Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

Abstract

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.

Physical resilience in the brain: The effect of white matter disease on brain networks in cognitively normal older adults

“…Participants were excluded for evidence of cognitive impairment determined by a combination of the Montreal Cognitive Assessment (MoCA) and additional cognitive testing as needed. Further exclusion criteria and a full description of cognitive inclusion criteria can be found here [20]. Three participants were excluded due to imaging artifacts resulting from excessive head motion; consequently, the resilience analyses presented here includes 189 participants.…”

“…Structural image segmentation, including removal of non-brain tissue and cerebrospinal fluid (CSF), was completed using SPM12s described in [20]. Briefly, co-registration of the functional and anatomical images was improved through creation of a whole-brain mask for each participant using both white and grey matter segments.…”

“…Participants were excluded for evidence of cognitive impairment determined by a combination of the Montreal Cognitive Assessment (MoCA) and additional cognitive testing as needed. Further exclusion criteria and a full description of cognitive inclusion criteria can be found here [20].…”

“…Here, we performed SI analyses using regions-of-interest (ROIs) to define a priori intrinsic brain subnetworks as described in [20]. In the current study, the SMN was chosen as the primary ROI due to its established involvement in motor function and our prior work establishing a relationship between higher scores of the SPPB and a more consistent SMN-CS [17].…”

Improvisational Movement to Improve Quality of Life in Older Adults With Early-Stage Dementia: A Pilot Study

“…Images were preprocessed using an established pipeline (Neyland et al, 2021). Segmentation of the T1-weighted structural image into gray matter, white matter and cerebrospinal fluid was done using Statistical Parametric Mapping version 12 (SPM12, http:// www.fil.ion.ucl.ac.uk/spm).…”