Elsevier

NeuroImage

Volume 54, Issue 3, 1 February 2011, Pages 2086-2095
NeuroImage

Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning

https://doi.org/10.1016/j.neuroimage.2010.09.086Get rights and content

Abstract

Multiple recent human imaging studies have suggested that the structure of the brain can change with learning. To investigate the mechanism behind such structural plasticity, we sought to determine whether maze learning in mice induces brain shape changes that are detectable by MRI and whether such changes are specific to the type of learning. Here we trained inbred mice for 5 days on one of three different versions of the Morris water maze and, using high-resolution MRI, revealed specific growth in the hippocampus of mice trained on a spatial variant of the maze, whereas mice trained on the cued version were found to have growth in the striatum. The structure-specific growth found furthermore correlated with GAP-43 staining, a marker of neuronal process remodelling, but not with neurogenesis nor neuron or astrocyte numbers or sizes. Our findings provide evidence that brain morphology changes rapidly at a scale detectable by MRI and furthermore demonstrate that specific brain regions grow or shrink in response to the changing environmental demands. The data presented herein have implications for both human imaging as well as rodent structural plasticity research, in that it provides a tool to screen for neuronal plasticity across the whole brain in the mouse while also providing a direct link between human and mouse studies.

Research Highlights

► Five days of learning causes neuroanatomical changes. ► MRI is capable of detecting these learning based anatomical changes in the mouse. ► Different brain regions change their shape depending on the type of learning. ► Brain shape changes correlate with GAP-43, an axonal growth cone marker.

Introduction

A series of studies have recently shown that the shape of the brain changes with learning at a scale detectable by MRI in human subjects. Grey matter density increases in the occipital and/or parietal lobes in juggling (Draganski et al., 2004), matched by fractional anisotropy increases in the right hemisphere (Scholz et al., 2009). These changes are detectable as early as 7 days after training (Driemeyer et al., 2008) and appear to occur in elderly as well as young subjects (Boyke et al., 2008). Similarly, musical instrument lessons in young childhood alter brain development (Hyde et al., 2009), and adult musicians feature multiple anatomical differences compared to non music-playing controls (Schlaug, 2001, Gaser and Schlaug, 2003, Bermudez et al., 2008). There is, in addition to the MRI studies cited above, a wealth of literature studying the effects of learning on the brain in the rodent (see Markham and Greenough, 2004, for a review). Training induces rapid alterations in synapse morphology and dendritic spine numbers (Lendvai et al., 2000, Holtmaat et al., 2005, Lippman and Dunaevsky, 2005), axonal and dendritic branch remodelling (De Paola et al., 2006), neurogenesis (Christie and Cameron, 2006, Kee et al., 2007), and astrocyte morphology (Haber et al., 2006, Todd et al., 2006). The link between the macroscopic alterations seen in the human MRI studies and microscopic changes investigated to date in the rodent is, however, still missing. Here we propose that high-resolution MRI imaging of mice trained on spatial mazes can offer this crucial link. The mouse makes for an excellent model organism to study brain plasticity, foremost due to the rich set of tools to manipulate its genome along with a plethora of extant mouse models relevant to brain plasticity research. Its small size, however, presents a challenge for MRI, which can be partially compensated for by increasing the scan time or using fixed specimens along with MR contrast agents to obtain exquisite resolution. We used the latter approach to scan fixed brains at 32-μm isotropic resolution for this study, with three specimens scanned simultaneously with independent transmit/receive coils to allow for adequate throughput (Bock et al., 2005, Nieman et al., 2005, Nieman et al., 2007). Anatomical measures obtained from these fixed specimens have been used in multiple phenotyping studies (Chen et al., 2005, Clapcote et al., 2007, Spring et al., 2007, Lerch et al., 2008a, Lerch et al., 2008b, Mercer et al., 2009, Ellegood et al., 2010, Yu et al., 2010), and validated against stereology (Lerch et al., 2008b, Spring et al., 2010), where the MR measures were found to have greater sensitivity at discriminating between groups compared to classic tissue slice-based techniques (Lerch et al., 2008b).

To study the macroscopic effects of learning on the brain, we trained mice on a spatial learning paradigm. There is a direct human correlate to spatial learning: in 2000, Eleanor Maguire published an intriguing study which showed that London Taxi drivers have enlarged midposterior hippocampi compared to non-taxi driving controls (Maguire et al., 2000) and bus drivers (Maguire et al., 2006). These results, later expanded by further studies (Bohbot et al., 2007), suggested that employing a relational spatial navigation strategy (i.e., forming a cognitive map of the environment) over a non-relational response strategy (navigating by using simple landmark cues) was reflected by the volumes of different brain structures, in particular the striatum and hippocampus. We thus trained 2.5-month-old inbred mice on one of three versions of the Morris water maze (MWM, 6 trials a day for 5 days), wherein the animal must learn to navigate a novel environment in order to find a hidden platform. Navigation is thought to be supported by multiple, anatomically distinct systems that may interact in a co-operative or competitive fashion (McDonald and White, 1993, Poldrack and Packard, 2003). It is well established that spatial memory based navigation, which involves learning relationships between environmental landmarks, engages the hippocampus (O'Keefe and Nadel, 1978), also known to be involved in relational or declarative memory (Squire and Zola-Morgan, 1991, Eichenbaum et al., 1992). On the other hand, navigation relying on non-spatial ‘response learning’ involves the striatum (Packard et al., 1989, McDonald and White, 1993, McDonald and White, 1994, Iaria et al., 2003), also known to be involved in procedural learning or habit formation (Squire and Butters, 1984). In using different variants of the MWM, we thus expected to be able to stimulate growth in the hippocampus and striatum selectively and furthermore take advantage of the whole brain coverage of MRI to assess for alterations outside these two key regions.

Section snippets

Mice

Male offspring (aged 2.5 months) from a cross between C57BL/6NTacfBr and 129Svev mice (Taconic Farms, Germantown, NY) were used in these experiments and housed in cage of 3–5 same-sex littermates. All animal experiments were approved by the animal ethics committee of the Hospital for Sick Children.

Water maze and general training procedures

Behavioural testing was conducted in a circular water maze pool (120 cm in diam.), located in a dimly lit room (see Teixeira et al., 2006, for details). The pool was filled with water (28 ± 1 °C) made

Results

Three versions of the MWM that differed with respect to cognitive demand were used. In the spatial MWM group, mice (n = 22) were trained to find a hidden platform in a fixed location by using visual landmarks located around the room, a task requiring the hippocampus (Morris et al., 1982, McDonald and White, 1994). Alternatively, in the non-spatial cued MWM group (n = 14), the platform was marked with a visible cue and located in a different position for each trial. The distal visual landmarks

Learning causes neuroanatomical volume changes

There is strong evidence that laboratory mice kept in the same environment have remarkably similar brain anatomy (Chen et al., 2005). Therefore, the null hypothesis is that there will be no differences between mice trained on the three versions of the MWM. The regional volume increases and decreases that are specific to the training regimen in our study thus provides strong evidence for a causal relationship between training and resulting neuroanatomical changes, as per our hypotheses. These

Acknowledgements

The authors would like to thank Shoshana Spring and Christine Laliberte for their technical assistance; Ashu Jain and Dulcie Vousden for their assistance in delineating ROIs for the immunohistochemistry, and Dr. Robert Zatorre for insightful comments on the manuscript and experiment design. This research was supported by the Canadian Institutes of Health Research and the Ontario Research Fund.

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