Biology subjects

Goubran, M.

Publications and source records attributed to Goubran, M..

16 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

Stable neuronal representations to repeated stimulation underlie cognitive resilience in Alzheimer's disease pathology

While Alzheimers Disease (AD) typically triggers cognitive decline, some individuals with significant AD pathology maintain normal cognition into late life. Understanding the neuronal underpinnings of such cognitive resilience would propel the development of interventions for delaying dementia. To this end, we used cognitive testing to identify a subset of cognitively resilient 13-month-old TgF344-AD rats (established AD) and their non-transgenic littermates, followed by Neuropixels recording from 8500 neurons during repeated somatosensory stimulation. Cognitively resilient TgF344-AD rats recruited fewer neurons yet displayed more stable neuronal representations during repeated stimulations in cortical excitatory and hippocampal inhibitory ensembles, with reduced excitatory spike burstiness during network activation and a distinct pattern of functional synaptic connectivity. These associations existed independently of amyloid and tau levels. For the first time, our study revealed neuronal population-level hallmarks of maintained cognition that may serve as a novel neurophysiological biomarker of cognitive resilience and a target for stabilizing cognition.

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↗

bioRxiv2024

NEUROG2 regulates a human-specific neurodevelopmental gene regulatory program

Unique hallmarks of human neocortical development include slower rates of neurogenesis and the establishment of an extracellular matrix-rich, outer-subventricular zone that supports basal neural progenitor cell expansion. How gene regulatory networks have evolved to support these human-specific neurodevelopmental features is poorly understood. Mining single cell data from cerebral organoids and human fetal cortices, we found that NEUROG2 expression is enriched in basal neural progenitor cells. To identify and purify NEUROG2-expressing cells and trace their short-term lineage, we engineered two NEUROG2-mCherry knock-in human embryonic stem cell lines to produce cerebral organoids. Transcriptomic profiling of mCherry-high organoid cells revealed elevated expression of PPP1R17, associated with a fast-evolving human-accelerated regulatory region, oligodendrocyte precursor cell and extracellular matrix-associated gene transcripts. Conversely, only neurogenic gene transcripts were enriched in mCherry-high cortical cells from Neurog2:mCherry knock-in mice. Finally, we show that Neurog2 is sufficient to induce Ppp1r17, which slows human neural progenitor cell division, and Col13a1, an extracellular matrix gene, in P19 cells. NEUROG2 thus regulates a human neurodevelopmental gene regulatory program implicated in supporting a pro-proliferative basal progenitor cell niche and tempering the neurogenic pace. SUMMARY STATEMENTTranscriptomic analyses of NEUROG2-mCherry knock-in human embryonic stem cell-derived cerebral organoids reveal a link between NEUROG2 and extracellular matrix remodeling during human cortical development.

developmental biology↗

bioRxiv2022

Microstructural alterations in tract development in college football: a longitudinal diffusion MRI study

Background and ObjectivesRepeated concussive and sub-concussive impacts in high-contact sports can affect the brains microstructure, which can be studied using diffusion MRI. Most prior imaging studies, however, employ a cross-sectional design, do not include low-contact players as controls, or use traditional diffusion tensor imaging without investigating novel tractspecific microstructural metrics. MethodsWe examined brain microstructure in 63 high-contact (American football) and 34 low-contact (volleyball) collegiate athletes with up to 4 years of follow-up (315 total scans) using advanced diffusion MRI, a comprehensive set of multi-compartment models, and automated fiber quantification tools. We investigated diffusion metrics along the length of tracts using nested linear mixed-effects models to ascertain the acute and chronic effects of sub-concussive and concussive impacts, as well as associations between diffusion changes with clinical, behavioral, and sports-related measures. ResultsSignificant longitudinal increases in fractional anisotropy and axonal water fraction were detected in volleyball players, but not in football players, along with decreases in radial and mean diffusivity as well as orientation dispersion index (all findings absolute T-statistic > 3.5, p < .0001). This pattern was present in the callosum forceps minor, left superior longitudinal fasciculus, left thalamic radiation, and right cingulum hippocampus. Longitudinal group differences were more prominent and observed in a larger number of tracts in concussed (previously or in-study) football players (p < .0001), while smaller effects were noted in un-concussed players. An analysis of immediate-post concussion scans in football players demonstrated a transient localized increase in axial diffusivity, mean and radial kurtosis in the left uncinate and right cingulum hippocampus (p < .0001). Finally, football players with high position-based sub-concussive risk demonstrated increased orientation dispersion index over time (p < .0001). DiscussionThe observed longitudinal changes in our volleyball cohort likely reflect normal development in this age range, while the relative attenuation of these effects seen in football, and especially concussed athletes, could possibly reveal diminished myelination, altered axonal calibers, or depressed pruning processes leading to a static, non-decreasing axonal dispersion. This prospective longitudinal study demonstrates significantly divergent tract-specific trajectories of brain microstructure, possibly reflecting a concussive and/or repeated sub-concussive impact-related alteration of normal white matter development in football athletes.

neuroscience↗

bioRxiv2021

Lower Aβ-PET signal in white matter lesions relates to higher extracellular free water in mixed small vessel disease and Alzheimer's pathology

White matter (WM) injury is frequently observed along with dementia. Positron emission tomography with amyloid-ligands (A{beta}-PET) recently gained interest for detecting WM injury. Yet, little is understood about the origin of the altered A{beta}-PET signal in WM regions. Here, we investigated the relative contributions of diffusion MRI-based microstructural alterations, including free water and tissue-specific properties, to A{beta}-PET in WM and to cognition. We included a unique cohort of 115 participants covering the spectrum of low-to-severe white matter hyperintensity (WMH) burden and cognitively normal to dementia. We applied a bi-tensor diffusion-MRI model that differentiates between (i) the extracellular WM compartment (represented via free water), and (ii) the fiber-specific compartment (via free water-adjusted fractional anisotropy [FA]). We observed that, in regions of WMH, a decrease in A{beta}-PET related most closely to higher free water and higher WMH volume. In contrast, in normal-appearing WM, an increase in A{beta}-PET related more closely to higher cortical A{beta} (together with lower free water-adjusted FA). In relation to cognitive impairment, we observed a closer relationship with higher free water than with either free water-adjusted FA or WM PET. Our findings support free water and A{beta}-PET as markers of WM abnormalities in patients with mixed dementia, and contribute to a better understanding of processes giving rise to the WM PET signal.

neuroscience↗

bioRxiv2021

Sex differences in central and peripheral fatigue induced by sustained isometric ankle plantar flexion

The main aim of this study was to determine sex differences in central and peripheral fatigue produced by a sustained isometric exercise of ankle plantar flexors in healthy young adults. Ten males and fourteen females performed a sustained isometric ankle exercise until task failure. Maximal voluntary isometric contraction torque (plantarflexion), voluntary activation level (using the twitch interpolation technique), and twitch contractile properties (twitch peak torque, twitch half relaxation time, and low frequency fatigue index) were measured before, immediately after, and throughout a recovery period (1, 2, 5, and 10 min) following the exercise protocol in order to characterize neuromuscular fatigue. Fatigue had a significant effect (p [≤] 0.05) on all dependent variables. Other than for the maximal voluntary contraction torque, where males showed a greater fatigue-related decrease than females, males and females showed generally similar changes with fatigue. Altogether, our findings indicate no major differences in central or peripheral fatigue mechanisms between males and females to explain a somewhat greater fatigability in males.

neuroscience↗

bioRxiv2021

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation

White matter hyperintensities (WMH) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D Convolutional Neural Network (CNN) with a U-Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. 432 subjects were recruited to train the CNNs from four multi-site imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multi-site study. We compared our model to two established state-of-the-art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U-Net achieved the highest Dice similarity coefficient of 0.89 {+/-} 0.08 and the lowest modified Hausdorff distance of 2.98 {+/-} 4.40 mm. We further validated our models highlighting their robustness on clinical adversarial cases simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io

neuroscience↗

bioRxiv2021

Investigating the Contribution of White Matter Hyperintensities and Cortical Thickness to Empathy in Neurodegenerative and Cerebrovascular Diseases

Introduction: Change in empathy is an increasingly recognised symptom of neurodegenerative diseases and contributes to caregiver burden and patient distress. Empathy impairment has been associated with brain atrophy but its relationship to white matter hyperintensities (WMH) is unknown. We aimed to investigate the relationships amongst WMH, brain atrophy, and empathy deficits in neurodegenerative and cerebrovascular diseases. Methods: 513 participants with Alzheimers Disease/Mild Cognitive Impairment, Amyotrophic Lateral Sclerosis, Frontotemporal Dementia (FTD), Parkinsons Disease, or Cerebrovascular Disease (CVD) were included. Empathy was assessed using the Interpersonal Reactivity Index. WMH were measured using a semi-automatic segmentation and FreeSurfer was used to measure cortical thickness. Results: A heterogeneous pattern of cortical thinning was found between groups, with FTD showing thinning in frontotemporal regions and CVD in left superior parietal, left insula, and left postcentral. Results from both univariate and multivariate analyses revealed that several variables were associated with empathy, particularly cortical thickness in the fronto-insulo-temporal and cingulate regions, sex(female), global cognition, and right parietal and occipital WMH. Conclusions: Our results suggest that cortical atrophy and WMH may be associated with empathy deficits in neurodegenerative and cerebrovascular diseases. Future work should consider investigating the longitudinal effects of WMH and atrophy on empathy deficits in neurodegenerative and cerebrovascular diseases.

neuroscience↗

bioRxiv2020

Cortical thickness estimation in individuals with cerebral small vessel disease, focal atrophy, and chronic stroke lesions.

BackgroundRegional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases can be estimated using specialised neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures. PurposeThe main goal of this study was to examine a correction procedure developed for enhancing FreeSurfers cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and cerebrovascular disease, with varying degrees of neurovascular lesions and brain atrophy. MethodsIn 155 cerebrovascular disease patients enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FreeSurfer outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures. ResultsCorrected procedures increased Acceptable QC ratings from 18% to 76% for the cortical ribbon and from 38% to 92% for tissue segmentation. Corrected procedures reduced Fail ratings from 11% to 0% for the cortical ribbon and 62% to 8% for tissue segmentation. FreeSurfer-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8mL vs. 15.9mL, p<0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p=0.018) and left insula (p=0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p<0.001). ConclusionsThese findings suggest that correction procedures that account for brain atrophy and neurovascular lesions can significantly improve FreeSurfers segmentation results, reduce failure rates, and potentially increase sensitivity to examine brain-behaviour relationships. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and neurodegenerative disease in the ONDRI study.

neuroscience↗

bioRxiv2020

Improved segmentation of the intracranial and ventricular volumes in populations with cerebrovascular lesions and atrophy using 3D CNNs

Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroimaging. We present iCVMapper and VentMapper, robust algorithms that use a convolutional neural network (CNN) to segment the ICV and ventricles from both single and multi-contrast MRI data. Our models were trained on a large dataset from two multi-site studies (N=528 subjects for ICV, N=501 for ventricular segmentation) consisting of older adults with varying degrees of cerebrovascular lesions and atrophy, which pose significant challenges for most segmentation approaches. The models were tested on 238 participants, including subjects with vascular cognitive impairment and high white matter hyperintensity burden. Two of the three test sets came from studies not used in the training dataset. We assessed our algorithms relative to four state-of-the-art ICV extraction methods (MONSTR, BET, Deep Extraction, FreeSurfer), as well as a ventricular segmentation tool (FreeSurfer). Our multi-contrast models outperformed other methods across all evaluation metrics, with average Dice coefficients of 0.98 and 0.94 for ICV and ventricular segmentation respectively. Both models were also the most time efficient, segmenting the structures in orders of magnitude faster than some of the other available methods. Our networks showed an increased accuracy with the use of a conditional random field (CRF) as a post-processing step. We further validated both segmentation models, highlighting their robustness to images with lower resolution and signal-to-noise ratio, compared to tested techniques. The pipeline and models are available at: https://icvmapp3r.readthedocs.io and https://ventmapp3r.readthedocs.io to enable further investigation of the roles of ICV and ventricles in relation to normal aging and neurodegeneration in large multi-site studies.

neuroscience↗

bioRxiv2019

Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI methods & outcome measures

The Ontario Neurodegenerative Research Initiative (ONDRI) is a 3 year multi-site prospective cohort study that has acquired comprehensive multiple assessment platform data, including 3T structural MRI, from neurodegenerative patients with Alzheimers disease, mild cognitive impairment, Parkinsons disease, amyotrophic lateral sclerosis, frontotemporal dementia, and cerebrovascular disease patients. This heterogeneous cross-section of patients with complex neurodegenerative and neurovascular pathologies pose significant challenges for standard neuroimaging tools. To effectively quantify regional measures of normal and pathological brain tissue volumes, the ONDRI neuroimaging platform implemented a semi-automated MRI processing pipeline that was able to address many of the challenges resulting from this heterogeneity. This paper describes the comprehensive neuroimaging pipeline methods used to generate regional brain tissue volumes & neurovascular markers.

neuroscience↗