Plasticity of resting state brain networks in recovery from stress

Abstract

Chronic stress has been widely reported to have deleterious impact in multiple biological systems. Specifically, structural and functional remodelling of several brain regions following prolonged stress exposure have been described; importantly, some of these changes are eventually reversible. Recently, we showed the impact of stress on resting state networks (RSNs), but nothing is known about the plasticity of RSNs after recovery from stress. Herein, we examined the “plasticity” of RSNs, both at functional and structural levels, by comparing the same individuals before and after recovery from the exposure to chronic stress; results were also contrasted with a control group. Here we show that the stressed individuals after recovery displayed a decreased resting functional connectivity in the default mode network (DMN), ventral attention network (VAN) and sensorimotor network (SMN) when compared to themselves immediately after stress; however, this functional plastic recovery was only partial as when compared with the control group, as there were still areas of increased connectivity in dorsal attention network (DAN), SMN and primary visual network (VN) in participants recovered from stress. Data also shows that participants after recovery from stress displayed increased deactivations in DMN, SMN and auditory network (AN), to levels similar to those of controls, showing a normalization of the deactivation pattern in RSNs after recovery from stress. In contrast, structural changes (volumetry) of the brain areas involving these networks are absent after the recovery period. These results reveal plastic phenomena in specific RSNs and a functional remodeling of the activation-deactivation pattern following recovery from chronic-stress, which is not accompanied by significant structural plasticity.

Full text

ORIGINAL RESEARCH ARTICLE
published: 27 December 2013
doi: 10.3389/fnhum.2013.00919
Plasticity of resting state brain networks in recovery from
stress
José M. Soares1,2,3*, Adriana Sampaio1,4, Paulo Marques1,2,3, Luís M. Ferreira1,2,3 , Nadine C. Santos1,2,3,
Fernanda Marques1,2,3, Joana A. Palha1,2,3, João J. Cerqueira1,2,3 and Nuno Sousa1,2,3
1Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal
2ICVS/3B’s - PT Government Associate Laboratory, Braga/Guimarães, Portugal
3Clinical Academic Center, Braga, Portugal
4Neuropsychophysiology Lab, CIPsi, School of Psychology, University of Minho, Braga, Portugal
Edited by:
Patrik Vuilleumier, University
Medical Center and University
Hospital Geneva, Switzerland
Reviewed by:
Hamdi Eryilmaz, Harvard University,
USA
Maarten Vaessen, University of
Geneva, Switzerland
*Correspondence:
José M. Soares, Life and Health
Sciences Research Institute (ICVS),
School of Health Sciences,
University of Minho, Campus
Gualtar, 4710-057 Braga, Portugal
e-mail: josesoares@ecsaude.
uminho.pt
Chronic stress has been widely reported to have deleterious impact in multiple biological
systems. Specifically, structural and functional remodeling of several brain regions
following prolonged stress exposure have been described; importantly, some of these
changes are eventually reversible. Recently, we showed the impact of stress on resting
state networks (RSNs), but nothing is known about the plasticity of RSNs after recovery
from stress. Herein, we examined the “plasticity” of RSNs, both at functional and
structural levels, by comparing the same individuals before and after recovery from the
exposure to chronic stress; results were also contrasted with a control group. Here we
show that the stressed individuals after recovery displayed a decreased resting functional
connectivity in the default mode network (DMN), ventral attention network (VAN), and
sensorimotor network (SMN) when compared to themselves immediately after stress;
however, this functional plastic recovery was only partial as when compared with the
control group, as there were still areas of increased connectivity in dorsal attention
network (DAN), SMN and primary visual network (VN) in participants recovered from
stress. Data also shows that participants after recovery from stress displayed increased
deactivations in DMN, SMN, and auditory network (AN), to levels similar to those of
controls, showing a normalization of the deactivation pattern in RSNs after recovery
from stress. In contrast, structural changes (volumetry) of the brain areas involving these
networks are absent after the recovery period. These results reveal plastic phenomena in
specific RSNs and a functional remodeling of the activation-deactivation pattern following
recovery from chronic-stress, which is not accompanied by significant structural plasticity.
Keywords: resting state networks, functional connectivity, deactivation, recovery from stress, plasticity
INTRODUCTION
When the homeostatic mechanisms are disrupted, namely
through prolonged stress exposure, maladaptive responses take
place and trigger inappropriate functional responses. It is well-
established that prolonged stress has deleterious impact in mul-
tiple biological systems, including the central nervous system. In
fact, prolonged stress exposure impairs spatial working memory,
perceptual attention, behavioral flexibility, and decision making
both in rodents and in humans (Joels et al., 2004; Cerqueira et al.,
2005; Dias-Ferreira et al., 2009; Soares et al., 2012; Yuen et al.,
2012), which has been associated with structural and functional
changes of several brain regions. Importantly, some of these mal-
adaptive structural and functional responses to increased chronic
stress were shown to be reversible (Sousa et al., 1998; Heine
et al., 2004; Cerqueira et al., 2005; Goldwater et al., 2009; Bian
et al., 2012; Soares et al., 2012), including evidence showing
that as trait positive affect may potentiate recovery and adaptive
response (Papousek et al., 2010). However, certain stress effects
and specific structural and functional changes may endure after
this recovery period (Joels et al., 2004; Gourley et al., 2013). Of
notice, most stress recovery studies were performed in rodent
models.
A growing field of functional magnetic resonance imaging
(fMRI) has provided new insights into the functional connectivity
across different brain regions. Indeed, resting state fMRI is being
widely used to assess brain regional interactions that comprise
the resting state networks (RSNs) (De Luca et al., 2006; Fox
and Raichle, 2007), both during resting periods and task-induced
deactivations. Moreover, alterations in the normal patterns of
RSNs have been associated with several disease states and neu-
ropsychiatric disorders (Zhang and Raichle, 2010; Meda et al.,
2012; Sripada et al., 2012), including stress exposure (Soares
et al., 2013). Indeed, we previously reported that stressed par-
ticipants had an hyperactivation pattern of the default mode
(DMN), dorsal attention (DAN), ventral attention (VAN), sen-
sorimotor (SMN), and primary visual (VN) networks, paralleled
by structural constriction of the DMN brain regions (Soares et al.,
2013).
The existence of plastic events in the RSNs after recovery from
chronic stress is, however, largely unknown. Indeed, Vai s v a ser
Frontiers in Human Neuroscience www.frontiersin.org December 2013 | Volume 7 | Article 919 |1
HUMAN NEUROSCIENCE

Soares et al. Brain plasticity in stress recovery
et al. (2013), using an acute social stress model, examined
stress-induced responses in the RSNs and cortisol levels before
stress, immediately after the acute stress exposure and 2h later.
The authors found a “recovery” pattern of the DMN connec-
tivity after stress exposure in two of the central hubs of the
DMN (seed ROIs at the posterior cingulate cortex and hippocam-
pus), but not in the amygdala-hippocampal disconnectivity that
was sustained at 2 h post-stress. Moreover, this increased con-
nectivity was inversely correlated with cortisol levels (Vaisvaser
et al., 2013). These results suggest that even acute psychosocial
stressors are associated with a prolonged post-stress DMN con-
nectivity response in specific brain regions. This study used only
an acute stress model and studies addressing how RSNs respond
to chronic stress and identifying specific networks that are asso-
ciated to an efficient recovery are absent. Therefore, the present
study examined the effects of chronic stress on the RSNs fol-
lowing recovery and investigated region-specific changes during
successful recovery from chronic stress exposure.
MATERIALS AND METHODS
PARTICIPANTS, PSYCHOLOGICAL TESTS, AND CORTISOL
MEASUREMENTS
The participants included in this study were 6 stress participants
submitted to prolonged psychological stress exposure (3 males, 3
females; mean age, 23.83 ±0.37), the same 6 stress participants,
6 weeks after the end of the exposure to stress and 6 controls
(3 males, 3 females; mean age, 24.33 ±1.24). Control partici-
pants included a cohort of medical students under their normal
academic activities, whereas the stress group included partici-
pantsthathadjustfinishedtheirlongperiodofpreparationfor
the medical residence selection exam (stress group). Participants
responded to a laterality test and to a self-administered question-
naire regarding stress assessment (Perceived Stress Scale—PSS)
(Cohen et al., 1983). Participants were further assessed with
the Hamilton anxiety scale—HAS (Hamilton, 1959)andthe
Hamilton depression scale—HDS (Hamilton, 1967)byacertified
psychologist. Upon filling of the questionnaires, and immedi-
ately before the imaging acquisitions, participants collected saliva
samples with the help of Salivette (Sarstedt, Germany) collection
devices. Collection took place between 9 and 5 p.m. in all partici-
pants. Samples were stored at −20◦C until the biologically active,
free fraction of the stress hormone cortisol was analyzed using an
immunoassay (IBL, Hamburg).
ETHICS STATEMENT
The study was conducted in accordance with the principles
expressed in the Declaration of Helsinki and was approved by
the Ethics Committee of Hospital de Braga (Portugal). The study
goals and tests were explained to all participants and all gave
informed written consent.
DATA ACQUISITION
Participants were scanned on a clinical approved Siemens
Magnetom Avanto 1.5 T (Siemens Medical Solutions, Erlangen,
Germany) on Hospital de Braga using the Siemens 12-channel
receive-only head coil. The imaging sessions, including one struc-
tural T1, one resting state functional, and two task related
functional acquisitions, were conducted in the same day and
the Siemens Auto Align scout protocol was used to minimize
variations in head positioning. For structural analysis, a T1 high-
resolution anatomical sequence, 3D MPRAGE (magnetization
prepared rapid gradient echo) was performed with the follow-
ing scan parameters: repetition time (TR)=2.4 s, echo time
(TE)=3.62 ms, 160 sagittal slices with no gap, field-of-view
(FoV )=234 mm, flip angle (FA)=8◦, in-plane resolution =
1.2×1.2mm
2and slice thickness =1.2 mm. During resting-state
fMRI acquisition, using gradient echo T2∗weighted echo-planar
images (EPIs), participants were instructed to keep the eyes
closed and to think about nothing in particular. The imaging
parameters were: 100 volumes, TR =3s,TE =50 ms, FA =90◦,
in-plane resolution =3.4×3.4mm
2, 30 interleaved slices, slice
thickness =5 mm, imaging matrix 64 ×64 and FoV =220 mm.
fMRI paradigm acquisition was acquired using: TR =2s, TE =
20 ms, FA =90◦, in-plane resolution and slice thickness 3.3 mm,
38 ascending interleaved axial slices with no gap and FoV =
212 mm. The functional paradigm acquisitions were previously
described (Soares et al., 2012) and the paradigm was presented
using the fully integrated fMRI system IFIS-SA.
IMAGE PRE-PROCESSING
Before any data processing and analysis, all acquisitions were visu-
ally inspected and confirmed that they were not affected by critical
head motion and that participants had no brain lesions.
To achieve signal stabilization and allow participants to adjust
to the scanner noise, the first 5 resting state fMRI volumes (15 s)
were discarded. Data preprocessing was performed using SPM8
(Statistical Parametrical Mapping, version 8, http://www.fil.ion.
ucl.ac.uk) analysis software. Images were firstly corrected for slice
timing using first slice as reference and SPM8’s Fourier phase shift
interpolation. To correct for head motion, images were realigned
to the mean image with a six-parameter rigid-body spatial trans-
formation and estimation was performed at 0.9 quality, 4 mm
separation, 5 mm FWHM smoothing kernel using 2nd degree B-
Spline interpolation. No participants exceed head motion higher
than 2 mm in translation or 1◦in rotation. Images were then spa-
tially normalized to the MNI (Montreal Neurological Institute)
standard coordinate system using SPM8 EPI template and trilin-
ear interpolation. Data were then re-sampled to 3 ×3×3 mm3
using sinc interpolation, smoothed to decrease spatial noise with
a 8 mm, full-width at half-maximum (FWHM), Gaussian kernel,
temporally band-pass filtered (0.01–0.08 Hz) and the linear trend
was removed. The pre-processing of fMRI paradigm images was
previously described (Soares et al., 2012).
INDEPENDENT COMPONENT ANALYSIS AND IDENTIFICATION OF RSN
Spatial independent component analysis was conducted for using
the Group ICA 2.0d of fMRI Toolbox (GIFT, http://www.icatb.
sourceforge.net) (Calhoun et al., 2001; Correa et al., 2005).
Concisely, spatial ICA analysis is a fully data-driven approach
that consists in extracting the non-overlapping spatial maps with
temporally coherent time courses that maximize independence.
The methodology employed by GIFT can be summarized in three
main stages: dimensionality reduction, estimation of the group
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Soares et al. Brain plasticity in stress recovery
independent components, and back-reconstruction of each sub-
ject’s corresponding independent components. The reduction of
dimensionality of the functional data and computational load
was performed with Principal Component Analysis (PCA) in
the concatenated dataset over all subjects, independently of the
groups. Then, 20 independent components were estimated, based
on a good trade-off (clustering/splitting) between preserving the
information in the data while reducing its size (Beckmann et al.,
2005; Zuo et al., 2010), using the iterative Infomax algorithm.
The ICASSO tool was used to assess the ICA reliability, and 20
computational runs were performed on the dataset, during which
the components were being recomputed and compared across
runs and the robustness of the results was ensured (Himberg
et al., 2004). The previous steps result in the estimation of a
mixing matrix with partitions, unique to each subject. The indi-
vidual independent components were then back-reconstructed
from the group-level components. This back-reconstruction step
is accomplished by projecting each subject’s data onto the inverse
of the partition of the calculated matrix corresponding to that
subject. The obtained independent components were expressed
in t-statistic maps, which were finally converted to a z-statistic.
Z-statistic describes the voxels that contributed more intensely to
a specific independent component, providing a degree of func-
tional connectivity within the network (Bartels and Zeki, 2005;
Beckmann et al., 2005). The final components were visually
inspected, sorted, and spatially correlated with resting state func-
tional networks from (Shirer et al., 2012). Each subject’s map
corresponding to the best-fit component of each RSN was used
to perform group statistical analyses.
RSN DEACTIVATION DURING fMRI TASK ANALYSIS
The fMRI decision-making paradigm analyzed to investigate
the task-induced deactivations consisted of two different event-
related jittered design sessions. First session of valued actions with
reward delivery and, after 30 min break, the second session con-
sisted of the devalued actions with the outcome devaluation and
extinction. Both sessions had 150 trials, each with 1.5 s for deci-
sion, 4 s with the choice highlighted, and 2 s for reward delivery,
followed by the interstimulus interval with mean duration of 4 s
[please see Soares et al. (2012), for further details].
fMRI paradigm was analyzed by creating a set of regressors at
resting and decision making periods, which were convolved with
the hemodynamic response function. In order to reliably map
task-induced deactivations, we combined all the resting periods
(resting baseline condition) and all the decision periods (decision
condition), given that decision periods were equally demanding.
The contrast used to assess task-induced deactivations was the
resting baseline condition minus decision condition. Resulting
functional patterns were masked with the previously described
RSNs masks (Shirer et al., 2012).
STRUCTURAL ANALYSIS
Structural analysis based on segmentation of brain structures
from T1 high-resolution anatomical data was performed using
the freely available Freesurfer toolkit version 5.0 (http://surfer.
nmr.mgh.harvard.edu). Intracranial volume (ICV) was used to
correct the volumes and the processing pipeline was the same as
previously described (Soares et al., 2012). DMN was defined by
the summed volume of the angular gyrus of inferior parietal lobe,
the posterior cingulate, the precuneus, and the frontopolar region
(Raichle et al., 2001; Buckner et al., 2008). The summed volume
of the middle frontal gyrus (dorsolateral and prefrontal region)
and the posterior parietal region constituted the DAN (Seeley
et al., 2007; Sridharan et al., 2008). VAN was constituted by the
sum of the temporal-parietal junction and the ventral frontal cor-
tex volumes (Fox et al., 2006). SMN was defined by the summed
volume of the paracentral, precentral postcentral, and the cere-
bellum (Shirer et al., 2012).Thesummedvolumeofthecuneus,
pericalcarine, and the lingual region constituted the primary VN
(Shirer et al., 2012). Auditory network (AN) was defined by the
summed volume of the temporal transverse and the temporal
superior (Shirer et al., 2012).
STATISTICAL ANALYSES
Results of the psychological scales, cortisol levels, and regional
volumes were analyzed in the IBM SPSS Statistics software, v.19
(IBM, New York). Comparisons between the stress recovered
andstressweredonewithpairedsamplest-test and between
stress recovered and control with two-tailed independent-samples
t-test. For all these comparisons significance level was set at 0.05.
Values are presented as mean ±standarderrorofthemean.
Group analysis of the resting state fMRI and task induced deac-
tivations were performed using the second level random effect
analyses in SPM8. Initially, within group analyses were performed
only to confirm the functional connectivity of the RSNs in the
different groups, using one-sample t-tests. Therefore, between
group analyses were implemented with directional two-sample t-
tests. Functional results for all RSNs were considered significant
at p<0.05 corrected for multiple comparisons using a combi-
nation of an uncorrected height threshold of p<0.025 with a
minimum cluster size. The cluster size was determined over 1000
Monte Carlo simulations using AlphaSim program distributed
with REST software tool (http://resting-fmri.sourceforge.net/).
AlphaSim input parameters were the following: individual voxel
probability threshold =0.025, cluster connection radius =3 mm,
gaussian filter width (FWHM) =8 mm, number of Monte Carlo
simulations =1000 and mask was set to the corresponding RSN
template mask. Anatomical labeling was defined by a combina-
tion of visual inspection and Anatomical Automatic Labeling atlas
(AAL) (Tzourio-Mazoyer et al., 2002).
RESULTS
PHYSIOLOGICAL AND BEHAVIORAL RESULTS
Stress impact was confirmed in several parameters: PSS
[Figure 1A;t(10)=2.52, P<0.05; Stress Group M=35.50;
SD =2.59; Control Group M=30.17; SD =4.49] and in the
HAS [Figure 1A;t(10)=2.37, P<0.05 Stress Group M=11.00;
SD =7.95; Control Group M=3.00; SD =2.28], and depres-
sion scores [HAD, Figure 1A;t(10)=3.65, P<0.01; Stress
Group M=7.50; SD =2.59; Control Group M=3.17; SD =
1.33], but only by a trend when it regards to salivary corti-
sol levels [Figure 1B;t(10)=1.69, P=0.12; Stress Group M=
0.44; SD =0.29; Control Group M=0.23; SD =0.10]. After a
stress-free period of 6 weeks after the end of the stress exposure,
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Soares et al. Brain plasticity in stress recovery
FIGURE 1 | Clinical characteristics of the cohort. (A) Perceived Stress
Scale (PSS), Hamilton Anxiety Scale (HAS); Hamilton Depression Scale
(HAD) and (B) Salivary Cortisol levels in the two groups (stress, stress
recovered, and control group); ∗P<0.05.
we observed that all the psychological changes were restored
[Figure 1A; SPP: t(5)=3.72, P<0.05; Stress Group M=35.50;
SD =2.59; Stress-Recovered Group M=30.00; SD =3.03; anx-
iety score: t(5)=2.86, P<0.05, Stress Group M=11.00; SD =
7.95; Stress-Recovered Group M=2.17; SD =2.71]; depres-
sion score: HAD: [t(4)=4.84, P<0.01; Stress Group M=7.50;
SD =2.59; Stress-Recovered Group M=2.5; SD =0.35], except
salivary cortisol levels [t(5)=0.67, P=0.53; Stress Group M=
0.44; SD =0.29; Stress-Recovered Group M=1.38; SD =0.8].
Importantly, stress-recovered group did not differ with the con-
trol group in all psychological and salivary cortisol measures [PSS:
t(10)=−0.08, P=0.94; HAS: t(10)=−0.58, P=0.58; HAD:
t(10)=−0.85, P=0.41; (Figure 1B) Cortisol: t(10)=1.42, P=
0.19].
FUNCTIONAL CONNECTIVITY RESULTS
The ICA analysis revealed the typical spatial pattern of functional
connectivity and deactivation in DMN, DAN, VAN, SMN, VN,
and AN in all experimental conditions (results not shown).
RSNs in stress and stress—recovered groups
Increased resting functional connectivity was identified in DMN,
VAN, and SMN and decreased connectivity in DAN and AN in
the stress group when compared to stress recovered participants
(Figure 2 and Ta b l e 1).
Regarding DMN, stress group displayed increased func-
tional connectivity mainly in the left cingulum, frontal medial
orbitofrontal, right precuneus, and in the left lingual (Tab l e 1 ).
Increased functional connectivity was also found in VAN in
stress group in the left parietal inferior and superior, right mid-
dle and superior frontal regions (Ta b l e 1 ) whereas in the SMN,
increased functional connectivity was found in the left cerebel-
lum (Tab l e 1 ). In contrast, decreased functional connectivity was
found in stress group in DAN, namely in the right parietal infe-
rior, supramarginal, frontal inferior opercularis, and precentral
regions (Tabl e 1 )aswellasintheAN(leftsuperiortemporal
region) (Tabl e 1 ).
RSNs in stress—recovered and control groups
Regarding the functional connectivity comparison between
stress-recovered and controls, we found that the former presented
an increased functional connectivity in the DAN, SMN, and VN.
Increased connectivity in the left superior occipital, bilateral supe-
rior parietal, right postcentral, left middle and superior frontal,
bilateral inferior frontal opercularis and bilateral precentral was
foundintheDANofstress-recoveredcomparedtocontrolgroup
(Tab l e 2 ). A differential pattern of functional connectivity was
observed for the VAN that is, while the stress-recovered group
presented higher connectivity in the left inferior parietal and
bilateral angular, they presented decreased functional connectiv-
ity in the bilateral inferior parietal, left angular, bilateral middle
frontal and left inferior frontal triangularis. Additionally, stress-
recovered group showed decreased connectivity in the DMN in
the right anterior cingulate, in the SMN in the bilateral precen-
tral, left paracentral, right postcentral, and bilateral cerebellum
and in the VN in the bilateral calcarine (Ta b l e 2 )whencompared
to controls (Figure 3).
Task-induced deactivations in stress and stress—recovered groups
In task-induced deactivations, decreased deactivations in DMN,
SMN, and AN were found in stress group when compared
to stress-recovered participants (Figure 4 and Ta b le 3 ). More
specifically, decreased deactivations in the left medial frontal
orbitofrontal and superior medial frontal were found in DMN
of stress group (Tabl e 3 ). In SMN, stress group presented lower
functional deactivation in the left cerebellum (Tab l e 3). The left
superior temporal and rolandic operculum in AN were less deac-
tivated in stress group compared to stress-recovered participants
(Tab l e 3 ). No significant region was found to display greater deac-
tivation in stressed participants than in stress recovered in any of
the studied RSNs.
Task-induced deactivations in stress—recovered and control
groups
To test for the degree of plasticity in RSNs, we compared deac-
tivation between stress-recovered participants and controls. In
this comparison, we found decreased deactivations in DMN,
both attention networks, and AN (Figure 5 and Ta b l e 4)in
stress-recovered group. In DMN, stress-recovered group showed
decreased deactivations in the left cuneus, anterior cingulate,
right medial frontal orbitofrontal, fusiform and middle temporal
and in the left inferior parietal in DAN (Tabl e 4 ). In VAN, stress-
recovered group showed lower deactivation in the left superior
parietal and in AN in the bilateral superior temporal (Ta b l e 4 ).
No significant region was found to display greater deactivation in
stress recovered than in control participants in any of the studied
RSNs.
EXPANSION/CONTRACTION MAPS OF THE RSNs
Whole brain analysis for relative ICVs did not differ between
experimental groups. We showed in a previous study (Soares
et al., 2013) that exposure to stress triggered a significant reduc-
tion in total DMN volume (corrected for ICV) with specific
contraction in the left pCC, and bilateral parietal inferior brain
regions. Herein, however, we did not find any significant dif-
ferences in the volume of any of the RSNs between stress par-
ticipants before and after recovery from stress. No significant
areas of expansion or constriction were found in the dorsal and
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Soares et al. Brain plasticity in stress recovery
FIGURE 2 | The recovery from stress in resting state networks
(RSNs) at rest. The images depict areas in which stress
participants display greater functional connectivity than stress
recovered in the default mode network (DMN) (A), ventral attention
network (VAN) (B) and sensorimotor network (SMN) (C) and lower
functional connectivity in the dorsal attention network (DAN) (D)
and auditory network (AN) (E). Results were extracted by
independent component analysis and using paired t-tests, with
results considered significant at a corrected for multiple
comparisons p<0.05 threshold.
Table 1 | Group differences (Stress vs. Stress recovered) at rest, in brain regions of the DMN, VAN, SMN, DAN, and AN maps (paired t-tests,
corrected for multiple comparisons, p<0.05).
Condition Regions Peak MNI Cluster size Maximum Z
coordinates (voxels) score
Stress >Stress
recovered
Default mode network Cingulum anterior (left) 0, 36, 3 141 4.31
Frontal medial orbitofrontal (left) −6, 54, −33.32
Precuneus (right) 9, −63, 27 137 4.17
Lingual (left) −9, −48, 3 3.60
Ventral attention network Parietal inferior (left) −33, −69, 45 225 3.80
Parietal superior (left) −33, −60, 48 3.64
Frontal middle (right) 33, 36, 39 108 3.43
Frontal superior (right) 30, 9, 63 2.97
Sensorimotor network Cerebellum (left) −15, −63, −24 49 3.82
Stress <Stress
recovered
Dorsal attention network Parietal inferior (right) 45, −36, 48 81 3.87
Supramarginal (right) 51, −30, 42 3.84
Frontal inferior opercularis (right) 51, 15, 33 58 3.45
Precentral (right) 51, 6, 27 3.22
Auditory network Temporal superior (left) −48, −9, 0 52 3.32
ventral attention networks, SMN, AN, and primary VN between
stress and stress-recovered participants (p=0.99, p=0.98, p=
0.87, p=0.84, and p=0.99, respectively) and between stress-
recovered and control groups (p=0.89, p=0.54, p=0.18, p=
0.47, and p=0.87, respectively).
DISCUSSION
In this study, we analzsed how the RSNs respond and change
following recovery after chronic stress exposure. Our hypothesis
wasofacontinuousrecoveryeffect,inwhichtheconnectiv-
ity would be decreasing from stress toward the control group.
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Soares et al. Brain plasticity in stress recovery
Table 2 | Group differences (Stress recovered vs. Controls) at rest, in brain regions of the DAN, VAN, SMN, VN, and DMNN maps (two sample
t-tests, corrected for multiple comparisons, p<0.05).
Condition Regions Peak MNI Cluster size Maximum Z
coordinates (voxels) score
Stress recovered >
Controls
Dorsal attention network Occipital superior (left) −24, −75, 42 504 5.53
Parietal superior (left) −24, −66, 54 5.21
Parietal superior (right) 21, −66, 57 322 5.05
Postcentral (right) 57, −21, 48 4.54
Frontal superior (left) −27, −6, 63 65 4.15
Frontal middle (left) −30, −3, 54 3.96
Frontal inferior opercularis (right) 55, 15, 33 93 3.93
Precentral (right) 51, 6, 24 3.85
Precentral (left) −51, 6, 27 92 3.15
Frontal inferior opercularis (left) −39, 3, 27 2.74
Ventral attention network Parietal inferior (left) −54, −48, 39 136 3.35
Angular (left) −48, −66, 33 3.14
Angular (right) 45, −60, 36 57 2.73
Sensorimotor network Precentral (left) −27, −21, 78 333 5.17
Paracentral (left) −15, −27, 72 4.46
Precentral (right) 15, −18, 75 237 4.33
Postcentral (right) 30, −30, 60 3.80
Cerebellum (right) 12, −51, 21 72 3.77
Cerebellum (left) −9, −48, −15 2.74
Visual network Calcarine (right) 6, −78, 12 331 4.77
Calcarine (left) −12 , −66, 12 3.93
Stress recovered <
Controls
Default mode network Cingulum anterior (right) 6, 36, 18 147 3.47
Ventral attention network Parietal inferior (left) −33, −74, 51 101 4.21
Angular (left) −36, −69, 45 2.78
Frontal middle (right) 33, 27, 39 75 3.77
Frontal inferior triangularis (left) −48, 42, 0 59 3.08
Frontal middle (left) −36, 45, 0 2.64
Parietal inferior (right) 51, −48, 48 58 3.04
Indeed, we observed a decreased resting functional connectivity
in the DMN, VAN, and SMN after stress recovery. Additionally,
decreased functional connectivity was also observed in the DAN,
SMN, and VN networks in controls, when compared with
stress-recovered group. However, only a specific brain region of
the DMN (the right anterior cingulate cortex—ACC) showed
increased functional connectivity in controls when compared
with stress-recovered participants. Results of increased functional
connectivity of the DMN at rest after chronic stress exposure are
consistent with those previously reported (Soares et al., 2013).
More recently, Vaisvaser et al. (2013) evidenced similar results
using an acute social stress model.
In the current study, we explored further the plasticity of the
RSNs after recovery from the impact of chronic stress-induced
changes and showed for the first time that all RSNs, with the
exceptionoftheDANandAN,displayedafunctionalrecovery
after the cessation of the exposure to stress. Notably, the compar-
ison with controls allowed us to observe a return to the initial
levels the functional connectivity of the DMN, VAN, and AN, but
still a sustained pattern of increased functional connectivity of the
DAN, SMN, and VN networks.
These results suggest that DAN, SMN, and VN are less plas-
tic when recovering from the impact of stress exposure. The
DAN network has been associated with top-down attention pro-
cesses as inhibitory control, working memory, and response
selection. These cognitive processes depend upon the prefrontal
integrity (dorsal frontal regions), which are brain regions vul-
nerabletotheeffectsofstress(Cerqueira et al., 2007). Indeed,
animal studies evidenced stress-related prefrontal remodeling
(e.g., selective atrophy of the prefrontal cortex, elimination of
dendritic spines) after chronic stress exposure (Cerqueira et al.,
2007; Gourley et al., 2013). This stress-related prefrontal struc-
tural reorganization has been associated with impaired perceptual
attention, behavioral flexibility, and decision making in rodents
and humans (Cerqueira et al., 2005; Dias-Ferreira et al., 2009;
Soares et al., 2012; Yuen et al., 2012). Interestingly, studies
analysing the recovery of posttraumatic stress disorder reported
that an increased thickness of the dorsolateral prefrontal cor-
tex was associated with greater symptomatic alleviation (Lyoo
et al., 2011). The concomitant SMN and VN sustained increased
functional connectivity are possibly associated with a motor and
visual readiness state that is required for the stress response. This
Frontiers in Human Neuroscience www.frontiersin.org December 2013 | Volume 7 | Article 919 |6

Soares et al. Brain plasticity in stress recovery
FIGURE 3 | Comparison between stress recovered participants and
controls in resting state networks (RSNs). The images show areas
in which stress recovered participants display greater functional
connectivity than controls in the dorsal attention network (DAN) (A),
sensorimotor network (SMN) (B), and primary visual network (VN)
(C). Lower functional connectivity was found in the default mode
network (DMN) (D). Ventral attention network (VAN) (E) displays
increased functional connectivity in different regions both in stress
recovered (orange) and in control (blue) participants. Results were
extracted by independent component analysis and using two-sample
t-tests, with results considered significant at a corrected for multiple
comparisons p<0.05 threshold.
FIGURE 4 | The recovery from stress in resting state networks
(RSNs) during task-induced deactivations. The images illustrate areas
of decreased deactivation in stress group when compared to stressed
recovered participants in the default mode network (DMN) (A),
sensorimotor network (SMN) (B), and auditory network (AN) (C),
extracted by general linear model analysis and using paired t-tests, with
results considered significant at a corrected for multiple comparisons
p<0.05 threshold. Importantly, no areas of increased deactivation of
these RSNs were found in stressed individuals when compared to
stress recovered.
specific pattern of plasticity suggests that some RSNs may be a
tool for monitoring effective anti-stress interventions, similar to
that proposed to verify the effect of the treatments in several
neuropsychiatric diseases (Achard and Bullmore, 2007).
Besides the functional plastic recovery in the connectivity of
the RSNs at rest, we also observed a continuum in the pattern of
deactivation—that is, there was an increased deactivation from
stress toward the control group in all the RSNs. DMN deactiva-
tion has been associated with reallocation of attentional resources
to cognitively demanding tasks (Hu et al., 2013). Moreover,
task-induced RSNs deactivation is correlated with behavioral per-
formance: for example, stronger DMN deactivation in a working
memory task predicts better performance (Uddin et al., 2009;
Mayer et al., 2010). Increased deactivation observed in our con-
trol group and in our stress-recovered participants (comparing
with stress). Additionally, abnormal patterns of RSNs deactiva-
tion have been associated with several neuropsychiatric diseases
(Pomarol-Clotet et al., 2008; Guerrero-Pedraza et al., 2012).
Frontiers in Human Neuroscience www.frontiersin.org December 2013 | Volume 7 | Article 919 |7

Soares et al. Brain plasticity in stress recovery
Table 3 | Group differences (Stress <Stress recovered) in brain regions of the DMN, SMN, and AD maps in task-induced deactivation (paired
t-tests, corrected for multiple comparisons, p<0.05).
Condition Regions Peak MNI Cluster size Maximum Z
coordinates (voxels) score
Stress <Stress recovered Default mode network Frontal medial orbitofrontal (left) −6, 56, −8 173 3.11
Frontal superior medial (left) −4, 64, 6 2.86
Sensorimotor network Cerebellum (left) −4, −58, −857 2.27
Auditory network Temporal superior (left) −58, 0, 2 82 3.28
Rolandic operculum (left) −50, −6, 4 2.48
Table 4 | Group differences (Stress recovered <Controls) in brain regions of the DMN, DAN, VAN, and AN maps in task-induced deactivation
(two sample t-tests, corrected for multiple comparisons, p<0.05).
Condition Regions Peak MNI Cluster size Maximum Z
coordinates (voxels) score
Stress recovered <Controls Default mode network Cuneus (left) −12 , −58, 24 74 2.58
Cingulum anterior (left) −4, 34, −8 260 2.56
Frontal medial orbitofrontal (right) 6, 52, −12 2.29
Fusiform (right) 26, −36, −16 5 2 2.46
Temporal middle (right) 42, −66, 22 157 2.44
Dorsal attention network Parietal inferior (left) −26, −44, 48 41 2.23
Ventral attention network Parietal superior (left) −28, −80, 50 57 2.52
Auditory network Temporal superior (left) −62, −8, 6 129 3.00
Temporal superior (right) 64, −12 , 6 3 1 2.65
FIGURE 5 | Comparison between stress recovered participants and
controls in resting state networks (RSNs) during task-induced
deactivations. The images demonstrate areas of decreased deactivation in
stress-recovered participants when compared to controls in the default mode
network (DMN) (A), dorsal attention network (DAN) (B), ventral attention
network (VAN) (C), and auditory network (AN) (D), extracted by general linear
model analysis and using two-sample t-tests, with results considered
significant at a corrected for multiple comparisons p<0.05 threshold.
Importantly, no areas of increased deactivation of these RSNs were found in
stress recovered when compared to controls participants.
Frontiers in Human Neuroscience www.frontiersin.org December 2013 | Volume 7 | Article 919 |8

Soares et al. Brain plasticity in stress recovery
This study shows that while the functional remodeling of RSNs
endures, the structural changes (volumetry) of the brain areas
involving these networks is still absent after this period of recov-
ery, as no significant areas of expansion or constriction were
found in the networks between stress and stress recovered partic-
ipants; however, in contrast to the difference previously reported
in the volumetry of the DMN after stress exposure (Soares et al.,
2013) we also did not find significant differences between stress
recovered and controls. Difference in results may be related with
the limited sample size; the fact that our groups did not dif-
fer in physiological cortisol levels and finally, because no direct
comparisons were made between stress and control groups.
In summary, the present study contributes to better under-
stand the plastic phenomena that occur in RSNs after the
cessation of stress exposure. While we have previously shown
the existence of stress-related impairments in the activation-
deactivation of RSNs (Soares et al., 2013), here we demon-
strate that a functional remodeling of the activation-deactivation
pattern of the RSNs takes place following chronic-stress recov-
ery. Although promising, our results should be interpreted
with caution mainly due to the reduced size of our sample;
therefore, future studies should try to replicate these obser-
vations in a larger sample, ideally using exactly the same
participants in all conditions, as controls, stressed, and after
recovery.
AUTHOR CONTRIBUTIONS
José M. Soares and Adriana Sampaio contributed in literature
search, figures, study design, data collection, data analysis, data
interpretation, and writing. Paulo Marques and Luís M. Ferreira
as contributed in data collection and data analysis. Nadine C.
Santos, Fernanda Marques, Joana A. Palha, João J. Cerqueira, and
Nuno Sousa contributed in study design, data interpretation, and
writing.
ACKNOWLEDGMENTS
We are thankful to all study participants. José M. Soares,
Paulo Marques, and Nadine C. Santos are supported by fel-
lowships of the project SwitchBox-FP7-HEALTH-2010-grant
259772-2; Fernanda Marques is supported by the fellowship
SFRH/BPD/33379/2008 funded by the Fundação para a Ciência
e Tecnologia (FCT, Portugal). The work was supported by
SwitchBox-FP7-HEALTH-2010-grant 259772-2.
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Conflict of Interest Statement: The authors declare that the research was con-
ducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 16 October 2013; accepted: 15 December 2013; published online:
December 2013.
Citation: Soares JM, Sampaio A, Marques P, Ferreira LM, Santos NC, Marques F,
Palha JA, Cerqueira JJ and Sousa N (2013) Plasticity of resting state brain networks in
recovery from stress. Front. Hum. Neurosci. 7:919. doi: 10.3389/fnhum.2013.00919
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