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Murtha, K.

Publications and source records attributed to Murtha, K..

15 recordsLinked to original sources
bioRxiv2026

Neuroanatomical Subtypes of Callous-Unemotional Traits in a Community Sample of Youth

ImportanceCallous Unemotional (CU) traits are associated with significant clinical and neurophysiological heterogeneity that may affect treatment effectiveness. ObjectiveTo uncover neuroanatomical subtypes of CU traits using weakly-supervised machine learning and assess whether emerging subtypes differ on relevant clinical, temperamental, and environmental constructs. Design, Setting, and ParticipantsImaging data was from the longitudinal Adolescent, Brain, Cognitive Development (ABCD) Study. Participants were 9-10 years old at baseline (M=9.925, 69.9% male) and included 222 children with CU traits and 234 typically developing controls matched on age, sex and income. Main Outcomes and MeasuresThe weakly-supervised heterogeneity through discriminative analysis (HYDRA) model was trained on grey matter (GM) volumes from 84 regions of interest (ROIs) and tested for reproducibility using cross-validation and permutation testing. Derived subtypes were compared cross-sectionally and prospectively on relevant clinical, temperamental, and environmental measures and subsequent GM volume at 2-year follow up. ResultsHYDRA revealed an optimal 2-subtype solution within children with CU traits. Subtypes showed comparable levels of aggression that were significantly higher than typically developing controls. At the same time, subtype 1 had larger GM volume, fewer internalizing symptoms, and less adversity exposure, while subtype 2 was characterized by smaller GM volume, more internalizing symptoms, and more adversity exposure. Conclusions and RelevanceThis study provides evidence of neuroanatomically distinct subtypes of CU traits characterized by different clinical and etiological profiles, with implications for diagnosis and treatment.

neuroscience↗
bioRxiv2026

Single-Nuclei Transcriptomic Characterization of APOE4-Associated Alzheimer's Disease

Apolipoprotein E (APOE) genotype contributes significantly to Alzheimers disease (AD) risk and pathogenesis. Cell-type specific effects of APOE alleles have been studied. However, due to the variable prevalence of APOE genotypes within human populations, characterization of cell-type specific transcriptomes across APOE genotypes has been challenging. Here, we integrated previous and newly generated single-nuclei sequencing (snRNA-seq) data in the prefrontal cortex (PFC) from individuals across APOE genotypes (2/2, 2/3, 3/3, 3/4, 4/4). Clustering analysis revealed distinct excitatory and microglial subpopulations that were uniquely enriched or depleted for APOE4/4 AD. Notably, an excitatory neuronal cluster exhibited neurofibrillary tangle (NFT) signatures and was selectively depleted in APOE4/4 AD cases. In addition, several microglial subpopulations were influenced by both APOE4 dosage and disease status. Among these, the putative AD risk gene FRMD4A emerged as APOE4 dose and AD-dependent. These findings were validated by RNAscope in an extended cohort. Together, our findings provide insights into how APOE4 reshapes cellular states and contributes to cell-type-specific vulnerability in AD.

neuroscience↗
bioRxiv2025

An Open, Fully-processed, Longitudinal Data Resource to Study Brain Development and Transdiagnostic Executive Function

Executive function (EF) develops rapidly during adolescence. However, deficits in EF also emerge in adolescence, representing a transdiagnostic symptom associated with many forms of psychopathology. To promote transdiagnostic research on EF during development, we introduce a new data resource - the Penn Longitudinal Executive functioning in Adolescent Development study (Penn LEAD) - that combines longitudinal multimodal imaging data with rich clinical and cognitive phenotyping. These data include 225 imaging sessions from 132 individuals (8-16 years old at the time of enrollment) who are typically developing (27.3%), or meet criteria for attention-deficit hyperactivity disorder (20.5%) or the psychosis-spectrum (52.3%). In addition to phenotypic data from multiple cognitive tasks focused on EF, the study includes data from structural MRI, diffusion MRI, n-back task fMRI, resting-state fMRI, and arterial spin-labeled MRI. Notably, all raw data, fully-processed derived data, and detailed quality control recommendations are publicly shared on OpenNeuro. We anticipate that such analysis-ready data will accelerate research on EF development in psychiatry.

neuroscience↗
bioRxiv2025

Comparing Brain-Behavior Relationships Across Dimensional, Tail-Sampled, and Propensity-Matched Models

Large population cohorts are needed to perform brain-wide association studies (BWAS), with evidence that sampling from the tails of a distribution increases effect sizes and improves reproducibility. However, studies rarely compare how variability in sample sociodemographic characteristics relates to imaging or behavioral phenotypes within BWAS. To address this gap, we derived estimates for brain-behavior associations using multivariate regression models, comparing effect sizes for dimensional, tail-sampled, and propensity matched groups. Data were obtained from the Adolescent Brain Cognitive Development (ABCD) Study(R). The independent variables were brain structural imaging phenotypes, with a range of biological and psychological outcomes as dependent variables. We found expected increases in the magnitude of effect sizes moving from full-sample dimensional models to tail-sampled group-based models. However, findings for the propensity-matched group models suggested a non-uniform impact on BWAS (i.e., both increased and decreased effect sizes). Results suggest that sampling from the tails of the distribution of measures of brain structure generally increases effect sizes across biological, clinical, and cognitive outcomes.

neuroscience↗
bioRxiv2024

Lack of Oncomodulin Increases ATP-Dependent Calcium Signaling and Susceptibility to Noise in Adult Mice

Tight regulation of Ca2+ is crucial for the function of cochlear outer hair cells (OHCs). Dysregulation of Ca2+ homeostasis in OHCs is associated with impaired hearing and contributes to increased vulnerability to insults such as noise exposure. Ca2+ signaling in developing OHCs is modulated by oncomodulin (OCM), an EF-hand calcium-binding protein. Here, we investigated whether the lack of OCM disrupts the control of intracellular Ca2+ in mature OHCs, and influences vulnerability to acoustic injury. Using young adult CBA/CaJ mice, we found that OHCs from Ocm-knockout (Ocm-/-) mice showed normal biophysical profiles, electromotile responses, and synaptic innervation compared to littermate controls. Moderate noise exposure (95 dB SPL, 2 hrs) caused temporary threshold shifts in Ocm+/+ and Ocm-/- mice. However, while Ocm+/+ fully recovered thresholds 2 weeks after noise exposure, Ocm-/- mice showed permanent threshold shifts. Additionally, Ocm-/- mice had auditory brainstem responses with highly variable latencies and amplitudes both before and after noise exposure compared to Ocm+/+ mice. Using a genetically encoded Ca2+ sensor (GCaMP6s) expressed in Ocm+/+ and Ocm-/- OHCs, we found that prolonged noise exposure (95 dB SPL, 9 hrs) significantly increased GCaMP6s fluorescence, ATP-induced Ca2+ signaling and also caused greater threshold shifts in Ocm-/- compared to Ocm+/+ OHCs. However, prolonged noise exposure had no significant change in the number of presynaptic OHC ribbons in either Ocm+/+ or Ocm-/- mice. We assessed whether the ATP-induced responses were due to changes in P2X2 receptor expression. Prior to noise exposure, P2X2 expression was higher in the cochlea of Ocm-/- mice compared to Ocm+/+ mice. Following prolonged noise, P2X2 receptors were upregulated in the cochlea of Ocm+/+ but not in the Ocm-/- mice, which retained their pre-noise expression level. We propose that the lack of OCM increases susceptibility to cochlear pathology and that purinergic signaling and dysregulation of cytosolic Ca2+ homeostasis likely contribute to early onset hearing loss in the Ocm-/- mice.

neuroscience↗
bioRxiv2023

Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

neuroscience↗
bioRxiv2022

ModelArray: a memory-efficient R package for statistical analysis of fixel data

Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel data exist, currently available tools are memory intensive, difficult to scale to large datasets, and support only a limited number of statistical models. Here we introduce ModelArray, a memory-efficient R package for mass-univariate statistical analysis of fixel data. With only several lines of code, even large fixel datasets can be analyzed using a standard personal computer. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n=938). ModelArray required far less memory than existing tools and revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides an efficient and flexible platform for statistical analysis of fixel data. HIGHLIGHTSO_LIModelArray is an R package for mass-univariate statistical analysis of fixel data C_LIO_LIModelArray is memory-efficient even for large-scale datasets C_LIO_LIModelArray supports linear and nonlinear modeling and is extensible to more models C_LIO_LIModelArray facilitates easy statistical analysis of large-scale fixel data C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/499631v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@1946469org.highwire.dtl.DTLVardef@14c3076org.highwire.dtl.DTLVardef@10197beorg.highwire.dtl.DTLVardef@19170cd_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗
bioRxiv2022

Curation of BIDS (CuBIDS): a workflow and software package for streamlining reproducible curation of large BIDS datasets

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled "Curation of BIDS" (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad--a version control software package for data--to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets. HIGHLIGHTSO_LICuBIDS is a workflow and software package for curating BIDS data. C_LIO_LICuBIDS summarizes the heterogeneity in a BIDS dataset. C_LIO_LICuBIDS prepares BIDS data for successful preprocessing pipeline runs. C_LIO_LICuBIDS helps users perform metadata-based quality control. C_LI

neuroscience↗
bioRxiv2021

Dissociable Multi-scale Patterns of Development in Personalized Brain Networks

The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derived personalized functional networks across 29 scales in a large sample of youths (n=693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling systematically adhered to and strengthened a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks dissociably mediated the emergence of executive function. These results delineate maturation of multi-scale brain networks, which varies according to a functional hierarchy and impacts cognitive development.

neuroscience↗
bioRxiv2021

Oncomodulin Delays Age-Related Hearing Loss in C57 and CBA Mice

Ca2+ signaling is a major contributor to sensory hair cell function in the cochlea. Oncomodulin (OCM) is a Ca2+ binding protein preferentially expressed in outer hair cells of the cochlea and few other specialized cell types. Here, we expand on our previous reports and show that OCM prevents early progressive hearing loss in mice of two different genetic backgrounds: CBA/CaJ and C57BI/6J. In both backgrounds, genetic disruption of Ocm leads to early progressive hearing loss as measured by auditory brainstem response (ABR) and distortion product otoacoustic emission (DPOAE). In both strains, loss of Ocm reduced hearing across lifetime (hearing span) by more than 50% relative to wild type (WT). Even though the two WT strains have very different hearing spans, OCM plays a considerable and similar role within their genetic environment to regulate hearing function. The accelerated ARHL of the Ocm KO illustrates the importance of Ca2+ signaling in maintaining hearing health.

neuroscience↗
bioRxiv2021

Mobile Footprinting: Linking Individual Distinctiveness in Mobility Patterns to Mood, Sleep, and Brain Functional Connectivity

Mapping individual differences in behavior is fundamental to personalized neuroscience. Here, we establish that statistical patterns of smartphone-based mobility features represent unique "footprints" that allow individual identification. Critically, mobility footprints exhibit varying levels of person-specific distinctiveness and are associated with individual differences in affective instability, circadian irregularity, and brain functional connectivity. Together, this work suggests that real-world mobility patterns may provide an individual-specific signature linking brain, behavior, and mood.

neuroscience↗
bioRxiv2021

FlywheelTools: Data Curation and Manipulation on the Flywheel Platform

The recent and growing focus on reproducibility in neuroimaging studies has led many major academic centers to use cloud-based imaging databases for storing, analyzing, and sharing complex imaging data. Flywheel is one such database platform that offers easily accessible, large-scale data management, along with a framework for reproducible analyses through containerized pipelines. The Brain Imaging Data Structure (BIDS) is a data storage specification for neuroimaging data, but curating neuroimaging data into BIDS can be a challenging and time-consuming task. In particular, standard solutions for BIDS curation are not designed for use on cloud-based systems such as Flywheel. To address these challenges, we developed "FlywheelTools", a software toolbox for reproducible data curation and manipulation on Flywheel. FlywheelTools includes two elements: fw-heudiconv, for heuristic-driven curation of data into BIDS, and flaudit, which audits and inventories projects on Flywheel. Together, these tools accelerate reproducible neuroscience research on the widely used Flywheel platform.

bioinformatics↗
bioRxiv2019

Advantages of Multi-shell Diffusion Models for Studies of Brain Development in Youth

Diffusion tensor imaging (DTI) has advanced our understanding of how brain microstructure evolves over development. However, the proliferation of multi-shell diffusion imaging sequences has coincided with notable advances in the modeling of neuronal diffusion patterns, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). The relative utility of these newer diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the relative vulnerability of these models to in-scanner motion has not been described. Accordingly, in a sample of 123 youth (ages 12-30) we evaluated DTI, NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales, including mean white matter values, voxelwise analyses, and tractography-based structural brain networks. Our results reveal that multi-shell diffusion imaging sequences can be leveraged to robustly characterize neurodevelopment, even within the framework of DTI. However, these metrics of diffusion are variably impacted by motion, highlighting the importance of modeling choices for studies of movement-prone populations. Our findings suggest that while traditional DTI is sensitive to neurodevelopmental trends, contemporary modeling techniques confer key advantages for neurodevelopmental inquiries.

neuroscience↗
bioRxiv2019

Accelerated Cortical Thinning within Structural Brain Networks is Associated with Irritability in Youth

BackgroundIrritability is an important dimension of psychopathology that spans multiple clinical diagnostic categories, yet its relationship to patterns of brain development remains sparsely explored. Here, we examined how trans-diagnostic symptoms of irritability relate to the development of structural brain networks.\n\nMethodsAll participants (n=144, 87 females) completed structural brain imaging with 3 Tesla MRI at two timepoints (mean age at follow-up: 20.9 years, mean inter-scan interval: 5.1 years). Irritability at follow-up was assessed using the Affective Reactivity Index, and cortical thickness was quantified using Advanced Normalization Tools software. Structural covariance networks were delineated using non-negative matrix factorization, a multivariate analysis technique. Both cross-sectional and longitudinal associations with irritability at follow-up were evaluated using generalized additive models with penalized splines. The False Discovery Rate (q<0.05) was used to correct for multiple comparisons.\n\nResultsCross-sectional analysis of follow-up data revealed that 11 of the 24 covariance networks were associated with irritability, with higher levels of irritability being associated with thinner cortex. Longitudinal analyses further revealed that accelerated cortical thinning within 9 networks was related to irritability at follow-up. Effects were particularly prominent in brain regions implicated in emotion regulation, including the orbitofrontal, lateral temporal, and medial temporal cortex.\n\nConclusionsCollectively, these findings suggest that irritability is associated with widespread cortical thickness reductions and accelerated cortical thinning, particularly within frontal and temporal cortex. Aberrant structural maturation of regions important for emotional regulation may in part underlie symptoms of irritability.

neuroscience↗