Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. In vivo MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive in vivo high-resolution microscopy and ex vivo molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies.

No abstract

In the brain, neural stem cells (NSC) are tightly regulated by external signals and biophysical cues mediated by the local microenvironment or “niche.” In particular, the influence of tissue elasticity, known to fundamentally affect the function of various cell types in the body, on NSC remains poorly understood. We, accordingly, aimed to characterize the effects of elastic substrates on critical NSC functions. Primary rat NSC were grown as monolayers on polydimethylsiloxane‐ (PDMS‐) based gels. PDMS‐coated cell culture plates, simulating the physiological microenvironment of the living brain, were generated in various degrees of elasticity, ranging from 1 to 50 kPa; additionally, results were compared with regular glass plates as usually used in cell culture work. Survival of NSC on the PDMS‐based substrates was unimpaired. The proliferation rate on 1 kPa PDMS decreased by 45% compared with stiffer PMDS substrates of 50 kPa (p < 0.05) whereas expression of cyclin‐dependent kinase inhibitor 1B/p27Kip1 increased more than two fold (p < 0.01), suggesting NSC quiescence. NSC differentiation was accelerated on softer substrates and favored the generation of neurons (42% neurons on 1 kPa PDMS vs. 25% on 50 kPa PDMS; p < 0.05). Neurons generated on 1 kPa PDMS showed 29% longer neurites compared with those on stiffer PDMS substrates (p < 0.05), suggesting optimized neuronal maturation and an accelerated generation of neuronal networks. Data show that primary NSC are significantly affected by the mechanical properties of their microenvironment. Culturing NSC on a substrate of brain‐like elasticity keeps them in their physiological, quiescent state and increases their neurogenic potential.