Farshid Sepehrband

This study examines age-related changes in perivascular and parenchymal fluid flow characteristics and their relationship with cognitive performance across development and aging.

The paper identifies a NOTCH3 variant, p.Arg1231Cys, enriched in South Asians and strongly associated with stroke risk, highlighting the importance of genetic diversity in stroke research.

The paper examines age-related changes in perivascular spaces (PVS) using MRI data from a large cohort, showing age and sex influence PVS size and distribution across the lifespan.

This study compares age-related volumetric changes in the medial temporal lobe between cognitively normal individuals and those with mild cognitive impairment, finding the rhinal cortex is less affected by aging and more indicative of Alzheimer's disease pathology.

The paper introduces SC-GAN, a 3D conditional GAN with self-attention and spectral normalization for multi-modal neuroimaging synthesis, showing improved performance over 2D and 3D baselines.

NiftyTorch provides a deep learning framework for neuroimaging tasks, aiming to simplify 3D deep learning for non-experts.

The paper shows that the human subiculum has similar laminar gene expression patterns to the mouse, supporting a conserved 3D anatomical organization.

The paper shows that early cognitive dysfunction is associated with altered perivascular space presence and distribution, linked to tau tangle deposition in Alzheimer's disease pathology.

The paper analyzes cerebral perivascular spaces (PVS) in healthy individuals to establish normal ranges and identifies factors like BMI, time of day, and genetics that influence PVS volume and distribution.

This paper introduces an image processing method to enhance perivascular space visibility and quantification using MRI by combining T1- and T2-weighted images with adaptive filtering.

This paper shows that perivascular space fluid affects diffusion tensor imaging metrics in white matter, challenging previous interpretations of DTI findings.

BDQC identifies data outliers and anomalies in large-scale genomic pipelines without domain knowledge, improving data quality and enabling new insights.