Deep convolutional neural networks can generate virtual histology from magnetic resonance imaging (MRI) data to map cellular structures in the mouse brain with high specificity, which enables neurobiologists to use MRI to characterize neuropathology effectively.

X-ray histotomography produces the first 3-dimensional images with the combined submicron resolution, centimeter fields-of-view, soft-tissue contrast, and potential throughput to enable quantitative, histopathological and centimeter-scale phenotyping for whole-organism phenomics.

Combining high-resolution imaging with automated image segmentation and supervised machine learning achieves accurate cellular feature extraction and automated cell type recognition in a large-scale developmental process.

Phase contrast X-ray tomography based on a combination of parallel and cone beam geometry extends conventional histology by a third dimension and enables full quantication of tissue remodeling in COVID-19.

MRI methods are promising techniques for investigating the human subcortical auditory system, and these publicly available data, atlases, and tools make researching human audition simpler and more reliable.

The BigBrainWarp toolbox facilitates integration of 3D human brain histology with neuroimaging, helping neuroscientists to utilise cytoarchitectural information in conjunction with in vivo imaging.

Most MRI-based myelin biomarkers are not statistically different regarding their relationship with histology.

Surface-based registration of the folded or unfolded human hippocampus achieves subfield alignment to within approximately 0.5 mm.

Metabotropic glutamate receptors modulate impulse control by the subthalamic nucleus, dissociating trait impulsivity from basal ganglia motor function.

Application of machine learning to serum miRNA profiles generated through next generation sequencing identifies a biologically relevant miRNA signature which can be deployed as a qPCR test to assist the diagnosis of epithelial ovarian cancer.