Effect of Dopamine Transporter Genotype on Intrinsic Functional Connectivity Depends
on Cognitive State
Evan M. Gordon1, Melanie Stollstorff2, Joseph M. Devaney3, Stephanie Bean2and Chandan J. Vaidya2,4
1Interdisciplinary Program in Neuroscience, Georgetown University Medical Center, Washington, DC 20057, USA,2Department of
Psychology, Georgetown University, Washington, DC 20057, USA,3Department of Integrative Systems Biology, Research Center for
Genetic Medicine and4Children’s Research Institute, Children’s National Medical Center, Washington, DC 20010, USA
Address correspondence to Chandan J. Vaidya, 306 White Gravenor Hall, Georgetown University, Washington, DC 20057, USA.
Functional connectivity between brain regions can define large-scale
neural networks and provide information about relationships
between those networks. We examined how relationships within
and across intrinsic connectivity networks were 1) sensitive to
individual differences in dopaminergic function, 2) modulated by
cognitive state, and 3) associated with executive behavioral traits.
We found that regardless of cognitive state, connections between
frontal, parietal, and striatal nodes of Task-Positive networks (TPNs)
and Task-Negative networks (TNNs) showed higher functional
connectivity in 10/10 homozygotes of the dopamine transporter
gene, a polymorphism influencing synaptic dopamine, than in 9/10
heterozygotes. However, performance of a working memory task (a
state requiring dopamine release) modulated genotype differences
selectively, such that cross-network connectivity between TPNs and
TNNs was higher in 10/10 than 9/10 subjects during working memory
but not during rest. This increased cross-network connectivity was
associated with increased self-reported measures of impulsivity and
inattention traits. By linking a gene regulating synaptic dopamine to
a phenotype characterized by inefficient executive function, these
findings validate cross-network connectivity as an endophenotype of
Keywords: DAT1, fMRI, functional connectivity, resting state, working
The functional architecture of the human brain is composed of
distinct networks whose regions show correlated activity
across time (Bullmore and Sporns 2009). This network
organization exists regardless of cognitive state, as the same
networks that demonstrate correlated activity during task
performance (Esposito et al. 2006; Fransson 2006; Fransson and
Marrelec 2008) also show correlated activity at low frequencies
(<0.08 Hz) during the task-free ‘‘resting state’’ (Beckmann et al.
2005). Furthermore, the spatial composition of these networks
matches patterns of regions that are activated by various tasks
(Smith et al. 2009; Gordon, Stollstorff, et al. 2011). For example,
correlated network activity is seen both between bilateral
auditory cortex and between bilateral visual cortex (Beckmann
et al. 2005), regions also activated by auditory and visual tasks,
respectively. Furthermore, several networks include regions
that are activated by complex cognitive tasks (e.g., working
memory), while others include regions that are deactivated
during those same tasks; these have been termed ‘‘Task-Positive’’
and ‘‘Task-Negative’’ (or default mode) networks, respectively
(Fox et al. 2005). This inverse relationship in activation between
Task-Positive networks (TPNs) and Task-Negative networks
(TNNs) during task performance is also reflected in the temporal
relationship between these networks, as they are anticorrelated
during the resting state (Fox et al. 2005). The regional
composition and temporal relationships within and between
networks (termed functional connectivity) are posited to be
established by repeated functional co-activation over a lifetime
(Dosenbach et al. 2007).
The strength of functional network connectivity appears to
be a key determinant of cognitive abilities. First, the strength of
within-network functional connectivity (between nodes of
a single network) predicts cognitive performance. Stronger
connectivity between major TNN nodes (medial prefrontal
cortex and posterior cingulate cortex) is associated with
superior working memory performance (Hampson et al. 2006,
2010; Sambataro et al. 2010) as well as superior processing
speed, memory, and executive function (Andrews-Hanna et al.
2007). Furthermore, stronger connectivity between major TPN
nodes (left and right lateral prefrontal cortex) is associated
with superior processing speed and executive function
(Gordon, Lee, et al. 2011). Second, the degree of anticorrelation
between TPN and TNN impacts cognition, as individuals who
have more negative TPN-TNN correlations demonstrated
reduced trial-to-trial behavioral variability (Kelly et al. 2008)
and superior working memory performance (Hampson et al.
2010). This negative or reduced cross-network connectivity is
thought to reflect reduced interference across networks (Kelly
et al. 2008). Third, many neuropsychiatric disorders associated
with cognitive deficits demonstrate atypical connectivity
patterns. Reduced connectivity within the TNN has been
observed in attention deficit hyperactivity disorder (ADHD)
(Castellanos et al. 2008), autism spectrum disorders (Kennedy
et al. 2006), Schizophrenia (Bluhm et al. 2007), and Alzheimer’s
disease (Greicius et al. 2004). Elevated or less negative cross-
network connectivity between TPN and TNN has been
observed in ADHD (Castellanos et al. 2008) and Schizophrenia
(Whitfield-Gabrieli et al. 2009). Together, these findings
suggest that integrity of cognition depends upon optimal
within- and between-network functional connectivity.
What factors may determine the nature of within- and
between-network relationships? One likely candidate is the
neurotransmitter dopamine (DA), as exogenous manipulations
of DA affect functional connectivity. In healthy volunteers, DA
depletion reduced resting state connectivity between striatum
and the TPN and disrupted the relationship between connec-
tivity and speed of executive task performance (Nagano-Saito
et al. 2008). Administration of a DA agonist altered resting state
striatal connectivity such that it was increased with TPN and
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motor networks but reduced with TNN (Kelly et al. 2009).
While the behavioral significance of these findings cannot be
determined, as subjects were not performing a task, these
findings indicate that altered DA levels have widespread effects
on temporal relationships of networks. In ADHD, a disorder
characterized by DA dysfunction, administration of stimulant
medications that enhance DA signaling normalized connectiv-
ity between cortical and striatal/cerebellar regions (Rubia et al.
2009), as well as between TPN and TNN (Peterson et al. 2009),
such that it was similar to control children. Together, these
results suggest that DA function is important for regulating
cross-network functional relationships.
Similar to experimental manipulations of exogenous DA, it is
possible that endogenous interindividual variation in DA
function is associated with functional connectivity differences
between individuals. One endogenous source of DA variation is
a widely studied genetic polymorphism, the variable number of
tandem repeats (VNTR) in the 3#-untranslated region of the
DAT1 gene coding for the DA transporter (DAT), a protein that
regulates DA signaling by reuptaking DA following its release
(Madras et al. 2005). The DAT1 gene’s 2 most common alleles, 9
and 10 repeats, appear to influence the expression of DAT in
vitro (Fuke et al. 2001; Mill et al. 2002; VanNess et al. 2005),
with greater striatal DAT expression associated with 10-repeat
compared with 9-repeat alleles (though in vivo findings have
been mixed; Heinz et al. 2000; Jacobsen et al. 2000; Krause et al.
2006). Inheritance of 2 copies of the 10-repeat allele (10/10)
has been associated with ADHD (Yang et al. 2007), a disorder
defined by reduced executive function (Willcutt et al. 2005).
Executive functioning was reduced in healthy adults with a 10/
10 genotype, as they showed worse inhibitory performance
(Caldu ´ et al. 2007) and reduced benefits of working memory
training despite similar baseline performance (Brehmer et al.
2009) relative to 9/10 heterozygotes.
In addition to behavioral effects, differences in DAT expres-
sion have also been associated with differences in the functional
engagement of brain regions important for executive function.
First, higher striatal DAT concentration was associated with less
deactivation of TNN regions in healthy adults during visual
attention (Tomasi et al. 2009). Second, 10/10 homozygotes had
reduced activation compared with 9-repeat carriers in the Task-
Positive lateral prefrontal cortex during working memory
(Bertolino et al. 2006, 2009, 2008; Caldu ´ et al. 2007; Stollstorff
et al. 2010) and response inhibition (Congdon et al. 2009);
reduced activation in the Task-Positive striatum during response
inhibition (Congdon et al. 2009) and reward processing (Dreher
et al. 2009; Forbes et al. 2009); and reduced deactivation in Task-
Negative medial prefrontal cortex during working memory
(Brown et al. 2011). These findings indicate that putative
differences in DAT expression induce individual variations in
both behavior and related brain activation. Whether DAT1
influences network connectivity is unknown.
Here, we investigated functional connectivity within and
between TPN and TNN during the resting state and during
performance of an N-back working memory task in healthy 9/
10 and 10/10 carriers. We first identified intrinsic connectivity
networks in the resting state, confirmed that network nodes
overlapped with activation during the working memory state
and then examined effects of DAT1 and cognitive state on
functional connectivity between these nodes. We had several
goals. First, we examined whether functional connectivity
differs by DAT1. As the 10/10 genotype has been associated
with ADHD (Yang et al. 2007), we predicted that 10/10
homozygotes would demonstrate connectivity patterns similar
to that observed in ADHD—that is, reduced connectivity
within TNN and increased (i.e., less negative) connectivity
between TPN and TNN (Castellanos et al. 2008). Second, we
examined whether subjects’ cognitive state (resting vs.
working memory task) modulates connectivity. Working
memory demands alter functional connectivity relative to rest
(Fransson 2006), both within networks (increases within TPN)
and across networks (decreases between TPN and TNN). We
expected to replicate these findings. Third, we examined
whether DAT1 and cognitive state would interact to modulate
functional connectivity. As working memory demands increase
DA release (Aalto et al. 2005), differences in DA regulation
associated with DAT1 ought to be magnified during working
memory relative to rest, yielding a DAT1 3 cognitive state
interaction on functional connectivity. Finally, as DAT1 has
been shown to affect executive control, we examined whether
DAT1 3 cognitive state interactions in functional connectivity
were associated with individual differences in executive
Materials and Methods
Two hundred and ninety-six Georgetown University undergraduates
aged 18 to 22 years provided saliva samples that were genotyped for
DAT1. Eighty-one subjects were randomly invited from the pool of 10/
10 and 9/10 carriers to participate (9/10: n = 37; 10/10: n = 44).
Exclusion criteria included self-reports of 1) use of psychotropic
medication (e.g., stimulants, SSRIs); 2) overt neurological injury or
disease, seizure disorder, psychiatric diagnosis; 3) contraindications for
MRI—for example, presence of metal, pregnancy. Four subjects (2 per
genotype) were excluded from analysis due to technical problems
during scanning. The final sample included thirty-five 9/10 hetero-
zygotes (mean ± standard deviation [SD] age = 20.37 ± 0.96; 14 males)
and forty-two 10/10 homozygotes (mean ± SD age = 20.26 ± 1.14; 14
males). Groups did not differ in either age or gender (Ps > 0.4). All
subjects gave informed consent in accordance with guidelines of the
Georgetown University Institutional Review Board.
DNA was extracted from Oragene saliva kits (DNA Genotek Inc.,
Ottawa, Ontario, Canada). The 40 bp VNTR polymorphism in the 3#
UTR of DAT1 was genotyped by PCR as previously described (Daly et al.
1999) using the following primers; Forward: 5#-TGTGGTGTAGG-
CAAGG-3#. PCR was performed using the Accuprime Taq DNA
polymerase system (Invitrogen) with the following PCR program: 94 ?C
for 2 min, followed by 35 cycles of 94 ?C for 30 s, 60 ?C for 30 s, and 68 ?C
for 1 min. The PCR products were then run out on a 2% agarose gel
stained with ethidium bromide. A 100 bp DNA ladder was then used to
identify the various repeat alleles by size: 7-repeat (360 bp), 8-repeat
(400 bp), 9-repeat (440 bp), 10-repeat (480 bp), and 11-repeat (520
bp). Genotyping was successful for 286 of 296 subjects in the original
sample. Observed genotypic frequencies in the sample were: 10/10--
59.1%; 9/10--29.4%; 9/9--8.7%; other, 2.8%.
Subjects completed the Adult ADHD Self-Report Scale v1.0 (Kessler et al.
2005) and the Barratt Impulsiveness Scale version 11 (Patton et al. 1995).
Subjects were scanned during performance of an N-back task and
during rest. The N-back task lasted for 6:26 min and consisted of nine
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30 s N-back blocks (3 blocks each at 1-, 2-, and 3-back) alternating with
eight 15-s blocks of fixation. Each N-back block consisted of 9 serially
presented consonants appearing for 500 ms, with an intertrial interval
of 2500 ms. The N-back load condition (1-, 2-, or 3-back) varied
between task blocks, with condition order pseudorandomized using
a modified Latin Square. Each block was preceded by a 3000-ms screen
informing the subject of the N-back condition. Subjects were
instructed to press a hand-held button with their right hand when
the current letter matched the letter n trials ago (e.g., for the 2-back
condition, subjects see: R V N W N—button-press for N). Targets were
present on 19% of trials; each block contained between 1 and 3 targets
with target frequency balanced across conditions. No condition
contained sequences of stimuli that were targets in any other
condition. Stimuli were presented using E-Prime (Psychology Software
Tools Inc., Pittsburgh, PA). The resting scan was always conducted
immediately following the conclusion of the N-back task. For the
resting run, which lasted 5:04 min, subjects were told to relax with eyes
closed and to not think of anything in particular.
fMRI Data Acquisition
Imaging was performed on a Siemens Trio 3-T scanner (Erlangen,
Germany). A high-resolution T1-weighted structural scan (magnetiza-
tion prepared rapid gradient echo [MPRAGE]) was acquired with the
parameters: time repetition (TR)/time echo (TE) = 2300/2.94 ms, time
to inversion = 900 ms, 90? flip angle, 1 slab, 160 sagittal slices with a 1.0-
mm thickness, field of view (FOV) = 256 3 256 mm2, matrix = 256 3
256, resulting in an effective resolution of 1.03-mm isotropic voxels.
For the N-back run, 197 whole-brain images were acquired using
a gradient echo pulse sequence (34 slices, TR = 2000 ms, TE = 30 ms,
256 3 256 mm FOV, 90? flip angle, voxel dimensions 4 3 4 3 4.2 mm).
For the resting run, 152 whole-brain images were acquired using
a gradient echo pulse sequence (37 slices, TR = 2000 ms, TE = 30 ms,
192 3 192 mm FOV, 90? flip angle, voxel dimensions 3-mm isotropic).
The first 4 images of each functional run were discarded to allow for
Using SPM8 (Wellcome Department of Cognitive Neurology, London,
UK) implemented in MATLAB (Version 7.10 Mathworks, Inc., Sherborn,
MA), images were corrected for translational and rotational motion by
realigning to the first image of the session, for each run. All subjects
demonstrated less than 2.0 mm of translational motion in any one
direction (max translation = 1.25 mm). One subject demonstrated
a transient large rotational motion in the first 3 TRs of the N-back run;
these TRs were removed from further analyses. Subsequently, all
subjects demonstrated less than 2? of rotation around any one axis
(max rotation = 1.44?). Two-sample t-tests showed that genotype
groups did not differ in maximum motion in any of the 3 translational
or 3 rotational directions (all Ps > 0.15). Images were slice-time
corrected, normalized to an EPI template, and smoothed using
a Gaussian kernel with full-width at half-maximum of 8 mm. For
connectivity analyses, a band-pass filter was applied to the resting and
working memory data in order to restrict signal variation to frequencies
between 0.01 and 0.1 Hz, corresponding to the frequency range
established in the literature for fluctuations in resting-state data (Biswal
et al. 1995).
Identification of Brain Regions Activated and Deactivated during
the N-Back Task
First-level analysis was performed using a general linear model as
implemented in SPM8. For each subject, 3 temporal regressors
consisting of boxcar time series convolved with a hemodynamic
response function were specified: one representing the presence of the
Fixation cross, one representing the presence of the N-back task, and
one representing the effect of load (constructed by reproducing the
N-back regressor and parametrically varying the boxcar height
according to the load condition). For each subject, Task > Fixation
and Fixation > Task contrasts were specified to delineate regions
activated and deactivated during the N-back task after removing
contributions of the N-back load condition (this was done for
consistency with the connectivity analysis—see below). For group
averaging, one-sample t-tests were conducted for both contrasts at P <
0.05, corrected for multiple comparisons using family wise error.
Identification of Functional Networks during Rest
A group-level ICA was performed on the preprocessed filtered resting-
state images using the MELODIC toolbox (Beckmann and Smith 2004)
implemented within FSL (Centre for Functional Magnetic Resonance
Imaging of the Brain, University of Oxford, London, UK). The
preprocessed filtered resting data from all subjects were temporally
concatenated to create a single time course, and a probabilistic ICA was
performed on this time course using the MELODIC toolbox, allowing
the program to select the optimal number of components to generate.
Within each component, MELODIC generated Z-scores for each voxel
by generating a mixture model combining a ‘‘noise’’ Gaussian function
with 2 gamma functions modeling ‘‘active’’ voxels and estimating the
probability of a given voxel’s intensity fitting the gamma functions
rather than the background noise Gaussian function (Beckmann and
The ICA delineated 20 components in the form of 3D Z-score images.
Components in which the areas of maximal covariation were non-
neuronal (e.g., white matter, cerebrospinal fluid, brain edge covariation
resulting from head motion) were visually identified (see Kiviniemi
et al. 2009) and removed from further analysis. The remaining group
components were visually identified based on similarities to known
brain networks. These components were identified as TPNs or TNNs
based on similarity to networks identified by Fox et al. (2005) or as
‘‘Task-Neutral’’ based on a lack of similarity to those networks.
Region of Interest Creation
For each TPN and TNN, the largest clusters of covariation were
delineated, and the voxel of peak network connectivity (i.e., with peak
Z-score values in the ICA-generated images) within each cluster was
identified as a network ‘‘node’’ from which functional connectivity
analysis was conducted. To restrict analysis to nodes that were
modulated by the N-back task, peak voxels were discarded from
further analysis if they did not fall within regions activated or
deactivated in the group-level Task >Fixation contrasts. The remaining
nodes thus represent regions that were both activated by the working
memory task and maximally connected within intrinsic connectivity
networks. Spherical regions of interest (ROIs) with radius 6 mm were
created centered on each of these node voxels using MARSBAR (Brett
et al. 2003) and were labeled based on the general anatomical location
of the node voxels (as in Duvernoy 1999); these ROIs were used for all
further connectivity analyses.
Removal of Nuisance Signals
To minimize the effects of motion, load (within the N-back run), and
physiological noise (such as respiration and heart rate) that would be
common to all ROIs, timecourses approximating these signals were
regressed out of each voxel. Physiological noise regressors were
approximated by obtaining signal timecourses from white matter and
CSF segmentations of the MP-RAGE image (Van Dijk et al. 2010).
Motion regressors were obtained as the 6 realignment parameter
timecourses from the motion correction preprocessing step. For the
N-back run, load-effect regressors were obtained by convolving 3
boxcar timecourses (one for each load condition) with a canonical
hemodynamic response. The effect of load was regressed out because
the manipulation of load in the N-back paradigm was expected to drive
substantial and systematic activation differences in many brain regions,
including both task-positive regions (Braver et al. 1997; Callicott et al.
1999; Veltman et al. 2003) and task-negative regions (McKiernan et al.
2003). If the load structure of the task was not regressed out, these
large activation differences would artificially inflate functional connec-
tivities, such that even regions with no moment-to-moment correla-
tions would appear functionally connected because they were both
driven by load effects over the course of the task (for further discussion
of this point, see Jones et al. 2010).
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Gordon et al.
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The regression of nuisance signals was conducted separately for each
run, and the postregression residual voxel timecourses were used for all
Functional Connectivity Analysis
For the rest and N-back runs separately, residual voxel timecourses
were averaged within each ROI, and mean residual timecourses from all
ROIs were then correlated against each other in a pairwise fashion to
assess functional connectivity. In the N-back run, analysis was restricted
to task performance by excluding the 15 s in each fixation block plus 6
subsequent seconds (to allow for hemodynamic response stabilization).
The correlations were thus performed on 148 resting timepoints and
108 N-back timepoints. The resulting Pearson’s r values (from each ROI
pair, for each subject in each run) were converted to normally
distributed Z-scores using Fisher’s transformation in order to allow
further analysis of correlation strengths.
To assess effects of genotype and cognitive state on each functional
connection between ROI pairs, a 2 3 2 DAT1 (10/10, 9/10; between
subjects) 3 state (resting state, N-back state; within subjects) analysis of
variance (ANOVA) was conducted on the connectivity between each
ROI pair using the LinStats software package within Matlab (http://
each ANOVA model, an F-test was performed testing the overall fit of
the model against a null model (intercept only), and the resulting
model fit P-values were tested for significance at P < 0.05 after
Bonferroni correction for the number of ANOVA models (corrected
alpha = 0.000416). ANOVA models that significantly fit the data were
subsequently examined for interaction effects and main effects.
Correlation with Executive Traits
To examine whether connectivity affected by DAT1 and state were also
associated with executive control traits, we calculated the state-related
change in connectivity (N-back state—resting state) for each subject in
each of the functional connections showing a significant DAT1 3 state
interaction. For each behavioral measure (Inattention and Hyperactivity
from the ADHD Self-Report Scale and Impulsivity from the Barratt
Impulsiveness Scale) separately, we conducted a stepwise multiple
regression to examine whether the calculated changes in connectivity
predicted individual differences in the behavioral trait.
On the ADHD Self-Report Scale, Inattention scores were
marginally higher in 10/10 than 9/10 subjects (9/10: 13.78 ±
3.74; 10/10: 15.63 ± 5.01; t79= 1.84, P = 0.069), but scores did
not differ on the Hyperactive/Impulsive subscale (9/10: 11.78 ±
4.31; 10/10: 12.23 ± 5.20; P > 0.6). Barratt Impulsiveness Scale
ratings were also marginally higher in 10/10 than 9/10 subjects
(9/10: 55.89 ± 6.79; 10/10: 59.30 ± 9.88; t79= 1.77, P = 0.080).
N-back Task Performance
Mean reaction time (RT) for correct N-back target responses
and N-back percent accuracy (% hits – % false alarms) were
computed for each subject. Genotype groups did not differ
in mean RT (9/10: 582 ms ± 172 ms; 10/10: 534 ms ± 145 ms;
P = 0.18). Both groups performed near ceiling and did not differ
on either accuracy (9/10: 95.4% ± 5.5%, 10/10: 96.0% ± 6.6%;
P = 0.66) or on the number of subjects in each group with
perfect accuracy (9/10 = 18; 10/10 = 25, P = 0.46).
Identification of Brain Regions Activated and Deactivated
by the N-Back Task
Group averages of activated (N-back >Fixation) and deactivated
(Fixation > N-back) regions are shown in Figure 1. Activated
regions included bilateral dorsolateral and ventrolateral pre-
frontal cortex, anterior insula, lateral parietal cortex, medial
supplementary motor area, and globus pallidus. Deactivated
regions included ventromedial prefrontal cortex and perigenual
anterior cingulate, anterior medial prefrontal cortex, and medial
parietal cortex (including posterior cingulate and precuneus), as
well as bilateral fusiform gyrus, hippocampus/amygdala, poste-
rior insula, anterior middle and superior temporal gyri, and
lateral/superior occipital cortex extending into bilateral angular
gyrus. These activation/deactivation patterns did not vary by
DAT1 genotype (see Supplementary Material I).
Identification of Functional Networks
Twenty components were delineated by ICA of the resting-state
data. Of these, 7 were visually identified as TPNs or TNNs based
on similarity to past reports (Fox et al. 2005), including:
a cingulo-opercular salience network, a left-lateralized fronto-
parietal control (lFPC) network, a right-lateralized frontoparietal
control (rFPC) network, a parietal-based bilateral dorsal atten-
tion network, and a bilateral striatal network, which were
classified as TPN; as well as a posterior default mode network
(pDMN) and an anterior default mode network (aDMN), which
were classified as TNN (see Fig. 2). Additionally, 6 networks
were identified which did not well-match TPN or TNN; these
networks, many of which were similar in appearance to
previously delineated networks (Kiviniemi et al. 2009), were
labeled Task-Neutral (see Supplementary Fig. S1). These in-
cluded networks with high connectivity in auditory cortex, in
primary visual cortex, in sensorimotor cortex, in left-lateralized
language regions, in medial posterior and middle cingulate
cortex, and in bilateral superior temporal and inferior frontal
cortex. As these networks are not relevant to our predictions,
they were not included in further analysis. The remaining 7
components were identified as deriving from nonneuronal
sources (Kiviniemi et al. 2009) and were thus excluded from
analysis: CSF (1), white matter (1), and subject head motion (5).
Identification of TPN and TNN nodes (as described in
Materials and Methods) resulted in 1--4 node ROIs for each
network (Table 1 and Fig. 2, green circles).
Functional Connectivity within and between TPN and
To display the correlational structure of TPN and TNN, we
created connectivity matrices by averaging across subjects’ Z-
transformed correlation coefficients for each connection, within
each genotype group and condition. These matrices are
presented in Figure 3. To statistically test for effects of DAT1
and cognitive state, subjects’ Z-transformed correlation coef-
ficients in each pairwise connection were subjected to a DAT1
(9/10, 10/10) 3 cognitive state (Nback, Rest) mixed ANOVA.
Bonferroni correction was conducted at P <0.05 for the number
of ANOVA models. The following significant effects emerged.
Main Effects of Cognitive State
Significant main effects of cognitive state were found in 38
TPN to TPN connections and 13 TPN to TNN connections
(Fs1,77ranged from 16.07 to 71.65). No significant TNN to
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TNN connections reached significance. In all TPN to TPN
connections, as well as in connections between TNN and the
TPN salience network, connectivity was higher during the N-
back task than during rest. By contrast, in connections
between TNN and the TPN FPC/Dorsal Attention networks,
connectivity was higher during rest than during the N-back
task (see Fig. 4). Thus, as predicted, working memory
demands strengthened connectivity between most nodes of
TPN. However, contrary to predictions, the hypothesis that
working memory would reduce TPN to TNN connectivity was
only supported for the FPC and Dorsal Attention TPN and not
for the Salience TPN.
Main Effects of DAT1
Significant main effects of DAT1 were found in 4 TPN to TPN
connections and 6 TPN to TNN connections (Fs1,77ranged
from 4.49 to 11.27) but not in any TNN to TNN connections. In
all connections showing effects, the 10/10 group exhibited
greater connectivity than the 9/10 group (see Fig. 5). The TPN
to TPN connections included one connection between bilateral
frontal nodes of the FPC networks (L pdlPFC to R pdlPFC), two
connections between bilateral parietal nodes of the Dorsal
Attention network and a frontal node of the Salience network
(L adlPFC to R and L aIPL), and one connection between Dorsal
Attention and Striatal networks (L Striatum to R aIPL). The TPN
Figure 1. Group average of Task [ Fixation (in red) and Fixation [ Task (dark blue) contrasts (P \ 0.05, FWE-corrected).
Figure 2. Seven networks delineated in the resting-state data by the ICA procedure. Network maps are thresholded for visual purposes at Z 5 15.0. Label shadings indicate
visual categorization of each network: light gray shading—TPNs; dark gray shading—TNNs. ROIs used in connectivity analyses are overlaid on top in green.
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Gordon et al.
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to TNN connections (Fig. 5, within green line) included 2
between DMN and FPC networks (PCC and vmPFC vs. R pIPL),
2 between DMN networks and the Dorsal Attention network
(PCC and vmPFC vs. L aIPL), and 2 between the pDMN and
bilateral insular nodes of the Salience network (L AG vs. R aIns
and L aINS).
DAT1 3 Cognitive State Interaction
Significant interaction between DAT1 and cognitive state (Fig.
6A) was observed in 6 connections, all between TPN and TNN
nodes (Fs1,75ranged from 4.33 to 8.47). Four of these were
between TNN and the TPN rFPC network (vmPFC and PCC vs.
R pdlPFC; vmPFC and PCC vs. R pIPL). The other 2 interactions
were observed between the TNN aDMN and the TPN Dorsal
Attention network (vmPFC vs. L aIPL) and between the TNN
pDMN and the TPN Salience network (L AG vs. R aINS). No
significant interactions were observed in any TPN to TPN or
TNN to TNN connections.
Two-sample t-tests evaluating effects of DAT1 on connec-
tivity in each cognitive state (Fig. 6B) revealed that in each
connection, the interaction was due to significantly greater
connectivity in 10/10 than in 9/10 subjects during the N-back
task (ts75> 2.72, Ps < 0.008) but not during rest (Ps > 0.25).
Paired t-tests evaluating effects of cognitive state in each group
separately revealed that state effects on connectivity varied by
network. In the 5 connections between TNN and the TPN FPC/
Dorsal Attention nodes, 9/10 subjects demonstrated ‘‘reduced’’
connectivity during the N-back task compared with rest, but
10/10 connectivity was unchanged. By contrast, in the
connection between the TNN left angular gyrus node and the
TPN right anterior insula node, 10/10 subjects demonstrated
‘‘increased’’ connectivity during the N-back task compared with
rest, but 9/10 connectivity was unchanged.
In sum, performing the N-back task increased connectivity in
several TPN to TPN and TNN to Salience network connections
compared with rest but decreased connectivity in several
connections between TNN and FPC/Dorsal Attention networks
in all subjects. Furthermore, regardless of cognitive state,
individuals with the 10/10 genotype showed greater connectiv-
ity in various regions of the brain, both within TPN and between
TPN and TNN, than individuals with the 9/10 genotype. DAT1
differences depended upon cognitive state only in cross-network
TPN to TNN connections, such that connectivity was higher in
10/10 than 9/10 subjects during working memory but not
during rest. Overall, these results support 2 posed hypotheses:
1) that 10/10 subjects would demonstrate elevated Task-
Positive to Task-Negative connectivity and 2) that these
genotype differences in cross-network connectivity would be
enhanced during working memory performance.
Association between Functional Connectivity and
Stepwise multiple regressions
whether state-related connectivity changes within connections
showing DAT1 3 state interactions predicted individual differ-
ences in behavioral measures of executive traits. These regres-
sions revealed one TPN to TNN connection—between the L aIPL
node of the Dorsal Attention network and the vmPFC node of the
aDMN—which significantly predicted both Inattention from the
ADHD Self-Report Scale (F1,75= 5.45, model R = 0.26, P = 0.022,
Fig. 7A) and Impulsivity from the Barratt Impulsiveness Scale
(F1,75 = 5.84, model R = 0.27, P = 0.018, Fig. 7C), such that
increased connectivity was associated with increased Inattention
and Impulsivity. Hyperactivity scores were not predicted by
connectivity changes within any connection (Ps > 0.4).
To determine the extent to which these associations
differed by DAT1 genotype, we examined these relationships
for each genotype group separately. Correlations were statis-
tically significant in the 10/10 group for both Inattention (R =
0.32, P = 0.038) and Impulsivity (R = 0.33, P = 0.031) but not in
the 9/10 group (Ps > 0.65) (Fig. 7B,D).
Thus, the degree to which the N-back task induced increases
in cross-network connectivity between vmPFC and L aIPL was
associated with self-reported inattention and impulsivity, and
these associations were strongest in the 10/10 genotype group.
The primary novel finding from this study was that a poly-
morphism of the dopamine transporter gene, which regulates
synaptic dopamine, influenced cross-network functional con-
nectivity, which in turn was associated with behavioral traits
associated with executive dysfunction. Regardless of cognitive
state, connections between frontal, parietal, and striatal nodes
Locations of ROIs constructed around peaks of maximal covariation in the resting-state data, within TPNs and TNNs
Network typeNetworkROI center (Montreal Neurological Institute coordinates)ROI locationROI abbreviation
Task-Positive rFPC 46, 14, 44
34, 58, 4
46, ?54, 48
?50, 18, 28
42, ?38, 52
?38, ?46, 56
34, 46, 28
?30, 46, 28
46, 14, ?8
?42, 10, ?4
10, ?2, 4
?10, ?6, 8
42, ?74, 28
?38, ?82, 32
Right posterior dorsolateral PFC
Right ventrolateral PFC
Right posterior inferior parietal lobule
Left posterior dorsolateral PFC
Right anterior inferior parietal lobule
Left anterior inferior parietal lobule
Right anterior dorsolateral PFC
Left anterior dorsolateral PFC
Right anterior insula
Left anterior insula
Ventromedial prefrontal cortex
Right angular gyrus
Left angular gyrus
aBoth the pDMN and the aDMN networks contained a node in posterior cingulate cortex (PCC). Because these 2 nodes overlapped, the aDMN PCC node (centered at peak voxel [6, ?58, 20]) was
discarded from further analysis (as it was not in the anterior portion of the network). There was no other overlap between ROIs.
Cerebral Cortex September 2012, V 22 N 9 2187
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of TPNs and TNNs showed higher functional connectivity in
10/10 homozygotes than in 9/10 heterozygotes. However,
performance of a working memory task modulated genotype
differences selectively, such that connectivity between TPN
and TNN was higher in 10/10 than 9/10 subjects during
working memory but not during rest. Such elevated cross-
network connectivity has been thought to signify cross-
network interference, suggesting inefficient cognition. Indeed,
the magnitude of elevated cross-network connectivity was
positively correlated with self-reported inattention and impul-
sivity in the present study. This association was primarily driven
by the 10/10 homozygotes, who also had marginally higher
scores on those measures than the 9/10 heterozygotes, despite
both groups performing equally well, with high accuracy, on
the working memory task. Furthermore, we also replicated
a previously reported finding that engagement in working
memory strengthened connectivity within TPN nodes and
reduced some cross-network TPN to TNN connectivities. By
linking a gene regulating synaptic dopamine to a phenotype
characterized by inefficient executive function, our primary
findings validate cross-network connectivity as an endopheno-
type of executive dysfunction.
Two methodological considerations are important for inter-
preting these results. First, our N-back paradigm varied working
memory load by including 1-back, 2-back, and 3-back blocks. As
our primary hypotheses concerned effects of DAT1 and
cognitive state, these load effects were regressed out, thereby
ensuring that observed genotype differences were not driven by
differential response to load. Thus, the results represent effects
on connectivity during working memory performance, without
including effects due to variability associated with changing
demands between the 1-back, 2-back, and 3-back conditions.
Figure 3. Connectivity matrices indicating Z-transformed r values for each genotype group in each state. Hot colors indicate positive connectivity between ROIs; cool colors
indicate negative connectivity. Shadings of ROI labels indicate network categorization: light gray shading—TPNs; dark gray shading—TNNs. The green dotted line demarcates
cross-network (Task-Negative to Task-Positive) connections from within-network connections.
Working Memory, Connectivity, and DAT1
Gordon et al.
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Importantly, analyses conducted without regressing out load
resulted in a similar pattern of DAT1 effects on connectivity,
with DAT1 effects observed primarily in cross-network con-
nections (Supplementary Figs S3 and S4), suggesting that
reported DAT1 differences were not driven by the load
manipulation. (The main effect of state was affected by
load—see next section).
Second, we interpret all results from the present study in
relative terms (more or less negative connectivity in different
groups/states) rather than in absolute terms (negative or
positive connectivity), as interpreting the negative connectivity
sometimes observed in cross-network connections is ambigu-
ous. While negative cross-network correlations may reflect
competitive or mutually antagonistic network relationships (Fox
et al. 2005), recent work has shown that the emergence of
negative connectivity strongly depends on the processing steps
used, as regression of the global signal can introduce widespread
and (arguably) artifactual negative connectivity (Chang and
Glover 2009; Fox et al. 2009; Murphy et al. 2009). Therefore, we
used alternate processing steps, including regression of motion
parameters and signal from white matter/cerebrospinal fluid,
which have been proposed as a middle ground (Van Dijk et al.
2010). While these steps are known to reduce the appearance of
negative connectivity, it is not possible to determine definitively
whether remaining negative connectivity actually represents
antagonistic relationships (Chang and Glover 2009).
Effect of Cognitive State on Connectivity
The present examination of the effect of working memory on
functional connectivity partially replicates past findings. In
support of the hypothesis that working memory would increase
connectivity within TPN compared with rest, we found that
connectivity indeed increased within TPN. However, the
hypothesis that working memory would decrease connectivity
between TPN and TNN was only partially supported, as we
found connectivity decreases between TNN and the TPN
Frontoparietal Control/Dorsal Attention networks but connec-
tivity ‘‘increases’’ between TNN and the TPN Salience network.
Previous work has shown that, when compared with
a resting state, performance of lower level sensory/motor tasks
increased connectivity within the regions engaged by the task,
whether that task was auditory (e.g., listening to speech,
Arfanakis et al. 2000), visual (e.g., watching a flashing check-
erboard, Arfanakis et al. 2000; Hampson et al. 2004; Nir et al.
2006), or motor (e.g., finger tapping, Arfanakis et al. 2000; Jiang
et al. 2004). These findings suggest a general principle that
connectivity during task-evoked states specifically increases
within activated regions. Similarly, performance of a higher
level cognitive task such as the N-back working memory task
increased connectivity within the same Task-Positive regions
(Fransson 2006) that are nominally activated during working
memory (Owen et al. 2005). This finding was replicated in the
present study. However, Fransson (2006) also found that
working memory decreased connectivity between TPN and
TNN regions, which in the present study was found to be true
only for the Frontoparietal Control and Dorsal Attention TPN
but not for the Salience TPN. The most likely explanation for
this discrepancy is that we removed effects of load, which was
not done by Fransson (2006). Increasing working memory load
is known to parametrically activate TPN regions (Braver et al.
Figure 4. Matrix indicating ROI pairs in which significant main effects of state were observed on connectivity, after correction for multiple comparisons at the model level. Hot
colors indicate increased connectivity during the N-back task compared with Rest; cool colors indicate increased connectivity during Rest compared with the N-back task.
Shadings of ROI labels indicate network categorization: light gray shading—TPNs; dark gray shading—TNNs. The green dotted line demarcates cross-network (Task-Negative to
Task-Positive) connections from within-network connections.
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by guest on December 31, 2015
1997; Veltman et al. 2003) and deactivate TNN regions
(McKiernan et al. 2003). If not removed from consideration
in the functional connectivity analysis, these activation changes
could artificially drive the connectivity analysis, such that
decreased connectivity might reflect opposing effects of task
condition on activation on a timeframe the length of a condition
block rather than decreased temporal synchronization on
a second-to-second timeframe (Jones et al. 2010). This could
reduce the ability to detect positive association between TPN
and TNN. Indeed, examination of effects of cognitive state
without removal of load (Supplementary Material II) showed
results similar to Fransson (2006): connectivity within TPN
increased, while connectivity between TNN and the TPN
Frontoparietal Control networks decreased, and the connec-
tivity increases between TNN and the TPN Salience network
almost completely disappeared (Supplementary Fig. S2).
Effect of DAT1 Genotype on Connectivity
Our study is the first to demonstrate effects of the DAT1
genotype on functional connectivity either during a task or
during the resting state. As hypothesized, DAT1 genotype
affected connectivity, and those effects also depended upon
cognitive state. Three main findings emerged from this
examination of DAT1 effects. First, regardless of cognitive state,
subjects with the 10/10 genotype demonstrated greater
connectivity than 9/10 subjects in connections within TPN, as
well as in connections between TPN and TNN. Second, cognitive
state selectively modulated DAT1 differences in connections
between TPN and TNN, such that higher connectivity in 10/10
than 9/10 groups was observed during working memory
engagement but not during the resting state. Third, within one
cross-network, TPN to TNN connection between ventromedial
prefrontal cortex and lateral parietal cortex, the state-related
connectivity increases predicted self-reported inattention and
impulsivity in everyday behavior, especially in 10/10 subjects.
DAT1 differences in connectivity emerged regardless of
cognitive state in connections between various TPN (such as
between right and left Frontoparietal Control networks,
between Salience and Dorsal Attention networks, and between
Striatal and Dorsal Attention networks), as well as in
connections between TPN and TNN (such as between Default
Mode and right Frontoparietal Control, Dorsal Attention, and
Salience networks). In all of these connections, 10/10 subjects
had higher functional connectivity than 9/10 subjects across
resting and N-back scans. In order to gain insight into these
overall genotype differences, we examined patterns of con-
nectivity effects in both cognitive states to determine whether
they were true main effects or whether they suggested
interactive effects of DAT1 and state (Supplementary Material
III). Notably, the pattern of mean connectivities in the cross-
network TPN to TNN connections resembled interactive
effects similar to the significant DAT1 3 state interactions
discussed below. Thus, these effects may be interpreted as
weak interactions that may require a higher sample size to
reach significance. By contrast, TPN to TPN connections
appeared to exhibit true main effects, with higher connectivity
in 10/10 than 9/10 subjects during both the task and rest
states. Overall differences by DAT1 in connectivity in bilateral 5
frontal, frontal--parietal,and striatal--parietalconnections
Figure 5. Matrix indicating ROI pairs in which significant main effects of DAT were observed on connectivity, after correction for multiple comparisons at the model level. All
significant effects were found to be driven by greater connectivity in 10/10 subjects than in 9/10 subjects (as indicated by hot color shading). Shadings of ROI labels indicate
network categorization: light gray shading—TPNs; dark gray shading—TNNs. The green dotted line demarcates cross-network (Task-Negative to Task-Positive) connections from
Working Memory, Connectivity, and DAT1
Gordon et al.
by guest on December 31, 2015
within TPN suggest baseline differences in the communication
of information across these regions that is sensitive to
dopaminergic differences. It remains to be seen whether these
overall connectivity differences are replicated in future DAT1
Performance of a working memory task was found to
modulate the effect of DAT1 genotype on connectivity only
in cross-network connections between TPN and TNN. These
included connections between the Default Mode network and
the right Frontoparietal Control, Dorsal Attention, and Salience
networks. Such elevated connectivity between TPN and TNN
has been interpreted as indicating more interference or
reduced segregation of the networks (Kelly et al. 2008); thus,
in the present study, carriers of the 10/10 DAT1 genotype
demonstrated reduced segregation of TPN and TNN, particu-
larly during a cognitive state that is associated with increased
dopamine release. There is growing evidence suggesting that
such reduced TPN-TNN segregation is associated with in-
efficient cognition. Specifically, higher connectivity between
TPN and TNN has been linked to increased trial-to-trial
response variability (Kelly et al. 2008) and reduced working
memory performance (Hampson et al. 2010). Increased in-
terference from TNN has also been linked to task-irrelevant
thought (Buckner et al. 2008), attention lapses (Weissman et al.
2006), and mind wandering (Mason et al. 2007). Such behaviors
are known consequences of reduced executive function. These
behavioral effects (Castellanos et al. 2005; Willcutt et al. 2005;
Klein et al. 2006) as well as elevated cross-network TPN to TNN
connectivity (Castellanos et al. 2008) also characterize ADHD,
a disorder defined by symptoms of inattention and impulsivity.
Therefore, elevated cross-network connectivity is believed to
reflect increased interference between networks that may
induce task-irrelevant thoughts, resulting in inattention and
impulsivity, which in turn yields inefficient cognitive process-
ing (Sonuga-Barke and Castellanos 2007). Indeed, in the
present study, greater working memory-related increases in
cross-network TPN to TNN connectivity predicted increased
self-reported behaviors of inattention and impulsivity in
everyday life, and this relationship was stronger (and signifi-
cant) in 10/10 subjects. The 10/10 genotype, which has been
associated with ADHD (Yang et al. 2007), has also been
associated with inefficient executive function, even in pop-
ulations without a diagnosis of ADHD. Worse performance was
observed on tasks of inhibitory control in healthy 10/10 adults
(Caldu ´ et al. 2007) and children (Loo et al. 2003; Cornish et al.
2005) relative to their 9/10 peers. Furthermore, hyperactivity,
a defining behavior of childhood ADHD, was higher in 10/10
than 9/10 children (Mill et al. 2005). Similarly, in the present
study, inattentiveness and impulsivity, which are associated
with adult ADHD, tended to be higher in 10/10 than 9/10
subjects. Thus, our findings in healthy subjects demonstrate
that cross-network connectivity increases are both associated
with 10/10 homozygosity—a genotype linked to ADHD—and
predict ADHD-like behaviors in that group. These findings
validate the proposal of elevated cross-network connectivity as
an endophenotype of ADHD (Sonuga-Barke and Castellanos
2007; Castellanos et al. 2008) and further contribute to an
emerging theme in the literature that functional connectivity
serves as an endophenotype for a variety of gene-behavior
relationships (Esslinger et al. 2009; Meyer-Lindenberg 2009;
Woodward et al. 2009; Smit et al. 2010; Walter et al. 2011).
For connections between TPN and TNN, the nature of DAT1
effects on connectivity varied by network. In frontal--parietal
and parietal--parietal connections between TNN and the TPN
Figure 6. (A) Matrix indicating ROI pairs in which significant DAT1 3 state interaction effects were observed on connectivity, after correction for multiple comparisons at the
model level. Shadings of ROI labels indicate network categorization: light gray shading—TPNs; dark gray shading—TNNs. The green dotted line demarcates cross-network (Task-
Negative to Task-Positive) connections from within-network connections. (B) Connectivity values by DAT and state for each significant ROI pair in A. Error bars represent standard
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Frontoparietal Control/Dorsal Attention networks, the 9/10
group demonstrated reduced connectivity during working
memory compared with rest, but minimal change was observed
in the 10/10 group. By contrast, in a parietal--insular
connection between TNN and the TPN Salience network, the
10/10 group demonstrated elevated connectivity during
working memory compared with rest, but minimal change
was observed in the 9/10 group. Thus, it appears that during
working memory, 10/10 subjects demonstrate an increase in
cross-network interference of the Salience network, along with
a failure to reduce cross-network interference of the Fronto-
parietal Control and Dorsal Attention networks. In light of past
findings, the observed pattern of results suggests that differ-
ences in segregation of TPN during working memory are
associated with DAT1 genotype. The Dorsal Attention network,
which primarily includes regions along the intraparietal sulcus,
has been argued to control voluntary, top-down orienting of
attention, and selection of behavior (Corbetta and Shulman
2002) and specifically to be involved in rehearsal during
working memory (Corbetta et al. 2002), while the Frontopar-
ietal Control networks, which include lateral frontal and
parietal regions, are believed to initiate and adjust executive
control processes (Dosenbach et al. 2007, 2008; Seeley et al.
2007; Vincent et al. 2008). During working memory, these
functions, which are primarily relevant to immediate task goals,
are likely to be strongly segregated from the TNN, which
process task-irrelevant thought (Fox et al. 2005; Spreng et al.
2010). By contrast, the Salience network, which includes
anterior insula, anterior middle frontal gyrus, and dorsal
anterior cingulate, is believed to maintain longer term task
goals and process stimuli salient to those goals (Dosenbach
et al. 2007, 2008; Seeley et al. 2007). Based on the involvement
in this canonical network of the dorsal anterior cingulate,
which is known to perform monitoring processes (Carter and
Van Veen 2007), as well as the involvement of the anterior
insula, which is believed to mediate dynamic interactions
between brain networks (Menon and Uddin 2010), we speculate
that increased TNN to Salience connectivity during working
memory may reflect an increased need to detect TNN-based
cross-network interference and segregate the networks appro-
priately. In this context, the failure to reduce connectivity
between TNN and Frontoparietal Control/Dorsal Attention
networks in 10/10 subjects reflects a lack of segregation
between networks, and those subjects’ increased connectivity
Figure 7. Associations between behavioral traits of executive control and the change in vmPFC to L aIPL connectivity from the Rest scan to the N-back task. (A) Significant
correlation between Inattention, as measured by the ADHD Self-Report Rating Scale, and the change in connectivity. (B) The correlation between connectivity and Inattention was
nonsignificant in 9/10 subjects (top) but significant in 10/10 subjects (bottom). (C) Significant correlation between Impulsivity, as measured by the Barratt Impulsiveness Scale,
and the change in connectivity. (D) The correlation between connectivity and Impulsivity was nonsignificant in 9/10 subjects (top) but significant in 10/10 subjects (bottom).
Working Memory, Connectivity, and DAT1
Gordon et al.
by guest on December 31, 2015
between TNN and Salience networks reflects increased effort
needed to prevent this lack of segregation from interfering with
The mechanism by which DAT1 may influence differences in
connectivity is not clear. DAT1 genotype affects expression of
dopamine transporter (DAT), as higher expression is associated
with the 10-repeat allele (Fuke et al. 2001; Mill et al. 2002;
VanNess et al. 2005), likely leading to genotype differences in
DA signaling (Madras et al. 2005). In light of observations of
greater phasic DA release during working memory than during
rest (Aalto et al. 2005), DAT1-related differences may be
enhanced during working memory, as more DA would be
available in the synapse to be reuptaken at differential rates
(depending on genotype); this likely explains why the effects of
DAT1 on cross-network connections emerged most strongly
during working memory. DAT concentrations are highest in
striatum (Hall et al. 1999; Madras et al. 2005), moderate in
parietal cortex (Lewis et al. 2001), and relatively low in
prefrontal cortex (Karoum et al. 1994). Therefore, DAT1 effects
on brain function should be strong within striatum and reduced
within prefrontal and parietal cortex—yet the present study, and
past functional magnetic resonance imaging (fMRI) studies
(Bertolino et al. 2006, 2009; Caldu ´ et al. 2007; Stollstorff et al.
2010), found effects of DAT1 in prefrontal and parietal cortex.
This is consistent with positron emission tomography studies,
which have found that the degree of DAT expression (Tomasi
et al. 2009) and dopamine synthesis (Braskie et al. 2011) within
the striatum predicts cortical activation. We speculate that these
DAT1 effects on corticocortical connectivity might be driven by
the degree to which the striatum ‘‘gates’’ communication
between different networks; as suggested by Braskie et al.
(2011), this gating is likely enabled through the striatal--palladal--
thalamiccortico loops which innervate both prefrontal and
parietal cortex (Alexander et al. 1986; Schmahmann and Pandya
2006). Both neurocomputational models (Hazy et al. 2007) and
imaging evidence (van Schouwenburg et al. 2010) suggest that
the striatum plays a causal role in allowing or preventing
(‘‘gating’’) information transfer between cortical regions. Thus,
increased cross-network TPN to TNN interference observed in
10/10 DAT1 subjects could be due to lower striatal DA function
reducing the ability of the striatum to gate information transfer
between networks. While DAT1 effects were observed on
connectivity between striatum and the parietal Dorsal Attention
network, these effects were insensitive to state, suggesting that
this connection is unlikely to be a gating signal mediating the
DAT1 3 cognitive state interaction effects on cross-network
connectivity. We speculate that the gating effect may be causally
‘‘upstream’’ of network connectivity effects and so undetectable
using connectivity assessed via pairwise correlations. Future
investigations using more complex connectivity measures such
as dynamic causal modeling might profitably investigate this
While associations between cross-network connectivity and
trait-level measures of executive function were successfully
observed, this study was notably limited in its inability to
investigate relationships between DAT1, connectivity, and
behavioral performance on the N-back task, as overall N-back
accuracy was very close to ceiling (mean ± SD = 96% ± 5.8%),
and likely as a result, the genotype groups did not differ in
accuracy in any condition (Ps >0.15). This lack of difference is
not unusual: while deficits in 10/10 subjects have been
observed on the N-back task in children (Stollstorff et al.
2010), such deficits have not been found in adults (Bertolino
et al. 2006, 2009, 2008; Caldu ´ et al. 2007; Blanchard et al. 2011).
Associations with connectivity were observed with Barratt
Impulsiveness Scale scores but not with the Hyperactive/
Impulsive scores of the ADHD Self-report Rating Scale. This is
likely because the ADHD Scale is designed to assess the
presence of ADHD in adults with a limited number of
questions, and therefore may not be sensitive to individual
variation in hyperactivity/impulsivity within a nonclinical
population. By contrast, the Barratt Scale is designed to assess
impulsivity in nonclinical populations within several domains.
By this interpretation, the fact that connectivity did correlate
with Inattention on the same clinically oriented ADHD Scale
suggests a particularly strong relationship between connectiv-
ity and inattentiveness.
We regressed out the effect of the N-back load condition to
avoid contaminating the N-back connectivity analysis with
load-related coactivations. However, using a block design
prevented us from being able to model and remove effects of
coactivation in individual trials; nor could we model and
remove error trials (though errors were sparse in both
genotype groups). The extent to which the inclusion of these
trial-by-trial coactivations and errors may be altering our
functional connectivity results is unknown.
The present results, which show that cross-network connec-
tivity is sensitive to a genetic polymorphism important for
regulating synaptic dopamine, provide an endophenotype for
inefficient executive function, as reflected in higher impulsivity
and inattention. This finding has important implications for
cognitive disorders associated with dopamine dysregulation,
such as schizophrenia and, especially, ADHD. Future studies are
required to address the mechanisms by which dopamine
transporter expression may lead to elevated cross-network
connectivity between networks engaged and suppressed
during externally oriented cognitive engagement.
R24HD050846-06 and UL1RR031988 to Children’s National
Medical Center); Canadian Institutes for Health Research grant
Institutes ofHealth (R03MH86709
andto E.M.G.,to J.M.D.,
We would like to thank Lindsay Anderson for assistance with subject
recruitment and behavioral testing and Rusan Chen for statistical
guidance. Conflict of Interest : None declared.
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