Biology subjects

Ravid, O.

Publications and source records attributed to Ravid, O..

5 recordsLinked to original sources

bioRxiv2025

CAR T-cells dysfunction in the central nervous system is mediated by BBB-induced activation-induced cell death

Chimeric Antigen Receptor (CAR) T-cells can eliminate leukemia and lymphoma within the central nervous system (CNS). Following a case with CD19+ relapse in the CNS in the presence of persisting Tisagenlecleucel, we generated several models to study CAR T-cells in the CNS. In both immunocompetent and humanized leukemia-bearing mice, CNS-residing CAR T-cells were shown to have inferior ex-vivo killing capacities compared to CAR T-cells harvested from the spleen or bone marrow of receptive mice. In a mechanistic work-up, we identified two contributing factors: First, CAR T-cells cultured in cerebrospinal fluid showed impaired cytotoxic and proliferative function compared to those in serum or other media. Second, using a unique in vitro human blood-brain barrier (BBB) model composed of endothelial cells and brain pericytes, we demonstrated that CAR T-cells that crossed the BBB exhibited antigen-independent activation and diminished cytolytic capacity. This led to increased activation-induced cell death of the BBB-migrating CAR T-cells and was confirmed to be independent of the CAR target and the costimulatory domain. Our findings add possible mechanisms of CAR T-cell resistance in the CNS, relevant for CNS hematologic and non-hematologic tumors.

cancer biology↗

bioRxiv2025

Image-Based Meta- and Mega-Analysis (IBMMA): A Unified Framework for Large-Scale, Multi-Site, Neuroimaging Data Analysis

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA produced stronger effect sizes and revealed findings in brain regions that traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

neuroscience↗

bioRxiv2023

Explaining Deep Learning-Based Representations of Resting State Functional Connectivity Data: Focusing on Interpreting Nonlinear Patterns in Autism Spectrum Disorder

BackgroundResting state Functional Magnetic Resonance Imaging fMRI (rs-fMRI) has been used to study brain function in psychiatric disorders, yielding insight into brain organization. However, the high dimensionality of the rs-fMRI data presents challenges, and requires dimensionality reduction before applying machine learning techniques. Neural networks, specifically variational autoencoders (VAEs), have been instrumental in extracting low-dimensional latent representations of resting state functional connectivity patterns, addressing the complex nonlinear structure of rs-fMRI. However, interpreting those latent representations remains a challenge. This paper aims to address this gap by creating explainable VAE models and testing their utility using rs-fMRI data in autism spectrum disorder (ASD). MethodsOne-thousand one hundred and fifty participants (601 HC and 549 patients with ASD) were included in the analysis. We extracted functional connectivity correlation matrices from the preprocessed rs-fMRI data using Power atlas with 264 ROIs. Then VAEs were trained in an unsupervised fashion. Lastly, we introduce our latent contribution scores to explain the relationship between estimated representations and the original rs-fMRI brain measures. ResultsWe quantified the latent contribution scores for the ASD and control groups at the network level. We found that both ASD and control groups share the top network connectivity that contribute to all estimated latent components. For example, latent 0 was driven by resting state functional connectivity patterns (rsFC) within ventral attention network in both the ASD and control. However, significant differences in the latent contribution scores between the ASD and control groups were discovered within the ventral attention network in latent 0 and the sensory/somatomotor network in latent 2. ConclusionThis study introduced latent contribution scores to interpret nonlinear patterns identified by VAEs. These scores effectively capture changes in each observed rsFC features as estimated latent representation changes, enabling an explainable deep learning model to better understand the underlying neural mechanism of ASD.

neuroscience↗

bioRxiv2022

Multimodal Imaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BackgroundCurrent clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. MethodsWe analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. ResultsWe found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. ConclusionOur findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD. Significance Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well ([~]75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities ([~]60% AUC). Using deep learning for feature reduction (denoising variational auto-encoder; DVAE) dramatically reduced the number of features with no concomitant performance degradation. Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks

neuroscience↗

bioRxiv2020

Endothelial iron homeostasis regulates BBB integrity via the HIF2α-Ve-cadherin pathway

The blood-brain barrier (BBB) serves as the guardian of the CNS, tightly regulating the movement of ions, molecules, and cells between the circulatory system and brain. This barrier is critical in maintaining brain homeostasis, allowing proper neuronal function and protecting the brain from injury and disease. Chronic and acute exposure to various chemicals lead to BBB breakdown through pathways that are also affected in neurological diseases. Therefore, we have created an in-vitro BBB injury model to gain a better understanding of the mechanisms controlling BBB integrity. This model exposes a co-culture of human stem-cell derived brain-like endothelial cells (BLEC) and brain pericytes that mimic the BBB, to the organophosphate paraoxon. This exposure results in rapid lipid peroxidation, initiating a ferroptosis-like process and leading to endothelium cell toxicity. Mitochondrial ROS formation (MRF) and increase in mitochondrial membrane permeability (MMP), which occur 8 - 10 h post paraoxon-induced injury, also trigger apoptotic cell death. Yet, these processes do not directly result in damage to barrier functionality since blocking them does not reverse the increased permeability. Looking for a crucial pathway affecting barrier functionality we analyzed the iron homeostasis in our model since the iron chelator, Desferal(C) (DFO) rescued endothelial cell viability. Upon BBB insult, the liable iron pool (LIP) is rapidly increased, preventing the increased expression of the stress related hypoxia-induced factor 2 (HIF2) transcription factor. This results in a decrease in surface expression of the adherens junction and permeability master regulator protein, Ve-cadherin, ultimately damaging BBB integrity. Unlike the apoptosis inhibitor ZVAD that rescues BLEC from cell toxicity, yet exacerbates damage to the barrier functionality, DFO significantly decreases MRF and apoptosis subsequent to PX exposure, while also rescuing barrier integrity by inhibiting the liable iron pool increase, inducing HIF2 expression and preventing the degradation of Ve-cadherin on the cell surface. Moreover, the novel nitroxide JP4-039 significantly rescues both injury-induced endothelium cell toxicity and barrier functionality. Collectively, we have elucidated the cellular processes initiated by chemical injury to the endothelium barrier that result in cell toxicity; yet, inhibiting these processes does not necessarily protect BBB integrity which is regulated by the iron mediated HIF2 - Ve-Cadherin axis. DFO protects BBB integrity by inhibiting the injury-induced deregulation of this axis. Additionally, we have discovered a novel compound, JP4-039, that inhibits both damage to endothelium functionality and cell toxicity. Elucidating a regulatory pathway that maintains BBB integrity and discovering both a novel and an FDA approved compound that interfere with this pathway elucidates a potential therapeutic approach to protect the BBB degradation that is evident in many neurological diseases.

neuroscience↗