Biology subjects

Dorr, A.

Publications and source records attributed to Dorr, A..

4 recordsLinked to original sources

bioRxiv2026

Aberrant cortical reorganization and astrogliosis underlie memory deficit post-traumatic brain injury: a longitudinal multiscale multimodal study

Integrating high-dimensional neuroimaging data across spatial scales remains a fundamental bottleneck in quantitative neuroscience. Here, we apply emerging analytical techniques including functional connectome gradient mapping and 3D segmentation of whole-brain cellular resolution microscopy data in a mouse model of traumatic brain injury. At the macroscale, longitudinal functional MRI revealed a transition from frontal hyperconnectivity to hippocampal hypoconnectivity and unimodal-transmodal gradient contraction indicating network dedifferentiation. At the cellular scale, deep-learning based segmentation of whole-brain light-sheet fluorescence microscopy volumes quantified regional astrogliosis, neuronal hyperactivity, and single-axon loss undetected by standard diffusion MRI. Multiscale multimodal data integration not only identified quantitative biomarkers predictive of memory performance post-injury but also enabled validation of in vivo modalities against more sensitive ex vivo histology. This study demonstrates a framework for probing complex multi-network cognitive functions and neurological disorders of network dysfunction.

neuroscience↗

bioRxiv2026

Chronic Stress Exacerbates Long-term Microvascular Network Dysfunction Following Brain Trauma

BackgroundPreexisting factors are among the strongest predictors of recovery following traumatic brain injury (TBI), with chronic stress closely associated with permanent disability and worse long-term outcomes. While chronic cerebrovascular dysfunction is linked to these poor trajectories, as impaired blood flow regulation drives secondary disease progression, the mechanisms regulating this vascular failure remain incompletely understood. Crucially, how premorbid chronic stress and TBI disrupt the fundamental coordination of the cerebrovascular network post-injury remains unknown. MethodsTo elucidate this coordination at rest and in response to increased neuronal activity, we used a model of three repeated moderate closed cortical impacts comorbid with chronic stress induced by social isolation (SI) post-weaning. Our previously developed vascular analysis pipeline (NOVAS3D) was employed to estimate changes in vascular radii across cerebrovasculature proximal to neuronal activation. Arteries and veins were annotated in the imaged volumes to allow for blood flow simulations. ResultsUsing graph-based network analysis, we demonstrate that TBI, when compounded with chronic stress, critically disrupts the long-range coordination of the capillary network. Specifically, the functional coordination of radius changes between nearby, non-adjacent capillaries was reduced by 40{+/-}20% in TBI+SI mice relative to controls. Consequently, simulations estimated that the vascular networks in TBI+SI mice experienced a 68{+/-}7% reduction in arterial red blood cell velocity (VRBC) responses to neuronal activation. These network-wide impairments were fundamentally driven by severely blunted vessel reactivity, including a 28{+/-}7% decrease in the magnitude of arteriolar dilations and a 47{+/-}7% decrease in the magnitude of arteriolar constrictions. ConclusionThese findings provide a mechanistic foundation for worse clinical outcomes seen in TBI patients with comorbid chronic stress, identifying arteriolar reactivity and long-range capillary coordination as critical therapeutic targets to mitigate secondary injury and improve long-term recovery.

neuroscience↗

bioRxiv2025

A self-supervised deep learning pipeline for segmentation in two-photon fluorescence microscopy

Two-photon fluorescence microscopy (TPFM) allows in situ investigation of the structure and function of the brain at a cellular level, but the conventional image analyses of TPFM data are labour-intensive. Automated deep learning (DL)-based image processing pipelines used to analyze TPFM data require large labeled training datasets. Here, we developed a self supervised learning (SSL) pipeline to test whether unlabeled data can be used to boost the accuracy and generalizability of DL models for image segmentation in TPFM. We specifically developed four pretext tasks, including shuffling, rotation, axis rotation, and reconstruction, to train models without supervision using the UNet architecture. We validated our pipeline on two tasks (neuronal soma and vasculature segmentation), using large 3D microscopy datasets. We introduced a novel density-based metric, which provided more sensitive evaluation to downstream analysis tasks. We further determined the amount of labeled data required to reach performance on par with fully supervised learning (FSL) models. SSL-based models that were fine-tuned with only 50% of data were on par or superior (e.g., Dice increase of 3% for neuron segmentation and Dice score of 0.88 {+/-} 0.09 for vessel segmentation) to FSL models. We demonstrated that segmentation maps generated by SSL models pretrained on the reconstruction and rotation tasks can be better translated to downstream tasks than can other SSL tasks. Finally, we benchmarked all models on a publicly available out-of-distribution dataset, demonstrating that SSL models outperform FSL when trained with clean data, and are more robust than FSL models when trained with noisy data.

neuroscience↗

bioRxiv2024

A Deep Learning Pipeline for Mapping in situ Network-level Neurovascular Coupling in Multi-photon Fluorescence Microscopy

1Functional hyperaemia is a well-established hallmark of healthy brain function, whereby local brain blood flow adjusts in response to a change in the activity of the surrounding neurons. Although functional hyperemia has been extensively studied at the level of both tissue and individual vessels, vascular network-level coordination remains largely unknown. To bridge this gap, we developed a deep learning-based computational pipeline that uses two-photon fluorescence microscopy images of cerebral microcirculation to enable automated reconstruction and quantification of the geometric changes across the microvascular network, comprising hundreds of interconnected blood vessels, pre and post-activation of the neighbouring neurons. The pipelines utility was demonstrated in the Thy1-ChR2 optogenetic mouse model, where we observed network-wide vessel radius changes to depend on the photostimulation intensity, with both dilations and constrictions occurring across the cortical depth, at an average of 16.1{+/-}14.3 m (mean{+/-}stddev) away from the most proximal neuron for dilations; and at 21.9{+/-}14.6 m away for constrictions. We observed a significant heterogeneity of the vascular radius changes within vessels, with radius adjustment varying by an average of 24 {+/-} 28% of the resting diameter, likely reflecting the heterogeneity of the distribution of contractile cells on the vessel walls. A graph theory-based network analysis revealed that the assortativity of adjacent blood vessel responses rose by 152 {+/-} 65% at 4.3 mW/mm2 of blue photostimulation vs. the control, with a 4% median increase in the efficiency of the capillary networks during this level of blue photostimulation in relation to the baseline. Interrogating individual vessels is thus not sufficient to predict how the blood flow is modulated in the network. Our computational pipeline, to be made openly available, enables tracking of the microvascular network geometry over time, relating caliber adjustments to vessel wall-associated cells state, and mapping network-level flow distribution impairments in experimental models of disease.

neuroscience↗