Biology subjects

Van Horn, J. D.

Publications and source records attributed to Van Horn, J. D..

12 recordsLinked to original sources

bioRxiv2026

Widely Used Calculations of Intracranial Volume Provide Inaccurate Estimates in Developmental Cohorts: Evidence and Solutions for the Neuroimaging Community

Intracranial volume (ICV) is often estimated as part of automated neuroimaging processing pipelines and commonly used to account for and characterize cranial size. FreeSurfer's estimated intracranial volume (eTIV) produces implausible longitudinal estimates in developmental samples when using versions prior to FreeSurfer 8. Across two different adolescent datasets, we document eTIV changes of up to 39% between 1.5 - 2 year sessions for FreeSurfer 7, with errors more pronounced on Siemens scanners and in female participants. These errors are markedly reduced in FreeSurfer 8 and AssemblyNet. However, the widespread use of FreeSurfer 7 raises concern about developmental neuroimaging studies that utilize eTIV without quality control.

neuroscience↗

bioRxiv2026

Altered biological aging-related brain profile in adolescents with autism: A neuroimaging study based on DunedinPACNI

Individuals with Autism Spectrum Disorder (ASD) show increased rates of physical health conditions across major organ systems, some of which are commonly linked with aging, suggesting that body-wide biological profiles may be altered compared to neurotypical controls. Since brain structure associates with peripheral physiology and biomarkers of aging, it may inform understanding of broader biological differences and physical health risks in ASD. Here, we assessed this possibility using DunedinPACNI, a neuroimaging-based model that estimates the pace of longitudinal aging in peripheral organ systems from brain structural information, in 329 adolescents (8-18 years) from the Autism Centers of Excellence network, with replication in an independent age-matched sample of comparable size from the Autism Brain Imaging Data Exchange. Across both datasets and sensitivity analyses, autistic individuals showed significantly larger DunedinPACNI values than controls, consistent with brain structural features that align with patterns associated with a faster pace of biological aging in adults. Group differences were mainly driven by the volume of the 3rd ventricle, cortical thickness of the left entorhinal cortex, grey matter volume of the right entorhinal cortex and grey-to-white matter ratio in the left temporal pole. No association between DunedinPACNI and core autistic traits was found. Our results provide novel evidence for a possible altered brain-body profile in ASD, motivating future studies combining neuroimaging with peripheral biomarkers to better understand the neurobiology of physical health conditions in autism.

neuroscience↗

bioRxiv2025

Brain morphology network alterations in adolescents with autism spectrum disorder: a sex-stratified study

Neuroimaging studies based on altered functional and structural networks have contributed to better characterizing males and females with Autism Spectrum Disorder (ASD), advancing our understanding of the male prevalence in diagnosis. However, much less is known about how brain networks are altered from a morphological perspective, and whether these alterations may help explain sex-related characteristics in ASD. Here, we used structural MRI from a sex- and diagnosis-balanced sample of 337 individuals in typical neurodevelopmental ages (8-18 years) from the Autism Center of Excellence to elucidate sex-specific alterations in morphology-based connectivity, calculated as the similarity between region-wise multivariate morphological signatures. Network-based statistics showed that ASD males had significantly increased connectivity involving the fusiform gyrus, medial orbitofrontal, entorhinal, and parahippocampal cortices. This network profile was further linked to a core social-communication trait within the autistic group. In females with ASD, increased connectivity was found in a subnetwork primarily implicating the entorhinal cortex, followed by the inferior parietal lobule and lateral occipital cortex. In contrast to males, females fusiform gyrus showed decreased connectivity with the superior temporal sulcus. No overlap between male- and female-specific profiles was found. Together, these findings offer new insights into the neurobiology underlying sex differences in autism.

neuroscience↗

bioRxiv2025

Structural Determinants of Signal Speed: A Multimodal Investigation of Face Processing in Autism Spectrum Disorder

It has not previously been possible to investigate the fundamental relationship between axonal structure - which dictates action potential transmission - and human neuronal function in vivo. Here, we introduce a novel metric of axonal signal speed, estimated axonal latency (EAL), derived from the relationship between axonal diameter, myelination, and length measured via MRI. We validate EAL along two pathways of the face processing network by relating it to N170 latency, an electrophysiological marker of face processing speed measured via EEG. Our results show that EAL along these pathways predicts N170 latency specifically during face processing. Moreover, we demonstrate that individuals with and without autism rely upon different pathways, potentially providing a structural account for autism-related face processing differences. By establishing this relationship between EEG-based electrical function and MRI-based axonal microstructure, we provide a non-invasive, spatially detailed estimate of neuronal processing speed that can inform our understanding of brain function, development, and disorder. TeaserEstimated axonal latency is a non-invasive, spatially detailed measure of neuronal speed to inform brain function and disorder.

neuroscience↗

bioRxiv2025

The NTU-DSI-122 template as a flexible platform for fiber orientation distribution registration of diffusion microstructure into stereotaxic space

The registration of neuroimaging subjects to a common stereotaxic space allows for comparisons between timepoints, subjects, or with reference atlases of regions of interest. Typically, this has been performed by computing a similarity metric between the voxel-wise intensity values in the subject image and a reference image. Diffusion MRI is a method that provides far more detailed voxel-wise information than a simple scalar intensity value. This allows for the potential to use this additional information to perform registration with added within-tissue contrast. In this study, we present a novel use of the NTU-DSI-122 template as a fiber orientation distribution (FOD) template, for the purpose of registering subject dMRI images to stereotaxic space. The reliability and accuracy of this FOD-based registration method are compared to the intensity-based registration method ANTs by registering cellular microstructure maps from two separate cohorts to the NTU-DSI-122 template. The stochastic FOD-based method significantly outperformed the stochastic intensity-based metric on reliability and was able to more consistently register the same subject multiple times independently. The FOD-based method also significantly outperformed the intensity-based metric on registration accuracy by more completely aligning the microstructure maps to the template as measured by the Sorenson-Dice coefficient at multiple percentile thresholds. The NTU-DSI-122 template has the additional benefit of including multiple b-value shells between a wide range of feasible acquisition schemes, making the platform a flexible option for registering acquisitions of varying quality, including clinically acquired data.

neuroscience↗

bioRxiv2025

Diffusion MRI microstructure markers of changes in the human brain across the lifespan

Understanding how the brain develops, matures, ages, and declines is one of the fundamental questions facing neuroscience. Recent advances in diffusion MRI microstructure analysis have allowed for detailed descriptions of neuronal change in humans. However, it is essential that findings from these studies are appropriately contextualized to general age-related changes in the brain. This study uses 3-tissue constrained spherical deconvolution (3T-CSD) to examine the relationship between brain diffusion microstructure and chronological age. 3T-CSD is able to quantify signal fraction measurements at the voxel-wise level from three different tissue microenvironments found in the brain: extracellular free water, intracellular isotropic, and intracellular anisotropic. This study applies 3T-CSD analysis to the Nathanial Kline Institutes Rockland cohort, a large-scale community sample of brain MRI data across the lifespan. Microstructural measurements were taken in a number of structures throughout the white matter, subcortical gray matter, and lobar cortical regions while additionally evaluating lateral differences in microstructural measurements. The general trajectory of signal fraction measurements was a positive relationship with age and extracellular signal fraction, a negative relationship between age and intracellular isotropic signal fraction, and an inverted U-shaped trajectory for the intracellular anisotropic signal fraction. In individual sub-areas these trends tended to still be present, with some notable exceptions. However there were large differences in 3T-CSD microstructure measurements between individual structures, including significant lateral differences between hemispheres for each of the subcortical gray matter structures and for each of the cortical regions. These results demonstrate that 3T-CSD is able to describe age-related change across the brain and lifespan. By using a healthy population cohort this study can be used as a point of comparison for 3T-CSD analysis of microstructure changes in the presence of pathology. Finally, the detailed analysis of lateralized ROI results can inform diffusion microstructure studies examining cortical and subcortical regions.

neuroscience↗

bioRxiv2024

Widespread Associations between Behavioral Metrics and Brain Microstructure in ASD

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. Our lab has previously found that g-ratio, the proportion of axon width to myelin diameter, and axonal conduction velocity, which is associated with the capacity of an axon to carry information, are both decreased in ASD individuals. By associating these differences with performance on cognitive and behavioral tests, this study aims to first associate a broad array of behavioral metrics with neuroimaging markers of ASD, and to explore the prevalence of ASD subtypes using a neuroimaging driven perspective. Analyzing 273 participants (148 with ASD) ages 8 to 17 through an NIH-sponsored Autism Centers of Excellence network (MH100028), we observe widespread associations between behavioral and cognitive evaluations of autism and between behavioral and microstructural metrics, alongside different directional correlations between different behavioral metrics. Stronger associations with individual subcategories from each test rather than summary scores suggest that different neuronal profiles may be masked by composite test scores. Machine learning cluster analyses applied to neuroimaging data reinforce the association between neuroimaging and behavioral metrics and suggest that age-related maturation of brain metrics may drive changes in ASD behavior. This suggests that if ASD can be definitively subtyped, these subtypes may show different behavioral trajectories across the developmental period. Clustering identified a pattern of restrictive and repetitive behavior in some participants and a second group that was defined by high sensory sensitivity and language performance.

neuroscience↗

bioRxiv2023

Conduction Velocity, G-ratio, and Extracellular Water as Microstructural Characteristics of Autism Spectrum Disorder

The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a novel metric termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel neuroimaging metric, aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.

neuroscience↗

bioRxiv2022

A Simple Model of Cortical Intraregional Metastability

Transient synchronization of bursting activity in neural networks, which occurs in patterns of metastable phase relationships between neurons, is a notable feature of network dynamics observed in vivo. However, the mechanisms that contribute to this dynamical complexity in neural circuits are not well understood. Local circuits in cortical regions consist of populations of neurons with diverse intrinsic oscillatory features. In this study, we numerically show that the phenomenon of transient synchronization, also referred to as metastability, emerges in an inhibitory neural population when the neurons intrinsic fast-spiking dynamics are appropriately modulated by slower inputs from an excitatory neural population. Using a compact model of a mesoscopic-scale network consisting of excitatory pyramidal and inhibitory fast-spiking neurons, our work demonstrates a relationship between the frequency of neural oscillations and the features of emergent metastability. In addition, a novel metric is formulated to characterize collective transitions in metastable networks. Finally, we discuss a blueprint to model the whole-brain resting-state dynamics using our scalable representation of intraregional network metastability.

neuroscience↗

bioRxiv2018

Reproducible big data science: A case study in continuous FAIRness

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility--thus ensuring that big data are not hard-to-(re)use data. We compare and contrast our approach with other approaches to big data analysis and reproducibility.

bioinformatics↗

bioRxiv2018

BDQC: a general-purpose analytics tool for domain-blind validation of Big Data

Translational biomedical research is generating exponentially more data: thousands of whole-genome sequences (WGS) are now available; brain data are doubling every two years. Analyses of Big Data, including imaging, genomic, phenotypic, and clinical data, present qualitatively new challenges as well as opportunities. Among the challenges is a proliferation in ways analyses can fail, due largely to the increasing length and complexity of processing pipelines. Anomalies in input data, runtime resource exhaustion or node failure in a distributed computation can all cause pipeline hiccups that are not necessarily obvious in the output. Flaws that can taint results may persist undetected in complex pipelines, a danger amplified by the fact that research is often concurrent with the development of the software on which it depends. On the positive side, the huge sample sizes increase statistical power, which in turn can shed new insight and motivate innovative analytic approaches. We have developed a framework for Big Data Quality Control (BDQC) including an extensible set of heuristic and statistical analyses that identify deviations in data without regard to its meaning (domain-blind analyses). BDQC takes advantage of large sample sizes to classify the samples, estimate distributions and identify outliers. Such outliers may be symptoms of technology failure (e.g., truncated output of one step of a pipeline for a single genome) or may reveal unsuspected \" signal\" in the data (e.g., evidence of aneuploidy in a genome). We have applied the framework to validate real-world WGS analysis pipelines. BDQC successfully identified data outliers representing various failure classes, including genome analyses missing a whole chromosome or part thereof, hidden among thousands of intermediary output files. These failures could then be resolved by reanalyzing the affected samples. BDQC both identified hidden flaws as well as yielded new insights into the data. BDQC is designed to complement quality software development practices. There are multiple benefits from the application of BDQC at all pipeline stages. By verifying input correctness, it can help avoid expensive computations on flawed data. Analysis of intermediary and final results facilitates recovery from aberrant termination of processes. All these computationally inexpensive verifications reduce cryptic analytical artifacts that could seriously preclude clinical-grade genome interpretation. BDQC is available at https://github.com/ini-bdds/bdqc.

bioinformatics↗

bioRxiv2018

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data Across 27 Tissue Types

There is intense interest in mapping the tissue-specific binding sites of transcription factors in the human genome to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting provides a means to predict genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. However, despite the public availability of DNase-seq data for hundreds of samples, there is neither a unified analytical workflow nor a publicly accessible database providing the locations of footprints across all available samples. Here, we implemented a workflow for uniform processing of footprints using two state-of-the-art footprinting algorithms: Wellington and HINT. Our workflow scans the footprints generated by these algorithms for 1,530 sequence motifs to predict binding sites for 1,515 human transcription factors. We applied our workflow to detect footprints in 192 DNase-seq experiments from ENCODE spanning 27 human tissues. This collection of footprints describes an expansive landscape of potential TF occupancy. At thresholds optimized through machine learning, we report high-quality footprints covering 9.8% of the human genome. These footprints were enriched for true positive TF binding sites as defined by ChIP-seq peaks, as well as for genetic variants associated with changes in gene expression. Integrating our footprint atlas with summary statistics from genome-wide association studies revealed that risk for neuropsychiatric traits was enriched specifically at highly-scoring footprints in human brain, while risk for immune traits was enriched specifically at highly-scoring footprints in human lymphoblasts. Our cloud-based workflow is available at github.com/globusgenomics/genomics-footprint and a database with all footprints and TF binding site predictions are publicly available at http://data.nemoarchive.org/other/grant/sament/sament/footprint_atlas.

bioinformatics↗