Transcranial Direct Current Stimulation Modulates - Specialized Research AI at MIT

Transcranial Direct Current Stimulation Modulates
Connectivity of Left Dorsolateral Prefrontal
Cortex with Distributed Cortical Networks

Kamin Kim1, Matthew S. Sherwood2, Lindsey K. McIntire3,
R. Andy McKinley4, and Charan Ranganath1

Abstract

■ Studies have shown that transcranial direct current stimula-
tion increases neuronal excitability of the targeted region and
general connectivity of relevant functional networks. However,
relatively little is understood of how the stimulation affects the
connectivity relationship of the target with regions across the
network structure of the brain. Here, we investigated the effects
of transcranial direct current stimulation on the functional
connectivity of the targeted region using resting-state fMRI
scans of the human brain. Anodal direct current stimulation
was applied to the left dorsolateral prefrontal cortex (lDLPFC;
cathode on the right bicep), which belongs to the frontoparietal
control network (FPCN) and is commonly targeted for neuro-
modulation of various cognitive functions including short-term
memory, long-term memory, and cognitive control. lDLPFC’s

connectivity characteristics were quantified as graph theory
measures, from the resting-state fMRI scans obtained prior to
and following the stimulation. Critically, we tested pre- to post-
stimulation changes of the lDLPFC connectivity metrics following
an active versus sham stimulation. We found that the stimulation
had two distinct effects on the connectivity of lDLPFC: for
Brodmann’s area (BA) 9, it increased the functional connectivity
between BA 9 and other nodes within the FPCN; for BA 46, net
connectivity strength was not altered within FPCN, but connec-
tivity distribution across networks (participation coefficient) was
decreased. These findings provide insights that the behavioral
changes as the functional consequences of stimulation may
come about because of the increased role of lDLPFC in the
FPCN. ■

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INTRODUCTION

Transcranial direct current stimulation (tDCS) is widely used
as a neuromodulation tool for clinical and research purposes
(for reviews, see Dedoncker, Baeken, De Raedt, &
Vanderhasselt, 2021; Filmer, Mattingley, & Dux, 2020; Razza
et al., 2020; Galli, Vadillo, Sirota, Feurra, & Medvedeva, 2019;
Osório & Brunoni, 2019; Bennabi & Haffen, 2018; Lucchiari,
Sala, & Vanutelli, 2018; Strobach & Antonenko, 2017;
Dedoncker, Brunoni, Baeken, & Vanderhasselt, 2016a,
2016b; Jantz, Katz, & Reuter-Lorenz, 2016; Kim, Ekstrom, &
Tandon, 2016; Nitsche & Paulus, 2011; Paulus, 2011;
Nitsche et al., 2008; Wagner, Valero-Cabre, & Pascual-Leone,
2007; Gandiga, Hummel, & Cohen, 2006). For example, sev-
eral tDCS studies have targeted left dorsolateral prefrontal
cortex (lDLPFC) to modulate long-term memory (Mizrak
et al., 2018; Leshikar et al., 2017; Sandrini et al., 2014, 2016;
Javadi, Cheng, & Walsh, 2012; Javadi & Walsh, 2012;
Zwissler et al., 2014), working memory (Talsma,
Broekhuizen, Huisman, & Slagter, 2018; Talsma, Kroese, &
Slagter, 2017; Hill, Fitzgerald, & Hoy, 2016; Mancuso, Ilieva,
Hamilton, & Farah, 2016; Trumbo et al., 2016; Carvalho
et al., 2015; Andrews, Hoy, Enticott, Daskalakis, &

1University of California, Davis, 2Science and Space, KBR Inc.,
Beavercreek, OH, 3Infoscitex, Beavercreek, OH, 4U.S. Air
Force, Dayton, OH

Fitzgerald, 2011; Keeser et al., 2011; Fregni et al., 2005;
Marshall, Mölle, Siebner, & Born, 2005), cognitive training
effects (Au et al., 2016; Martin, Liu, Alonzo, Green, & Loo,
2014), and attention and cognitive control (London &
Slagter, 2021; McIntire, McKinley, Nelson, & Goodyear,
2017; Nejati, Salehinejad, Nitsche, Najian, & Javadi, 2020;
Nelson, McKinley, Golob, Warm, & Parasuraman, 2014;
McKinley et al., 2013; Gladwin, den Uyl, Fregni, & Wiers,
2012). Prefrontal cortex is thought to contribute to these
functions by using information about behavioral goals to
modulate activity in posterior cortical areas (Miller &
Cohen, 2001; Tomita, Ohbayashi, Nakahara, Hasegawa, &
Miyashita, 1999). Accordingly, to the extent that stimulating
lDLPFC produces behavioral effects, we would expect that
stimulation should modulate potential interactions between
lDLPFC and other cortical areas. However, our under-
standing is still sparse on how the tDCS might influence
the interregional dynamics of lDLPFC.

We investigated whether tDCS applied to lDLPFC can
modulate network-level functional connectivity between
lDLPFC and other regions of the brain. We used the tem-
poral correlation of BOLD signal fluctuations as the mea-
sure of functional connectivity between brain regions (i.e.,
resting-state connectivity; Cole, Ito, Bassett, & Schultz,
2016; Cole, Bassett, Power, Braver, & Petersen, 2014;
Vincent, Kahn, Snyder, Raichle, & Buckner, 2008; Fox &

Journal of Cognitive Neuroscience 33:7, pp. 1381–1395
https://doi.org/10.1162/jocn_a_01725

Raichle, 2007; Damoiseaux et al., 2006; De Luca,
Beckmann, De Stefano, Matthews, & Smith, 2006;
Vincent et al., 2006). The spatial structure of these corre-
lations suggests that the brain is organized in multiple
semimodular “small-world” networks characterized by
high within-network connections and sparse between-
network connections (Ashourvan, Telesford, Verstynen,
Vettel, & Bassett, 2019; Power et al., 2011; Bullmore &
Sporns, 2009; Kaiser, Martin, Andras, & Young, 2007;
Sporns & Zwi, 2004), and we aim to investigate how stim-
ulation modulates the target region’s connectivity within
the network structure. Prior studies have shown that
tDCS applied to lDLPFC influences general connectivity
strength of the target or neighboring networks. In
Keeser et al. (2011), participants went through two exper-
imental sessions, one with active stimulation and the other
with sham stimulation, and each stimulation session was
preceded and followed by fMRI scanning. Authors showed
that the stimulation induced significant increases in
lDLPFC activation and significant changes of connectivity
within networks that included brain regions that are close
to lDLPFC. However, the connectivity changes specific to
lDLPFC or between lDLPFC and the networks were not di-
rectly tested in this study. In another study, connectivity
analyses using lDLPFC as the seed region showed that
the stimulation increased connectivity between lDLPFC
and bilateral parietal regions inferior parietal lobule
[IPL], superior parietal lobule [SPL]); however, this study
focused on pairwise functional connectivity relationships
and did not take the network structure into consideration
(Mondino et al., 2020).

We investigated how lDLPFC stimulation modulates the
way in which the target region interacts with other parts of
the brain network structure. Given that lDLPFC has been
identified as a node in the “frontoparietal control network
(FPCN)” (Schaefer et al., 2018), we considered the possi-
bility that tDCS might increase the relative importance of
lDLPFC within the FPCN compared to its importance
across other networks. An alternative possibility is that
stimulation might increase the breadth of lDLPFC connec-
tivity across different networks beyond FPCN. To test
these predictions, we conducted a combined stimulation
and imaging experiment, in which different groups of par-
ticipants underwent an active or sham stimulation.
Functional connectivity between regions was estimated
using resting-state fMRI scans acquired prior to and follow-
ing the stimulation, and used to compute graph theory
metrics that characterize a node’s connectivity in the brain
network structure. Finally, we examined how stimulation
altered the network relationships of lDLPFC.

METHODS

Participants

A total of 60 healthy participants completed this study
(Day 1 study session; see Procedure section for details).

Participants were active duty Air Force military members
recruited from Wright-Patterson Air Force Base. Inclusion
criteria for this study included the absence of neurological
or psychological disorders, head injury (concussion his-
tory, traumatic brain injury), recent trauma or hospitaliza-
tion, impairment of vision, hearing or motor control,
dependency on alcohol, caffeine, or nicotine, and any
current medication that may affect cognitive functions.
Of the 60 participants who completed the session, nine
participants were excluded from analysis because of ex-
cessive motion during fMRI scanning (> 3 mm) and a to-
tal number of 51 participants were included in the final
analyses (13 female, 38 male). Participants were randomly
assigned to the sham, 1 mA, or 2 mA active stimulation
group (Nsham = 22, Nactive = 29 [N1mA = 13, N2mA =
16]). All data were collected at Dayton Children’s
Hospital, and all experimental procedures were approved
by the Air Force Research Laboratory Institutional Review
Board at Wright-Patterson Air Force Base and were fully
described to the participants before they consented to
participate in the study.

Procedure

This study was conducted as part of a larger multiday
testing study. This study focused on noncumulative,
immediate effects of stimulation using data from Day 1
resting-state fMRI. Figure 1 depicts the procedure of the
pretesting and Day 1 sessions. One or 2 days prior to the
actual testing, participants came in for the consenting pro-
cedure and task practice. They were given a 5-min-long
practice training on the Mackworth Clock test (McKinley,
2018; McIntire et al., 2017) that was to be used during stim-
ulation. On each testing day, participants went through a
stimulation session and two MRI sessions: one preceding
and one following the stimulation session. An imaging ses-
sion included one block of resting-state fMRI, three blocks
of task fMRI (n-back task), structural MRI, diffusion tensor
imaging, magnetic resonance spectroscopy (MRS), and
arterial spin labeling (ASL). During resting-state fMRI scan-
ning, participants were instructed to keep their eyes open
and to focus on a fixation dot at the center of the screen.
Participants were taken out from the scanner after the first
(prestimulation) imaging session and guided into a behav-
ioral testing room for a stimulation session. A scanning
session was about 70 min long, and the average interval
between the start of the prestimulation and poststimula-
tion scanning sessions was 158 min. Poststimulation
resting-state fMRI was conducted within 1 hr from the
stimulation, and this interval is well within the duration
that our stimulation regime shows behavioral effects
for (McIntire, McKinley, Goodyear, & Nelson, 2014).
Nevertheless, one may argue that tDCS effects might wear
out before the scanning and mere epiphenomenal effects
of stimulation (i.e., thinking about the tingling sensation
during the stimulation session) could result in false-
positive findings. In order to address such possibility,

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Journal of Cognitive Neuroscience

Volume 33, Number 7

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Figure 1. Experiment procedure. tDCS setup figure is reused with permission (McIntire et al., 2014).1

we obtained participants’ rating on the arousal level (fa-
tigued vs. energized) and four types of sensation that are
often associated with stimulation (see under Stimulation
section below for details).

meant unbearable. The protocol was to turn off the stim-
ulation in the case one reports a sensation of 7 or higher
on any category, but there was no incident. Fatigue was
rated on a 7-point scale, with 1 feeling most fatigued
and 7 feeling energized.

Stimulation

Transcranial DC stimulation was delivered with a MagStim
DC stimulator (magstim.com, Whiteland). Instead of the
standard wet sponge electrodes, custom, composite elec-
trodes were used as anode and cathode (Sherwood,
Madaris, Mullenger, & McKinley, 2018; McIntire et al.,
2014, 2017; McKinley et al., 2013; Park, Hong, Kim, Suh,
& Im, 2011). As in the prior studies that used composite
electrodes, a composite electrode consisted of five
Na/NaCl EEG electrodes that were arranged in a circular
pattern with an inner diameter of 1.6 cm. EEG electrodes
in the array were separated by 0.1 cm from the neighbor-
ing ones (measured from outer edge to outer edge). The
anode was placed on the lDLPFC (approximately F3 in
International 10–20 EEG system), and the cathode was
placed on the contralateral (right) bicep. Electrical field
modeling using the Finite Element Method has shown
that this approach delivers the current evenly distributed
among the five EEG electrodes and, when used with a 2 mA
current intensity, delivers stimulation with an estimated
current density of 0.199 mA/cm2 to the target area
(McIntire et al., 2017; McKinley et al., 2013; see Figure 2
in McKinley et al., 2013, for the Finite Element Method
model of estimated current distribution). Conductive gel
was applied on the electrodes to ensure current conduc-
tion, and electrodes were secured to the target area using
medical bandages. A constant current of 1 mA or 2 mA
was delivered for 30 min in the active stimulation group
and for 30 sec in the sham stimulation group. Electrodes
remained in place for the full 30 min in both groups.

One minute into the stimulation, participants completed
a sensation and fatigue questionnaire. On this question-
naire, participants rated the intensity of four types of sensa-
tion (heat, itching, pain, and discomfort) on a 10-point
scale, where 0 corresponded to feeling nothing and 10

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Figure 2. Stimulation target ROIs belong to the FPCN. (A) FPCN
is depicted in color: violet = parcels corresponding to BA 9
(LH_Cont_PFCl_4 in Schaefer atlas); blue = parcels corresponding to
BA 46 (labeled as LH_Cont_PFCl_3 the in Schaefer atlas); orange =
other parcels that belong to FPCN. (B–C) Left DLPFC ROIs are depicted
in volumetric views. Sections at the center of BA 9 (B, violet) and BA 46
(C, blue). Coordinates are in MNI space.

Kim et al.

1383

For the remaining duration of the 30-min stimulation,
participants completed a vigilance task (an adapted ver-
sion of Mackworth Clock Test; McIntire et al., 2017).
This was done as neuronal modulatory effects via electrical
stimulation are largely influenced by the neuronal state
before and during the stimulation (Silvanto, Muggleton,
& Walsh, 2008). Specifically for tDCS, prior research indi-
cated that anodal stimulation applied while the target
region is activated by task engagement has increased effec-
tiveness (Filmer, Lyons, Mattingley, & Dux, 2017; Ruf,
Fallgatter, & Plewnia, 2017; Andrews et al., 2011). Left
DLPFC, our stimulation target region, is highly engaged
in attentional/cognitive control functions such as vigilance
(Kim, Kim, & Im, 2017; Nelson et al., 2014; Langner &
Eickhoff, 2013) and working memory ( Jansma, Ramsey,
de Zwart, van Gelderen, & Duyn, 2007; Ranganath,
Johnson, & D’Esposito, 2003; D’Esposito, Postle, Ballard,
& Lease, 1999; D’Esposito et al., 1998; Barch et al., 1997;
Braver et al., 1997).

translation and three rotation parameters, and their first-
order derivatives), BOLD time series from subject-specific
white matter (top 5 PCA parameters) and CSF (top 5 PCA
parameters), and a binary vector that marked outlier time
points identified by ART (www.nitrc.org/projects/artifact
_detect). ART outliers were defined as images with a
framewise displacement greater than 0.5 mm from the
previous image and images with a global mean intensity
more than 2 SDs away from the mean image intensity of
all scans. These thresholds are slightly more conservative
than the default thresholds in the CONN toolbox
(0.9 mm, 5 standard deviations), which are considered
to be “intermediate” thresholds (97th percentile). On av-
erage, approximately 17% of scans were outliers (ranges:
2%–53%). The resulting BOLD time series were band-pass
filtered (0.009 Hz < F < 0.08 Hz) and averaged within each ROI. Fisher-transformed correlations between the resulting filtered time series of ROI pairs were used as the final estimates of connectivity between ROI pairs. Resting-State fMRI Data Acquisition MRI images were collected using a 3T GE Discovery scanner with a 24-channel head/neck coil at Dayton Children’s Medical Center. T1-weighted structural images were acquired using a spoiled gradient recalled acquisi- tion sequence (flip angle = 16°, FoV = 25.6 cm, image dimension: 256 × 256 × 164, voxel size = 1 × 1 × 1 mm). Resting-state functional images were acquired using gradient EPI sequences (TR = 2000 msec, TE = 20 msec, flip angle = 90°, FoV = 24 cm, image dimension = 64 × 64 × 41, slice thickness = 3 mm, voxel size = 3.75 × 3.75 × 3.5 mm, top–down interleaved). The functional image acquisition included four initial dummy scans to ensure signal stabilization, and the scanning lasted 12 min and 14 sec. Dummy scans were discarded, and a total number of 363 volumes were further processed for analyses. Resting-State fMRI Data Processing Preprocessing and data analyses were performed using SPM12 (www.fil.ion.ucl.ac.uk/spm) and the CONN tool- box ( Whitfield-Gabrieli & Nieto-Castanon, 2012). Functional images were slice-time corrected for differ- ences in slice acquisition times, realigned, normalized, spatially smoothed (FWHM = 8 mm), and segmented using a local-global parcellation atlas (Schaefer et al., 2018; the atlas provides parcellations of 10 different levels of granularity ranging from 100 to 1000 parcellations, and we used the atlas with 200 parcels). Data were denoised using the component-based noise correction (CompCor; Behzadi, Restom, Liau, & Liu, 2007) method and scrubbing as implemented in the CONN toolbox. Specifically, the following time series were used as temporal covariates in the first-level analysis and removed from the BOLD functional data using linear regression: 12 time series of the estimated motion (three Graph Measures Graph theory measures that characterize a node’s connec- tivity strength (node degree) and connectivity distribution (participation coefficient) were computed using the resting-state connectivity measures and an independent, predefined network structure. For valid testing of the stim- ulation effects on graph measures, it was crucial to obtain graph measures based on a common, objective network structure across individuals and scanning sessions. To achieve this, the following steps were taken: 1) We acquired stable solutions of modular/network structures from our resting-state connectivity data using a consensus clustering approach (see below for details), and 2) we com- puted graph measures by assessing the outcome connec- tions against a network structure of our ROIs that was predefined using resting-state functional connectivity from a large sample (n = 1489; Schaefer et al., 2018). The Schaeffer atlas assigns ROIs into networks of two different granularity—seven and 17 networks—and we used the seven-network solution that includes visual, somato-motor, dorsal attention, ventral attention, limbic, frontoparietal control, and default networks. The functional connectivity network structure from each individual and session was obtained using a consen- sus clustering approach (Lancichinetti & Fortunato, 2012), which allows for obtaining stable community solu- tions when the community detection method is not deter- ministic. A consensus matrix was obtained as following. First, an adjacency matrix of ROI-by-ROI connectivity strength (Fisher’s Z ) was obtained for each participant and session (see Resting-state fMRI Data Processing section). The adjacency matrices were entered into a clustering algorithm (community_louvain) implemented in BrainConnectivityToolbox (sites.google.com/site /bctnent; Rubinov & Sporns, 2011), and this procedure was repeated 1000 times. For each ROI pair, the ratio that 1384 Journal of Cognitive Neuroscience Volume 33, Number 7 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 3 8 1 1 9 2 1 2 3 2 / / j o c n _ a _ 0 1 7 2 5 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 the two ROIs were partitioned into the same community out of the 1000 solutions (i.e., “consensus” rate) was com- puted, and the consensus rates were thresholded at 0.035 (consensus rates smaller than the threshold were set to zero). The outcome ROI-by-ROI matrix of thresholded consensus rate was used as an input to the next iteration. This procedure (clustering an ROI-by-ROI matrix 1000 times, obtaining a consensus matrix, and thresholding the consensus matrix) was repeated iteratively until the resulting matrix reached a complete consensus (all ROI pairs would have a consensus rate of 0 or 1). We consid- ered an “edge” (i.e., connection) to exist between ROIs if the consensus rate was 1 between the pair of ROIs. Using the predefined network memberships of ROIs and the consensus matrix solutions that are described above, we estimated graph measures that quantify a node’s con- nectivity strength with given networks (node degree) and how well-distributed across different networks a node’s connectivity is (participation coefficient). Connectivity strength of an ROI (i.e., a network node), node degree, was estimated as total number of the node’s edges divided by the number of all possible edges. For example, Brodmann’s area (BA) 9’s node degree within FPCN was computed as the number of edges between BA 9 and other nodes within the FPCN divided by the number of possible edges between BA 9 and all FPCN nodes (i.e., the denom- inator is N-1 in a network with N nodes). Participation co- efficient was computed using the participation_coef_sign function in BrainConnectivityToolbox. Statistical Analysis Statistical analyses were conducted using R statistical tool- box 3.4.3. (www.r-project.org/) and custom MATLAB (www.mathworks.com) codes. One-way between-group ANOVA was used to test whether participant groups (sham vs. active stimulation) differed in their age, scan- ning time intervals, or the sensation and fatigue ratings. A general linear mixed-effects model (GLMM) and permutation-based nonparametric tests were used to test for stimulation effects on node degree and participation coefficient of targets. Graph theory measures (i.e., node degree) were entered as the predicted variable, and stim- ulation group (sham/active), scanning session (pre-/poststim- ulation), and a Group × Session interaction were entered as predictor variables, with the subject variable included in the model as a random intercept. To determine whether the variability of graph measures is explained by the stimulation, the statistical significance of the inter- action term (Sham/Active × Pre-/Poststimulation interac- tion) was critically tested using permutation tests. A permutation distribution of interaction coefficients was acquired by running the regression model 1000 times after shuffling group membership (active/sham) and session (pre-/poststimulation) labels. Specifically, each participant was randomly reassigned to one of the groups (sham/active) while keeping the group size the same (Nsham = 22, Nactive = 29). Session labels (pre-/poststimula- tion) were flipped in randomly selected participants. Shuffling variable labels in this manner guaranteed that the within-subject variability was kept intact. The interac- tion coefficient obtained from unshuffled, real data was tested against the permutation distribution. The sign of the interaction term is irrelevant to the interpretation of results here; therefore, coefficients smaller than the 5th or greater than the 95th percentile of the distribution were considered statistically significant. RESULTS Demographic, Scanning, and Poststimulation Questionnaire Data Stimulation groups were comparable in age and the inter- vals between the scanning sessions prior to and following the stimulation (Table 1; all p > .1). On average, both
groups reported ratings below 2 (from 0 being nothing
and 10 being unbearable) on all four types of sensation,
and importantly, the rating did not differ between groups
for any type (all p > .1). In addition to sensation, partici-
pants also reported how fatigued or energized they felt on
a 7-point scale (with 1 being most fatigued and 7 being
energized ). The fatigue level that the sham and active
stimulation groups reported were identical (sham group =
4.09, active group = 4.10; p > .1). Therefore, it can be
assured that effects of the stimulation were not driven
by participants’ awareness of the stimulation condition.
When the active stimulation group was further broken
into 1- and 2-mA stimulation intensity groups, the three
groups (sham, active-1 mA, active-2 mA) did not differ in
any metrics reported here (Table 2).

ROIs and Functional Categories of the lDLPFC

Among the 200 parcels defined in the Schaefer atlas, ones
that were centered in BA 9 or BA 46 (MacDonald, Cohen,
Stenger, & Carter, 2000) were identified as lDLPFC. This
approach identified two neighboring parcels in the left
prefrontal cortex—a posterior dorsal one corresponding
to BA 9 (violet in Figure 2; Figure 2B for volumetric views)
and an anterior ventral one mapping to BA 46 (blue in
Figure 2; Figure 2C for volumetric views). Both of these
ROIs have been identified as nodes in the FPCN
(Schaefer et al., 2018; Figure 2A). In addition, to test the
specificity of stimulation effects, we used the rDLPFC as a
control region. Three parcels were identified as rDLPFC
using the same approach as for the lDLPFC described
above, and all three nodes belonged to the FPCN.

Active Stimulation Increased Connectivity of BA 9
within FPCN

We first examined how the stimulation influenced the
within-FPCN connectivity strength of lDLPFC ROIs. To

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Table 1. Comparisons of Sham and Active Simulation Groups

Stimulation Group

Group Difference

demographic

scanning time

sensation

fatigue

sex

age

interval

itching

pain

heat

discomfort

Sham (n = 22)

Active (n = 29)

Female = 5

28.1 (5.22)

Female = 8

28.2 (5.85)

159.1 (16.86)

158.9 (15.54)

1.73 (1.70)

0.36 (0.85)

0.23 (0.43)

1.05 (1.43)

4.09 (1.02)

1.38 (1.21)

0.59 (0.91)

0.55 (0.91)

0.69 (0.97)

4.10 (1.35)

0.009

0.003

0.733

0.796

2.387

1.112

0.001

.92

.96

.40

.38

.13

.30

.97

Demographics, time interval between scanning sessions prior to and following the stimulation, and stimulation questionnaire scores in participant
groups that received sham and active stimulation. The sham and active stimulation group did not differ in age, scanning time, and sensation reports.

test this, GLMM was conducted on lDLPFC nodes’ within-
FPCN connectivity strength with the stimulation group
(active/sham), scanning session (pre-/poststimulation),
and an interaction term as predictor variables (see
Statistical Analysis section for details of the regression
model). GLMM permutation tests revealed that within-
FPCN connectivity strength was significantly increased
by stimulation for BA 9 (|z| = 1.90; Figure 3A), but not
for BA 46 (|z| = 1.39; Figure 3B) or any of the rDLPFC
control ROIs (all |z| < 0.7). We additionally investigated whether the BA 9-FPCN connectivity change was stimula- tion intensity-dependent. Specifically, we tested the effects of the stimulation intensity (Intensity × Session interaction) on BA 9-FPCN connectivity using GLMM per- mutation tests. The analysis revealed no significant differ- ence in the stimulation-driven BA 9-FPCN connectivity changes between the 1- and 2-mA stimulation groups (interaction term |z| = 0.11). Connectivity between lDLPFC and Regions Outside FPCN: Stimulation May Bias BA 46 Connectivity to Be Less Distributed across Networks Next, we assessed stimulation effects on the stimulation target’s connectivity characteristics outside FPCN. We approached the outside-network connectivity via two dif- ferent measures: the connectivity strength and the distri- bution of connections outside FPCN. First, outside-FPCN connectivity strength of an lDLPFC ROI was quantified as node degree that only took nodes that are not part of FPCN into consideration. For example, BA 9’s outside-FPCN connectivity strength was computed Table 2. Comparisons of Sham and Two Active Stimulation Groups Stimulation Group Group Difference Sham (n = 22) Active - 1mA (n = 13) Active - 2mA (n = 16) demographic sex age F = 5 28.1 (5.2) F = 4 28.8 (6.2) F = 4 27.8 (5.7) scanning time interval 159.1 (16.86) 158.9 (17.39) 158.9 (14.45) sensation itching pain heat 1.73 (1.70) 0.36 (0.85) 0.23 (0.43) discomfort 1.05 (1.43) fatigue 4.09 (1.02) 1.31 (1.18) 0.77 (1.17) 0.54 (0.97) 0.85 (1.21) 3.69 (1.32) Stimulation groups did not differ in age, scanning time, and sensation reports. 1.44 (1.26) 0.44 (0.63) 0.56 (0.59) 0.56 (0.73) 4.44 (1.31) F NA 0.108 0.001 0.388 0.906 1.173 0.756 1.396 p NA .9 .99 .68 .41 .32 .48 .26 1386 Journal of Cognitive Neuroscience Volume 33, Number 7 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 3 8 1 1 9 2 1 2 3 2 / / j o c n _ a _ 0 1 7 2 5 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 as the number of edges between BA 9 and non-FPCN nodes divided by possible number of edges between BA 9 and all non-FPCN nodes (i.e., denominator was the total number of non-FPCN nodes). GLMM permutation tests revealed that the connectivity strength of the lDLPFC ROIs (BA 9, BA 46) with nodes outside FPCN was not significantly altered by the stimulation (BA 9: |z| = 1.23, Figure 4A; BA 46 |z| = 0.29, Figure 4B). Similarly, stimulation did not alter rDLPFC nodes’ connectivity with nodes outside FPCN (all |z| < 0.8). In summary, the stim- ulation did not alter the net strength of the connectivity between lDLPFC ROIs and brain regions outside FPCN. In our next analyses, we assessed the effects of tDCS on the participation coefficient, the measure that character- izes how well-distributed a node’s connections are (Meunier, Lambiotte, & Bullmore, 2010; Bullmore & Sporns, 2009; Guimerà, Sales-Pardo, & Amaral, 2007; Guimerà & Amaral, 2005) across the seven networks that were predefined in the atlas (Schaefer et al., 2018). GLMM regression revealed that the participation coefficient of BA 46 was reduced by lDLPFC stimulation relative to sham (|z| = 1.98; Figure 5B), whereas stimulation did not significantly affect participation coefficient values in BA 9 (|z| = 0.37; Figure 5A) or the rDLPFC (|z| < 0.2). We also tested whether the modulation of BA 46 participa- tion coefficient was stimulation intensity-dependent, and found no difference in the stimulation effects be- tween the two intensity groups (|z| = 0.25). Close examination of the data indicated potential between-group differences in the baseline (prestimula- tion) BA 46 participation coefficient values (Figure 5B, light blue), and we further inspected this result. Consistent with this impression, we found that prestimu- lation BA 46 participation coefficient values were signifi- cantly higher for participants in the active stimulation condition relative to participants in the sham conditions (|z| = 1.82). To address whether the interaction effect was merely a consequence of prestimulation group differ- ence, we conducted follow-up analyses on subgroups of sham and active condition participants that were closely matched in the prestimulation participation coefficient values. Participation coefficient-matched groups were identified as follows: First, we identified the range of the BA 46 participation coefficients that overlapped between the active and sham stimulation groups, and excluded participants that fell outside the range. This eliminated a few participants from each stimulation group, and yielded 20 and 27 participants for the sham and active stimulation groups, respectively. Next, for the participation coeffi- cient value of each participant in the sham stimulation condition, the closest participation coefficient value was identified from active stimulation participants. The search for a matching pair was conducted through the sham participants’ participation coefficient values in a random order. This matching process was repeated 1000 times with a new random order each time. The goodness of matching was assessed as the mean difference of the Kim et al. 1387 Figure 3. FPCN-connectivity changes of lDLPFC ROIs. Bar graphs depict lDLPFC ROI’s connectivity with FPCN in each stimulation group and session, and insets depict permutation test results for the interaction effect (Stimulation Session × Group). Histogram: permutation distribution, x-axis: coefficient size, y-axis: probability, red line: regression coefficient, gray shade: < 5th percentile of the permutation distribution. (A) BA 9-FPCN connectivity was significantly increased by active stimulation. (B) BA 46-FPCN was not significantly modulated by active stimulation. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 3 8 1 1 9 2 1 2 3 2 / / j o c n _ a _ 0 1 7 2 5 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 lDLPFC and networks of the brain. We used functional connectivity measures computed from resting-state fMRI scans acquired before and after the participants under- went a stimulation session. Relative to sham stimulation, active stimulation altered the functional connectivity of lDLPFC in two ways. First, stimulation increased the func- tional connectivity between BA 9 and other nodes within the FPCN. Second, stimulation impacted the connectivity of BA 46 across different networks. While the net connec- tivity strength of BA 46 was not altered within FPCN or outside FPCN, the connectivity distribution (participation coefficient) of BA 46 was decreased by stimulation. In con- trast to the effects of stimulation on lDLPFC, stimulation had no significant effects on network-level functional con- nectivity metrics in homologous rDLPFC regions that are also part of the FPCN. Our results build on prior findings showing that lDLPFC stimulation impacts the connectivity of FPCN and the functional connectivity of lDLPFC. Keeser et al. (2011) ex- amined stimulation-induced changes in the functional connectivity of resting-state networks. Using independent component analysis on resting-state fMRI scans obtained from 13 healthy participants, they identified four net- works: default mode, left frontoparietal, right frontoparie- tal, and self-referential networks. Next, authors used dual regression method (Nickerson, Smith, Öngür, & Beckmann, 2017; Beckmann, DeLuca, Devlin, & Smith, 2005) to identify subject-specific spatial maps for the net- works, which were entered into the group-level statistical tests to assess stimulation effects in a network (contrast: (after real tDCS > baseline1) > (after sham tDCS > base-
line)). Analysis of the left frontoparietal network revealed
increased coactivation between regions within the superior
and inferior frontal gyri, the inferior parietal lobule, and
the posterior cingulate gyrus. Apart from the connectivity
changes, authors also found significant increase of local
activation in the anodal target region (BA 6). However,
the independent component analysis did not identify
the stimulation target as part of the left frontoparietal net-
work, and as a consequence, the dual regression analysis
did not assess the connectivity relationship between the
stimulation target and other regions in the network. In
this study, we capitalized on a network atlas that is prede-
fined from a large, independent sample (Schaefer et al.,
2018), and tested how stimulation impacts the stimula-
tion target’s network relationships with FPCN and other
networks.

Whereas Keeser et al. (2011) focused on the connectiv-
ity strength changes in different brain networks, other re-
search focused on pairwise connectivity between lDLPFC
and other brain regions. Using a seed-based connectivity
analysis, Mondino et al. (2020) showed that the lDLPFC
stimulation increased resting-state functional connectivity
between lDLPFC and bilateral parietal regions (IPL, SPL).
IPL and SPL belong to FPCN, and therefore these findings
are consistent with our finding that lDLPFC connectivity
within FPCN was increased by stimulation. In this study,

Figure 4. lDLPFC connectivity changes outside FPCN. Bar graphs
depict lDLPFC ROI’s connectivity with regions outside FPCN in each
stimulation group and session, and insets depict permutation test
results for the interaction effect (Stimulation Session × Group).
Histogram: permutation distribution, x- axis: coefficient size, y-axis:
probability, red line: regression coefficient, gray shade: < 5th percentile of the permutation distribution. (A) BA 9 Connectivity with non-FPCN regions was not significantly altered by active stimulation. (B) BA 46 Connectivity with non-FPCN regions was not significantly altered by active stimulation. participation coefficient, and the matching solution with the smallest mean participation coefficient difference was selected for further analysis (difference mean = 0.0258; standard deviation = 0.0291). Permutation GLMM revealed that 1) BA 46 participation coefficients were matched be- tween the sham and the active stimulation groups (|z| = 1.26) in the selected subsample and that 2) stimulation significantly reduced the BA 46 participation coefficients (|z| = 1.68, significant interaction effects) in this subset of participants. DISCUSSION The goal of this study was to delineate how tDCS applied to the lDLPFC modulates interactions between the 1388 Journal of Cognitive Neuroscience Volume 33, Number 7 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / 3 3 7 1 3 8 1 1 9 2 1 2 3 2 / / j o c n _ a _ 0 1 7 2 5 p d . f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 we further took the network structure into consideration, and found that the lDLPFC stimulation might increase the role of lDLPFC in the FPCN via modulating the con- nectivity between lDLPFC and other regions within the network. Given that the participation coefficient reflects the dis- tribution of connectivity across networks, it is somewhat counterintuitive that stimulation significantly strength- ened connectivity of BA 9 within FPCN but stimulation did not significantly affect its participation coefficient. We suspected that this might reflect a concomitant in- crease of connectivity between BA 9 and other networks in addition to the FPCN. The numerical increase of outside-FPCN connectivity of BA 9 (Figure 4A) was consis- tent with this idea, and when we further probed BA 9’s connectivity with each network, tDCS numerically in- creased connectivity between BA 9 and all other networks with the exception of Salience Ventral Attention Network. In other words, BA 9 obtained stronger affinity/functional connectivity with a broad range of networks, which did not significantly alter the relative distribution of its con- nections across networks. In contrast, there was a significant Group × Session inter- action effect on the participation coefficient of BA 46. This finding is interesting as it suggests that the stimulation may not only modulate connectivity within the targeted network but also modulate the target’s connectivity with other net- works. Given that BA 46 connectivity differed between the sham and active stimulation groups in the prestimulation data (see interaction effects and prestimulation differences in Results section), we performed a follow-up analysis on subsamples of participants who were equated in the presti- mulation participation coefficient values. Even after match- ing prestimulation participation coefficient values, we found a significant effect of stimulation on the BA 46 partic- ipation coefficient, suggesting that our finding was not sim- ply because of sampling error. The finding that the connectivity distribution (participation coefficient) of BA 46 was decreased by stimulation suggests that BA 46 be- came less likely to be interacting with multiple other networks. There are some limitations of this study. The study de- sign included only sham and active stimulation groups and did not have an active control condition. Although the current design with a sham stimulation condition al- lows for comparing effects of the stimulation to the ab- sence of stimulation, we cannot conclude from this study that lDLPFC is the only stimulation target that can lead to the connectivity changes shown in our results. Relatedly, one may argue that connectivity changes might be mere epiphenomena of stimulation-related sensation, or arousal. We ruled out this possibility via demonstrating that there was no between-group difference in the sensa- tion and arousal ratings. However, we acknowledge that an optimal approach is to include an active control condi- tion in the design and that studies following up on the pres- ent findings should include an active control condition. Kim et al. 1389 Figure 5. Changes of lDLPFC participation coefficient. (A) Stimulation did not significantly alter participation coefficient of BA 9. (B) Participation Coefficient of BA 46 was significantly reduced by stimulation. The bar graph depicts participation coefficients in each stimulation group and session, and insets depict permutation test results for the interaction effect (Stimulation Session × Group). Histogram: permutation distribution, x- axis: coefficient size, y-axis: probability, red line: regression coefficient, gray shade: > 95th
percentile of the permutation distribution.

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Another caveat is that the active stimulation group in-
cluded participants who received different stimulation in-
tensities (1 mA or 2 mA). We used these intensities as
lDLFPC stimulation delivered at 1 mA or 2 mA has been
shown to be effective for yielding behavioral effects in
prior research (e.g., for vigilance/attention, 1 mA: Nejati
et al., 2020; Nelson et al., 2014; Gladwin et al., 2012;
2 mA: McIntire et al., 2017; McKinley et al., 2013), but
we treated them together as the active stimulation group
in the analyses as we did not have strong a priori predic-
tions on the effects of stimulation intensity. Nevertheless,
to address the possibility that stimulation effects shown
in this study could have been driven by stimulation of
one intensity and not the other, we first assured that
there was no preexisting difference between 1 and 2 mA
groups (Table 2; demographic feature, scanning intervals,
and stimulation-related sensation reports). More impor-
tantly, we tested whether the two groups differed in any
of the stimulation effects using GLMM and permutation
tests, and found no between-group difference (see
Results section for details). Therefore, it is unlikely that
the connectivity changes observed in this study were pre-
dominantly driven by one stimulation intensity group.

With the present data, we are not able to directly relate
the observed network changes to cognitive performance
measures and, therefore, cannot draw conclusions on
what would be the functional benefit of the lDLPFC-
FPCN connectivity changes. Building on the present
results, future studies can investigate whether lDLPFC
stimulation improves cognition by modulating connectiv-
ity of BA 9 within the FPCN. Accumulated fMRI findings
have established that the lDLPFC plays essential roles in
various cognitive functions including cognitive control
(meta-analysis and reviews: Vanderhasselt, De Raedt, &
Baeken, 2009; Neumann, von Cramon, & Lohmann,
2008; Nee, Wager, & Jonides, 2007; Owen, McMillan,
Laird, & Bullmore, 2005), long-term memory processing
(meta-analysis and reviews: Blumenfeld & Ranganath,
2007; Fletcher & Henson, 2001; Nolde, Johnson, & Raye,
1998) and working memory maintenance (meta-analysis
and reviews: Wager & Smith, 2003). Therefore, on the
one hand, increasing neuronal excitability in the lDLPFC
via anodal tDCS might be sufficient for modulating these
task functions. On the other hand, it is notable that these
cognitive functions also engage other regions in the FPCN
(Lemire-Rodger et al., 2019; Roberts, Libby, Inhoff, &
Ranganath, 2018; Thakral, Wang, & Rugg, 2017; Spaniol
et al., 2009; Blumenfeld & Ranganath, 2006; Owen et al.,
2005; Wager & Smith, 2003; Dobbins, Foley, Schacter, &
Wagner, 2002; Pessoa, Gutierrez, Bandettini, & Ungerleider,
2002). This brings up the possibility that the tightened
lDLPFC-FPCN connectivity may contribute to behavioral
modulations associated with lDLPFC stimulations. Future
studies using a wider array of cognitive tasks should allow
for directly investigating this question.

The present results raise the question as to whether the
kinds of modulations seen following lDLPFC stimulation

would generalize to targeted stimulation of other nodes
of FPCN. For example, if tDCS is applied to a node of
FPCN other than lDLPFC, would the connectivity of that
node be modulated the same way as lDLPFC? Or is it a
unique characteristic of lDLPFC that its network relation-
ships are particularly malleable? Future research should
also address the importance of the duration and additivity
of modulation effects. Are the modulation effects on the
target connectivity long-lasting? If so, what does the decay
function look like? In addition to the duration of stimula-
tion effects, it would be informative to understand whether
the observed stimulation effects can be cumulative over
repeated stimulation. A prior transcranial magnetic
stimulation study showed that stimulation repeated over
five consecutive days (i.e., five stimulation sessions)
brought about memory enhancement that lasted for a
long term (e.g., 15 days; Wang & Voss, 2015). Some
behavioral and imaging studies have tested the effects of
repeated tDCS, but so far evidence is sparse for additive
effects. tDCS repeatedly applied with a 10-hr-long gap
between stimulation sessions did not have an additive be-
havioral benefit (McIntire, McKinley, Goodyear, McIntire,
& Nelson, 2020). tDCS applied repeatedly over three con-
secutive days significantly influenced brain perfusion
measured using arterial spin labeling (Sherwood et al.,
2018); however, we do not know whether these tDCS
effects additively increased across sessions in a dose-
dependent manner. Future research is needed to under-
stand whether the target connectivity modulation effects
can be sustained and/or enhanced via repeated applica-
tion of tDCS.

Acknowledgments

We thank Casserly R. Mullenger and Aaron T. Madaris for their
assistance in data collection.

Reprint requests should be sent to Kamin Kim or Charan
Ranganath, Center for Neuroscience, University of California
Davis, 1544 Newton Cir., Davis, CA, or via e-mail: kmikim
@ucdavis.edu; cranganath@ucdavis.edu.

Author Contributions

Kamin Kim: Conceptualization; Formal analysis;
Investigation; Software; Visualization; Writing—Original
draft; Writing—Review & editing. Matthew S. Sherwood:
Conceptualization; Data curation; Investigation;
Methodology; Project administration; Writing—Review &
editing. Lindsey K. McIntire: Data curation; Investigation;
Project administration; Writing—Review & editing. Andy
R. McKinley: Conceptualization; Funding acquisition;
Resources; Supervision; Writing—Review & editing. Charan
Ranganath: Conceptualization; Funding acquisition;
Resources; Supervision; Writing—Original draft; Writing—
Review & editing.

1390

Journal of Cognitive Neuroscience

Volume 33, Number 7

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Funding Information

This project was supported by the Air Force Research
Laboratory (AFRL) under the Human Interface and
Research Technology program (https://dx.doi.org/10
.13039/100000005), grant number: FA8650-14-D-6500;
and by a Vannevar Bush Faculty Fellowship (ONR grant
N00014-15-1-0033) to C.R. Any opinions, findings, and
conclusions or recommendations expressed in this mate-
rial are those of the authors and do not necessarily reflect
the views of the U.S. Department of Defense.

Diversity in Citation Practices

A retrospective analysis of the citations in every article
published in this journal from 2010 to 2020 has revealed
a persistent pattern of gender imbalance: Although the
proportions of authorship teams (categorized by estimated
gender identification of first author/last author) publishing
in the Journal of Cognitive Neuroscience ( JoCN) during
this period were M(an)/M = .408, W(oman)/M = .335,
M/ W = .108, and W/ W = .149, the comparable proportions
for the articles that these authorship teams cited were
M/M = .579, W/M = .243, M/ W = .102, and W/ W = .076
(Fulvio et al., JoCN, 33:1, pp. 3–7). Consequently, JoCN
encourages all authors to consider gender balance ex-
plicitly when selecting which articles to cite and gives
them the opportunity to report their article’s gender cita-
tion balance. The authors of this article report its propor-
tions of citations by gender category to be as follows:
M/M = .523, W/M = .233, M/ W = .116, and W/ W = .128.

Note

1. A stimulation session was preceded and followed by a scan-
ning session, and resting-state fMRI scans were acquired at the
beginning of each scanning session. Therefore, the interval be-
tween the prestimulation resting-state fMRI scanning and the
stimulation session (approximately 75 min) included some in-
scanner time, and stimulation setup time. During the in-scanner
time following resting-state fMRI scanning, participants engaged
in a working memory task for 30 min and then stayed task-free
for the remaining time in the scanner (∼22 min, structural MRI,
DTI, ASL, and MRS). Participants were instructed to relax and re-
main still during structural MRI, DTI, and MRS scanning. They
were further informed that they could close their eyes but needed
to remain awake. The fixation point remained on the screen; how-
ever, no other stimuli (auditory or visual) were provided. During
ASL scanning, participants were instructed to relax, clear their
mind and let their thoughts wander freely, and focus on the fixa-
tion dot. The interval between and the stimulation session and
the poststimulation resting-state fMRI scanning (approximately
10–40 min) included stimulation wrap-up and waiting time (be-
cause of scanner logistics, some participants had waiting time of
up to 30 min before the poststimulation scanning). During the
waiting time, participants stayed in the preparation area at the
testing site.

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