Imaging whole-brain cytoarchitecture of mouse with MRI-based quantitative susceptibility mapping
Introduction
The complex central nervous system (CNS) consists two main cell types, neurons and glia (Steinbusch, 1981). The average diameter of most neurons and glia cells is on the order of microns in the adult mouse brain (Geisert et al., 2002, Magavi et al., 2000). These cells are characterized by a wide variation in shape and are often location specific, e.g. pyramidal neurons are abundant in the cortical regions (Spruston, 2008). An understanding of these structures and their locations is essential to understand functional circuit properties and their relation to behaviors (Fields et al., 2015). However, mapping the entire brain at near cellular resolution is still challenging. Several imaging techniques are currently being used for acquiring high resolution data from a mouse brain, e.g. the conventional two-dimensional (2D) histology methods (Halliday et al., 2007, Lein et al., 2007) and optical microscopy (Dodt et al., 2007, Magavi et al., 2000). However, conventional 2D histology-based methods are limited by the orientation of available sections and sectioning-related damage and deformation. Optical approaches have limited tissue contrast for differentiating substructure within the brain. Dedicated high field (> 7 T) animal MRI scanners have been shown to provide superior contrast and reveal fine anatomical details in the mouse brain (Jiang and Johnson, 2010, Wu et al., 2013). Advances in MRI techniques continue to improve resolution and contrast, providing a means to achieve a mesoscopic resolution (on the order of 10 μm) bridging gross neuroanatomy to the cellular architecture of the brain.
Several MRI contrast mechanisms that are thought to be sensitive to cellular organization have been applied to evaluate the mouse brain at high resolution. For example, current state-of-the-art DTI methods enable imaging an ex vivo adult mouse brain at approximately 40-μm isotropic resolution (Jiang and Johnson, 2010). But, DTI is inherently based on signal attenuation and is limited by T2 and T2⁎ decay, B0 inhomogeneity, and limited signal-to-noise ratio (SNR) (Jaermann et al., 2006). Thus, DTI and other diffusion-based methods are problematic in resolving small fiber tracts, such as the structures of medium-sized spiny neurons (MSN) which are also complex with frequent branches (Matamales et al., 2009). Studies have shown that the use of MRI signal phase in gradient-echo (GRE) can uncover a fine structure in the brain tissue (Duyn et al., 2007, He and Yablonskiy, 2009, Rauscher et al., 2005). Phase imaging allows an enhanced contrast within gray matter and white matter that are not resolved with conventional imaging at ultra-high field (> 7.0 T) MRI (Abduljalil et al., 2003, Duyn et al., 2007, Marques et al., 2009). However, phase values are non-local, i.e. the phase at one location not only depends on the local tissue properties but also depends on the neighboring magnetic susceptibility distribution. Quantitative susceptibility mapping (QSM) addresses this limitation by computing the spatial distribution of the underlying source of the phase, i.e. magnetic susceptibility (Bilgic et al., 2012, Carpenter et al., 2016, Li and van Zijl, 2014, Liu, 2010, Liu et al., 2014, Liu et al., 2015, Schweser et al., 2010, Wei et al., 2016, Xie et al., 2015). QSM reveals excellent image contrast and quantifies the magnetic properties of brain tissue, affected by e.g., iron in the cell body and myelin in the axons (Argyridis et al., 2014, Benner et al., 2013, Bilgic et al., 2012, Lee et al., 2012, Liu et al., 2012, Wang and Liu, 2015), which indicates that magnetic susceptibility may be used to improve the spatial resolution and contrast for cytoarchitecture of the whole brain. Current QSM techniques suffer from the severe streaking artifacts in the computed susceptibility maps from the single-orientation data (Li et al., 2015, Liu et al., 2013, Wei et al., 2015). A number of recent studies have aimed to improve the accuracy of susceptibility estimation and reduce the streaking artifacts (Li et al., 2015, Liu et al., 2013, Wei et al., 2015, Wu et al., 2012). One method called the streaking artifact reduction for quantitative susceptibility mapping (STAR-QSM) reduces streaks by limiting the strong susceptibility components (Wei et al., 2015).
In this study, mouse brains (n = 2) were scanned ex vivo at a nominal 10-μm isotropic resolution using a three-dimensional (3D) GRE sequence at 9.4 T. We applied STAR-QSM to address current issues of streaking artifacts. In this dataset, QSM offers a powerful tool to resolve fine detailed magnetic susceptibility contrast in many structures, e.g. retina cell layers, olfactory sensory neurons, corpus callosum, putamen axon, cerebral cortical layers, barrel cortex, hippocampus layers, cerebellum, striatal neurons, and the brainstem. Using STAR-QSM, we are able to achieve in susceptibility mapping at a resolution and contrast exceeding traditional MR images.
Section snippets
Perfusion and fixation
Mice (adult male C57BL/6) were provided with free access to food and water before experiments. Mice were anesthetized with isoflurane, a midline abdominal incision was made, and a catheter was inserted into the heart. Transcardial perfusion fixation was used with inflow to the left ventricle and outflow from the right atrium. The animals were perfused with saline and 0.1% heparin followed by a solution of 2.5 mM ProHance (Gadoteridol, Bracco Diagnostics Inc., Princeton, NJ) in 10% formalin. Both
Results
High contrast in the brain tissue can be observed in both magnitude and susceptibility images (Fig. 2A & B). Note that susceptibility values are inverted and the bright glomeruli are diamagnetic. Higher structural contrast can be observed in susceptibility images (Fig. 2B). For instance, the glomeruli in the olfactory bulb, the mitral cell layer, and the hippocampal cell layers can be seen in much higher detail in QSM compared to magnitude. Furthermore, the olfactory bulb, putamen, and
Discussion
In this study, we demonstrate the utility of QSM in visualizing the cytoarchitecture of the mouse brain based on MRI phase images acquired at a 10-μm nominal resolution. At this scale, we are approaching the classical MRI diffusion-limited resolution and are beginning to reach the scale of average cell diameters in the mouse brain. Moreover, such high resolution helps reduce the volume averaging that leads to ambiguity in fiber tracts. Indeed, anatomical maps are needed at different spatial
Conclusions
In this study, we demonstrate the utility of QSM in visualizing the microstructure of the intact mouse brain at a 10-μm resolution. QSM at near cellular resolution provides an exquisite delineation of brain microstructure, which overcomes limitations of current imaging methodologies. QSM offers a tool to assess the brain cytoarchitecture and the dataset achieved can serve as a reference for quantitative analysis of mouse brain microstructure.
Acknowledgements
This study was supported in part by the National Institutes of Health through grants NIBIB P41EB015897, NIBIB T32EB001040, NIMH R01MH096979, NINDS R01-NS079653, NIMH R24MH106096 and NHLBI R21HL122759, and by the National Multiple Sclerosis Society through grant RG4723.
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