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BACKGROUNDPhysical resilience with age is considered a key feature of healthy aging, but current understanding of the neural contributions to resilience is limited. Additionally, few methods exist to identify physical resilience and observe the mechanisms through which resilience manifests.METHODSTo address these gaps, we used data from 189 participants from the Brain Networks and Mobility (B-NET) study who completed the short physical performance battery (SPPB) as well as its expanded version (eSPPB), magnetic resonance imaging (MRI), and functional MRI (fMRI). Functional brain networks were generated using graph theory methods. We grouped participants based on SPPB scores (RESULTSHigher SPPB scores were associated (pCONCLUSIONSIncreased connectivity between SMN and ACC may be a marker of physical resilience within the brain.

Imaging markers of cerebrovascular disease and Alzheimer’s disease (AD) are implicated in mobility impairment in older adults, but few studies have examined these relationships longitudinally in a racially-diverse population-based sample. At Visit 5 (2011–13) of the ARIC Study, 1859 participants had usual pace gait speed (cm/s) assessed and brain MRI (mean age = 76.3, 28.5% Black) and PET (n = 343; mean age = 75.9, 42.6% Black) measures including total/regional brain volume (cm(3)), white matter hyperintensities (WMH; cm(3)), infarcts (present/absent), microbleeds (count) and global beta-amyloid (Aβ). Participants returned at Visit 6 (n = 1264, 2016–17) and Visit 7 (n = 1108, 2018–19) for follow-up gait speed assessments. We used linear regression to estimate effects of baseline infarct presence, higher microbleed count, and a one interquartile range (IQR) poorer measures of continuous predictors (−1 IQR total brain volume, temporal-parietal lobe meta region of interest(ROI); +1 IQR WMH volume, global Aβ SUVR) on cross-sectional gait speed and change in gait speed adjusting for age, sex, education, study site, APOE e4, estimated intracranial volume, BMI, and cardiovascular risk factors. Cross-sectionally, slower gait speed outcome was associated with higher WMH volume, −3.38 cm/s (95%CI:−4.71, −2.04), infarct presence, −5.60 cm/s (−7.69, −3.51), microbleed count, −2.20 cm/s (−3.20, −0.91), smaller total brain volume, −9.26 cm/s (−12.1, −6.43), and smaller temporal-parietal lobe ROI −6.28 cm/s (−8.28, −4.28). Longitudinally, faster gait speed outcome decline was associated with higher WMH volume, −0.27 cm/s/year, (−0.51, −0.03) and higher global Aβ SUVR, −0.62 cm/s/year (−1.20, −0.03). Both cerebrovascular and AD pathology may contribute to mobility decline commonly seen with aging.

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

Brain pathologies are increasingly understood to confer mobility risk, but the malleability of functional brain networks may be a mechanism for mobility reserve. In particular, white matter hyperintensities (WMH) are strongly associated with mobility and alter functional network connectivity. To assess the potential role of brain networks as a mechanism of mobility reserve, 116 participants with MRI from the Brain Networks and Mobility Function (B-NET) were categorized into 4 groups based on median splits of SPPB scores and WMH burden: Expected Healthy (EH: low WMH, SPPB>10, N=45), Expected Impaired (EI: high WMH, SPPB10, N=24), Unexpected Impaired (EI: low WMH, SPPB

Declining mobility is associated with increased accumulation of white matter hyperintensities (WMH). However, a high WMH burden is not always accompanied by impaired mobility. Our previous work demonstrates that some variance in mobility may be explained by brain network connectivity. Here, we extended this work by measuring WMHs and brain networks in older adults participating in a lifestyle intervention. The Short Physical Performance Battery (SPPB) and resting state functional magnetic resonance imaging (fMRI) were collected before and after a 5-month caloric restriction plus aerobic exercise intervention in 57 obese, sedentary adults aged 65-78. Participants were categorized based on median splits of baseline SPPB scores and WMH burden: Expected Healthy (EH: low WMH, SPPB≥11, n=16), Expected Impaired (EI: high WMH, SPPB≤10, n=17), Unexpected Healthy (UH: high WMH, SPPB≥11, n=12), and Unexpected Impaired (UI: low WMH, SPPB≤10, n=12). Graph theory-based methods were used to characterize brain networks and compare the four groups. At baseline, the somatomotor cortex community structure (SMC-CS) was less consistent in EI (p=0.05) and UI (p=0.23) compared to EH. The EI (mean=1.25, p=0.003) and UI (mean=1.57, p=0.001) significantly improved their SPPB scores following the intervention. Although both groups had equivalent SPPB scores, SMC-CS was less consistent in the UH than EH (p=0.16). However, UH displayed a significant (p=0.004) increase in second-order connections to the precuneus compared to EH. These data suggest that studying brain networks could improve the understanding of the development of mobility disability and the CNS contributions to mobility independent of white matter disease.

It is well established that aging-related changes in brain structure such as white matter lesions and atrophy are linked with changes in gait and mobility. However, changes in neural function may precede irreversible changes in brain structure. Previous work has shown that functional brain networks are associated with mobility function in older adults. Here, we sought to extend that work by replicating previous associations between mobility function and brain networks, and testing whether improving mobility through an exercise intervention produced concomitant improvements in brain networks. Short Physical Performance Battery (SPPB) and resting state functional magnetic resonance imaging (fMRI) were collected in 66 obese, sedentary adults 65–80 years old before and after a randomized trial adding weight loss to a 5-month aerobic exercise intervention. Graph theory-based methods were used to characterize brain network organization and compare participants in the highest (SPPB=12, n=22) and lowest (SPPB=3–9, n=21) tertiles of SPPB score. We replicated findings that the community structure of somatomotor cortex was less consistent in the Low SPPB group compared to the High SPPB group (p=0.15). The Low SPPB group improved SPPB significantly after the intervention (mean=1.73, 95%CI [1.02–2.45], p