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Simpson, S. L.

Publications and source records attributed to Simpson, S. L..

6 recordsLinked to original sources

MIND-Map; A Comprehensive Toolbox for Estimating Brain Dynamic States

Studying dynamic brain states has offered new insights into understanding functional connectivity. One of the promising approaches for estimating these brain states is the hidden semi-Markov model (HSMM). However, despite its potential, its adoption in the neuroscience community has been limited due to its complexity. We developed the Markov Inference Dynamic Mapping (MIND-Map) toolbox to overcome this limitation. This interactive user-friendly toolbox leverages HSMM to identify brain states, analyze their dynamics, and perform two-sample statistical comparisons of network dynamics. Furthermore, it introduces a new approach, not yet used in conjunction with these models, for determining the optimal number of states, addressing a key challenge in the field. We assessed the performance of the HSMM and our method for identifying the optimal number of states using two datasets, including a unique dataset explicitly developed for this purpose.

neuroscience↗

Insulin resistance, cognition, and functional brain network topology in older adults with obesity

ObjectiveCross-sectional data from a sample of older adults with obesity was used to determine how peripheral and neuronal insulin resistance (IR) relate to executive function and functional brain network topology. MethodsOlder adults (n=71) with obesity but without type 2 diabetes were included. Peripheral IR was quantified by HOMA2-IR. Neuronal IR was quantified according to a proposed neuron-derived exosome-based method (NDE-IR). An executive function composite score, summed scores to the Auditory Verbal Learning Test (AVLT) trials 1-5, and functional brain networks generated from resting-state functional magnetic resonance imaging were outcomes in analyses. We used general linear models and a novel regression framework for brain network analysis to identify relationships between IR measures and brain-related outcomes. ResultsHOMA2-IR, but not NDE-IR, was negatively associated with executive function. Neither IR measure was associated with AVLT score. Peripheral IR was also related to hippocampal network topology in participants who had undergone functional neuroimaging. Neither peripheral nor neuronal IR were significantly related to network topology of the central executive network. ConclusionsCognitive and functional imaging effects were observed from HOMA2-IR, but not NDE-IR. The hippocampus may be particularly vulnerable to effects of peripheral IR.

neuroscience↗

The USH3A causative gene clarin1 functions in Muller glia to maintain retinal photoreceptors

Mutations in CLRN1 cause Usher syndrome type IIIA (USH3A), an autosomal recessive disorder characterized by hearing and vision loss, and often accompanied by vestibular balance issues. The identity of the cell types responsible for the pathology and mechanisms leading to vision loss in USH3A remains elusive. To address this, we employed CRISPR/Cas9 technology to delete a large region in the coding and untranslated (UTR) region of zebrafish clrn1. Retina of clrn1 mutant larvae exhibited sensitivity to cell stress, along with age-dependent loss of function and degeneration in the photoreceptor layer. Investigation revealed disorganization in the outer retina in clrn1 mutants, including actin-based structures of the Muller glia and photoreceptor cells. To assess cell-specific contributions to USH3A pathology, we specifically re-expressed clrn1 in either Muller glia or photoreceptor cells. Muller glia re-expression of clrn1 prevented the elevated cell death observed in larval clrn1 mutant zebrafish exposed to high-intensity light. Notably, the degree of phenotypic rescue correlated with the level of Clrn1 re-expression. Surprisingly, high levels of Clrn1 expression enhanced cell death in both wild-type and clrn1 mutant animals. However, rod- or cone-specific Clrn1 re-expression did not rescue the extent of cell death. Taken together, our findings underscore three crucial insights. First, clrn1 mutant zebrafish exhibit key pathological features of USH3A; second, Clrn1 within Muller glia plays a pivotal role in photoreceptor maintenance, with its expression requiring controlled regulation; third, the reliance of photoreceptors on Muller glia suggests a structural support mechanism, possibly through direct interactions between Muller glia and photoreceptors mediated in part by Clrn1 protein. AUTHOR SUMMARYMutations in USH-associated genes profoundly impact patients, affecting auditory, visual, and vestibular function. While the basis of inner ear defects is reasonably well understood for USH and auditory devices can improve hearing, the mechanisms underlying photoreceptor loss are unknown, and there are no approved treatments for vision deficits. In USH3A, the affected gene, clarin1 (clrn1), is predominantly expressed in Muller glia. The role of Muller glia in maintaining photoreceptor health and contributions to USH3 pathology is understudied, in part as Clrn1 mutant mice - the traditional experimental model used to study retinal diseases - do not phenocopy the photoreceptor loss of USH3 patients. In the present study, we developed a zebrafish model of USH3A that displays many features of the human disease. Our research shows that the loss of Clrn1 affects actin-based structures of the outer retina, including those of photoreceptor cells and Muller glia. Importantly, we demonstrate that the expression of Clrn1 in Muller glia, but not rods and cones, alleviated light-induced damage in clrn1 mutant zebrafish. We also highlight that the dosage of Clrn1 in Muller glia is critical for maintaining proper photoreceptor function. These findings demonstrate the key contribution of Muller glia to USH pathology and can guide strategies for gene-replacement therapies.

cell biology↗

Desire and Craving ratings vary significantly for healthy alcohol consumers: Differences in semantic interpretation?

Craving is a central concept in alcohol, and other substance, research. Beginning in 1955 the World Health Organization outlined a working definition of the term to be used in research and clinical settings. However, the semantic interpretation of "craving" as a concept is not widely agreed upon. Since the publication of this first craving definition, a handful of studies have been conducted to investigate differences in operational definitions of "craving", and have demonstrated a lack of agreement between studies and across research subjects. With this background as evidence, our research group investigated, when left to their own semantic understanding of the terms, if regular alcohol consumers would rate craving for alcohol and desire for alcohol in similar ways using related descriptors. Thirty-nine healthy, non-binging regular alcohol consumers were studied across periods of their typical alcohol consumption and imposed alcohol abstinence, collecting ratings of desire and craving for alcohol approximately every two hours across the two experimental periods, and during neutral and alcohol related imagery viewing. Among these non-binging regular drinkers, ratings of desire and craving for alcohol are consistently different while drinking according to a persons typical routine or abstaining, throughout the day, and when viewing alcohol cue imagery.

neuroscience↗

A Regression Framework for Brain Network Distance Metrics

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances between connection matrices, and adapt several standard methods for estimation and inference within our framework: Standard F-test, F-test with individual level effects (ILE), Feasible Generalized Least Squares (FGLS), and Permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing Multivariate Distance Matrix Regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data. HighlightsO_LIRelated distances between connection matrices to differences in covariates. C_LIO_LIAdapted methods for estimation and inference in this framework. C_LIO_LIAssessment of methods and distance metrics via simulation. C_LIO_LICompared our methods to existing MDMR methods via simulation. C_LIO_LIAnalysis of the HCP data with the best approach for each distance metric. C_LI

neuroscience↗

A Mixed-Modeling Framework for Whole-Brain Dynamic Network Analysis

The emerging area of dynamic brain network analysis has gained considerable attraction in recent years. While current tools have proven useful in providing insight into dynamic patterns of brain networks, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. Unlike current tools which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks using resting-state fMRI (rfMRI) data from 200 subjects in the Human Connectome Project (HCP) study. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits.

neuroscience↗