An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

What’s retinoic acid got to do with it? Retinoic acid regulation of the neural crest in craniofacial and ocular development

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151310/1/dvg23308.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151310/2/dvg23308_am.pd

Grid Computing Simulations of Ion Channel Block Effects on the ECG Using 3D Anatomically-Based Models

In this study, a computational framework combining state-of-the-art cardiac simulation software and Grid computing is used to investigate the impact of the block of the HERG current on the ECG waveform using state-of-the-art 3D ventricular models of electrophysiology. The technology developed enables (i) automated parameter sweeping using multiscale models (from ion channel to ECG) and (ii) reduced execution time of the simulations performed