Biology subjects

Bryant, A. G.

Publications and source records attributed to Bryant, A. G..

9 recordsLinked to original sources

bioRxiv2026

Subcortex visualization: A toolbox for custom data visualization in the subcortex and cerebellum

Recent years have seen an expanding repertoire of code-based tools for visualizing neuroimaging data, promoting reproducibility and interpretability of brain-mapping findings. However, most open-source visualization packages for the human brain are geared toward the cerebral cortex, and the comparatively fewer options for the subcortex and cerebellum are limited in scope (i.e., atlas support) and flexibility. We address this critical gap by introducing subcortex_visualization, an open-source package offered in both Python and R, that provides a unified and accessible framework for programmatically visualizing non-cortical region-level data across many popular atlases for the subcortex (including thalamic nuclei and the brainstem) and cerebellum. These two-dimensional vector-based visuals are inspired by the ggseg R package for cortical data, which implements standardized rendering conventions to facilitate comparison across atlases in a vectorized two-dimensional format. In addition to the vectorized versions of twelve atlases--to our knowledge, the largest collection of non-cortical atlases in a single vector-based brain visualization toolbox--we also provide a step-by-step tutorial for users to generate custom vector-based visualizations from any given brain segmentation, enabling flexible extension to new atlases and structures. Collectively, subcortex_visualization and the accompanying documentation support reproducible and interpretable visualization of region-level data below the cortical mantle.

neuroscience↗

bioRxiv2025

Mapping functional, structural, and transcriptomic correlates of homotopic connectivity

Homotopic functional connectivity (HoFC)--the synchronous activity between homologous regions of the left and right cortical hemispheres--is a hallmark of inter-hemispheric brain architecture, yet its biological underpinnings remain incompletely understood. Here, we characterize spatial variation in resting-state HoFC and its relation to dominant natural axes of anatomical, functional, and transcriptomic organization across the human cerebral cortex. We show that the regional distribution of HoFC, from the lowest in association areas (especially limbic) to the highest in primary sensorimotor regions, is preserved in neuropsychiatric disorders in a separate neuroimaging cohort (including schizophrenia, bipolar I disorder, and attention-deficit hyperactivity disorder) despite disease-associated perturbations in magnitude. Regional variation in HoFC is not fully explained by properties of physical embedding or vascular innervation in the brain, suggesting involvement of alternative physiological processes. Furthermore, we find that regions with stronger HoFC are more functionally connected within and across hemispheres at rest, suggesting that homotopic functional coupling is positively associated with brain-wide functional synchrony. Our results collectively suggest that HoFC is inherently aligned with a fundamental natural axis of macroscopic cortical organization and shared global signal--advancing our understanding of the biological correlates of cortical homotopy and suggesting potential mechanisms to investigate in the future.

neuroscience↗

bioRxiv2025

A data-driven approach to identifying and evaluating connectivity-based neural correlates of consciousness

Identifying the neural correlates of conscious visual perception remains a major challenge in neuroscience, requiring theories that bridge between subjective experience and measurable neural correlates. However, theoretical interpretation of empirical evidence is often post hoc and susceptible to confirmation bias. Building upon the adversarial collaboration mediated by the COGITATE Consortium, we present a generalizable approach for the data-driven identification, evaluation, and theoretical modeling of connectivity-based neural correlates of conscious visual perception. Using the same magnetoencephalography (MEG) dataset and accompanying pre-registered hypotheses from the COGITATE Consortium, we systematically compared 246 functional connectivity (FC) measures between regions predicted to underlie conscious vision by Integrated Information Theory (IIT) and/or Global Neuronal Workspace Theory (GNWT). We identified a family of FC measures based on the barycenter--tracking the center of mass between two signals--as the top-performing stimulus decoding measures that generalize across regions central to predictions of both IIT and GNWT. To interpret these findings within a theoretical framework, we developed neural mass models that recapitulate the neural dynamics hypothesized to underlie conscious perception by each theory. Comparing simulated barycenter values from these models against empirically measured MEG data revealed that both the GNWT-based model, featuring delayed ignition dynamics, and the IIT-based model, which relied on synchronous sensory dynamics, captured the observed connectivity patterns. These results lend tentative support to GNWT, as the presence of ignition dynamics independent of task-demand conditions contradicts the predictions of IIT. Beyond dataset-specific conclusions and limitations, we introduce a framework for systematically identifying and testing candidate neural correlates of conscious visual perception in an unbiased and interpretable manner.

neuroscience↗

bioRxiv2025

Benchmarking overlapping community detection methods for applications in human connectomics

Brain networks exhibit non-trivial modular organization, with groups of densely connected areas participating in specialized functions. Traditional community detection algorithms assign each node to one module, but this representation cannot capture integrative, multi-functional nodes that span multiple communities. Despite the increasing availability of overlapping community detection algorithms (OCDAs) to capture such integrative nodes, there is no objective procedure for selecting the most appropriate method and its parameters for a given problem. Here we overcome this limitation by introducing a datadriven method for selecting an OCDA and its parameters from performance on a tailored ensemble of generated benchmark networks, assessing 22 unique algorithms and parameter settings. Applied to the human structural connectome, we find that the Order Statistics Local Optimization Method (OSLOM) best identifies ground-truth overlapping structure in the benchmark ensemble and yields a seven-network decomposition of the human cortex. These modules are bridged by fifteen overlapping regions that generally sit at the apex of the putative cortical hierarchy--suggesting integrative, higher-order function-- with network participation increasing along the cortical hierarchy, a finding not supported using a non-overlapping modular decomposition. This data-driven approach to selecting OCDAs is applicable across domains, opening new avenues to detecting and quantifying informative structures in complex real-world networks.

neuroscience↗

bioRxiv2025

Cerebellar and Subcortical Contributions to Working Memory Manipulation

Working memory enables us to temporarily store and manipulate information, a crucial function for problem-solving. However, most working memory models emphasize cortical interactions ignoring contributions from subcortical and cerebellar regions. Given the dense connectivity between the cerebellum, subcortex, and cortex, we hypothesize that these regions provide unique contributions during working memory manipulation. We tested this hypothesis using functional Magnetic Resonance Imaging (fMRI) to measure blood oxygen-level dependent (BOLD) activity during a mental rotation task, where participants judged whether rotated pairs of three-dimensional stimuli were identical. Our results revealed a distributed network spanning the cortex, subcortex, and cerebellum that differentiates rotated from non-rotated stimuli and correct from incorrect responses. BOLD recruitment in these regions increased with larger angles of rotation. We observed delayed responses in premotor, subcortical, and cerebellar regions during incorrect trials. These findings suggest that cerebellar and subcortical regions support working memory manipulation, highlighting a broader mechanism by which distributed brain regions interact to coordinate higher cognitive functions.

neuroscience↗

bioRxiv2024

Molecular profiling of frontal and occipital subcortical white matter hyperintensities in Alzheimer's disease

White matter hyperintensities (WMHs) are commonly detected on T2-weighted magnetic resonance imaging (MRI) scans, occurring in both typical aging and Alzheimers disease. Despite their frequent appearance and their association with cognitive decline, the molecular factors contributing to WMHs remain unclear. In this study, we investigated the transcriptomic profiles of two commonly affected brain regions with coincident AD pathology--frontal subcortical white matter (frontal-WM) and occipital subcortical white matter (occipital-WM)--and compared with age-matched healthy controls. Through RNA-sequencing in frontal- and occipital-WM bulk tissues, we identified an upregulation of genes associated with brain vasculature function in AD white matter. To further elucidate vasculature-specific transcriptomic features, we performed RNA-seq analysis on blood vessels isolated from these white matter regions, which revealed an upregulation of genes related to protein folding pathways. Finally, comparing gene expression profiles between AD individuals with high-versus low-WMH burden showed an increased expression of pathways associated with immune function. Taken together, our study characterizes the diverse molecular profiles of white matter changes in AD compared to normal aging and provides new mechanistic insights processes underlying AD-related WMHs.

neuroscience↗

bioRxiv2024

Extracting interpretable signatures of whole-brain dynamics through systematic comparison

The brains complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

neuroscience↗

bioRxiv2023

Endothelial Cells are Heterogeneous in Different Brain Regions and are Dramatically Altered in Alzheimer's Disease

Vascular endothelial cells play an important role in maintaining brain health, but their contribution to Alzheimers disease (AD) is obscured by limited understanding of the cellular heterogeneity in normal aged brain and in disease. To address this, we performed single nucleus RNAseq on tissue from 32 AD and non-AD donors each with five cortical regions: entorhinal cortex, inferior temporal gyrus, prefrontal cortex, visual association cortex and primary visual cortex. Analysis of 51,586 endothelial cells revealed unique gene expression patterns across the five regions in non-AD donors. Alzheimers brain endothelial cells were characterized by upregulated protein folding genes and distinct transcriptomic differences in response to amyloid beta plaques and cerebral amyloid angiopathy (CAA). This dataset demonstrates previously unrecognized regional heterogeneity in the endothelial cell transcriptome in both aged non-AD and AD brain. Significance StatementIn this work, we show that vascular endothelial cells collected from five different brain regions display surprising variability in gene expression. In the presence of Alzheimers disease pathology, endothelial cell gene expression is dramatically altered with clear differences in regional and temporal changes. These findings help explain why certain brain regions appear to differ in susceptibility to disease-related vascular remodeling events that may impact blood flow.

neuroscience↗

bioRxiv2022

Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer's disease

Astrocytes play a critical role in brain homeostasis and normal functions but their changes along the spatiotemporal progression of Alzheimers disease (AD) neuropathology remain largely unknown. Here we performed single-nucleus RNA-sequencing on brain regions along the stereotypical progression of AD pathology from donors ranging the entire normal aging-AD continuum comprising 628,943 astrocyte nuclei from 32 donors across 5 brain regions. We discovered temporal gene-expression-trajectories with gene sets differentially activated at various disease stages. Surprisingly, a gene set enriched in proteostasis and energy metabolism, was upregulated in late-stage but unexpectedly returned to baseline levels in end-stage, suggesting exhaustion of response in "burnt-out" astrocytes. The spatial gene-expression-trajectories revealed that astrocytic genes of tripartite synapses are dysregulated in parallel to the stereotypical progression of tangle pathology across regions. We identified astrocyte heterogeneity across brain regions with a continuum from homeostatic to reactive cells through "intermediate" transitional states. These findings suggest complex astrocytic dysfunction in AD neurodegeneration.

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