Characterization of Functional and Structural Integrity in
Experimental Focal Epilepsy: Reduced Network Efficiency
Coincides with White Matter Changes
Willem M. Otte1,2*, Rick M. Dijkhuizen2, Maurits P. A. van Meer1,2, Wilhelmina S. van der Hel1,
Suzanne A. M. W. Verlinde1, Onno van Nieuwenhuizen1, Max A. Viergever2, Cornelis J. Stam3,
Kees P.J. Braun1
1Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands, 2Image Sciences Institute, University Medical Center Utrecht,
Utrecht, The Netherlands, 3Department of Clinical Neurophysiology, VU University Medical Center, Amsterdam, The Netherlands
Background: Although focal epilepsies are increasingly recognized to affect multiple and remote neural systems, the
underlying spatiotemporal pattern and the relationships between recurrent spontaneous seizures, global functional
connectivity, and structural integrity remain largely unknown.
Methodology/Principal Findings: Here we utilized serial resting-state functional MRI, graph-theoretical analysis of complex
brain networks and diffusion tensor imaging to characterize the evolution of global network topology, functional
connectivity and structural changes in the interictal brain in relation to focal epilepsy in a rat model. Epileptic networks
exhibited a more regular functional topology than controls, indicated by a significant increase in shortest path length and
clustering coefficient. Interhemispheric functional connectivity in epileptic brains decreased, while intrahemispheric
functional connectivity increased. Widespread reductions of fractional anisotropy were found in white matter regions not
restricted to the vicinity of the epileptic focus, including the corpus callosum.
Conclusions/Significance: Our longitudinal study on the pathogenesis of network dynamics in epileptic brains reveals that,
despite the locality of the epileptogenic area, epileptic brains differ in their global network topology, connectivity and
structural integrity from healthy brains.
Citation: Otte WM, Dijkhuizen RM, van Meer MPA, van der Hel WS, Verlinde SAMW, et al. (2012) Characterization of Functional and Structural Integrity in
Experimental Focal Epilepsy: Reduced Network Efficiency Coincides with White Matter Changes. PLoS ONE 7(7): e39078. doi:10.1371/journal.pone.0039078
Editor: Alice Y. W. Chang, Kaohsiung Chang Gung Memorial Hospital, Taiwan
Received November 18, 2011; Accepted May 16, 2012; Published July 12, 2012
Copyright: ? 2012 Otte 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 research was supported by the Dutch National Epilepsy Fund (NEF nr. 08-10) and Utrecht University’s High Potential program. The funders had no
role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Widespread, bilateral structural and functional abnormalities
have been reported in people with epilepsy, even when the
epileptic syndrome is localization-related, idiopathic or crypto-
genic, and the brain appears normal on conventional magnetic
resonance imaging (MRI) [1,2]. Such tissue damage distant from
the epileptogenic zone has been observed in both white [2,3] and
gray matter [4,5,6].
These subtle progressive changes in tissue integrity that are
mostly undetectable with conventional MRI and extend outside
the margins of the primary epileptogenic area , are presumed to
be the result of recurrent seizure propagation . This is thought
to play a crucial role in epilepsy as these changes may significantly
modify the global structural and functional network topology .
The characterization of brain networks has been greatly
facilitated by the exact mathematical formalism provided by
graph theoretic analysis . In graph analysis a network is
represented as a set of vertices and edges. Different network classes
can be characterized based on the type of configuration vertices
and edges. More specifically, three network classes, namely
regular, small-world, and random, can be differentiated based
on their clustering, a property of segregation, and shortest path, a
property of integration. High values of both clustering and shortest
path are found in regular networks. At the other extreme, if the
nodes are randomly interconnected, both measures are low. Low
values of shortest path and high values of clustering reflect a small-
world network topology, which is proposed to be an optimal
network configuration for global information transfer and local
Significant changes in clustering and shortest path have been
reported, based on electrophysiology, in people with epilepsy .
Several studies have shown that during seizures the global network
shifts from a small-world topology towards a more regular
topology. Based on these findings, it has been hypothesized that
interictal epileptic functional networks have a more random, that
is, opposite from regular, topology . More recently, interictal
brain networks in individuals with focal, most often temporal,
epilepsy were characterized functionally and structurally and
compared to control networks [14,15,16,17,18,19]. Results,
PLoS ONE | www.plosone.org1July 2012 | Volume 7 | Issue 7 | e39078
however, are not unequivocal. Both an increase in clustering and
shortest path [14,17,19], and a decrease in these network
properties have been described in the interictal epileptic state
. In addition, a decreased clustering and increased path length
has been reported . Methodological differences or incompa-
rability of study populations could attribute to the different
network topologies found. Incomparability of natural history is a
well-known threat for most observational studies . Specific
confounders in the study of network differences in patients with
temporal epilepsy include a possible history of febrile seizures, age
at onset, duration of epilepsy at time of inclusion, the presence of
dual pathology , and the use of antiepileptic drug treatment.
In particular the latter may contribute to changes in network
topology as antiepileptic drugs can affect brain development with
long-term neurological consequences . Adequate separation of
these extraneous influences from the effect that spontaneous
recurrent seizures have on the network topology is very difficult in
a clinical setting.
A preclinical study in a well-characterized animal model of
neocortical epilepsy allows to assess – both spatially and
temporally – the effect of focal epilepsy on the interictal network
topology, without the aforementioned confounders, in two groups
that are identical except for the occurrence of spontaneous
recurrent seizures. In this study we aimed to characterize the effect
of spontaneous recurrent seizures occurring from a focal
epileptogenic area on the functional network configuration. To
that aim, we measured the spatiotemporal evolution of changes in
interictal brain networks in a rat model of neocortical focal
epilepsy by means of serial in vivo resting-state functional MRI (rs-
fMRI) acquisition over ten weeks of time. Measurement of
spontaneous low-frequency blood oxygenation level-dependent
(BOLD) fluctuations with rs-fMRI allows the assessment of
changes in signal synchronization at the level of hemispheres,
regions or voxels. Functional networks are based on neuronal
signal synchronizations underlying brain communication. Our
longitudinal setup of rs-fMRI acquisition provides information on
the stability of the network topology in brains subjected to focal
epilepsy. We had the following hypotheses: (a) functional interictal
networks shift towards a more random topology; (b) these shifts are
consistent over time; (c) network changes are associated with
changes in intra- as well as interhemispheric functional connec-
An additional unsolved issue is the relation between changes in
functional connectivity, network topology, and the microstructural
white matter damage, which is known to occur in focal epilepsy
[2,3]. We therefore included diffusion tensor imaging (DTI), which
enables the assessment of white matter structural reorganization
non-invasively. This interrogation of white matter structure in vivo
is based on measurement of the diffusion process that is effectively
captured as a diffusion tensor. The most frequently used diffusion
tensor parameter is the fractional anisotropy, a measure of
preferred directionality of diffusion within a voxel. DTI has been
indicated to be much more sensitive in detecting microstructural
alterations in white matter as compared to conventional structural
MRI [3,23,24]. We hypothesized that epilepsy-induced network
topology and functional connectivity alterations are accompanied
by remote changes in tissue water diffusion properties in bilateral
Unravelment of the extent and time course of shifts in network
topology and the relation with structural white matter integrity in
a controlled setting may provide new insights into the pathogenesis
of focal epilepsy and its consequences for brain function.
We used a well known focal epilepsy tetanus toxin rat model
[25,26], with the right primary motor cortex as injection side
[27,28]. The tetanus toxin injection induced frequent, mild, but
persistent facial motor seizures in all animals. Spontaneous, well-
tolerated seizures occurred in clusters, and persisted for multiple
weeks. Seizures started around one week after tetanus toxin
injection, with a peak in frequency (on average 8 seizure clusters
per 30 minutes) at seven weeks, followed by a decline towards the
latest time point (Figure 1, left). Seizure clusters typically lasted
from ten seconds to three minutes. In addition, seizures specifically
occurred during onset of and recovery of anesthesia, which was
confirmed by electroencephalography (EEG) recordings, showing
high amplitude rhythmic spiking on EEG at 0% isoflurane
anesthesia, not related to motion artifacts (Figure 1, right). At 1%
isoflurane concentrations, clinical seizures were absent and no ictal
discharges were recorded with EEG, although interictal spikes
were occasionally observed in epileptic rats. Five animals that
developed repeated generalized tonic-clonic seizures or status
epilepticus in the second week after induction were excluded from
further analysis. Four rats developed aggressive behavior after the
second week and were housed individually. Normal body weight
gain over time was slightly reduced in the tetanus toxin treated
rats, with weight values being lower only at day 21 (control group:
415620 g; epilepsy group: 381631 g (mean 6 SD); p=0.03).
Structural damage at the injection site was inspected on the
anatomical images. T2-weighted scans are sensitive for edematous
brain alterations as a result of frequent seizure propagation .
We found hypointensities at day 7, but not at the subsequent time
Resting-state functional MRI
Graph analysis of functional networks.
clustering coefficients (c) and normalized characteristic shortest
path lengths (l) in the brain network at seven, 21, 49 and seventy
days are depicted in Figure 2 for both experimental groups.
Based on the repeated linear mixed model fits, we found a
significant c difference for group (p=0.04) and time (p=0.01). c
increased over time in the epilepsy group (p=0.009), but not in the
control group. We found a significant l interaction between group
and time (p=0.02). In the epilepsy group, but not the control
group, l increased over time (p=0.003). In the epilepsy group,
differences in c and l normalized over time, reaching control
values at ten weeks after epilepsy induction.
Mean functional connectivity maps of the left
and right sensorimotor cortices (regions-of-interest (ROIs) outlined
in Figure 3A) with the rest of the brain clearly demonstrate strong
intrahemispheric functional connectivity enhancement within the
contiguous cortex and subcortical caudate putamen in the injected
hemisphere (Figure 3C), and to a lesser extent in the contralateral
hemisphere (Figure 3E), in the epilepsy group up to seven weeks.
Interhemispheric functional connectivity of both sensorimotor
ROIs and the contralateral hemisphere was reduced at 21 days
after epilepsy induction, and recovered thereafter (Figure 3).
The inter- and intrahemispheric functional connectivities of
both the left and right cortical sensorimotor areas remained stable
over time in the control animals (Figure 4). In contrast, the overall
interhemispheric functional connectivity was diminished in the
epileptic group (group effect: p=0.04; Figure 4). This was due to
the reduction at day 21 (p=0.01), where seven out of eight
epileptic rats had strong negative z’ values (anti-correlations).
Focal Epilepsy Changes Function and Structure
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Figure 1. Spontaneous seizures during study follow-up. Left: Number of seizure clusters scored during 30 minutes observation epochs. Right:
Representative 5 seconds EEG selected from ten minutes recordings from a control rat at 1.0% isoflurane (C); and from an epileptic rat seven weeks
post induction, interictally at 1.0% isoflurane (I), and during a motor seizure at 0% isoflurane (S). The interictal EEG is characterized by a similar
baseline rhythm as the control EEG with infrequent interictal spikes. The ictal EEG involved high amplitude rhythmic spike series (only one shown).
Figure 2. Graph analysis results. The temporal pattern of global brain functional network characteristics, i.e. the normalized clustering coefficient
(c) and normalized characteristic shortest path length (l), at days seven, 21, 49 and seventy in control (C) and epilepsy animals (E). No significant
changes over time were found for the control group. The epilepsy networks were characterized by increased c and l up to seven weeks. Differences
between interictal networks and controls were absent at the last time point. Statistical significance obtained from the linear mixed model analysis is
indicated for factors with p,0.05.
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Increased intrahemispheric functional connectivity was found in
both hemispheres in epilepsy rats (group effects: p=0.001
(ipsilateral), p=0.003 (contralateral)), which returned to control
levels at day 70.
Diffusion tensor imaging
Focal epilepsy resulted in significant lower fractional anisotropy
(FA) values in white matter, which was observed as early as one
week post induction. Figure 5 shows results from the Tract-Based
Spatial Statistics (TBSS) analysis for all time points. Significant FA
reduction was found in the ipsilateral internal and external
capsules. At later time points, reduced FA was found in all major
white matter bundles in both hemispheres, most profoundly at the
seven week time point which recovered thereafter. We found no
abnormal signal enhancement on T2-weighted images (data not
In addition to the whole brain voxel-wise white matter statistics,
ROI analysis was performed in the medial corpus callosum
(delineated in Figure 6, most right). The average FA, trace of the
apparent diffusion coefficient (ADCtrace), and axial and radial
diffusivity values at each time point for both groups are shown in
Figure 6. Callosal FA values in the epilepsy group were lower as
compared to controls at all time points (p=0.006). Increases in FA
were seen over time for both groups (p,0.0001). ADCtrace, axial
and radial diffusivity values did not differ between groups, but a
significant time effect was found for radial diffusivity in both
groups (p=0.002). ADCtraceand axial diffusivity did not change
In this study, we applied serial rs-fMRI and DTI in a rat model
of refractory focal neocortical epilepsy to longitudinally charac-
terize functional connectivity, global network configuration and
white matter integrity associated with chronic epilepsy.
By acquiring whole brain connectivity data at multiple time
points after epilepsy induction, we gained new insights in the
temporal profile of interictal network topology. Our main findings
are that (a) graph-based network properties c and l increase in the
interictal state, indicating a more regular brain network config-
uration; (b) interhemispheric functional connectivity in epileptic
brain decreases, whereas intrahemispheric functional connectivity
increases in both hemispheres; and (c) concomitantly, structural
white matter integrity is disrupted, not restricted to bundles in
close vicinity to the epileptogenic focus, but including the main
commissural structure, the corpus callosum.
The importance of network organization for seizure spread in
epilepsy has been emphasized in multiple modeling studies
[30,31,32,33] and confirmed with EEG [17,34], magnetoenceph-
alography (MEG) , rs-fMRI [15,18] and DTI . In this
study we hypothesized that the focal epileptic brain, during
seizure-free periods, would have a state of increased susceptibility
to seizure generation and spread, which has been proposed to be
associated with a more random network organization [30,35].
This hypothesis is largely based on the previously reported shift
towards a more ordered network configuration during seizures, as
compared to the interictal states in temporal lobe and absence
epilepsy syndromes [12,36]. Interictal network topology in cortical
focal epilepsy, however, has until now not been directly compared
to the healthy control network state. Our study in a neocortical
focal epilepsy model demonstrates that the interictal epileptic
brain is characterized by a more ordered configuration, with
higher c and l as compared to the healthy brain. In contrast with
previous global network epilepsy studies, we assessed the network
topology serially. This provided unique insights in the unknown
interictal neuronal network dynamics. Most importantly, the
affected network topology recovers within a time span of ten
weeks. This recovery coincides with the reduced seizure frequency.
This substantial alterations in network topology could be one of
the explanations of the conflicting results found in previous cross-
sectional studies. We speculate that the increased intrahemispheric
functional connectivity is related to local neuronal sprouting
instead to distant functional interactions in the interictal brain
status. However, future research is required to address this issue
using longitudinal (immuno) histopathological experiments with
Figure 3. Spatial functional connectivity maps for the bilateral
sensorimotor cortices. Functional connectivity maps of right and left
sensorimotor cortices with the rest of the brain. Right (ipsilateral)
(white) and left (contralateral) sensorimotor cortical ROIs (orange)
overlaid on coronal slices from a T2-weighted rat brain template (A).
Maps of functional connectivity with right, ipsilateral (B: control group;
C: epilepsy group) and left, contralateral sensorimotor cortex (D:
control group; E: epilepsy group). Time points (days) are shown on the
left. Functional connectivity (z’) values range from 0.15 to 1.0. The
control group shows strong and consistent functional connectivity
between both sensorimotor cortices at all time points. In the epileptic
brain, functional connectivity was elevated at day seven, extending into
the adjacent secondary somatosensory and medial cingulate cortices,
the subcortical caudate putamen, and contralateral homologous areas.
At day 21 after epilepsy induction, interhemispheric functional connec-
tivity from both ROIs was clearly reduced, which recovered at the later
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stainings for both myelination and axonal integrity, preferably at
multiple time points after focal epilepsy induction.
The longitudinal changes in global network properties closely
matched with the patterns of the intrahemispheric functional
connectivity. We suppose a relationship between the increase in
both c and intrahemispheric functional connectivity as c is a
measure of the degree to which functional nodes tend to cluster
together. The increased c corroborates with previous seizure-free
network findings in patients with absence epilepsy  and
temporal neocortical epilepsy , where higher interictal c was
most pronounced in the EEG and MEG delta bands. This increase
is in line with the idea that neural disturbances are correlated with
changes in functional network organization [37,38] and probably
occur in a wide range of epilepsy syndromes. On the other hand, a
different temporal lobe epilepsy (TLE) study reported interictal
functional networks with lower l . This dissimilarity with our
findings may be explained by differences between location in focus
(temporal versus primary motor cortex), duration of epilepsy (more
than 13 years versus weeks), use of antiepileptic drugs, and
differences between network organization in humans and rats.
l is a measure of the ability to rapidly combine specialized
information from distributed brain areas . The observed
increase in interictal l in our study was accompanied by a
decrease in interhemispheric functional connectivity, which points
toward a relationship between these parameters. The largest
deviation in both measures was found at day 21, which
Figure 4. Functional connectivity between and within hemispheres. Interhemispheric functional connectivity (as normalized correlation: z’)
between the ipsilateral and contralateral sensorimotor cortical regions (left graph), and intrahemispheric functional connectivity (as volume of voxels
connected with the sensorimotor ROI within the same hemisphere) for the ipsilateral (middle graph) and contralateral hemisphere (right graph) in the
controls (C) and epilepsy (E) groups. Interhemispheric functional connectivity was stable over time in controls, but lowered at day 21 in the epilepsy
group. The intrahemispheric functional connectivity was increased in the epilepsy group up to seven weeks, but declined to control levels at the
latest time point. Statistical significance obtained from the linear mixed model analysis is indicated for factor with p,0.05.
Figure 5. Tract-based spatial statistics results. Tract-Based Spatial Statistics output, illustrating significant differences in white matter fractional
anisotropy (FA) values in epileptic brain as compared to controls (blue: reduced; red: increased. Color codes represent p,0.01 – p,0.001; false
discovery rate corrected), overlaid on average FA maps of adjacent coronal rat brain slices. White matter skeleton is shown in green. Time points
(days) are shown on the left.
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subsequently normalized towards the latest time point. Although
functional network paths represent sequences of statistical
associations, making an analogy with the structural network
difficult, modeling has shown that functional resting-state networks
largely overlap with the underlying structural network . The
disturbed corpus callosum integrity may be held responsible for
the decrease of both l and interhemispheric connectivity. The
potential relationship between increased l and reduced inter-
hemispheric functional connectivity is also in agreement with a
recent study, that reported a striking loss of interhemispheric low-
frequency blood oxygenation level-dependent (BOLD) signal
correlations after corpus callosotomy, while intrahemispheric
networks were preserved . However, whether the integrity of
the connecting white matter between the two hemispheres is truly
related to the decreased interhemispheric functional connectivity
needs further study, for example using computational models .
Knowledge of the status of the epileptic brain’s structural
connections is important as the above described global network
alterations could be caused by white matter abnormalities, such as
disruption of association fibers that may underlie the presumed
long-distance functional connections. Our structural analyses add
to the previous DTI epilepsy work by comparing controls to drug-
naı ¨ve subjects, longitudinally. The temporal pattern of white
matter FA changes resembled the temporal change in l,
suggesting a close relationship: widespread abnormalities at
day 21 and 49, and recovery at ten weeks. The TBSS results
indicated that the corpus callosum was substantially affected,
which was confirmed by specific ROI analysis.
The diffuse structural abnormalities found in both hemispheres
with DTI are in agreement with previous partial epilepsy DTI
studies (for overview see: [2,3]). Possible explanations for both the
structural damage and associated changes in functional network
organization include synaptic changes, neuronal death or glial cell
damage. Synaptic alterations have been observed during the
process of secondary epileptogenesis , suggesting that the
anatomically distant areas undergo a physiological change
consequent to neuronal alterations at the primary epileptogenic
In addition, experimental studies have shown that repeated
seizures produce neuronal damage and cell death in the
hippocampus [44,45]. Despite the lack of histological studies
examining the relationship between recurrent seizures and
extrahippocampal remote damage, we know from hippocampal
studies that axonal demyelination, formation of axonal spines,
increase in interstitial fluid volume due to edema, replacement of
axons with glial cells, and astrocyte proliferation may all be
associated with the damage caused by seizure activity .
Although rodent epilepsy models may differ from human
epilepsy, they allow us to study specific pathophysiological
mechanisms that are associated with the development and
progression of epilepsy in a detailed and controlled manner. The
tetanus toxin model is relative mild as compared, for example, to
the lithium-pilocarpine  and kainate  TLE models, that
require a prolonged status epilepticus inducing diffuse damage.
The functional and structural changes that we found in the tetanus
toxin model are therefore more likely to result from frequent
seizure propagation alone, rather than a direct effect of tetanus
toxin-induced brain damage. This idea is strengthened by the
temporal relationship we found between seizure frequency and the
changes in graph properties, functional connectivity and fractional
A limitation of our animal study is the necessity to use
anesthesia. Isoflurane anesthesia is known to suppress overall
functional connectivity in a dose-dependent manner .
Although we have demonstrated that low-frequency BOLD
fluctuations are largely preserved under light to mild isoflurane
anesthesia , the correlation of spontaneous BOLD fluctuations
during resting-state fMRI acquisition and therefore the strengths
of the graph edges and ROI-based functional connectivity may
have been lower than under awake conditions.
The TBSS method we used has some disadvantages [51,52].
TBSS allows, similarly to voxel-based morphometry , the
comparison of whole-brain maps on a group level, but it is more
suited for FA analysis as no spatial smoothing is required.
Nonetheless, partial volume effects may still exist . TBSS
may also result in wrong estimates in regions with multiple,
crossing fiber populations . Another potential drawback of
TBSS is the thinning preprocessing step. The thinning procedure
causes the statistical analysis to focus on voxels with highest FA
only. White matter changes in the lower FA regions of white
Figure 6. Corpus callosum diffusion tensor imaging measures. Fractional anisotropy (FA), axial and radial diffusivity, and ADCtracevalues in
the medial corpus callosum at each time point for the control and epilepsy groups. FA increased gradually in control animals. In epilepsy rats, this
age-related increase was delayed and absolute values were lower. None of the other three parameters showed significant group or group 6time
interaction differences. The corpus callosum ROI is overlaid on the FA template (ROI in white; shown on slices in three orthogonal directions) (most
right). Statistical significance obtained from the linear mixed model analysis is indicated for factor with p,0.05.
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matter bundles are therefore ignored. These drawbacks may apply
to our data as well, although we believe they have a minor impact.
The white matter bundles we analyzed do not contain areas with
significant crossing fibers. In addition, the major white matter
bundles we analyzed in rats are thin structures by nature (i.e.,
external capsule, corpus callosum, internal capsule). The effect of
thinning will therefore be modest.
Unfortunately we were not able to measure EEG simultaneous
with MRI acquisition, which is technically challenging and can
affect the fMRI quality because of potential artifacts that
electrodes would cause on the T2*-weighted images. This prevents
us to rule out any effect of spontaneous seizures on the resting-state
fMRI BOLD fluctuations. We do however believe that this effect is
unlikely to have happened. We anesthetized the rats during the
resting-state fMRI acquisition using isoflurane, which is a potent
inhibitor of spontaneous seizures . Directly after each MRI
session in all animals, we acquired EEG at identical isoflurane
levels and did not observe spontaneous clinical or electrographical
seizures. Interictal epileptic spikes were rare. Seizures started to
occur only when isoflurane anesthesia was stopped. Therefore we
are convinced that functional connectivity, as measured with
resting-state fMRI under the anesthetic protocol used in this study,
reflects interictal functional connectivity and is not related to
frequent spontaneous seizures.
Lastly, although the neocortical tetanus toxin rat model is not
related to neuronal cell death , direct correlations between
gray and white matter MRI measures and histologically measured
microstructural integrity are needed. In particular a direct
correlation between functional and structural MRI parameters
and adaptations at the cellular level will be useful in the
characterization of the plasticity process that likely plays a role
in brain tissue prone to recurrent spontaneous seizures.
Taken together, frequent focal seizures induce global abnor-
malities of white matter and of functional brain networks,
characterized by increased functional network segregation and
ipsilateral functional connectivity, decreased interhemispheric
functional connectivity, and concomitantly increased shortest path
lengths, for which spontaneous recurrent seizures may be held
responsible. We speculate that increased global network segrega-
tion and decreased integration may contribute to cognitive
dysfunction in patients with focal epilepsy.
Materials and Methods
The animal experimental protocol was approved by the Utrecht
University Ethical Committee on Animal Experiments. The
experiments were carried out in accordance with the guidelines
of the European Communities Council Directive. A total of 26,
nine weeks old, juvenile male Sprague-Dawley rats (Charles River
Laboratories International, Inc., MA, USA), weighing 283625 g
(mean 6 SD) at day 0, were included in the study. Animals were
group-housed under standard conditions (food and water provided
ad libitum, 12 h light/12 h dark cycle, temperature 22–24uC).
Chronic focal epilepsy was induced in 13 rats by injection of
tetanus toxin (Sigma-Aldrich, Zwijndrecht, The Netherlands) into
the right primary motor cortex, which is known to induce
frequent, mild facial seizures [25,26,27,28] (epilepsy group). Rats
were anesthetized with a subcutaneous (s.c.) injection of a mixture
of 0.315 mg/mL fentanyl citrate and 10 mg/mL fluanisone
(0.55 mL/kg, HypnormH, VetaPharm, Leeds, United Kingdom)
and 50 mg/mL midazolam (0.55 mL/kg, DormicumH, Roche
Nederland B.V., Woerden, The Netherlands). During surgery rats
were kept warm on a heating pad to prevent hypothermia. A small
medial incision was made in the skin covering the skull and the
pericranium. A hole in the skull above the right primary motor
cortex was made with a 300 mm micro drill. The dura was
carefully opened with a micro needle. A volume of 0.6 mL tetanus
toxin solution (100 ng/mL) was stereotaxically injected (0.5 nL/
min) in the cortex with a 0.5 mL Hamilton syringe with 32 G
needle, at coordinates 0.5 mm anterior, 2.5 mm lateral from
Bregma and at 1.8 mm depth from the cortical surface. To
prevent loss of toxicity, tetanus toxin was dissolved in sterile saline
with 0.2% bovine gelatin (Sigma-Aldrich, Zwijndrecht, The
Netherlands). After injection the needle was left in situ for
15 minutes and removed very slowly and stepwise from the brain.
Immediately after surgery, animals were given 0.3 mg/mL s.c.
buprenorphine analgesia (0.1 mL/kg, TemgesicH, Schering-
Plough Nederland B.V., Houten, The Netherlands). During the
next days, if tetanus toxin injected rats became aggressive, they
were housed individually. Thirteen age-matched healthy rats
served as controls.
Animals were monitored, to detect behavioral changes and
clinical seizures, for 30 min at a weekly basis and prior to scan
sessions. Seizure activity was defined as behavioral arrest with
motor signs, including (a) bilateral whiskers twitching (b) bilateral
facial twitching, and (c) facial twitching together with bilateral
myoclonic jerks of muscles around the skull and in the neck region
In a random subset of animals (three controls, seven epileptic
rats) EEG activity was recorded continually for ten minutes at
1.0% isoflurane outside the MR scanner, immediately following rs-
fMRI acquisitions. Bilateral subcutaneous needle EEG electrodes
were inserted at the position of the primary motor cortices with a
reference electrode above the cerebellum. EEG measurements
were conducted using a homebuilt multichannel amplifier, band
pass filtered between 0.1 Hz and 250 Hz, a National Instru-
mentsTMNI USB-6211 DAQ with a sampling rate of 1000 Hz per
channel and LabWindowsTM
software. After ten minutes, isoflurane anesthesia (which is known
to suppress epileptic activity ) was lowered to 0%, while EEG
monitoring and mechanical ventilation continued, until animals
woke up. EEGs were visually inspected for interictal spiking, and
for the occurrence of electrographical seizure discharges, being
previously defined as series of rhythmic EEG spikes .
programmed data acquisition
Structural and functional MRI
All MRI experiments were performed on a 4.7 T SISCO/
Varian system (Palo Alto, CA, USA) at seven, 21, 49 and seventy
days after epilepsy induction. Radiofrequency excitation and
signal detection were accomplished with a Helmholtz volume coil
(diameter, 9 cm) and an inductively coupled surface coil (diameter,
2.5 cm), respectively.
Before MRI the animals were endotracheally intubated and
mechanically ventilated with 2.0% isoflurane in a mixture of O2/
air (1/2 volume/volume; 55 beats/min). During MRI, expired
CO2, blood oxygen saturation and heart rate were continuously
monitored and kept within physiological levels. A feedback-
controlled heating pad was used to maintain body temperature at
First, multiecho multislice T2-weighted MRI [repetition time
(TR)/echo time (TE), 3000/17.5 ms; 19 coronal slices; field of
view (FOV), 32632 mm2; acquisition matrix, 1286128; voxel
resolution, 0.2560.2561.0 mm3; echo train length, 12] was
conducted to assess possible changes in brain T2relaxation times.
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Second, a gradient echo T2*-weighted 3D dataset was collected
40640640 mm3; data matrix, 12861286256; voxel resolution,
0.31360.31360.234 mm3; pulse angle, 20u].
Third, high angular resolution diffusion imaging [TR/TE,
3500/26 ms; 25 transverse slices; FOV, 32632 mm2; acquisition
matrix, 64664; voxel resolution, 0.560.560.5 mm3; d, 6 ms; D,
11 ms; b-value, 1184.33 s/mm2; non-diffusion weighted images,
2] was acquired to assess microstructural white matter integrity. A
spherical acquisition scheme with fifty unique gradient directions,
determined with electrostatic repulsion , was used.
Finally, repetitive BOLD MRI was conducted [TR/TE, 500/
19 ms; 7 coronal slices; FOV, 32632 mm2; acquisition matrix,
64664; voxel resolution, 0.560.561.5 mm3; pulse angle, 35u;
temporal resolution, 500 ms; number of scans, 1200; total scan
time, 10 min] using a gradient echo single shot EPI sequence.
Exactly ten minutes prior to rs-fMRI acquisition, end-tidal
isoflurane anesthesia concentration was reduced to, and main-
tained at 1.0%. At this level of isoflurane anesthesia, coherence of
low-frequency BOLD signal fluctuations between functionally
connected regions has been shown to be preserved .
After bias-field inhomogeneity correction  and masking out
nonbrain structures , the T2*-weighted 3D volume was
nonlinearly registered to the a stereotaxic rat brain atlas .
Next, BOLD MR images were linearly registered with the T2*-
weighted 3D image. Registrations were performed with the elastix
toolkit (http://elastix.isi.uu.nl; ). Matching of MR images with
the atlas and the functional images allowed functional anatomy-
based delineation of (bilateral) ROIs. We combined the primary
and secondary motor cortices, and the fore- and hindlimb region
of the primary somatosensory cortices to create a single
sensorimotor cortical ROI in the left and right hemispheres
Resting-state fMRI analysis
Resting-state fMRI enables the assessment of functional
connectivity in the brain. If neuronal signaling between two areas
– measured as low-frequency BOLD fluctuations – is temporally
coherent, these areas are considered to be functionally connected.
Several methods exist to calculate such temporal coherency. We
used the Pearson’s correlation coefficient r. We subsequently
normalized r into z’ using the Fisher’s z’-transformation . Non-
neuronal signal contributions were minimized by means of spatial
smoothing (the smoothing kernel full-width at-half-maximum was
set to 1.0 mm), band-pass filtering (between 0.01 and 0.1 Hz) and
linear regression with nuance signals, including the mean brain
BOLD signal oscillations, the mean signal form the white matter,
the mean signal from the cerebrospinal fluid, and the rotation and
translation parameters as obtained from the motion correction
. After these preprocessing steps, two rs-fMRI analyses were
performed; (a) a global network analysis, including all cortical and
subcortical voxels, and (b) a ROI-based analysis in left and right
Each functional dataset was considered as
a weighted undirected network, described by the graph G = (V,
W), where V is the number of nodes and W is the collection of
edges wijis the V6V symmetric weight matrix, where wii=0. In
our data, V was the collection of N cortical and subcortical gray
matter voxels and wijthe normalized correlation coefficient z’,
defined between voxel time series i and j. N varied slightly between
animals and time points (mean 6 SD: 20036126). Edges with
negative correlation values were set to 0.
We quantified the local and global graph structures via the
weighted undirected clustering coefficient  and the shortest
path length , using the C++ Boost Graph Library (www.boost.
The overall clustering coefficient was defined as:
with the clustering coefficient for node i:
Taking into account weights of all edges in a triangle, but not
considering weights not participating in any triangle. L is defined
as the mean geodesic length over all couples of nodes:
The harmonic mean approach avoided inclusion of disconnect-
ed nodes in calculating L and resembles the global efficiency
measure (i.e. 1/‘ R 0) . For each functional dataset, L and C
were normalized using 100 surrogate networks. This number was
sufficient to result in stable surrogate network properties (data not
shown). Surrogate networks were constructed using the random
rewiring procedure described in . Normalized weighted L and
C were defined as:
i u j(sum(1=wij)).
es in global functional network topology we also performed three
different ROI-based analyses: (a) Correlation of the average
sensorimotor cortical signal, for left and right ROIs separately,
with all brain voxels. The obtained connectivity maps were
conservatively thresholded at z’.0.15 and overlaid on an
anatomical template; (b) correlation of the average left and right
sensorimotor cortical ROI signals, as a measure of interhemi-
spheric functional connectivity ; (c) for each hemisphere
separately, calculation of total volume of all voxels that correlated
significantly (i.e., z’.0.15) with the average signal from the
sensorimotor ROI within that hemisphere, as a measure of
intrahemispheric functional connectivity.
To identify the basis of chang-
Diffusion tensor imaging analysis.
angular resolution diffusion scans were registered to the average
non-diffusion weighted image with an affine transformation to
correct for head motion and eddy-current distortions, and brain
tissue was masked out . The set of gradient vectors was
adjusted according to the rotation of the individual scans. The
average non-diffusion weighted image was matched with the T2*-
weighted 3D dataset using affine registration. Next, the effective
The acquired high
Focal Epilepsy Changes Function and Structure
PLoS ONE | www.plosone.org8 July 2012 | Volume 7 | Issue 7 | e39078
diffusion tensor, the corresponding eigensystem, and the subse-
quently derived FA, ADCtrace, and axial and radial diffusivity
maps were computed for each voxel . A total of forty control
FA maps were nonlinearly registered to a common reference to
construct a FA template. Next, localized statistical testing of FA
data was carried out using TBSS [51,52]. TBSS overcomes many
of the problems inherent to conventional voxel-based morphom-
etry analysis , potentially resulting in spurious findings if
spatial misalignment is present . Using TBSS, all registered FA
maps were averaged, ‘thinned’, and individual FA values were
projected onto this thinned white matter skeleton and fed into
voxel-wise randomization testing. In addition to the TBSS
analysis, a ROI analysis was carried out. The corpus callosum
was manually outlined on the FA template, to calculate its FA,
ADCtrace, and axial and radial diffusivity values.
Repeated measures linear mixed models  were employed to
characterize changes in graph properties, inter– and intrahemi-
spheric functional connectivity and tissue diffusion measures over
Random group and time effects, first order interaction between
time and group, and continuous AR1 correlation structure 
were added to the model. The model parameters were estimated
by the restricted maximum likelihood method and considered
significant if p,0.05 (corrected using Tukey’s method). All
statistical analyses were performed in R (www.r-project.org; )
using the nlme package . Voxel-wise statistical analysis of the
FA data was carried out using permutation t-testing and
thresholded at p,0.05 (false discovery rate corrected ).
The authors thank Gerard van Vliet, Annette van der Toorn and Ward
Jennekens for technical assistance.
Conceived and designed the experiments: WMO RMD WSvDH KPJB.
Performed the experiments: WMO MPAvM SAMWV. Analyzed the data:
WMO. Contributed reagents/materials/analysis tools: MPAvM WSvDH
MAV CJS. Wrote the paper: WMO RMD WSvDH OvN MAV CJS
KPJB. Designed the software used in the analysis: WMO. Managed the
running of the project: OvN MAV RMD KPJB.
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