Biology subjects

Lyday, R. G.

Publications and source records attributed to Lyday, R. G..

2 recordsLinked to original sources

bioRxiv2022

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

neuroscience↗

bioRxiv2021

A Regression Framework for Brain Network Distance Metrics

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances between connection matrices, and adapt several standard methods for estimation and inference within our framework: Standard F-test, F-test with individual level effects (ILE), Feasible Generalized Least Squares (FGLS), and Permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing Multivariate Distance Matrix Regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data. HighlightsO_LIRelated distances between connection matrices to differences in covariates. C_LIO_LIAdapted methods for estimation and inference in this framework. C_LIO_LIAssessment of methods and distance metrics via simulation. C_LIO_LICompared our methods to existing MDMR methods via simulation. C_LIO_LIAnalysis of the HCP data with the best approach for each distance metric. C_LI

neuroscience↗