Marshall University Music Department Presents a BFA Junior Recital, Dustin Moraczewski, guitar

https://mds.marshall.edu/music_perf/1514/thumbnail.jp

Functional organization of social-motivation brain systems during social interaction in autism spectrum disorder

The motivation to interact with others and the feeling of reward following a social interaction is integral to the development and maintenance of successful so- cial relationships. For those with autism spectrum disorder (ASD) successful social interaction is often more challenging relative to those who are neurotypical (NT) and atypical social reward processing may contribute to such deficits. However, our understanding of the relationship between brain systems associated with re- ward and higher-order social-cognitive processing during both typical and atypical development is limited. Middle childhood is an important time to examine the de- velopment of the functional relationship between these brain systems as this is a time when children’s social worlds expand in size and complexity and those with ASD often fall behind. The goal of the current dissertation is to characterize the development of the functional relationship between the ventral striatum (VS)—a hub of reward processing—and other brain regions implicated in reward and social-cognitive processing during an interactive social context in middle childhood. Using novel Bayesian multilevel modeling, Aim 1 examines VS functional connectivity within the NT group while Aim 2 examines group differences between the ASD and NT groups. Finally, given that heterogeneity is ubiquitous in both NT and ASD populations, Aim 3 takes a dimensional perspective through examining VS connectivity as a function of individual differences in autistic traits and subjective reports of social reward within the entire sample. Results suggest that participant age may be particularly important for the development of the relationship between reward and social-cognitive brain systems, such that older children of both groups exhibit greater sensitivity the absence of a social reward and to the contingency of a non-social reward. This dissertation underscores the importance of examining multidimensional heterogeneity in both NT and ASD populations

Marshall University Music Department Presents the Marshall University Guitar Ensemble Recital

https://mds.marshall.edu/music_perf/1511/thumbnail.jp

Improving accuracy and precision of heritability estimation in twin studies through hierarchical modeling: reassessing the measurement error assumption

Introduction: The conventional approach to estimating heritability in twin studies implicitly assumes either the absence of measurement error or that any measurement error is incorporated into the nonshared environment component. However, this assumption can be problematic when it does not hold or when measurement error cannot be reasonably classified as part of the nonshared environment.Methods: In this study, we demonstrate the need for improvement in the conventional structural equation modeling (SEM) used for estimating heritability when applied to trait data with measurement errors. The critical issue revolves around an assumption concerning measurement errors in twin studies. In cases where traits are measured using samples, data is aggregated during preprocessing, with only a centrality measure (e.g., mean) being used for modeling. Additionally, measurement errors resulting from sampling are assumed to be part of the nonshared environment and are thus overlooked in heritability estimation. Consequently, the presence of intra-individual variability remains concealed. Moreover, recommended sample sizes are typically based on the assumption of no measurement errors.Results: We argue that measurement errors in the form of intra-individual variability are an intrinsic limitation of finite sampling and should not be considered as part of the nonshared environment. Previous studies have shown that the intra-individual variability of psychometric effects is significantly larger than the inter-individual counterpart. Here, to demonstrate the appropriateness and advantages of our hierarchical linear modeling approach in heritability estimation, we utilize simulations as well as a real dataset from the ABCD (Adolescent Brain Cognitive Development) study. Moreover, we showcase the following analytical insights for data containing non-negligible measurement errors: i) The conventional SEM may underestimate heritability. ii) A hierarchical model provides a more accurate assessment of heritability. iii) Large samples, exceeding 100 observations or thousands of twins, may be necessary to reduce imprecision.Discussion: Our study highlights the impact of measurement error on heritability estimation and introduces a hierarchical model as a more accurate alternative. These findings have significant implications for understanding individual differences and improving the design and analysis of twin studies

Reliability and predictability of phenotype information from functional connectivity in large imaging datasets

One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as latent phenotypes from resting state functional connectivity. For this task, we predicted phenotypes in two large neuroimaging datasets: the Human Connectome Project (HCP) and the Philadelphia Neurodevelopmental Cohort (PNC). We illustrate the importance of regressing out confounds, which could significantly influence phenotype prediction. Our findings reveal that both phenotypes and their corresponding latent phenotypes yield similar predictive performance. Interestingly, only the first five latent phenotypes were reliably identified, and using just these reliable phenotypes for predicting phenotypes yielded a similar performance to using all latent phenotypes. This suggests that the predictable information is present in the first latent phenotypes, allowing the remainder to be filtered out without any harm in performance. This study sheds light on the intricate relationship between functional connectivity and the predictability and reliability of phenotypic information, with potential implications for enhancing predictive modeling in the realm of neuroimaging research

A bimodal taxonomy of adult human brain sulcal morphology related to timing of fetal sulcation and trans-sulcal gene expression gradients

We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (n = 34,725; 45–82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bimodal distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex, and complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (n = 228; 21–36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes.</p

A bipolar taxonomy of adult human brain sulcal morphology related to timing of fetal sulcation and trans-sulcal gene expression gradients

We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (N=34,725; 45-82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bipolar distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex; complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (N=228; 21-36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes.</p

The interplay between brain and behavior during development:A multisite effort to generate and share simulated datasets

One of the challenges in the field of neuroimaging is that we often lack knowledge about the underlying truth and whether our methods can detect developmental changes. To address this gap, five research groups around the globe created simulated datasets embedded with their assumptions of the interplay between brain development, cognition, and behavior. Each group independently created the datasets, unaware of the approaches and assumptions made by the other groups. Each group simulated three datasets with the same variables, each with 10,000 participants over 7 longitudinal waves, ranging from 7 to 20 years-of-age. The independently created datasets include demographic data, brain derived variables along with behavior and cognition variables. These datasets and code that were used to generate the datasets can be downloaded and used by the research community to apply different longitudinal models to determine the underlying patterns and assumptions where the ground truth is known.</p

DSST fmriprep Helpers

&lt;p&gt;This repo provides shell scripts to help summarize fmriprep error reports across an entire dataset and to generate motion censor files from the fmriprep confounds.&lt;/p&gt