Biology subjects

Wilson, T. W.

Publications and source records attributed to Wilson, T. W..

5 recordsLinked to original sources

bioRxiv2026

Altered Somatosensory Cortical Entrainment in Cerebral Palsy

Somatosensory dysfunction contributes substantially to mobility impairments in cerebral palsy (CP), yet their somatosensory cortical responses to rhythmic sustained input remains unstudied. This study examined somatosensory cortical entrainment in 28 ambulatory youth with CP (Gross Motor Function Classification System levels I-III; 21.9 {+/-} 6.3y; 14 unilateral CP) and 34 neurotypical controls (23.4 {+/-} 3.8y). Participants underwent magnetoencephalography while an 8 Hz cutaneous electrical stimulation was applied to the right posterior tibial nerve. Rhythmic neural responses were mapped to the contralateral somatosensory cortices using beamforming, and virtual sensor time series were extracted to quantify neural dynamics. The entrainment response declined more steeply over time in CP compared to controls, whereas the concurrent alpha response strengthened progressively in CP but changed minimally in controls (Group x Time interactions, ps<.001). Significant cross-spectral coupling of entrainment x concurrent alpha responses was observed only in CP (p<.001) and was most pronounced in those with bilateral CP. Weaker cross-spectral coupling was associated with better functional mobility in controls (p=.004) but brain-behavior links in CP were phenotype-specific. Finally, the second harmonic neural response in CP was weaker (p<.001) and demonstrated a blunted temporal modulation relative to controls (p=.003). These findings implicate disrupted somatosensory cortical entrainment responses as possible neurophysiological features of youth with CP.

neuroscience↗

bioRxiv2026

Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models

Functional magnetic resonance imaging (fMRI) data are inherently complex, characterized by high dimensionality, intricate inter-regional dependencies, and substantial individual variability across experimental paradigms. Traditional linear mixed models (LMMs) provide a principled framework that models population-level fixed effects while estimating variance components arising from subject-level random effects; however, they often fail to adequately capture nonlinear relationships inherent in neuroimaging data. To address these limitations, we introduce the nonlinear mixed model (NMM) approach, an innovative extension of the LMM framework that integrates neural networks to flexibly model complex fixed-effect relationships while preserving the random-effects structure to account for individual differences. NMM advances fMRI analysis by: (1) identifying robust functional connectivity (FC) patterns consistently observed across multiple paradigms; (2) leveraging SHapley Additive exPlanations (SHAP) analysis to provide post-hoc interpretability of the nonlinear fixed effects, quantifying how age, sex, and paradigm contribute to predicted FC and how these effects are distributed across large-scale brain networks; and (3) using subject-specific random effects as neural fingerprints that not only show systematic variability across attention and default mode systems but also predict standardized cognitive scores, demonstrating biological relevance. Applied to the Philadelphia Neurodevelopmental Cohort (PNC) across emotion, n-back, and resting-state paradigms, NMM achieved superior model fit relative to classical LMMs, as evidenced by lower mean squared error (MSE) in predicting FC. This framework offers a statistically rigorous and practically explainable approach for modeling large-scale FC from modest covariates while explicitly separating population-level effects from stable individual variability in functional brain organization.

bioengineering↗

bioRxiv2024

Dev-Atlas: A reference atlas of functional brain networks for typically developing adolescents

Adolescence is a critical period for neural changes, including maturation of the brains cognitive networks, but also a period of increased vulnerability to psychopathology. It is well accepted that the brain is functionally organized into multiple interacting networks and extensive literature has demonstrated that the spatial and functional organization of these networks shows major age-related changes across the lifespan, but particularly during adolescence. Yet, there is limited option for a reference functional brain atlas derived from typically developing adolescents, which is especially problematic as the reliable and reproducible identification of functional brain networks crucially depends on the use of such reference functional atlases. In this context, we utilized resting-state functional MRI data from a total of 1,391 typically developing youth between the ages of 8 and 17 years to create a new adolescent-specific reference atlas of functional brain networks. We further investigated the impact of age and sex on these networks. Using a multiscale individual component clustering algorithm (MICCA), we identified 24 reliable functional brain networks, classified within six domains: Default-Mode (5 networks), Control (4 networks), Salience (3 networks), Attention (4 networks), Somatomotor (5 networks), and Visual (3 networks). We identified reliable and large effects of age on the spatial topography of these majority of networks, as well as on the functional network connectivity (FNC) between networks. The DMN showed reduced FNC with the other networks with older age. Sex effects were not as widespread. No significant sex-by-age interactions were detected. Overall, we created a novel brain atlas, named Dev-Atlas, focused on a typically developing sample, with the hope that this atlas can be used in future independent developmental network neuroscience studies. Dev-Atlas is freely available to the research community.

neuroscience↗

bioRxiv2024

Anterior pituitary gland volume mediates associations between pubertal hormones and changes in transdiagnostic symptoms in youth

The pituitary gland (PG) plays a central role in the production and secretion of pubertal hormones, with documented links to the emergence and increase in mental health symptoms known to occur during adolescence. Although much of the literature has focused on examining whole PG volume, recent findings suggest that there are associations among pubertal hormone levels, including dehydroepiandrosterone (DHEA), subregions of the PG, and elevated mental health symptoms (e.g., internalizing symptoms) during adolescence. Surprisingly, studies have not yet examined associations among these factors and increasing transdiagnostic symptomology, despite DHEA being a primary output of the anterior PG. Therefore, the current study sought to fill this gap by examining whether anterior PG volume specifically mediates associations between DHEA levels and changes in dysregulation symptoms in an adolescent sample (N = 114, 9 - 17 years, Mage = 12.87, SD = 1.88). Following manual tracing of the anterior and posterior PG, structural equation modeling revealed that greater anterior, not posterior, PG volume mediated the association between greater DHEA levels and increasing dysregulation symptoms across time, controlling for baseline dysregulation symptom levels. These results suggest specificity in the role of the anterior PG in adrenarcheal processes that may confer risk for psychopathology during adolescence. This work not only highlights the importance of separately tracing the anterior and posterior PG, but also suggests that transdiagnostic factors like dysregulation are useful in parsing hormone-related increases in mental health symptoms in youth.

neuroscience↗

bioRxiv2020

Modular and state-relevant connectivity in high-frequency resting-state BOLD fMRI data: An independent component analysis

Resting-state fMRI (rs-fMRI) data are typically filtered at different frequency bins between 0.008[~]0.2 Hz (varies across the literature) prior to analysis to mitigate nuisance variables (e.g., drift, motion, cardiac, and respiratory) and maximize the sensitivity to neuronal-mediated BOLD signal. However, multiple lines of evidence suggest meaningful BOLD signal may also be parsed at higher frequencies. To test this notion, a functional network connectivity (FNC) analysis based on a spatially informed independent component analysis (ICA) was performed at seven different bandpass frequency bins to examine FNC matrices across spectra. Further, eyes open (EO) vs. eyes closed (EC) resting-state acquisitions from the same participants were compared across frequency bins to examine if EO vs. EC FNC matrices and randomness estimations of FNC matrices are distinguishable at different frequencies. Results show that FNCs in higher-frequency bins display modular FNC similar to the lowest frequency bin, while r-to-z FNC and FNC-based measures indicating matrix non-randomness were highest in the 0.31-0.46 Hz range relative to all frequency bins above and below this range. As such, the FNC within this range appears to be the most temporally correlated, but the mechanisms facilitating this coherence require further analyses. Compared to EO, EC displayed greater FNC (involved in visual, cognitive control, somatomotor, and auditory domains) and randomness values at lower frequency bins, but this phenomenon flipped (EO > EC) at frequency bins greater than 0.46 Hz, particularly within visual regions. While the effect sizes range from small to large specific to frequency range and resting state (EO vs. EC), with little influence from common artifacts. These differences indicate that unique information can be derived from FNC between BOLD signals at different frequencies relative to a given restingstate acquisition and support the hypothesis meaningful BOLD signal is present at higher frequency ranges.

neuroscience↗