Recently, Mn(II)-containing nanoparticles have been explored widely as an attractive alternative to Gd(III)-based T -weighted magnetic resonance imaging (MRI) contrast agents (CAs) for cancer diagnosis. However, as far as it is known, no Mn-based MRI CAs have been reported to sensitively respond to a very weakly acidic environment (pH 6.5-7.0, i.e., the pH range in a tumor microenvironment) with satisfactory imaging performance. Here, recently devised pH-ultrasensitive Mn-based layered double hydroxide (Mn-LDH) nanoparticles with superb longitudinal relaxivity (9.48 mm s at pH 5.0 and 6.82 mm s at pH 7.0 vs 1.16 mm s at pH 7.4) are reported, which may result from the unique microstructure of Mn ions in Mn-LDH, as demonstrated by extended X-ray absorption fine structure. Further in vivo imaging reveals that Mn-LDH nanoparticles show clear MR imaging for tumor tissues in mice for 2 d post intravenous injection. Thus, this novel Mn-doped LDH nanomaterial, together with already demonstrated capacity for drug and gene delivery, is a very potential theranostic agent for cancer diagnosis and treatment.

The neocortex is the largest component of the mammalian cerebral cortex. It integrates sensory inputs with experiences and memory to produce sophisticated responses to an organism's internal and external environment. While areal patterning of the mouse neocortex has been mapped using histological techniques, the neocortex has not been comprehensively segmented in magnetic resonance images. This study presents a method for systematic segmentation of the C57BL/6J mouse neocortex. We created a minimum deformation atlas, which was hierarchically segmented into 74 neocortical and cortical-related regions, making it the most detailed atlas of the mouse neocortex currently available. In addition, we provide mean volumes and relative intensities for each structure as well as a nomenclature comparison between the two most cited histological atlases of the mouse brain. This MR atlas is available for download, and it should enable researchers to perform automated segmentation in genetic models of cortical disorders.

We examined whether quantitative density measures of cerebral tissue consistent with histology can be obtained from diffusion magnetic resonance imaging (MRI). By incorporating prior knowledge of myelin and cell membrane densities, absolute tissue density values were estimated from relative intra-cellular and intra-neurite density values obtained from diffusion MRI. The NODDI (neurite orientation distribution and density imaging) technique, which can be applied clinically, was used. Myelin density estimates were compared with the results of electron and light microscopy in ex vivo mouse brain and with published density estimates in a healthy human brain. In ex vivo mouse brain, estimated myelin densities in different sub-regions of the mouse corpus callosum were almost identical to values obtained from electron microscopy (Diffusion MRI: 42±6%, 36±4% and 43±5%; electron microscopy: 41±10%, 36±8% and 44±12% in genu, body and splenium, respectively). In the human brain, good agreement was observed between estimated fiber density measurements and previously reported values based on electron microscopy. Estimated density values were unaffected by crossing fibers.

The recently proposed track-density imaging (TDI) technique was introduced as a means to achieve super-resolution using diffusion MRI. This technique is able to increase the spatial resolution of the reconstructed images beyond the acquired MRI resolution by incorporating information from whole-brain fibre-tracking results. It not only achieves super-resolution, but also provides very high anatomical contrast with a new MRI contrast mechanism. However, the anatomical informationcontent of this novel contrast mechanism has not yet been validated. In this work, we perform such