Default mode network abnormalities in idiopathic generalized epilepsy
Megan L. McGilla, Orrin Devinskya, Clare Kellyb, Michael Milhamb, F. Xavier Castellanosb, Brian T. Quinna,
Jonathan DuBoisa, Jonathan R. Younga, Chad Carlsona, Jacqueline Frencha, Ruben Kuznieckya,
Eric Halgrenc, Thomas Thesena,c,⁎
aNYU Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, USA
bPhyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Langone Medical Center, New York, NY, USA
cMultimodal Imaging Laboratory, University of California-San Diego, San Diego, CA, USA
a b s t r a c ta r t i c l ei n f o
Received 17 November 2011
Revised 13 January 2012
Accepted 23 January 2012
Available online 29 February 2012
Idiopathic generalized epilepsy
Default mode network
Resting state functional connectivity
Idiopathic generalized epilepsy (IGE) is associated with widespread cortical network abnormalities on elec-
troencephalography. Resting state functional connectivity (RSFC), based on fMRI, can assess the brain's global
functional organization and its disruption in clinical conditions. We compared RSFC associated with the ‘de-
fault mode network’ (DMN) between people with IGE and healthy controls. Strength of functional connectiv-
ity within the DMN associated with seeds in the posterior cingulate cortex (PCC) and medial prefrontal
cortices (MPFC) was compared between people with IGE and healthy controls and was correlated with sei-
zure duration, age of seizure onset and age at scan. Those with IGE showed markedly reduced functional net-
work connectivity between anterior and posterior cortical seed regions. Seizure duration positively correlates
with RSFC between parahippocampal gyri and the PCC but negatively correlates with connectivity between
the PCC and frontal lobe. The observed pattern of disruption provides evidence for integration- and
segregation-type network abnormalities and supports aberrant network organization among people with
© 2012 Elsevier Inc. All rights reserved.
Idiopathic generalized epilepsy (IGE) is characterized by wide-
spread cortical hyperexcitability with myoclonic, absence or general-
ized tonic–clonic seizures . People with IGE do not exhibit
abnormal brain anatomy or an identified focus of seizure activity
but rather, widespread atypical cortical activity typified by general-
ized spike-and-wave discharges and seizures. Deviant neuronal
activity stems from aberrant thalamo-cortical or cortico-cortical in-
teractions that spread throughout the brain . Abnormal neuronal
activity at a distinct anatomic location that is part of a larger cortical
network may be the basis for rapid propagation of aberrant neuronal
firing that contributes to generalized seizures.
Distant neuronal assemblies that fire synchronously at very low
frequencies maintain large-scale networks throughout the brain .
These low-frequency oscillations are identified by spontaneous
increases in the blood oxygen level dependent (BOLD) signal on
functional MRI (fMRI) at 0.01–0.1 Hz while the brain is at “rest” or
not engaged in cognitive demands or goal-directed tasks . Meta-
bolic demands of the brain during active engagement only increase
slightly over the rest condition, and several distinct regions of the
brain are noted to decrease in activity during tasks [5–7]. These
areas, together termed the default mode network (DMN), are com-
posed of the precuneus/posterior cingulate cortex (PCC), medial pre-
frontal cortex (MPFC), lateral parietal cortex and inferior temporal
cortex and have been the focus of much investigation in recent
years [8–10]. The nodes of this network spontaneously but synchro-
nously show increases in the BOLD signal on fMRI, which give rise
to the resting state functional connectivity (RSFC) of neuronal assem-
blies that show temporal coherence .
The DMN is referred to as the ‘task-negative’ network because its
nodes show decreased activity during specific cognitive demands
while the ‘task-positive’ network, composed of the dorsolateral pre-
frontal cortex, inferior parietal cortex and supplementary motor
area, shows increased activity in response preparation [12,13].
Though the alternating synchrony of the spontaneous activity of
these two networks suggests that they must be organized in some
manner into one well-orchestrated unit, we refer to functional inte-
gration as temporally correlated activity in distinct nodes while segre-
gation is the network of regions in which the time series of activation
is anti-correlated . The organization of the integration and segre-
gation is well-preserved in healthy individuals but has been shown to
break down in some neurological populations.
Healthy individuals reliably exhibit robust, positive correlations
between regions of the DMN and negative correlations between
DMN regions and other cortical areas. Abnormal DMN functional
Epilepsy & Behavior 23 (2012) 353–359
⁎ Corresponding author at: Department of Neurology, 423 East 23 St, #17087c, New
York, NY 10016, USA.
E-mail address: email@example.com (T. Thesen).
1525-5050/$ – see front matter © 2012 Elsevier Inc. All rights reserved.
Contents lists available at SciVerse ScienceDirect
Epilepsy & Behavior
journal homepage: www.elsevier.com/locate/yebeh
connectivity occurs in several brain disorders, including Alzheimer's
disease , schizophrenia , ADHD , Parkinson's , and de-
pression . Decreased functional connectivity within the DMN
has also been found in people with temporal lobe epilepsy .
There is conflicting evidence that people with IGE show differences
in resting state functional connectivity (RSFC). Both Luo et al. and
Song et al. demonstrated decreases in DMN integration in people
with absence and general tonic–clonic epilepsy, respectively [21,22].
However, Moeller et al. showed no differences in functional connec-
tivity in areas that deactivate the most during general spike-and-
wave discharges, including nodes of the DMN . Given the
spontaneous, deviant neuronal activity that spreads throughout the
brain in people with IGE, we aim to identify abnormal regions in the
DMN and the extent to which these pathologic areas are a part of a
larger network. We predict disruptions of the default mode network
in the frontal lobes, consistent with previous studies showing
frontal lobe abnormalities in IGE .
Fifteen people with IGE were recruited from the patient popula-
tion at New York University Medical Center, Comprehensive Epilepsy
Center (8 women, age range 20–48, mean age 30.13) and were age-
and sex-matched with 15 healthy control subjects recruited from
the general population (8 women, age range 21–43, mean age 29.8).
Patients met criteria for IGE and had to have a history of seizures
with no history of developmental delay or structural brain abnormal-
ities. Standard, diagnostic structural imaging studies were normal.
Electrophysiologic evaluation with interictal, and in most patients,
ictal EEG demonstrated typical generalized epileptiform spikes; pa-
tients with focal epileptiform discharges or focal slowing on EEG
were not eligible. People with IGE were classified according to the In-
ternational League Against Epilepsy (ILAE) classification as having ju-
venile myoclonic epilepsy (JME) (40%), absence seizures (40%),
unspecified myoclonic seizures (60%) or general tonic–clonic seizures
(93%) (Table 1). All people diagnosed with IGE were under medical
treatment at the time of study. All subjects gave their written in-
formed consent to participate in this study, which was approved by
the Institutional Review Board of NYU Langone School of Medicine.
2.2. Data acquisition
Functional MRI data were acquired on a Siemens Allegra 3.0 T
scanner. We collected 197 contiguous echo planar imaging functional
volumes for each subject (TR=2000 ms; TE=25 ms; flip angle=90,
39 slices, matrix=64×64; FOV=192 mm; acquisition voxel size=
3×3×3 mm). All participants were instructed to lie as still as possible
with their eyes closed for the duration of the 6-min, 38-second scan. A
T1-weighted anatomical image was also acquired for spatial normali-
zation and localization using a magnetization prepared gradient echo
sequence (TR=2530 ms; TE=3.25 ms; T1=1100 ms; flip angle=7;
176 slices; FOV=256 mm).
2.3. fMRI data preprocessing
AFNI  was used to perform slice timing correction, motion cor-
rection, and detection and reduction of extreme time series outliers.
The first 5 volumes of each participant's scan were discarded. All
other data processing was done with FSL (FMRIB Software Library;
www.fmrib.ox.ac.uk). Further processing included spatial smoothing
using a Gaussian kernel (FWHM=6 mm), mean-based intensity nor-
malization (each subject's 4-D dataset was scaled by its global mean),
and temporal bandpass filtering (0.01–0.1 Hz). To control for the ef-
fects of motion, as well as normal physiologic processes such as cardi-
ac and respiratory rhythms, each participant's 4-dimensional (4-D)
preprocessed volume was regressed on 9 predictors that modeled
nuisance signals from white matter, cerebrospinal fluid and the global
signal and 6 motion parameters. Correction for time series autocorre-
lation (prewhitening) was performed. Each voxel's time series was
then scaled by its standard deviation, and the volume was spatially
normalized to MNI152 standard space using linear registration.
These methods were described in detail elsewhere .
2.4. DMN identification with anatomically distinct regions
Spherical seed regions of interest (ROIs) with radii of 6 mm were
placed in areas consistently implicated in the DMN: the PCC (BA 31)
and MPFC (BA 10), centered at MNI coordinates x=3, y=−57,
z=26 (PCC) and x=8, y=59, z=19 (MPFC) . Although there
are many distinct nodes in the DMN, these two structures were cho-
sen as seed regions because they robustly elicit DMN maps, are not
lateralized, and are distant anatomic regions that belong to a well-
PatientGender AbsenceMyoclonic JMEConvulsionOnset age Age at
LEV, LTG, RUF, TPM
LEV, TPM, VPA
CBZ = carbamazepine, ETX = ethosuximide, CLZ = clonazepam, LEV = levetiracetam, LTG = lamotrigine, OXC = oxcarbazepine, RUF = rufinamide, TPM = topiramate, VPA =
valproic acid, and ZNG = zonisamide.
aPatients were diagnosed with IGE based on scalp EEG and presentation to a hospital after first general tonic–clonic convulsion. It is unclear, though possible, that they
experienced staring spells that would be consistent with absence epilepsy prior to this age of diagnosis. They did show clinical evidence of absence seizures after onset age,
which were captured on video EEG.
M.L. McGill et al. / Epilepsy & Behavior 23 (2012) 353–359
characterized network . The mean time series of each seed was
obtained by applying these seed ROIs to each participant's 4-D resid-
ual standard space volume and averaging across the time series of
each voxel within the ROI (see Fig. 1A). Subject-level RSFC maps of
all voxels that were positively and negatively correlated with the
seed ROI were generated by regressing each participant's 4-D residual
volume on the seeds' time series. Group level RSFC analyses were car-
ried out using an ordinary least squares model implemented in FSL,
which generated Z-score maps (“networks”) of positive and negative
RSFC for each seed. Correlation coefficients of each voxel were nor-
malized to Z-scores with Fisher's r-to-Z transformation. Maps of cor-
related networks were thresholded at Z>2.3 and were corrected for
multiple comparisons with a FDR criterion cluster level significance
2.5. Differences in DMN functional connectivity
To examine differences between positively and negatively corre-
lated networks with the seed regions, a direct voxel-wise comparison
was performed using a mixed-effects ordinary least squares model
implemented in FSL, thresholded at Z>2.3. Group difference maps
were generated using both the PCC and MPFC seeds. Individual pa-
rameter estimates for each participant were generated for degree of
correlation between the average time series in the voxels comprising
the seed and all voxels included in the group difference ROI. This was
done for each seed region and plotted with average values and stan-
dard error of the mean calculated.
Finally, to fully characterize all the brain regions showing a differ-
ence in connectivity with the abnormally integrated region elicited in
the previous analysis, a 4-mm seed was centered on the point of
maximum difference in connectivity. Using the mixed-effect ordinary
least squares model, a direct voxel-wise comparison between the
people with IGE and healthy controls was performed.
2.6. Correlation analysis
The effects of seizure duration, age of seizure onset and age at time
of scan on RSFC were examined by using these measures as covariates
of positive and negative connectivity with each of the seeds. Seizure
duration is defined as the time in years from first seizure to time of
mented in FSL's general linear model, maps were generated that show
clusters that co-vary positively and negatively with RSFC of both the
PCCand MPFCseeds.The parameterestimatesbetweentheclusterseli-
cited and seed voxels were correlated with each parameter mentioned
above to determine the correlation coefficient.
3.1. Integrated and segregated networks with MPFC and PCC seeds
Connectivity maps for each group and for each node of the DMN
were generated. Both groups exhibited functional connectivity within
areas of the DMN. Regions exhibiting positive RSFC with the MPFC
seed in both groups included medial prefrontal cortex, extending
to the paracingulate gyrus, superior frontal gyrus, middle frontal
gyrus (superiorly and laterally), ventral ACC, precuneus/PCC, angular
gyrus extending to the lateral occipital cortex, and middle temporal
gyrus. In both groups, negative correlations were observed in the
supramarginal gyrus extending to the superior occipital cortex and
insular cortex. However, there were differences seen between the
groups on visual inspection. Healthy controls had greater negative
correlations with the planum temporale extending to the parietal
Fig. 1. Positively and negatively correlated time series with seed regions. A. Voxel-wise correlation map in a single control subject with a seed region in medial prefrontal cortex
(MPFC) indicated by the circle in green. Brain regions in orange/yellow, such as the angular gyrus (AG), are positively correlated with the seed region, and brain regions in blue,
such as the supramarginal gyrus (SMG) are anti-correlated with the seed region. Time courses of the average BOLD signal from the seed region (green trace), positively correlated
AG (orange trace) and negatively correlated SMG (blue trace) are shown below. B. Red/yellow colors show brain regions that are positively correlated with the seed ROI (green
circle) in the MPFC in controls and people with IGE. Blue colors show areas that are negatively correlated. C. Areas positively and negatively correlated with the seed in the PCC
are shown in controls and people with IGE. PCC/Pr, posterior cingulate cortex/precuneus; vACC, ventral anterior cingulate cortex; SFG, superior frontal gyrus; MPFC, medial pre-
frontal cortex; AG, angular gyrus; IPS, intraparietal sulcus; SMC, supplementary motor cortex; SMG, supramarginal gyrus; and LPFC, lateral prefrontal cortex.
M.L. McGill et al. / Epilepsy & Behavior 23 (2012) 353–359
operculum, the lateral edge of the thalamus on the right and areas in
the cerebellum (Fig. 1B).
Fig. 1C shows the areas positively and negatively correlated with
the seed placed in the PCC in healthy control subjects and in people
with IGE. Regions exhibiting positive RSFC with the PCC in both
healthy controls and people with IGE included proximal areas of
PCC and precuneus, angular gyrus, MPFC, ACC, paracingulate gyrus,
and inferior temporal poles extending to the middle temporal gyrus.
In both groups, negative correlations were observed in dorsolateral
prefrontal cortex, insular cortex, planum polare, supramarginal
gyrus, left intraparietal sulcus and dorsal anterior cingulate extending
to the supplementary motor cortex. People with IGE failed to show
negative correlations in the precentral gyrus, extending rostrally to
the superior frontal gyrus and paracingulate gyrus, central opercular
cortex, posterior inferior temporal gyrus, antero-superior aspects of
the temporal lobe, left precentral gyrus, and cerebellum.
3.2. Direct group comparisons of RSFC
The direct comparison functional connectivity that was cluster-
based thresholded at Z>2.3 to control for false discovery rates be-
tween controls and people with IGE identified a group difference in
the integrated network between each of the seeds in the PCC and
MPFC and a cluster elicited more ventrally in the MPFC (Fig. 2).
This area is consistently shown to be integrated in the DMN, but
people with IGE lacked extensive positive RSFC in this area of the
DMN compared to healthy controls and failed to show strong posi-
tive correlations (mean r=0.07, SEM 0.02) compared to controls
(mean r=0.29, SEM 0.04). Similarly, DMN connectivity based on
the seed in the MPFC exhibited a group difference within the same
area of the prefrontal cortex. People with IGE did not reliably show
strong positive correlations between these areas (IGE group mean
r=0.22, SEM 0.03; control group mean r=0.41, SEM 0.03). Scatter
plots in both panels of Fig. 2 show the correlation values for each
To further examine the group differences in RSFC in prefrontal
cortex areas, we performed an additional analysis to investigate the
connectivity network of the discordant region in the prefrontal cortex
seen in warm colors in Fig. 2. A seed with a radius of 4 mm was placed
where differences in PCC RSFC between controls and people with IGE
were maximal in the prefrontal cortex cluster at MNI coordinates −2,
44, 0 (Fig. 3). The difference from the PCC RSFC was the most robust
though the point of maximal difference elicited from the MPFC seed
was adjacent. Maps of significant group differences in RSFC were gen-
erated in the same manner as for the primary PCC and MPFC seed
analyses. The comparison map (healthy controls v. people with IGE)
thresholded at Z>2.3 and corrected for multiple comparisons
showed robust group differences in both positive and negative RSFC
between the disrupted node in the prefrontal cortex and areas
typically integrated in the DMN and segregated from the DMN. Spe-
cifically, patients did not consistently show positive correlations
with areas included in the ‘task-negative’ network, namely the dor-
sal–medial prefrontal cortex, extending from the superior frontal
gyrus to the paracingulate gyrus and ventral ACC, and the PCC/
precuneus (control group mean r=0.20, SEM=0.02 IGE group
mean r=0.01, SEM=0.02). Group differences also appeared in
areas typically negatively correlated with the DMN, in the ‘task-posi-
tive’ network. All controls showed negative correlations between the
seed in the PFC and the areas shown in blue on the group difference
map, such as the supramarginal gyrus, superior parietal lobule, and
intraparietal sulcus, while the majority of IGE patients showed posi-
tive correlations between these areas (control group r=−0.15,
SEM 0.02; IGE group r=0.02, SEM 0.01). Scatter plots show that all
healthy controls exhibit positive RSFC between the seed in the PFC
(green) and areas typically integrated in the DMN (red) while people
with IGE exhibit overall decreased or negative functional connectivity
between these areas. Similarly, the negative connectivity scatter plot
illustrates that healthy controls consistently show negative RSFC
between the seed and areas in the task-negative network (blue)
though our cohort of people with epilepsy fail to show strong segre-
gation in these areas.
Fig. 2. Group differences in functional connectivity between patients and controls. The cortical cluster shown in red in both panels is the cluster that exhibits group differences be-
tween healthy controls and people with IGE upon direct comparison with cluster-based thresholding at Z>2.3. Both differences between the functional connectivity of the seed
regions in the MPFC (A) and the posterior cingulate cortex (PCC) (B) show differences in the same prefrontal cortex region (red cluster in both A and B). Scatter plots show the
parameter estimate or correlation of the low-frequency oscillations of the BOLD signal between the seed regions and the entire ROI (red) in the prefrontal cortex. The scatter
plot in (A) shows that healthy controls (NC) tend to show stronger correlations in resting state functional connectivity (RSFC) between both the MPFC seed (green) and the PFC
cluster (red). Similarly, the scatter plot in (B) shows that NC subjects have greater RSFC between the PCC seed (green) and PFC (red) than people with IGE.
M.L. McGill et al. / Epilepsy & Behavior 23 (2012) 353–359
3.3. Correlation analyses
When parameters such as seizure onset age, age at time of scan
and seizure duration were used as regressors with the correlation
analysis for functional connectivity of both the PCC and MPFC seeds,
only seizure duration correlated with network differences from the
PCC node (Fig. 4). Seizure duration was positively correlated with in-
creased RSFC between the PCC and bilateral anterior temporal lobes,
primarily in the parahippocampal cortex (R2=0.71, pb0.005).
Seizure duration was also negatively correlated with decreased RSFC
between the PCC and a cluster in the frontal cortex (R2=0.64,
pb0.005), dorsal to the cluster in the DMN that showed decreased
Fig. 3. Group differences in functional connectivity from abnormal region in the prefrontal cortex. A. When a seed was placed in the abnormal PFC region (green), the cortical maps
of group differences upon direct comparison with cluster-based thresholding at Z>2.3 show that healthy controls exhibit greater positive RSFC than people with IGE in areas typically
included in the DMN (red areas) and greater negative RSFC in areas typically included in the ‘task-positive’ networks (blue areas). B. The scatter plot shows the degree of RSFC (the
parameter estimate) between the green seed region and all red clusters in both the healthy controls (NC) and people with IGE. C. The scatter plot shows the parameter estimate of the
green seed region and all blue clusters in NC and IGE. Yellow bars show the mean and standard error of the mean in each plot.
Fig. 4. Functional connectivity with PCC seed regressed with seizure duration. A. Seizure duration positively varies with the correlations between the average time series of the seed
in the PCC (green) and clusters bilaterally in the anterior temporal lobes (yellow). The scatter plot shows the relationship between seizure duration and parameter estimates be-
tween the regions specified (R2=0.71, pb0.005). B. Seizure duration negatively varies with the parameter estimates between the PCC and a cluster near the anterior cingulate cor-
tex in the frontal lobe. The scatter plot shows the relationship between seizure duration and parameter estimates between the regions specified (R2=0.62, pb0.005). Each green
point on the scatter plots represents a person with IGE's RSFC between the seed and ROI and their seizure duration.
M.L. McGill et al. / Epilepsy & Behavior 23 (2012) 353–359
integration compared to controls (Fig. 4). These areas failed to show
any correlation with onset age or age at time of scan. None of the pa-
rameters used as regressors showed any differences with network in-
tegration or segregation from the MPFC seed.
Using R-fMRI, we examined the integrity of the DMN at rest in
people with IGE and found abnormalities in interictal RSFC. Specifical-
ly, those with IGE have diminished RSFC between nodes of the DMN
and a cluster in the PFC, relative to healthy controls. People with
IGE exhibit both disrupted functional network integration (positive
RSFC between nodes of the DMN) and functional segregation (nega-
tive RSFC between areas of the DMN and “task-positive” regions),
supporting aberrant functional network organization in people with
4.1. Decreased positive functional connectivity in the frontal cortex
Collectively, the participants with IGE showed abnormal function-
al connectivity within the DMN. They exhibited decreased positive
RSFC between areas in the frontal lobe and the rest of the DMN.
These areas serve various cognitive and emotional functions, includ-
ing “mentalizing” (i.e., understanding the mental states of one's self
and others) [27–29]. Patients with IGE show difficulties in social pro-
cessing similar to those exhibited by patients with frontal lobe lesions
, including limited self-control, suggestibility, distractibility, and
indifference to physical needs [31,32]. Such deficits may be the result
of impaired mentalizing abilities such as concept formation, abstract
reasoning, mental flexibility, cognitive speed and planning . Fur-
thermore, the cluster in the frontal lobe showed abnormal segrega-
tion from regions typically considered to be part of ‘task-positive’
network including lateral parietal and occipital regions. Though the
cognitive implications of decreased segregation among these areas
would be speculative, these group differences strengthen the argu-
ment that an abnormal area in the frontal cortex is neither well inte-
grated into the default mode network nor segregated from task-
positive networks. Future studies should investigate whether the de-
creased frontal DMN RSFC observed in this study is related to these
IGE-related social-cognitive impairments.
Physiological recordings in rat models support our findings of
frontal lobe dysfunction, where aberrant firing rates and patterns
have been observed in MPFC neurons during spike-and-wave dis-
charges . Source localization utilizing MEG has identified frontal
lobe localizations in people with IGE [35,36]. In people with juvenile
myoclonic epilepsy, spike-and-slow wave discharges were modeled
to the medial prefrontal region . An EEG-fMRI study of a patient
with IGE revealed frontal deactivation during a generalized spike-
wave discharge . These studies, together with our results, suggest
that frontal deactivations may disrupt the DMN and contribute to the
cognitive and behavioral deficits in IGE.
Differences in RSFC with the PCC that correlate with seizure dura-
tion are not limited to the MPFC and are specific to the PCC seed only.
These abnormalities extend beyond the DMN, but we address them
here because the PCC is a prominent node of the DMN. The frontal
lobe cluster that shows decreased RSFC with the PCC seed negatively
correlates with seizure duration. This suggests that the chronic effects
of seizures, epileptiform activity, and the underlying abnormalities
disrupt the functional integration between medial posterior and fron-
tal regions. fMRI studies show negative BOLD responses (NBR) in
frontal, parietal and posterior cingulate cortices during GSW dis-
charges . Surprisingly, we found seizure duration significantly
correlated with increased connectivity of both parahippocampal gyri
to the PCC seed. Though frontal–thalamic circuits are implicated in
IGE, temporal lobe function may also be affected in generalized epi-
lepsies. Frontal and temporal volumes are decreased in childhood
absence epilepsy , and perirhinal kindling increases discharges
in a rat model of absence epilepsy . Our finding of increased para-
hippocampal connectivity to the PCC seed may reflect either 1) in-
creased synchronization of these limbic regions in people with IGE,
2) a disinhibition reflecting loss of frontal inhibitory input on this cir-
cuit, or 3) other processes.
IGE-related disruption in DMN RSFC may reflect chronic disorga-
nization of the functional architecture due to neurophysiologic dys-
function (e.g., channelopathy) or intermittent disruptions to that
functional architecture that perturbs cortical networks (e.g., frequent
abnormal discharges). Differences between the RSFC of a region in the
PFC to the DMN and task-positive areas suggest that the disruptions
affect the DMN as well as extensive cortical networks. Further work
combining R-fMRI with electrophysiological recordings providing ac-
cess to high-frequency neuronal signals may help adjudicate between
4.2. Limitations and future directions
In our study, healthy subjects expectedly and uniformly showed
either strong positive or negative correlations within the DMN. In
turn, people with IGE showed both a decrease and greater variability
in connectivity strength, potentially identifying a feature of the popu-
lation or a potential effect of other disease-related variables. As this
study utilized a cross-sectional sample of controls as well as patients,
it is not known whether the network differences we found mani-
fested before seizure onset, or result from seizures, medications,
spike-and-wave discharges, and other disease-related factors. Longi-
tudinal studies would confirm that seizure duration correlates with
changes in DMN RSFC over time. Future studies should use simulta-
neous EEG-fMRI to determine whether periodic epileptiform dis-
charges contribute to the observed group differences in DMN
functional connectivity. Further studies would also benefit from in-
vestigating the relationship of the specific cognitive deficits seen in
this patient population and its relation to DMN integrity.
Conflict of interest statement
None of the authors has any conflicts of interest to disclose. We
confirm that we have read the journal's position on issues involved
in ethical publication and affirm that this report is consistent with
This project was supported funded by the Epilepsy Foundation
(M.L.M), FACES (Finding a Cure for Epilepsy and Seizures), as well as
NIH grant 18741 (E.H.).
 Gloor P. Generalized epilepsy with bilateral synchronous spike and wave discharge.
New findings concerning its physiological mechanisms. Electroencephalogr Clin
Neurophysiol Suppl 1978:245–9.
 TyvaertL, Chassagnon S,Sadikot A,LeVan P,Dubeau F,Gotman J.Thalamic nucleiac-
tivity in idiopathic generalized epilepsy: an EEG-fMRI study. Neurology 2009;73:
 Buzsaki G, Draguhn A. Neuronal oscillations in cortical networks. Science
 Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor
cortex of resting human brain using echo-planar MRI. Magn Reson Med
 Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A de-
fault mode of brain function. Proc Natl Acad Sci 2001;98:676–82.
 Esposito F, Bertolino A, Scarabino T, et al. Independent component model of the
default-mode brain function: assessing the impact of active thinking. Brain Res
 Fransson P. Spontaneous low-frequency BOLD signal fluctuations: an fMRI inves-
tigation of the resting-state default mode of brain function hypothesis. Hum Brain
M.L. McGill et al. / Epilepsy & Behavior 23 (2012) 353–359
 Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting Download full-text
brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A
 UddinLQ, KellyAM,Biswal BB,Castellanos FX,Milham MP.Functional connectivity
of default mode network components: correlation, anticorrelation, and causality.
Hum Brain Mapp 2009;30:625–37.
 Raichle ME, Snyder AZ. A default mode of brain function: a brief history of an
evolving idea. Neuroimage 2007;37:1083–90.
 Friston KJ. Functional and effective connectivity in neuroimaging: a synthesis.
Hum Brain Mapp 1994;2:56–78.
 Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human
brain is intrinsically organized into dynamic, anticorrelated functional networks.
Proc Natl Acad Sci U S A 2005;102:9673–8.
 Sonuga-Barke EJS, Castellanos FX. Spontaneous attentional fluctuations in im-
paired states and pathological conditions: a neurobiological hypothesis. Neurosci
Biobehav Rev 2007;31:977–86.
 Fair DA, Dosenbach NU, Church JA, et al. Development of distinct control networks
through segregation and integration. Proc Natl Acad Sci U S A 2007;104:
 Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity dis-
tinguishes Alzheimer's disease from healthy aging: evidence from functional MRI.
Proc Natl Acad Sci U S A 2004;101:4637–42.
 Garrity AG, Pearlson GD, McKiernan K, Lloyd D, Kiehl KA, Calhoun VD. Aberrant
“default mode” functional connectivity in schizophrenia. Am J Psychiatry 2007;164:
 Tian L, Jiang T, Wang Y, et al. Altered resting-state functional connectivity patterns
of anterior cingulate cortex in adolescents with attention deficit hyperactivity dis-
order. Neurosci Lett 2006;400:39–43.
 Van Eimeren T, Monchi O, Ballanger B, Strafella AP. Dysfunction of the default
mode network in Parkinson disease: a functional magnetic resonance imaging
study. Arch Neurol 2009;66:877–83.
 Greicius MD, Florew BH, Menon V, et al. Resting-state functional connectivity in
major depression: abnormally increased contributions from subgenual cingulate
cortex and thalamus. Biol Psychiatry 2007;62:429–37.
 Zhang Z, Lu G, Zhong Y, et al. Altered spontaneous neuronal activity of the
default-mode network in mesial temporal lobe epilepsy. Brain Res 2010;1323:
 Luo C, Li Q, Lai Y, et al. Altered functional connectivity in default mode network in
absence epilepsy: a resting-state fMRI study. Hum Brain Mapp 2011;32:438–49.
 Song M, Du H, Wu N, et al. Impaired resting-state functional integrations within
default mode network of generalized tonic–clonic seizures epilepsy. PLoS One
 Moeller F, Maneshi M, Pittau F, et al. Functional connectivity in patients with idi-
opathic generalized epilepsy. Epilepsia 2011;52(3):515–22.
 Holmes MD, Brown M, Tucker DM. Are “generalized” seizures truly generalized?
Evidence of localized mesial frontal and frontopolar discharges in absence. Epilepsia
 Cox RW. AFNI: software for analysis and visualization of functional magnetic res-
onance neuroimages. Comput Biomed Res 1996;29:162–73.
 Kelly C, De Zubicaray G, Di Martino A, et al. L-dopa modulates functional connec-
tivity in striatal cognitive and motor networks: a double-blind placebo-controlled
study. J Neurosci 2009;29:7364–78.
 Gusnard DA, Akbudak E, Shulman GL, Raichle ME. Medial prefrontal cortex and
self-referential mental activity: relation to a default mode of brain function.
Proc Natl Acad Sci U S A 2001;98:4259–64.
 Simpson JR, Drevets WC, Snyder AZ, Gusnard DA, Raichle ME. Emotion-induced
changes in human medial prefrontal cortex: II. During anticipatory anxiety. Proc
Natl Acad Sci U S A 2001;98:688–93.
 Amodio DM, Frith CD. Meeting of minds: the medial frontal cortex and social cog-
nition. Nat Rev Neurosci 2006;7:268–77.
 Damasio H, Grabowski T, Frank R, Galaburda AM, Damasio AR. The return of Phi-
neas Gage: clues about the brain from the skull of a famous patient. Science
 Bech P, Pedersen K, Simonsen N, Lund M. A multidimensional study of personality
traits ad modum Sjobring. Acta Neurol Scand 1976;54:348–58.
 De Araujo Filho GM, Jackowski AP, Lin K, et al. Personality traits related to juvenile
myoclonic epilepsy: MRI reveals prefrontal abnormalities through a voxel-based
morphometry study. Epilepsy Behav 2009;15:202–7.
 Devinsky O, Gershengorn J, Brown E, Perrine K, Vazquez B, Luciano D. Frontal
functions in juvenile myoclonic epilepsy. Neuropsychiatry Neuropsychol Behav
 Lorincz M, Olah M, Baracskay P, Szilagyi N, Jhuasz G. Propagation of spike and
wave activity to the medial prefrontal cortex and dorsal raphe nucleus of WAG/Rij
rats. Physiol Behav 2007;90:318–24.
spike and wave complexes in juvenile myoclonic epilepsy. Seizure 2002;11:320–4.
 Stefan H, Paulini-Ruf A, Hopfengartner R, Rampp S. Network characteristics of idio-
pathic generalized epilepsies in combined MEG/EEG. Epilepsy Res 2009;85:187–98.
 Laufs H, Lengler U, Hamandi K, Kleinschimdt A, Krakow K. Linking generalized
spike-and-wave discharges and resting state brain activity by using EEG/fMRI in
a patient with absence seizures. Epilepsia 2006;47:444–8.
 Hamandi K, Laufs H, Noth U, Carmichael DW, Duncan JS, Lemieux L. BOLD and
perfusion changes during epileptic generalized spike wave activity. Neuroimage
 Caplan R, Levitt J, Siddarth P, et al. Frontal and temporal volumes in childhood ab-
sence epilepsy. Epilepsia 2009;50(11):2466–72.
 Ackman O, Karson A, Aker RG, Ates N, Onat FY. Perirhinal cortical kindling in rats
with genetic absence epilepsy. Neurosci Lett 2010;479:74–8.
M.L. McGill et al. / Epilepsy & Behavior 23 (2012) 353–359