also present in DTI data. This study aims to develop white-matter-specific age estimation by focusing solely on microstructural features and ignoring macrostructural information when predicting age from DTI scalar images.

Two distinct methods were employed. The first method involved extracting only microstructural features from regions of interest. The second method utilized 3D residual neural networks (ResNets) to learn features directly from images that were non-linearly registered and warped to a template, minimizing macrostructural variations.

When tested on unseen data, the first method yielded a mean absolute error (MAE) of 6.11 years for cognitively normal participants and 6.62 years for cognitively impaired participants. The second method, using ResNets, achieved a MAE of 4.69 years for cognitively normal participants and 4.96 years for cognitively impaired participants. These results suggest that the ResNet model effectively captures subtler, non-macrostructural features for brain age prediction, highlighting its potential for more accurate age estimation from DTI data.