Maged Goubran

This study uses multiscale multimodal data integration in a mouse model of traumatic brain injury to identify biomarkers of memory deficits and network dedifferentiation.

This study shows that chronic stress worsens microvascular network dysfunction after brain trauma, reducing capillary coordination and vascular reactivity.

This paper develops vCATCH to map covalent drug targets across the entire mouse body, revealing new tissue and cell type engagements for afatinib and ibrutinib.

The paper identifies stable neuronal representations in cognitively resilient Alzheimer's disease rats during repeated stimulation, suggesting a potential biomarker for cognitive resilience.

MAPL3 enables whole-brain projectome mapping at single-axon resolution using deep learning and light sheet microscopy, outperforming existing methods across diverse datasets.

This paper develops a self-supervised deep learning pipeline for TPFM image segmentation, showing that SSL models can match or exceed fully supervised models with less labeled data.

The paper identifies NEUROG2's role in regulating a human-specific gene regulatory program in neural progenitor cells through transcriptomic analysis of knock-in organoids.

The paper develops a deep learning pipeline to analyze microvascular network changes in response to neuronal activity using two-photon microscopy, revealing network-level coordination in blood flow adjustments.

This study examines the relationship between amyloid-PET signals in white matter and microstructural changes like free water and fiber integrity, finding that free water and WMH volume are more closely linked to Aβ-PET in WMH regions.

This study uses longitudinal diffusion MRI to examine microstructural changes in brain tracts of college football and volleyball players, finding divergent developmental patterns between the groups.

This study examines the relationship between white matter hyperintensities, cortical thickness, and empathy in neurodegenerative and cerebrovascular diseases, finding associations with specific brain regions and demographic factors.

This paper introduces a Bayesian 3D U-Net for WMH segmentation that provides uncertainty estimates and is robust to imaging variations, achieving high accuracy and efficiency.

The paper introduces CNN-based models for segmenting intracranial and ventricular volumes in neuroimaging data, showing improved accuracy and efficiency compared to existing methods.

This study improves FreeSurfer's cortical thickness estimation in patients with cerebral small vessel disease and stroke by applying correction procedures, leading to better segmentation and cognitive association results.

This paper describes the neuroimaging pipeline methods used in the ONDRI study to generate regional brain tissue volumes and neurovascular markers from structural MRI data.