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Vaclavu, L.

Publications and source records attributed to Vaclavu, L..

2 recordsLinked to original sources

Preliminary cross-sectional investigations into the human glymphatic system using multiple novel non-contrast MRI methods.

We discuss two potential non-invasive MRI methods to cross-sectionally study two distinct facets of the glymphatic system and its association with sleep and aging. We apply diffusion-based intravoxel incoherent motion (IVIM) imaging to evaluate pseudodiffusion coefficient, D*, or cerebrospinal fluid (CSF) movement across large spaces like the subarachnoid space (SAS). We also performed perfusion-based multi-echo, Hadamard encoded multi-delay arterial spin labeling (ASL) to evaluate whole brain cortical cerebral blood flow (CBF) and transendothelial exchange (Tex) of water from the vasculature into the perivascular space and parenchyma. Both methods were used in young adults (N=9, 6F, 23{+/-}3 years old) in the setting of sleep and sleep deprivation. To study aging, 10 older adults, (6F, 67{+/-}3 years old) were imaged after a night of normal sleep only and compared with the young adults. D* in SAS was significantly (p<0.05) lesser after sleep deprivation (0.014{+/-}0.001 mm2/s) than after normal sleep (0.016{+/-}0.001 mm2/s), but was unchanged with aging. Cortical CBF and Tex on the other hand, were unchanged after sleep deprivation but were significantly lower in older adults (37{+/-}3 ml/100g/min, 476{+/-}66 ms) than young adults (42{+/-}2 ml/100g/min, 624{+/-}66 ms). IVIM was thus, sensitive to sleep physiology and multi-echo, multi-delay ASL was sensitive to aging.

neuroscience↗

ExploreASL: an image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. The toolbox adheres to previously defined international standards for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts to increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

neuroscience↗