Biology subjects

Shappell, H.

Publications and source records attributed to Shappell, H..

5 recordsLinked to original sources

bioRxiv2026

Linking Covariates to State Durations in Dynamic Brain Networks via Hidden Semi-Markov Models

Hidden semi-Markov models (HSMMs) provide a flexible framework for studying dynamic functional connectivity because they represent brain activity as transitions between latent network states with explicit dwell time distributions. Although dwell times are often associated with behavioral and demographic variables, these associations are typically assessed only after model fitting using indirect group comparisons, limiting formal covariate modeling and the simultaneous inclusion of multiple predictors. In this work, we extend the HSMM framework by introducing covariate-dependent dwell time distributions through a regression-based formulation, allowing expected state duration to vary with subject-level covariates and enabling direct inference on how these factors relate to state persistence. Model parameters are estimated with an expectation-maximization algorithm, and uncertainty in the dwell-time regression coefficients is quantified using a subject-level nonparametric bootstrap. In simulations, the proposed model accurately recovered covariate effects under both shifted-Poisson and Gamma dwell time specifications, with low bias and variability. We applied the method to resting-state fMRI data from 1,000 UK Biobank participants and found that age and sex were associated with dwell times in a subset of brain states. Together, these results show that modeling dwell-time behavior directly provides a more flexible framework for studying individual differences in brain dynamics, revealing not only which states occur, but also how long individuals remain in them.

neuroscience↗

bioRxiv2026

The individuality of single-frame functional brain connectivity

AbstractConverging evidence from studies on brain network "fingerprinting" and precision functional mapping suggest that brain networks are highly individualized in functionally meaningful ways. Concurrently with a growth in studies on this topic, there has been a rise in interest on dynamics (approximately second-to-second changes) in brain networks within scan sessions. While analyses of traditional static networks have increasingly grown towards emphasizing the importance of individual differences in brain network topology, studies of dynamic networks typically follow methodology that require brain states to be considered at a group level. Recent studies have begun to assess the individuality of recurring dynamic brain "states". In this work, we extend this recent work by exploring the extent to which functional connectivity fingerprinting is feasible at single-frame temporal resolution. We estimate connectivity at individual volumes using phase coherence. We find that the identity of participants can be classified based on single volumes given sufficient database scan data and that having more highly parcellated atlases facilitates identification. Finally, we find that tasks can be identified more readily within subjects than between subjects. We conclude that participant identity may be an important driver of observed single-volume connectivity patterns. Further, the single-volume neural correlates of a task appear to be more consistent within subjects than between subjects. This highlights the importance of considering individual variability in studies of brain network dynamics.

neuroscience↗

bioRxiv2025

Dynamic Resting-State Network Markers of Disruptive Behavior Problems in Youth

BackgroundChildhood disruptive behavior problems are linked to aberrant integrity within large-scale cognitive control networks. However, it is unclear if transitory or dynamic variation in the functional brain architecture is a marker of disruptive behavior problems. The current study tested whether functional connectivity across dynamic networks is distinctly associated with the transdiagnostic symptom domain of disruptive behavior problems in children. MethodsParticipants were aged 9-10 years from the Adolescent Brain Cognitive Development (ABCD) Study, who completed resting-state fMRI (N=877). We employed a dynamic connectivity approach leveraging a hidden semi-Markov model (HSMM) to identify transient properties of brain networks and states. Models estimated the time spent in each state (occupancy time) and the number of consecutive timepoints in a state (dwell time) for each participant. Linear regression models were utilized to identify distinct associations between dynamic properties (occupancy and sojourn times) and severity of disruptive behavior problems, accounting for other commonly co-occurring symptoms. ResultsDynamic network markers of disruptive behavior problems included increased time in network states characterized by globally aberrant connectivity patterns in circuitry involved in cognitive control including frontoparietal and dorsal attention networks. Replication of findings was found in a held-out sample of resting-state fMRI runs in which greater severity of disruptive behavior problems was uniquely linked to greater occupancy time in similarly characterized brain states. ConclusionTransdiagnostic, dynamic resting-state markers of disruptive behavior problems in youth may assist in the development of brain-based biomarkers for monitoring treatment outcomes, assessing circuit target engagement and informing clinical decisions.

neuroscience↗

bioRxiv2025

Accounting for Edge Uncertainty in Stochastic Actor-Oriented Models for Dynamic Network Analysis

Stochastic Actor-Oriented Models (SAOMs) were designed in the social network setting to capture network dynamics representing a variety of influences on network change. The standard framework assumes the observed networks are free of false positive and false negative edges, which may be an unrealistic assumption. We propose a hidden Markov model (HMM) extension to these models, consisting of two components: 1) a latent model, which assumes that the unobserved, true networks evolve according to a Markov process as they do in the SAOM framework; and 2) a measurement model, which describes the conditional distribution of the observed networks given the true networks. An expectation-maximization algorithm is developed for parameter estimation. We address the computational challenge posed by a massive discrete state space, of a size exponentially increasing in the number of vertices, through the use of the missing information principle and particle filtering. We present results from a simulation study, demonstrating our approach offers improvement in accuracy of estimation, in contrast to the standard SAOM, when the underlying networks are observed with noise. We apply our method to functional brain networks inferred from electroencephalogram data, revealing larger effect sizes when compared to the naive approach of fitting the standard SAOM.

neuroscience↗

bioRxiv2024

Transdiagnostic Symptom Domains are Associated with Head Motion During Multimodal Imaging in Children

BackgroundHead motion is a challenge for neuroimaging research in developmental populations. However, it is unclear how transdiagnostic symptom domains including attention, disruptive behavior (e.g., externalizing behavior), and internalizing problems are linked to scanner motion in children, particularly across structural and functional MRI. The current study examined whether transdiagnostic domains of attention, disruptive behavior, and internalizing symptoms are associated with scanner motion in children during multimodal imaging. MethodsIn a sample of 9,045 children aged 9-10 years in the Adolescent Brain Cognitive Development (ABCD) Study, logistic regression and linear mixed-effects models were used to examine associations between motion and behavior. Motion was indexed using ABCD Study quality control metrics and mean framewise displacement for the following: T1-weighted structural, resting-state fMRI, diffusion MRI, Stop-Signal Task, Monetary Incentive Delay task, and Emotional n-Back task. The Child Behavior Checklist was used as a continuous measure of symptom severity. ResultsGreater attention and disruptive behavior problem severity was associated with a lower likelihood of passing motion quality control across several imaging modalities. In contrast, increased internalizing severity was associated with a higher likelihood of passing motion quality control. Increased attention and disruptive behavior problem severity was also associated with increased mean motion, whereas increased internalizing problem severity was associated with decreased mean motion. ConclusionTransdiagnostic domains emerged as predictors of motion in youths. These findings have implications for advancing development of generalizable and robust brain-based biomarkers, computational approaches for mitigating motion effects, and enhancing accessibility of imaging protocols for children with varying symptom severities.

neuroscience↗