Bosshardt, A. | Search bioRxiv

bioRxiv2025

SimulScan and Partial Least Squares: Visualizing swallowing through functional and dynamic imaging correlations

PurposeSwallowing is a complex function involving the precise coordination of muscles, nerves, and brain areas, and can be disrupted in a variety of neurological conditions. Current imaging studies to visualize the central control of swallowing cannot examine both the biomechanics of the swallow and the brain activity associated with swallow events. An updated version of SimulScan is introduced that provides high-quality and high-speed dynamic imaging, together with fMRI acquisitions, to enable data-driven analysis of swallowing function through a partial least squares correlation (PLSC) analysis. MethodsIntegrating updated dynamic imaging approaches, SimulScan can achieve dynamic MRI at 23.75 frames per second and BOLD fMRI at a 1.6 s TR. Five subjects were recruited and scanned with SimulScan twice and with videofluoroscopy to compare the preliminary reliability of measuring swallowing biomechanics and the test-retest relationship in correlated functional and dynamic components of PLSC. ResultsHigh reliability of biomechanical measures of swallowing were achieved across the two SimulScan runs. In addition, these showed moderate correlation with videofluorscopy measures. Correlations between dynamic and functional imaging across runs also showed high reliability indicating that SimulScan with PLSC can extract maps of linked correlations between the brain and the oropharyngeal region. ConclusionThe updated version of SimulScan with PLSC analysis enables the study of central control of swallowing, providing simultaneous biomechanical visualization of the swallow along with brain functional signals.

neuroscience↗

bioRxiv2020

Optimization and scaling of patient-derived brain organoids uncovers deep phenotypes of disease

Cerebral organoids provide unparalleled access to human brain development in vitro. However, variability induced by current culture methodologies precludes using organoids as robust disease models. To address this, we developed an automated Organoid Culture and Assay (ORCA) system to support longitudinal unbiased phenotyping of organoids at scale across multiple patient lines. We then characterized organoid variability using novel machine learning methods and found that the contribution of donor, clone, and batch is significant and remarkably consistent over gene expression, morphology, and cell-type composition. Next, we performed multi-factorial protocol optimization, producing a directed forebrain protocol compatible with 96-well culture that exhibits low variability while preserving tissue complexity. Finally, we used ORCA to study tuberous sclerosis, a disease with known genetics but poorly representative animal models. For the first time, we report highly reproducible early morphological and molecular signatures of disease in heterozygous TSC+/- forebrain organoids, demonstrating the benefit of a scaled organoid system for phenotype discovery in human disease models.

neuroscience↗