Biology subjects

Chang, S.-E.

Publications and source records attributed to Chang, S.-E..

6 recordsLinked to original sources

bioRxiv2026

Fixel-wise Differences in White Matter Fiber-bundle Cross-section identified in Children who Stutter

Developmental stuttering has been repeatedly linked to decreased white matter integrity and structural connectivity differences in major white matter tracts. Prior diffusion-weighted MRI studies reporting such white matter structural differences involved voxel-averaged tensor measures (e.g., fractional anisotropy (FA)). A well-acknowledged limitation of this approach, however, has been that it cannot resolve distinct fiber populations within crossing-fiber regions. Fixel-based analysis addresses this limitation by quantifying individual fiber populations within a voxel and providing complementary measures of fiber density (FD), fiber-bundle cross-section (FC), and their combined measure (FDC).Here we applied Fixel-based analysis to diffusion MRI data from 171 children aged 3 - 13 years, including 84 children who stutter and 87 typically developing controls, to test whether white-matter differences associated with developmental stuttering were expressed as differences in FD, FC, and FDC and whether these measures show group-specific age-related patterns. We evaluated this at both the whole-brain level and within a priori white-matter tracts.FC, but not FD or FDC, showed significant Group and Group by Age effects, indicating that white-matter differences were primarily expressed in fiber-bundle macrostructure. Tract-of-interest analyses identified developmental differences in bilateral arcuate fasciculus FC that remained significant across tracts, with controls showing greater age-related increases in FC than children who stutter. Additional FC differences were observed across corticospinal and thalamocortical pathways, although these tract-wise effects were less robust to across-tract correction. These findings suggest that white-matter differences in developmental stuttering are prominently expressed in fiber-bundle macrostructure rather than fiber density and highlight divergent development of bilateral arcuate pathways. FBA provides fiber-specific information that complements conventional tensor-based diffusion measures and may improve characterization of white-matter development in childhood stuttering.

neuroscience↗

bioRxiv2026

Neural subtypes in developmental stuttering

Developmental stuttering is a complex neurodevelopmental disorder characterized by disfluent speech. At the individual level, the behavioral manifestations of stuttering vary considerably, likely reflecting heterogeneity in underlying neural mechanisms. In this study, we examined individual-specific differences in the brains of children who stutter (CWS), by implementing normative modeling, a framework that quantifies how an individual deviates from an age- and sex-matched reference population. We applied this approach to identify individual-specific structural brain atypicalities using gray and white matter volumes. These volumes were derived from MRI scans from a large mixed-longitudinal dataset of 235 and 240 scans from CWS and fluent controls respectively, aged between 3 and 12 years. Individual deviation maps capturing these atypicalities were then used to cluster CWS into subtypes based on similarities in their neuroanatomical profiles. This analysis identified four neural subtypes with distinct neuroanatomical atypicalities relative to fluent controls. The key findings were a basal ganglia-thalamo-cerebellar subtype associated with higher stuttering severity and lower rates of recovery, and a white matter subtype characterized by mild severity and a higher likelihood of recovery. The remaining two subtypes showed cerebellar differences alongside alterations in brain regions involved in sensorimotor integration. Moreover, cerebellar volume atypicalities were present in all four subtypes, indicating that cerebellar alterations were present across otherwise distinct neural profiles and may represent a shared neuroanatomical feature of stuttering. These findings indicate that examining individual-specific neural differences and subtyping based on patterns of neural atypicalities provides valuable insight into the heterogeneity of developmental stuttering and represents a promising direction for improving our understanding of the disorder.

neuroscience↗

bioRxiv2022

Neural oscillatory activity and connectivity in children who stutter during a non-speech motor task

Neural motor control rests on the dynamic interaction of cortical and subcortical regions, which is reflected in the modulation of oscillatory activity and connectivity in multiple frequency bands. Motor control is thought to be compromised in developmental stuttering, particularly involving circuits in the left hemisphere that support speech, movement initiation and timing control. However, to date evidence comes from adult studies, with limited understanding about motor processes in childhood, closer to the onset of stuttering. In this study, we investigated the neural control of movement initiation in children who stutter and children who do not stutter by evaluating transient changes of EEG oscillatory activity and connectivity during a simple button press motor task. We found reduced modulation of left hemisphere oscillatory power, phase locking to button press and phase connectivity in children who stutter compared to children who do not stutter, consistent with previous findings of dysfunction within the left sensorimotor circuits. Interhemispheric connectivity was also weaker at lower frequencies (delta, theta) and stronger in the beta band in children who stutter than in children who do not stutter. Taken together, these findings indicate weaker engagement of the contralateral left motor network in children who stutter even during low-demand non-speech tasks, and suggest that the right hemisphere might be recruited to support sensorimotor processing in childhood stuttering. Differences in oscillatory dynamics occurred despite comparable task performance between groups, indicating that altered balance of cortical activity might be a core aspect of stuttering, observable during normal motor behavior.

neuroscience↗

bioRxiv2021

Charting Brain Growth and Aging at High Spatial Precision

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and use normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1,985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision making.

neuroscience↗

bioRxiv2020

Leveraging big data for classification of children who stutter from fluent peers

IntroductionLarge datasets, consisting of hundreds or thousands of subjects, are becoming the new data standard within the neuroimaging community. While big data creates numerous benefits, such as detecting smaller effects, many of these big datasets have focused on non-clinical populations. The heterogeneity of clinical populations makes creating datasets of equal size and quality more challenging. There is a need for methods to connect these robust large datasets with the carefully curated clinical datasets collected over the past decades. MethodsIn this study, resting-state fMRI data from the Adolescent Brain Cognitive Development study (N=1509) and the Human Connectome Project (N=910) is used to discover generalizable brain features for use in an out-of-sample (N=121) multivariate predictive model to classify young (3-10yrs) children who stutter from fluent peers. ResultsAccuracy up to 72% classification is achieved using 10-fold cross validation. This study suggests that big data has the potential to yield generalizable biomarkers that are clinically meaningful. Specifically, this is the first study to demonstrate that big data-derived brain features can differentiate children who stutter from their fluent peers and provide novel information on brain networks relevant to stuttering pathophysiology. DiscussionThe results provide a significant expansion to previous understanding of the neural bases of stuttering. In addition to auditory, somatomotor, and subcortical networks, the big data-based models highlight the importance of considering large scale brain networks supporting error sensitivity, attention, cognitive control, and emotion regulation/self-inspection in the neural bases of stuttering.

neuroscience↗

bioRxiv2019

Linking lysosomal enzyme targeting genes and energy metabolism with altered gray matter volume in children with persistent stuttering

Developmental stuttering is a childhood onset neurodevelopmental disorder with an unclear etiology. Subtle changes in brain structure and function are present in both children and adults who stutter. It is a highly heritable disorder, and up to 12-20% of stuttering cases may carry a mutation in one of four genes involved in mannose-6-phosphate mediated protein intracellular trafficking. To better understand the relationship between genetic factors and brain structural changes, we used gene expression data from the Allen Institute for Brain Science (AIBS) and voxel-based morphometry (VBM) to investigate the spatial correspondence between gene expression patterns and differences in gray matter volume (GMV) between children with persistent stuttering (n=26, 87 scans) and their fluent peers (n=44, 139 scans). We found that expression patterns of two stuttering-related genes (GNPTG and NAGPA) in the brain exhibit a strong positive spatial correlation with the magnitude of GMV differences between groups. Further gene set enrichment analyses revealed that genes whose expression was highly correlated with the GMV differences were enriched for glycolysis and oxidative metabolism in mitochondria. Although the results are correlational and cannot inform us about underlying casual mechanisms, our results suggest a possibility that regions with high expression level of genes associated with stuttering may be particularly vulnerable to the effect of alterations in these genes. This effect may be further exacerbated by the relatively high energy utilization in those brain during the period of a sharp increase in brain energy utilization, which coincides with a period of rapid language development and the onset of stuttering during childhood.

neuroscience↗