Biology subjects

Attarpour, A.

Publications and source records attributed to Attarpour, A..

6 recordsLinked to original sources

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

Quantitative profiling of whole-brain connectomes at single-axon resolution using deep learning and high-resolution light sheet microscopy

Revealing how individual axons create a brain-wide connectome would be indispensable for understanding brain function and behavior, yet remains technically challenging. We introduce MAPL3, an end-to-end pipeline that integrates self-supervised learning with an innovative deep architecture to capture local and global brain-wide axonal projections. MAPL3 enables subject- and population-level quantitative laminar analysis, generalizes across experiments, and outperforms state-of-the-art methods. We showcase its ability to map the circuitry of the orbitofrontal cortex from single axons to whole-brain projectome.

neuroscience↗

bioRxiv2025

Mapping cellular targets of covalent cancer drugs in the entire mammalian body

As our understanding of biological systems reaches single-cell and high spatial resolutions, it becomes imperative that pharmacological approaches match this precision to understand drug actions. This need is particularly urgent for the targeted covalent inhibitors that are currently re-entering the stage for cancer treatments. By leveraging the unique kinetics of click reactions, we developed volumetric clearing-assisted tissue click chemistry (vCATCH) to enable deep and homogeneous click labeling across the 3D mammalian body. With simple and passive incubation steps, vCATCH offers cellular resolution drug imaging in the entire adult mouse. We combined vCATCH with HYBRiD imaging and virtual reality to visualize and quantify in vivo targets of two clinical cancer drugs, afatinib and ibrutinib, which recapitulated their known pharmacological distribution and revealed previously unreported tissue and cell type engagement potentially linked to off-target effects. vCATCH provides a body-wide, unbiased platform to map covalent drug engagements at unprecedented scale and precision.

pharmacology and toxicology↗

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↗

bioRxiv2020

Vascular origins of low-frequency oscillations in the cerebrospinal fluid signal in resting-state fMRI: Interpretation using photoplethysmography

Slow and rhythmic spontaneous oscillations of cerebral blood flow are well known to have diagnostic utility, notably frequencies of 0.008-0.03 Hz (B-waves) and 0.05-0.15Hz (Mayer waves or M waves). However, intracranial measurements of these oscillations have been difficult. Oscillations in the cerebrospinal fluid (CSF), which are influenced by the cardiac pulse wave, represent a possible avenue for non-invasively tracking these oscillations using resting-state functional MRI (rs-fMRI), and have been used to correct for vascular oscillations in rs-fMRI functional connectivity calculations. However, the relationship between low-frequency CSF and vascular oscillations is unclear. In this study, we investigate this relationship using fast simultaneous multi-slice rs-fMRI coupled with fingertip photoplethysmography (PPG). We not only extract B-wave and M-wave range spectral power from the PPG signal, but also derive the pulse-intensity ratio (PIR, a surrogate of slow blood-pressure oscillations), the second-derivative of the PPG (SDPPG, a surrogate of arterial stiffness) and heart-rate variability (HRV). The main findings of this study are: (1) signals in different CSF regions (ROIs) are not equivalent in their vascular contributions or in their associations with vascular and tissue rs-fMRI signals; (2) the PPG signal contains the highest signal contribution from the M-wave range, while PIR contains the highest signal contribution from the B-wave range; (3) in the low-frequency range, PIR is more strongly associated with rs-fMRI signal in the CSF than PPG itself, and than HRV and SDPPG; (4) PPG-related vascular oscillations only contribute to < 20% of the CSF signal in rs-fMRI, insufficient support for the assumption that low-frequency CSF signal fluctuations directly reflect vascular oscillations. These findings caution the use of CSF as a monolithic region for extracting physiological nuisance regressors in rs-fMRI applications. They also pave the way for using rs-fMRI in the CSF as a potential tool for tracking cerebrovascular health through, for instance the strong relationship between PIR and the CSF signal.

physiology↗