Brain Imaging and Behavior (2015) 9:854–867
DOI 10.1007/s11682-014-9347-3
ORIGINAL RESEARCH
Errors on interrupter tasks presented during spatial and verbal
working memory performance are linearly linked to large-scale
functional network connectivity in high temporal resolution
resting state fMRI
Matthew Evan Magnuson & Garth John Thompson &
Hillary Schwarb & Wen-Ju Pan & Andy McKinley &
Eric H. Schumacher & Shella Dawn Keilholz
Published online: 7 January 2015
# Springer Science+Business Media New York 2015
Abstract The brain is organized into networks composed of
spatially separated anatomical regions exhibiting coherent
functional activity over time. Two of these networks (the
default mode network, DMN, and the task positive network,
TPN) have been implicated in the performance of a number of
cognitive tasks. To directly examine the stable relationship
between network connectivity and behavioral performance,
high temporal resolution functional magnetic resonance imaging (fMRI) data were collected during the resting state, and
behavioral data were collected from 15 subjects on different
days, exploring verbal working memory, spatial working
memory, and fluid intelligence. Sustained attention performance was also evaluated in a task interleaved between resting state scans. Functional connectivity within and between
the DMN and TPN was related to performance on these tasks.
Decreased TPN resting state connectivity was found to significantly correlate with fewer errors on an interrupter task
presented during a spatial working memory paradigm and
decreased DMN/TPN anti-correlation was significantly correlated with fewer errors on an interrupter task presented during
a verbal working memory paradigm. A trend for increased
DMN resting state connectivity to correlate to measures of
M. E. Magnuson : G. J. Thompson : W.<J. Pan : S. D. Keilholz (*)
Georgia Institute of Technology and Biomedical Engineering, Emory
University, 1760 Haygood Dr, HSRB W230, Atlanta, GA, USA
30322
e-mail: [email protected]
H. Schwarb : E. H. Schumacher
Georgia Institute of Technology School of Psychology, 654 Cherry
Street, Atlanta, GA, USA 30313
A. McKinley
Air Force Research Laboratory Wright-Patterson Air Force Base,
Atlanta, OH, USA 45433
fluid intelligence was also observed. These results provide
additional evidence of the relationship between resting state
networks and behavioral performance, and show that such
results can be observed with high temporal resolution fMRI.
Because cognitive scores and functional connectivity were
collected on nonconsecutive days, these results highlight the
stability of functional connectivity/cognitive performance
coupling.
Keywords Cognitive processing . High temporal resolution
fMRI . Resting state . Default mode network . Task positive
network . Working memory . Interrupter task
Abbreviations
PVT
SST
OST
RAPM
DMN
TPN
Functional network
psychomotor vigilance task
symmetry span task
operation span task
Raven’s advanced progressive matrices
default mode network
task positive network
functionally connected network
Introduction
Understanding how the functional organization of brain activity manifests as complex cognitive processes and behaviors
has been a goal of neuroscientists for decades. The advent of
functional magnetic resonance imaging (fMRI) allows for
non-invasive, whole brain imaging of changes in blood oxygenation coupled to neural activity, opening an unexplored
world of possibilities for probing the functional organization
of the brain (Ogawa et al. 1990). Early fMRI studies were
Brain Imaging and Behavior (2015) 9:854–867
primarily focused on the relationship between specific tasks
and the resultant brain function correlated with that task
(Logothetis 2008). Insights obtained from these fMRI studies
advanced the understanding of brain organization and functional topology tremendously; however, higher order cognitive processes such as working memory, fluid intelligence,
and combinational sensory and motor integration seemingly
involve complex neural interactions between spatially distinct
brain regions (Bressler and Menon 2010; Fuster 2000) that
cannot be easily evaluated using the traditional stimulus–
response approach.
The development of “resting state” fMRI techniques that
map functional networks has provided new insight into these
large scale network interactions. Biswal and colleagues
showed that low frequency (<0.1 Hz) BOLD fluctuations in
the motor cortex of one hemisphere were highly correlated
with the contralateral cortical region; they proposed that this
synchronous activity represented functional connectivity of
neurophysiological origin (Biswal et al. 1995). Similar functional networks were soon discovered including the default
mode network (DMN), task positive network (TPN), and
many others (Cordes et al. 2000; Fox et al. 2006; Hampson
et al. 2002; Raichle et al. 2001). Several recent studies have
examined the relationship between coordinated patterns of
neural activity and corresponding coordinated BOLD networks. In a wide range of experimental variables including
animal models and humans, patients and subjects, and anesthesia or no anesthesia, strong links elucidating the underlying
neural correlates of functional networks have begun to emerge
(He et al. 2008; Nir et al. 2008; Pan et al. 2011; Shmuel and
Leopold 2008). Similarly functional networks have also been
evaluated and reproduced using electroencephalography
(Hlinka et al. 2010), magnetoencephalography (Brookes
et al. 2011), and voltage sensitive dyes (Carlson and Coulter
2008; Xu et al. 2010). Functional networks have also been
evaluated in disease states and are significantly altered in cases
of Alzheimer’s disease, Parkinson’s disease, schizophrenia,
epilepsy, and many other conditions (Garrity et al. 2007;
Grady et al. 2001; Greicius et al. 2007; Liu et al. 2007; Lowe
et al. 2002; Villalobos et al. 2005). Extrapolating, it has been
posited that variability in functional network connectivity in
the healthy population may be associated with cognitive integrity and ability. Studies have now begun to focus on the
relationship between specific aspects of functional network
activity and cognitive performance (Greicius and Menon
2004; Sala-Llonch et al. 2012).
The relationship between functional network activity and
cognitive performance has been evaluated with a variety of
tasks that, in general, show a relationship between functional
connectivity and affective behavior. Activity within functional
networks, just prior to the presentation of a stimulus (seconds
before), accurately predicts subject performance on attention
based and working memory based tasks (Boly et al. 2007;
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Eichele et al. 2008; Hesselmann et al. 2008; Sadaghiani et al.
2009, 2010). Weissmann et al. suggest that a decrease of
activity within the DMN facilitates increased selective attention (Weissman et al. 2006), while Hampson et al. determined
that increased connectivity between the anterior medial prefrontal cortex and the posterior cingulate cortex (two nodes of
the DMN) was correlated with increased working memory
performance (Hampson et al. 2006). Seeley et al. found decreased connectivity between the executive control network
(anatomical brain regions include the dorsolateral prefrontal
cortex and the superior parietal cortex) activity and the bilateral intraparietal sulcus predicted increased spatial memory
performance (Seeley et al. 2007). Kelly et al. evaluated the
relationship between the anti-correlation of DMN and TPN
activity and cognitive variability; they discovered greater anticorrelation was associated with reduced attentional variability
using the Flanker task (Kelly et al. 2008). Van den Heuvel
et al. discovered a correlation between normalized functional
network path length and IQ. This finding was localized to
frontal and parietal brain regions; the data suggests that the
efficiency of an individual’s functional brain architecture
bounds ones cognitive capacity (van den Heuvel et al.
2009). Another study by Stevens et al. indicated a tight coupling between network modularity and variability in working
memory capacity, indicating more local modular communication and less communication with distant modules was associated with increased cognitive capacity (Stevens et al. 2012).
These studies suggest that working memory and attention
are highly related to functional network activity. Working
memory is the ability to actively maintain and manipulate
information in memory (Baddeley and Logie 1999) and is
critically important to successful performance on most of the
tasks that humans carry out each day. There exist considerable
differences in working memory capacity among individuals
and these differences are predictive of performance on a wide
variety of both basic cognitive tasks and higher-order reasoning tasks (Engle 2007; Kane et al. 2007). Given the vast
individual differences in working memory capacity, investigating functional network integrity as it relates to working
memory performance seems a fruitful arena for understanding
the relationship between functional networks and cognition.
The present work builds upon a study from our group that
investigated a single task, the psychomotor vigilance task
(PVT), which involves sustaining attention over time, using
high temporal resolution fMRI (Thompson et al. 2013). One
of the benefits of high temporal resolution fMRI is that data
can be examined on short time scales (seconds) as well as the
longer time scales (minutes) typically used for resting state
studies. The study focused on the correlation and difference in
magnitude between activity in the DMN and TPN in short
windows prior to each task performance and found that the
functional connectivity metrics predicted performance both on
an intra-individual and inter-individual basis. In every metric
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where significance was found, the direction of the relationship
was identical; a greater difference, or more anti-correlation
between the TPN and DMN timecourses, was linked to better
performance on the PVT. The findings were consistent with
the “default mode interference hypothesis” (Sonuga-Barke
and Castellanos 2007) and led us to hypothesize that functional connectivity measures have two components related to
performance, a “steady state” value averaged over the length
of the imaging session that ties to the overall performance of
the subject and transient fluctuations that are linked to performance on individual trials. Part of the motivation for the
current study was to show that both components can be
assessed from a single data set. The individuals who participated in our study (Thompson et al. 2013) also had, on an
earlier day, performed three additional cognitive tasks involving verbal working memory, spatial working memory and
fluid intelligence. Based on previous findings, we hypothesized that an exploratory evaluation of the relationship between working memory and corresponding errors on a
distractor task, attention, or fluid intelligence tasks correlated
with resting state functional connectivity in the DMN, TPN,
and network anti-correlation should reveal a relationship that
persisted over time. To test this hypothesis, we examined the
linear relationship between cognitive performance and static
measures of large-scale network connectivity from resting
state fMRI scans. Specifically, for each subject average correlation for voxels within the DMN and TPN and the correlation
between the two networks’ averaged functional time courses
were calculated. We then compared the resulting resting state
metrics to each subject’s scores on the four cognitive tasks (the
PVT sustained attention task collected concurrently with resting state imaging sessions and the three working memory and
fluid intelligence tasks recorded outside of the scanner from a
prior day). We found that intra- and inter-network connectivity
was significantly correlated with distractor task errors for two
of the cognitive tasks and that the relationship between intranetwork connectivity and fluid intelligence approached
significance.
Materials and methods
All studies were performed in compliance with the World
Medical Association Code of Ethics as well as the Georgia
Institute of Technology Institutional Review Board. Data was
collected at the Center for Advanced Brain Imaging (CABI), a
joint venture between Georgia Institute of Technology and
Georgia State University.
Seventeen subjects were in a previous study using the same
data analyzed here (Thompson et al. 2013). In this work, one
subject was not included because both resting state scans
contained excess head motion (>1 voxel), and another subject
was excluded due to insufficient recording of cognitive
Brain Imaging and Behavior (2015) 9:854–867
performance data. The remaining data came from 15 healthy
human subjects (8 men and 7 women; ages 18–26) who were
recruited to perform cognitive behavioral tasks and a series of
fMRI scans. Data were collected over a two to 10 day period.
On the first day, informed consent was obtained from all
subjects and three cognitive performance tasks were conducted. Structural and functional MRI data were obtained on the
second day of the study, which occurred 1–9 days following
the initial study date (average 2.8 days).
Cognitive performance testing
Four cognitive tasks were performed with the intent of
obtaining a breadth of information regarding an individual’s
brain function: PVT, operation span task (OST), symmetry
span task (SST), and Raven’s advanced progressive matrices
task (RAPM). The PVT task with concurrent fMRI recording
was interleaved with resting state fMRI performed on the
second study day, while the other three tasks were performed
on the first day. Cognitive data was omitted from the analysis
if it was>2.5 standard deviations from the task mean (one
from PVT and one from OST score were removed, no data
was omitted from the findings that passed FWER correction).
Psychomotor vigilance task
Subjects were asked to fixate on a black dot comprising 0.280
of their central visual angle and were told that when it changed
from black to navy they should respond as quickly as possible
with a button press using their right index finger. The task was
performed over an 8 min fMRI scan in which the dot changed
colors 3 to 6 times. Stimulation onset time and total number of
changes was randomized for each subject. Short-time scale
results, using the same data as we use here, focusing on the
relationship between PVT performance and transient network
activity can be found in the Thompson et al. report (Thompson
et al. 2013).
This task assesses sustained attention in subjects and is
described in detail by Dinges et al. (Dinges and Powell
1985). The output is a series of response times for each
stimulus presentation. A response time average was calculated
for each subject. There were not enough instances where the
individual committed errors on the PVT (“miss” instances,
only 18 total for all 17 subjects) to analyze in this report.
Operation span task
In the OST, subjects were presented with a string of letters,
one at a time. Between the presentations of each letter subjects
performed an interrupter verbal task involving solving a simple math problem (division or multiplication paired with addition or subtraction for each problem). Three to seven letters
were presented and the participant was then asked to recall the
Brain Imaging and Behavior (2015) 9:854–867
letters in order. This procedure was repeated until 75 letters
and math problems have been presented.
This task measures verbal working memory performance.
The output from this cognitive task is a score out of 75 which
indicates how many letters the subject was able to recall, and a
total number of errors, also a score out of 75, which is a count
of how many math problems the subject answered incorrectly.
This task is described in further detail by Unsworth et al., and
has been related to verbal working memory capacity
(Unsworth et al. 2005).
Symmetry span task
This task is similar to the OST; however, the SST evaluates
spatial working memory (Unsworth et al. 2005). A 4x4 matrix
of two dimensional outlined squares was presented on each
trial. One block was red, and the rest were white. Between
each matrix presentation the subject performed an interrupter
symmetry judgment task in which he/she was asked to make a
judgment on the symmetry of image (vertical symmetry of an
8x8 black and white grid). Two to five repetitions of the 4x4
matrix were presented, and then the subject was asked to recall
the locations of the red blocks in order of presentation. Symmetry memory score is the total number of right answers for
the order of red block presentation, and symmetry errors is the
total number of incorrect answers on the interrupter spatial
symmetry evaluation (both of these scores are out of 48).
Raven’s advanced progressive matrices
In this task, subjects were presented a 3x3 matrix containing
various shapes on each trial. The cell in the bottom right
corner was always empty, and the subject was asked to choose
the best shape to fill in the cell based on a pattern present in the
other rows and columns. Eight possible choices were given for
the participant to choose from. Subjects were given 10 min to
perform 18 problems, which increase in difficulty as the
experiment proceeds (Jaeggi et al. 2008; Shipstead et al.
2012). RAPM is hypothesized to measure fluid intelligence
(Raven 2000). The RAPM score represents the number of
problems answered correctly.
fMRI data acquisition
All analysis was performed on the imaging data from the
study by Thompson et al. (Thompson et al. 2013); spatiotemporal dynamic analysis of this data set can also be found in the
Majeed et al. paper (Majeed et al. 2011). As the purpose of
both studies was to investigate comparatively rapid changes in
functional connectivity, a faster repetition time was used than
is typical for studies of functional connectivity (300 ms vs. 2–
4 s). This rapid imaging limited the volume of the brain that
could be imaged, so only four 5 mm slices were acquired. We
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were not able to cover all nodes of the DMN and TPN but
slices were positioned to include as many critical regions as
possible; manual positioning of slices was done to contain the
precuneus, medial prefrontal cortex, inferior parietal cortex,
dorsolateral prefrontal cortex, premotor cortex, and angular
gyri due to their prominence in the DMN and TPN. Many
more DMN and TPN relevant nodes were also included in the
slices and are discussed further in the results section. Fox et al.
and Fransson et al. and subject specific anatomical markers
were used as a reference during slice positioning (Fox et al.
2005; Fransson 2005).
Imaging was conducted using a Siemens Trio 3 T whole
body scanner equipped with a Siemens 12-channel transmit/
receive RF head coil. All subjects first underwent a structural
T1 weighted MPrage 3D anatomical scan (5 min and 14 s)
with 1 mm isotropic resolution. Before functional imaging
began, subjects were told that there would be both task blocks
and rest blocks, and they would be informed prior to each scan
which of the two it would be. Six functional scans were then
obtained in one of two acquisition orders: Rest – PVT – PVT –
Rest – PVT – PVT or PVT – PVT – Rest – PVT – PVT – Rest.
During the PVT scan subjects performed the PVT task (described in previous section) throughout the duration of the
scan. For the resting state scans it was made clear that there
was no task to perform; the subjects were told to lay still,
relax, and to look towards the fixation dot.
Resting state scans and PVT scans were acquired using an
identical Gradient Echo-Planar Imaging (EPI) sequence: Repetition Time (TR) – 300 ms, Echo Time (TE) – 30 ms, 1600
repetitions, four horizontal slices, 3.4 mm resolution in the
frequency and phase encoding direction, 5 mm resolution in
the slice direction, and 8 min of total scan time.
fMRI data preprocessing
Preliminary preprocessing was performed in Statistical Parametric Mapping 8 (SPM8) (London, UK). T1 weighted images were segmented into white matter, gray matter, and
cerebrospinal fluid. The left and right precuneus ROIs were
obtained from the automated anatomical library (AAL)
(Tzourio-Mazoyer et al. 2002), combined into a single
precuneus ROI and were reversed normalized (Chang and
Glover 2010) from the MNI brain to the individual space of
each subject’s anatomical image. Reverse normalization was
conducted because of the difficulties and inaccuracies of
normalizing subject data to MNI space with only a few slices
as opposed to the whole brain (the registration done for Fig. 2
was for visualization purposes only and was not used for
analysis).
Using Analysis of Functional Images (AFNI), EPI images
were corrected for slice timing and were registered to the
mean EPI image to measure and correct for motion artifacts.
The total maximum movement in each direction (X, Y, and Z)
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was recorded and runs were excluded if the total range of
motion in any direction was greater than the size of a voxel in
the phase or frequency encoding direction (3.4 mm). Following the same procedures as in Thompson et al. 2013, the
average global signal, white matter signal and motion parameters were used as nuisance regressors. One hundred TRs
(30 s) were removed from the beginning of each scan to
account for potential scanner or subject momentary instability.
Resting state EPI data were spatially blurred using a 3×3×1
voxel Gaussian filter (sigma, 2×2×1) and band pass filtered
between 0.01 and 0.08 Hz (Biswal et al. 1995) (the finite
impulse response filter was 150 TRs in length). fMRI data
were quadratically de-trended and motion parameters were
regressed from the EPI data (Majeed et al. 2011). All voxels
were normalized to unit variance by subtracting the mean
value from each time course and dividing by one standard
deviation. Finally, data were reset to zero mean, and the final
result was a normalized and motion corrected BOLD signal.
Both resting state scans were used in 10 of the subjects;
connectivity scores for these subjects were averaged between
scans to create one value for each network metric. One scan
was removed from five of the subjects due to excess head
motion (>1 voxel).
Generation of DMN and TPN correlation maps, time courses,
and inter-network correlation
See Fig. 1 for a visual representation of the network generation paradigm. For each resting state data set, the ROI comprising the precuneus (as defined by the reversed normalized
MNI brain) was used as the seed for creating network masks.
The whole precuneus (contained in our slices) was chosen as
the seed region, because it is a primary component of the
default mode network (Fox et al. 2005, 2006; Fransson
2005), centrally located in the brain, and present in all subject’s resting state scans. A mean time course was obtained for
the activity of voxels within the precuneus (using
preprocessed data). The mean time course obtained from the
precuneus was then correlated, using Pearson Correlation (r),
with the normalized time course from all other voxels in the
fMRI data. A spatial map of r values for each voxel is created
(Fig. 1.).
The 10 % of voxels from the imaged slices (1639 voxels=
0.1×64×64×4) most highly correlated with the precuneus time
course and located within the gray matter were selected as the
DMN; similarly the least correlated 10 % of voxels (or most
anti-correlated voxels) were chosen as the TPN (Fox et al.
2005). The resultant DMN and TPN masks look qualitatively
similar to those defined by Fransson (Fransson 2005) and Fox
et al. (Fox et al. 2005) (both authors used a seed based
approach, with a seed of the precuneus, for generation of
functional networks).
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A correlation coefficient matrix was calculated
representing correlation between each voxel pair within a
network mask (DMN and TPN). Resulting correlation values
were normalized using the Fisher transform (z=0.5 ln [(1+r)/
(1-r)]). An average network correlation was calculated from
the upper triangular half of the voxel by voxel Fisher transformed correlation matrix. This method is similar to methods
used for finding global coherence in EEG analysis (Jelles et al.
2008); it is also very similar to the methods used by Kelley
et al. who compared resting state fMRI network activity to
cognitive processing variations (Kelly et al. 2008). Mean
values were calculated for both the DMN and TPN networks.
Similar to the method used to create the initial seed time
course, a time course was created based on the averaged time
course from all voxels within the previously defined DMN
and TPN masks. A second Pearson correlation was then
performed between the average time course from each network, and a single value was obtained defining the average
correlation between the DMN and TPN over the duration of
the resting state scan. The resulting correlation value was then
Fisher transformed.
Boot-strapping: connectivity vs. cognitive score analysis
The output from the analysis described in the previous section
was three resting state network metrics from each subject:
average connectivity within the DMN, average connectivity
within the TPN, and correlation between the two networks.
These final metrics are each a static measure of network
activity occurring over the duration of a resting state scan
(8 min).
Connectivity metrics were plotted and compared to all six
cognitive testing scores (PVT reaction time, RAPM score,
OST memory, OST math errors, SST memory, SST symmetry
errors), resulting in eighteen total comparisons (significant
results shown in Fig. 4). A least-square linear fit was performed for each comparison and a test statistic (t-score) was
obtained from each regression by dividing the slope by the
standard error.
Boot-strapping was implemented to create null distributions for each comparison by performing 100,000 random
permutations of each connectivity vs. cognition comparison.
To create each random permutation, the connectivity vector
was left as-is, while the cognitive metric vector was randomly
deranged, i.e. rearranged randomly such that no element was
in its original position (ensuring no subject-matched
connectivity/cognition results; creating null data). Linear regression was performed on each set of shuffled data to obtain a
t-score for each randomized set. The 100,000 t-scores obtained from each randomized data comparison were used as a null
distribution; such a null distribution was developed for each
pair of connectivity and behavioral metrics. The t-scores obtained from the actual data (non-randomized) were each
Brain Imaging and Behavior (2015) 9:854–867
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Fig. 1 Visual map of the data analysis process. This method is based off
previously published results and is effective when limited spatial
coverage may preclude use of more standard methods such as ICA
(Thompson et al. 2013). The precuneus is chosen as the ROI for
analysis. A time course is obtained from the voxels and correlated with
the time courses from all other voxels in the brain. The most correlated
voxels create the DMN (top 10 % of voxels in gray matter correlated with
precuneus); while the least correlated voxels create the TPN (bottom 10 %
of image voxels in gray matter correlated with precuneus). For intranetwork evaluation, average correlation within each of the networks is
obtained (Metric 1, DMN; Metric 2, TPN). For inter-network evaluation,
average time courses are generated for each network and correlated to
determine the relationship between activity in the two networks (Metric 3;
DMN/TPN)
compared to the null distribution, using its cumulative distribution function to get a p-value for the statistical significance
of the actual t-score vs. the boot-strapped t-distributions.
To correct for multiple comparisons, sequential goodness
of fit method was used to control for family-wise error rate
(Carvajal-Rodriguez et al. 2009). SGoF performs a binomial
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test on the expected distribution from the p-values in the
family based on a null hypothesis. SGoF controls for type I
errors, minimizing the chance that false-positives are included
as significant results. SGoF controls family-wise error rate in
the weak sense; it looks for a significantly large group of low p
values rather than increasingly small p values. Therefore, it
does not lose statistical power as the number of p values tested
increases. This method was also used by Thompson et al.
2013. For this study, the 18 hypotheses were divided into
two families, connectivity versus connectivity (3 hypotheses)
and connectivity versus cognition (15 hypotheses) and SGoF
was run twice, once on each family.
Results
Cognitive performance
Performance was measured in several areas of cognitive ability using four task paradigms: PVT measuring sustained attention (Dinges and Powell 1985); OST evaluating verbal
working memory (Unsworth et al. 2005); SST, a measure of
spatial working memory (Unsworth et al. 2005); and RAPM,
a measure of fluid intelligence (Raven 2000). PVT performance was measured interleaved with resting state brain scans
as reported in (Thompson et al. 2013); while the other three
tasks were recorded on an earlier day.
Average PVT reaction time in response to a slight color
change in the visual fixation point was 1184 ms±451 ms
(average±standard deviation); faster responses indicate better
performance. OST memory scores averaged 58.5±12 correct
response out of 75 (73 %) and 4.8±2.5 math errors out of 75
(6.4 %). Average SST memory scores were 25.7±7.5 correct
responses out of 48 (53 %) with 1.7±1.2 errors out of 48
(3.5 %). Finally the average RAPM score was 13.2±2.4 out of
18 (73 %). Higher scores with fewer errors indicate better
performance.
Brain Imaging and Behavior (2015) 9:854–867
section 2.5 for a detailed description of the boot-strapping
paradigm).
Group incidence maps representing the DMN and TPN for
all subjects are displayed in Fig. 2. Anatomical regions associated with high levels of connectivity in the DMN are shown
in Fig. 2 (middle) and include the posterior cingulate cortex/
precuneus, angular gyri, medial prefrontal cortex, middle
frontal gyrus, and the superior frontal gyrus. The highest
correlation values within the TPN mask are associated with
the dorsal prefrontal cortex, premotor cortex, inferior parietal
lobules, and the medial frontal gyrus (Fig. 2 - bottom). Generated DMN and TPN networks are very similar to those seen
by Fox et al. (Fox et al. 2005), Fransson et al. (Fransson 2005),
and Uddin et al. (Uddin et al. 2009), corroborating accurate
formation of the networks.
Average Z scores for correlation within the DMN mask
was 0.22±0.10 (average±standard deviation) (Fig. 1 - metric
1), for voxels comprising the TPN the average value was 0.14
±0.06 (Fig. 1 - metric 2), and correlation between the DMN
and TPN was 0.10±0.54 (Fig. 1 - metric 3). Inter-subject
variability is greater than intra-subject variability for the
DMN, TPN, and DMN/TPN metrics (Inter-subject standard
deviation: DMN=0.10, TPN=0.06, DMN/TPN=0.56 Intrasubject standard deviation: DMN=0.06, TPN=0.03; DMN/
TPN=0.46).
Correlating resulting functional metrics
Figure 3 contains plots of the relationships between connectivity values obtained for DMN, TPN, and the correlation
between the networks. None of the three connectivity metrics
indicate a linear statistical relationship with any of the other
metrics. DMN vs. TPN show the most similarity between
functional network measures; however, the relationship between the metrics is not significant. DMN’s correlation with
the DMN/TPN correlation is slightly less than that of DMN
and TPN. There was essentially no relationship between TPN
and DMN/TPN metrics.
fMRI
Functional network connectivity vs. cognitive performance
Maps of the DMN and TPN created from the resting state data
using the precuneous as a seed region are shown in Fig. 1.
This approach is identical to that used in the report motivating
this study (Thompson et al. 2013) and similar to early studies
identifying the DMN/TPN relationship by Fox et al. and
Fransson et al. (Fox et al. 2005; Fransson 2005). Our analysis
method produces network maps highly similar to those in the
original works. Defining the TPN based on a DMN seed
timecourse will bias data towards intra-network correlation
and inter-network anticorrelation; therefore all significance
testing is done against a boot-strapped distribution which
would be sensitive to the same biases (see section 3.4 and
Three sets of connectivity metrics were independently
paired with each of six cognitive performance data sets
(PVT, OST memory score, OST math errors, SST memory score, SST symmetry errors, and RAPM), resulting
in 18 independent measures of the relationship between
functional networks and cognitive performance. Due to
multiple hypotheses being tested, family-wise error rate
was used to correct against false positive statistical
errors (see sections 3.5 and 2.5). Z-scores were calculated for intra-network correlation in the DMN, intranetwork correlation in the TPN, and the correlation
between the two networks. Performance score vs.
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Fig. 2 DMN and TPN group incidence maps. Four transverse T1
anatomical slices are shown in the top frame. Group probability maps
for voxels contained within the default mode network (middle) and task
positive network maps (bottom). A probability of zero indicates that the
voxel never appeared in that network, one would indicate that it always
appeared. For visualization purposes only, all subjects were registered on
a per-slice basis using rigid-body registration (Sochor, http://www.
mathworks.com/matlabcentral/fileexchange/19086). Network maps are
highly reproducible and localized. Anatomical areas indicated on the
incidence maps specified by the nearest areas composed of red/yellow
voxels: a.) precuneus/posterior cingulate cortex b.) angular gyrus c.)
medial prefrontal cortex d.) middle frontal gyrus e.) precuneus f.)
superior frontal gyrus g.) dorsolateral prefrontal cortex h.) premotor
cortex i.) inferior parietal lobe j.) medial frontal gyrus. Compare to
previous reports of these networks (Eichele et al. 2008; Fox et al. 2005;
Fransson 2005)
861
Fig. 3 Inter-relationship between the three calculated FC metrics. There
is no significant relationship between any of the connectivity metrics (no
p value less than 0.05). Top) Mean DMN correlation vs. mean TPN
correlation. Middle) DMN/TPN correlation vs. mean DMN correlation.
Bottom) DMN/TPN correlation vs. mean TPN correlation. Note that the y
axis on the top graph covers a smaller range than for the other two graphs
to allow better visualization of the data
connectivity was plotted, and a line was fit to the data
to determine linear significance. The p-values referenced
in the corresponding figures are calculated from the
cumulative distribution function of the same results calculated on boot-strapped data where behavioral results
and connectivity results were matched randomly. The
significant relationships found between functional connectivity and cognitive performances are presented in
Fig. 4. Findings for all tasks are described below, and
862
Brain Imaging and Behavior (2015) 9:854–867
Fig. 4 Relationships between Fisher transformed (Z-score) resting state
functional connectivity metrics and cognitive processing scores. Shown
are the four strongest relationships between resting state functional metrics and cognitive scores. * indicates a sequential goodness of fit corrected
result with>95 % confidence of no type I errors. Top left) Increased
DMN/TPN anti-correlation is correlated with increased errors on OST
interrupter math task. Top right) increased mean correlation in the TPN
network during rest correlates with increased errors on SST interrupter
symmetry task. Also worth noting is the strong, though not significant,
relationships found between increased DMN mean correlation and
RAPM performance (bottom left) and increased DMN/TPN anticorrelation and PVT performance (which replicates Thompson et al.
2013; bottom right). For OST errors, SST errors, and PVT results increasing along the x-axis indicates decreased performance; conversely, on
the RAPM chart the positive direction on the x-axis indicates increased
performance
a summary of these results can be found in Table 1
(summary of results from sections 3.4.1–3.4.4).
DMN/TPN was not significant. Higher SST errors represent
decreased performance. The SST memory score was not
significantly correlated with any functional network activity.
Psychomotor vigilance task and functional connectivity
Operation span task and functional connectivity
The DMN/TPN correlation indicated a correlative relationship
with the PVT response time (Fig. 4); however this p-value was
not significant after multiple comparison correction (see section 3.5 for a discussion of multiple comparison correction).
The correlation of this data is in agreement with the results
from the Thompson et al. and Kelly et al. work (Kelly et al.
2008; Thompson et al. 2013) indicating that better interindividual PVT performance is correlated with increased
DMN/TPN anti-correlation. The positively increasing slope
in this finding indicates that greater anti-correlation is linked
to increased PVT performance (Fig. 4). Note that the lower
response times indicate better performance. Default mode
connectivity and PVT response times did not show a significant relationship; similarly the relationship between PVT
response times and TPN connectivity was not significant.
Symmetry span task and functional connectivity
There was a significant relationship between errors on the
interrupter symmetry task and TPN connectivity which passed
family-wise error rate (FWER) correction. Increased connectivity in resting state TPN was linked to a greater number of
errors. There was no significant relationship between SST
interrupter symmetry errors and DMN connectivity; also the
relationship between SST interrupter symmetry errors and
Operation span errors and the DMN/TPN were significantly
correlated, with a p-value that passed FWER correction.
Higher anti-correlation between the DMN and TPN was
linked to an increased number of errors; these results are
opposite of the trend seen between PVT and DMN/TPN
correlation. Neither the relationship between OST math errors
and DMN connectivity or TPN connectivity was significant.
Raven’s advanced progressive matrices and functional
connectivity
Comparing RAPM score (higher scores mean better performance) and DMN connectivity resulted in a correlative relationship; however, this p-value did not pass correction for
multiple comparisons (see section 3.5). The relationship between RAPM and TPN connectivity and RAPM and DMN/
TPN was not significant.
Correcting for multiple comparisons
Sequential goodness of fit (SGoF) was used to correct for
multiple comparisons (Carvajal-Rodriguez et al. 2009). SGoF
controls family-wise error rate in the weak sense; therefore,
unlike Bonferroni or False Detection Rate methods, FWER
Brain Imaging and Behavior (2015) 9:854–867
863
Table 1 Summary of results. Horizontal axis is the functional
connectivity metric (DMN, TPN or DMN versus TPN), vertical axis is
the task performance metric (PVT reaction time, OST score, OST errors,
SST score, SST errors, RAPM score)
DMN
TPN
DMN/TPN
PVT
p=0.452
Pearson=0.199
R2 =0.040
p=0.577
Pearson=−0.162
R2 =0.026
p=0.040
Pearson=0.547
R2 =0.299
OSS
p=0.502
Pearson=0.184
R2 =0.034
p=0.675
Pearson=0.123
R2 =0.015
p=0.071
Pearson=0.454
R2 =0.207
OSE
p=0.787
Pearson=−0.073
R2 =0.005
p=0.299
Pearson=−0.269
R2 =0.072
p=0.005 *
Pearson=−0.612
R2 =0.374
p=0.445
Pearson=0.196
R2 =0.038
p=0.949
Pearson=0.018
p=0.4214
Pearson=0.209
R2 =0.044
p=0.008 *
Pearson=0.635
p=0.863
Pearson=−0.051
R2 =0.003
p=0.426
Pearson=−0.209
R2 =0.000
p=0.029
Pearson=0.540
R2 =0.291
R2 =0.403
p=0.202
Pearson=0.319
R2 =0.102
R2 =0.044
p=0.174
Pearson=0.352
R2 =0.124
SSS
SSE
RAPM
Each entry lists the p value for slope, Pearson’s linear correlation coefficient, and R2 value resulting from a linear regression analysis. Asterisks
(*) indicate statistical significant following multiple comparisons correction. While more than two results had p<0.05, only two results survived
multiple comparisons correction: DMN versus TPN compared to OST
errors, and TPN compared to SST errors
does not decrease in predictive power as the number of p
values tested increases. Following SGoF for family wise error
rates correction there were no significant relationships in the
family containing comparisons made between the resting state
metrics.
There were two significant comparisons within the connectivity vs. cognition family after SGoF correction. The strongest relationship between connectivity and cognition was
found between errors occurring on an interrupter math task
presented during the OST and DMN/TPN correlation; we can
reject the null hypothesis with 99.9 % certainty. There is a
second significant correlation between TPN connectivity and
interrupter symmetry task errors during the SST. Including
this significant result as well as the previous significant result
(OST interrupter math errors - DMN/TPN and SST interrupter
symmetry errors - TPN) we can reject the null hypothesis with
98.9 % certainty.
There are two additional relationships with significant pvalues but neither passed 5 % FWER correction. The next
most significant correlation was found between the default
mode network and the RAPM score, if we include this relationship with the two mentioned in the previous paragraph we
can reject the null hypothesis with 94.2 % certainty. Finally
including the next most significant relationship, found between PVT and DMN/TPN correlation, the null hypothesis
can be rejected with 77.4 % certainty.
Discussion
Summary of results Fifteen healthy individuals performed a
battery of cognitive tests (OST, SST and RAPM) and had
fMRI resting state data collected on a subsequent day, interleaved with a fourth task (PVT). Performance metrics from all
tasks were compared with linear regression to three resting
state metrics: functional connectivity in the DMN, functional
connectivity in the TPN, and functional connectivity between
these two networks. Results were tested for statistical significance and controlled against multiple comparisons to reduce
false positive errors. Only two comparisons survived this
correction. First, more errors on the OST interrupter task were
related to greater DMN/TPN anticorrelation, suggesting that
worse performance (less resistant to errors on the interleaved
interrupter task) is associated with less connectivity (or negative connectivity) between these two networks at rest. This
result is somewhat surprising given the literature (as discussed
in more detail below). It is possible that the complex nature of
the math task (keeping track of intermediate products and
sums) and switching between it and the memory task benefits
from correlated network activity in DMN and TPN – perhaps
to shift between internally and externally focused attention
(Fox et al. 2005). Second, more errors on the SST interrupter
task were related to higher correlation within the TPN, suggesting that decreased performance (less resistant to errors on
the interleaved interrupter task) is associated with a strongly
connected TPN at rest. There are several possible explanations
for this relationship. One possibility is that good performance
on switching between spatial tasks benefits from efficient
brain network connectivity. Efficient brain network activity
involves activating the task relevant network as needed.
During rest, the DMN is most relevant, so increased
connectivity in the unneeded network (TPN during rest)
may lead to decreased performance during task performance. Another possibility is the increased connectivity
of the TPN during rest inhibits the ability to switch
from the spatial memory to the spatial interrupter task.
Finally, although not significant, there was a correlation
between performance on RAPM and DMN correlation
at rest. Specifically, higher DMN correlation at rest was
associated with higher RAPM scores. A network efficiency explanation also holds for this result as well.
Subjects who show increased connectivity during rest
(when that is the most appropriate brain network) are
able to activate task related networks when those are
needed (during the RAPM).
864
Context of results The results of the current study provide
further support for the hypothesis that cognitive performance
is related to aspects of functional connectivity (Albert et al.
2009; Hampson et al. 2006; Kelly et al. 2008; Prado and
Weissman 2011; Sadaghiani et al. 2010; Stevens et al. 2012;
Tambini et al. 2010). Specifically, increased errors on an
interrupter task presented during a spatial working memory
paradigm (SST) were significantly correlated with increased
task positive network connectivity during rest. Similarly, increased errors on an interrupter task presented during a verbal
working memory paradigm (OST) were significantly correlated with greater DMN/TPN anti-correlation during rest. Additionally, the relationship between RAPM performance and
DMN connectivity approached significance. These results
support conclusions drawn from other experiments using different tasks (Eichele et al. 2008; Polli et al. 2005) suggesting
that working memory capacity and problem solving can depend upon (or are at least related) to functional connectivity in
these large scale networks, and thus variations in connectivity
are related to complex task performance.
This study links better performance (fewer errors due to the
interleaved interrupter task) to either increased DMN/TPN
correlation, decreased TPN connectivity, or increased DMN
connectivity. While consistent with the general message of
previous studies linking functional connectivity to performance, the specific direction of these linear relationships is
not completely consistent with what has been seen previously.
For example, increased intra-network correlation (Fries 2005;
Prado and Weissman 2011) and increased DMN/TPN anticorrelation have historically been associated with increased
performance (Eichele et al. 2008; Hampson et al. 2006; Polli
et al. 2005). However, previous research is not completely
consistent with each other (e.g., Weissman et al. 2006 and
Hampson et al. 2006 report different relationships between
DMN activity and connectivity with attention and working
memory performance). The results presented here are not
necessarily expected to be consistent with that previous work
because the present study used different tasks, and the previous studies examined performance directly (during the the
task performance) rather than errors due to task performance
from previous sessions. In addition, the relationship between
functional connectivity and cognitive output has been shown
to be more complex than a simple linear relationship that is
consistent across different tasks. Prado et al. found that increased anti-correlation between the DMN/TPN is important
for current-trial attention performance while decreased DMN/
TPN anti-correlation during the trial is an indicator of increased future performance (Prado and Weissman 2011)
which is more similar to the relationship identified here.
Another study found that the match/scan portion of the RAPM
paradigm requires co-activation of the angular gyri (major
DMN component) and the supramarginal gyri (major TPN
component) (Prabhakaran et al. 1997), suggesting that
Brain Imaging and Behavior (2015) 9:854–867
decreased DMN/TPN correlation is unlikely to increase performance on that task. But increased DMN network activity
may improve performance, as suggested by our data. Thus, as
different tasks and different contexts for tasks may require
very different (or even opposite (Prado and Weissman 2011))
functional connectivity for good performance, it is thus not
contradictory that the results seen here are different from
previous studies.
While our results were not able to show significance in the
relationship between the PVT and DMN/TPN correlation, the
results are similar to the findings the Kelly et al. work (Kelly
et al. 2008). Similarly our previous work using this data set did
show significance in this relationship using a paired method
rather than a linear fit (Thompson et al. 2013). Note that these
methods will not necessarily give the same results in each case
(See Thompson et al. 2013, supporting information supplemental Fig. 3).
Further importance As dynamic analysis of functional connectivity is generating increasing interest in the fMRI imaging
community (Chang and Glover 2010; Honey et al. 2007;
Hutchison et al. 2013; Keilholz et al. 2013; Majeed et al.
2011; Sakoglu et al. 2010; Thompson et al. 2013) faster
imaging becomes more and more important. Our results demonstrate that significant relationships can be found between
resting state functional brain networks and cognitive performance when using fast imaging sequences. This suggests that
a single data set collected for future functional connectivity
studies can be analyzed in the traditional steady-state manner
and in a dynamic fashion, providing complementary information about average network connectivity and network transitions.. High temporal resolution fMRI has additional benefits
such as reducing the aliasing of higher frequencies, enhancing
fluctuations due to the sensitivity to inflow effects, and providing more data points per unit time resulting in more accurate correlation values.
It is also important to note that the present study
collected fMRI data and the behavioral data that was
ultimately found to be statistically significant on different
days. This suggests that resting state functional networks
may be sufficiently stable within individuals to be indicators of performance even if said performance does not
occur concurrent with scanning. Further investigation
should examine the stability of networks in more depth.
Limitations The use of a fast imaging protocol resulted in
limited spatial coverage, which is a major caveat for interpretation of this study’s results. (Our data include four 5 mm thick
slices were used spanning the Talairach atlas (Lancaster et al.
2000) z-plane from approximately +24 through +64, with
5 mm of space between every acquired slice.) For this reason,
we have purposefully limited our discussion of the neural
substrates of behavior in this paper. However, the majority
Brain Imaging and Behavior (2015) 9:854–867
of nodes in the DMN and TPN networks were imaged. Brain
areas contained in both the slices from our work and the Fox
et al. (Fox et al. 2005) and Fransson et al. (Fransson 2005)
works include the following anatomical regions defining the
DMN: posterior cingulate cortex, lateral parietal cortex (right
and left angular gyri), superior frontal cortex, and areas of the
prefrontal cortex (right and left medial prefrontal cortex and
dorsolateral prefrontal cortex). The anatomical areas include
in our slices containing the TPN are: Intra-parietal cortex
(right and left inferior parietal lobules, right and left inferior
parietal cortices, right and left posterior parietal cortex), orbital
gyrus, frontal eye fields, and regions of the prefrontal cortex
(right and left dorsal premotor cortex, supplementary and presupplementary motor areas). The medial frontal gyrus was
both an area of positive and negative correlation (depending
on the Talairach Z-plane / dorsal-ventral plane) to the
precuneus/PCC from the Uddin et al. work (Uddin et al.
2009) and is included in our slices. It should be noted that
the method used to define the TPN in this work as the network
most anticorrelated to the precuneus seed is unconventional,
though the resulting networks appeared similar to those previously reported.
Because of the limited coverage in our study, several areas
of the DMN and TPN were not included in this analysis. In the
DMN, these areas include: Retro-splenial area, medial and
inferior prefrontal cortex, inferior temporal lobe,
parahippocampal gyrus, cerebellar tonsils, and ventral anterior
cingulate cortex. In the TPN areas not imaged include: right
and left ventral premotor area, left and right medial temporal
cortex, right occipital fusiform gyrus, left inferior occipital
lobe, and the right and left insula. In the DMN our slices
contain the three brain regions most highly correlated with
the precuneus/posterior cingulate cortex (medial prefrontal
cortex, dorsolateral prefrontal cortex, and the anglar gyri)
according to the Fransson et al.’s work (Lancaster et al.
2000). Similarly our images contain four of the six most
anti-correlated regions to the precuneus posterior cortex according to Frannson’s findings (Lancaster et al. 2000) including the inferior parietal lobe, inferior parietal cortex, posterior
parietal cortex, and the supplementary motor area. Because
anatomical nodes of functional networks largely activate and
deactivate in concert, we expect that the large areas of the
DMN and TPN that our slices did cover contain “enough” of
the networks to be highly representative of network wide
function. It should be noted that while the fast imaging sequence used in this study limited the number of slices, recent
developments in multiband imaging can now provide wholebrain coverage with subsecond TRs (Moeller et al. 2010).
These new acquisition methods will greatly improve dynamic
and steady state functional connectivity experiments in the
future.
Global brain signal regression was performed on all resting
state data. Early reports questioning global signal removal
865
suggested that this process could induce artifactual anticorrelation in the brain (Gavrilescu et al. 2002). Fox et al.
later asserted that global signal removal does not act in a
spatially preferential manner on the data, arguing that anatomically specific artifactual networks could not possibly be a
result of global signal regression alone (Fox et al. 2009). Fox
makes the case for the improvement of functional results and
the anatomy/correlation relationship after the removal of global signal. Continuing he showed that the formation of the
DMN and TPN networks was highly reproducible, with or
without global signal removal, suggesting that these observed
networks were not attributable to the removal of global signal
(Fox et al. 2009). In addition, regarding the dynamic changes
in functional connectivity that high temporal resolution data
may provide, an early study suggests that the global signal
likely does not represent the neural basis of these dynamic
changes (Thompson et al. 2013).
Another caveat in our data set is that the PVT was interleaved between resting state scans. Previous studies have
indicated that prolonged cognitive tasks occurring prior to
resting state scans can influence resulting resting state connectivity (Evers et al. 2012; Waites et al. 2005). It is possible
that the performance of the PVT task prior to resting state
induces conditions that allow functional distinction of performance variation. Our study is typical in this regard; rarely are
resting state scans conducted independently of task paradigms
in the experimental environment due to the economic costs of
subject time. Cognitive task performance may induce specific
functional network activity brain states uncovering functional
network relationships that are otherwise undetectable; this
may be a fruitful arena for further evaluation.
Finally, another limitation to these data is that different
functional connectivity-cognitive performance relationships
were identified for the different tasks. This study does not
allow us to isolate the cause of these differences. It may be that
important differences between the cognitive processes involved in each task lead to the recruitment of different networks. Therefore, different network relationships at rest predict performance. Additional research is necessary to resolve
this issue.
Conclusion While data-driven, and while incomplete coverage of the brain limits interpretation, this study provides
several important results. We have shown that increased errors
on an interrupter spatial task presented during a spatial working memory task are significantly correlated with a more
coherent TPN network during rest. Similarly, increased errors
on an interrupter math task presented during verbal working
memory task are significantly correlated with increased
DMN/TPN anti-correlation during rest. Thirdly, increased
performance on a fluid intelligence task is correlated with
increased DPN correlation during rest. Finally, these results
can be seen using high temporal resolution fMRI, and when
866
the tasks are performed on different days than the fMRI
scanning. Our results thus provide further evidence of the
strong connection between TPN functional connectivity,
DMN versus TPN functional connectivity, DMN functional
connectivity and performance on a variety of cognitive tasks.
Acknowledgments Funding was provided in part by the Bio-nanoenabled Inorganic/Organic Nanostructures and Improved Cognition
(BIONIC) Air Force Center of Excellence at the Georgia Institute of
Technology. This research was also partially funded under an appointment to the U.S. Department of Homeland Security (DHS) Scholarship
and Fellowship Program, administered by the Oak Ridge Institute for
Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and DHS (contract number
DE-AC05-06OR23100). All opinions expressed in this paper are the
author’s and do not necessarily reflect the policies and views of DHS,
DOE, or ORAU/ORISE. We would also like to thank Dr. Waqas Majeed
for his suggestions regarding data preprocessing, Nytavia Wallace for her
assistance with data collection, and Brian Roberts for his insightful
discussions.
Conflict of interest Matthew Evan Magnuson, Garth John Thompson,
Hillary Schwarb, Wen-Ju Pan, Andy McKinley, Eric H. Schumacher, and
Shella Dawn Keilholz declare that they have no conflicts of interest.
Informed consent All procedures followed were in accordance with
the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of
1975, and the applicable revisions at the time of the investigation.
Informed consent was obtained from all patients for being included in
the study.
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