An increasing focus has been placed on differential functions of the medial Prefrontal Cortex (mPFC) in mammals. This region is involved in a number of regulatory functions in humans, and in rodents has been most associated with regulation of behavior related to appetitive and aversive processing (Choi et al.; Corcoran and Quirk, 2007
; LaLumiere et al.; Milad and Quirk, 2002
; Milad et al., 2004
; Peters et al., 2008
). The processing of conditioned fear is among the most well-understood neural circuits related to a specific behavior, and recently the mPFC has been shown to intimately regulate the amygdala in the modulation of learned fear (Corcoran and Quirk, 2007
; Vidal-Gonzalez et al., 2006
). The prelimbic (PL) component of the mPFC in mice and rats has been associated with the learning and expression of conditioned fear, but with no effect on innate fear behavior (McLaughlin et al., 2002
; Powell et al., 2001
). In contrast, the infralimbic (IL) component of the mPFC appears to have an opposite function, with its requisite involvement in the inhibition of learned fear behavior (Milad and Quirk, 2002
; Nieuwenhuis and Takashima, 2011
; Quirk et al., 2000
) . Similarly, with appetitive behaviors, the PL appears to drive drug seeking while the IL suppresses such behaviors (LaLumiere et al., 2012
; Rocha, et al 2010
). What is particularly interesting about the mPFC regions is that despite their apparently opposite functions in emotion regulation, they are adjacent, sharing a thin cortical region of only a few hundred microns within the rodent mPFC. Furthermore, although the PL/IL connectivity has been examined in the rat (Vertes, 2004
), these connections have not been described in the mouse, which is increasingly used for understanding the molecular genetic mechanisms of brain function.
Therefore, it is critical to further understand the complementary and differential roles of these regions, as well as to better understand their connectivity relationships to other brain regions. However, classical anatomical techniques, while feasible and serving as the gold standard, are slow - often requiring weeks from injection to image generation, low throughput as limited by the number of stereotaxic setups and animal surgery time, are limited to a few injections of tracer in regions of interest in each animal, and are not easily applied in small animal models. For example (and in the case of this paper), when working with a mouse brain, the injection targets are significantly smaller (leading to a high “miss” rate) and the diffusion of the tracer may impact surrounding regions of interest unless the volume of tracer injection is reduced (also reducing the neural tracing fidelity).
Besides classical tracing are other methods including Magnetic Resonance Imaging (MRI), which provides an excellent and powerful set of customizable methodologies to allow non-invasive in vivo and ex vivo analysis of the mouse brain. A number of different MRI methods have been developed and implemented to allow high-throughput ex vivo imaging of fixed tissue or in vivo samples. Several groups have developed sophisticated workflows that allow rapid in vivo analysis in mice, although this often requires the construction of custom enclosures, receiver coils, or modifications to the underlying hardware. For anatomical scans and morphometric analysis of brain structures, many of the studies focus on T2 and T2* weighted pulse sequences, which provide a good balance of tissue contrast, signal-to-noise ratio (SNR) and resolution. The choice of optimal scan parameters of course remains a tradeoff, as there is inherently a tradeoff between SNR, image resolution, scan time, and sample preparation which can be a particular problem in diffusion tensor imaging which requires multiple acquisition volumes.
Diffusion Tensor Imaging (DTI) is a MRI technique that generates its signal and contrast primarily based on the degree and direction that water diffuses along neuronal axons (Zhang et al., 2003
). DTI has had some preliminary success when used to characterize the developing mouse embryo (Zhang et al., 2003
). DTI has also been extensively used in both human (Douaud et al., 2011
; Gutman et al., 2009
; Wedeen et al., 2008
) and primate studies (Li et al., 2010
; Rilling et al., 2008
; Wedeen et al., 2008
) to compare and contrast the structural connectivity of brain regions between the model species imaged. Using computation techniques, it is subsequently possible to reconstruct putative connections in silico
via various computational algorithms. A few studies have also directly compared the results of tractography to the gold standard tracer-injection studies in the porcine model and have generally shown a high concordance between the histologically defined pathways and those defined based on MRI Tractography results (Dyrby et al., 2011
; Dyrby et al., 2007
). Furthermore, one study has compared the DTI analysis of the tracts of the murine olfactory bulb to corresponding MRI based tracing conducted using MEMRI (Gutman et al., 2012).
The generation of high-throughput MRI DTI presents a number of challenges in mice—largely related to the small size of the mouse brain. For example, the resolution required to gather isotropic voxels less than a fifth of a millimeter (or about the size of the smaller nuclei in the mouse brain) makes a highfield magnet (e.g 7 or greater teslas) with a complimentary gradient and RF coil set ideal. Additionally, the higher resolution increases the length of the scan tremendously. Furthermore, in order to compute probabilistic tractography, a minimum of 30 gradient directions is recommended (although technically only 6 are required to compute a ‘tensor’), necessitating the collection of at least 31 separate volumes (when including at least one B0), which dictates and even longer scanning session. Considering both the resolution and number of gradients such work is often not feasible with in vivo
models. Importantly, the addition of 30 or more directions and higher resolution are imperative as it allows estimations of a more sophisticated tensor model that allows tracking through regions of “crossing fibers” (Behrens et al., 2007
; Behrens et al., 2003
; Johansen-Berg and Behrens, 2006
In this study, we report our implementation of a high-throughput ex vivo
diffusion tensor imaging protocol that allows for both high-resolution and a high number of diffusion weighted directions. For these studies, groups of fixed ex vivo
mice brains were scanned in a single acquisition session. We then tested the feasibility and fidelity of using probabilistic tractography in the mouse by analyzing the connectivity of the IL and PL cortices (areas separated by less than a millimeter) with the results validated against previous tract-tracing experiments in rats, and also against a preliminary BDA tracing of the mouse PL and IL projections. Comparisons to rat anatomy are important a major theme of this paper as there is an assumed direct and corresponding homology. Furthermore, comparisons to the classical tracing within mice provides a validation of the DTI technique. Specifically, the mPFC region was chosen as it has received both increasing interest within the neuroscience community (Maddux and Holland, 2011
; Nieuwenhuis and Takashima, 2011
), as well as provides some of the technical challenges in terms of proximity.
Our DTI and classical tracing data suggest that IL and PL share a majority of projections but also have unique connectivity as well. More importantly, it demonstrates that these methods can be utilized to examine differential connectivity of mPFC (and other regions of interest) in genetically modified models as well as pre- and post-experimental manipulation, e.g. following stress, fear conditioning, drug sensitization, etc. In this paper, we demonstrate the feasibility of tracking complicated pathways in the mouse through the use of probabilistic tractography, and we validate these results using a comparative anatomy approach of known anatomical connections of the rat IL/PL (Vertes, 2004
) and also using a preliminary tracing study of the mouse IL/PL. Finally, our data provides a preliminary comparison of rat and mouse IL/PL connectivity, often assumed to be homologous.