Differential synchronization in default and task-specific networks of the human brain.

Abstract

On a regional scale the brain is organized into dynamic functional networks. The activity within one of these, the default network, can be dissociated from that in other task-specific networks. All brain networks are connected structurally but evidently are only transiently connected functionally. One hypothesis as to how such transient functional coupling occurs is that network formation and dissolution is mediated by increases and decreases in oscillatory synchronization between constituent brain regions. If so, then we should be able to find transient differences in intra-network synchronization between the default network and a task-specific network. In order to investigate this hypothesis we conducted two experiments in which subjects engaged in a Sustained Attention to Response Task while having brain activity recorded via high-density electroencephalography (EEG). We found that during periods when attention was focused internally (mind wandering) there was significantly more neural phase synchronization between brain regions associated with the default network, whereas during periods when subjects were focused on performing the visual task there was significantly more neural phase synchrony within a task-specific brain network that shared some of the same brain regions. These differences in network synchrony occurred in each of theta, alpha, and gamma frequency bands. A similar pattern of differential oscillatory power changes, indicating modulation of local synchronization by attention state, was also found. These results provide further evidence that the human brain is intrinsically organized into default and task-specific brain networks, and confirm that oscillatory synchronization is a potential mechanism for functional coupling within these networks.

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HUMAN NEUROSCIENCE
ORIGINAL RESEARCH ARTICLE
published: 21 May 2012
doi: 10.3389/fnhum.2012.00139
Differential synchronization in default and task-specific
networks of the human brain
Aaron Kirschner1, JuliaWingYan Kam1,Todd C. Handy1,2and Lawrence M.Ward1,2*
1Department of Psychology, University of British Columbia, Vancouver, BC, Canada
2Brain Research Centre, University of British Columbia, Vancouver, BC, Canada
Edited by:
ShuheiYamaguchi, Shimane
University, Japan
Reviewed by:
Hidenao Fukuyama, Kyoto University,
Japan
Douglas Owen Cheyne, Hospital for
Sick Children, Canada
*Correspondence:
Lawrence M. Ward, Department of
Psychology, University of British
Columbia, 2136West Mall,Vancouver,
BC, Canada V6T 1Z4.
e-mail: lward@psych.ubc.ca
On a regional scale the brain is organized into dynamic functional networks. The activity
within one of these, the default network, can be dissociated from that in other task-specific
networks. All brain networks are connected structurally but evidently are only transiently
connectedfunctionally.Onehypothesisastohowsuchtransientfunctionalcouplingoccurs
is that network formation and dissolution is mediated by increases and decreases in oscil-
latory synchronization between constituent brain regions. If so, then we should be able
to find transient differences in intra-network synchronization between the default network
and a task-specific network. In order to investigate this hypothesis we conducted two
experiments in which subjects engaged in a Sustained Attention to ResponseTask while
having brain activity recorded via high-density electroencephalography (EEG). We found
that during periods when attention was focused internally (mind wandering) there was
significantly more neural phase synchronization between brain regions associated with
the default network, whereas during periods when subjects were focused on performing
the visual task there was significantly more neural phase synchrony within a task-specific
brain network that shared some of the same brain regions.These differences in network
synchrony occurred in each of theta, alpha, and gamma frequency bands. A similar pat-
tern of differential oscillatory power changes, indicating modulation of local synchronization
by attention state, was also found.These results provide further evidence that the human
brainisintrinsicallyorganizedintodefaultandtask-specificbrainnetworks,andconfirmthat
oscillatory synchronization is a potential mechanism for functional coupling within these
networks.
Keywords: default network, task-specific network, neural synchronization, oscillatory power, theta band, alpha-
band, gamma-band, mind wandering
INTRODUCTION
At a regional scale the brain is organized into functionally spe-
cific networks (Passingham et al., 2002; Bullmore and Sporns,
2009). Several networks with different functional properties have
beendelineated,includingthoseforsensory/perceptualprocessing
(Mishkin et al., 1983; van Essen and Maunsell, 1983; Rauschecker
and Tian, 2000; Alain et al., 2001), orienting attention (Corbetta
et al., 2008), and memory encoding and retrieval (Maguire and
Frith, 2004; Habecka et al., 2005; Burianova et al., 2010). Impor-
tantly, a network distinct from most task-specific networks has
been labeled the “default network” (Raichle et al., 2001; Buckner
et al., 2007). Whereas task-specific networks tend to become acti-
vated when attention is directed externally toward behaviorally
relevant stimuli, the default network has been shown to increase
in activity during rest (Buckner et al., 2007). Functional connec-
tivityandcorrelationanalyseshavealsoshownthedefaultnetwork
to be anticorrelated with task-specific networks in the sense that
when task-specific networks become active the default network
decreases in activity and vice versa (Greicius et al.,2003;Fox et al.,
2005; Jerbi et al., 2010).
The dominant brain regions comprising the default network –
the medial-frontal cortex, the temporal lobe, the hippocampal
formation, and the parietal lobe (Buckner et al., 2007) have been
shown to be functionally linked (Greicius et al., 2003, 2009;
Honey et al., 2009). It is still unknown, however, how the brain
switches functionally between default and task-specific networks,
or whether default and task-specific networks are always anticor-
related or can be simultaneously active. One attractive hypothesis
is that transient functional organization of such large-scale neural
assembliesisbroughtaboutbymodulationsofphasesynchroniza-
tionof neuraloscillations(vonSteinetal.,2000;Varelaetal.,2001;
Ward,2003). If inter-regional phase synchronization is a plausible
mechanism for the transient formation and dissolution of default
andtask-specificfunctionalnetworks,thenphasesynchronization
within task-specific networks should be greater when attention is
focusedonaspecifictask,whereasthatwithinthedefaultnetwork
shouldbegreaterwhenattentionisfocusedinward,awayfromthe
externaltask.Itisprobablyunrealistic,however,toexpectextreme
anticorrelation of synchronization in these networks if a task is
ongoing and performed at a reasonable level, as synchroniza-
tion in the task-specific network would be required to maintain
performance if the synchronization is functionally important.
Such a pattern of differential synchronization between default
and task-specific networks should be manifested at several
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Kirschner et al. Differential synchronization in default and task-specific networks
oscillation frequencies, particularly theta (4–8Hz), alpha (9–
12Hz),and gamma (30–50Hz),given the links between cognitive
processesandoscillationsinthesefrequencybands(vonSteinand
Sarnthein, 2000; Varela et al., 2001; Ward, 2003). Previously, dif-
ferential synchronization of 40-Hz oscillations has been produced
inasimplifiedcorticalnetworksimulation(Decoetal.,2009),and
alpha-bandsynchronizationwithingroupsofEEGscalpelectrodes
hasbeeninferredtobeactivatedbydefaultprocessing(Fingelkurts
andFingelkurts,2011).Moreover,seed-basedcorrelationmapping
of powerchangesinMEGdatahasbeenusedtodemonstratetran-
sientformationofrestingstatenetworksseparatelyfromthedorsal
attention network (de Pasquale et al., 2010).
Alpha-band (8–14Hz) spectral power in the occipital lobe
increaseswhenvisualprocessingissuppressedanddecreaseswhen
visualprocessingisenhanced,whereasgamma-bandpowershows
the opposite behavior (Kelly et al., 2006; Klimesch et al., 2007;
Doesburg et al., 2009; Jensen and Mahazeri, 2010). Thus, when
the occipital lobe is fully engaged in visual task-specific pro-
cessing there should be lower alpha power and greater gamma
power than when it is not so engaged. Consistent with this, pre-
stimulus parietal–occipital alpha power is negatively correlated
with attention state ratings in a rapid serial visual presentation
task (MacDonald et al., 2011).
During most prolonged tasks attention waxes and wanes,alter-
nating periods of “on-task” and “mind wandering” (Smallwood
and Schooler, 2006). Given that attention is directed inward dur-
ing periods of mind wandering,the default network is expected to
be active during this time. The Sustained Attention to Response
Task (SART; Robertson et al.,1997) can be used to induce periods
of mind wandering as well as periods of task-directed attention.
The highly repetitive nature of this task automates responses and
subjects’attentionoftendriftsinward.UsingSARTandfMRIithas
beenshownthatthedefaultnetworkismoreactiveduringperiods
of mindwandering,whereasatask-specificnetworkismoreactive
during periods of task-directed attention (Christoff et al., 2009).
Thus, we implemented a SART in two high-density EEG experi-
mentsinordertoinvestigatewhetheroscillatoryactivityandphase
synchrony within default and task-specific brain networks would
differinwayssimilartothoseobservedinfMRIBOLDactivations.
MATERIALS AND METHODS
SUBJECTS
In Experiment 1, 15 subjects (11 women, mean age±SD=21.5
±3.2) completed a SART for $20; in Experiment 2, 10 subjects
(seven women, mean age±SD=21.5±2.0) completed a similar
SART for $20. One subject in Experiment 2 indicated no off-task
periodsandherdatawerediscarded.Allsubjectswererighthanded
with no history of neurological conditions and had normal or
corrected-to-normal vision. Subjects provided written informed
consenttotheexperimentalprocedure,whichwasapprovedbythe
UBCClinical(Experiment1)orBehavioral(Experiment2)Ethics
Review Board.
SUSTAINED ATTENTION TO RESPONSE TASK
The SART involved presenting a serial stream of visual stimuli to
subjects (Figure 1). In Experiment 1 subjects pressed a button
if the stimulus was one of the numerals 0–9 and withheld their
FIGURE 1 |The SART for Experiments 1 and 2. In Experiment 1 at the top,
open boxes illustrate video screen frames in the sequence presented to the
subject.The target stimuli requiring responses were the randomly selected
numerals 0–9 and the no-response target was an “X.” Non-target stimuli,
which were never responded to, were either a grating presented on the
screen above the fixation point or a brief sound.These occurred on every
trial but in random order from trial to trial. In Experiment 2 at the bottom,
only the sequence of stimuli is illustrated there.The target stimuli requiring
responses were the numerals 0–2 and 4–9 and the no-response stimulus
was a “3”; there were no non-target stimuli.The dark boxes illustrate the
experience (attention state) probes that interrupted the ongoing SART from
time to time, and which were different in the two experiments.
response to the letter X. Within each block of 12–36 stimuli, one
or two stimuli to which response had to be withheld (letter X)
occurred at random. Subjects completed 38.2 blocks on average.
During the interval between task-relevant stimuli (numerals or
X), two task-irrelevant stimuli were presented, in random order,
to the subject (see Figure 1). The “task-irrelevant” stimuli were
added to the standard SART in order to allow for the measure-
ment of peripheral, sensory processing during mind wandering.
Analyses of event-related potential responses to these stimuli were
reported by Kam et al. (2011). In Experiment 2 subjects pressed
a button for numerals 0–2 and 4–9 and withheld their response
to the numeral 3, which occurred randomly on 10% of the tri-
als. Each stimulus was presented for 500ms and a response was
allowed up to 2s after stimulus onset.
In Experiment 1 subjects were asked by the experimenter, who
wasintheroomwiththesubject,toidentifyverballytheirattention
state immediately prior to the experience probe as being“on-task”
(fully attentive to task performance at block’s end) or “off-task”
(inattentive to the task at the block’s end). The block duration
randomly varied between 30 and 90s. In Experiment 2, subjects,
alone in the room,were queried by the computer as to their atten-
tion state 115 times during the experiment at random intervals
(block length) averaging approximately 42s after the response to
thepreviousexperienceprobe.Theyrespondedbyindicatingtheir
state on a 1 (“completely off-task”) to 7 (“completely on-task”)
attention state scale by pressing the appropriate computer key.
EEG RECORDING
In Experiment 1, EEGs were recorded from 64 active electrodes
(Bio-Semi Active2 system) distributed evenly over the head, rel-
ative to two scalp electrodes located over medial-frontal cortex
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Kirschner et al. Differential synchronization in default and task-specific networks
(CMS/DRL), using a second order high-pass filter of 0.05Hz,
with a gain of 0.5 and digitized on-line at a sampling rate of
256 samples-per-second. The vertical EOG was recorded from an
electrode inferior to the right eye, and the horizontal EOG from
an electrode on the right outer canthus. In Experiment 2, EEGs
were recorded from 60 passive electrodes in a standard electrode
cap (Electro-cap,Inc.) at locations based on the International 10–
10 System, relative to an electrode over the right mastoid with
ground at AFz. Data were sampled at 500Hz through an analog
pass band of 0.01–100Hz (SA Instrumentation, San Diego, CA,
USA). The EOG was recorded from four periocular electrodes.
Electrode impedance was below 10kΩ. Prior to analysis all signals
were re-referenced to an average reference, resampled to 250Hz,
and digitally high-pass filtered at 1Hz.
EEG DATA ANALYSIS
EEG data were analyzed using EEGLAB software (Delorme and
Makeig, 2004), an open source MATLAB toolkit (MathWorks,
Natick,USA),andcustomscripts.FirstweperformedanIndepen-
dentComponentAnalysis(ICA)onthecontinuousdata.ICAuses
temporal informational independence to separate out the unique
EEG components corresponding to the volume-conducted pro-
jectionsof partiallysynchronouslocalcorticalfieldactivitywithin
a variety of single,compact,cortical domains (Bell and Sejnowski,
1995; Stone, 2004; Delorme et al., 2012). The brain sources of the
Independent Components (ICs) produced from this analysis were
localizedusingdipolefittingof theirsingle-dipolescalpmaps.The
canonical three-dimensional locations of the electrodes were co-
registeredwiththeMontrealNeurologicalInstitute(MNI)average
brain, and the EEGLAB dipfit2 algorithm was used to find dipole
locations. Only ICs whose best-fitting single dipoles were local-
ized within Talairach brain space and with less than 15% residual
variance (termed“valid”ICs) were included in subsequent analy-
ses. Note, however, that the data analyzed further were the time
series of the ICs themselves, not the activations of their best-
fittingdipoles,whoselocationsareusedhereonlytogivemeaning
to the ICs.
Wavelet coefficients of the sinusoidal oscillations between 5
and 70Hz were obtained from a Morlet wavelet analysis on each
IC time series. Wavelet analysis divides the total broadband IC
signal into a set of frequency bands for which amplitude (power)
and phase at each time point are computable from the wavelet
coefficients.
Becausewecouldnotascertainpreciselyhowlongasubjectwas
inaparticularattentionstatebeforeeachattentionprobe,weused
a fixed time window of 12s before the probe. This window has
beenusedsuccessfullyinprevioussimilarstudies.ForExperiment
1, windows preceding subjects’ indication that they were focused
on the experiment were labeled “on-task,” whereas for Experi-
ment 2 windows preceding responses five, six, and seven on the
1–7 attention state scale were labeled “on-task.” For Experiment
1, windows preceding subjects’ indication that they were off-task
were labeled “off-task,” whereas for Experiment 2 windows pre-
cedingattentionscaleresponses1,2,and3werelabeled“off-task.”
Windows preceding attention scale response “4” in Experiment 2
were not analyzed. No non-response stimuli occurred in the ana-
lyzed 12-s intervals in either experiment. This was by design, as
we wished to analyze a stable on-task or off-task state, and a non-
responsestimulusoccurringinthisperiodcouldhavechangedthe
subject’s attention state.
Cluster analysis based on Talairach locations of dipoles associ-
ated with all valid ICs was performed to find neural sources that
were common across subjects. In Experiment 1 a total of 230 ICs
for the 15 subjects were separated into 13 clusters (Experiment 2:
266ICsforninesubjectsinto15clusters)byapplyingthek-means
algorithm of EEGLAB. For Experiment 1, seven clusters that con-
tainedthedipolesof atleast9of the15subjects’ICswithinasingle
Brodmann brain area were retained (Experiment 2: seven clusters
with at least five of nine subjects) for further analysis. Clusters
were pruned so that they contained only a single IC from each
represented subject; in case of multiple ICs from a single subject
only that subject’s IC whose dipole was closest to the centroid of
the cluster was retained.
In order to assess neural synchrony within the brain sources
of the retained ICs we performed a time–frequency analysis of
event-related spectral perturbation (ERSP). Within the epochs
assignedtoeachattentioncondition,eachofwhichconsistedofthe
timeperiodfromstimulusonsetuntil650msafterstimulusonset,
oscillatory amplitude relative to a baseline (−150 to −50ms), in
decibels,was determined at each time point at frequencies from 5
to 70Hz. Statistical significance of differences between the ERSPs
in the on- and off-task conditions at each time point was assessed
using the EEGLAB permutation test.
In order to assess the functional connections between differ-
ent areas of the brain (inter-regional connectivity), phase locking
analysiswasperformed.Thephaselockingvalue(PLV)wascalcu-
lated between every pair of valid ICs for which a subject had one
in each of two clusters. Phase cross-coherence (PLV) values were
obtained using the following formula:
CC1,2(f ,t) =
1
N
N
?
k=1
W1,k
??W1,k
?f ,t?W∗
2,k
?f ,t?
?f ,t?W2,k
?f ,t???
where Wi,k(f,t) are the wavelet coefficients for each time, t, and
frequency, f, point for each IC, i, and k =1 to N is the index
of trials (Delorme and Makeig, 2004). PLV values range from 0,
which indicates no phase locking, to 1, which indicates perfect
phaselocking(constantphasedifference).Approximateconstancy
of phase difference indicates stochastic synchronization, and thus
a functional connection, between the relevant brain areas. Note
that although the ICs are maximally informationally independent
by design, they are not completely independent. There is always a
(usually small) residual amount of mutual information between
ICsthatcannotbereduced.Moreover,ICAwasdoneonthebroad-
band EEG signals,which are dominated by low frequencies. Thus,
there is the possibility that significant phase locking can occur
transientlybetweenpairsof ICs,particularlyforfrequenciesabove
5Hz. It is this phase locking that our analysis assesses. Note also
that because PLV was measured between maximally independent
ICs,whose sources were localized to disparate brain regions,there
is virtually no possibility of volume-conduction-induced spuri-
ous PLV. Indeed the entire point of the ICA is to disentangle the
various neural sources from the composite, volume-conducted
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Kirschner et al. Differential synchronization in default and task-specific networks
Table 1 | Cluster properties Experiment 1.
Cluster brain region No. of subjects involved Total no. of ICsBACentroidTalairach x, y, zMean RV% of dipole fit
Occ
L ACC
R MTG
OFC
L MTG
PPC
R ACC
13/15
12/15
9/15
13/15
10/15
13/15
13/15
33
21
20
32
19
32
16
17
24
21
11
21
7
24
−10, −97 , −5
−21, −16, 45
62, −25, −5
0, 45, −26
−73, −27 , 1
3, −62, 30
11, 8, 32
6.84
5.31
10.27
7 .18
8.67
5.23
6.96
BA, Brodmann area; IC, independent component; L, left; Occ, occipital cortex; ACC, anterior cingulate; R, right; RV, residual variance; SD, standard deviation; OFC,
orbito-frontal cortex; MTG, middle temporal gyrus; PPC, posterior parietal cortex.
Table 2 | Cluster properties Experiment 2.
Cluster brain regionNo. of subjects involvedTotal no. of ICsBA CentroidTalairach x, y, z Mean RV% of dipole fit
Occ
L ACC
R MTG
OFC
L MTG
PPC
R MFG
9/9
5/9
9/9
9/9
9/9
9/9
8/9
19
11
21
12
21
20
13
18
33
42
11
42
2
8
15, −84, −7
−3, 10, 19
71, −12, 3
9, 59, −22
−73, −22, 2
−41, −25, 41
55, 9, 38
4.65
8.64
4.45
8.32
5.86
6.4
8.64
BA, Brodmann area; IC, independent component; L, left; Occ, occipital cortex; ACC, anterior cingulate; R, right; RV, residual variance; SD, standard deviation; OFC,
orbito-frontal cortex; MTG, middle temporal gyrus; PPC, posterior parietal cortex; MFG, middle frontal gyrus.
signal (see Delorme et al., 2012). Also, entrainment of ongoing
low-frequency oscillations in the 100- to 200-ms after stimulus
onset (Gruber et al.,2005) is not a compelling explanation for the
observed inter-regional synchronization in theta and alpha bands,
as our ICs typically did not show coherent ERP components,with
theexceptionof ICsinoccipitalandparietalcortex,andinter-trial
coherence (typically <0.2) was far below that required (typically
>0.8) for such entrainment to explain a significant part of the
evoked activity (Makeig et al., 2004).
To obtain statistically significant differences between PLVs in
theon-andoff-taskconditions,amethodrelyingonsurrogatedis-
tributions was utilized (Maris and Oostenveld, 2007; Maris et al.,
2007). The PLVs were averaged for all subjects within a cluster for
each condition (on-task vs. off-task) and compared to generate a
difference between mean PLVs across conditions. All PLVs from
both conditions were then combined and randomly shuffled. The
shuffled PLVs were separated into two pseudo-conditions and the
difference between their means was computed. This process was
repeated 1000 times for each IC comparison and the results were
rank ordered to produce a distribution of comparison results that
would have been found by chance. The mean difference between
ICsintheon-andoff-taskconditionswasthenrankedwithrespect
to the surrogate distribution and its rank used to measure the sta-
tistical likelihood of finding the result by chance. Comparisons
were performed within a standard time window of 0–650ms post
stimulusonset.Thefrequencywindowsusedforcomparisonwere
theta(4–8Hz),alpha(9–14Hz),andgamma(30–50Hz).Inorder
tominimizeexperiment-wiseerror,eachtestwasconductedatthe
0.001probabilitylevel(meanPLVdifferencerequiredtobegreater
than or less than all surrogate differences).
Phase locking value differences were only considered to be sig-
nificant if a second criterion was also met. We used the EEGLAB
procedure for determining whether a group of PLVs is generally
different from zero to filter the PLV values. In this procedure,
individual subjects’ PLVs were masked at p <0.01 for each of
a number of smaller time–frequency windows (the grain of the
waveletanalysis,hereaftercalled“pixels”)withineachlargertime–
frequency window in each condition separately, and the group of
masked PLVs was masked at p <0.001 or less. Masking for indi-
vidualPLVsineachpixelwasdonewithapermutation(surrogate)
methodbasedon200shufflingsoftheepochsforeachICinvolved,
and that for the group was done with a binomial probability cal-
culation. In the latter case, the p-value used for the individual
tests was taken as the probability of a “success” in a binomial
distribution with P(failure)=1−P(success), and the binomial
probability of k or more of n individuals with a significant PLV
at p <0.01 was kept at or less than 0.001 (the minimum binomial
probability was determined by the number of individual IC pairs
available).
We discuss only those significant PLV differences between the
on-task and off-task conditions (from the first test) in which a
cluster of pixels in the more significant condition also was signif-
icantly different from zero within the indicated time–frequency
window,meaning that all or most of the subjects had significantly
greater than zero PLV for each of those pixels. The one exception
was for the default network in Experiment 2, where none of the
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Kirschner et al. Differential synchronization in default and task-specific networks
indicated significant results passed this latter stringent test. There
were a number of IC pairs for which PLV was significantly differ-
ent from zero by the binomial test in both on-task and off-task
conditions,includingabouthalf of thosewherethePLVsweresig-
nificantly different between conditions by the permutation test.
This was to be expected because, even when mind wandering and
thus off-task, subjects were still performing the SART task at an
acceptable albeit slightly reduced level. Moreover, and especially
in Experiment 2, there were also a number of pairs for which the
off-task PLV was different from zero and the on-task was not but
thepermutationtestwasnotpassed.Inordernottoover-interpret
our data we do not discuss these cases further, although they are
predominantly consistent with our conclusions.
RESULTS
BEHAVIORAL DATA
Subjects reported being off-task on 57.5% and on-task on 42.5%
of the experience probes (SE=3.5%) in Experiment 1,and 35.5%
(SE=10.9%)and49.4%(SE=11.7%)respectivelyinExperiment
2 (15.1% discarded as neither;“4”on the 7-point response scale).
Average false alarms were 6.1 (SD=4.4) and 11.0 (SD=4.3) in
on-task and off-task epochs, respectively, in Experiment 1, and
14.8 (SD=13.2) and 25.0 (SD=22.7) respectively in Experiment
2.Becauseamountof timespentinon-taskandoff-taskstatesdif-
fered across attention state, subjects, and experiments, however,
we normalized false alarm errors with respect to these times. First,
we assumed that the proportion of total time spent in each state
was the same as the proportion of experience probes that indi-
cated that state. Then we divided the proportion of false alarms in
each state by the proportion of experience probes indicating that
state,yieldinganormalizedfalsealarmmeasureforeachattention
state. We then aggregated this measure across experiments. As is
commoninSART(e.g.,Christoff etal.,2009),theaveragenormal-
izedmeasureofoff-taskfalsealarms,1.34,wasgreaterthanthatfor
on-taskfalsealarms,0.84(t =2.00,df=23,p =0.029,one-tailed).
We used a one-tailed test in this case because we only wished to
confirm that our SART results were in the typical direction. There
were no significant differences in response time. This latter result,
too, is typical in this SART paradigm (e.g., Christoff et al., 2009).
LOCALIZATION OF ACTIVE BRAIN REGIONS
Tables1 and 2 summarize the results of IC analysis and dipole fit-
ting followed by cluster analysis to identify the neural sources that
were common to the majority of subjects. Six cluster dipole cen-
troidsineachexperimentwerefoundtocorrespondtobrainareas
identified in previous fMRI studies as belonging to the default or
executive networks (Figure2).A task-specific source in the occip-
ital lobe was also identified in both experiments. Five cortical
regions (Figure 3, left) were identified as similar across experi-
ments within typical EEG localization error based on Talairach
coordinatesof clusterdipolecentroidsandBrodmannarea:occip-
ital (Talairach −10, −97, −5 and 15, −84, −7 for Experiments 1
and 2 respectively), left (−73, −27, 1 and −73, −22, 2) and right
(62, −25, −5 and 71, −12, 3) middle temporal, left anterior cin-
gulate (−21, −16, 45 and −3, 10, 19), and orbito-frontal (0, 45,
−26and9,59,−22).Weemphasizethat,asexpectedfromtheICA
(Delormeetal.,2012),alloftheseICsdisplayedsingle-dipolescalp
FIGURE 2 | Brain regions identified with the default network in several
fMRI studies (Buckner et al., 2007; Christoff et al., 2009) and in the
current study.
maps for both their centroids and the individual subjects’ICs. It is
thus reasonable to consider that the dipole locations represent at
leastregionalsources,andideallysingle,compactcorticaldomains
(Delorme et al., 2012), in the indicated brain regions.
INTRA-REGIONAL SYNCHRONY IN TASK-SPECIFIC AND DEFAULT
NETWORKS
Figure3 (middle and right) displays the results of on-task vs. off-
task ERSP comparisons. Although there are some differences, the
convergence across experiments is remarkable. The occipital cor-
tex displays significantly greater power in the theta band during
on-taskepochs.Suppressioninthealpha-bandoccursinbothcon-
ditions (a sign of active stimulus processing). There is also greater
power in the gamma-band during on-task epochs in Experiment
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Kirschner et al. Differential synchronization in default and task-specific networks
FIGURE 3 | Event-related spectral perturbation (ERSP) results for ICs
localized to five critical brain regions. Leftmost columns show the locations
in the brain of clusters of ICs from the two experiments: Experiment 1 in red
dots and Experiment 2 in blue dots. Rightmost three columns display ERSP
results (Off-task and On-task) and the On-task Off-task difference masked at
p =0.05 by permutation test.
1, and weaker evidence of this in Experiment 2. From these indi-
cations, we concluded that this region is a task-specific region.
The right middle temporal cortex, however, displays significantly
greater power throughout the alpha and gamma bands in off-task
epochs without regard to target onset. We therefore concluded that
this region is mostly involved in default processing. The other
areas either display similar patterns in on- and off-task epochs
(orbito-frontal and anterior cingulate) or display a mixed pattern
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Kirschner et al. Differential synchronization in default and task-specific networks
(leftmiddletemporal).Wethereforecharacterizedtheseregionsas
possiblybeinginvolvedinbothtask-specificanddefaultnetworks.
INTER-REGIONAL SYNCHRONY IN TASK-SPECIFIC AND DEFAULT
NETWORKS
Forthisanalysis,weaddedtwoadditionalregionsfromeachexper-
imenttothefivediscussedabove:Experiment1:posteriorparietal
(Talairach 3, −62, 30) and right anterior cingulate (11, 8, 32);
Experiment 2: posterior parietal (−41, −25, 41) and right mid-
dle frontal gyrus (55, 9, 38). These were not sufficiently close in
Talairachspacetobeconsideredanalogousacrosstheexperiments
butwereimportantinthephaselockinganalysis.Therightmiddle
frontal gyrus (Experiment 2) showed on-task off-task ERSP dif-
ferences indicating that it might be more strongly involved in the
off-tasknetwork,whereastheotherthreeareasdisplayedERSPdif-
ferences more consistent with a task-related function (Figure 4).
Results from the analysis of phase locking between all pairs of the
seven brain regions for each experiment are shown in Figure 5.
Thepatternofnetworkconnectivityintheta,alpha,andgamma
frequency bands is clearly different when PLV is greater in off-
taskthaninon-taskepochs,presumablyinvolvingdefaultnetwork
processing in addition to task-specific processing,from that when
PLV is greater for on-task epochs,presumably involving primarily
task-specificprocessing.Notably,occipitalcortexwassignificantly
more synchronized with other brain regions only during on-task
epochs. Moreover, in Experiment 1 occipital cortex was never
more synchronized with right middle temporal cortex, a default
region,duringeithertypeofepochwhereasduringoff-taskepochs
right middle temporal cortex was more synchronized with several
other regions. In Experiment 2, the right middle frontal gyrus
seemed to be a more focal region for the network when PLV
was greater in off-task epochs. Again, however, the occipital cor-
tex was not involved in the off-task network and was the focal
pointof theon-tasknetwork.Finally,theoverallpatternof greater
on-task synchronization had a distinctive fronto-parietal charac-
ter, whereas the pattern of greater off-task synchronization was
distinctly more lateralized.
DISCUSSION
The data reported here support the idea that synchronization
within default and task-specific networks in the cerebral cortex
differs in ways similar to previous studies. That is, functional
connectivity within these dissociable brain-regional networks
varies transiently during off-task and on-task epochs. The fact
thatsynchronizationdifferswithinthesenetworksasafunctionof
whether both default and task-specific processing (off-task, mind
wandering) or mostly task-specific processing (on-task) is occur-
ring,isconsistentwiththeideathatinter-regionalsynchronization
is a mechanism that modulates functional coupling within these
networks.
Also important is our finding that blind source separation
(ICA) of EEG data can be used to localize brain activity to areas
FIGURE 4 | Event-related spectral perturbation (ERSP) results
for two additional ICs (see text forTalairach locations of the
ICs in the brain) in each experiment. From right to left the
columns display ERSP results for Off-task epochs, On-task
epochs, and the On-task Off-task difference masked at p =0.05 by
permutation test.
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Kirschner et al. Differential synchronization in default and task-specific networks
FIGURE 5 | Synchronization between brain regions. (E1) Experiment 1 on
left, (E2) Experiment 2 on right. In each part the left column displays blue lines
between regions that were more synchronized during off-task epochs (default
network), and the right column displays red lines between regions that were
more synchronized during on-task epochs. In all but the Experiment 2
off-task>on-task network, lines are displayed only if both the difference
between average PLV was significant at p =0.001 in the indicated direction
and the more significant coherence was also significantly different from zero
for most or all of the subjects in the cluster by binomial test at p =0.001.The
off-task network in Experiment 2 passed only the first of these criteria.
within default and task-specific networks. Overall, several core
locations in the default network were identified. Some discrep-
ancy between the results in this study and previous findings is to
be expected, however, since, even in fMRI studies, differences in
default network locations are found across experiments,probably
reflectingdifferencesintaskcontextsornormalizationprocedures.
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Kirschner et al.Differential synchronization in default and task-specific networks
Overall, the convergence in spatial locations between fMRI and
high-densityEEGwithICAprovidesnewevidenceforthevalidity
of themethodologiesusedinthisstudy.Italsoprovidesadditional
evidence for the reality of the default network as a robust subset
of brain areas whose study is not contingent on a particular brain
imaging method.
The pattern of results obtained converges well with the char-
acterization of the activities of default network regions posited by
Buckner et al. (2007). As the default network shares several brain
regions with the networks responsible for memory retrieval and
simulationof futureevents,themajorfunctionof defaultnetwork
processing may be using past experiences to conduct simulations
relevant to anticipating and planning for future events so as to
maximize adaptive responses to them. Consistent with this spec-
ulation, in our study areas in the left and right middle temporal
regions displayed a high level of synchronization at all frequen-
ciesduringoff-taskepochs,suggestingthatwhileoff-task,subjects
were more likely to retrieve and manipulate information from
memory at the expense of that from the immediate environment
(more false alarms). Perhaps the most striking characterization
of the pattern of synchrony in off-task epochs is its left-to-right
patternoffunctionalconnectionsincontrasttothemoreanterior-
to-posterior pattern during on-task epochs (Figure 4). Rather
than emphasizing processing information from posterior sensory
regions,off-taskprocessingseemedtobecharacterizedinaddition
bymemorysystemsinlateraltemporalstructuresinteractingwith
frontal regions and with each other.
In contrast, synchronization results also revealed a distinct
on-task network that was clearest when subjects indicated that
they were paying attention to the task. During the on-task con-
dition for Experiment 1, greater inter-regional synchronization
was found in at least one frequency band in all clusters examined,
with the notable exception of the right middle temporal cortex.
The synchronization results were slightly weaker for Experiment
2,but indicated extensive synchronization throughout an on-task
network. Importantly, in both experiments synchronization was
robust between occipital, parietal, and frontal regions during on-
task periods. This set of areas has been linked repeatedly to the
control of goal-oriented attention.
The SART often involves potential response conflicts. Because
the number of target stimuli to be responded to is much higher
than the single no-response stimulus, responses often become
automated. In the unlikely event that a no-response stimulus does
appear, a potential conflict occurs between the subject’s habitual
behavior (press the button) and the correct response (withhold
thebuttonpress).Thistendencylikelyexplainsthegreateron-task
synchrony between the anterior cingulate and frontal and parietal
systems,given that the anterior cingulate has been robustly linked
to conflict detection in the brain (Kerns et al., 2004).
In both experiments there was overlap between areas within
the default and on-task networks. In Experiment 1, of the seven
common IC clusters found, only the occipital and right mid-
dle temporal clusters displayed significantly greater inter-regional
synchronization in only one condition. In Experiment 2, only
occipital cortex displayed such synchronization. The result that
occipital cortex was less synchronized with other areas during off-
task epochs was to be expected, given that sensory processing is
attenuated during default network activity (c.f.,Kam et al.,2011).
Likewise, memory processes associated with the right temporal
lobe were probably unnecessary in the context of the SART. More
importantly,however,itseemsthatacorenetworkofbrainregions
was at play during both on- and off-task epochs (c.f., Christoff
et al.,2009).A tentative explanation for this finding is that a set of
basicneuraloperationssustainedactivitiesinbothon-andoff-task
conditions.Whetherattentionwasfocusedinternallyorexternally,
some basic attention-related, executive, and memory processes
were likely active. Within this core network, which may under-
liebothdefaultandtask-specificnetworksandwhichmayinclude
high-capacity network “hubs” such as the ACC and OFC, basic
brain processes remain activated, although they might vary con-
siderablyfrommomenttomoment(Decoetal.,2011).Depending
on this core network’s functional integration with other areas and
networks, however, it attains the unique characteristics associated
with particular states of mind.
In conclusion, our results contribute to the evolving picture of
the cognitive brain as intrinsically organized into somewhat over-
lapping default and task-specific brain networks, and support the
idea that oscillatory synchronization is a potential mechanism for
functional coupling within these networks.
ACKNOWLEDGMENTS
ThisresearchwassupportedbyDiscoveryGrantsfromtheNatural
Sciences and Engineering Research Council (NSERC) of Canada
to Lawrence M.Ward and to Todd C. Handy,by a NSERC predoc-
toralfellowshiptoJuliaWingYanKam,andbyaUBCAURAgrant
to Lawrence M. Ward.
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Conflict of Interest Statement: The
authors declare that the research was
conducted in the absence of any com-
mercial or financial relationships that
could be construed as a potential con-
flict of interest.
Received: 02 March 2012; paper pend-
ing published: 29 March 2012; accepted:
30 April 2012; published online: 21 May
2012.
Citation: Kirschner A,Kam JWY,Handy
TC and Ward LM (2012) Differen-
tial synchronization in default and
task-specific networks of the human
brain. Front. Hum. Neurosci. 6:139. doi:
10.3389/fnhum.2012.00139
Copyright © 2012 Kirschner, Kam,
Handy and Ward. This is an open-access
article distributed under the terms of
the Creative Commons Attribution Non
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commercial use, distribution, and repro-
duction in other forums, provided the
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