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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.

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 (<10=unhealthy & 10-12=healthy) and median splits of white matter hyperintensity volumes: Expected Healthy (EH: low WMH, healthy SPPB, n=81), Expected Impaired (EI: high WMH, unhealthy SPPB, n=42), Unexpected Healthy (UH: high WMH, healthy SPPB, n=53), and Unexpected Impaired (UI: low WMH, unhealthy SPPB, n=13). UH is considered the “resilient” group due to their maintained function despite elevated WMH burden. Continuous analyses assessed the relationships between network properties, mobility, and cognition.RESULTSHigher SPPB scores were associated (p<0.01) with greater sensorimotor cortex community structure (SMN-CS) consistency. While no main effect of the resilience interaction term (SPPB*WMH) was found on SMN-CS, UH showed higher numbers of second-order connections between the SMN and anterior cingulate cortex (ACC) than EI (p<0.01).CONCLUSIONSIncreased connectivity between SMN and ACC may be a marker of physical resilience within the brain.

Background Undetected AD pathology in cognitively normal older adults may increase risk for mobility decline by both increasing white matter pathology and directly interfering with functional networks through Aβ deposition. Methods Thirty‐one cognitively normal older adults (75.01 ± 4.15 y, 35.5% female) enrolled in the Brain Networks and Mobility Function (B‐NET) study received PiB PET and MRI. A global cortical PiB average for each participant was calculated by coregistering PET to MRI using Freesurfer v5.6 to generate masks and thresholding at 1.21 to create PiB positivity groups (PiB‐ = 16, PiB+ = 15). White matter hyperintensity (WMH) volume was calculated with the Lesion Segmentation Toolbox (LST) implemented in SPM12. Mobility function was assessed using the expanded Short Physical Performance Battery (eSPPB), a 4‐meter walk, and a 400‐meter walk. Cognitive measures included the Montreal Cognitive Assessment (MOCA) and Digit Symbol Substitution Task (DSST). Associations were explored using linear regression, correcting for age, sex, and BMI in the fully adjusted mobility model while age, sex, and education were used in the fully adjusted cognition and WMH model. Results PiB+ individuals had significantly faster 4‐meter gait speed (p<0.05) and a trend for higher DSST (p=0.052). These associations were independent of WMH volume. PiB+ individuals also had significantly higher total (p<0.05) and motor WMH (p<0.05). Conclusions The observation that PiB+ individuals had better physical function than PiB‐ was unexpected given existing literature showing associations between regional Aβ volume and slower gait speed. It was also unexpected given that the PiB+ group had higher WMH volume, which is associated with slower gait speed. The current sample, which includes primarily individuals with good physical and cognitive function, may represent a resilient phenotype. Continued recruitment of more diverse participants with lower physical function will be an important addition to this sample. Future analyses will allow for the inclusion of APOE and an increased sample size. Whole‐brain functional connectivity will also be assessed in this cohort using graph‐theory based methods.

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.