Biology subjects

Lyday, R. G.

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

9 recordsLinked to original sources

bioRxiv2025

Poorer physical function is associated with elevated spatial entropy in the aging brain network landscape

Life is a constant struggle against disorder. As we age, our ability to maintain internal order declines. In the healthy human brain, order is observable in the form of functionally segregated brain network communities that exhibit spatial consistency. These communities associate with distinct cognitive and physical functions. When mapped into the brain, they form a functional "landscape". We assessed the spatial disorder of these landscapes in older adults with a wide range of mobility using a modified version of Shannon entropy. We found that compared to younger adults, older adults had significantly higher entropy in the sensorimotor cortex, basal ganglia, hippocampus, thalamus, and occipital lobe. Higher entropy in many of these regions was associated with worse physical function and higher body mass index in older adults. Findings suggest that spatial entropy in brain network landscapes may be a marker of declining physical function. Modifiable factors, such as losing excess weight, may help to ameliorate aging-related brain entropy increases in regions such as the sensorimotor cortex, which may in turn help to preserve physical function in older adults.

neuroscience↗

bioRxiv2025

Spatial entropy of brain network landscapes: a novel method to assess spatial disorder in brain networks

In this work, we introduce a method for mapping the spatial entropy of functional brain network community structure images in brain space. Entropy maps indicate the extent to which the network communities present in a local area are ordered or disordered. We demonstrate how spatial entropy can be quantified for each voxel in the brain according to the network community affiliations of surrounding voxels. This process results in interpretable maps of brain network entropy. We show that local entropy decreases in predictable brain regions during working memory and music-listening tasks. We suggest that these regional entropy reductions reflect self-organization of neural processes in support of functionally localized cognitive tasks. Analyses in this work provide a framework for future analyses of spatial entropy in complex networks that can be mapped to Euclidean space - both within the brain and in other contexts. Significance StatementWe introduce an approach for quantifying the spatial entropy of functional brain network community structure. We demonstrate the biological relevance of the measure in three independent datasets. This approach for analyzing brain network data is data-driven, easy to implement, and highly interpretable. It also allows investigators to visualize complex data by mapping values into the brain rather than storing values in extremely high-dimensional and abstract data structures. We believe this will make the method highly accessible even to investigators with minimal experience analyzing human neuroimaging data.

neuroscience↗

bioRxiv2025

The Effects of Mindfulness on Brain Network Dynamics Following an Acute Stressor in a Population of Moderate to Heavy Drinkers

Previous research has found that mindfulness-based techniques are beneficial for reducing stress in heavy drinking individuals. However, the underlying neurobiology of these stress-reducing effects are unclear. Moreover, much of the research examining neurobiological correlates of mindfulness have used static functional connectivity, suggesting brain activity goes unchanged for the entire length of an MRI scan. In the current study, we used a state-based dynamic functional connectivity model to examine brain states during either a 10-minute mindfulness session or resting control that followed an individually tailored stress imagery task. Using a Hidden Semi-Markov Model (HSMM), six brain states and the associated dynamics of state traversal were estimated for the population. Participants that experienced the mindfulness session had more transitions and longer time spent in states in which the salience network was more active. Participants assigned to the control group had more transitions and increased time spent in states in which nodes of the default mode network were more active. Moreover, for control participants, increased occupancy time to SN-dominant states were associated with lower perceived stress. Using HSMM provided unique insight into network connectivity during mindful states; we believe it offers a novel approach to testing and optimizing the content of mindful-based therapies.

neuroscience↗

bioRxiv2024

Insulin resistance, cognition, and functional brain network topology in older adults with obesity

ObjectiveCross-sectional data from a sample of older adults with obesity was used to determine how peripheral and neuronal insulin resistance (IR) relate to executive function and functional brain network topology. MethodsOlder adults (n=71) with obesity but without type 2 diabetes were included. Peripheral IR was quantified by HOMA2-IR. Neuronal IR was quantified according to a proposed neuron-derived exosome-based method (NDE-IR). An executive function composite score, summed scores to the Auditory Verbal Learning Test (AVLT) trials 1-5, and functional brain networks generated from resting-state functional magnetic resonance imaging were outcomes in analyses. We used general linear models and a novel regression framework for brain network analysis to identify relationships between IR measures and brain-related outcomes. ResultsHOMA2-IR, but not NDE-IR, was negatively associated with executive function. Neither IR measure was associated with AVLT score. Peripheral IR was also related to hippocampal network topology in participants who had undergone functional neuroimaging. Neither peripheral nor neuronal IR were significantly related to network topology of the central executive network. ConclusionsCognitive and functional imaging effects were observed from HOMA2-IR, but not NDE-IR. The hippocampus may be particularly vulnerable to effects of peripheral IR.

neuroscience↗

bioRxiv2024

Triple network dynamics and future alcohol consumption in adolescents

BackgroundHuman neuroimaging increasingly suggests that the brain is best modeled as a highly interconnected and dynamic system. However, novel methodology for studying functional brain network dynamics have never been applied to the study of adolescent alcohol consumption. We sought to determine whether brain network dynamics are related to future drinking behavior in teenagers. MethodsResting-state functional magnetic resonance imaging (fMRI) time series from 17-year-old non/low drinking participants (n=295) of the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study were used to fit a Hidden Semi-Markov Model (HSMM). Regions of the default mode network (DMN), salience network (SN), and central executive network (CEN), collectively known as the Triple Network, were included in modeling. The HSMM identified the most-likely state sequence for each participant, a trajectory through distinct brain network states over the course of their fMRI scan. Poisson regression models were used to assess relationships between state sequence metrics and future drinking frequency. Potential sex differences in state sequence metrics or the relationship between sequence metrics and future drinking were assessed with permutation testing and interactions in regression models. ResultsNo sex differences in state sequence metrics were observed. However, the relationship between occupancy times and future drinking frequency differed by sex for two brain states. In the full sample, occupancy time in a state characterized by high interconnectivity between the SN and CEN was negatively associated with drinking. Occupancy time in a separate state characterized by high activation in the DMN and SN, but low activation in the CEN, was negatively associated with future drinking. ConclusionsBrain network dynamics may be useful neural markers of predisposition to drinking in adolescents. Brain states which make teens vulnerable or resilient to drinking may differ between sexes.

neuroscience↗

bioRxiv2024

Resting-state connectivity modifies the effects of amyloid on cognitive and physical function: evidence for network-based cognitive reserve

Cognitive and physical function are interrelated in aging co-occurring impairments in both domains can be debilitating and lead to increased risk of developing dementia. Amyloid beta (A{beta}) deposition in the brain is linked to cognitive decline and is also associated with poorer physical function in older adults. However, significant inter-individual variability exists with respect to the influence of increased brain A{beta} concentrations on cognitive and physical outcomes. Identifying factors that explain inter-individual variability in associations between A{beta} and clinical outcomes could inform interventions designed to delay declines in both cognitive and physical function. Cognitive reserve (CR) is considered a buffer that allows for cognitive performance that is better than expected for a given level of brain injury or pathology. Although the neural mechanisms underlying CR remain unknown, there is growing evidence that resting-state brain networks may serve as a neural surrogate for CR. The currently study evaluated whether functional brain networks modified associations between brain A{beta} and cognitive and physical function in community-dwelling older adults from the Brain Networks and Mobility (B-NET) study. We found that the integrity of the central executive and basal ganglia networks modified associations of A{beta} with cognitive and physical performance. Associations between brain A{beta} and cognitive and physical function were less pronounced when brain network integrity was high. The current study introduces novel evidence for brain networks underlying CR as a buffer against the influence of A{beta} accumulation on cognitive and physical function. Significance StatementThere is a growing number of medications targeting beta amyloid for the treatment of Alzheimers disease. The treatments effectively lower brain amyloid but do not have as robust of an effect on clinical outcomes. The current study introduces novel evidence for brain networks as a buffer against the influence of A{beta} accumulation on cognitive and physical function in older adults with normal cognition. Future studies should examine if brain network integrity underlies the variability in treatment response to amyloid-lowering drugs in patients with cognitive decline.

neuroscience↗

bioRxiv2024

Contrasting topologies of synchronous and asynchronous functional brain networks

We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy (oCSE) and compared aFN topology to the correlation-based synchronous functional networks (sFNs) which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the Default Mode Network (DMN) in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity which the other type of network does not.

neuroscience↗

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↗