Brain Struct Funct
DOI 10.1007/s00429-014-0774-0

ORIGINAL ARTICLE

Whole-brain mapping of behaviourally induced neural activationin mice
Dulcie A. Vousden • Jonathan Epp • Hiroyuki Okuno • Brian J. Nieman •Matthijs van Eede • Jun Dazai • Timothy Ragan • Haruhiko Bito •Paul W. Frankland • Jason P. Lerch • R. Mark Henkelman

Received: 29 November 2013 / Accepted: 3 April 2014
! Springer-Verlag Berlin Heidelberg 2014

Abstract The ability to visualize behaviourally evokedneural activity patterns across the rodent brain is essential forunderstanding the distributed brain networks mediating particular behaviours. However, current imaging methods arelimited in their spatial resolution and/or ability to obtainbrain-wide coverage of functional activity. Here, we describe
a new automated method for obtaining cellular-level, wholebrain maps of behaviourally induced neural activity in themouse. This method combines the use of transgenic immediate-early gene reporter mice to visualize neural activity;
serial two-photon tomography to image the entire brain at
cellular resolution; advanced image processing algorithms tocount the activated neurons and align the datasets to the Allen
Mouse Brain Atlas; and statistical analysis to identify the
network of activated brain regions evoked by behaviour. We
demonstrate the use of this approach to determine the wholebrain networks activated during the retrieval of fear
D. A. Vousden (&) ! B. J. Nieman ! M. van Eede ! J. Dazai !
J. P. Lerch ! R. M. Henkelman
Mouse Imaging Centre, The Hospital for Sick Children,
25 Orde St., Toronto ON M5T 3H7, Canadae-mail: d.vousden@utoronto.ca
D. A. Vousden ! J. Epp ! P. W. Frankland ! J. P. Lerch
Program in Neuroscience and Mental Health, The Hospital for
Sick Children, 555 University Ave., Toronto ON M5G 1X8,
CanadaD. A. Vousden ! B. J. Nieman ! J. P. Lerch ! R. M. Henkelman
Department of Medical Biophysics, University of Toronto,
Toronto, Canada

memories. Consistent with previous studies, we identified alarge network of amygdalar, hippocampal, and neocorticalbrain regions implicated in fear memory retrieval. Our proposed methods can thus be used to map cellular networks
involved in the expression of normal behaviours as well as toinvestigate in depth circuit dysfunction in mouse models ofneurobiological disease.
Keywords Neuroimaging ! Immediate-early genes !
Memory ! Functional networks ! Serial two-photon
tomography

Introduction
A key challenge in neuroscience is to understand the neuralcircuits that give rise to particular behaviours. Complex
B. J. Nieman ! R. M. Henkelman
Program in Physiology and Experimental Medicine,
The Hospital for Sick Children, Toronto, Canada
T. Ragan
TissueVision, Inc., 955 Massachusetts Ave Ste 316, Cambridge,
MA 02139, USA
P. W. Frankland
Institute of Medical Science, University of Toronto, Toronto,
CanadaP. W. Frankland
Department of Physiology, University of Toronto, Toronto,
CanadaH. Okuno ! H. Bito
Department of Neurochemistry, The University of Tokyo
Graduate School of Medicine, CREST-JST, Bunkyo-ku, Hongo,
113-0033 Tokyo, Japane-mail: hbito@m.u-tokyo.ac.jp

123Brain Struct Functbehaviours, such as memory retrieval, depend on thecoordinated activity of distributed networks of neurons
across cortical and subcortical brain regions (Alivisatos
et al. 2012; Guzowski et al. 2005). Understanding these
networks therefore requires whole-brain approaches. The
mouse is an excellent model for studying brain andbehaviour relationships due to its physiological and genetic
similarity to humans (Chinwalla et al. 2002), the ease withwhich its genome can be manipulated (Peters et al. 2007),and our ability to train mice to perform behavioural tasks
relevant to human cognitive processes. Although techniques exist to assess neural activation in rodents at both
macro- and micro-scopic levels, current methods are limited in their ability to do so at high resolution across thewhole rodent brain. For instance, functional magnetic resonance imaging (fMRI) can obtain whole-brain coverageof neural activation patterns in vivo, but does so at a relatively coarse spatial resolution. Moreover, the types of
behaviours that can be investigated with rodent fMRI are
limited by the fact that the animal must be immobilized in
the scanner and is therefore generally anaesthetized during
imaging (Ferris et al. 2011). In vivo two-photon microscopy and electrophysiological recording, on the other hand,enable high-resolution visualization of neural activity;
however, both techniques are very limited in their spatial
coverage (Kerr and Denk 2008; Alivisatos et al. 2012).
Thus, new imaging methods are needed to map the brainwide neural circuits involved in the expression of behaviours, in a systematic and unbiased manner.
With this objective, we describe a novel method forautomated, brain-wide mapping of behaviourally induced
neural activation patterns in the mouse. Moving towards afunctional connectomics requires the integration of several
technologies. First, we need reliable reporters of neuralactivity evoked by particular behaviours or stimuli infreely behaving rodents. Transgenic mice expressing
fluorescent reporters under immediate-early gene (IEG)
promoters such as c-fos and Arc/Arg3.1 have been used tovisualize recent neural activity related to cognitive processes (Guzowski et al. 2005; Barth 2007; Okuno 2011;
Shepherd and Bear 2011; Kawashima et al. 2013). Here,
we used a transgenic Arc-Venus mouseline, which
expresses the destabilized fluorescent Venus protein under
the control of the activity-dependent Arc promoter (Kawashima et al. 2009; Okuno et al. 2012; Mikuni et al.
2013). Like other IEGs, Arc expression is induced byheightened synaptic activity and is believed to play a
critical role in synaptic plasticity and memory consolidation and retrieval (Gusev et al. 2005; Tzingounis andNicoll 2006; Okuno 2011; Shepherd and Bear 2011;
Okuno et al. 2012; Yamasaki et al. 2012). Moreover,since resting-state levels of Arc expression are low, Arc
induction is a reliable molecular readout of recent

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neuronal activity induced by cognitive processes (Okuno
2011; Shepherd and Bear 2011).
Secondly, to visualize the neuronal activity reporter, weneed methods for high-resolution, whole-brain imaging of
brains. Here, we used a recently developed imaging tool,
serial two-photon tomography (STPT), which combines
two-photon microscopy with an integrated vibratome forsectioning and produces well-aligned, high-resolution 3D
datasets (Ragan et al. 2012).
Finally, we need unbiased automated image-processing
and analysis methods to identify and quantify areas ofactivity and to compare the resulting activity networks.
Here, we used the open-source, automated cell segmentation tool CellProfiler to count fluorescently labelled neuronal cell bodies (Carpenter et al. 2006) and image
registration algorithms (Lerch et al. 2011) to align thesamples to the Allen Institute for Brain Science (AIBS)
mouse brain reference atlas (Lein et al. 2007). This process
enables us to quantify neuronal activation in each of the
AIBS atlas’ annotated regions and compare the activation
patterns across subjects. Aligning the datasets to the AIBS
atlas also ties the functional activation maps into a common coordinate space, allowing patterns of activity to becompared both with those evoked by other behaviours, andwith maps of gene and protein expression.
The method presented here combines the use of transgenic mice that act as reports of neural activity, STPT,
automated image processing algorithms, and statisticalanalysis (Fig. 1). We demonstrate the use of this method to
identify the whole-brain neural activity patterns evoked by
two well-characterized behaviours: retrieval of contextual
and tone fear memories. In addition to mapping networks
involved in the expression of normal behaviours, this is a
powerful method to investigate network dysfunction inmouse models of neurobiological disease.

Materials and methods
Mice
Twenty-four homozygous transgenic male mice expressing
destabilized Venus protein under control of the full-length
Arc promoter (Arc-Venus mice) were used. Lines oftransgenic mice harbouring the Arc-pro-Venus-pest transgene were generated in C57BL/6 by a procedure similar tothat used to establish Arc-pro-EGFP-Arc transgenic mice
(Okuno et al. 2012). Briefly, the Venus cDNA (Nagai et al.
2002) was subcloned into a plasmid containing the 7-kb
regulatory region of the Arc gene (Kawashima et al. 2009).
The Venus reporter was destabilized by adding a PEST
signal sequence from the mouse ornithine decarboxylasewith D433A/D434A mutations to accelerate its degradation

Brain Struct Funct

Fig. 1 Overview of the method. a Behavioural stimulation, such fear
memory retrieval, evokes neural activation and induces immediateearly genes (IEGs), such as Arc. Transgenic mice expressing fluorophores following IEG induction can be used to visualize recent neuralactivity. b Each brain sample is imaged as a series of coronal opticalsections using serial two-photon tomography (STPT). STPT consists ofa two-photon microscope and integrated vibratome and translation
stages. c A 3D volume rendering of an Arc-Venus mouse brain imaged
with STPT. An enlarged view of the region outlined in red is shown inthe bottom panels: i Visualization of fluorescently labelled neurons in
the dentate gyrus using STPT. ii Automated cell segmentation software
is used to identify fluorescent cell bodies in each coronal section. iii The
samples and counts are stacked into 3D volumes and resampled at
25 lm isotropic resolution to obtain counts of the number of fluorescentneurons per 25 lm voxel. d Brain regions activated during recall ofcontextual fear memories: aligning the datasets to the Allen Mouse
Brain Atlas enables statistical analysis to relate counts of number ofactivated neurons to retrieval of fear memories. Scale bar is 0.1 mm

rate (Li et al. 1998). The Arc-pro-Venus-pest transgene was
microinjected into the pronucleus of fertilized mouse eggs.
Detailed characterization of Arc-pro-Venus-pest transgenic
mice will be described elsewhere (HO and HB, in preparation). Genomic integration of the transgene and reporter
expression was analysed by PCR, southern blotting, western blotting and histological assays. Mice were housed incages of 3–5, maintained on a 12-h light/dark cycle, andallowed ad libitum access to food and water. Mice were
8-week old when tested. All experiments were approved by
the Animal Ethics Committee of the Hospital for Sick
Children in Toronto and by Recombinant DNA and Animal
Experiments Committees of the University of Tokyo
Graduate School of Medicine. All mouse requests should
be directed to Haruhiko Bito and Hiroyuki Okuno at the
University of Tokyo.

chambers with stainless steel grid floors through whichfootshocks could be delivered. All 24 Arc-Venus mice
were trained on a fear-conditioning task. During training,each mouse was placed individually in the training chamber. Two minutes later, it received five tone (2,800 Hz) andfootshock pairings (30 s tone, which co-terminated with a
2 s shock, 0.5 mA) at 2 min intervals (Fig. 2a). Mice werereturned to their homecage 1 min after the final footshock.

Fear conditioning
Training
Prior to training, mice were handled for 2 min per day for
3 days. Mice were trained and tested in conditioning

Testing
Forty-eight hours after training, memory recall was
induced by exposing the mice to either the training context(Context, n = 8) or the tone cue in a novel context (Tone,n = 8), while a control group remained in their home cageduring the retrieval session (Control, n = 8). The context
group was placed in the training chamber without the tonecue, and freezing behaviour was assessed for 5 min(Fig. 2b). The tone group was placed in a novel, unfamiliar
chamber, distinct from the training chamber, and presented
with the training tone (2 min without tone followed by
3 min with tone) (Fig. 2b). Freezing behaviour was measured automatically (FreezeFrame, Actimetrics).

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Brain Struct FunctabFig. 2 Fear conditioning of Arc-Venus mice. a There was nodifference in freezing in mice trained on the cued fear-conditioning
paradigm. b Following presentation of the conditioned stimulus (CS)to the tone group, there was no difference in conditioned fear in micerecalling either contextual or tone fear memories (Post-CS). Pre-CS

refers to the time prior to tone presentation to the tone group, during
which only the context group is exposed to their CS, the training
environment, and exhibits a fear response. Error bars represent
standard error of the mean

Sample preparation

optical imaging, an integrated vibratome mechanicallysections the sample. Here, we used a 516-nm single-edge
dichroic beamsplitter (Semrock FF516-di01-25 9 36),
which reduces background autofluorescence by splitting
the light at 516 nm and collecting emitted light on two
channels (channel 1: [516 nm, channel 2 \516 nm). Only
channel 1 data were used for subsequent image processing
and analysis.
Once the imaging parameters are set, the instrument
software automatically controls scanning, stage motion, vibratome control, and data acquisition. The following sectioning
parameterswerespecified:sectioning
speed = 0.5 mm/s, blade vibration frequency = 60 Hz, cut
velocity = 20 mm/s, delay = 8 s. For image acquisition,
signal was collected from 40 lm below the sample surface,
as a mosaic of 12 9 8 overlapping tiles, each 832 9 832
pixels. The xy stage movement was 900 lm and pixel size
1.37 lm. The total imaging time was approximately 9.5 h
per sample. All imaging experiments and analyses wereconducted blind to the experimental group of the mouse.

The mouse brains were prepared for imaging as follows:
three and a half hours following memory recall testing, themice were anaesthetized by intraperitoneal (i.p.) injectionof chloral hydrate and transcardially perfused with 50 mLof phosphate-buffered saline (PBS, pH 7.4, 25 "C) followed by 50 mL of cold 4 % formaldehyde. This timepoint was chosen as it is the peak expression of the Venus
fluorophore. The skulls were dissected and further fixed in
4 % formaldehyde at 4 "C for 24 h. The brains were thentransferred to PBS and 0.02 % sodium azide at 4 "C before
being embedded in 4 % agarose for imaging.
Imaging
We used serial two-photon tomography (STPT) to image
the brains as datasets consisting of 200 coronal sections,
evenly spaced by 75 lm with an in-plane resolution of
1.37 lm (Fig. 1b–c) (Ragan et al. 2012). STPT works asfollows: the brain sample is glued to a microscope slide andmounted in a phosphate buffer bath on integrated xyz
translation stages. Light (here 920 nm) from a Chameleon
Ultra titanium-sapphire laser (coherent) passes through atube and scan lens assembly toward a pair of scanningmirrors before being reflected by a short-pass dichroic
toward a microscope objective (169 Nikon lens, 0.8 NA,
WD 3.0 mm) (Ragan et al. 2012). The fluorescent signal
from the sample is collected by the same objective, andthen split by a dichroic mirror and directed onto two
photomultiplier tubes (channel 1 and channel 2). After

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Image pre-processing
The individual tiles were saved as 16-bit tiff files. We usedcustom programs written in matlab and Python to process
the data as follows: each tile was cropped by 15 pixels oneach edge to remove illumination artefacts near the borders. Then to correct for uneven illumination, each tile wasdivided by the average-intensity image of all tiles in thesample. The illumination-corrected tiles were then stitched
together into 2D coronal slices by computing the crossBrain Struct Funct

Fig. 3 Quantification of Arc-Venus? neurons. Using the cellsegmentation software CellProfiler, each 2D coronal image (a) ineach brain sample was processed as follows: intensities were rescaled(b) and the image was blurred with small median and Gaussianbilateral filters (c) to reduce noise. Bright speckles were enhanced(d) and used to identify cell bodies based on intensity and shape (e).
The cell bodies in each 2D image were counted. Then, images andcounts were stacked and resampled at 25 lm isotropic resolution tocreate 3D volumes of cell counts at each voxel (f). Scale bar
represents 0.1 mm

correlation between tiles. At the overlapping regions, thepixels were blended using a weighted average of pixel
intensities from contributing tiles.

smoothed 25-lm optical sections and separately, theircorresponding counts, into 3-dimensional, 25-lm isotropic
voxel datasets. This produces two 3D volumes for eachbrain: an anatomical volume and a volume of cell counts.
For a full list of CellProfiler modules and parameters, see
Appendix.
To ensure appropriate selection of CellProfiler parameters, the full dataset was divided into a training set and avalidation set, each comprising half the samples from eachgroup. The training set was used to tune the segmentation
parameters listed above. Three sets of size, shape, andintensity filter parameter combinations were selected andused to process the training set. Once the optimal parameter set was found, the data were analysed as described
below. The validation set was used to confirm the accuracyof this analysis. For the final analysis, the data werecombined.

Quantification of fluorescent neuronal cell bodies
To identify and localize Arc-Venus-labelled neurons thatwere activated during memory recall, we used the automated, open-source cell segmentation software CellProfiler
to count fluorescent neuronal cell bodies in every 2D section of each sample (Figs. 1, 3) (Carpenter et al. 2006).
A CellProfiler pipeline was created to identify neuronal cellbodies based on intensity, size, and shape. Each image wasblurred to reduce noise, and then bright speckles of an
appropriate size were enhanced to identify cell-body candidates. The cell bodies were then identified and filteredbased on size, shape, and intensity, using a robust adaptive
background filter. Finally, each identified object was
reduced to one pixel and 25-lm blocks were used for cellcounting. The sequential 2D optical sections acquired with
STPT are inherently aligned (Ragan et al. 2012). We
therefore used a custom python script to stack both the

Image registration
To identify and compare the brain networks involved inrecalling contextual and cued fear memories across all123

Brain Struct Funct

Fig. 4 Image registration process. The 3D serial two-photon tomography images and accompanying cell counts from all 24 mice werealigned to create an average of all brains in the study. This average
was then aligned to the Allen Mouse Brain Atlas via a synthetic

intermediate. Using these transforms, the Allen Mouse Brain Atlasannotations were backpropagated onto individual samples, enabling
quantification of the number of cells in each segmented brain region.
Full registration parameters are given in Appendix

subjects, all brains in the study were aligned using in-house
image registration software (Fig. 4, for full details see
Appendix) (Lerch et al. 2008, 2011; Kovacevic et al.
2005). Image registration finds a set of smooth transformations to align all brains in the study to a common
coordinate system so that corresponding anatomical features are superimposed. Given the biased sampling
acquired, with high in-plane resolution and 75-lm slice
gaps, the datasets were resampled to isotropic 25 lm
sampling using trilinear resampling for the images andnearest-neighbour resampling for the counts. Therefore,
each downsampled voxel contained an estimate of thenumber of identified fluorescent cell bodies in that25 9 25 9 25 lm3 voxel. Isotropic sampling allows thebrain to be digitally resliced in any arbitrary direction,
which is essential for aligning all the brains in the study
into the same space for statistical comparisons between
groups.
We used an automated intensity-based group-wise registration approach that first linearly aligns all images
through a series of rotations, translations, scales, andshears, and then locally deforms each image through an
iterative nonlinear process, bringing all brains into exactalignment (Lerch et al. 2008, 2011; Kovacevic et al. 2005)(Fig. 4, Appendix). Two iterations were used for the nonlinear stage. The registration was performed using in-house
software based on the MNI autoreg tools, which use anelastic registration algorithm (Collins et al. 1994, 1995;
Collins and Evans 1997). This process yields an average
image of all brains in the study, as well as transforms thatbring each individual image into exact alignment with thisaverage (Fig. 4). These transforms were then applied to thecorresponding datasets of cell counts so they too werealigned and could be compared. After resampling to 25 lm
resolution, the STPT datasets were each *400 MB in size.
The registration of 24 samples took approximately 500 h of
CPU time. However, this process is easily parallelizable,
and here was run on a computing cluster. The registration
process completes in approximately 10 h on a mid-sizedcluster of 8 machines each with an 8-core processor. For a
more detailed description of registration parameters foreach stage see Appendix.
To localize fluorescence to particular brain regions wealigned the STPT average to the AIBS mouse brain reference atlas, which divides the brain into 670 annotatedregions (Lein et al. 2007). This allows us to compute cellcounts in each of the Allen Mouse Brain Reference Atlas’
structures in each individual brain. The goal of image
registration is to align homologous points or brain regions
between samples; image registration is thus an ill-posed
problem if these homologous points/regions do not exist or
cannot be identified between samples. The Allen Mouse
Brain Reference Atlas is Nissl stained, while the intensityof our Arc-Venus average depends on the pattern of Venus
expression throughout the brain samples and on inherent
variation in the amount of autofluorescence from different
brain regions. As such, different brain regions are visible in
the Arc-Venus and Allen Nissl samples, making precise
registration difficult. To overcome this challenge, the STPT
average was first aligned to an intermediate synthetic atlasof the whole brain that was in the same space as the Allen
Mouse Brain Atlas. This atlas was created by merging
structures in the Allen Mouse Brain Atlas annotations suchthat a similar set of structures as in the Arc-Venus average
was visible (Fig. 4). Because our registration algorithms
depend on computing the cross correlation between image

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Brain Struct Functintensities, we then replaced the Allen Mouse Brain Atlasannotations with the corresponding structure’s intensity in
the STPT average (Fig. 4). This enabled precise alignmentof the Arc-Venus average to the Allen Mouse Brain Atlas.
The linear steps of the registration were performed using
the MNI program register and in-house software using theautoreg tools described above, and the final nonlinear
registration used a greedy symmetric diffeomorphic registration (the SyN algorithm in ANTS, Avants et al. 2008).
Statistical analysis
All analyses were performed using the R statistical software (version 2.15.3). For each structure in the Allen
Mouse Brain Reference Atlas, a negative binomial
regression model was used to determine whether there wasan effect of testing group (Control, Context, or Tone) on
the number of counted neurons. The incident rate ratio(IRR) is the ratio between one group and the referencegroup. For example, an IRR of 5 means that the expected
count is five times greater in, for example, the context micecompared to the control animals. A negative binomial
model was used because the count distributions did not
follow a normal distribution, and the data were overdispersed (conditional mean \ variance). Shapiro–Wilks
tests were used to determine that the count distributions
deviated significantly from normality.
To determine whether differences in mean intensity
could be used to analyse group-wise differences in activation networks, we computed the mean fluorescenceintensity in each structure segmented in the Allen Mouse Brain Atlas. To control for global differences in intensity, we also normalized the mean intensity in eachstructure in three separate ways: first, by dividing by theaverage intensity over the sample; and secondly, by
dividing by the mean intensity in the corpus callosum or
separately by the arbor vitae—white matter structures, inwhich we would expect no activation under any condition.
A Shapiro–Wilks test was used to show that the intensities
did not deviate significantly from normality. We therefore
used a linear regression to determine whether there was aneffect of testing condition on the mean intensity or on
normalized mean intensity in each structure.
Negative binomial regression models were also used totest the relationship between the percentage of time spent
freezing during testing and the number of counted neurons
in each brain structure. For this analysis, only the tested
mice (Context and Tone) were used. For all analyses,
multiple comparisons were controlled for using the falsediscovery rate at a 5 % threshold (Genovese et al. 2002).
Throughout the text, q values will be used, which are
p values adjusted for multiple testing using the FDR
approach.

Results and discussion
Arc-Venus mice recalled different types of fear
memories
Twenty-four Arc-Venus mice were trained on a fear-conditioning task in which they received five tone and shock
pairings (Fig. 2a). Forty-eight hours after training, the mice
were divided into three groups. Memory recall was induced
by exposing the mice to either the training context (Context, n = 8) or the tone cue in a novel context (Tone,n = 8), while a control group remained in their home cageduring the retrieval session (Control, n = 8) (Fig. 2b).
Following presentation of the tone stimulus to the tonegroup, there was no difference in the conditioned freezing
response between mice recalling contextual and tone
memories (Fig. 2b, post-CS). Thus, although the mice wererecalling different types of fear memories, the strength ofthe memory was the same between groups.
Brain-wide mapping of functional activation networks
To visualize Arc-Venus expression, we used serial twophoton tomography (STPT) to image the brains as 3D
datasets of optical sections. We then used the automatedcell segmentation software CellProfiler to count activated
Arc-Venus-labelled neurons (Carpenter et al. 2006) andimage registration to align all brains in the study along with
their corresponding cell counts. This enabled us to compare
the activation patterns across samples and to determine thebrain networks involved in recalling contextual and tone
fear memories.
Consistent with previous studies, we found that fear
memory retrieval is associated with activation in a distributed network of brain areas, including regions of the
hippocampus, amygdala, and neocortex (Fig. 5). Amongthe most significant results were the findings that compared
to untested controls, mice recalling contextual fear memories showed significantly greater activation in the hippocampal formation (including dentate gyrus) (Maren 2001;
Lee and Kesner 2004; Huff et al. 2006; Mamiya et al.
2009), lateral, medial, cortical amygdalar nuclei (Maren
2001; Ploski et al. 2008; Mamiya et al. 2009), subiculum,as well as visual (Frankland et al. 2004), ento- and ectorhinal (Corcoran et al. 2011; Tayler et al. 2013), retrosplenial (Corcoran et al. 2011), perirhinal (Campeau andDavis 1995; Corodimas and LeDoux 1995), and posteriorparietal association areas of the cortex (Tayler et al. 2013)(q \ 0.05 for all regions shown, Fig. 5). Similarly, compared to untested controls, mice recalling tone memories
had significantly greater activation in the hippocampus(Mamiya et al. 2009), basolateral, lateral, medial, central,and cortical amygdalar nuclei (Ploski et al. 2008; Mamiya

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Brain Struct Functb Fig. 5 Whole-brain networks involved in fear memory recall. (Left)

Brain regions in which there were a significantly greater number ofactivated neurons in mice recalling contextual fear memories,
compared to untested controls. (Middle) Regions where there werea significantly greater number of activated neurons in mice recalling
tone fear memories compared to controls. (Right) Regions where
there was a significant difference in the number of activated neurons
in mice recalling contextual fear memories compared to tone memories (with colour indicating which showed more activated neurons).
In all columns, the left half of the image shows individual labelled
structures coloured according to the ratio of mean counts in one groupcompared to another, i.e. the fold increase of counts of activatedneurons in one group relative to the reference group. The backgroundimage is the average of all brains in the study. The right half of theimage shows the corresponding Allen Mouse Brain Atlas referenceatlas annotations. For all coloured structures in the statistical maps
(left hemisphere), there was a significant effect of testing group at afalse-discovery rate (FDR) of 0.05. Allen Mouse Brain Atlas images
obtained from the Allen Institute for Brain Science website: http://
mouse.brain-map.org/experiment/thumbnails/100048576?image_
type=atlas (Image numbers: 405, 382, 297, 261, 209, 169. Image
credit: Allen Institute for Brain Science). Abbreviations: 1–6: Corticallayers 1–6; ACA anterior cingulate, AUD auditory (d dorsal,
p primary, v ventral), BLA Basolateral amygdala, CA1-3 Cornu ammonis 1-3, CEA Central amygdala, COA Cortical amygdala, DG
dentate gyrus, ECT ectorhinal, ENT Entorhinal area (l lateral,m medial), Epd endopiriform nucleus, HPF hippocampal formation,
ILA infralimbic, LA Lateral amygdala, LS lateral septal nucleus(r rostral, c caudal), MEA medial amygdala, MO Motor area
(p primary motor area, s secondary), NTB nucleus of the trapezoid
body, PAR parasubiculum, PERI perirhinal, PIR piriform, POST
postsubiculum, PTLp posterior parietal association, PRE presubiculum, SS Somatosensory area (bfd barrel field, ll lower limb, p primary,s supplemental, tr trunk), TEA temporal association, Ttd Taenia tecta,
RSP retrosplenial, VISC visceral, VIS Visual area (p primary, l lateralpl posterolateral, pm posteromedial visual)et al. 2009), and many cortical areas, including the auditory(LeDoux 2000), retrosplenial, somatosensory (Wei et al.
2002), and infralimbic cortices (q \ 0.05 for all regions,Fig. 5).
While the contextual and tone memory networks weresimilar, we were also able to distinguish contextual fear
memory networks from those involved in tone memory
recall (Fig. 5). Compared to tone memory-recalling mice,
mice recalling contextual memories showed significantly
greater activation in the lateral areas of the entorhinal
cortex, postsubiculum, and temporal association areas(q \ 0.05 for all). In mice recalling tone memories, wefound significantly greater activation in the auditory,
somatosensory and anterior cingulate cortices, and subregions of the hippocampal formation and amygdala, compared to contextual memory-recalling mice (q \ 0.05 forall, Fig. 5).
In addition to mapping the fold increase in counts onto
the Allen Mouse Brain Atlas annotations, we also looked at
the pattern of level of significance across the brain (Fig. 6).
We found that the overall pattern in these mappings wasvery similar. Indeed, areas where there was a large foldincrease in neuronal activation in mice recalling either
context or tone memories compared to controls were generally those with very small q values. Similarly, areas
where there was a small fold increase in activation inmemory-recalling animals compared to controls weregenerally those with larger q values.
Although the functional activation maps we obtained arelargely an amalgam of regions previously reported in moretargeted studies, we also saw significant activation in someless-expected brain regions. For instance, although theanterior cingulate (ACC) is typically implicated in more
long-term fear memories (Frankland et al. 2004), we founda significant increase in Arc-Venus? neurons in the micerecalling tone fear memories (Fig. 5). This is consistent,
however, with reports that auditory fear memory retrieval
induces a significant increase in the amount of cyclic-AMP
responsive element-binding protein (CREB), a key transcription factor involved in memory formation and synaptic plasticity (Josselyn et al. 2001; Silva et al. 1998), in
the ACC (Wei et al. 2002). Additionally, there wasincreased activation in the visual cortex of both groups ofmemory-recalling animals. This result may be due toincreased Arc expression in visual areas following spatial
exploration (Shepherd and Bear 2011). These results suggest that once a whole-brain functional activation map isobtained, further investigations (e.g. lesion studies or
pharmacological/optogenetic interventions) may be beneficial to determine whether a particular brain region isstrictly necessary for the expression of a behaviour ofinterest.
Quantification of neuronal activity in annotated brain
regions
An advantage of this approach is its quantitative nature.
Because the datasets were aligned to the AIBS mousereference atlas, we were able to obtain counts of thenumber of activated neurons in each annotated region foreach mouse. While this process was done automaticallyover each brain region, Fig. 6 shows the estimates of thenumber of activated Arc-Venus? neurons in two areas ofinterest: the lateral amygdala and the granule cell layer ofthe dentate gyrus. There were five times as many activatedneurons in the granule cell layer of the dentate gyrus inmice recalling both contextual (IRR = 5.19, q \\ 0.0001)
and tone (IRR = 5.14, q \\ 0.0001) fear memories compared to untested controls (Fig. 7). Similarly, mice recalling contextual and tone fear memories had, respectively,
three and four times as many activated neurons in the lateral amygdala as untested control mice (IRR = 3.16,
q = 0.03 and IRR = 4.03, q = 0.004, respectively;
Fig. 7). Thus, this method can be used to quantify the
proportion of recently activated neurons in a particular123

Brain Struct Funct

Fig. 6 Comparison of fold-increase and significance activation maps.
Brain regions from Fig. 5 showing regions with a significantly greaternumber of activated neurons in mice recalling contextual (a) and tone(c) fear memories, compared to untested controls. Columns b andd show the same brain regions with the colour representing thesignificance of the difference or q value (FDR-adjusted p values).
Generally, the areas in which there was a greater increase in micerecalling context or tone fear memories were also those in which thedifferences were highly significant

brain region of interest, which could be useful in determining the size of the neuronal subpopulation needed tosupport a memory.

relationship between the percentage of time spent freezing
during testing and the number of neurons in that region. For
instance, in the posterior amygdalar nucleus, an area
known to be critical for fear memory formation andretrieval (Ledoux 2000; Maren 2001), a greater amount oftime freezing in the test was associated with a greaternumber of activated neurons (IRR = 1.02, q = 0.037)(Fig. 8a, b).
Conversely, in layer 6 of the posterolateral visual cortex,
the extent of time freezing in the test was inversely correlated with the number of activated neurons in this region(IRR = 0.924, q = 0.0005) (Fig. 8c, d). One possible
explanation for these seemingly opposing correlations in
distinct areas is that mice that spend a greater amount oftime freezing during testing may have formed a stronger
association between the conditioned stimulus andEffect of behaviour on neuronal activation
In fear-conditioning tasks, the amount of time the animal
spends freezing during the recall test is commonly used as
an indication of the strength of the animal’s memory for theaversive event. With our approach, we obtain 3D volumesof counts of the number of recently active neurons in eachdefined brain region for each mouse. This approach
therefore enables in-depth quantitative analyses of therelationship between the number of activated neurons in agiven region and the mouse’s behaviour during testing. We
found several regions in which there was a significant

123Brain Struct Funct

Fig. 7 Quantification of number of activated neurons across thebrain. There was a significant increase in the number of activatedneurons in the dentate gyrus of mice recalling contextual and tone fear
memories compared to untested control animals (left plot,
q \ 0.0001, both). There was also a significant increase in numberof activated neurons in the lateral amygdala in mice recalling
contextual (q = 0.03) and tone (q = 0.004) fear memories, compared
to controls (right plot). Error bars represent standard error of themean. In the brain image in the middle, the left half shows the average
image of all brains in the study and the right half shows the Allen
Mouse Brain Atlas reference atlas annotations (image 297). Allen
Mouse Brain Atlas images obtained from the Allen Institute for Brain
Science website: http://mouse.brain-map.org/experiment/thumbnails/
100048576?image_type=atlas (Image credit: Allen Institute for Brain
Science)Fig. 8 Relationship between behaviour and neural activity.
a Increased amount of time spent freezing during the memory recall
test was associated with an increased number of activated ArcVenus? neurons in the posterior amygdala (negative binomial
regression, q = 0.037). b, c Average of all samples acquired with
serial two-photon tomography with Allen Mouse Brain Atlas
reference atlas annotations overlayed to delineate the posterior
amygdala and layer 6a of the posterolateral visual cortex.
d Increased amount of time spent freezing during the memory recall

test was associated with a decreased number of activated Arc-Venus?
neurons in layer 6a of the posterolateral visual cortex (q = 0.0005).
Dots represent individual samples. Fitted curve represents thepredicted number of neurons in each brain area over the range offreezing percentages. Grey bands represent 95 % confidence intervals. Allen Mouse Brain Atlas images obtained from the Allen
Institute for Brain Science website: http://mouse.brain-map.org/
experiment/thumbnails/100048576?image_type=atlas (Image numbers: 309 and 388. Image credit: Allen Institute for Brain Science)footshock, which may lead to an increased response inamygdalar nuclei and a subsequent stronger fear-mediatedmodulation of downstream structures. The increased
freezing may thus perhaps gate and reduce neuronal
activity in the visual cortical neurons involved in spatial
exploration during fear retrieval. Alternatively, since IEG
induction in the visual cortex has been shown to become
stronger in response to a more novel/less familiar visual
stimuli (Zhu et al. 1995), one may imagine that the mice
which visually recognized less familiarity in the context(i.e. more novelty and hence more IEG expression in visual
related systems) were the ones which showed comparably
weaker freezing. Overall, the ability to quantitatively relate

individual differences in behaviour and neuronal activity
patterns will enable future studies to more precisely probe
the contribution of particular brain areas to behaviour.
Counting activated neurons is required as intensity
measures are insufficient to dissociate functional
activation networks
Obtaining the cellular-level resolution needed to extract
neuronal counts from the data comes at the expense of long
imaging times. We therefore asked whether this level ofquantification is necessary to map differences in neuralactivation networks between testing conditions, or whether

123Brain Struct Funct

Fig. 9 Intensity-based methods are insufficient to dissociate activation networks. There was no difference in mean intensity in thedentate gyrus granule cell layer and lateral amygdala in mice recalling
contextual and tone fear memories, compared to untested controls,

despite there being a significant difference in the number of activatedneurons (q [ 0.05, and see Fig. 5). There were no significant groupwise differences in intensity in any brain region, after multiple
comparisons correction. Error bars are standard error of the meana coarser intensity-based analysis could suffice to dissociate the networks involved in recalling fear memories.
There were no brain structures in which there was a significant effect of testing condition on the mean intensity ofthat structure, after correction for multiple comparisons,even if the false-discovery threshold was relaxed from 5 to
20 % (Fig. 9). There was also no significant effect of
testing condition on the normalized mean intensity, whennormalized by average intensity over the sample or bymean intensity in the corpus callosum or arbor vitae.
Together, these results suggest that cognitively activated
Arc-Venus? neurons represent only a small number inmost brain areas and are too scattered in space to beresolved using low-resolution, intensity-based approaches.
These are clearly insufficient to distinguish the differencesin Arc-Venus expression in mice recalling different typesof fear memories, and thus cannot be used to determinefunctional activation networks evoked by memory recall.

Denk 2008). Secondly, fluorescence microscopy or
immunohistochemistry of mounted serial brain sections isvery laborious to do across the whole-brain, and it is difficult to align the resulting 2D sections, which limits its use
for whole-brain quantitative analyses at neuron-level resolution (Lein et al. 2007). Similarly, electrophysiologicalrecordings of neural activity presently sample only sparse
subsets of neurons within only a few brain regions (Alivisatos et al. 2012). Finally, though fMRI does acquire
whole-brain activity patterns, it images at a much coarser
spatial resolution, and the present requirement of anaesthesia restricts the types of behaviours that can be investigated (Ferris et al. 2011).
While the method described here overcomes these limitations, it is important to note that our automated approachis currently restricted to imaging fixed brain samples andthus does not permit in vivo imaging of neural activity inthe freely behaving rodent, which limits the temporal resolution. However, we note that the use of transgenic orviral reporters of IEG expression is promising in this regard
as well, as it allows the visualization of recent neuralactivation over the timescales of minutes to hours (Barth
2007; Eguchi and Yamaguchi 2009; Kawashima et al.
2009; Okuno et al. 2012). Additionally, while we used an
Arc-Venus mouseline, this approach generalizes to anytransgenic IEG reporter.

Comparison with existing methodologies
To our knowledge, the imaging and image analysis workflow presented here is the first automated method able toidentify and quantify behaviourally evoked neural activation across the whole mouse brain at single-neuron resolution. Compared to existing methods for identifying neuralcircuits involved in behaviour, this approach has several
advantages. First, while in vivo two-photon fluorescencemicroscopy can be combined with calcium indicators or
fluorescent reporters to image neural activity in the awake,
behaving rodent, it is currently challenging to image large
areas of the rodent cortex, and the depth achievable within vivo imaging is limited by optical scattering (Kerr and123

Considerations for future studies
While we investigated the brain networks activated in
response to fear memory retrieval, in the future, thismethod could be used to determine the networks evoked bydifferent stimuli or that are involved in the expression ofBrain Struct Functmany other behaviours, including learning and memory
formation in many domains (e.g. fear or spatial learning),
object recognition, or social interaction. Additionally, thismethod could be used to investigate how functional activation networks are disrupted in mouse models of diseaseby crossing disease mouse models with immediate-earlygene reporter mouselines such as Arc-Venus or cfos-eGFP(Barth 2007; Reijmers et al. 2007). However, we note thatany observed differences in functional activation networks
between control and disease models may also be due todifferences in synapse density. In such studies, it would
therefore be important to control for this as much as possible, by comparing neural activation in wildtype animals and the model of interest in some sort of control
activation task engaging the brain areas of interest, such asa tactile stimulation of the paw or visual stimulation.
Integration with the Allen Mouse Brain Atlas
Finally, although we aligned our dataset to the Allen
Mouse Brain Atlas, the data can in principle be aligned toany atlas, provided the appropriate intermediate is created
if necessary. For instance, the functional activation networks could be aligned to an MRI-based atlas or a mouse
vascular atlas, enabling comparisons across imaging
modalities. Additionally, the group-wise comparisons ofneuronal activation can be performed voxel-wise, enabling
the quantification of highly localized differences in activity. We note, however, that aligning the functional activation networks of different behaviours to the Allen Mouse
Brain Atlas provides a standardized space for comparison.
This enables the networks to be compared with maps ofgene expression or cell types and will be particularly usefulin comparing how activation networks are altered inmodels of disease.

Conclusion
In summary, we developed an automated method to detect
neural activation patterns across the whole mouse brain.
We used this method to obtain brain-wide maps of theareas involved in recalling different types of fear memories. Consistent with previous studies, we identified a large
network of hippocampal, amygdala, and neocortical brain
regions implicated in fear memory retrieval, which gives
strong face validity to our approach. In the future, thismethod can be applied to identify the networks involved inless well-characterized behaviours, to understand how
memory networks evolve with time, and to investigate howthese neural circuits are disrupted in the pathogenesis andtreatment of mouse models of complex brain disorders,such as Alzheimer’s disease, schizophrenia, and autism(Rudinskiy et al. 2012). In addition, this approach could be
combined with graph-theoretical analyses to understandboth healthy and disordered circuits from a network perspective (Bullmore and Sporns 2009; Wheeler et al. 2013).
The combination of transgenic reporters of IEG expression,
serial two-photon tomography (STPT) for high-resolution
whole-brain imaging, and automated image registrationand segmentation techniques provides a powerful means ofmapping functional circuitry over the whole mouse brain.
Acknowledgments Research was supported by the CanadianFoundation for Innovation, Canadian Institute for Health Research(CIHR), Natural Science and Engineering Research Council(NSERC-DV), Ontario Research Foundation, a CREST-JST grant(HB and HO), and by Grants-in-Aid from JSPS (KIBAN to HO,
WAKATE to HB) and from MHLW of Japan (to HB and HO). RMH
was supported by a Canada Research Chair.
Conflict of interest
Vision, Inc.

TR is a shareholder and employee of Tissue-

Appendix: CellProfiler modules and parameters
The CellProfiler modules we used were as follows:
1.
2.

3.
4.

5.

6.
7.

Rescale intensity: rescaled the image intensity range tobe from 0 to 1 (Fig. 3b).
Smooth: blur each image with a small median filter(size = 3 pixels) and an edge-preserving bilateral
Gaussian filter to reduce noise (Fig. 3c).
Enhance or suppress features: enhance bright speckles(size = 15 pixels) using a white tophat filter (Fig. 3d).
Identify primary objects: identify neuronal nuclei
based on size (4–40 pixels), shape (threshold correction factor = 6), and intensity, using a robust adaptive
background filter.
Measure object intensity and measure object size
shape: measure the intensity and size of each identified
object.
Filter objects: filtered objects based on intensity andshape (Fig. 3e).
Expand or shrink objects: reduce each identified object
to one pixel for counting (Fig. 3f).

Image registration parameters
Aligning individual samples acquired with serial twophoton tomography (STPT) to achieve a consensus
average
The registration was achieved using in-house software
using the MNI autoreg tools (Collins et al. 1994; Kovacevic et al. 2005; Collins and Evans 1997). First, all brains123Brain Struct Functwere rigidly registered towards one of the samples in study.
Then all possible pairwise 12-parameter registrations were
performed to create a linear consensus average model of all
the brains in the study. This linear average was used as theinitial target for the final part of the registration. The
individual images were locally deformed to this target
using a multi-scale nonlinear alignment procedure using
the Automatic Nonlinear Image Matching and Anatomical
Labeling (ANIMAL) algorithm (Collins et al. 1997). This
process was iterated a second time, using a finer resampling
grid, with the resulting atlas used as the target for thesecond nonlinear stage.
Aligning average STPT image to Allen Mouse Brain
Atlas
1.

2.

The STPT average and the synthetic intermediate atlas(in the same space as the Allen Mouse Brain Atlas)were first coarsely aligned manually with a 6 parameter registration using the MNI program register. Thenthe MNI autoreg tools (version 0.99.6) were used toalign the two samples with a 12 parameter linear registration (3 shears, 3 scales, 3 translations, and 3
rotations (Kovacevic et al. 2005). We use the crosscorrelation of intensities for the linear optimization
objective function with translation weights of 0.2 ineach direction. The step size along each dimension was
1, and the simplex volume was 1.
The images were then aligned using nonlinear registration procedure using the Advanced Normalization
Tools (ANTs) algorithm (Avants et al. 2008). We firstblurred the images with a 0.05 mm fwhm Gaussian
blurring kernel, and computed the gradients for each
image. We used symmetric normalization, a diffeomorphic transformation model, using as similarity
metrics a combination of cross correlation on theregular image intensities and cross correlation on the
3D gradient magnitudes. A Gaussian regularizer wasused that operates both on the similarity metric and on
the deformation field. The regularization blurring useda sigma of two for the gradient field and one for the
deformation field. The gradient descent step size was
0.3. The radius for the similarity metric was three. At
most 150 iterations were performed at each level.

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