Identification of a Functional Connectome for Long-Term
Fear Memory in Mice
Anne L. Wheeler1,2, Cátia M. Teixeira1, Afra H. Wang1,2, Xuejian Xiong3, Natasa Kovacevic4,
Jason P. Lerch1,5, Anthony R. McIntosh4,6, John Parkinson3,7, Paul W. Frankland1,2,8*
1 Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, Canada, 2 Institute of Medical Science, University of Toronto, Toronto, Canada,
3 Program in Molecular Structure and Function, The Hospital for Sick Children, Toronto, Canada, 4 Rotman Research Institute, Baycrest Centre, Toronto, Canada,
5 Department of Medical Biophysics, University of Toronto, Toronto, Canada, 6 Department of Psychology, University of Toronto, Toronto, Canada, 7 Departments ofBiochemistry and Molecular Genetics, University of Toronto, Toronto, Canada, 8 Department of Physiology, University of Toronto, Toronto, Canada

AbstractLong-term memories are thought to depend upon the coordinated activation of a broad network of cortical and subcorticalbrain regions. However, the distributed nature of this representation has made it challenging to define the neural elements ofthe memory trace, and lesion and electrophysiological approaches provide only a narrow window into what is appreciated amuch more global network. Here we used a global mapping approach to identify networks of brain regions activatedfollowing recall of long-term fear memories in mice. Analysis of Fos expression across 84 brain regions allowed us to identify
regions that were co-active following memory recall. These analyses revealed that the functional organization of long-term
fear memories depends on memory age and is altered in mutant mice that exhibit premature forgetting. Most importantly,
these analyses indicate that long-term memory recall engages a network that has a distinct thalamic-hippocampal-corticalsignature. This network is concurrently integrated and segregated and therefore has small-world properties, and contains hublike regions in the prefrontal cortex and thalamus that may play privileged roles in memory expression.
Citation: Wheeler AL, Teixeira CM, Wang AH, Xiong X, Kovacevic N, et al. (2013) Identification of a Functional Connectome for Long-Term Fear Memory inMice. PLoS Comput Biol 9(1): e1002853. doi:10.1371/journal.pcbi.1002853
Editor: Olaf Sporns, Indiana University, United States of America
Received August 16, 2012; Accepted November 7, 2012; Published January 3, 2013
Copyright: ß 2013 Wheeler et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by a grant from the Canadian Institutes of Health Research (MOP-77561) to PWF. ALW and AHW received support from theOntario Mental Health Foundation (OMHF, www.omhf.on.ca) and The Hospital for Sick Children (www.sickkids.ca), respectively. CMT received support from the
Graduate Program in Areas of Basic and Applied Biology (GABBA, www.gabba.up.pt) and the Portuguese Foundation for Science and Technology (FCT, www.fct.
pt). XX received support from a Canadian Institutes of Health Research team grant(CTP-82940). The funders had no role in study design, data collection andanalysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: paul.frankland@sickkids.ca

long-term contextual fear memories in mice (Figure 1). Sustainedneural activity leads to the induction of activity-regulated genes
such as c-fos [11,12]. As Fos protein may be resolved at the level ofthe nucleus, immunohistochemical approaches may be used togenerate high resolution, brain-wide maps of Fos expression
induced by memory recall. Subsequent computation of interregional correlations allows us to identify collections of brain
regions where Fos expression co-varies across mice, and presumably form components of a network that are co-active during recallof long-term fear memory. This analysis of functional connectivity
suggests that expression of a long-term fear memory is an emergent property of large scale neural network interactions. This
network has a distinct thalamic-hippocampal-cortical signature
and, like many real-world networks [13] as well as other anatomical and functional brain networks [14], has small-worldarchitecture with a subset of highly-connected hub nodes that may
play more central roles in memory expression.

Introduction
Long-term memories are thought to be represented by changes
in the strength of connections among neurons in the brain [1,2].
While much is understood about the molecular, cellular andstructural changes that contribute to the changes in connection
strength between neurons [3,4,5], it has been challenging toprecisely define which subsets of neurons constitute the memory
trace for at least two reasons. First, long-term memories arethought to be distributed, and depend on the collective activity ofgroups of neurons (or cell assemblies [6]) across a broad network ofcortical and subcortical brain regions [7]. Second, memory
expression likely depends upon network-wide, coordinated activation of these cell assemblies, rather than an overall, net increase in
network activity [8,9]. Electrophysiological approaches have been
useful in linking regional activity and coordination of interregional activity to memory processing [10]. However, theynecessarily provide only a narrow window into what is appreciatedto be a much more global network.
Imaging-based approaches can detect coordinated activity
across distributed and spatially remote brain regions, and therefore
have been useful in defining functional networks (see Supplementary Note in Text S1). Here we have developed a brainwide imaging approach to study the network organization of
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Results
Fos expression is induced by contextual fear memory
recallIn order to characterize regional activation following long-term
memory recall, we used a contextual fear conditioning task in
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Long-Term Fear Memory Networks in Miceassociated with contextual fear memory recall and control for nonspecific aspects of the testing procedure, the density of Fos positive
cells in each brain region in trained mice was compared to control
animals that did not receive footshocks during conditioning.
Importantly, multivariate task partial least squares (PLS) analyses
revealed that patterns of Fos expression depended both on the
training condition as well as the retention delay (significant condition
6delay interaction (P,0.05). To better we understand the nature of
the interaction we conducted separate ‘post-hoc’ analyses. These
revealed distinct patterns of Fos expression in the trained vs. control(no shock) mice following testing at both the 1 day (P,0.05;
Figure 2C) and 36 day (P,0.01; Figure 2D) delay. At the 1 day
retention delay, these distinct patterns of activation were primarily
driven by increases in Fos expression in the hippocampus (CA1-d,
CA1-p and DG-low) in trained mice (Figure 2C). In contrast, at the
36 day retention delay, distinct patterns of activation were associatedwith increases in Fos expression in distributed brain regions in
trained mice. These included cortical (PrL, Cg-a), thalamic (Hb, C),
hypothalamic (LH, SHy, LPO and MPA) regions, as well as the
BST, Cpu-dm, AcbSh, VP, LSI, LSV and PAG (Figure 2D).
Therefore, these data indicate that memory recall is associated with
the induction of Fos, and patterns of Fos expression in trained miceare distinct from those in controls.

Author Summary
Memory retrieval is thought to involve the coordinatedactivation of multiple regions of the brain, rather thanlocalized activity in a specific region. In order to visualize
networks of brain regions activated by recall of a fear
memory in mice, we quantified expression of an activityregulated gene (c-fos) that is induced by neural activity.
This allowed us to identify collections of brain regions
where Fos expression co-varies across mice, and presumably form components of a network that are co-active
during recall of long-term fear memory. This analysis
suggested that expression of a long-term fear memory isan emergent property of large scale neural network interactions. This network has a distinct thalamic-hippocampalcortical signature and, like many real-world networks aswell as other anatomical and functional brain networks,
has small-world architecture with a subset of highlyconnected hub nodes that may play more central roles inmemory expression.
which mice learn an association between a context and an aversive
event (i.e., the delivery of a mild footshock). When returned to thesame context, contextual fear memory is inferred from an increasein freezing behavior [15]. The advantage of this task is that a single
training episode produces robust memory that is easily-quantifiable
and long-lasting [16]. During training wild-type (WT) mice (F1 froma cross between C57B6/N and 129) received 5 footshocks, and thenwere tested either 1 day or 36 days later (Figure 2A). As expected,
conditioned freezing levels in trained mice were similar at both theshort and long retention delay (planned, unpaired t-test: t(14) = 1.31,
P = 0.21), indicating that no forgetting occurred across this time
period. Control groups, that underwent the same procedure but did
not receive footshocks during training, showed little freezing during
testing (Figure 2B) (2 way between subjects ANOVA; main effectof training only, F(1,28) = 63.08, P,0.0001).
Neuronal stimulation is associated with increases in intracellular
calcium levels through NMDA receptor activation or voltage-gatedcalcium channels. Because these increases in calcium lead to therapid upregulation of activity-regulated genes such as c-fos, measuring Fos protein levels allows for the detection of recently
activated neurons [11,12,17] (see Supplementary Note in Text
S1). Therefore, in order to characterize regional activation
following contextual fear testing, Fos was quantified in 84 brain
regions (Figure S1). These included cortical, thalamic, hippocampal, cerebral and midbrain nuclei and were defined according to a
standard mouse brain atlas [18] (for complete listing of brain regions,see Table S1). In order to isolate changes in gene expression

Generation of functional networks
Memory recall is thought to involve coordinated activation ofmultiple brain regions. Therefore, examination of how activity covaries across brain regions has been used to study these large scaleinteractions in both human and experimental animal imaging
studies and define functional networks engaged during memory
recall. In human imaging studies regional brain activity can beassessed by measuring an electrical signal (EGG, MEG) or
indirectly by measuring a metabolic signal such as blood flow
(fMRI). Functional connectivity is then typically evaluated byassessing covariance of these signals across different brain regions
during performance of a given task. In contrast, in experimental
animal studies, neuronal activity is typically assessed post-mortem
by evaluating, for example, changes in expression of the activityregulated genes such as c-fos. As such measures provide a single
index of activation per region per animal, functional connectivity
is necessarily estimated by computing covariance across subjects,
rather than within subjects. Both within and across subjects
covariance can be used to infer interactions between neuralelements [9,19].
Accordingly, we next computed a complete set of inter-regionalcorrelations in the groups of mice tested at each retention delay(Figure 3A). This allowed us to identify collections of brain
regions where Fos expression co-varied across mice, presumably

Figure 1. Overview of experimental approach. Mice were fear conditioned, and fear memory was tested after either a short- or long retention
delay. In order to identify neurons activated by memory recall, following testing brain sections were stained for the activity-dependent gene c-fos. Fos
expression was subsequently quantified in 84 brain regions, and a complete set of inter-regional correlations computed in order to identify
collections of brain regions where Fos expression co-varies across mice. The most robust correlations were then used to generate functional networks
for long-term fear memory, and network properties and hubs were characterized using graph theoretical approaches.
doi:10.1371/journal.pcbi.1002853.g001

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Figure 2. Fos is induced by contextual fear memory recall. A. Experimental design. Mice were trained, and fear memory was assessed either 1(short delay) or 36 (long delay) days later. Ninety minutes following this test, brains were removed and expression of the activity-regulated gene,c-fos, was evaluated immunohistochemically. B. Percent freezing at the 1 day or 36 day retention test in trained (black bars) or control (open bars)
mice. C–D. Task PLS analysis of Fos expression in trained vs. control mice tested 1 or 36 days following training. These analyses identified LVs (leftgraph) that strongly differentiated the trained vs. control conditions at both the (C) short and (D) long retention delays. Salience scores (right)
identify regions that maximally differentiate between these conditions at both the (C) short and (D) long retention delays. The hatched line reflects asalience score of 3, above which the contribution of the regions is considered reliable. At the short delay, Fos expression in the hippocampus
contributed strongly to this contrast, whereas at the longer retention delay, Fos expression in multiple brain regions contributed to the contrast.
doi:10.1371/journal.pcbi.1002853.g002

found that Fos- and Egr-1-derived patterns of inter-regionalcorrelations were similar (Figure S5), consistent with previous
studies showing that immediate early genes are typically expressedin the same or largely overlapping neuronal populations [12].
Finally, while functional connections in these networks reflectstatistical (rather than physical) relationships between regions, wefound that there was excellent correspondence with known
neuroanatomy. For example, in the 36 day retention delay network,
comparison of functional connections for one brain region (reuniens
thalamic nucleus; Re) with published, anatomical data revealed that
all functional connections had corresponding anatomical connections in the primary and high confidence networks (Figure S6 andTable S2). While direct anatomical connections are not necessary
for two regions to be functionally connected (e.g., they might be comodulated by a third region), nonetheless this finding is consistent
with previous analyses indicating that there is typically goodcorrespondence between functional and underlying structuralconnections in brain networks [21].

constituting components of a common network engaged by recallof long-term fear memory [19,20]. Network graphs for eachcondition were subsequently generated by considering only thestrongest correlations (Pearson’s r$0.83, P,0.005) (Figure 3B).
In addition, networks were generated using either more (r$0.87,
P#0.0025) or less (r$0.79, P#0.025) conservative thresholds(Figure S2) or alternate correlation coefficients (Spearman’s rank;
Figure S3) in order to evaluate whether network properties werestable across a range of conditions. In all resulting undirected
graphs, nodes (brain regions) are connected by edges representing
super-threshold inter-regional correlations.
The structure of these network graphs varied across conditions,but, importantly, these differences were not simply an artifact ofgroup differences in signal strength and/or variance (Figure S4).
Moreover, a number of other immediate early genes are regulated
by neural activity, including egr-1 [11]. In order to explore thegenerality of our effects we additionally quantified Egr-1 expression in a subset of brain regions in the WT/36 day group. We
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Figure 3. Generation of long-term fear memory networks. A. Matrices showing inter-regional correlations for Fos expression at the short(upper) and long (lower) retention delays. Axes are numbered, and correspond to brain regions listed in Table S1. Colors reflect correlation strength(scale, right). B. Network graphs were generated by considering only the strongest correlations (Pearson’s r$0.83). In these graphs, regions aregrouped by major brain subdivision and node size is proportional to the number of connections (degree) while the weight of the connection is
proportional to correlation strength.
doi:10.1371/journal.pcbi.1002853.g003

In our matrices, Fos expression in the anterodorsal thalamic
nucleus consistently showed negative correlations with other brain
regions. In constructing our networks we focused on only positive
correlations. However, while relatively few in number, suchnegative correlations are interesting, and may reflect repression
networks. Indeed, future studies using activity-regulated genes thathave higher basal levels of expression (e.g., Egr-1) may offer
greater sensitivity at detecting negative correlations (as levels canbe regulated bi-directionally).

network organization at a global level. Accordingly, we categorized our 84 regions into major brain subdivisions (e.g., neocortex,
hippocampus, midbrain, cerebral nuclei; Table S1) and asked
whether connection strength between these major subdivisions
differed at the 1 day and 36 day time-points (see Table S3). We
focused in particular, on three a priori predictions of systems
consolidation models [26,27]: Memory aging would be expected to
be associated with 1) strengthening of connections between
different neocortical modules, 2) a gradual disengagement of the
hippocampus and 3) an emergent role for prefrontal corticalregions in memory expression.
This analysis revealed three main forms of reorganization. First,
Fos expression (or activity) among cortical regions was morestrongly correlated at the 36-day, compared to 1-day, retention
delay (Figure 4A–B). This pattern was particularly evident within
subdivisions of the somatosensory cortex, where activity was more
tightly coupled at the longer retention delay (Figure 4C). This
increase in inter-cortical correlated activity as a function ofmemory age is consistent with the idea that consolidation is

Networks change with memory age
Regional inactivation or lesions may disrupt expression of apreviously-acquired memory. As these retrograde effects are often
temporally-graded (with either newer or older memories differentially affected) [15,22,23,24], these data provide evidence thatmemory organization changes with memory age (or systems
consolidation [25]). In the present experiment, we evaluated longterm contextual fear memory at two time-points after training, andthis therefore provides an opportunity to track these changes in
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Figure 4. Functional connectivity changes as a function of memory age. A. Color-coded matrices showing inter-regional correlations for Fos
expression within the neocortex at the short (1 day) and long (36 days) retention delays. The inset matrices correspond to inter-regional correlations
for subregions of the somatosensory (SS) cortex. Mean inter-regional correlation coefficients were greater at the long delays for (B) cortical regionsand (C) somatosensory regions (for this, and other comparisons, see Table S3). D. Color-coded matrices showing inter-regional correlations for Fos
expression between regions of the prefrontal cortex and other cortical, thalamic and hippocampal regions at the short (1 day) and long (36 days)
retention delays. E. Mean correlation coefficients were greater at the long delay, suggesting that medial prefrontal cortex regions play increasingly
important roles in memory expression as a function of memory age. F. Color-coded matrices showing inter-regional correlations for Fos expression
between hippocampal and cortical regions at the short (1 day) and long (36 days) retention delays. G. Correlation strength increased over time,suggesting that the hippocampus plays a sustained role in memory expression. In all graphs error bars indicate 95% confidence intervals.
doi:10.1371/journal.pcbi.1002853.g004

associated with the strengthening of functional connections
between anatomically-distinct regions of the neocortex, leading
to the coordination of activity across cortical cell assemblies [6].
Second, previous studies suggested that subregions of the medial
prefrontal cortex (including, in particular, the anterior cingulate[Cg-a] [22,23,28] and prelimbic cortex [PrL] [23,29,30,31]) play
an increasingly important role in memory expression as a functionof memory age. Consistent with this, we found an increase incorrelated activity between the medial prefrontal cortex and othercortical, thalamic, and hippocampal regions at the longer retention
delay (Figure 4D–E). Third, whether the hippocampus plays a
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transient or more sustained role in memory expression is more
controversial [26,27,32]. While the magnitude of the hippocampal
Fos signal declined with retention delay (Figure 2), nonetheless
we found that this activity either remained tightly coupled or
became more tightly coupled with Fos expression in other brain
regions following recall at the remote time-point. For example,
correlated activity between hippocampal regions and thalamic
regions and between hippocampal regions and the midbrain wereequivalent at the short and long retention delays (see Table S3),
whereas coupling of activity between hippocampal and neocorticalregions increased over time (Figure 4F–G). That hippocampal
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Figure 5. Fear memory networks have small-world structure. A. Histogram showing degree distribution for fear memory networks
corresponding to the 1 day (upper) and 36 day (lower) retention delay. B. Mean clustering coefficient for fear memory vs. random network. At both
short (upper) and long (lower) retention delays the fear memory network was more clustered. C. Mean global efficiency for fear memory vs. random
network. At both short (upper) and long (lower) retention delays global efficiency (or integration) was equivalent in the fear memory vs. random
networks. Error bars represent 95% confidence intervals.
doi:10.1371/journal.pcbi.1002853.g005

or less (r$0.79, P#0.025) conservative thresholds were used for
network generation (Figure S8). Moreover, a similar pattern ofresults was found using alternate segregation measures (local
efficiency and transitivity; Figure S9).
Integration may be evaluated by quantifying either path length(average shortest path length between all node pairs) or global
efficiency (the average inverse path length between all node pairs).
Using global efficiency as a measure of integration, we found thatfear memory networks exhibited equivalent integration comparedto random networks (Figure 5C), as would be anticipated for
networks with small-world topology. This pattern was evident at
both retention delays and across a range of thresholds for network
generation (Figure S10). Moreover, we found a similar pattern ofresults using path length as a measure of integration (Figure S11).
Therefore, like other macroscale (i.e., inter-regional structural andfunctional connections) and micro/meso-scale (i.e., intra-regional
connectivity) brain networks [14], these analyses indicate thatfunctional networks engaged by recall of long-term fear memories
have properties that are consistent with small-world topology.

activity (albeit reduced in magnitude) remains tightly coupled withactivity in other thalamic and cortical regions suggests thathippocampal regions play a sustained role in the expression ofcontextual fear memories. Together, these changes in functional
connectivity are consistent with the idea that networks supporting
contextual fear evolve as a function of time. These changes inorganization are unlikely to be related to changes in behavior sincelevels of conditioned freezing were similar at both time points.
However, as we only measured freezing we cannot exclude that
other indices of conditioned fear (e.g., changes in respiration orheart rate) differ at the short and long retention delays.

Networks engaged by fear memory recall have smallworld organization
Our data suggest that recall of long-term fear memories involves
coordinated activation of a broad network of brain regions.
Graphical representation of these networks suggest that they arecomplex in nature, with the majority of brain regions (or nodes)
having relatively few connections, but a minority of nodes being
highly connected (Figure 3B). This non-Gaussian degree
distribution was observed at both short and long retention delays(Figure 5A), and when either more (r$0.87, P#0.0025) or less
(r$0.79, P#0.025) conservative thresholds were used to generate
the networks (Figure S7).
Many complex networks, including both anatomical andfunctional brain networks, have small-world organization [13,14].
This type of organization ensures that networks are concurrently
segregated—allowing for specialized processing in more denselyconnected clusters—and integrated—allowing for efficient information flow through the network [14]. To evaluate whether fear
memory networks have similar small-world organization for each
network we generated control networks with random topology(matched for node, degree and degree distribution; see Supplementary Note in Text S1). To assess segregation we initiallycomputed the clustering coefficient for each network. The clustering
coefficient measures the tendency for a node’s nearest neighbors
to be also connected to each other (quantified as the number ofconnections between a node’s nearest neighbors as a proportion of
all possible connections [33]). Using this measure, we found thatfear memory networks were consistently more segregated comparedto random networks. This was evident at both short and longretention delays (Figure 5B) and when more (r$0.87, P#0.0025)
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Fear memory network includes a densely-connectedthalamic-hippocampal- cortical core
Networks engaged by recall of a long-term fear memory werehighly clustered. To examine this structure in more detail we next
focused on the network engaged by memory recall at the longretention delay, as this network was densely-connected and fear
memory was robustly expressed. We applied the Markov clustering
algorithm to systematically organize nodes into discrete modules
based on their common inter-connections. This analysis identified
eight distinct clusters in the fear memory network, and, inparticular, a large densely-connected central component containing
two groupings (green and blue nodes; Figure 6A). The greengrouping consisted almost entirely of hippocampal and corticalregions (Table S4) that appear to cluster based on their common
interaction with the Cg-a. Interestingly, many of the regions within
this grouping, including the Cg-a, have previously been implicatedin long-term memory expression, especially at remote time-points
following training (Figure 6B and Table S5). Within the blue
cluster, thalamic regions were over-represented (Table S4), and,similar to the green cluster, this grouping contained several regionsthat play important roles in remote memory expression, including
PrL, Ca1-m, C, LD and MD (Figure 6B and Table S5).
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Figure 6. The long-term fear memory network is clustered and resilient. A. Network summarizing functional connections after memory
recall at the long delay. Brain regions were categorized into discrete (color-coded) clusters with similar connectivity to the rest of the network using
the Markov Clustering Algorithm. This network contains a densely-interconnected core (green and blue clusters). B. Proportion of brain regions ingreen, blue and remaining clusters that have previously been implicated in remote memory expression (darkly-shaded portion of bars). Remotememory brain regions are over-represented in the green cluster compared to remaining network (for complete list of regions see Table S5). C.
Assortativity for the fear memory vs. random network. Higher assortativity in the fear memory network indicates that highly-connected nodes tend to
be connected to one another. Error bars represent 95% confidence interval. D. Consequence of random node deletion on the size of the largest
connected component in the fear memory network (grey circles) vs. matched random control network (white circles). In both networks, as nodes
were successively removed the size of the largest connected component declined. E. Consequence of targeted node deletion on the size of thelargest connected component in the fear memory network (grey circles) vs. matched random control network (white circles). In this simulation, nodes
were removed in order of descending degree value. The fear memory network was more resilient to successive deletion of high degree nodes. In
both graphs, component size is shown as a proportion of the largest original component.
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Many of these brain regions in the green and blue clusters werehighly connected, suggesting that this central core is composed ofmutually-interconnected high degree ‘hub’ nodes. We formally
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evaluated this by computing assortativity—the tendency for nodes
with same or similar degree (i.e., the number of connections for agiven node) to be directly connected with one another [33,34].
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Figure 7. Identification of hub regions in the long-term memory network. Brain regions ranked in descending order for (A) degree and (B)betweenness. C. Venn diagram shows the overlap between brain regions ranked above the 80th percentile for degree and betweenness in each of
the primary, high and low confidence networks. D. The three putative hub regions (Cg-a, PrL and Re) that were ranked above the 80th percentile for

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degree and betweenness in all three networks were used as seeds in a multi-seed PLS analysis. These analyses identified a LV (upper graph) thatstrongly differentiated the patterns of correlations between the seed and other brain regions in the trained (closed bars) vs. control (open bars)
conditions. The salience scores (lower graph) identify brain regions whose activity correlates strongly with all three seed regions in the trained (butnot control) mice. The hatched lines reflect a salience of 63, above or below which the contribution of the regions is considered reliable.
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Compared to control, random topology networks, our fear
memory network had a strongly positive assortativity coefficient(Figure 6C), consistent with the observation that high degree
nodes tend to be connected to one another. Such organization isthought to offer additional resilience, as this central core is moreable to withstand removal of multiple hub nodes before network
failure. We tested this idea by evaluating network resiliency in ourfear memory vs. random network. We simulated the effects of bothrandom node removal and targeted attacks (progressive removal ofhighest degree nodes) [35,36]. In both networks, successive
random node removal led to gradual disintegration of the
network: as nodes were successively removed the size of thelargest connected component declined (Figure 6D). The rate ofdisintegration was similar in both networks, suggesting that thefear memory and control networks exhibit equivalent resilience torandom node removal. However, the fear memory network wasconsiderably more resilient to targeted attacks. Whereas removalof ,37% of the most connected nodes led to complete disintegration of the control network, removal of ,64% of the most connected nodes was necessary to see similar disintegration of the fear
memory network (Figure 6E).

and not engaged by other non-specific factors of the testingprocedure.

Altered fear memory networks in a-CaMKII+/2 mice withaccelerated forgetting
Usually, fear memories are very stable, and can be maintained
over extended periods with little decay [16]. However, aCaMKII+/2 mice exhibit accelerated forgetting [22], andtherefore provide an opportunity to contrast initial organizationof functional networks for fear memories that are destined topersist with those that are destined to fade with time. Accordingly,
we trained a-CaMKII+/2 mice (in parallel with their WT
littermate controls, described above) and tested their memory 1
or 36 days later. As expected, the mutants exhibited accelerated
forgetting, with freezing levels greatly reduced at the longer(12.465.2%) compared to shorter (49.5610.5%) retention delay.
Importantly, freezing levels at the shorter retention delay weresimilar to WT mice (66.568.7%; planned, unpaired t-test,
t(14) = 1.25, P = 0.23). An ANOVA confirmed the differential
impact of this mutation on memory at the longer retention delay (2way between subjects ANOVA; delay 6 genotype interaction,
F(1,28) = 10.62, P,0.001). Following testing, Fos expression was
subsequently quantified in 84 brain regions, and networks
generated as previously described (Figure 8A–B).
Similar to WT mice, fear memory networks in the a-CaMKII+/2
mice had a non-Gaussian degree distribution and small-world
topology. As in the WT networks, the majority of brain regions had
relatively few connections, but a minority of nodes was highly
connected (Figure S14A). Moreover, compared to random control
networks, mutant networks were more segregated, but equivalently
integrated, at both the short and long retention delays (Figure
S14B–C). Finally, network density declined (rather than increased)
as a function of time most likely reflecting no (or very weak) memory
expression in the mutants at the remote retention delay(Figure 8C).
While general network organization was similar in WT and aCaMKII+/2 mice, nevertheless there were some notable differences. In particular, at the 1-d retention delay, while WT and aCaMKII+/2 mice exhibited equivalent levels of conditioned
fear, functional connections differed within the neocortex (andespecially the somatosensory cortex), within the hippocampus
and between the hippocampus and neocortex between groups(Figure 8D). These differences between WT and a-CaMKII+/2
networks at this shorter retention delay indicate equivalentbehavior may be generated by distinct networks. As the aCaMKII+/2 mice showed accelerated forgetting, they further
suggest that such degenerate solutions may not always be sorobust. Indeed, they raise the possibility that functional connections within the neocortex, within the hippocampus and between
the hippocampus and neocortex are especially important for
memory stability.

Identification of fear memory network hubs
Our analyses reveal that expression of a long-term fear memory
involves widespread interactions between brain regions. While thisis consistent with the idea that these types of memories are
distributed, nonetheless highly-connected regions (or hubs) may
disproportionately influence network function. In order to identify
specific hub regions within our fear memory network, we therefore
next ranked all nodes by degree. As expected, nodes within thelarge, central component were highly ranked. They accounted forthe vast majority of regions .80th percentile rank in our primary
network (Figure 7A), as well as in networks generated using either
more (r$0.87, P#0.0025) or less (r$0.79, P#0.025) conservative
thresholds (Figure S12). However, how any given node interacts
with the rest of the network depends not only on the number ofconnections but also the nature of these connections. We therefore
additionally ranked nodes using another measure of centrality,
betweenness (Figure 7B, Figure S12). Betweenness centrality
computes the number of shortest paths between node pairs thatpass through a given node, and nodes that have a large number ofintermodular connections tend to have high betweenness centrality[37]. Nodes ranked high in betweenness differ from those thatwere computed in random networks (Figure S13). Of the highlyconnected nodes, three regions—Cg-a, PrL, and Re—were alsoranked above the 80th percentile in terms of betweenness in eachof the low, moderate and high confidence networks (Figure 7C),
suggesting that this subset of high-degree regions may function asconnector hubs and facilitate global intermodular integration.
We next used seed PLS analysis to examine patterns ofinteractions of the three putative hubs (Cg-a, PrL, and Re)(Figure 7D). These analyses verified that these regions interacted
most strongly with cortical, thalamic, and hippocampal regions,consistent with the network graphs. Most importantly, this patternof interactions differentiated the fear memory and control
conditions (P,0.001), suggesting that regions that show reliable
connectivity with the seed regions form a functional network thatis engaged during contextual fear memory expression specifically,
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Discussion
Here we used global mapping and graph theoretical approaches
to characterize functional networks engaged by recall of a longterm memory in mice. These experiments yielded four principal
findings. First, like many real-world networks, functional networks
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Long-Term Fear Memory Networks in Mice

Figure 8. Fear memory networks are altered in a-CaMKII+/2 mice. A. Matrices showing inter-regional correlations for Fos expression at the 1day (upper) and 36 day (lower) retention delays for a-CaMKII+/2 mice. Axes are numbered, and correspond to brain regions listed in Table S1. Colors
reflect correlation strength (scale, right). B. Network graphs generated for Pearson’s r$0.83. In these graphs, regions are grouped by major brain
subdivision and node size is proportional to the number of connections (degree) while the weight of the connection is proportional to correlation

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January 2013 | Volume 9 | Issue 1 | e1002853

Long-Term Fear Memory Networks in Micestrength. C. Comparison of mean degree (observed connections per node [K]/all possible connections for that node [N]) for a-CaMKII+/2 mice at the
short (1 day; closed circles) and long (36 days; open circles) retention delays. The number of connections per node is greater at the 1 day delay foralmost all correlation (r) thresholds, reflecting greater network density at the shorter delay. D. Individual correlations differ between WT and aCaMKII+/2 matrices. These were determined by permutation testing, using a false discovery rate of 5% to account for multiple comparisons.
Correlations where WT.a-CaMKII+/2 are shown in white, and where a-CaMKII+/2.WT are shown in black. Notably, at this short retention delay
correlation strength within the somatosensory cortex (outlined in purple) and between hippocampal regions and other brain regions (outlined ingreen) was stronger in a-CaMKII+/2 mice.
doi:10.1371/journal.pcbi.1002853.g008

of remote contextual fear memories [39], as well as conceptualizations that the hippocampus is re-engaged regardless of the ageof the context memory as proposed in alternate models [26] (seealso: [32]).
What might these changes in network organization reflect?
Levels of freezing were similar at the short and long delays, andtherefore distinct networks are unlikely to be directly related todifferent levels of fear. However, while behavioral output may besimilar at these two time points, it is possible that underlying
context representations differ. Such representational changes aresuggested by time-dependent increases in context generalization following fear conditioning [40]. Indeed, using an identicaltraining protocol, we found that mice froze more when tested in
the training vs. a novel context at the short delay, but froze
equivalently when tested in these two contexts at the longer delay(see Figure S15). Therefore, time-dependent changes in thenature of functional connections might reflect the transformationof the contextual fear memory from a precise, detailed form into aless precise, generalized form [40]. A second possibility is that
network changes reflect an interaction between new and existing
memories. In addition to changes in the nature of connections, thedensity of connections also increased as a function of retention
delay. This increase in connection density suggests that recall of anolder fear memory involves the coordinated activation of abroader network, perhaps reflecting the engagement of a more
expansive associative net. Such time-dependent network expansion is predicted by models that propose that consolidation
involves, in part, the integration of new information into existingknowledge bases or schemas [41,42].
Fear memory networks had a distinct thalamic-hippocampalcortical signature, and this was most pronounced at the longer
retention delay. Interestingly, electrophysiological recording studies in awake, behaving animals suggest similar networks areengaged during encoding and subsequently re-engaged during
post-encoding periods such as sleep or even retrieval [43]. For
example, encoding-related situations (e.g., spatial exploration or
exploration-based learning) are associated with coordinated activation of the hippocampus and neocortex, with especially pronounced coupling between the hippocampal and prefrontal
cortical activity [44,45,46]. Moreover, these same patterns ofhippocampal and cortical activity are spontaneously replayed insubsequent ‘offline’ periods such as quiet wakefulness or sleep [47],and hippocampal-cortical activity remains coupled [10,48].
Moreover, similar coupling of hippocampal and prefrontal activity
is observed during retrieval of a contextual fear conditioning [49].
While hippocampal-cortical replay has been studied in most detail,post-encoding replay also been observed hippocampal-thalamicand hippocampal-striatal circuits [50,51], suggesting that replay
occurs in brain-wide networks. This brain-wide reactivation isthought to play an essential role in systems consolidation, leading
to the incremental remodeling of networks for long-term memorystorage. Indeed, disruption of replay impairs consolidation of
spatial memory [52,53].
In our networks, functional connections reflect statistical, rather
than necessarily physical, relationships between brain regions.
Accordingly, our network analyses allowed us to generate hypo-engaged by recall of long-term memory have a small-world
topology. This type of organization ensures that networks areconcurrently segregated—allowing for specialized processing in
more densely-connected clusters—and integrated—allowing for
efficient information flow through the network [14]. Second, while
networks maintain small-world topology across time, the specificnature of functional connections changes as a function of memory
age. Memory aging was associated with strengthening of intercortical functional connections and with strengthening of connectivity between prefrontal areas and thalamic, cortical andhippocampal areas. Third, the functional network supporting
long-term fear memory had a characteristic thalamic-hippocampal-cortical signature. Many thalamic, hippocampal and corticalregions formed a central core of mutually-interconnected regions,and the dense interconnectivity of these high-degree regions likely
enhances network resilience. Fourth, several regions (including theanterior cingulate cortex and prelimbic cortex) were identified asimportant hubs within this long-term memory network andtherefore might play disproportionately important roles in network
function and resilience. While previous imaging and electrophysiological studies have examined the role of specific regions and smallcollections of regions in expression of a long-term memory, thisanalysis provides global picture of how a long-term fear memory isorganized in mice.
In our experiments, we evaluated contextual fear memory atboth short and long delays after training. We found that functional
networks differed at these two time-points, consistent with the ideathat memory organization changes as a function of memory age, a
process known as systems consolidation [25]. One prominentmodel of systems consolidation [27] proposes that memories are
initially encoded in hippocampal-cortical networks, and thatreactivation of these networks then leads to the incremental
strengthening of inter-cortical connectivity, and an emergent rolefor prefrontal cortical regions in memory expression coupled withdisengagement of the hippocampus. Consistent with this model,
we found that functional connections within the neocortex
strengthened over time, and this was especially evident within
the somatosensory cortex. Moreover, functional connections
between prefrontal cortical regions (including Cg-a and PrL) andthalamic, posterior cortical and hippocampal regions werestronger at the longer retention delay, supporting the idea thatthese regions play important roles in the memory expression atremote time points after encoding [22,23,29,38,39]. However, thepicture in the hippocampus was more complex. At the remote
time-point, hippocampal activity was reduced in magnitude, yetremained tightly coupled with activity in other thalamic andcortical regions. Clearly, consideration of Fos expression levels
alone may have suggested that the hippocampus is not importantat remote time points. However, the co-activity data paint a morenuanced picture of the role of the hippocampus in expression of
remote contextual fear memory: That hippocampal activity (albeit
reduced in magnitude) remains tightly coupled with activity inother thalamic and cortical regions suggests that hippocampal
regions play a sustained role in the expression of contextual fear
memories. This more sustained role is consistent with the findingthat light-induced inactivation of CA1 neurons blocks expression
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Long-Term Fear Memory Networks in MiceIn these studies we quantified expression of the immediate early
gene, c-fos, to track changes in neural activity. Like other IEGs, Fos
is induced rapidly in neurons after sustained activity and can bequantified at single-cell resolution across the entire brain [12,66].
However, as Fos is an indirect measure of activity, there somepotential limitations worth noting. Foremost, promoter-specificdifferences in regional and cellular expression mean that it isunlikely that there is a uniform relationship between neuralactivity and Fos across the brain, and regional differences inmagnitude of the behaviorally-induced Fos signal inevitably would
compromise mapping and subsequent network generation. However, regional differences in signal did not appear to be limiting as
correlation strength (or connectivity) was not strongly influenced
by Fos signal. A closely-related issue is whether networks based on
different activity markers might differ markedly from one another.
Accordingly, in additional analyses we quantified expression ofanother activity-regulated gene, egr-1, in a subset of brain regions.
While there are inevitably some regional differences in Egr-1 andFos expression following memory recall, we found that the overall
patterns of inter-regional correlations derived from Egr-1 vs. Fos
expression were very similar, consistent with idea that many
activity-regulated genes are expressed in the same subpopulationsof neurons after regional activation [12]. Future refinement of thisapproach might focus on improving temporal resolution (e.g.,
examining mRNA for single or multiple activity markers) [11] anddata acquisition and analysis (e.g., optical projection tomography
or 3D digital reconstruction from serial block-face scanning ofactivity marker expression [67,68,69] and derivation of voxelbased, rather than region-based maps of co-activity). Indeed, alimitation of the current study was the relatively small sample sizes(,8 mice/group). In the future, implementation of these morehigh-throughput data acquisition and analysis methods, in particular, should make it is feasible to use larger sample sizes andconsequently maximize the power of the approach.

theses about which brain regions may play privileged roles inmemory expression, with highly-connected hub-like regions
likelier to influence overall network function. In particular, ouranalysis revealed that the network engaged by recall of a fear
memory more than one month after training contains a denselyinterconnected central core of highly-connected hub-like regions.
Importantly, this central core included many brain regions thathave previously been implicated in the expression of contextual
fear memories at such remote time-points [25] (Table S4),
consistent with the idea that hubs play more integral roles in
network function. For example, loss of function manipulations(including pharmacological inactivation, cytotoxic lesions or optogenetic silencing) targeting the anterior cingulate cortex[22,28,39,54], hippocampus (including CA1 and CA3 fields)[39,55], retrosplenial cortex [56] and prelimbic cortex [30] disrupt
expression of contextual fear memories at extended retention
delays. Similarly, loss of function manipulations targeting theseregions, as well as thalamic (e.g., LD, MD, C) and orbitofrontal(DLO, VO, LO) regions affect expression of other forms of
remote memory, including spatial and olfactory-based memories[23,57,58]. While correspondence between the predictions derived
from the network analysis and these published findings provides
some validation of our approach, perhaps greater value lies inusing this approach for the identification of novel, candidate brain
regions. In this regard, it is interesting that many thalamic regions
were identified within this central core, including Re, Sub, VPM
and Po. These regions, by virtue of their widespread connectivity
with both the hippocampus and neocortex, are ideally positioned
to influence network function. In particular, thalamic regions
strongly influence cortical activity by modulating up/down states[59] and therefore may play central roles in coordinating memory
replay and retrieval. Indeed a recent lesion study provided
evidence that Re plays a key role in remote memory [60].
One interesting observation was that the same (or very similar)
behavioral output was associated with distinct functional networks,
suggesting that there is a degree of degeneracy at the network level(i.e., an ability of distinct constellations of neural elements to
produce same output [61,62]). This was the case for networks
engaged by recall of contextual fear memory at the short vs. long
delay in WT mice. Likewise, a-CaMKII+/2 mice exhibited
equivalent levels of conditioned freezing compared to WT
littermates at the short delay, yet underlying functional networks
differed considerably. Together, this suggests that a population of
network solutions may support contextual fear memory, and raise
two related issues. First, because of this degeneracy, pre-training
manipulations of key brain regions may not necessarily prevent theformation of a contextual fear memory. For example, pre-training
lesions of the hippocampus surprisingly do not always impair
acquisition of a contextual fear memory [63,64], and this ispresumably because alternate networks can support this type ofmemory in the absence of an intact hippocampus at the time oftraining. If multiple networks can support the formation of acontextual fear memory, then a second issue is whether all
networks perform equivalently under all conditions? In the case ofanimals trained without a hippocampus this does not seem to be
the case. While hippocampal lesioned animals may form acontextual fear memory, acquisition is less efficient [64], and theresultant memory is less precise [63] and less durable [65].
Similarly, in our study, the a-CaMKII+/2 mice were able to form
a contextual fear memory, but this memory faded rapidly overtime. This highlights that not all degenerate solutions necessarily
have the same robustness, although it remains to be determinedwhich specific alterations in network organization in a-CaMKII+/2
mice might be causally related to premature forgetting.
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Methods
Ethics statement
All experimental protocols were approved by the Animal Care
Committee at The Hospital for Sick Children.

Behavioral and immunohistochemical procedures
In these experiments, Fos and Egr-1 expression was analyzed
following recall of long-term contextual fear memory at two timepoints after training. A sub-analysis of these brain sections waspreviously published in which gene expression was quantified insix brain regions [22]. In the current analyses, quantification was
extended to 84 brain regions, and the network properties of fear
memory were analyzed using graph theoretical approaches. The
genetic background of all mice used in these experiments was 50%
C57Bl/6NTacfBr and 50% 129Sv/J. Briefly, experimental mice
were generated by crossing a-CaMKII+/2 mutants that were 50%
C57Bl/6NTacfBr and 50% 129Sv/J with mice that were F1
hybrids of the same genetic background. Male and female
littermate WT and a-CaMKII+/2 mice were used in experiments.
All mice were maintained on a 12 h light/dark cycle with freeaccess to food and water. Behavioral experiments were conductedduring the light phase of the cycle, and mice were at least 8 weeks
old at the time of training.
Context fear conditioning experiments were conducted in awindowless room containing four conditioning chambers. The chambers were stainless steel (31 cm624 cm621 cm; Med Associates, St.
Albans, VT). The shock-grid floor consisted of stainless steel shockgrid bars (diameter 3.2 mm) that were spaced 7.9 mm apart. The
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Long-Term Fear Memory Networks in Mice

front, top, and back of the chamber were made of clear acrylic andthe two sides made of modular aluminum. Mouse freezing behavior
was monitored via four overhead cameras. Freezing was assessedusing an automated scoring system (Actimetrics, Wilmette, IL), which
digitized the video signal at 4 Hz and compared frame by frame
movement to determine the amount of time spent freezing. During
training, WT and a-CaMKII+/2mice were placed in the conditioning chamber for seven minutes. After two minutes they were
presented with five unsignalled footshocks (2 s duration, 0.75 mA,
1 minute apart). Following the last footshock mice remained in thecontext for an additional minute, and then were returned to theirhome cage. Separate groups of mice were tested either 1 day (WT
mice, n = 8, CaMKII+/2 mice, n = 8) or 36 days (WT mice, n = 8,
CaMKII+/2 mice, n = 8) later. During testing, mice were placed backin the conditioning chamber for two minutes, and freezing wasassessed. Control mice underwent the same procedure but did notreceive the footshocks during the conditioning session (WT mice, 1
day delay, n = 8, WT mice, 36 day delay, n = 9).
Ninety minutes following the completion of testing, mice weredeeply anesthetized then perfused transcardially with phosphatebuffered saline (PBS) followed by 4% paraformaldehyde (PFA).
The brains were removed, fixed overnight in PFA, thentransferred to 30% sucrose solution and stored at 4uC. Fifty mm
coronal cryostat sections were cut from the anterior to theposterior of the brain. Every fourth section contributed to a set,creating four sets with sections spaced 200 mm apart. One set wasused for Fos and another for Egr-1 immunohistochemical staining.
Brains were prepared for immunocytochemistry using an anti Fos(1:20000) or an anti Egr-1 (1:7500) primary rabbit polyclonal
antibody. A biotinylated goat anti-rabbit antibody (1:2000) wasused as a secondary antibody. Staining was revealed using theavidin-biotin peroxidase method (ABC kit) coupled to diaminobenzidine as a chromogen, as previously described [22].

Task PLS analysis

Immediate early gene quantification

Functional connectivity analysis

PLS is a multivariate statistical technique that is used to identify
optimal patterns of functional activity or connectivity thatdifferentiate conditions [9]. Task PLS is used in the analysis ofbrain region activity to describe the relationship between experimental conditions and functional activity [70]. PLS is able to pullout similarities and differences between groups by identifying brain
regions where activation varies with the experimental condition.
Through singular value decomposition, PLS produces a set ofmutually orthogonal latent variable (LV) pairs. One element of the
LV depicts the contrast, which reflects a commonality or difference between conditions. The other element of the LV, the brain
region salience, identifies brain regions that show the activation
profile across tasks, indicating which brain areas are maximally
expressed in a particular LV.
Statistical assessment of PLS was performed by using permutation testing for LVs and bootstrap estimation of standard error
for the brain region saliences. For the LV, significance wasassessed by permutation testing; resampling without replacementby shuffling which condition (trained vs. control) each mouse was
assigned to. Following each resampling, the PLS was recalculated.
This was done 500 times in order to determine whether the effects
represented in a given LV were significantly different to random
noise. For brain region salience, reliability was assessed usingbootstrap estimation of standard error. Bootstrap tests were
performed by resampling 500 times with replacement, while
keeping the subjects assigned to their conditions. This reflects thereliability of the contribution of that brain region to the LV. Brainregions with a bootstrap ratio greater than 3 (roughly corresponding
to a confidence interval of 0.01) were considered as reliably contributing to the pattern. An advantage to using this approach overunivariate methods is that no corrections for multiple comparisons
are necessary because the brain region saliences are calculated on allof the brain regions in a single mathematical step [70].

Fos expression was analyzed in 84 brain regions (see Table S1)and included cortical, thalamic, hippocampal, cerebral andmidbrain nuclei. The borders of regions were defined manuallyaccording to the Franklin and Paxinos mouse brain atlas [18]. This
was accomplished at low magnification (26 objective), using thetrace contour function in the Stereo Investigator imaging system(Microbrightfield Inc., Colchester, VT). Images were subsequently
acquired using either a Nikon Eclipse 80i microscope (Nikon,
Instruments, Melville, NY) or Olympus BX61 epifluorescent
microscope (Olympus America, Center Valley, PA) at a higher
magnification (106 objective) for offline quantification of Fospositive or Egr-1-positive cells (Figure S1).
Quantitative analysis of Fos-positive and Egr-1-positive nuclei
was performed on 8-bit grey scale images using Image J software(National Institute of Health, Bethesda, MD). The threshold for
detection of positive nuclei was set at a consistent level for eachbrain region, and only nuclei 25–125 mm2 in area were counted.
Immunoreactive nuclei were counted in all 84 regions of interestby an experimenter blind to the condition and expressed as theaverage number of Fos or Egr-1 positive cells per standard unit ofarea (10,000 mm2). For the majority of brain regions three sections
were quantified bilaterally and then a mean count was computedfor each animal. These sections were evenly spaced, and typicallydid not include the most anterior and posterior portions of a regionin order to avoid sampling outside the region of interest. In a small
subset of regions, counts were based on ,3 sections, usually
because the anterior-posterior extent of the region #600 mm (e.g.,
VTA).
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Within each of the four experimental groups of animals (WT/1
day, WT/36 days, a-CaMKII+/2/1 day, a-CaMKII+/2/36 days),
all possible pairwise correlations between the Fos signal in the 84
regions were determined by computing Pearson correlation
coefficients (totalling 3486 correlations). Each complete set ofcorrelations was computed from a vector of size 8, and weredisplayed as color-coded correlation matrices using Matlab
software (Mathworks, Natick, MA). A correlation matrix was also
constructed for analysis of Egr-1 expression in 8 regions in the
WT/36 day group.

Wild-type correlation matrix comparisons
In order to evaluate how functional connectivity changed as afunction of memory age in WT mice, we categorized our 84 brain
regions into major brain subdivisions (hippocampus, thalamus,
hypothalamus, cerebral nuclei and neocortex). We then contrasted
mean correlation strength between different major subdivisions at
the short vs. long retention delays. These contrasts were selected apriori according to models of systems consolidation [27] thatpredict time-dependent changes in the strength of hippocampal,
prefrontal cortical and inter-cortical functional connections.
Specifically, to assess time-dependent changes in hippocampal
functional connectivity, mean correlations were calculated between hippocampal subregions (CA1-a, CA1-m, CA1-d, CA1-p,
CA3, DG-up, DG-low) and the other major subdivisions of thebrain (thalamus, hypothalamus, cerebral nuclei, midbrain andneocortex). To assess changes in medial prefrontal cortical functional connectivity, mean correlations were calculated between
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Long-Term Fear Memory Networks in Micecharacteristic path length is then the average shortest path length
for all pairs of nodes in the network [13]. Global efficiency is theaverage inverse shortest path length in the network [71]. Global
efficiency differs from characteristic path length in that it considers
unconnected node pairs in its calculation since the inverse of
infinity is zero.
Segregation. Segregation in a brain network allows for
specialized processing in more densely-connected clusters [33].
In our analyses we computed three common measures of segregation: mean clustering coefficient, transitivity and local efficiency.
The clustering coefficient is computed by dividing the number ofexisting connections among a node’s directly connected neighbours by the number of possible connections between them [13].
The mean clustering coefficient for the network is then the averageclustering coefficient for all of the active nodes in the network.
Transitivity is a weighted version of the clustering coefficient thatis less biased by low degree nodes [72]. The local efficiency is theglobal efficiency computed among all directly connected neighbours of a node [71], and the mean local efficiency of all active
nodes in the network was considered.
Identification and characterization of clusters. Cluster
analysis was conducted with the Markov Cluster Algorithm(inflation parameter set at 2.6), a scalable, unsupervised cluster
algorithm for networks based on simulation of stochastic flow ingraphs (http://www.micans.org/mcl/). Additionally, we validated
our results using a number of alternate clustering algorithms,
including community clustering, clusterONE, affinity propagation,connected components and k means clustering. These yielded
mostly overlapping results. However, we chose to present results
based on Markov clustering since previous studies indicated that
this procedure is significantly more tolerant to noise and behaves
more robustly than other algorithms [73]. Clusters were visualizedusing Cytoscape (http://www.cytoscape.org/). The assortativity
coefficient is a correlation coefficient between the degrees of all
directly-connected node pairs. A positive assortativity coefficientindicates that nodes tend to link to other nodes with the same orsimilar degree [34].
Hub identification. Hub regions play disproportionately
important roles in the function of a network [33]. Two measuresof centrality, degree and betweenness, were computed for all nodes
in the network and used to identify candidate hub regions. Degreecorresponds to the number of edges that are incident upon a node.
Node betweenness is the number of all shortest paths in the
network that pass through a given node, and nodes with highvalues of betweenness centrality participate in a large number ofshortest paths. All regions were ranked by degree and betweenness, and candidate hub regions were considered to be regions thatwere ranked .80th percentile for both measures in primary, as
well as high and low confidence, networks. For comparison
betweeness values were also computed in 100 random networks.
Resilience of networks to sequential node attack. We
assessed network resilience to random error and targeted attack
using an approach previously described by Achard et al (2006)[36]. Random error was modeled by sequentially removing nodes
and all of their connections from the network at random and thenrecalculating the size of the largest connected component (i.e.,collection of connected nodes) in the new network. Targeted
network attack was simulated using the same process except that
node removal began with the most highly-connected node (i.e.,
node with the highest degree) and progressed in order ofdescending degree value. Curves describing the effect of random
and targeted node removal on largest component size in the fear
memory network were compared to curves for a control network
matched for node, degree and degree distribution.

regions in the medial prefrontal cortex (Cg-a, Cg-p, IL, PrL) andthe other subdivisions of the brain (thalamus, hypothalamus,
cerebral nuclei, midbrain and neocortex). To assess changes ininter-cortical connectivity mean correlations were calculated
within the entire neocortex, as well as within only the somatosensory cortex. Ninety-five % confidence intervals were computed bybootstrapping which involves resampling subjects with replacement 1000 times and each time recalculating the mean
correlation. Differences between mean correlation coefficients at
the 1 and 36 day delay were assessed by calculating the 95%
confidence interval of the difference between mean correlations,and correlation differences where this confidence interval $0 wereconsidered reliably different.

Functional network construction
Networks were constructed by thresholding inter-regionalcorrelations in each group of animals. The primary networks
were constructed by considering correlations with Pearson’s r$0.8343, which corresponds to a one tailed significance level ofP,0.005, uncorrected for multiple comparisons. Higher and lower
confidence networks were constructed as well to insure that
network properties were not dependent on threshold levelselection. A threshold of r$0.8697 (corresponding to a significancelevel of P,0.0025 [one tailed, uncorrected]) was used to generate
the high confidence networks and a threshold of r $0.7887(corresponding to a significance level of P,0.01 [one tailed,uncorrected]) was used to generate the low confidence networks.
In addition, networks were constructed using Spearman’s rankcorrelation coefficient, with a threshold of r $0.8343, whichcorresponds to a one tailed significance level of P,0.005. The
nodes in the networks represent brain regions and the correlations
that survived thresholding were considered connections. NetDraw(Analytic Technologies, Lexington, KY) was used to visualize
networks, with node size set proportional to degree (number ofconnections) and connection line weights reflecting the strength ofthe correlation. While potentially interesting, we did not consider
negative correlations in the current network analyses.

Graph theory analysis
Graph theoretical measures were used to characterize propertiesof the long-term memory networks. For each primary, low andhigh confidence fear memory network, 1000 random, null
hypothesis networks were generated with the same number ofactive nodes, connections and same degree distribution. Thenlong-term memory network properties were contrasted withaveraged values from these corresponding random, control
networks. Network measures and random null hypothesis
networks were generated using functions from Olaf Sporns’ brain
connectivity toolbox (https://www.brain-connectivity-toolbox.
net/). Definitions and formulae for these graph theory measures
have been described in elsewhere [33]. Ninety-five percent
confidence intervals for the network measures are reported inorder to determine whether network properties differ reliably
between fear memory network and random, control networks.
Means and confidence intervals for the network measures were
derived by bootstrapping which involves resampling subjects withreplacement one thousand times and recalculating the network
measures.
Integration. Integration in a brain network gives rise tocoordinated activation of distributed neuronal populations andbrain areas [33]. In our analyses we computed two measures ofintegration: characteristic path length and global efficiency.
Shortest path length is the mean number of connections thatneed to be traversed to get from one node to another. The
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Long-Term Fear Memory Networks in Miceelevated in the WT mice that were tested at the long retention
delay. B. Scatterplot of mean correlation strength (r2) vs.
magnitude of Fos signal for each brain region. Data points aretaken from all 4 groups tested. There was no relationship between
correlation strength and the magnitude of the Fos signal,
indicating that group differences in number of functional
connections (or network density) cannot be attributed to overall
differences in Fos expression. C. Overall coefficient of variation(standard deviation/mean Fos) in WT and a-CaMKII+/2 micetested at the 1 and 36 day delay. Variability did not differ acrossgroups. D. Scatterplot of mean correlation strength (r2) vs.
coefficient of variation for each brain region. Data points aretaken from all 4 groups tested. While correlation strength typicallyincreased as a function of variance (or coefficient of variation),variance was equivalent across groups and therefore cannot
account for increased network connectivity in WT mice at the longretention delay.
(TIF)

Seed Partial Least Squares
Seed PLS is used in the analysis of functional connectivity to
explore if seed regions exhibit task-related changes in activity inrelation to the rest of the brain. Like task PLS described above,
seed PLS identifies LVs through singular value decomposition ofcorrelation maps [74]. The LVs maximally describe patterns ofbrain region interactivity with seed regions that are similar ordifferent between groups. The three brain regions identified as
hubs with graph theory measures were used as seeds in thisanalysis. This analysis was used to verify that activity in hubs covaries with activity in other brain regions, and to determine if thispattern of interactivity is unique to the experimental condition or
shared between the experimental and control condition.

a-CaMKII+/2 and WT 1 day correlation matrix
comparisons
In order to compare correlation matrices for the WT and aCaMKII+/2 1 day retention delay groups, all individual correlations were compared with no a priori predictions (3486 comparisons). Significance differences in individual correlations were
determined with permutation testing. Permutation testing involved
shuffling subject labels to produce several permuted combinationsof the original two groups of data. Following each permutation thetest statistic (correlation) was recalculated. The number of events
that exceeded the observed correlation statistic was determined
and a probability of the number of observed events being greaterthan expected was assigned. The false discovery rate was controlled
at the 5% level because of the number of multiple comparisons.

Figure S5 Patterns of inter-regional correlations derived from Fos and Egr-1 expression are similar. Matrices
showing inter-regional correlations for (A) Fos and (B) Egr-1
expression in a subset of brain regions in WT mice tested at the 36
day retention delay. Colors reflect correlation strength (scale,right). Overall correlation strength did not differ in the Fos vs. Egr1 matrices (by permutation testing; P = 0.76), nor were any
individual inter-regional correlations different (by permutation
testing; all Ps.0.05, corrected for multiple comparisons with thefalse discovery rate set at 5%).
(TIF)

Supporting Information

Correspondence between structural andfunctional connectivity for the reuniens thalamic nucleus (Re). Of the 84 regions investigated, 47 demonstrated direct
structural (afferent or efferent) connectivity with the Re in
published tract tracing studies in rodents (green circles, for alisting of all connections see Table S2). Regions that demonstrate
functional connectivity with the Re in the high (left), primary(center) and low (right) confidence long term memory networks are
represented by red circles. For the high and primary networks, allfunctional connections had corresponding structural connections.
For the low confidence network, the majority (22/24) of functional
connections had corresponding structural connections.
(TIF)

Figure S6

Figure S1 Fos quantification. Sample images of Fos

immunohistochemical staining from a representative brain section.
Images were captured with the 26 objective (red border) and 106
objective (blue border). Higher magnification images are shownfor specific hippocampal (CA3), cortical (S1Tr), cerebral nuclei(Ce), hypothalamic (DM) and thalamic (VPM) regions. Dottedlines indicate region borders.
(TIF)
Figure S2 High and low confidence networks in WT
mice. Networks formed from thresholding inter-regional Fos
correlations in mice tested at the 1 (upper) and 36 day (lower)
retention delay using either a high confidence threshold of r .0.87(left) or a low confidence threshold of r .0.79 (right). Brain regions
are grouped by major brain subdivision and node size is
proportional to the number of connections (degree) while theweight of the connection is proportional to correlation strength.
(TIF)

Figure S7 Degree distribution in high and low confidence networks. Histogram showing degree distribution for fear
memory networks in WT mice tested at the 1 day (upper) and 36 day(lower) retention delay. These networks were constructed by imposing
either (A) more (r.0.87, high confidence) or (B) less (r.0.79, lowconfidence) stringent thresholds on correlation matrices.
(TIF)

Figure S3 Spearman rank correlation networks in WT
mice. A. Matrices showing inter-regional Spearman rankcorrelations for Fos expression at the 1 (upper) and 36 day (lower)
retention delays. Axes are numbered, and correspond to brain
regions listed in Table S1. Colors reflect correlation strength(scale, right). B. Network graphs were generated by considering
only the strongest correlations (Spearmans’s rs$0.83). In thesegraphs, regions are grouped by major brain subdivision and node
size is proportional to the number of connections (degree) while theweight of the connection is proportional to correlation strength.
(TIF)

Figure S8 Segregation in high and low confidence

networks. Mean clustering coefficient constructed by imposing(A) more (r.0.87, high confidence) or (B) less (r.0.79, lowconfidence) stringent thresholds on correlation matrices for WT
mice tested at the 1 day (upper) and 36 day (lower) delay. The
bootstrapped mean clustering coefficient for the fear memory
networks are compared to the average of 1000 random networks
matched for node, degree and degree distribution. Error bars
represent 95% confidence intervals.
(TIF)

Figure S4 Relationship between magnitude and variance of regional Fos signal and correlation strength. A.
Overall Fos signal levels (Fos+ nuclei/10,000 mm2) in WT and aCaMKII+/2 mice tested at the 1 and 36 day delay. Fos levels were
PLOS Computational Biology | www.ploscompbiol.org

Figure S9 Alternate measures of segregation for WT
mice tested at the 1 and 36 day delay. (A) Transitivity and(B) mean local efficiency measured in fear memory networks for
15

January 2013 | Volume 9 | Issue 1 | e1002853

Long-Term Fear Memory Networks in Micein context A). Freezing was then assessed either 1 day (n = 12) or 36
days (n = 14) later in the training context (context A) and an alternate
context (context B). B. Percent time freezing in contexts A and B at
the short (1 day) vs. long (36 day) retention delay. C. Context discrimination ([freezingA2freezingB]/[freezingA+freezingB]) declined asa function of retention delay.
(TIF)

WT mice tested at the 1 day (upper) and 36 day (lower) retention
delay. The mean of bootstrapped measures in the fear memory
networks are compared to the average of 1000 random networks
matched for node, degree and degree distribution. Error bars
represent 95% confidence intervals. These alternate measuresindicate that fear memory networks are more segregated thanwould be expected by chance.
(TIF)

List of brain regions in which Fos wasquantified with corresponding abbreviations (according
to supplementary reference [8] in Text S1) and reference
numbers for the correlation matrices. Brain regions are
categorized by major brain subdivision.
(PDF)

Table S1

Integration measures in high and low
confidence networks. Mean global efficiency in graphs
constructed by imposing (A) more (r.0.87, high confidence) or(B) less (r.0.79, low confidence) stringent thresholds oncorrelation matrices for WT mice tested at the 1 day (upper)and 36 day (lower) delay. The bootstrapped mean global efficiency
for the fear memory networks are compared to the average of1000 random networks matched for node, degree and degree
distribution. Error bars represent 95% confidence intervals.
(TIF)

Figure S10

Table S2 Direct afferent and efferent connections withthe reuniens thalamic nucleus (Re). Connections wereidentified from published tract tracing studies in rodents (seesupplementary references [9–45] in Text S1). Brain structurefrom which each connection originates from and terminates arelisted, together with tracer method and strength of projection (ifreported).
(PDF)

Alternate measure of integration for WT
mice tested at the 1 and 36 day delay. Characteristic path
length for fear memory networks for WT mice tested at the 1 day(upper) and 36 day (lower) retention delay. The bootstrappedcharacteristic path length in the fear memory networks iscompared to the average of 1000 random networks matched for
node, degree and degree distribution. Error bars represent 95%
confidence intervals. Fear memory and random networks had
similar characteristic path length, suggesting they have equivalentlevels of integration.
(TIF)

Figure S11

Table S3 Mean correlation coefficients within or between major brain subdivisions for WT mice tested 1 vs.
36 days after training, with corresponding 95% confidence interval of the difference. Time-dependent changes incorrelation strength were only considered significant if the 95%
confidence interval of the difference was greater than zero (in red).
mPFC = medial prefrontal cortex (includes Cg-a, PrL, IL andCg-p).
(PDF)

Ranked centrality measures in high and low
confidence networks. Brain regions are ranked in descendingorder for degree and betweenness in the (A) high and (B) low
confidence long term memory networks. Regions to the left of thehatched line are ranked above the 80th percentile and dark colors
indicate regions that are additionally ranked above 80th percentile
for both degree and betweenness. These regions were used for hub
identification, shown in Figure 7C.
(TIF)
Figure S12Table S4 Over-representation of cortical, hippocampal
and thalamic regions in the densely-interconnected
central component. Using a clustering algorithm, the fear
memory network (for the WT/36 day group) was organized into
eight distinct clusters that included, in particular, a large denselyconnected central component containing two groupings (green
and blue nodes). This table reports the proportion of brain regionsin major brain subdivisions with respect to the total number ofregions analyzed, as well as the proportion of brain regions inmajor brain subdivisions with respect to the total number ofregions found in the blue and green clusters. Cortical andhippocampal regions were over-represented in the green cluster,and thalamic regions were over-represented in the blue cluster(highlighted in red).
(PDF)

Nodes ranked high in betweenness differ
from those in random networks. Mean betweenness values
for nodes in 100 random networks matched for node, degree anddegree distribution is superimposed on the ranked betweenness
values in the primary (A), high (B) and low (C) confidence
networks. High betweenness nodes differ from those derived fromthe random networks.
(TIF)
Figure S13

Table S5 Brain regions implicated in long-term mem-

Figure S14

Networks have small-world properties in aCaMKII+/2 mice. A. Histogram showing degree distribution forfear memory networks corresponding to 1 day (upper) and 36 day(lower) retention delay. B. Mean clustering coefficient for fear
memory vs. random network (matched for node, degree anddegree distribution). At both short (upper) and long (lower)
retention delays the fear memory network was more clustered. C.
Mean global efficiency for fear memory vs. random network. At
both short (upper) and long (lower) retention delays global
efficiency (or integration) was equivalent in the fear memory vs.
random networks. Error bars represent 95% confidence intervals.
(TIF)

ory expression at remote time-points following training.
A pubmed-based search was conducted to identify brain regions
where targeted, loss-of-function manipulations affect expression of
remote memory expression (see supplementary references [46–73] inText S1). Behavioral paradigms were considered if acquisition and/or initial memory expression (e.g., 24 hours after training) weredependent upon the hippocampus. Loss-of-function manipulations
included anatomical lesions, pharmacological inactivation, DNA
demethylation, and testing occurred at least 25 days after thecompletion of training. DNMT = DNA methyltransferases. adorsal
hippocampus, bventral hippocampus, cdorsal+ventral hippocampus.
(PDF)

Figure S15 Context generalization increases as a function of retention delay. A. Experimental design. Mice weretrained in an identical way as in the main experiment (5 footshocks

Text S1 Supplementary methods, results, notes andreferences.
(PDF)

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Long-Term Fear Memory Networks in Mice

Contributed reagents/materials/analysis tools: ARM. Wrote the paper:
ALW PWF. Advised on statistical and graph theoretical analyses: XX NK
JPL ARM JP.

Author Contributions
Conceived and designed the experiments: ALW PWF. Performed theexperiments: ALW CMT AHW PWF. Analyzed the data: ALW.

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