NeuroImage 107 (2015) 182–189

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NeuroImage
journal homepage: www.elsevier.com/locate/ynimg

Rotarod training in mice is associated with changes in brain structure
observable with multimodal MRIJan Scholz a,⁎, Yosuke Niibori c, Paul W Frankland c,d,e,f, Jason P Lerch a,b,c
aMouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, CanadadDepartment of Psychology, University of Toronto, Toronto, ON, CanadaeDepartment of Physiology, University of Toronto, Toronto, ON, CanadafInstitute of Medical Science, University of Toronto, Toronto, ON, Canadabca r t i c l e

i n f oArticle history:
Accepted 1 December 2014
Available online 10 December 2014a b s t r a c t
The brain has been shown to remain structurally plastic even throughout adulthood. However, little is known
how motor-skill training affects different MRI modalities in the adult mouse brain. The aim of this study is to investigate whether rotarod training, a simple motor training task taken from the standard test battery, is associated with structural plasticity observable with different MRI modalities in adult C57BL/6 mice. The rotarod is astandard test that taxes motor coordination and balance. We use T2-weighted MRI followed by deformationbased morphometry to assess local volume and fractional anisotropy (FA) derived from diffusion MRI to assess
microstructure ex-vivo. Using deformation-based morphometry we found that the hippocampus, frontal cortex
and amygdala are larger in rotarod-trained mice compared to untrained controls. Surprisingly, the cerebellum
and white matter in the corpus callosum underlying the primary motor cortex are smaller after training. We
also found that the volume of the motor cortex is positively correlated with better rotarod performance. Diffusion
imaging indicates group differences and behavioral correlations with FA, a measure of microstructure. Trained
mice have higher FA in the hippocampus. Better rotarod performance is associated with higher FA in the hippocampus and lower FA in the primary visual cortex. This is the ﬁrst study to reveal the substantial structural reorganization of the adult mouse brain following only a relatively brief period of motor-skill training by using
complementary measures of microstructure and volume.
© 2014 Elsevier Inc. All rights reserved.

Introduction
Behavioral change depends on the reorganization of neural circuitry.
This reorganization might be achieved by the modiﬁcation of alreadyexisting synapses through protein synthesis (Schacher et al., 1988). In
rodents more persistent behavioral changes are associated with more
dramatic structural changes, such as neurogenesis, dendritic/axonal arborization, myelinogenesis, and gliogenesis (Demerens et al., 1996;
Gross, 2000; Hickmott and Steen, 2005; Loseva et al., 2009). Thesechanges, albeit small, might occur in sufﬁcient number or density to potentially alter the tissue structure to a degree that is observable withmagnetic resonance imaging (MRI). The aim of this study is to investigate whether motor skill training can elicit marked structural plasticity
observable with multimodal MRI in adult mice of a common background strain.
Of particular scientiﬁc interest is the question whether the adult brain
still exhibits marked plasticity. Being able to modulate this structural⁎ Corresponding author. Fax: +1 647 837 5832.
E-mail address: jan.scholz@mouseimaging.ca (J. Scholz).

http://dx.doi.org/10.1016/j.neuroimage.2014.12.003
1053-8119/© 2014 Elsevier Inc. All rights reserved.

plasticity might help behavioral recovery in humans suffering from neurological diseases. Recent human MRI studies have suggested that bothgray and white matter continue to be malleable throughout adulthoodin response to training a motor skill (Bezzola et al., 2011; Draganski
et al., 2004; Jäncke et al., 2009; Scholz et al., 2009; Taubert et al., 2010).
Animal studies have been able to reveal the cellular changes
associated with changes in experience. In adult rodents increases
in cell number/proliferation have been found in response to spatial
navigation tasks (Drapeau et al., 2003), environmental enrichment(Kempermann et al., 1997, 1998), and exercise (van Praag et al.,1999). These previous studies have relied on histology to quantify the
structural changes and have thus focused on a region of interest, usually
the hippocampal formation, which is known to be structurally plastic
throughout adulthood. Thus, relatively little is known about the effectof experience on the whole brain. Further, while the association between cellular changes and behavior is informative, the cause of the cellular changes remains largely unknown.
A small number of unimodal studies have found experience-relatedchanges using MRI in adult mice, thus allowing detection of structuralchanges all over the brain. We found volumetric changes in mice trained

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on the Morris water maze for ﬁve days (Lerch et al., 2011b). Different
brain areas increased in size (2–4%) depending on the strategy
employed by the mice. The volume changes were related to GAP-43
staining, an axonal growth cone marker, but not to markers linked tonumber and size of neurons and astrocytes. This suggests an increasein neuronal processes in speciﬁc training-related brain areas.
Local volume is not the only useful MRI measure of structural plasticity. Ding et al. (2013) found changes in fractional anisotropy (FA) after
only 1 h of fear conditioning, most of which seemed to return to baseline
after one day. Fractional anisotropy is a measure of tissue organization
derived from diffusion-weighted MRI. It is high in well-aligned tissue
such as white matter tracts and lower in gray matter where dendritic
trees point in various orientations. The authors hypothesize that the observed changes in amygdala, hippocampus and cingulum might be due
to changes in dendritic branching or glial remodeling.
Further experience-related structural changes have been reported in
response to Morris water maze training in rats using diffusion MRI(Blumenfeld-Katzir et al., 2011). Changes in apparent diffusion coefﬁcient (ADC), a measure related to overall diffusivity, were found in a
number of regions, such as the dentate gyrus and piriform cortex.
Here ADC was decreased in learners compared to exercise and home
cage controls. Interestingly, ADC seemed to be decreased in exercise
controls in S1/S2 compared to the two other groups. This suggeststhat while the majority of the changes were learning-related some microstructural changes might have been speciﬁc due to exercise (i.e.
swimming). A recent rat study found myelin-related FA increases in
the corpus callosum following reaching training but restricted the analysis to white matter tracts (Sampaio-Baptista et al., 2013).
Firstly, this study aims to establish a standard motor skill task that
reliably elicits structural changes in adult mice. Here we chose thewell-tested rotarod task, which is part of a battery of tasks used to assessrodent motor function (Brooks and Dunnett, 2009; Shiotsuki et al.,2010). Brieﬂy, mice are placed on a rotating drum and the time ismeasured until they fall off or the maximum trial length is reached.
The rotation speed is increased smoothly to raise difﬁculty over time.
Secondly, this study aims to identify a mouse strain that might serve
as a background for genetic modiﬁcation necessary to determine thecause of the cellular changes. Here we use C57BL/6 mice, the most widely used background strain for genetically modiﬁed mice.
This study will thus establish the structurally modulating brain regions and the size and direction of the changes using ex vivo MR imaging.
We test for volume changes based on T2-weighted images and microstructural changes based on FA maps derived from diffusion-weighted
MRI to cover a wider variety of underlying cellular changes. Multimodal
whole-brain imaging will potentially give us access to a wider array ofunderlying cellular changes. Although the rotarod task is widely-used
for motor assessment in mice its effects on whole brain structure are unknown. Importantly, this work will therefore pave the way for studies inmice genetically engineered to allow the investigation of the cause of
these structural changes utilizing a task similar to the motor training paradigms commonly used in human imaging plasticity studies.
The rotarod task trains the motor system symmetrically as micealign orthogonally to the axis of the rotarod drum. We therefore expect
bilateral brain changes both in volume and microstructure, especially inbrain areas involved in motor execution, balance, and learning. This hypothesis allows us to speciﬁcally test for bilateral group differences, thusincreasing statistical power by exploiting the symmetry of the expectedstructural changes. Finally, we hypothesize that if structural alterations
are behaviorally relevant they would correlate with overall latency.
Methods
Mice
A total of 48 C57BL/B6 mice, aged 16 weeks, were used in this experiment and housed in cages of 3–5 separated according to sex (21 male).

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All animal experiments were approved by the animal ethics committeeof the Hospital for Sick Children.
Behavior
Out of the experimental mice 25 (14 male) were trained on therotarod (Med Associates Inc.) with a rod diameter of 32 mm and an accelerating speed of 4 to 40 rpm over 5 min. Four mice were tested simultaneously on the rotarod, ten times a day for eight days in a row
resulting in 80 trials per animal. Latency was measured as the timefrom the beginning of the trial (start of the accelerated rod rotation)
until the mouse falls off and onto the lever that stops the timer. The
maximum trial length was 300 s. The remaining mice functioned as controls with no behavioral testing. Controls were handled 2 min a day for 5
consecutive days. Mice were perfused one day after the last training day.
Behavioral analysis
Behavioral analysis of the latencies measured at each trial was carried out using the R software package. Average latency per animal
across trials and days was estimated using a Bayesian model that
accounted for the ‘censoring’, i.e. the end of the trial after a maximumof 300 s. The resulting parameter estimates are thus more a measureof skill than performance, i.e. the model takes into account that an animal could potentially perform past the artiﬁcially imposed performance
limit.
In the following we will use the notation from Gelman (2007). Briefly, the basic model assumes average latencies μ for each mouse j camefrom a common normal distribution μ j ∼Nðμ; τÞ. The likelihood function
for latencies y read yi ∼ N(yj[i], τ), where j[i] refers to subject j = 1..25
and measurement i = 1..2000. We used a minimally informative normal
prior on the mean of the distribution of average performances μ∼N0; 10−5 and a uniform prior on the precisions τ; τ∼U ð0; 100Þ−2 . The
precision is deﬁned as the reciprocal of the variance. Posteriors were estimated using Gibbs sampling with 30,000 samples after burn-in. Latencies of more than 300 s were drawn from the likelihood function.
Similarly, a quadratic model was used to estimate the number of trials till peak performance was achieved for each mouse j. Latency y ateach measurement i was modeled as a function of trial number x =^i = β0j[i] + β1j[i]xi + β2j[i]x2i . Data as well
1..80 and three parameters: y^j[i], τ), βn j ∼N
as betas were assumed to be normally distributed: yi ∼ N(yyβn ; τβn , for n = 0..2. This model seemed to have a reasonable ﬁt(Pearson's r = 0.84, see Fig. 1C) and captured the main trends in thedata. The peak trial was deﬁned as the trial that contained the maximumof the quadratic ﬁt. We adopted a signiﬁcance threshold of p b 0.001 fortests on behavioral measures. Standard deviations are reported in parenthesis following means.
Image acquisition
Mice were perfused and brains were subsequently immersion
ﬁxated (Cahill et al., 2012). Brains were left within skulls for scanningon a multi-channel 7.0 T, 40 cm diameter bore magnet (Varian Inc.
Palo Alto, CA) to avoid deformations and damage during removal. A
T2-weighted sequence was used to obtain images for deformation
based morphometry. A custom-built 16-coil solenoid array was used toimage 16 samples concurrently (Lerch et al., 2011b) at 56 μm isotropic
voxel resolution. The 3D fast spin-echo sequence had the following parameters: TR = 2000 ms, echo train length = 6, TEeff = 42 ms, ﬁeldof-view (FOV) = 25 × 28 × 14 mm and matrix size = 450 × 504 ×250. Total imaging time was 11.7 h.
Additionally, brains were scanned with a custom built 3-coilsolenoid array in an insert gradient using a 3D diffusion-weighted

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Fig. 1. Volumetric group differences. Differences between rotarod trained mice and controls are estimated across both hemispheres as bilateral effects. Signiﬁcant clusters are depicted only
on the right hemisphere of the average as they are symmetric. Labels from a 291-structure atlas are displayed on the left hemisphere for orientation. (10% FDR corrected; rotarod trained N
controls, red; controls N rotarod trained, blue; Abbreviations: 29b/30, retrosplenial cortex; AMYG, amygdala; aTHAL, anterior thalamus; BF, barrel ﬁeld; CC, corpus callosum; CENT9, cerebellar central lobule 9; cst, corticospinal tract; FL, primary somatosensory, forelimb region; HIP, hippocampus; HL, primary somatosensory, hindlimb region; INS, insular cortex; int, internal capsule; lORB, lateral orbital cortex; MED, medualla; MVN, medial vestibular nucleus; PC, piriform cortex; PFL, paraﬂocculus; S1, primary somatosensory cortex; st, stria terminalis;
SVN, superior vestibular nucleus; V2, secondary visual cortex; V4, 4th ventricle; vsc, ventral spinocerebellar tract.)

fast-spin echo sequence (30 directions with b = 1917 smm−2, 5 b0images, TR = 325 ms, echo train length = 6, ﬁrst TE = 30 ms, TE =6 ms for the remaining 5 echoes, two averages, FOV = 25 mm ×
14 mm × 14 mm, matrix size = 192 × 108 × 108, yielding 130 μm isotropic voxels, total imaging time = 13.5 h).

Image analysis
High-resolution anatomical images were corrected for geometricdistortions based on phantom data prior to image registration. Brieﬂy,
for each coil we estimated the displacement ﬁeld to correct for the deformations by aligning scans of machine-milled plastic phantoms withknown surface geometry to their actual geometry. Brains were aligned
using nonlinear image registration with iterative template reﬁnement
encapsulated into a custom pipeline (Kovacević et al., 2005; Lerchet al., 2011a). Non-linear image registration relied on the diffeomorphic
registration algorithm which is part of the Advanced Normalization
Tools (ANTS) (Avants et al., 2011). First, all brains were rigidly registered towards a pre-existing mouse brain average. The left-right ﬂippedversion of each rigidly aligned brain was included in all following registration steps, to test for bilateral changes. Thus, 48 original brains plus
another 48 brains ﬂipped across the midline were passed to the next
registration step, which consisted of estimating the afﬁne transformation between each pair of brains. The pair-wise transformations werethen averaged per brain to obtain the ﬁnal afﬁne registration used toalign each brain and create an average. This afﬁne template functioned
as the target for the ﬁrst non-linear registration step, yielding the next
reﬁned template (Avants et al., 2011). At each non-linear registration
step all brains and their ﬂipped versions were aligned to the template.
Three nonlinear targets with progressively ﬁner non-linear registration
parameters were generated. For the high resolution anatomical images
the log Jacobian determinants of the deformation ﬁelds produced byaligning each individual image to the ﬁnal target were then smoothed(0.2 mm FWHM) and fed into statistical analysis to test for volumetricchanges. All individual Jacobian determinants had linear components

removed to account for differences in overall brain size, i.e. estimatedexpansions and contractions were relative to total brain volume.
Dtiﬁt of the FSL software package (Jenkinson et al., 2012) was used
to ﬁt tensors to the diffusion data and create maps of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). Similarly
to the high-resolution anatomical data, FA maps were non-linearly registered using iterative template reﬁnement. After registration FA maps
were smoothed (0.2 mm FWHM) and fed into statistical analysis totest for differences in FA.
Image statistics
Group differences between trained and control mice and correlations
with behavior within trained mice were estimated with linear mixed effects modeling at each voxel using the RMINC tools. We tested for increases and decreases (two-tailed). We included group as a ﬁxed effectto test for group differences and average latency to test for behavioral correlations. Mouse ID and brain hemisphere were included as random effects. Gender was included as a co-variate to account for potential sex
differences. FA averaged across the brain was included as a covariate in
the analysis of the diffusion data to account for a slowly varying (global)
bias ﬁeld. The resulting p-values were corrected for multiple comparisonswith false discovery rate (FDR) (Genovese et al., 2002).
A q-value of 10% was chosen as a threshold of signiﬁcance based on
previous studies and simulations (Lerch et al., 2008; van Eede et al.,2013). To localize the signiﬁcant clusters anatomical labels were aligned
to the ﬁnal average. An atlas with 62 structures was used for rough localization (Dorr et al., 2008) and a more elaborate atlas with 291 structures for more precise localization within the cortex (Ullmann et al.,2013; Steadman et al., 2013). Additionally, we adopted explorative
thresholding for the FA maps of p b 0.005, uncorrected. To reduce therisk of false positives we employed an additional arbitrary cluster-size
threshold of 0.01 mm−3.
For each signiﬁcant cluster, the size, center of gravity (CoG), and anatomical structure are reported. For the statistically most signiﬁcantvoxel within each cluster we report the percentage difference between

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control mice and rotarod-trained mice for both volume and FA. The behavioral correlations are reported in percent volume (or FA) change e
per 10 s average latency change.

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sex difference in number of trials till peak performance was achieved(p = 0.65).
Volumetry

Results
Behavior
Mice received ten rotarod trials on each of eight successive days.
Mice were allowed to stay on the rotarod for a maximum of 300 s pertrial. Latencies increased signiﬁcantly by 100 s (±14) when comparing
performance on the ﬁrst day to the last day (p b 0.001, Fig. 2C). Males
had overall lower latency (p = 0.002), but there was no interaction between sex and day (p = 0.45), suggesting that both sexes learned similarly well. According to a quadratic ﬁt to the latency data mice reachedtheir maximum performance on average at trial 64 (±12). There was no

Brains of rotarod-trained mice did not differ in total brain volume
from controls (p = 0.47), nor was there a signiﬁcant sex difference(p = 0.14).
Testing for voxel-wise differences revealed a distinct pattern ofchanges of rotarod-trained mice relative to controls. In general, volume
increases were found in the gray matter of the forebrain and decreases
in the gray matter of the hindbrain and the white matter of the forebrain(Fig. 1, SI Table 1). In the following we will brieﬂy localize all signiﬁcantclusters (10% FDR corrected).
Volume was increased in rotarod-trained mice in two clusters in thearea of the insular, piriform and orbitofrontal cortices (Fig. 1, slices 1, 2).

Fig. 2. Behavioral correlations with MR measures. (A) Regions where FA correlates with the average rotarod latency of the whole training period. (B) Regions where local volume correlateswith average latency (p b 0.005, uncorrected; longer latency correlated with higher FA or larger volume, red; shorter latency correlated with lower FA or smaller volume, blue). Correlations were estimated across both hemispheres as bilateral effects. Signiﬁcant clusters are depicted only on the right hemisphere of the average as they are symmetric. (C) Quadratic ﬁt tolatency (s) averaged across trials within day (whiskers represent 90% conﬁdence intervals). Labels from a 291-structure atlas are displayed on the left hemisphere for orientation. (Abbreviations: aTHAL, anterior thalamus; CENT3/8, cerebellar central lobule 3/8; DG, dentate gyrus; dlENT, dorsolateral entorhinal cortex; FrA, frontal association cortex; HIP, hippocampus;
HYP, hypothalamus; IC, inferior colliculus; ic, internal capsule; lem, mendial lemniscus; M1/2, primary/secondary motor cortex; MED, medualla; mlf, medial longitudinal fasciculus;OB, olfactory bulb; RF, reticular formation; STR, striatum; THAL, thalamus; TRN, thalamic reticular nucleus; V1, primary visual cortex.)

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Another large cluster of increased volume was found around the molecular layer of the hippocampus (slice 7). Further increases were foundin the anterior thalamus just dorsally of the mammilothalamic tract(slice 3), the amygdala (slices 4, 5), and the paraﬂocculus of thecerebellum (slices 11). Several white matter tracts contained regionsof decreased volume, such as the corpus callosum underlying the
primary somatosensory cortex (slice 4), the cortico spinal tract andthe ventral spinocerebellar tracts (slice 8). Finally, gray matter decreases were found in the central lobule 9 of the cerebellum (slice 12),the medial/superior vestibular nuclei (slices 9, 10), and the retrosplenialcortex (slice 8).
Testing for voxel-wise differences revealed several signiﬁcant clusters where average latency correlated with local volume (10% FDR
corrected, Fig. 2B, SI Table 2). In the rotarod-trained mice longer averagelatency was associated with larger volume of an extensive region of the
primary/secondary motor cortex (Fig. 2B, slice 2). There were several
smaller clusters with the same direction of correlation, one in the olfactory bulb (slice 1) and two in the reticular formation of the medulla(4,6), and one in the frontal association cortex (slice 1). There were negative correlations in the reticular nucleus of the thalamus (slice 3), andin the central lobule 3 of the cerebellum (slice 5).

Fractional anisotropy
Voxel-wise FA differences and behavioral correlations did not survive whole-brain correction for multiple comparisons. Although overall
FA contrast was well preserved in all samples, it is possible that the ﬁxation process might have caused cellular alterations through protein
cross-linking which normalized the diffusion characteristics, thus decreasing the chance of detecting brain differences (Shepherd et al.,2009).
Therefore, in a second explorative analysis we tested for increases in
FA at an explorative statistical threshold. FA was increased in the hippocampus of rotarod-trained mice compared to controls (p b 0.005, uncorrected, Fig. 3, SI Table 3).
The behavioral correlation revealed that higher average latency was
associated with higher FA in two hippocampal regions immediately adjacent to the area where FA differed between groups (p b 0.005, uncorrected, Fig. 2A, slice 7, SI Table 4). Further positive correlations were
found in the hypothalamus (slice 4), the thalamus (3,4,5), the medial
longitudinal fasciculus (slice 12), lobule 8 of the cerebellum (slice 11),the striatum (slice 2), and the olfactory bulbs (slice 1). An extended

area where lower latency was correlated with higher FA was found in
the visual cortex and entorhinal cortex (9,10).
Discussion
The aim of this study was to investigate whether a simple motor
training task taken from the standard test battery could elicit marked
structural plasticity observable with MRI in adult mice of a common
background strain. Our analysis took into account that rotarod runningtaxes the motor system and by extension the brain symmetrically, i.e.
the rotarod could be described as a bilateral movement training task
(Nakagawa et al., 2013). We found that eight days of training was associated with volume increases in the prefrontal cortex, the hippocampus,
the thalamus, and the amygdala. Interestingly, volume decreases were
found in the cerebellum, the retrosplenial cortex, and the vestibular nuclei of the brainstem. Further, volume decreases were found in whitematter, such as the corpus callosum underlying the primary somatosensory cortex, the cortico spinal tract, and the ventral spinocerebellar
tract. Among rotarod-trained mice, longer latency was associated withincreased volume across a large cluster in the primary/secondary
motor cortex.
An explorative analysis showed tentative increases in FA in rotarodtrained mice in the hippocampus. Behavioral correlations were morewide-spread and included the hippocampus where longer rotarod
time was associated with higher FA. Further areas of positive correlations were found in the cerebellum, the striatum, the thalamus, andthe hypothalamus. The relationship was reversed in the primary visualcortex and the dorsolateral entorhinal cortex.
Behavior
The behavioral improvement followed a time course similar to the
one reported in previous studies of mice and rats (Buitrago et al.,
2004; Cendelín et al., 2008). Most mice improved most quickly at the
beginning of the training period and reached peak performance after
about seven days of training. This indicates that most mice had reacheda performance plateau by the end of the eight-day training period. Thus,
the training elicited maximal behavioral change while not substantiallyovertraining the mice. Analysis of gait patterns has shown that therotarod task trains motor skills and goes beyond locomotor ability andﬁtness (Buitrago et al., 2004). Rats learned to change from ‘slow
stepping’ to ‘running’ at lower speeds during the course of the training,
thus adopting an advanced and more ﬂuid movement sequence. Thissuggests that the rotarod task has a substantial skill training component
and that the resulting behavioral and structural brain changes are notpurely the results of exercise.
Structural changes

Fig. 3. Volumetric changes, FA changes and FA-behavior correlations can be found in the
hippocampus. Hippocampal clusters from previous ﬁgures are added to the FA changes
to illustrate spatial proximity. Volume is increased in the dorsal hippocampus (10% FDR
corrected; rotarod trained N controls). FA is increased in the dorsal hippocampus and correlates positively with longer average latencies (p b 0.005, uncorrected; rotarod trained N
controls, green; longer latency correlated with higher FA, yellow; abbreviations: HIP,
hippocampus).

The structural differences between rotarod-trained mice and controls formed a distinct pattern that could have only been revealedwith a whole-brain technique such as MRI. The brain regions implicatedin these changes and behavioral correlations fall into three broad categories: brain regions which could be considered to be directly related
to rotarod running/balancing, brain regions which are associated withlearning in general, and brain regions which seem to be neither directly
nor indirectly related to rotarod running/balancing.
The motor cortex (M1/M2), the cerebellum, the striatum, and connected ﬁber tracts can be subsumed under regions that are to a certain
degree directly related to motor-learning. They span almost the entire
network of motor-related brain areas.
It has been shown that the transcription factor c-Fos, an indirect
marker of neuronal activity, is upregulated in the cerebellum, themotor cortex, the cingulate cortex, and dorsal striatum of rotarodtrained mice (Bureau et al., 2010). The same study found that extracellular signal-regulated kinase, marker of long-term memory

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stabilization, is activated in the cerebellum, the anterior cortex and the
hippocampus speciﬁcally during the ﬁrst day of training. This suggeststhat the brain regions observed in this study might be regions of increased neuronal activity and synaptic plasticity after rotarod training.
Two-photon imaging in the motor cortex of rotarod-trained micehas shown a transient increase in spine formation about two days
after training (Yang et al., 2009). However, due to the concomitant increase in spine elimination the total gain in spine number was negligible. The number of persistent spines formed during the ﬁrst two daysof training correlated with rotarod performance. Similarly, spine elimination over the entire seven-day training period correlated with performance. These observations ﬁt with our volumetric ﬁndings in the motor
cortex where we did not ﬁnd a volume increase in trained mice compared to controls; but we found an association between better performance and larger volume.
Apart from these directly motor-related regions, we found changes
in the vestibular nuclei (MVN/SVN). These nuclei are involved in thevestibulo-ocular reﬂex (VOR), which stabilizes the retinal image during
head motion by transforming vestibular input into oculomotor output(Beraneck and Cullen, 2007; Sekirnjak and du Lac, 2006). It is conceivable that a mouse which is otherwise living on the even surface of ashoe box-like container will have to learn how to adjust head and compensatory eye movements to the constantly shifting ground of a rotating rotarod. Vestibular system plasticity continues throughout life
(Gittis and du Lac, 2006). It has been shown that in the short term modulation of the VOR depends on the cerebellar ﬂocculus, an area where
we also found volume increases. Consolidation of the motor memories
is then assumed to happen in the vestibular nuclei (Boyden et al.,
2004; Nagao and Kitazawa, 2003; Shutoh et al., 2006).
The visual cortex might be directly related to the balancing component of the rotarod task, although it is not part of the motor network.
We found a large area in the deep layers of the primary visual cortex
close to the white matter of the external capsule where longer latencies
were associated with lower FA. These microstructural differences might
be the result of differences in development and could be related to how
efﬁciently the visual cortex processes visual cues which support estimation of head and body orientation.
In addition to these gray matter regions, we also found a number ofwhite matter tracts that might be directly related to rotarod training.
The corticospinal tract (CST) connects to spinal motor neurons that innervate the muscles necessary to accomplish the rotarod task. The ventral spinocerebellar tract (VSC) transmits information towards the
cerebellum. VSC neurons are excited by reticulospinal, vestibulospinal,
rubrospinal and especially corticospinal neurons which might provide
early feedback about motor neuron activation in the spine (Hammar
et al., 2011; Shrestha et al., 2012). Finally, we found volume decreases
that could be either attributed to the deep layers of primary somatosensory cortex or the underlying corpus callosum. The direction of thechange ﬁts better with the direction of changes in the above describedwhite matter tracts and suggests that the volume decrease might be related to white matter. In general, it is not immediately obvious why
white matter tracts should have decreased volume compared to controls. A simple hypothesis might posit that increased activity is related
to increased myelination (Demerens et al., 1996), which would most
likely lead to an increase in volume due to the membranes added by oligodendrocytes. The fact that the volume decreases are focal and localized underneath the primary somatosensory cortex could indicate thatthe volume decrease is more related to axons innervating the corpus
callosum at this particular location than to axons traveling along the
corpus callosum. However, we did not ﬁnd signiﬁcant changes in FA inthis region, which would support these changes in microstructure. The
absence of FA differences in a white matter region of volume change ispuzzling, but could be explained by the fact that ﬁbers underlying thecortex make sharp turns to enter the cortex. In this scenario newly
growing ﬁbers or increases in myelin could modulate both radial andaxial diffusivity similarly in a voxel containing ﬁbers tangential and187

perpendicular to the cortical surface, yielding no substantial change in
FA. Alternatively, a decrease in tract volume might also increase
partial-voluming in the lower resolution diffusion data. An FA increasein the underlying white matter tract could thus be obscured by an increased proportion of less anisotropic gray matter within the samevoxel.
In summary these changes and behavioral correlations suggest thata whole network of brain regions associated with sensorimotor, vestibular, and visual signal processing is recruited and adapts during rotarodtraining.
A number of brain regions, such as the hippocampal formation andthe entorhinal cortex are probably not directly related to the motorand vestibular components of the rotarod task. The hippocampus does
not seem to be necessary during acquisition of the motor learningtask; it is rather involved in spatial and recognition memory (Gould
et al., 2002). In addition, the hippocampal formation is an exceptionallyplastic region with ongoing neurogenesis in the dentate gyrus of the
hippocampus (Kempermann et al., 1997). Therefore the hippocampus
might be considered a prime candidate within the adult brain to produce bulk cellular changes observable with MRI. Indeed it has beenfound to change structurally in a number of studies using MRI in learning rodents (Blumenfeld-Katzir et al., 2011; Lerch et al., 2011b;
Shiotsuki et al., 2010).
Longer latencies were associated with higher FA in the hippocampus. The largest cluster was located dorsally in the hippocampal region
with increased volume and around the molecular layer of CA1 in theintermediate hippocampus between the areas of volume and FA increases. While the association between FA and latency suggests behavioral relevancy, it should be noted that physical exercise (30 days ofaccess to a running wheel) in young and aged mice has been shown toincrease cell proliferation and neurogenesis in the hippocampus(Madroñal et al., 2010; van Praag et al., 2005). In addition, changes invasculature have been observed in hippocampus following exercise(Van der Borght et al., 2009). These previous ﬁndings might indicatethat the volume and FA changes we observed could come from different
underlying cellular changes.
The anterior thalamus could be added to this second group of regions on the basis that it is heavily interconnected with the hippocampal formation, but not directly related to motor training (Shibata,1993). Apart from direct connections between the anterior thalamic nuclei and the hippocampal formation there are also indirect projections
via the retrosplenial cortex where we found volume decreases (Van
Groen and Wyss, 1995). Speciﬁcally, we found volume increases in theventral hippocampus spanning the layers between dentate gyrus andthe molecular layer of CA1 and FA increases dorsally in this region between the molecular layer and the pyramidal layer of CA1. This suggeststhat the changes might be related to the apical dendrites of the pyramidal neurons of layer CA1 or interneurons residing in these layers.
In concert, these ﬁndings indicate that the hippocampus is the structurally most malleable in response to rotarod training. More importantly it also seems to be the only brain region that is both subject to change
and behaviorally relevant in terms of volume and microstructure.
Finally, we found a third set of brain regions which seem neither directly nor indirectly related to motor skill training. Of these regions theamygdala could show up due to the stress and anxiety that the mice experience when they are suspended over ground on top of a rotating barrel. Dendritic complexity has been shown to increase in the amygdala
after fear conditioning (Heinrichs et al., 2013).
Both insular and orbitofrontal cortices are part of a network of regions that have been related to interoception, i.e. they represent andprocess body temperature, pain, muscular, and visceral sensations(Christianson et al., 2008; Craig, 2002). These sensations are related tohomeostatic needs and might be associated with behavioral motivations. In addition, the insula shares bi-directional connectivity withthe ventral CA1/subiculum close to where we found volume increases(Fanselow and Dong, 2010; Moraga-Amaro and Stehberg, 2012). The

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anterior hypothalamus has been related to temperature regulation andpanting/sweating, autonomous responses that might be associated withthe exercise component of the rotarod training.
Why small clusters within the olfactory bulb change volume or microstructure after rotarod training is less obvious. The olfactory bulb isin addition to the hippocampus, the only brain region that stays
neuronally plastic throughout life. New neurons continue to migratefrom the subventricular zone to the olfactory bulbs via the rostral migratory stream in the mature brain (Ghashghaei et al., 2007). Any
form of experience, such as exercise, might have a net structural effect,by altering neuronal proliferation and survival globally, even if the stimulus is not olfactory in nature. However, current evidence favors speciﬁc
olfaction-related stimuli as causes of neurogenesis in the olfactory bulb
(Ma et al., 2009).
In summary, it seems that most of the regions in this category
are associated with emotional, visceral, and interoceptive processes.
Although it is interesting to see that these processes might be related
to structural plasticity, this experiment aimed at the motor skill aspectof the rotarod task. Future experiments could potentially control for
emotional/visceral differences by inducing similar levels of anxiety
and exercise in the control group.
It is important to realize that the reported clusters can result fromvery different cellular processes depending on whether they are training or behavior-related and depending on whether they were detectedusing volumetry or diffusion imaging derived fractional anisotropy.
Due to the cross-sectional nature of this study it is not possible todisambiguate pre-existing differences from experience-related differences. For example, we found that mice with larger motor cortices are
better at the rotarod task. This difference could be due to differencesin development i.e. better performing mice had larger motor cortices
before the start of the experiment. However, it is also possible thatmice that spent more time on the rotarod had larger cortices at theend of the experiment as a result of the more intense training.
The differences in MRI-derived volume and fractional anisotropy
cannot be unambiguously related to differences in underlying cellular
structure and morphology. Hypotheses that have been proposed include difference in number and size of neurons and glial cells andtheir processes (Lerch et al., 2011b). Volume might be predominantly
related to the volume taken up by the neuropil as well as extra andintra-cellular water content. Volume is measured across multiple voxels
and thus requires (MR) contrast to estimate a shift in tissue boundaries.
Fractional anisotropy is a intra-voxel measure linked to myelination,ﬁber density, and ﬁber coherence in white matter (Beaulieu, 2002). In
gray matter fractional anisotropy might be related to the orientational
distribution of anisotropic structures such as dendrites and axons.
Conclusion
Multiple human imaging studies have shown structural plasticity
with motor learning (Bezzola et al., 2011; Draganski et al., 2004;
Jäncke et al., 2009; Scholz et al., 2009; Taubert et al., 2010). However,
it is often difﬁcult to tightly control the training regime in human studies nor can the mechanisms underlying the structural brain changes
be pursued. Here we show that, similar to previous studies of spatial
memory in rodents, motor-skill learning can alter the brain at a scaledetectable by MRI (Blumenfeld-Katzir et al., 2011; Lerch et al., 2011b).
The three categories of directly task-related, learning-related andemotional/visceral regions show a distinct pattern of structural change
and behavioral relevance across the mouse brain. Regions associatedwith sensorimotor and balance processing together with the generally
learning-related and structurally plastic hippocampus dominate theemerging pattern. We also ﬁnd evidence for regions linked to anxiety,stress, and visceral processing. The results of this study suggest thatthe standard rotarod training task is associated with changes in awhole network of motor learning-related and task-associated brain regions, detectable with multimodal MRI. It also seems to be the case thatvolume and microstructure are differentially sensitive to underlying tissue changes and complement each other. The fact that the results wereobtained in the common background strain of C57BL/6 mice should encourage further studies using genetically-modiﬁed mice to investigate
the underlying cellular process of experience-related structural changes
in the adult brain.
Appendix A. Supplementary data
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.neuroimage.2014.12.003.
References
Avants, B.B., Tustison, N.J., Wu, J., Cook, P.A., Gee, J.C., 2011. An open source multivariate
framework for n-tissue segmentation with evaluation on public data. Neuroinformatics
9, 381–400.
Beaulieu, C., 2002. The basis of anisotropic water diffusion in the nervous system — atechnical review. NMR Biomed. 15, 435–455.
Beraneck, M., Cullen, K.E., 2007. Activity of vestibular nuclei neurons during vestibularand optokinetic stimulation in the alert mouse. J. Neurophysiol. 98, 1549–1565.
Bezzola, L., Mérillat, S., Gaser, C., Jäncke, L., 2011. Training-induced neural plasticity in golf
novices. J. Neurosci. 31, 12444–12448.
Blumenfeld-Katzir, T., Pasternak, O., Dagan, M., Assaf, Y., 2011. Diffusion MRI of structuralbrain plasticity induced by a learning and memory task. PLoS ONE 6, e20678.
Boyden, E.S., Katoh, A., Raymond, J.L., 2004. Cerebellum-dependent learning: the role ofmultiple plasticity mechanisms. Annu. Rev. Neurosci. 27, 581–609.
Brooks, S.P., Dunnett, S.B., 2009. Tests to assess motor phenotype in mice: a user's guide.
Nat. Rev. Neurosci. 10, 519–529.
Buitrago, M.M., Schulz, J.B., Dichgans, J., Luft, A.R., 2004. Short and long-term motor skill
learning in an accelerated rotarod training paradigm. Neurobiol. Learn. Mem. 81,
211–216.
Bureau, G., Carrier, M., Lebel, M., Cyr, M., 2010. Intrastriatal inhibition of extracellularsignal-regulated kinases impaired the consolidation phase of motor skill learning.
Neurobiol. Learn. Mem. 94, 107–115.
Cahill, L.S., Laliberté, C.L., Ellegood, J., Spring, S., Gleave, J.A., Eede, M.C.V., Lerch, J.P.,
Henkelman, R.M., 2012. Preparation of ﬁxed mouse brains for MRI. NeuroImage 60,
933–939.
Cendelín, J., Korelusová, I., Vozeh, F., 2008. The effect of repeated rotarod training on
motor skills and spatial learning ability in Lurcher mutant mice. Behav. Brain Res.
189, 65–74.
Christianson, J.P., Benison, A.M., Jennings, J., Sandsmark, E.K., Amat, J., Kaufman, R.D.,
Baratta, M.V., Paul, E.D., Campeau, S., Watkins, L.R., Barth, D.S., Maier, S.F., 2008. The
sensory insular cortex mediates the stress-buffering effects of safety signals but not
behavioral control. J. Neurosci. 28, 13703–13711.
Craig, A.D., 2002. How do you feel? Interoception: the sense of the physiological conditionof the body. Nat. Rev. Neurosci. 3, 655–666.
Demerens, C., Stankoff, B., Logak, M., Anglade, P., Allinquant, B., Couraud, F., Zalc, B.,
Lubetzki, C., 1996. Induction of myelination in the central nervous system by electrical activity. Proc. Natl. Acad. Sci. U. S. A. 93, 9887–9892.
Ding, A.Y., Li, Q., Zhou, I.Y., Ma, S.J., Tong, G., McAlonan, G.M., Wu, E.X., 2013. MR diffusion
tensor imaging detects rapid microstructural changes in amygdala and hippocampus
following fear conditioning in mice. PLoS ONE 8, e51704.
Dorr, A.E., Lerch, J.P., Spring, S., Kabani, N., Henkelman, R.M., 2008. High resolution threedimensional brain atlas using an average magnetic resonance image of 40 adult
C57Bl/6 J mice. NeuroImage 42, 60–69.
Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., May, A., 2004.
Neuroplasticity: changes in grey matter induced by training. Nature 427, 311–312.
Drapeau, E., Mayo, W., Aurousseau, C., Moal, M.L., Piazza, P.-V., Abrous, D.N., 2003. Spatial
memory performances of aged rats in the water maze predict levels of hippocampal
neurogenesis. Proc. Natl. Acad. Sci. U. S. A. 100, 14385–14390.
Fanselow, M.S., Dong, H.-W., 2010. Are the dorsal and ventral hippocampus functionallydistinct structures? Neuron 65, 7–19.
Gelman, A., 2007. Data analysis Using Regression and Multilevel/Hierarchical Models.
Cambridge University Press, Cambridge New York.
Genovese, C.R., Lazar, N.A., Nichols, T., 2002. Thresholding of statistical maps in functional
neuroimaging using the false discovery rate. NeuroImage 15, 870–878.
Ghashghaei, H.T., Lai, C., Anton, E.S., 2007. Neuronal migration in the adult brain: are we
there yet? Nat. Rev. Neurosci. 8, 141–151.
Gittis, A.H., du Lac, S., 2006. Intrinsic and synaptic plasticity in the vestibular system. Curr.
Opin. Neurobiol. 16, 385–390.
Gould, T.J., Rowe, W.B., Heman, K.L., Mesches, M.H., Young, D.A., Rose, G.M., Bickford, P.C.,
2002. Effects of hippocampal lesions on patterned motor learning in the rat. Brain
Res. Bull. 58, 581–586.
Gross, C.G., 2000. Neurogenesis in the adult brain: death of a dogma. Nat. Rev. Neurosci. 1,
67–73.
Hammar, I., Krutki, P., Drzymala-Celichowska, H., Nilsson, E., Jankowska, E., 2011. A transspinal loop between neurones in the reticular formation and in the cerebellum.
J. Physiol. 589, 653–665.
Heinrichs, S.C., Leite-Morris, K.A., Guy, M.D., Goldberg, L.R., Young, A.J., Kaplan, G.B., 2013.
Dendritic structural plasticity in the basolateral amygdala after fear conditioning andits extinction in mice. Behav. Brain Res. 248, 80–84.

J. Scholz et al. / NeuroImage 107 (2015) 182–189
Hickmott, P.W., Steen, P.A., 2005. Large-scale changes in dendritic structure during reorganization of adult somatosensory cortex. Nat. Neurosci. 8, 140–142.
Jäncke, L., Koeneke, S., Hoppe, A., Rominger, C., Hänggi, J., 2009. The architecture of thegolfer's brain. PLoS ONE 4, e4785.
Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M., 2012. FSL.
NeuroImage 62, 782–790.
Kempermann, G., Kuhn, H.G., Gage, F.H., 1997. More hippocampal neurons in adult miceliving in an enriched environment. Nature 386, 493–495.
Kempermann, G., Kuhn, H.G., Gage, F.H., 1998. Experience-induced neurogenesis in thesenescent dentate gyrus. J. Neurosci. 18, 3206–3212.
Kovacević, N., Henderson, J.T., Chan, E., Lifshitz, N., Bishop, J., Evans, A.C., Henkelman, R.M.,
Chen, X.J., 2005. A three-dimensional MRI atlas of the mouse brain with estimates of
the average and variability. Cereb. Cortex 15, 639–645.
Lerch, J.P., Carroll, J.B., Spring, S., Bertram, L.N., Schwab, C., Hayden, M.R., Henkelman, R.M.,
2008. Automated deformation analysis in the YAC128 Huntington disease mousemodel. NeuroImage 39, 32–39.
Lerch, J.P., Sled, J.G., Henkelman, R.M., 2011a. MRI phenotyping of genetically alteredmice. In: Modo, M., Bulte, J.W.M. (Eds.), Magnetic Resonance Neuroimaging SE —17. Humana Press, pp. 349–361.
Lerch, J.P., Yiu, A.P., Martinez-Canabal, A., Pekar, T., Bohbot, V.D., Frankland, P.W.,
Henkelman, R.M., Josselyn, S.A., Sled, J.G., 2011b. Maze training in mice induces
MRI-detectable brain shape changes speciﬁc to the type of learning. NeuroImage54, 2086–2095.
Loseva, E., Yuan, T.-F., Karnup, S., 2009. Neurogliogenesis in the mature olfactory system:
a possible protective role against infection and toxic dust. Brain Res. Rev. 59,
374–387.
Ma, D.K., Kim, W.R., Ming, G.-l., Song, H., 2009. Activity-dependent extrinsic regulation of
adult olfactory bulb and hippocampal neurogenesis. Ann. N. Y. Acad. Sci. 1170,
664–673.
Madroñal, N., López-Aracil, C., Rangel, A., del Río, J.A., Delgado-García, J.M., Gruart, A.,
2010. Effects of enriched physical and social environments on motor performance,
associative learning, and hippocampal neurogenesis in mice. PLoS ONE 5, e11130.
Moraga-Amaro, R., Stehberg, J., 2012. The insular cortex and the amygdala: shared functions and interactions. In: Ferry, B. (Ed.), The Amygdala — A Discrete Multitasking
Manager. InTech.
Nagao, S., Kitazawa, H., 2003. Effects of reversible shutdown of the monkey ﬂocculus on
the retention of adaptation of the horizontal vestibulo-ocular reﬂex. Neuroscience
118, 563–570.
Nakagawa, H., Ueno, M., Itokazu, T., Yamashita, T., 2013. Bilateral movement training promotes axonal remodeling of the corticospinal tract and recovery of motor function
following traumatic brain injury in mice. Cell Death Dis. 4, e534.
Sampaio-Baptista, C., Khrapitchev A.A., Foxley, S., Schlagheck, T., Scholz, J., Jbabdi, S.,
DeLuca, G.C., Miller, K.L., Taylor A., Thomas, N., Kleim, J., Sibson, N.R., Bannerman,
D., Johansen-Berg, H., 2013. Motor skill learning induces changes in white matter microstructure and myelination. J. Neurosci. 33, 19499–19503.
Schacher, S., Castellucci, V.F., Kandel, E.R., 1988. cAMP evokes long-term facilitation in
Aplysia sensory neurons that requires new protein synthesis. Science 240,
1667–1669.

189

Scholz, J., Klein, M.C., Behrens, T.E.J., Johansen-Berg, H., 2009. Training induces changes inwhite-matter architecture. (Supplement). Nat. Neurosci. 12, 1370–1371.
Sekirnjak, C., du Lac, S., 2006. Physiological and anatomical properties of mouse medial
vestibular nucleus neurons projecting to the oculomotor nucleus. J. Neurophysiol.
95, 3012–3023.
Shepherd, T.M., Thelwall, P.E., Stanisz, G.J., Blackband, S.J., 2009. Aldehyde ﬁxative solutions alter the water relaxation and diffusion properties of nervous tissue. Magn.
Reson. Med. 62, 26–34.
Shibata, H., 1993. Direct projections from the anterior thalamic nuclei to theretrohippocampal region in the rat. J. Comp. Neurol. 337, 431–445.
Shiotsuki, H., Yoshimi, K., Shimo, Y., Funayama, M., Takamatsu, Y., Ikeda, K., Takahashi, R.,
Kitazawa, S., Hattori, N., 2010. A rotarod test for evaluation of motor skill learning.
J. Neurosci. Methods 189, 180–185.
Shrestha, S.S., Bannatyne, B.A., Jankowska, E., Hammar, I., Nilsson, E., Maxwell, D.J., 2012.
Excitatory inputs to four types of spinocerebellar tract neurons in the cat and the rat
thoraco-lumbar spinal cord. J. Physiol. 590, 1737–1755.
Shutoh, F., Ohki, M., Kitazawa, H., Itohara, S., Nagao, S., 2006. Memory trace of motor
learning shifts transsynaptically from cerebellar cortex to nuclei for consolidation.
Neuroscience 139, 767–777.
Steadman, P.E., Ellegood, J., Szulc, K.U., Turnbull, D.H., Joyner, A.L., Henkelman, R.M., Lerch,
J.P., 2013. Genetic effects on cerebellar structure across mouse models of autism usinga magnetic resonance imaging atlas. Autism Res. 1–14.
Taubert, M., Draganski, B., Anwander, A., Müller, K., Horstmann, A., Villringer, A., Ragert,
P., 2010. Dynamic properties of human brain structure: learning-related changes incortical areas and associated ﬁber connections. J. Neurosci. 30, 11670–11677.
Ullmann, J.F.P., Watson, C., Janke, A.L., Kurniawan, N.D., Reutens, D.C., 2013. A segmentation protocol and MRI atlas of the C57BL/6 J mouse neocortex. NeuroImage 78C,
196–203.
Van der Borght, K., Kóbor-Nyakas, D.E., Klauke, K., Eggen, B.J.L., Nyakas, C., Van der Zee,
E.A., Meerlo, P., 2009. Physical exercise leads to rapid adaptations in hippocampal
vasculature: temporal dynamics and relationship to cell proliferation andneurogenesis. Hippocampus 19, 928–936.
van Eede, M.C., Scholz, J., Chakravarty, M.M., Henkelman, R.M., Lerch, J.P., 2013. Mapping
registration sensitivity in MR mouse brain images. NeuroImage 82, 226–236.
Van Groen, T., Wyss, J.M., 1995. Projections from the anterodorsal and anteroventral nucleus of the thalamus to the limbic cortex in the rat. J. Comp. Neurol. 358, 584–604.
van Praag, H., Christie, B.R., Sejnowski, T.J., Gage, F.H., 1999. Running enhances neurogenesis,
learning, and long-term potentiation in mice. Proc. Natl. Acad. Sci. U. S. A. 96,13427–13431.
van Praag, H., Shubert, T., Zhao, C., Gage, F.H., 2005. Exercise enhances learning and hippocampal neurogenesis in aged mice. J. Neurosci. 25, 8680–8685.
Yang, G., Pan, F., Gan, W.-B., 2009. Stably maintained dendritic spines are associated withlifelong memories. Nature 462, 920–924.