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Merritt, H.

Publications and source records attributed to Merritt, H..

8 recordsLinked to original sources
bioRxiv2026

Dynamic social envirotypes demonstrate adolescent brain dysconnectivity is contextual

The adolescent social environment and dynamics therein have been linked to divergent brain organization and greater risk of psychopathology later in life, suggesting the neural underpinnings of mental health may depend on social context. To examine this, we leveraged longitudinal ABCD Study data to identify dynamic social envirotypes, or distinct trajectories of social environment change over four years. Dynamic social envirotypes differed in mental health outcomes, changes in mental health, and whole-brain functional connectivity, with each dynamic social envirotype exhibiting a characteristic pattern of brain network organization. Crucially, functional connectivity associations with internalizing and externalizing problems diverged across dynamic social envirotypes, with the highest and lowest social resource envirotypes showing largely opposing associations between connectivity and symptom severity. These findings suggest that dysconnectivity is contextual: there is no single brain connectivity pattern that is universally associated with better mental health. Rather, adolescent brain connectivity relates to mental health in context-specific ways, challenging normative narratives of the neural underpinnings of psychopathology and centering the role of context for understanding and treating mental health.

neuroscience↗
bioRxiv2025

The Topological Architecture of Brain Identity

Accurately identifying individuals from brain activity--functional fingerprinting--is a powerful tool for understanding individual variability and detecting brain disorders. Most current approaches rely on functional connectivity (FC), which measures pairwise correlations between brain regions. However, FC is limited in capturing the higher-order, multiscale structure of brain organization. Here, we propose a novel fingerprinting method based on homological scaffolds, a topological repre-sentation derived from persistent homology of resting-state fMRI data. Using data from the Human Connectome Project (n = 100), we show that scaffold-based fingerprints achieve near-perfect identification accuracy ([~] 100%), outperforming FC-based methods (90%), and remain robust across preprocessing pipelines, atlas choices, and even with drastically shortened scan durations. Unlike FC, in which fingerprinting features localize within networks, scaffolds derive their discriminative power from inter-network connections, revealing the existence of individual mesoscale organizational signatures. Finally, we show that scaffolds bridge redundancy and synergy by balancing redundant information along high-FC border edges with synergistic interactions across the topological voids they enclose. These findings establish topological scaffolds as a powerful tool for capturing individual variability, revealing that unique signatures of brain organization are encoded in the interplay between mesoscale network integration and information dynamics.

neuroscience↗
bioRxiv2024

The dual interpretation of edge time series: Time-varying connectivity versus statistical interaction

Functional connectivity (FC) is frequently operationalized as a correlation. Many studies have examined changes in correlation networks across time, claiming to link time-varying fluctuations to ongoing mental operations and physiological processes. Other studies, however, have called these results into question, noting that statistically indistinguishable patterns of time-varying fluctuations can be obtained by windowing synthetic time series generated from ground-truth stationary correlation structure. Recently, we developed a technique for tracking rapid (framewise) fluctuations in network connectivity over time. Here, we show that these "edge time series" are mathematically equivalent to interaction terms in a specific family of general linear models. We exploit this fact to further demonstrate that time-varying connectivity carries explanatory power above and beyond brain activations. This observation suggests that time-varying connectivity is likely more than a statistical artifact. SUMMARYBrain activity and connectivity have been linked to ongoing behavior and mentation but usually in isolation and almost never in the same model. Here, we show that "edge time series" - a recently proposed method for tracking moment-to-moment connectivity changes - are equivalent to an interaction term in a linear model. By including terms for activations in the same model, it provides an elegant framework for assessing the relative explanatory power of edges and activations. In our work, we use this modeling framework to study time-varying behavior in zebrafish, worms, and humans. We find that connectivity contains unique explanatory power above and beyond activity.

neuroscience↗
bioRxiv2024

Social 'envirotyping' the ABCD study contextualizes dissociable brain organization and diverging outcomes

The environment, especially social features, plays a key role in shaping the development of the brain, notably during adolescence. To better understand variation in brain-environment coupling and its associated outcomes, we identified "envirotypes," or different patterns of social environment experience, in the Adolescent Brain Cognitive Development Study by hierarchically clustering subjects. Two focal clusters, which accounted for 89.3% of all participants, differed significantly on eight out of nine youth-report social environment quality measures, representing almost perfect complements. We then applied tools from network neuroscience to show different envirotypes are associated with different patterns of whole brain functional connectivity. Differences were distributed across the brain but were especially prominent in Default and Somatomotor Hand systems for these focal clusters. Finally, we examined how social envirotypes change over development and how these patterns of change are associated with a suite of outcomes. The resulting dynamic envirotypes differed along dimensions of stability and quality, but outcomes diverged based on stability. Specifically, the stable, high quality envirotype was most easily distinguished from the improving envirotype, while the unstable envirotype was associated with the worst outcomes. Altogether, our findings represent significant contributions to both social developmental neuroscience and network neuroscience, emphasizing the variability and dynamicity of brain-environment coupling and its consequences.

neuroscience↗
bioRxiv2024

A Bioluminescent Assay for Quantification of Cellular Glycogen Levels

Glycogen is a large polymer of glucose that functions as an important means of storing energy and maintaining glucose homeostasis. Glycogen synthesis and degradation pathways are highly regulated and their dysregulation can contribute to disease. Glycogen storage diseases are a set of disorders that arise from improper glycogen metabolism. Glycogen storage disease II, known as Pompe disease, is caused by a genetic mutation that leads to increased glycogen storage in cells and tissues, resulting in progressive muscle atrophy and respiratory decline for patients. One approach for treating Pompe disease is to reduce glycogen levels by interfering with the glycogen synthesis pathway through glycogen synthase inhibitors. To facilitate the study of glycogen synthase inhibitors in biological samples, such as cultured cells, a high-throughput approach for measuring cellular glycogen was developed. A bioluminescent glycogen detection assay was automated and used to measure glycogen content in cells grown in 384-well plates. The assay successfully quantified reduced glycogen stores in cells treated with a series of glycogen synthase 1 inhibitors, verifying inhibitor activity in biological systems and validating the utility of the assay for glycogen metabolism studies. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/595028v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1ae263borg.highwire.dtl.DTLVardef@50124eorg.highwire.dtl.DTLVardef@e1ca53org.highwire.dtl.DTLVardef@1aa41dc_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗
bioRxiv2023

Stability of brain-behavior correlation patterns across measures of social support

The social environment has a critical influence on human development, cognition, and health. By using network approaches to map and analyze the connectivity between all pairs of brain regions simultaneously, we can clarify how relationships between brain regions (e.g. connectivity) change as a function of social relationships. Here we apply multilayer modeling and modularity maximization-both established tools in network neuroscience-to jointly cluster patterns of brain-behavior associations for seven social support measures. Our analyses build on both neuroecological findings and network neuroscientific approaches. In particular we find that subcortical and control systems are especially sensitive to different constructs of perceived social support. Network nodes in these systems are highly flexible; their community affiliations, which reflect groups of nodes with similar patterns of brain behavior associations, differ across social support measures. The multilayer approach used here enables direct comparison of the roles of all regions of the brain across all social support measures included. Additionally, our application of multilayer modeling to patterns of brainbehavior correlations, as opposed to just functional connectivity, represents an innovation in how multilayer models are used in. More than that, it offers a generalizable technique for studying the stability brain-behavior correlations.

neuroscience↗
bioRxiv2019

Two distinct mechanisms of small molecule inhibition of LpxA acyltransferase essential for lipopolysaccharide biosynthesis

The lipopolysaccharide biosynthesis pathway is considered an attractive drug target against the rising threat of multidrug-resistant Gram-negative bacteria. Here, we report two novel small-molecule inhibitors (compounds 1 and 2) of the acyltransferase LpxA, the first enzyme in the lipopolysaccharide biosynthesis pathway. We show genetically that the antibacterial activities of the compounds against efflux-deficient Escherichia coli are mediated by LpxA inhibition. Consistently, the compounds inhibited the LpxA enzymatic reaction in vitro. Intriguingly, using biochemical, biophysical, and structural characterization, we reveal two distinct mechanisms of LpxA inhibition; compound 1 is a substrate-competitive inhibitor targeting apo LpxA and compound 2 is an uncompetitive inhibitor targeting the LpxA-product complex. Compound 2 exhibited more favorable biological and physicochemical properties than compound 1, and was optimized using structural information to achieve improved antibacterial activity against wild type E. coli. These results show that LpxA is a promising antibacterial target and imply the advantages of targeting enzyme-product complexes in drug discovery.

biochemistry↗