BRAIN-2015-0374-ver9-Kadis_5P 76..83


Characterizing Information Flux Within the Distributed
Pediatric Expressive Language Network:

A Core Region Mapped Through fMRI-Constrained
MEG Effective Connectivity Analyses

Darren S. Kadis,
1–3

Andrew Dimitrijevic,
4–6

Claudio A. Toro-Serey,
1

Mary Lou Smith,
7,8

and Scott K. Holland
1,3,4,9

Abstract

Using noninvasive neuroimaging, researchers have shown that young children have bilateral and diffuse language
networks, which become increasingly left lateralized and focal with development. Connectivity within the distributed
pediatric language network has been minimally studied, and conventional neuroimaging approaches do not distinguish
task-related signal changes from those that are task essential. In this study, we propose a novel multimodal method to
map core language sites from patterns of information flux. We retrospectively analyze neuroimaging data collected in
two groups of children, ages 5–18 years, performing verb generation in functional magnetic resonance imaging (fMRI)
(n = 343) and magnetoencephalography (MEG) (n = 21). The fMRI data were conventionally analyzed and the group
activation map parcellated to define node locations. Neuronal activity at each node was estimated from MEG data
using a linearly constrained minimum variance beamformer, and effective connectivity within canonical frequency
bands was computed using the phase slope index metric. We observed significant ( p £ 0.05) effective connections
in all subjects. The number of suprathreshold connections was significantly and linearly correlated with participant’s
age (r = 0.50, n = 21, p £ 0.05), suggesting that core language sites emerge as part of the normal developmental trajec-
tory. Across frequencies, we observed significant effective connectivity among proximal left frontal nodes. Within the
low frequency bands, information flux was rostrally directed within a focal, left frontal region, approximating Broca’s
area. At higher frequencies, we observed increased connectivity involving bilateral perisylvian nodes. Frequency-
specific differences in patterns of information flux were resolved through fast (i.e., MEG) neuroimaging.

Key words: Broca’s area; causal network; children; functional magnetic resonance imaging; linearly constrained
minimum variance beamformer; magnetoencephalography; multimodal; parcellation; phase slope index

Introduction

Normal developmental changes in gross languagerepresentation have been well characterized using
noninvasive neuroimaging. With functional magnetic res-
onance imaging (fMRI), and more recently, magnetoen-

cephalography ( MEG), researchers have shown that
healthy young children have bilateral and diffuse lan-
guage networks, which become increasingly left later-
alized and focal with development (Brown et al., 2005;
Holland et al., 2001; Kadis et al., 2011; Ressel et al.,
2008).

1
Pediatric Neuroimaging Research Consortium, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio.

2
Division of Neurology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio.

3
Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio.

4
Communication Sciences Research Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio.

5
Division of Pediatric Otolaryngology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio.

6
Department of Surgery, College of Medicine, University of Cincinnati, Cincinnati, Ohio.

7
Department of Psychology, University of Toronto, Toronto, Ontario, Canada.

8
Department of Psychology, Hospital for Sick Children, Toronto, Ontario, Canada.

9
Division of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio.

ª Darren S. Kadis, et al., 2016; Published by Mary Ann Liebert, Inc. This Open Access article is distributed under the terms of the Creative
Commons Attribution Noncommercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits any noncommercial use,
distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

BRAIN CONNECTIVITY
Volume 6, Number 1, 2016
Mary Ann Liebert, Inc.
DOI: 10.1089/brain.2015.0374

76



An early distributed network is thought to confer a pediat-
ric advantage in the event of cerebral insult. Adults with left
perisylvian injury tend to develop severe and lasting apha-
sias, whereas young children with comparable insults experi-
ence minimal language disturbance and develop essentially
normal language through childhood and adolescence (Bal-
lantyne et al., 2008; Bates et al., 2001; Reilly et al., 1998;
Vargha-Khadem et al., 1985; see also, Jacola et al., 2006;
Tillema et al., 2008). Sparing of language is realized through
functional engagement of the extracanonical cortex. The po-
tential for both interhemispheric and intrahemispheric plas-
ticity (i.e., establishment of an atypical language network)
decreases with age, and relatively poor outcomes and failure
to form atypical networks are seen with insults occurring
after about age 5 or 6 years (Branch et al., 1964; Brazdil
et al., 2003; Helmstaedter et al., 1997; Kadis et al., 2007,
2009; Rasmussen and Milner, 1977; Saltzman-Benaiah et al.,
2003; Satz et al., 1988).

Normal developmental changes in language representation
provide a compelling context in which the potential for ef-
fective plasticity would be expected to decrease with age.
However, the timing for establishment of adult-typical repre-
sentation does not perfectly track with the clinical outcome
data. In fMRI, mature left lateralization and focalization of
the BOLD activity is protracted in the developmental period,
with adult-typical representation emerging in adolescence or
young adulthood (e.g., Szaflarski et al., 2006). Similarly, in
MEG language studies, dipoles fit to average waveforms and
signature oscillatory activity tend to left lateralize and focalize
in the teen years (Gummadavelli et al., 2013; Kadis et al., 2008,
2011; Ressel et al., 2008). The discord suggests that conven-
tional neuroimaging approaches to language mapping fail to
distinguish task-correlated hemodynamic or neuronal activity
from that which is task essential (i.e., necessary and sufficient
for task completion). The result is relatively broad ‘‘activa-
tion’’ maps for childhood function, which fail to delineate
the core regions necessary for task completion.

Recent developments in neuroimaging technology, and
particularly the high temporal resolution afforded by MEG,
permit assessment of neuronal population dynamics, includ-
ing characterization of transient activity and estimation of
causal networks. In this study, we seek to estimate informa-
tion flux (directional or effective connectivity) within the
distributed pediatric expressive language network. We report
on findings from two groups of children completing verb
generation tasks in fMRI and MEG. Large-scale fMRI data
are used to provide a spatial template of task-related activa-
tion. The group activation map is then segmented using a
200-unit random parcellation scheme (Craddock et al.,
2012), with parcel centroids used to define network node lo-
cations. The neuromagnetic activity at each node is estimated
from MEG recordings using a beamformer, and the time
courses are subjected to effective connectivity analyses
using the phase slope index (PSI) metric recently introduced
by Nolte and associates (2008). Unlike many other electro-
physiological metrics of effective connectivity, PSI remains
insensitive to mixing/volume conduction, providing robust
estimates of information flux between both proximal and dis-
tal node pairs. The pipeline capitalizes on the relative
strengths of fMRI and MEG (spatial and temporal resolution,
respectively) and is sensitive to the regional dynamics that is
known to underlie all higher cognitive processes.

Method

We analyze fMRI and MEG data that were collected for
two separate studies investigating developmental changes
in language representation. The participants, tasks, and
data acquisition have been documented previously (Holland
et al., 2007; Kadis et al., 2011; see also, Kadis et al., 2008;
Karunanayaka et al., 2010, 2011; Pang et al., 2011) and are
described only briefly below. The focus of the current
study is on integration of data from the two imaging modal-
ities and the novel application of effective connectivity ana-
lyses for mapping a core functional network.

Data acquisition

Functional magnetic resonance imaging

Participants. The fMRI cohort included 343 typically de-
veloping children (170 males), ages 5–18 years (M = 12.0,
SD = 3.8), studied at Cincinnati Children’s Hospital Medical
Center (Cincinnati, OH) between 2000 and 2006. This a
slightly larger cohort than initially described in Holland and
associates (2007), as recruitment continued beyond initial
analysis and publication. All participants in the study were na-
tive English speakers, free from language disability, learning
disability, and neurological disorder. Edinburgh Handedness
Inventory (EHI; Oldfield, 1971) scores indicate that 318
were right handed, 22 left handed, and 3 ambidextrous.
Parents provided consent, and children aged 8 years and
older assented to participate. The study was approved by the
Hospital’s Institutional Review Board.

fMRI verb generation. Children alternately listened to re-
cordings of concrete nouns generated by an adult female
speaker (task block) or speech-frequency warble tones (control
block). During the task block, children covertly generated ac-
tion words corresponding to each noun. The noun stimuli were
presented at a rate of once every 5 sec, in five 30-sec blocks.
During the control block, children were asked to simply listen
to each tone. Warble tones were presented every 5 sec, in six
30-sec blocks. Participants listened to a total of 30 nouns
and 36 warble tones during the 5.5 min fMRI recordings.

Functional and structural MRI scanning was performed on
a Bruker BioSpec 30/60 3T system (Bruker Medizintechnik,
Karlsruhe, Germany). The fMRI scans were acquired using a
T2*-weighted gradient echo EPI sequence (TE = 38 msec,
TR = 3000 msec, FOV = 256 · 256 mm, slice thickness = 5 mm).
At each of the 110 time points, 24 slices were acquired. The
initial 10 time points (corresponding to a control block) were
discarded to allow protons to reach T1 relaxation equilib-
rium. A T1-weighted whole brain structural image was
acquired using a 3D MDEFT scan (TE = 4.3 msec, TR =
15.7 msec, voxel size = 1.0 · 1.0 · 1.5 mm).

Magnetoencephalography

Participants. The MEG cohort included 21 typically de-
veloping children (13 males), ages 5–18 years (M = 12.2,
SD = 4.6), scanned at the Hospital for Sick Children (Tor-
onto, ON) between 2007 and 2008. This a subset of partici-
pants described in Kadis and associates (2011); only subjects
with structural MRIs of sufficient quality for automated seg-
mentation and single-shell head modeling in SPM were in-
cluded in this study (see forward modeling description

MAPPING EFFECTIVE CONNECTIVITY WITH FMRI-CONSTRAINED MEG 77



under ‘‘Extraction of nodal time courses from MEG data’’
section, below). Participants were native English speakers,
negative for history of neurological disorder, learning disabil-
ity, and language disturbance. Children showed a typical
distribution of hand preference, with 19 right handed and 2
ambidextrous, based on EHI scores. Parents provided consent,
and children and adolescents assented to participate. The study
was approved by the Hospital’s Research Ethics Board.

MEG verb generation. Participants alternately viewed
color pictures of everyday objects (task condition) or scrambled
color images with a superimposed central fixation cross (con-
trol condition). Task stimuli were presented for 500 msec,
and control stimuli were presented for 1500–2500 msec (jit-
tered). The order of presentation was random within each con-
dition. For the task condition, children were required to
covertly generate a verb for each object viewed, as quickly as
possible. For the control condition, children were asked to
look at the central fixation cross. Overt assessment following
MEG acquisition showed that all participants were able to per-
form the task, and children aged 5–10 had a mean accuracy of
85% across trials.

MEG scanning was performed on a 151-channel whole-
head CTF system ( MEG International Services Ltd., Coqui-
tlam, BC, Canada). Subjects were tested in the supine
position, with the projection screen positioned above the
lower face and neck region, for comfortable viewing. Stimuli
were presented within 2–3� of the center of the visual field to
promote foveal projection. Head localization coils were
placed at nasion and preauricular points to monitor move-
ment. Data were acquired at 625 Hz, with an online 100-
Hz low-pass filter. In all cases, head displacement was less
than 5 mm from the beginning to end of acquisition. To facil-
itate MEG-MRI coregistration (required for accurate forward
modeling in source analyses), multimodal radiographic
markers were placed at the nasion and preauricular positions
before acquiring structural images. MRI was conducted on a
GE Signa Advantage 1.5T scanner (GE Medical, Milwaukee,
WI). A whole brain T1-weighted image was acquired using a
3D-SPGR scan (TE = 4.2 msec, TR = 9 msec, voxel size =
0.94 · 0.94 · 1.50 mm).

Analyses

fMRI analyses. Image preprocessing and first-level ana-
lyses were carried out using the Cincinnati Children’s Hos-
pital Image Processing Software (CCHIPS; Schmithorst
and Dardzinski, 2000). EPI data were corrected for geomet-
ric distortion due to B0 field inhomogeneity (Schmithorst
et al., 2001) and coregistered to minimize motion effects
(Thevenaz et al., 1998). An experienced rater identified land-
marks on each subject’s structural image, which were used to
linearly transform structural and functional data to a standard
space (Talairach and Tournoux, 1988). Functional data were
cross correlated with a reference waveform reflecting the
time course of task and control blocks, and a t-statistic
(verbs minus tones) was computed on a voxel-wise basis.

Second-level analyses were carried out in SPM8 (www
.fil.ion.ucl.ac.uk/spm/) running in MATLAB R2014a (The
MathWorks, Inc., Natick, MA). Individual contrast images
were submitted to group analyses in a single sample t test. We
identified brain regions showing significantly increased acti-

vation for verb generation, using a family-wise error correc-
tion of p < 0.01 and clustering threshold of k = 8 voxels.

Parcellation of the activation map. As an initial step to in-
terrogating the distributed expressive language network for
patterns of effective connectivity, we parcellated the group
map and established a set of cortical node locations. The
group activation map was normalized to the MNI 152 subject
average T1-weighted image template and binarized such that
only suprathreshold voxels were retained. We cropped the
resulting activation map using a gray matter mask (to isolate
cortex; mask supplied with SPM8), resampled the image to
4.0 mm isotropic, and multiplied the activation by a random,
200-unit parcellation scheme (provided as a 4.0 mm isotropic
image) recently introduced by Craddock and associates
(2012). Centroids of cortical parcels with >10 active voxels
serve as network nodes. The 200-unit parcellation scheme
provides a good trade-off between anatomical interpretabil-
ity and functional homogeneity (Craddock et al., 2012),
and the node density approximates the specificity of our sub-
sequent beamformer analyses (i.e., limited by the under de-
terminacy of the inverse; source analyses described below).

Extraction of nodal time courses from MEG data. Pre-
processing was carried out using FieldTrip (Oostenveld
et al., 2011) in MATLAB R2014a. Each participant’s
MEG recording was imported and epoched from �450 to
950 msec, relative to the onset of target picture presentation.
Trials were baseline corrected (using the �450 to 0 msec
window as a baseline), and power line noise was attenuated
using a 60-Hz discrete Fourier transform filter. Scanner jump
artifacts were automatically identified and trials containing
artifacts rejected from each dataset.

Forward modeling was conducted in SPM8. Each sub-
ject’s T1-weighted MRI was imported, segmented, and
then warped to the MNI 152 template to establish a normal-
ization deformation field. The source model was constructed
from a standard 8196-vertex cortical mesh that was warped
to the individual subject’s cortex using the inverse of the
deformation. Fiducial locations were manually identified
on each subject’s structural MRI, facilitating coregistration
with MEG data. Finally, realistic single-shell head models
were constructed from the segmented images and lead fields
computed using default conductivity parameters.

The neuronal activity at each network node was com-
puted using FieldTrip’s linearly constrained minimum vari-
ance beamformer (with 0.1% regularization, for spheres of
5 mm radius), implemented in SPM8. Trial data were then
cropped to 350–750 msec from picture onset, to isolate neu-
romagnetic changes reflecting the generative period of the
expressive language task (Kadis et al., 2011) in subsequent
connectivity analyses.

Effective connectivity analyses. We estimated effective
connectivity between each pair of network nodes using the
PSI metric, recently introduced by Nolte and associates
(2008; see also, Nolte and Müller, 2010; Haufe et al.,
2013). PSI is computed from the complex coherency func-
tion for a pair of signals, and directionality is determined
from phase differences in signals over a specified frequency
range. When signal i drives (is driven by) signal j, the mean
phase differences between i and j will increase (decrease)

78 KADIS ET AL.



with frequency and PSI will be positive (negative). For con-
venience, PSI is often reported as a normalized value,
obtained through division by an estimate of standard devia-
tion (here, jackknife resampling was used). The normalized
value, PSInorm, can then be interpreted as any z-value
would, facilitating statistical thresholding and group-wise
quantitation.

In the current study, we computed PSInorm for each node
pair within canonical bands (delta, *2–4 Hz, low frequency
limited by time window; theta, 4–8 Hz; alpha, 8–12 Hz; beta,
13–30 Hz; and gamma, 31–70 Hz) for each subject. To iden-
tify significant connections, data are thresholded at PSInorm ‡
1.96. The value of –1.96 corresponds to the critical value in a
two-tailed normal deviate (z) test conducted at alpha = 0.05;
because the PSI metrics for any given node pair are an addi-
tive inverse (i.e., the PSI for signal i driving j is the nega-
tive of PSI for j driving i), only one direction of flux need
be considered.

Results

Network definition

Group analyses of the fMRI data revealed consistent acti-
vation across individuals in bilateral frontal (including in-
sular cortex) and posterior temporal regions, and the left
anterior cingulate and right occipital cortices. As expected,
the activation encompasses canonical language regions of
the left hemisphere (i.e., Broca’s and Wernicke’s areas)
and, to a lesser extent, their right hemisphere homologues.
Occipital activation has been previously noted in this task
and likely relates to visualization associated with the stimu-
lus and response (i.e., visualization of the everyday object
and/or associated activities). Group activation is shown in
Figure 1.

The parcellation procedure yielded 27 cortical network
nodes. Of these, 20 were located in the left hemisphere,
with the largest cluster focused around the inferior, middle,
and superior frontal cortices. The application of a 200-unit
random parcellation scheme and the resulting set of network
nodes are depicted in Figure 2. Node coordinates and their
anatomical labels are presented in Supplementary Table S1
(Supplementary Data are available online at www.liebertpub
.com/brain).

Density of effective connections

Subjects had between 7 and 44 (M = 25.9, SD = 9.7) supra-
threshold effective connections among network nodes,
summed across all frequency bands. The number of surviv-
ing connections did not differ among frequency bands
( p > 0.05).

Age-related changes in density of effective connec-
tions. The total number of suprathreshold connections was
positively and linearly correlated with subject age (r = 0.50,
n = 21, p £ 0.05), suggesting that older children and adoles-
cents meaningfully engage a greater portion of the distrib-
uted network during verb generation than younger children
do (Figure 3). Within any particular frequency band, the
number of surviving effective connections failed to correlate
with age ( p > 0.05, uncorrected; Supplementary Fig. S1).

Patterns of effective connectivity across frequency spectra

To characterize frequency-related differences in patterns
of information flux during verb generation in children, we
plotted suprathreshold effective connections for each fre-
quency bin across all subjects on a template brain (Fig. 4).
We observed distinct patterns of information flux across
the spectra. In general, low-frequency information flux was
focused within the left frontal region; at higher frequencies,
effective connectivity was increasingly observed between
distal nodes.

Within the delta band, information flux was predominantly
rostrally directed and focused among proximal nodes of the
left inferior and middle frontal gyri. In the theta band, the spa-
tial distribution of suprathreshold connections was somewhat
broader.

We observed a high density of effective connections be-
tween putative Wernicke’s and Broca’s areas in the theta,
beta, and gamma bands, which were predominantly rostrally
directed.

In the alpha, beta, and gamma bands, we observed signif-
icant interhemispheric effective connectivity. In alpha and
beta, bidirectional information transfer was observed be-
tween left and right frontal nodes and between left and
right posterior temporal nodes. In gamma, the right posterior
temporal region emerged as an important driver of both left

FIG. 1. Group fMRI acti-
vation for verb generation.
Colored areas depict signifi-
cant fMRI activation in 343
children performing verb
generation in fMRI, projected
on a template brain ( p < 0.01,
FWE corrected; minimum
clustering threshold of k = 8
voxels of 1.0 · 1.0 · 1.5 mm
dimension). fMRI, functional
magnetic resonance imaging.

MAPPING EFFECTIVE CONNECTIVITY WITH FMRI-CONSTRAINED MEG 79



posterior temporal (Wernicke’s) and left frontal (Broca’s) re-
gions, although this region appears to be driven by the left
posterior region at lower frequencies.

Across frequency bins, we observed significant informa-
tion flux between Wernicke’s and Broca’s areas. However,
only minimal effective connectivity was observed between

right posterior temporal and right frontal regions, indicating
that the right hemisphere language homologues do not sim-
ply mirror the function/connectivity of the canonical lan-
guage regions in typically developing children.

Discussion

In this study, we integrated fMRI and MEG data to
characterize information flux within the distributed pediatric
expressive language network. The approach extends the con-
ventional analysis of task-related changes in BOLD signal or
oscillatory power in fMRI and MEG; in this study, we map
function through patterns of significant effective connectiv-
ity, estimated from fast recordings of neuronal population
activity. The framework can be easily generalized to charac-
terize cortical transmission of information for other cogni-
tive domains. The parcellation and MEG analysis approach
could also be used to interrogate any arbitrarily defined
brain network for patterns of effective connectivity (e.g., as-
sess information flux within a theoretically derived network
defined by anatomical boundaries), in the absence of avail-
able fMRI data.

Using thresholded normalized PSI to identify regions of
significant information flux, we clearly resolved a core left
frontal subnetwork consisting of 11 nodes in the inferior,
middle, and superior frontal gyri and insula, which support
verb generation (expressive language) in our pediatric sam-
ple. Although some degree of suprathreshold connectivity
was observed between all nodal regions, only the left frontal
regions showed significant information flux at all frequency
bands studied. This preferential localization is consistent

FIG. 2. Parcellation and
definition of the functional
network. (a) Depiction of
parcellation scheme in axial
slices; each colored patch
represents a distinct parcel;
the centroids of active parcels
are used to define network
node locations. (b) The
resulting 27 nodes are
depicted as colored spheres
on a template brain; left
hemisphere nodes (n = 20) are
shown in blue and right
hemisphere nodes (n = 7) are
shown in red.

FIG. 3. Age-related increase in number of significant ef-
fective connections. For each subject, the number of signifi-
cant effective connections was summed across all frequency
bands. Plot shows the significant age-related increase in the
total number of suprathreshold (PSInorm ‡ 1.96) effective
connections observed within the distributed network
(r = 0.50, n = 21, p £ 0.5). PSI, phase slope index.

80 KADIS ET AL.



with the earliest accounts of expressive language disturbance
following left inferior frontal injury (e.g., Broca, 1861; see
also, Dronkers, 1996) and subsequent clinically informed
models of gross language representation championed by
Geschwind (1970, 1972). Localization is also consistent
with previous neuroimaging studies of expressive language
in both children (e.g., Karunanayaka et al., 2010, 2011)
and adults (e.g., McCarthy et al., 1993; Szaflarski et al.,
2006). In general, the left inferior and middle frontal cortex
has been implicated in semantic processing and word retriev-
al; the insular cortex is involved in speech or articulatory
planning (Price, 2012).

Uniquely, we observed changing patterns of effective
connectivity among the 27 network nodes, depending on
the frequency analyzed. In the delta band, rostrally directed
effective connections were focused within the left frontal
nodes. Progressing through theta, alpha, and beta bands,
we see a transition to predominantly caudally directed con-
nections among the same nodes. Findings highlight the var-
iable nature of information flow that can occur among a fixed
set of nodes, at different frequencies. Similarly, we observed
changes in the spatial distribution of information flux across
the spectra. In the delta and theta bands, we observe effective
connectivity primarily between proximal left inferior frontal
nodes; in the alpha and beta bands, we see increasing in-
volvement of midline regions, and at beta and gamma, we
observe bilateral posterior temporal nodes emerging as im-
portant drivers in the network. These spectrally resolved
changes in patterns of information flux cannot be resolved
in conventional neuroimaging. We expect that increased ac-
cess to MEG for studying language will necessitate updates
to the current network models, to account for the distinct pat-
terns of connectivity that occur at various timescales.

Across frequency bands, the total number of suprathres-
hold effective connections was found to increase with age
in our pediatric sample. The finding initially appears to run
counter to the established literature showing relatively bilat-
eral and extensive representation in the youngest children
(e.g., Szaflarski et al., 2006; Kadis et al., 2011). However,
we propose that patterns of significant information flux re-
veal the core components of a network—those regions that
are engaged in a consistent coordinated manner and are nec-
essary for function—reflecting the neural strategy used for
task completion. We do not expect developmental changes
in effective connectivity to track with developmental
changes in task-related BOLD signal or oscillatory power
distribution. Collectively, the data suggest that young chil-
dren possess a broadly distributed expressive language net-
work, as evidenced from conventional neuroimaging, with
relatively few core (vulnerable) nodes. Relative plasticity
for language representation in young children is possible be-
cause the broader network lacks established core nodes. With
development, the extent of the overall network decreases,
while patterns of information flux become entrained. A left
lateralized, focal, core expressive language network emerges
as part of the normal developmental trajectory, and plasticity
is diminished. In the future, we hope to assess the stability of
these connectivity patterns through adulthood.

In this study, fMRI data served as a ‘‘hard constraint’’
on subsequent MEG effective connectivity analyses. By ap-
plying an activation map that was established through
large-scale investigation, we unambiguously focus on brain
regions known to undergo task-related hemodynamic
changes during verb generation in children. Parcellation of
the fMRI activation map serves to reduce the number of
comparisons needed to characterize information flux. We

FIG. 4. Effective connec-
tivity within canonical fre-
quency bands. Significant
node-to-node effective con-
nections are represented as
green arrows, which indicate
the direction of information
flow. The thickness of each
arrow reflects the relative
frequency of suprathreshold
effective connectivity across
subjects.

MAPPING EFFECTIVE CONNECTIVITY WITH FMRI-CONSTRAINED MEG 81



preferred a multimodal approach over unimodal MEG, since
methods used to identify language cortex in fMRI have been
relatively well established and the extent of cortex involved
is easily interpreted from an activation map. In contrast, the
choice of source localization approach in MEG will impact
ensuing functional maps and most attempts to solve the in-
verse solution produce localizations that are difficult to in-
terpret in terms of extent (e.g., equivalent current dipole
analysis, beamforming).

The tasks used in each imaging modality were highly
similar, but not identical. In fMRI, auditory noun stimuli
were used; in MEG, pictures of everyday objects were pre-
ferred. According to current language models, the auditory
or visual stimuli preferentially engage their respective
sensory cortices; however, both versions of the task will ul-
timately require engagement of a broader stimulus modality-
independent language network necessary for semantic
processing, word retrieval, and articulatory planning related
to verb generation (Price, 2012). Since the broad activation
map was defined using an auditory fMRI paradigm, and ef-
fective connectivity was assessed from MEG data collected
using a visual paradigm, the resulting effective connectivity
map should reflect only the elements of the expressive lan-
guage network that are modality nonspecific. In this way,
we isolate the language-specific subprocesses involved in
verb generation.

In the current analyses, we study information flux occur-
ring between node pairs, without imparting any theoretical
or anatomical restrictions on where network edges may be
established. The data-driven approach is objective, but
could potentially yield connectivity maps that do not reflect
the underlying physiological structure of the language net-
work. With the current approach, it is indeed possible to
observe significant information flux between two regions
that are not directly connected by any known fiber pathway.
Furthermore, we limited our analyses to regions shown ac-
tive in large-scale fMRI analyses; it is possible that nodes re-
siding outside the defined network could also be involved in
expressive language processing, particularly in individual
subjects. These connections could not be resolved in the cur-
rent analyses.

It is important to note that the PSI metric requires specifi-
cation of both time and frequency ranges for computation. In
this study, we cropped our data to a time window known to
be relevant for verb generation in children (Kadis et al.,
2011) and restricted analyses to within canonical frequency
bands. Had we assessed phase slope across the broadband
data, we would have failed to capture the changing direction
of information flux among left frontal nodes and missed the
changing patterns of connectivity that occur across the spec-
tra. The choice of temporal and spectral window is not triv-
ial; with additional experience, we anticipate proposing
guidelines so researchers can make informed decisions
about the parameters used in effective connectivity analyses
with PSI and related metrics.

We are currently investigating other methods to integrate
fMRI and MEG data, to map core language sites in healthy
children and those undergoing investigations for epilepsy
surgery. With continued development of multimodal neuro-
imaging and connectivity analyses, we will gain access to a
better understanding of normal language development, plas-
ticity, and representation in the brain.

Acknowledgments

The fMRI data were obtained through support from the
National Institute of Child Health and Human Development
(R01-HD38578; S.K.H.), and the MEG data were obtained
through support from the University of Toronto (D.S.K./
M.L.S.). The authors wish to acknowledge Akila Rajagopal
and Thomas Maloney for their technical assistance with
the study.

Author Disclosure Statement

No competing financial interests exist.

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Address correspondence to:
Darren S. Kadis

PNRC and Division of Neurology
Cincinnati Children’s Hospital Medical Center

3333 Burnet Avenue
Cincinnati, OH 45229

E-mail: darren.kadis@cchmc.org

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