Biology subjects

ODonnell, S. M.

Publications and source records attributed to ODonnell, S. M..

3 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↗