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\begin{document}

\title{Dose-dependent response of prefrontal transcranial direct current
stimulation on heart rate variability: an electric field modeling study}



\author[1]{Lais Razza}%
\author[2]{Stefanie De Smet}%
\author[3]{Stevan Nikolin}%
\author[4]{Xander Cornelis}%
\author[5]{Matias Pulopulos}%
\author[6]{Rudi De Raedt}%
\author[7]{André Brunoni}%
\author[4]{Marie-Anne Vanderhasselt}%
\affil[1]{Ghent University Faculty of Medicine and Health Sciences}%
\affil[2]{UZ Gent}%
\affil[3]{Univ New South Wales}%
\affil[4]{Ghent University}%
\affil[5]{Ghent University Faculty of Psychology and Educational Sciences}%
\affil[6]{Affiliation not available}%
\affil[7]{Universidade de São Paulo}%


\vspace{-1em}



  
  \date{May 28, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Transcranial direct current stimulation (tDCS) of the prefrontal cortex
(PFC) modulates the autonomic nervous system by activating deeper brain
areas via top-down pathway. However, effects on the nervous system are
heterogeneous and may depend on the amount of current that penetrates
the brain due to individual brain anatomical differences. Therefore,
investigated the variable effects of tDCS on heart rate variability
(HRV), a measure of the functional state of the autonomic nervous
system. Using three prefrontal tDCS protocols (1.5mA, 3mA and sham), we
associated the simulated individual electric field (E-field) magnitude
in brain regions of interest with the HRV effects. This was a
randomized, double-blinded, sham-controlled and within-subject trial, in
which participants received tDCS sessions separated by two weeks. The
brain regions of interest were the dorsolateral PFC (DLPFC), anterior
cingulate cortex, insula and amygdala. Overall, 37 participants (mean
age = 24.3 years, standard deviation = 4.8) were investigated,
corresponding to a total of 111 tDCS sessions. The findings suggested
that HRV, measured by Root Mean Squared of Successive Differences
(RMSSD) and high-frequency HRV (HF-HRV), were significantly increased by
the 3.0mA tDCS when compared to sham and 1.5mA. No difference was found
between sham and 1.5mA. E-field analysis showed that all brain regions
of interest were associated with the HRV outcomes. However, this
significance was associated with the protocol intensity, rather than
inter-individual anatomical variability. To conclude, our results
suggest a dose-dependent effect of tDCS for HRV. Therefore, further
research is warranted to investigate the optimal current dose to HRV.%
\end{abstract}%



\sloppy


Title: Dose-dependent response of prefrontal transcranial direct current
stimulation on heart rate variability: an electric field modeling study

La\selectlanguage{ngerman}ís B. Razza¹ ²*; Stefanie De Smet¹ ²; Stevan Nikolin³; Xander
Cornelis²; Matias M. Pulopulos\selectlanguage{english}4; Rudi De Raedt4; \selectlanguage{english}Andre R. Brunoni 5 6 7;
\selectlanguage{english}Marie-Anne Vanderhasselt\selectlanguage{ngerman}¹ ²

1.Department of Head and Skin, Psychiatry and Medical Psychology, Ghent
University Hospital, Ghent University, 9000 Ghent, Belgium. 2.Ghent
Experimental Psychiatry (GHEP) Lab, Ghent University, 9000 Ghent,
Belgium. 3. School of Psychiatry, University of New South Wales, Sydney,
Australia; Black Dog Institute, Sydney, Australia. 4.Department of
Experimental Clinical and Health Psychology, Ghent University, Ghent,
Belgium. 5. Departamento de Clínica Médica, Faculdade de Medicina da
Universidade de São Paulo \& Hospital Universitário, Universidade de São
Paulo, Av. Prof Lineu Prestes 2565, 05508-000, São Paulo, Brazil. 6.
Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil. 7.
Serviço Interdisciplinar de Neuromodulação, Laboratório de Neurociências
(LIM-27), Departamento e Instituto de Psiquiatria, Hospital das
Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo,
Brazil

*Correspondence: Laís Boralli Razza, PhD, Department of Head and Skin -
Psychiatry and Medical Psychology, Ghent University Hospital, Corneel
Heymanslaan 10 -- 9000, Ghent,Belgium.

E-mail address:\emph{lais.razza@ugent.be}.

Abstract

Transcranial direct current stimulation (tDCS) of the prefrontal cortex
(PFC) modulates the autonomic nervous system by activating deeper brain
areas via top-down pathway. However, effects on the nervous system are
heterogeneous and may depend on the amount of current that penetrates
the brain due to individual brain anatomical differences. Therefore, we
aimed to investigate the variable effects of tDCS on heart rate
variability (HRV), a measure of the functional state of the autonomic
nervous system. Using three prefrontal tDCS protocols (1.5mA, 3mA and
sham), we associated the simulated individual electric field (E-field)
magnitude in brain regions of interest with the HRV effects. This was a
randomized, double-blinded, sham-controlled and within-subject trial, in
which participants received tDCS sessions separated by two weeks. The
brain regions of interest were the dorsolateral PFC (DLPFC), anterior
cingulate cortex, insula and amygdala. Overall, 37 participants (mean
age = 24.3 years, standard deviation = 4.8) were investigated,
corresponding to a total of 111 tDCS sessions. The findings suggested
that HRV, measured by Root Mean Squared of Successive Differences
(RMSSD) and high-frequency HRV (HF-HRV), were significantly increased by
the 3.0mA tDCS when compared to sham and 1.5mA. No difference was found
between sham and 1.5mA. E-field analysis showed that all brain regions
of interest were associated with the HRV outcomes. However, this
significance was associated with the protocol intensity, rather than
inter-individual anatomical variability. To conclude, our results
suggest a dose-dependent effect of tDCS for HRV. Therefore, further
research is warranted to investigate the optimal current dose to
modulate HRV.

Keywords: transcranial direct current stimulation; heart rate
variability; autonomic nervous system; electric field; dose-dependency

Background

Dysregulation in the autonomic nervous system is common in a variety of
psychiatric disorders. Heart rate variability (HRV), an index of
beat-to-beat variation in the heart, is frequently used to evaluate
autonomic (dys)function, and, as such, changes in this measurement are
confirmed to be associated with psychiatric illnesses, including
depression (Koch et al., 2019)(Borrione et al., 2018).

According to the central autonomic network model, the prefrontal cortex
(PFC) and deeper brain regions are involved in high-order autonomic
control (Benarroch, 1993). Neuromodulation of the PFC can activate the
parasympathetic branch of the autonomic nervous system via top-down
influences. This subsequently alters cardiovascular autonomic responses,
including HRV (Shaffer et al., 2014)(Thomas et al., 2019). In this
sense, previous studies showed that the manipulation of PFC activity
using non-invasive brain stimulation (NIBS) techniques can modulate HRV,
confirming the prediction of the brain-body connection of central
autonomic network theory in humans(Vanderhasselt \& Ottaviani,
2022)(Nikolin et al., 2017; Schmaußer et al., 2022).

A NIBS intervention that is particularly promising, due to its safety
profile and accessible use, is transcranial direct current stimulation
(tDCS). TDCS is able to modulate brain activity via a low-intensity
direct electric current applied to the scalp (Lefaucheur \& Wendling,
2019). The electric current can increase or decrease cortical
excitability in both locally stimulated regions and downstream connected
brain networks (Makovac et al., 2017). Although the technique might be
able to modulate HRV of healthy and depressive patients by means of
targeting the PFC network, the overall findings are still heterogeneous
(Razza, Wischnewski, et al., 2023; Wischnewski et al., 2021). This
heterogeneity can be explained in part by interindividual differences in
brain morphology, which might alter electric current distributions in
the brain (Polanía et al., 2018). Hence, it is plausible that variance
in the electric current that penetrates specific brain areas due to
individual anatomical variability may underlie the subsequent variance
observed in the measured HRV response. Recently, technological
developments allow evaluating simulations of the electrical current
injected into the brain (Saturnino et al., 2019). While some studies
have shown that the simulated electric field (E-field) strength might be
associated with cognitive and affective prefrontal tDCS
effects(Caulfield et al., 2022; Suen et al., 2020), no study so far has
investigated its impact on HRV.

In this study the modulatory effects of distinct prefrontal tDCS
intensities (1.5mA, 3mA and sham) on the parasympathetic effects (HRV)
is investigated in healthy individuals, and it is explored whether the
magnitude of the E-field in brain regions of interest is associated with
this outcome. Based on the central autonomic network model, the brain
regions of interest were the anterior cingulate cortex (ACC), insula,
amygdala and the PFC - focusing on the dorsolateral region. We
hypothesize that 1) greater current intensities (1.5mA vs 3.0mA) will
increase HRV at a population level (reflecting increased parasympathetic
control); 2) individual E-field magnitudes in brain regions of interest
will explain inter-individual heterogeneity in HRV modulation, with
individuals experiencing relatively higher E-fields demonstrating
greater HRV modulation (Wei et al., 2018).

\textbf{Design}

A randomized, double-blind, sham-controlled within-subjects trial design
was employed, in which participants received different tDCS protocols
(sham tDCS, 1.5mA tDCS, and 3.0mA tDCS). A two-week interval was
incorporated between the experimental sessions to mitigate any carryover
effects. The research is in line with the Declaration of Helsinki, and
it received the approval of the Ethics Committee of the Ghent University
Hospital (UZ Ghent - B6702021000839). The study was conducted at the
Ghent University Hospital of Ghent University, from March 2022 to March
2023. The current study is part of a larger project (Razza, De Smet, et
al., 2023).

\textbf{Participants}

Participants were between 18 to 45 years old without any current or past
mental or neuropsychiatric disorder. A psychologist screened all
potential participants using the Diagnostic and Statistical Manual of
Mental Disorders - version 5 (DSM-5) and the Beck Depression Inventory
(BDI) (Beck et al., 1961). Exclusion criteria for the study were as
follows: (1) contraindications for the applied techniques, including
tDCS and Magnetic Resonance Imaging (MRI), (2) habitual smoking (more
than 10 cigarettes per day) or abuse/dependence on other drugs, (3)
pregnancy, (4) use of psychoactive drugs, including antidepressant
drugs, benzodiazepines, and Z-drugs, and (5) serious clinical
conditions. The participants included in the study were contacted from a
pool of participants who have previously undergone other studies
conducted by our laboratory. Prior to participation, all participants
provided written informed consent.

The required sample size for this study was determined by employing a
small to moderate effect size of f = 0.25, alpha of 0.05, and a power of
0.80, based on three repeated measurements (G*Power 3.1 software), which
generated a required sample size of 28 participants. To account for
potential dropouts or data loss, a total of 40 participants were
recruited.

\subsection*{Experiment Procedure}

{\label{experiment-procedure}}

The experiment consisted of four separate visits. Firstly, an anatomical
3-Tesla MRI acquisition was performed (T1- and T2-weighted sequences;
repetition time (TR) = 1900 milliseconds, echo time (TE) = 2.2
milliseconds, flip angle = 9°, 176 slices/volume, slice thickness = 0.8
mm). These images were posteriorly used for neuronavigation and for
electric field modeling analyses. Afterwards, participants underwent
three tDCS experiment sessions (Figure 1).

All experiment sessions took place in a well-controlled laboratory
environment. Participants were asked to sleep sufficiently, and abstain
from intense physical activity and alcohol/caffeine consumption 2 hours
prior to the session. Throughout all three sessions, participants were
seated in a comfortable chair and positioned their knees at a 90-degree
angle. In the first experiment session, a neuronavigation procedure
(Brainsight, Rogue Resolutions, Inc) based upon the individual subject's
MRI image was performed to individually determine the targeted DLPFCs.
These targets were marked in a cap that was used in the following
sessions.

At the beginning of each session, self-report baseline measurements were
collected and the physiological equipment to record cardiac activity was
set up. Cardiac activity was then collected for 5 minutes at rest (i.e.
baseline period). The neuromodulation session started after the end of
the baseline period. A 0-back task was performed together with the tDCS
to control and standardize ongoing neural activity during tDCS. For the
0-back, random words appeared on a computer screen and the participant
had to press a specific letter on the keyboard of the occurrence of a
specific word on the screen. Cardiac activity was collected during the
entire tDCS session (20 minutes) (Figure 1).

Figure 1. Study Design\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Abbreviation: ECG: Electrocardiogram; E-Field: Electric Field; HRV:
Heart Rate Variability; MRI: Magnetic resonance Imaging; tDCS:
transcranial direct current stimulation.

Heart rate variability

Cardiac activity was measured continuously and then separated in four
time-points: 5-min of baseline (baseline), initial 5 minutes of the tDCS
session (5-min tDCS), 10-min during the 0-back task (during 0-back) and
the last 5-min of the tDCS session (last 5-min tDCS). Cardiac activity
was acquired using a Biopac ECG100C amplifier (Biopac Systems Inc., USA)
and Biopac Acqknowledge software 4.3. For the amplifier, the gain was
set at 5000, with a high pass filter of 0.05 Hz and a low pass filter of
35 Hz. The sampling rate was set at 1000 Hz. The electrocardiogram was
set-up using the lead I configuration (Francis, 2016), with two Ag/Agcl
electrodes attached below the right and left clavicle and a third
reference electrode placed under the left ribs. The electrocardiogram
data were analyzed with PhysioData Toolbox (version 0.6.3) which allows
for automated R-peak detection and inter-beat-interval (IBI) extraction.
Misidentified R-peaks were manually corrected after visual inspection of
the data.

HRV (in milliseconds) was assessed by calculating the Root Mean Squared
of Successive Differences (RMSSD) of the detrended IBI data (Tarvainen
et al., 2002 )and High-Frequency-HRV (HF-HRV, 0.15 to 0.40 Hz), using
the absolute power of the HF band, as calculated using the Lomb-Scargle
method. Importantly, although there are several ways to investigate HRV,
only RMSSD and HF-HRV were analyzed here because they present robust
effects when using NIBS intervention over the PFC and they are markers
of the parasympathetic system (Makovac et al., 2017).

RMSSD and HF-HRV were calculated for time epochs of 5 min, as
recommended elsewhere (Malik et al., 1996).Therefore, for the time-point
in which we recorded the HRV during the 0-back task (10min), the average
of two epochs of 5 min was obtained.

tDCS

TDCS was performed using a NeuroConn device (DC-Stimulator Plus,
NeuroConn, Germany). The session consisted of a 20-minute direct
current, delivered using two rubber electrodes of 25cm\selectlanguage{ngerman}² (5x5cm) applied
to the scalp with a conductive gel. The anode and cathode were placed
over the left and right neuronavigated DLPFC, respectively ( left: x =
-38, y = +44, y = +26; right: x = 38, y = +44, y = +26) (Blumberger et
al., 2018), pointing towards Cz. Stimulation at these coordinates has
been found to achieve optimal anticorrelation with the ACC in functional
brain studies (Fox et al., 2012)(Razza et al., 2022). The active tDCS
sessions were performed using currents of 1.5mA and 3mA. The sham
protocol was identical but consisted of a brief active period of 30
seconds fade-in and 30 seconds fade-out at the beginning and end of the
session to a current intensity of 3mA with the device set to 0mA for the
remainder. The `study mode' of the tDCS device was used to deliver
active or sham current based on a randomized imputed code, allowing for
double-blinding of participants and study personnel. The codes were
randomized via\emph{https://www.randomizer.org}and were managed by an
independent party not directly involved in data acquisition.

Self-reported measures

The State-Trait-Anxiety Inventory (STAI) state inventory was
administered at the beginning of each session (Spielberger et al.,
1970). The STAI state measures how participants are feeling at that
moment using a 4-point Likert scale (1 = not at all, 2 = a little, 3 =
moderately, 4 = very much). This measurement was collected to identify
the baseline psychological state of each participant before the tDCS
session. Moreover, the Visual Analogue Scale (VAS) was collected two
times (at baseline and after the tDCS session). In VAS participants are
asked to indicate from `zero' to `one-hundred' (``I do not experience
this at all'', ``I experience this very much'') how much they were
feeling the following moods: `angry', `tense', `sad' `happy', `stressed'
and `anxious'. Higher scores indicate higher levels of perceived stress
or negative affect.

\textbf{Computational Modeling Analysis}

E-field simulation was performed using SimNIBS (Version 4.0, Copenhagen,
Denmark) (Saturnino, Madsen, and Thielscher 2019), an open-source
software that allows the estimation of the tDCS-induced E-field
distribution in individual brains. Firstly, head models of each
participant were generated using \emph{charm} (from individual T1- and
T2-weighted structural MRI data (Puonti et al., 2020)), which segments
the MRI images into nine distinct tissue types. For this analysis, the
default conductivity values for each tissue were applied. Subsequently,
the software generated a 3D tetrahedral mesh structure for each
segmented tissue, allowing the simulation of the tDCS-induced E-field in
each participant's brain. Segmentations were manually verified to
investigate any potential errors.

The tetrahedral head meshes resulting from the segmentation procedure
were used to simulate the E-field distribution resulting from the two
active tDCS protocols (1.5 mA and 3 mA). The sham protocol was not taken
into consideration for our analysis as it does not produce an E-field in
the head. To simulate the E-field in the brain, the tDCS set-up applied
was identical as used in the experimental sessions. The electrodes
(5x5cm) were placed over the MNI coordinates retrieved from
neuronavigation, pointing towards Cz, with a thickness of 5 mm
(electrode + conductivity paste). SimNIBS scripts were executed in
MATLAB (version R2022a).

Finally, the E-field magnitude within predefined brain regions of
interest were extracted. These regions were the bilateral DLPFC,
bilateral ACC, bilateral insula and bilateral amygdala. The DLPFC was
extracted from the Sallet atlas (Sallet et al., 2013), whereas the ACC,
insula and amygdala were extracted from the Brainnetome atlas (Fan et
al., 2016) (Sup. Material - Appendix 1).

Values analyzed in this study were the mean magnitude of the electric
field (magn-E component).

\textbf{Statistical Analysis}

All statistical analyses were performed with R software (version 4.1.2,
R Core Team,\emph{2021}). To investigate tDCS-induced changes in HRV, we
calculated the change from baseline to the tDCS periods (5-min tDCS,
during 0-back, and last 5-min tDCS) using delta scores (i.e: \selectlanguage{greek}Δ \selectlanguage{english}score =
RMSSD of 5-min tDCS minus RMSSD of baseline or HF-HRV of 5-min tDCS
minus HF-HRV of baseline, etc). Outliers were inspected via boxplot
distribution and three observations were excluded for the RMSSD data,
while none was excluded for the HF-HRV (Sup. Material - Appendix 2). A
linear mixed model (LMM) (`lme4' package) was used to assess the effects
of tDCS on the HRV. The model included either delta RMSSD or HF-HRV
scores as the dependent variable, with a fixed effect of tDCS protocol,
time and their interaction (tDCS protocol*time). `Subject' was employed
as a random intercept (full statistical model: `RMSSD/HF-HRV
\textasciitilde{} tDCS protocol*time + (1\textbar{}Subject))`. Pairwise
analyses were performed using the `emmeans' function.

Secondly, we investigated whether the HRV changes were associated with
E-field magnitude induced by tDCS in the brain regions of interest. In
the first step we employed LMM models having the RMSSD/HF-HRV delta
scores as dependent variables, whereas the mean E-field in each region
of interest were the fixed factors. `Subject' was considered a random
intercept. To investigate whether the mean individual E-fields were
associated with the outcome, the same model was used but the variable
`protocol' was included as a fixed factor (RMSSD/HF-HRV
\textasciitilde{}E-field + tDCS protocol + (1\textbar{}Subject)). In
total, eight models were performed per analysis (4 brain regions of
interest x 2 hemispheres). Therefore, multiple comparison corrections
were conducted using the false discovery rate (`stats' package). Only
the corrected p-values are presented here.

Exploratory analyses investigated whether the HRV changes could be
influenced by baseline mood or state anxiety. For this analysis a LMM
was fitted with the RMSSD/HF-HRV as the dependent variable and the
interaction between mood and tDCS protocol (i.e: VAS baseline*tDCS
protocol and STAI*tDCS protocol) as the independent variable (full
model: RMSSD/HF-HRV \textasciitilde{} mood baseline*tDCS protocol +
(1\textbar{}Subject)). For all statistical tests, the significance level
was set to alpha = 0.05.

Results

A total of 40 healthy volunteers were included. Two dropped-out after
the first session and one was excluded due to non-normal alterations in
the heart rate as indicated by a cardiologist. The final analyses were
performed with 37 subjects (mean age = 24.3 years, standard deviation
(SD) = 4.8), representing a total of 111 sessions being performed. Due
to electrocardiogram artifacts during data collection (especially during
the 0-back) a few time points were dropped from our dataset. Therefore,
our analyses were conducted with a total of 103 time-points each for
sham and 1.5mA, and 96 time-points for the 3.0mA protocol. For more
information, a comprehensive table can be found in the Sup. Material -
Appendix 3.

\emph{tDCS protocol and HRV}

The results of our analysis revealed a significant effect of tDCS
protocol (chi-square (\selectlanguage{greek}χ\selectlanguage{english}2) = 8.02, p = 0.018) in the RMSSD measure.
Post-hoc analyses revealed that HRV during 3.0mA tDCS, measured with
RMSSD, was significantly increased compared to sham (beta= -9.97,
standard error (SE) = 1.96, p \textless{} 0.001) and 1.5mA (beta= -7.92,
SE = 1.94, p = 0.001). No significant difference was found between 1.5mA
tDCS and sham (beta= -2.06, SE = 1.9, p = 0.28) (Figure 2A and Sup.
Material - Appendix 4). The interaction between tDCS protocol and time
is presented in the Sup. Material - Appendix 5.

The same results were found for the HF-HRV measure (tDCS protocol: \selectlanguage{greek}χ\selectlanguage{english}2 =
6.88, p = 0.03). Post-hoc analyses revealed that HF-HRV were greater
with 3.0mA compared to sham (beta= -606, SE = 112, p \textless{} 0.001)
and 1.5mA (beta= -454, SE = 112, p = 0.001) (Figure 2B and Sup. Material
- Appendix 6). The interaction between tDCS protocol and time is
presented in the Sup. Material - Appendix 7.

No association was found between HRV changes and the interaction between
protocol and psychological state at the baseline (STAI: \selectlanguage{greek}χ\selectlanguage{english}2 = 2.18, p =
0.33; VAS baseline: \selectlanguage{greek}χ\selectlanguage{english}2 = 0.45, p = 0.79).

Figure 2. Effects of tDCS on HRV. A) Results of RMSSD; B) Results of
HF-HRV.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\emph{HRV and E-field magnitude}

A significant association was found between both measures of HRV (RMSSD
and HF-HRV) and the E-field magnitude in all brain regions of interest,
showing that stronger E-field are able to induce greater HRV changes
(Table 1 and Figures 3 and 4).

Table 1. Mixed effects model outcomes showing the association between
E-field strength in the brain regions of interest and HRV modulation.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\textbf{RMSSD} & \textbf{RMSSD} & \textbf{RMSSD} & \textbf{RMSSD} &
\textbf{RMSSD} & \textbf{RMSSD} & \textbf{RMSSD} & \textbf{RMSSD} &
\textbf{RMSSD} & \textbf{RMSSD}\tabularnewline
& \textbf{Left} & \textbf{Left} & \textbf{Left} & \textbf{Left} & &
\textbf{Right} & \textbf{Right} & \textbf{Right} &
\textbf{Right}\tabularnewline
& Beta & SE & t-value & p-value & & Beta & SE & t-value &
p-value\tabularnewline
DLPFC & 37.54 & 8.6 & 4.36 & \textless{}0.001 & & 35.54 & 8.8 & 4 &
\textless{}0.001\tabularnewline
ACC & 48.3 & 11.4 & 4.2 & \textless{}0.001 & & 51.1 & 12.7 & 4 &
\textless{}0.001\tabularnewline
Amygdala & 52.2 & 12.9 & 4.03 & \textless{}0.001 & & 52.6 & 13.6 & 3.9 &
\textless{}0.001\tabularnewline
Insula & 53.3 & 12.8 & 4.16 & \textless{}0.001 & & 48.7 & 12.5 & 3.9 &
\textless{}0.001\tabularnewline
\textbf{HF-HRV} & \textbf{HF-HRV} & \textbf{HF-HRV} & \textbf{HF-HRV} &
\textbf{HF-HRV} & \textbf{HF-HRV} & \textbf{HF-HRV} & \textbf{HF-HRV} &
\textbf{HF-HRV} & \textbf{HF-HRV}\tabularnewline
& \textbf{Left} & \textbf{Left} & \textbf{Left} & \textbf{Left} & &
\textbf{Right} & \textbf{Right} & \textbf{Right} &
\textbf{Right}\tabularnewline
& Beta & SE & t-value & p-value & & Beta & SE & t-value &
p-value\tabularnewline
DLPFC & 2063 & 482 & 4.3 & \textless{}0.001 & & 1986 & 496 & 4 &
\textless{}0.001\tabularnewline
ACC & 2696 & 640 & 4.2 & \textless{}0.001 & & 2891 & 714 & 4.1 &
\textless{}0.001\tabularnewline
Amygdala & 2994.5 & 725.7 & 4.12 & \textless{}0.001 & & 3054.5 & 764 & 4
& \textless{}0.001\tabularnewline
Insula & 3020 & 721 & 4.18 & \textless{}0.001 & & 2820 & 708 & 3.9 &
\textless{}0.001\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
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\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 3. Association between RMSSD and E-field in all brain regions of
interest.

Figure 4. Association between HF-HRV and E-field in all brian regions of
interest.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

In turn, no significance was found when including \emph{tDCS protocol}in
the model, showing that changes in HRV might not be associated with
individual E-field variability in brain regions of interest (Table 2).
E-field head models are presented in the Sup. Material - Appendix 8.

Table 2. Mixed effects model outcomes controlled by `tDCS protocol'
showing no association between E-field strength in the brain regions of
interest and HRV modulation.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\textbf{RMSSD} & \textbf{RMSSD} & \textbf{RMSSD} & \textbf{RMSSD} &
\textbf{RMSSD} & \textbf{RMSSD} & \textbf{RMSSD} & \textbf{RMSSD} &
\textbf{RMSSD} & \textbf{RMSSD}\tabularnewline
& \textbf{Left} & \textbf{Left} & \textbf{Left} & \textbf{Left} & &
\textbf{Right} & \textbf{Right} & \textbf{Right} &
\textbf{Right}\tabularnewline
& Beta & SE & t-value & p-value & & Beta & SE & t-value &
p-value\tabularnewline
DLPFC & 65.9 & 42.9 & 1.5 & 0.13 & & -0.2 & 48.2 & -0.005 &
0.99\tabularnewline
ACC & 59.1 & 53 & 1.1 & 0.26 & & 7.6 & 67.6 & 0.1 & 0.9\tabularnewline
Amygdala & 0.67 & 79.3 & 0.01 & 0.98 & & -83.9 & 86.6 & -0.96 &
0.33\tabularnewline
Insula & 62.9 & 83.4 & 0.75 & 0.45 & & -69.3 & 83.6 & -0.8 &
0.4\tabularnewline
\textbf{HF-HRV} & \textbf{HF-HRV} & \textbf{HF-HRV} & \textbf{HF-HRV} &
\textbf{HF-HRV} & \textbf{HF-HRV} & \textbf{HF-HRV} & \textbf{HF-HRV} &
\textbf{HF-HRV} & \textbf{HF-HRV}\tabularnewline
& \textbf{Left} & \textbf{Left} & \textbf{Left} & \textbf{Left} & &
\textbf{Right} & \textbf{Right} & \textbf{Right} &
\textbf{Right}\tabularnewline
& Beta & SE & t-value & p-value & & Beta & SE & t-value &
p-value\tabularnewline
DLPFC & 3350 & 542 & -0.57 & 0.56 & & 361 & 2610 & 0.14 &
0.89\tabularnewline
ACC & 2892 & 2876 & 1 & 0.32 & & 935 & 3621 & 0.26 & 0.79\tabularnewline
Amygdala & 2596 & 4204 & 0.62 & 0.53 & & -648 & 4746 & -0.14 &
0.89\tabularnewline
Insula & 4458 & 4584 & 0.97 & 0.33 & & -1499 & 4745 & -0.31 &
0.75\tabularnewline
\bottomrule
\end{longtable}

Discussion

This study aimed to investigate the modulatory effects of distinct
prefrontal tDCS intensities (1.5mA, 3mA and sham) on HRV and explored
whether the magnitude of the E-field in brain regions of interest was
associated with this outcome. Our hypotheses posited that higher
electric currents would lead to increased HRV, while individual
anatomical variability would also play a significant role in this
modulation. Specifically, we expected that individuals with higher
magnitudes of E--fields in brain regions of interest would exhibit
greater increases in HRV, as measured by RMSSD and HF-HRV. As
hypothesized, the results showed that tDCS was able to modulate both
cardiovascular measures via a top-down route. However, only the highest
electric intensity (of 3.0mA) increased HRV compared to sham and 1.5mA
current. According to our findings, this modulation was not associated
with anatomical individual differences per se, as evaluated by
computational modeling analysis.

Although this study did not provide evidence to support our hypothesis
that inter-individual variability contributes to the heterogeneous
effects of tDCS, the results presented here are aligned with the
dose-dependent effects of tDCS - with higher electric current
intensities producing increased RMSSD and HF-HRV (Goldsworthy \&
Hordacre, 2017). In this context, a recent meta-analysis showed that the
effects of prefrontal tDCS might be only small to moderate for both
RMSSD and HF-HRV measures of healthy subjects (Schmau\selectlanguage{ngerman}ßer et al., 2022).
However, it is important to note that a limitation of the aforementioned
study was that potential moderators of response to tDCS were not
investigated, including tDCS protocols. Therefore, our findings suggest,
for the first time, that the variability of tDCS effects on
cardiovascular measures might be associated with the heterogeneity of
tDCS protocol (as different electric current is applied across published
studies, i.e: 1mA, 1.5mA and 2mA), rather than inter-individual
anatomical variability.

Following the central autonomic network model, the brain-body connection
is important for regulating parasympathetic control and autonomic
balance (Cameron, 2009). This occurs when the modulation of cortical and
subcortical brain regions - such as the ones discussed here - has the
potential to activate parts of the autonomic nervous system that can
regulate oscillations of the heart rate (Mulcahy et al., 2019). Hence,
it's important to ensure that this top-down approach (from PFC, to
subcortical areas to autonomic nervous system) using tDCS and other
non-invasive brain stimulation interventions seems effective. As tDCS
delivers a low electric current into the brain and almost 75\% of this
current is deflected by different layers including skin, bone, hair and
cerebrospinal fluid, only a small percentage of the current is indeed
able to reach cortical tissue (Vöröslakos et al., 2018). In this sense,
we believe that higher currents (i.e: 3mA), compared to lower currents
(i.e: 1.5mA) are better able to penetrate into the (sub-) cortical
regions, and thereby efficiently modulate the PFC as well as the
parasympathetic system via a top-down regulation.

Moreover, it is worth mentioning that the present study employed a
neuronavigation method aiming to accurately target the MNI coordinate in
the DLPFC optimally associated with the subgenual ACC in depressed
patients, using transcranial magnetic stimulation (Fox et al., 2012).
Although the tDCS electrodes are larger (5x5cm), the utilization of this
precise targeting approach might have increased the connection between
PFC and subcortical areas. This is important to note, as previous
studies used target location based on the 10-20 EEG system or Beam-F3,
both of which are valid methods in the field, but are less accurate than
neuronavigation. Therefore, this approach should be suggested for future
researchers evaluating tDCS on HRV of depressed patients.

Although our study did not reveal significant inter-individual
differences, the analysis utilizing E-field modeling yielded two
important findings. Firstly, all the brain regions of interest exhibited
associations with the outcome measure, indicating that greater simulated
electric current in these areas was associated with more effective
manipulation of HRV. This finding further supports the notion that when
prefrontal tDCS is applied to healthy individuals, it impacts and
modulates all regions that are correlated with the central autonomic
network. Secondly, the graphical representation of the E-field results
indicates that the administration of a higher current intensity (3.0mA)
is associated with increased variability in the E-field within the brain
regions of interest. This observation suggests a greater potential for
modulatory effects and room for improvement when higher currents are
applied.

Both ours and Nikolin and colleagues (Nikolin et al., 2017) studies
showed overall increased HF-HRV for active tDCS conditions relative to
sham. Overall, the results of the 1.5mA protocol of our study seems
really close to what they presented using a current of 2mA. This
comparison also supports our hypothesis of a dose-dependent
relationship. Finally, our study did not demonstrate a reduction in HRV
measures during the concurrent cognitive task when combined with tDCS,
as seen by Nikolin and colleagues (Nikolin et al., 2017) . In fact, the
graphical representations visually depicted an increase in
parasympathetic effects during the 0-back performance, although this
finding did not reach statistical significance. It is important to note
that while HRV measures typically decrease in stressful situations, the
individuals in our study were exposed to an attentional task that
engaged PFC activity, which is known to increase HRV. Thus, the
engagement of PFC activity during the attentional task may have
contributed to the observed increase in HRV, despite the absence of
statistical significance.

\emph{Limitation}

While this study has several strengths, such as a well-powered
within-subjects design and the measurement of various parasympathetic
indicators, it is important to acknowledge and address some notable
limitations. First, a measure of the sympathetic activity was not
evaluated. Therefore, we were not able to investigate how tDCS, via
increasing parasympathetic control, can affect the sympathetic nervous
system. Second, the neuronavigation method used here was based on a
predetermined MRI coordinate. Perhaps, applying individualized DLPFC
location based on functional MRI targeting (Cash et al., 2021) may be a
further improvement for future studies. Third, due to the within-subject
design, blinding was not assessed as it could increase participants'
awareness applying individualized DLPFC location regarding the
intervention protocol in the subsequent session.

\textbf{Conclusion}

This study examined the modulatory effects of different tDCS intensities
on HRV in healthy individuals. The findings support the dose-dependent
effects of tDCS, with the highest electric current intensity (3.0mA)
demonstrating a significant increase in HRV compared to both sham and
1.5mA current. While the individual amount of electric current
penetrating the brain did not appear to significantly influence the tDCS
effects on HRV modulation, E-field modeling analysis suggests that the
heterogeneity of tDCS protocols may contribute to the variability in
tDCS effects on cardiovascular measures. This study highlights the
effects of tDCS on parasympathetic activity of healthy subjects via
prefrontal tDCS, showing a dose-dependent effect. Based on our results,
we believe that further research is warranted to investigate the optimal
tDCS parameters for HRV modulation.

Acknowledgement

LBR is currently supported by a Research Foundation Flanders (FWO) grant
(G0F4619N). RDR and MMP received support from the Research Foundations
FWO and F.R.S.-FNRS under the Excellence of Science (EOS) program (EOS
40007528). MAV received funding from FWO-Flanders for research projects
for fundamental research (Grant Numbers: G044222N; G044019N). ARB
receives grants from the National Council for Scientific and
Technological Development (PQ-1B), and FAPESP (Grants: 2018/10861-7,
2019/06009-6). ARB has a small equity of Flow\selectlanguage{english}, whose devices were not
used in the present study. The LIM-27 laboratory receives grants from
the Associacao Beneficente Alzira Denise Hertzog da Silva. SDS is funded
by a FWO-Flanders PhD fellowship (Grant Number: 11J7521N).The other
authors have no conflicts of interest to disclose.

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