Biology subjects

Shappell, H. M.

Publications and source records attributed to Shappell, H. M..

7 recordsLinked to original sources

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

MIND-Map; A Comprehensive Toolbox for Estimating Brain Dynamic States

Studying dynamic brain states has offered new insights into understanding functional connectivity. One of the promising approaches for estimating these brain states is the hidden semi-Markov model (HSMM). However, despite its potential, its adoption in the neuroscience community has been limited due to its complexity. We developed the Markov Inference Dynamic Mapping (MIND-Map) toolbox to overcome this limitation. This interactive user-friendly toolbox leverages HSMM to identify brain states, analyze their dynamics, and perform two-sample statistical comparisons of network dynamics. Furthermore, it introduces a new approach, not yet used in conjunction with these models, for determining the optimal number of states, addressing a key challenge in the field. We assessed the performance of the HSMM and our method for identifying the optimal number of states using two datasets, including a unique dataset explicitly developed for this purpose.

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↗

bioRxiv2021

A Mixed-Modeling Framework for Whole-Brain Dynamic Network Analysis

The emerging area of dynamic brain network analysis has gained considerable attraction in recent years. While current tools have proven useful in providing insight into dynamic patterns of brain networks, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. Unlike current tools which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks using resting-state fMRI (rfMRI) data from 200 subjects in the Human Connectome Project (HCP) study. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits.

neuroscience↗

bioRxiv2019

Improved state change estimation in dynamic functionalconnectivity using hidden semi-Markov models

The study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring time-varying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subjects most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms.

neuroscience↗