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Orbitofrontal cortex neurofeedback produces lasting changes in contamination anxiety and resting-state connectivity

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Anxiety is a core human emotion but can become pathologically dysregulated. We used functional magnetic resonance imaging (fMRI) neurofeedback (NF) to noninvasively alter patterns of brain connectivity, as measured by resting-state fMRI, and to reduce contamination anxiety. Activity of a region of the orbitofrontal cortex associated with contamination anxiety was measured in real time and provided to subjects with significant but subclinical anxiety as a NF signal, permitting them to learn to modulate the target brain region. NF altered network connectivity of brain regions involved in anxiety regulation: subjects exhibited reduced resting-state connectivity in limbic circuitry and increased connectivity in the dorsolateral prefrontal cortex. NF has been shown to alter brain connectivity in other contexts, but it has been unclear whether these changes persist; critically, we observed changes in connectivity several days after the completion of NF training, demonstrating that such training can lead to lasting modifications of brain functional architecture. Training also increased subjects' control over contamination anxiety several days after the completion of NF training. Changes in resting-state connectivity in the target orbitofrontal region correlated with these improvements in anxiety. Matched subjects undergoing a sham feedback control task showed neither a reorganization of resting-state functional connectivity nor an improvement in anxiety. These data suggest that NF can enable enhanced control over anxiety by persistently reorganizing relevant brain networks and thus support the potential of NF as a clinically useful therapy.
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Orbitofrontal cortex neurofeedback produces lasting
changes in contamination anxiety and resting-state
connectivity
D Scheinost
1
, T Stoica
2
, J Saksa
3
, X Papademetris
1,2
, RT Constable
1,2,4
, C Pittenger
3,5,6
and M Hampson
2
Anxiety is a core human emotion but can become pathologically dysregulated. We used functional magnetic resonance imaging
(fMRI) neurofeedback (NF) to noninvasively alter patterns of brain connectivity, as measured by resting-state fMRI, and to reduce
contamination anxiety. Activity of a region of the orbitofrontal cortex associated with contamination anxiety was measured in real
time and provided to subjects with significant but subclinical anxiety as a NF signal, permitting them to learn tomodulate the target
brain region. NF altered network connectivity of brain regions involved in anxiety regulation: subjects exhibited reduced resting-
state connectivity in limbic circuitry and increased connectivity in the dorsolateral prefrontal cortex. NF has been shown to alter
brain connectivity in other contexts, but it has been unclear whether these changes persist; critically, we observed changes in
connectivity several days after the completion of NF training, demonstrating that such training can lead to lasting modifications of
brain functional architecture. Training also increased subjects’ control over contamination anxiety several days after the
completion of NF training. Changes in resting-state connectivity in the target orbitofrontal region correlated with these
improvements in anxiety. Matched subjects undergoing a sham feedback control task showed neither a reorganization of resting-
state functional connectivity nor an improvement in anxiety. These data suggest that NF can enable enhanced control over anxiety
by persistently reorganizing relevant brain networks and thus support the potential of NF as a clinically useful therapy.
Translational Psychiatry (2013) 3, e250; doi:10.1038/tp.2013.24; published online 30 April 2013
Introduction
Normal and pathological patterns of behavior and thought
correspond to the activity of particular brain circuits. Interven-
tions that alter patterns of behavior and thought therefore
must act on the organization of the underlying circuits; some
clinical interventions, such as deep brain stimulation, do so
explicitly through anatomically targeted manipulations of brain
function.
1
The ability to manipulate targeted brain circuits of
relevance to particular behavior patterns in a non-invasive
manner would be of immense interest and clinical utility.
Poorly controlled anxiety reduces the quality of life of many
healthy individuals and is a key symptom of numerous
neuropsychiatric conditions. Contamination anxiety, in part-
icular, is prevalent in the healthy population and is a common
symptom in obsessive-compulsive disorder (OCD).
2
Pharma-
cological and behavioral interventions are widely used in the
treatment of anxiety and of OCD, but for many individuals these
are of little efficacy or are associated with troublesome side
effects. In extreme cases, invasive anatomically targeted inter-
ventions are sometimes used for OCD and can be effective.
1
Neurofeedback (NF) describes the process of learning to
control neural processes via an explicit feedback signal. Real-
time functional magnetic resonance imaging (rt-fMRI) NF is a
novel approach in which subjects can receive direct feedback
regarding neural activity, as reflected in the BOLD signal, of a
defined brain region. Recent studies have reported success in
training subjects to manipulate activity in specific target
brain regions using this approach.
3–5
Several studies have
also reported that such training can translate into changes in
behavioral measures
6–9
or clinical symptoms.
10,11
Learned
control over a specific region of the brain has been shown to
lead to alterations in brain networks, with documented
alterations in brain function lasting only during
3
or immediately
after the presentation of feedback.
5,12
For NF-induced
changes in brain networks to be of clinical utility, it is essential
that such changes persist beyond the scanning session in
which training occurred.
We asked whether NF can produce functional alterations in
the circuitry associated with contamination anxiety, thereby
reducing it, and whether such changes can persist over the
days following training. Activation in the orbitofrontal cortex
(OFC) has been implicated in contamination anxiety in healthy
individuals
13
and in patients with OCD.
14
We hypothesized
that feedback training that permitted subjects to manipulate
neural activity in the OFC would result in a reorganization
of associated functional brain networks, as measured by
resting-state fMRI, and in a reduction in experienced anxiety.
We investigated this in individuals with significant contamina-
tion anxiety but without any clinical diagnosis of an anxiety
disorder.
1
Department of Biomedical Engineering, Yale University, New Haven, CT, USA;
2
Department of Diagnostic Radiology, Yale School of Medicine, New Haven, CT, USA;
3
Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA;
4
Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA;
5
Department
of Psychology, Yale University, New Haven, CT, USA and
6
Child Study Center, Yale School of Medicine, New Haven, CT, USA
Correspondence: Dr M Hampson, Magnetic Resonance Research Center (MRRC), Yale School of Medicine, The Anlyan Center, N121, 300 Cedar Street, PO Box
208043, New Haven, CT 06520-8043, USA.
E-mail: michelle.hampson@yale.edu
Received 26 November 2012; revised 24 January 2013; accepted 18 February 2013
Keywords: contamination anxiety; neurofeedback; obsessive-compulsive disorder; real-time fMRI; resting connectivity
Citation: Transl Psychiatry (2013) 3, e250; doi:10.1038/tp.2013.24
&
2013 Macmillan Publishers Limited All rights reserved 2158-3188/13
www.nature.com/tp
Materials and methods
Subject recruitment. Subjects with high contamination-
related anxiety were recruited and consented in accordance
with a protocol reviewed and approved by the Yale University
Human Research Protection Program. Individuals with
a history of neuropsychiatric disorder or currently using
medication (other than antibiotics or birth control) were
excluded from the study. All included subjects had a score of
X8 on the Padua Inventory—Washington State University
Revision
15
—Contamination Obsessions and Washing Compul-
sions subscale. A total of 12 subjects were recruited for the NF
group, and 11 subjects, matched for age and gender, were
recruited for the sham-feedback (SF) control group. One NF
subject was removed from analysis for not following instructions
and was not matched with a SF subject. Further, one NF
subject and the matched SF subject were removed from
analysis due to an error in the localization of the target region
discovered after their data collection (but before assessment of
imaging outcome measures). A total of 10 NF and 10 matched
SF subjects were included in the final analysis (4 females in
each group). Each of the 20 subjects had four separate
scanning sessions over the course of 3 weeks (80 scanning
session in total were analyzed). There was no significant
difference in age or scores on the Padua Contamination
subscale between these two groups.
Study protocol. Our rt-fMRI NF protocol has been
described in detail in a methods publication
16
using the
system described in Scheinost et al.
17
We provide here a
general description but refer the reader there for more
specific information. Briefly, the experimental paradigm
consisted of four MRI scanning sessions, spaced roughly
half a week apart. The first visit began with an imaging
session involving high-resolution anatomical images, two
resting-state functional runs (5 min each) and a functional
localizer that alternately presented neutral and contamina-
tion-related images. The localizer was used to identify the
target region of interest, defined as the 30 OFC voxels,
bilaterally, that were maximally differentially activated by
contamination-related stimuli. After this first imaging session,
subjects met with a clinical psychologist to help them develop
initial strategies for manipulating their brain activity that could
later be refined via NF. The second visit began with an out-of-
magnet assessment of how well subjects could control their
anxiety. In this assessment session, subjects viewed 25
contamination-related images and were instructed to try to
minimize their anxiety in response to each image and to
indicate on a 1–5 scale the anxiety they experienced.
Subjects then participated in a 90-min NF (or SF) imaging
session involving 10 functional runs during which they tried to
control brain activity, as reported to them in a line graph on
the visual display, while viewing contamination-related and
neutral images (as further described below). The first and last
two runs were performed without NF; the middle six were
performed with NF. During the NF runs, subjects were
instructed to try to learn what worked the best for controlling
their OFC activity. During the non-NF runs, subjects were
instructed to do whatever they felt worked best for controlling
OFC activity. The non-NF runs, referred to as control task
runs, were used to assess changes in the OFC in the
absence of feedback. The third visit was identical to the
second. The fourth visit consisted of a final assessment of
anxiety evoked by contamination-associated stimuli and a
final imaging session in which two resting-state runs were
collected to assess changes in functional connectivity. The
precise scheduling of the visits varied based on availability of
scan slots, but the final visit was always scheduled 1–7 days
after the third visit. The average was 3 days, and there was
no significant difference between the sham and real NF
subjects in this variable. As the final visit, in which we
assessed the effects of the NF on both anxiety and brain
functional connectivity, occurred several days after feedback
training, the observed effects do not reflect transient results
of feedback training but rather reflect changes that persisted
for several days. In total, each subject participated in four
MRI sessions during the experiment and 480 scanning
sessions were performed in this experiment.
Subjects never viewed the same stimulus twice. The sets of
contamination and neutral images used at different time
points in the protocol were balanced for induced anxiety, as
described previously.
16
Task during NF sessions. During NF, subjects viewed an
arrow on the left side of the screen that cued them regarding
the current task. A red up-arrow indicated they should try
to increase activity in their OFC, a blue down-arrow
indicated they should try to decrease activity in the region,
and a white forward-arrow indicated they should rest and not
try to control the region. To the right of this arrow, a large
contamination-related image was shown during the increase
and decrease conditions and a neutral image was shown
during the rest condition. The arrows and images changed
every 26 s, cycling through the three conditions. During NF
runs, a line graph was included at the bottom of the screen
indicating the activity in the subject’s OFC. The line was
color-coded to indicate the current task and was updated
after each newly acquired volume was analyzed, approxi-
mately every 2 s. Although the NF group viewed the
actual time course of the OFC, the SF group viewed the
time course of the corresponding run performed previously of
their paired NF subject. As a result, the SF subjects viewed
exactly the same stimuli as their paired NF subjects. An
example of what subjects viewed during the NF runs is
provided in Figure 1.
MR imaging protocol. All imaging was done on a 1.5-T
Siemens Sonata scanner (Siemens Medical Systems, Erlan-
gen, Germany). A sequence designed to optimize signal in
the OFC was used for all functional data collection
(repetition time ¼2000 ms, echo time ¼30 ms, flip angle ¼80,
bandwidth ¼2604, 200 mm field of vi ew for 3.1 mm isotro pic
voxels, 31 axial-oblique slices covering the OFC and brain
above).
Preprocessing. Images were slice time corrected using
sinc interpolation in Matlab (www.mathworks.com) and
motion corrected using SPM5 (http://www.fil.ion.ucl.ac.uk/
spm/software/spm5/). Unless noted, all analyses were
conducted using BioImage Suite.
18
Neurofeedback modulates connectivity and anxiety
D Scheinost et al
2
Translational Psychiatry
Resting-state connectivity analysis. Several covariates of
no interest were regressed from the data, including linear and
quadratic drift, six rigid-body motion parameters and mean
cerebral–spinal fluid, white matter and global signals. The data
were low-pass filtered (approximate cutoff frequency ¼0.12
Hz) and a gray matter mask was applied to the data so that
only voxels in the gray matter were included. The network
measure of degree
19,20
was computed for each voxel.
Comparison of degree maps before and after NF allows
exploration of functional connectivity changes, unbiased by a
priori assumptions regarding regions of interest. First, the time
series for a voxel was correlated with every other voxel in the
gray matter with degree defined as the number of connection
with a correlation greater than r¼0.40. The process was
repeated for each voxel. Degree maps were normalized as
described previously.
21,22
For group comparisons, single
subject results were smoothed (6 mm Gaussian kernel) and
warped to common space through a series of linear and non-
linear registrations as described previously.
19,22
Control task analysis. Control task data collected immedi-
ately before and after NF on the second and third
experimental days were used to assess changes in the target
region of interest. Task regressors for increase and decrease
blocks were convolved with a hemodynamic response
function and included in the design matrix of a general linear
model. Motion parameters and drift terms were added as
regressors of no interest. Activity in the target OFC voxels
during decrease blocks was subtracted from activity during
increase blocks to yield an estimate of control over the region
during control task runs.
Statistical analyses
Resting connectivity data. Group level statistics were per-
formed in a voxel-wise manner to identify regions showing
changes in functional connectivity after rt-fMRI training. For
each group, paired differences in the resting data collected
before and after training were identified using Wilcoxon’s
signed-rank test. Between-group differences were identified
using Wilcoxon’s rank-sum test, implemented in Analysis of
Functional NeuroImages (AFNI) (http://afni.nimh.nih.gov/
afni). To assess the relationship between behavior and
intrinsic connectivity patterns, as previous studies have
done,
23
we performed a voxel-by-voxel Pearson correlation
analysis between the change in behavior and change in
degree. Significance was assessed at a Po0.05 level after
correcting for multiple comparisons across the grey matter
via AFNI’s AlphaSim program. To check for possible
confounds caused by group differences in head motion,
24
we computed the mean frame-to-frame displacement for
each subject across all time points. Wilcoxon’s rank-sum
tests revealed no group difference in motion (P¼0.67,
U¼44) and no difference in motion between the first and last
imaging session (P¼0.57, U¼42).
Behavioral data. The anxiety ratings were averaged across
stimuli in each assessment session. The average in the final
session was subtracted from the average in the first session
to yield an estimate of how much each subject increased
control over their contamination anxiety. Wilcoxon’s signed-
rank tests were used to assess changes within each group,
and a Wilcoxon’s rank-sum test was used to contrast
changes between groups. Because we hypothesized a priori
that feedback subjects would increase control over their
anxiety, and would have a greater increase in control than
the sham subjects, significance was assessed using one-
tailed tests at a Po0.05 level.
Control task data. Within-group changes in OFC (percentage
of signal change at the final time point compared with per-
centage of signal change at the first time point) were tested
with Wilcoxon’s signed-rank tests and between-group differ-
ences were tested with a Wilcoxon’s rank-sum test. Because
we hypothesized a priori that feedback subjects would
increase control over their OFC activity, and would have a
greater increase in control than the sham subjects, signifi-
cance was assessed using one-tailed tests at a Po0.05 level.
Results
Changes in resting state functional connectivity patterns before
and after NF were assessed using a network theory metric,
degree of connectivity, computed on a voxel-wise basis.
20
The
group composite map of whole-brain changes in degree of
connectivity in the feedback group showed significant (Po0.05
corrected) decreases in brain regions associated with emotion
processing, including the insula and adjacent regions, the
hippocampi, parahippocampal and entorhinal cortex, the right
amygdala, the brainstem in the vicinity of the substantia nigra,
the temporal pole, superior temporal sulcus, thalamus and
fusiform gyrus. By contrast, significantly (Po0.05 corrected)
increased degree of connectivity was seen in prefrontal areas
associated with emotion regulation and cognitive control,
25
including right lateral prefrontal cortex and bilateral portions of
Brodmann’s area 8 (Figure 2a). The composite map from the
sham group did not show any loci of significant changes in
connectivity surviving multiple comparisons correction (not
Figure 1 A screen-shot showing the visual display at the end of a
neurofeedback run, which ended with a rest block and a corresponding neutral
image. The images shown during increase and decrease blocks were designed to
induce contamination anxiety. The time course of the orbitofrontal cortex, shown at
the bottom, tended to be higher in the red relative to the blue periods, indicating
some control over their target region.
Neurofeedback modulates connectivity and anxiety
D Scheinost et al
3
Translational Psychiatry
shown). The contrast between the two groups at Po0.05
corrected was similar to the feedback group composite map
(Figure 2b), further suggesting that the changes in connectivity
were due specifically to the feedback and not to non-specific
aspects of the task.
Control over contamination anxiety was assessed by
showing subjects contamination-associated images before
and after NF and instructing them to control their anxiety and
report how much anxiety they experienced. NF subjects
showed a significant reduction in contamination anxiety
several days following the feedback sessions (P¼0.02,
median ¼0.27), based on these self-report measures, while
sham subjects did not (P¼0.45, median ¼0.04). There was a
significant difference between the groups (P¼0.034,
U¼25.5; Figure 3), confirming that the improvement in the
intervention group was due to the feedback rather than to
habituation or some other non-specific aspect of training.
To identify the specific connectivity changes most asso-
ciated with improved control over anxiety, we created a map
for the feedback group of the correlation between changes in
degree of connectivity (Figure 2a) and increased control over
anxiety (Figure 3). Two areas of significant correlation
(Po0.05 corrected) are apparent: the target region of the
OFC, bilaterally, and a right lateral parietal area (Figure 4).
Changes in control over OFC activity were assessed in the
absence of a NF signal at the start and end of each of the two
90-min feedback sessions. Comparison of the first and final
assessments revealed that the NF subjects changed activity
in the target area of their OFC (Po0.01, median ¼0.2689),
while the sham subjects did not (P¼0.43, median ¼
0.0642; Figure 5). The contrast between groups
approached statistical significance (P¼0.07, U¼30).
Discussion
Mitigation of maladaptive behavioral states entails inducing
change in the underlying brain circuitry. We demonstrate that
rt-fMRI NF can modulate intrinsic brain connectivity patterns
associated with anxiety control, thereby enhancing control
over contamination anxiety.
These results extend previous applications of rt-fMRI NF in
two critical ways. First, we show changes in both anxiety
regulation and brain connectivity that persist for several days
following the end of NF training. Previous rt-fMRI studies have
reported success in training subjects using specific target
brain regions
4,26
and in altering relevant connectivity patterns
during task performance
5,27
or at rest.
12
However, these
changes in brain network function have been reported only
during NF or immediately after it and may therefore represent
state changes, rather than lasting alterations in brain
functional architecture. Transient symptomatic improvements
or changes in brain network connectivity would clearly be of
only limited clinical utility and interest. We demonstrate that
rt-fMRI training can induce changes in both resting-state brain
patterns and behavior that last for days after the last NF
session: both the post-feedback resting-state data and the
post-intervention behavioral data were collected several days
after the completion of the NF intervention. Therefore, the
increased control over anxiety produced by NF (Figure 3)
reflects a persistent change, not an acute consequence of the
training. Similarly, the changes in connectivity we observe
Figure 2 (a) Changes in degree of connectivity in the feedback group. Increases are shown in red/yellow and decreases in blue/purple. Decreases in connectivity are seen
in limbic areas, and increases are seen in prefrontal regions. (b) Contrast between the feedback and sham groups. This contrast is similar to the feedback group composite
map, suggesting that the changes in connectivity in the feedback group were a result of the feedback rather than habituation or some other non-specificaspect of training. All
slices are shown with radiological convention (left is on the right) at a whole-brain-corrected Po0.05 threshold.
Figure 3 Change in control over contamination anxiety in the two groups. The
neurofeedback subjects significantly increased their control over anxiety (indicated
by an asterisk) and the sham subjects did not.
Neurofeedback modulates connectivity and anxiety
D Scheinost et al
4
Translational Psychiatry
(Figures 2a, b and 4) reflect persistent alterations produced by
the intervention. These lasting alterations suggest that rt-fMRI
is a promising mechanism to induce clinically meaningful
changes in the brain.
Second, we show that this approach can be used to
modulate a particular form of anxiety. Anxiety is a core
symptom of many psychiatric conditions and a substantial
source of distress in both clinical and non-clinical populations.
Contamination anxiety, in particular, is a cardinal symptom of
OCD and is common in the non-clinical population,
2
such as
the subjects described here. The ability of rt-fMRI NF to
modulate this core emotional state creates clear possibilities
for clinical applications.
The observed alterations in resting-state connectivity in the
feedback group (Figures 2a and b) show that rt-fMRI is
capable of reorganizing the functional brain architecture
associated with emotion processing. The pattern of change
in functional connectivity, with decreased connectivity in
limbic and paralimbic areas and increased connectivity in
lateral and anterior frontal areas associated with cognitive
control, is consistent with a model in which dorsolateral
prefrontal cortical areas have a role in emotional control by
downregulating the function of areas involved in emotion
generation.
25
The specificity of changes in functional con-
nectivity to the feedback group (and absence of these
changes in the sham subjects) indicates that it was not a
result simply of repeated exposure to contamination-related
images, or of practice effects, but was contingent upon the
receipt of accurate, subject-specific NF.
By correlating changes in resting-state connectivity with
changes in anxiety, it is possible to probe the neural basis of
anxiety control. We found a negative correlation between
OFC global connectivity and improved anxiety regulation,
suggesting a critical role for this region in controlling
contamination anxiety and confirming that we have targeted
an appropriate region of the brain with our intervention
(Figure 4). The correlation between increased connectivity
in the right lateral parietal region and improved control over
anxiety was not predicted, but it is interesting to note that this
area has been implicated in OCD.
28
Susceptibility artifacts make the OFC a difficult region to
image. However, using an optimized pulse sequence, we
were able to successfully train individuals to reorganize
their brain patterns so as to better control activity in this
region. This has implications beyond this study and beyond
the management of anxiety, as this critical limbic area has
been implicated in many different disorders involving dis-
rupted emotional processing, including conduct disorder,
bipolar disorder and addiction.
29
Despite a growing literature on rt-fMRI NF, there is, to date,
limited evidence that it can induce persistent changes in brain
function. Most NF studies have not included a follow-up
imaging session. At least two previous studies have reported
persistent alterations in behavioral measures days after
NF,
30,31
supporting the view that rt-fMRI NF can induce a
lasting reorganization of brain function. On the other hand,
one study that identified changes in brain function during the
NF found that those changes did not persist after the
feedback.
3
Two factors may have contributed to the more
persistent changes in resting-state connectivity in the current
study. First, by having two feedback sessions spaced several
days apart, subjects in this study had a chance to consolidate
their learning overnight; sleep is a critical part of some forms of
learning
32
and may have enhanced the persistence of plastic
changes in this case. Second, anxiety-related brain regions
may be more subject to lasting changes than other brain
circuits. More studies are needed examining the persistence
of changes induced by rt-fMRI NF in a variety of contexts to
clarify these issues.
It is possible that the neural basis of contamination anxiety
in patients with OCD differs from that of healthy subjects.
However, the region that we targeted (the OFC) has been
reported to have elevated activation during the experience of
contamination anxiety in both healthy individuals and OCD
patients.
13,14
Thus, it is plausible that a protocol such as this
one, which uses rt-fMRI NF to train subjects to control activity
in their OFC, will translate into improved control over
contamination anxiety in OCD patients. A study in the patient
population is needed to address this issue definitively.
As our NF protocol was limited to two sessions, we are not
able to identify the optimal number of sessions for this
intervention. Our results suggest that two sessions are better
than one: the subjects in our study did not show a significant
increase in control over their OFC after a single session, but
Figure 4 Regions of the brain where changes in degree of connectivity correlated with increased control over contamination anxiety, at a whole-brain-corrected Po0.05
threshold. Positive correlations between increased control over anxiety and increased degree of connectivity are shown in red/yellow and negative correlations are shown in
blue/purple. Increased control over anxiety was associated with decreased connectivity in the orbitofrontal cortex and increased connectivity in a right parietal region.
Neurofeedback modulates connectivity and anxiety
D Scheinost et al
5
Translational Psychiatry
they did improve with two NF sessions (Figure 5). It is possible
that a larger number of NF sessions would be able to produce
still greater control of OFC activity. Future work should include
studies aimed at optimizing the number of NF sessions.
Rt-fMRI NF was used to reorganize the intrinsic functional
brain patterns of subjects so as to allow them to gain greater
control over their contamination anxiety. The alterations
lasted for several days after the completion of NF training.
These results have implications for mechanism, as they imply
brain plasticity rather than just a state change induced in the
short term by the feedback experience. More generally, these
data indicate that rt-fMRI NF can produce persistent changes
in the brain circuitry underlying maladaptive behavioral states
and thereby support its potential a treatment for neuropsy-
chiatric disease.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgements. This work was supported by funding from the
National Institutes of Health (MH090384, EB012969, MH081190) and the Doris
Duke Charitable Foundation. We thank Maolin Qiu, Jitendra Bhawnani and Hedy
Sarofin for technical assistance and the State of Connecticut for its support of the
Ribicoff Research Facilities at the Connecticut Mental Health Center.
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Translational Psychiatry is an open-access journal
published by Nature Publishing Group.Thisworkis
licensed under a Creative Commons Attribution-NonCommercial-
ShareAlike 3.0 Unported License. To view a copy of this license, visit
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Figure 5 Control over the orbitofrontal cortex target area at the four different
time points when control over the brain area was assessed. The median and quartile
ranges are shown for each of the two groups at each time point. BF, biofeedback;
SF, sham feedback.
Neurofeedback modulates connectivity and anxiety
D Scheinost et al
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Translational Psychiatry
... In most studies, the nature of the neurofeedback signal is made explicit to the subject, but there have also been paradigms where training occurs covertly (Bray et al., 2007;Shibata et al., 2011). Some studies present feedback while subjects are processing auditory, visual, or tactile stimuli (Bruhl et al., 2014;deCharms et al., 2005;Scheinost et al., 2013;Yoo et al., 2007) or while they are performing an assigned cognitive task (Chiew et al., 2012). Other rtfMRI studies employ an unconstrained paradigm in which the neurofeedback and the cues to increase or decrease brain activity are the only stimuli provided and the subjects are free to use any cognitive strategy to control brain function and neurofeedback (Caria et al., 2007;Hampson et al., 2011;Rota et al., 2009). ...
... Finally, the experimenter will need to design the study to adequately capture potential brain changes related to training over time, which could include reduced activity in the target ROI(s), change in the extent of activation within the ROI(s), and/or recruitment of different neural systems to support improved performance. The experimenter could consider including a resting state fMRI scan before and after training as one strategy for capturing complex brain changes over time (Hampson et al., 2011;Scheinost et al., 2013). ...
... doi: bioRxiv preprint 2013). However, with one exception (Scheinost et al., 2013), these studies measured behavior at the time of the rtfMRI study, did not test retention, and, with few exceptions (Scheinost et al., 2013;Subramanian et al., 2011) the dependent measure of behavioral change with rtfMRI was assessed with self-report measures that may reflect non-specific training effects. ...
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While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health (BRAIN, 2013), the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain-behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders.
... Third, although we determined our target connectivity based on comprehensive data-driven analyses of the resting-state fMRI data [17], an adjustment of the anatomical location of the target using a task-based fMRI paradigm (i.e., a functional localizer) would be beneficial. The challenge is that, unlike motor, affective, or executive functional regions, e.g., [54][55][56][57][58][59][60][61][62], RNT is a higher order cognitive process, and we are still uncertain what kind of tasks would have the maximum power to identify an RNT-related circuit. Fourth, although we observed a more prominent reduction in RNT for the active group compared to the sham, both groups showed a reduction in general depression. ...
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Introduction: Repetitive negative thinking (RNT) is a cognitive process focusing on self-relevant and negative experiences, leading to a poor prognosis of major depressive disorder (MDD). We previously identified that connectivity between the precuneus/posterior cingulate cortex (PCC) and right temporoparietal junction (rTPJ) was positively correlated with levels of RNT. Objective: In this double-blind, randomized, sham-controlled, proof-of-concept trial, we employed real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) to delineate the neural processes that may be causally linked to RNT and could potentially become treatment targets for MDD. Methods: MDD-affected individuals were assigned to either active (n = 20) or sham feedback group (n = 19). RNT was measured by the Ruminative Response Scale-brooding subscale (RRS-B) before and 1 week after the intervention. Results: Individuals in the active but not in the sham group showed a significant reduction in the RRS-B; however, a greater reduction in the PCC-rTPJ connectivity was unrelated to a greater reduction in the RRS-B. Exploratory analyses revealed that a greater reduction in the retrosplenial cortex (RSC)-rTPJ connectivity yielded a more pronounced reduction in the RRS-B in the active but not in the sham group. Conclusions: RtfMRI-nf was effective in reducing RNT. Considering the underlying mechanism of rtfMIR-nf, the RSC and rTPJ could be part of a network (i.e., default mode network) that might collectively affect the intensity of RNT. Understanding the relationship between the functional organization of targeted neural changes and clinical metrics, such as RNT, has the potential to guide the development of mechanism-based treatment of MDD.
... Several investigations demonstrated that emotion regulation can be mediated by voluntary regulation of key emotional brain centers such as the amygdala (Bruhl et al., 2014;Hellrung et al., 2018;Herwig et al., 2019;Marxen et al., 2016;Paret et al., 2014Paret et al., , 2018Zotev et al., 2011), anterior insula (AI) (Berman et al., 2013;Caria et al., 2007Caria et al., , 2010Cohen Kadosh et al., 2016;Lawrence et al., 2013;Veit et al., 2012;Yao et al., 2016;Zilverstand et al., 2015) and anterior cingulate cortex (Grone et al., 2015;Hamilton et al., 2011). Remarkably, several studies demonstrated that real-time fMRI neurofeedback induced modifications of emotional behavior along with local and distributed changes of neural activity in both healthy (Caria et al., 2010;Koush et al., 2017;Yao et al., 2016;Zilverstand et al., 2015) and diseased conditions such as depression (Mehler et al., 2018;Yuan et al., 2014;Zotev et al., 2016), anxiety disorders (Morgenroth et al., 2020;Scheinost et al., 2013;Zilverstand et al., 2015) and posttraumatic stress disorder Zotev et al., 2018;Zweerings et al., 2018), elicited by learned regulation of BOLD activity in emotional brain areas. Altogether these findings proved real-time fMRI-neurofeedback a promising neuromodulatory technique for developing novel treatments of emotional disorders (Paret & Hendler, 2020). ...
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Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including fronto-occipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix significantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings highlighted specific neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms.
... Several investigations demonstrated that emotion regulation can be mediated by voluntary regulation of key emotional brain centers such as the amygdala (Bruhl et al., 2014;Hellrung et al., 2018;Herwig et al., 2019;Marxen et al., 2016;Paret et al., 2014Paret et al., , 2018Zotev et al., 2011), anterior insula (AI) (Berman et al., 2013;Caria et al., 2007Caria et al., , 2010Cohen Kadosh et al., 2016;Lawrence et al., 2013;Veit et al., 2012;Yao et al., 2016;Zilverstand et al., 2015) and anterior cingulate cortex (Grone et al., 2015;Hamilton et al., 2011). Remarkably, several studies demonstrated that real-time fMRI neurofeedback induced modifications of emotional behavior along with local and distributed changes of neural activity in both healthy (Caria et al., 2010;Koush et al., 2017;Yao et al., 2016;Zilverstand et al., 2015) and diseased conditions such as depression (Mehler et al., 2018;Yuan et al., 2014;Zotev et al., 2016), anxiety disorders (Morgenroth et al., 2020;Scheinost et al., 2013;Zilverstand et al., 2015) and posttraumatic stress disorder Zotev et al., 2018;Zweerings et al., 2018), elicited by learned regulation of BOLD activity in emotional brain areas. Altogether these findings proved real-time fMRI-neurofeedback a promising neuromodulatory technique for developing novel treatments of emotional disorders (Paret & Hendler, 2020). ...
Article
Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain gray matter and white matter features predictive of self-regulation of anterior insula cortex (AI) activity, a neuromodulation procedure that has been previously shown to support emotion regulation and to result in changes of emotional stimuli perception in both healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several gray matter regions including fronto-occipital and medial temporal areas and the basal ganglia as well as white matter regions including the fronto- occipital fasciculus, tapetum and fornix were significant predictors of learned AI regulation. Notably, we observed a major contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings indicated that specific neurostructural features contribute to individual differences of AI- guided emotion regulation. Intriguingly, the neuroanatomical topography that we observed partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting partially common neural mechanisms.
... Several investigations demonstrated that emotion regulation can be mediated by voluntary regulation of key emotional brain centers such as the amygdala (Bruhl, et al., 2014;Hellrung, et al., 2018;Herwig, et al., 2019;Marxen, et al., 2016;Paret, et al., 2014;Paret, et al., 2016b;Paret, et al., 2018;Zotev, et al., 2011), anterior insula (AI) (Berman, Horovitz, & Hallett, 2013;Caria, Sitaram, Veit, Begliomini, & Birbaumer, 2010;Caria, et al., 2007;Cohen Kadosh, et al., 2016;Lawrence, et al., 2013;Veit, et al., 2012;Yao, et al., 2016;Zilverstand, Sorger, Sarkheil, & Goebel, 2015) and anterior cingulate cortex (Grone, et al., 2015;Hamilton, Glover, Hsu, Johnson, & Gotlib, 2011). Remarkably, several studies demonstrated that real-time fMRI neurofeedback induced modi cations of emotional behavior along with local and distributed changes of neural activity in both healthy (Caria, Sitaram, Veit, Begliomini, & Birbaumer, 2010;Koush, et al., 2017;Yao, et al., 2016;Zilverstand, Sorger, Sarkheil, & Goebel, 2015) and diseased conditions such as depression (Mehler, et al., 2018;Young, et al., 2017a;Young, et al., 2018a;Young, et al., 2017b;Young, et al., 2018b;Yuan, et al., 2014;Zotev, et al., 2016), anxiety disorders (Morgenroth, et al., 2020;Scheinost, et al., 2013;Zilverstand, Sorger, Sarkheil, & Goebel, 2015) and posttraumatic stress disorder Zweerings, et al., 2018), elicited by learned regulation of BOLD activity in emotional brain areas. Altogether these ndings proved real-time fMRI-neurofeedback a promising neuromodulatory technique for developing novel treatments of emotional disorders (Paret & Hendler, 2020). ...
Preprint
Full-text available
Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including fronto-occipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix significantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings highlighted specific neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms.
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Real-time functional magnetic resonance imaging (rt-fMRI) has recently gained interest as a possible means to facilitate the learning of certain behaviors. However, rt-fMRI is limited by processing speed and available software, and continued development is needed for rt-fMRI to progress further and become feasible for clinical use. In this work, we present an open-source rt-fMRI system for biofeedback powered by a novel Graphics Processing Unit (GPU) accelerated motion correction strategy as part of the BioImage Suite project ( www.bioimagesuite.org ). Our system contributes to the development of rt-fMRI by presenting a motion correction algorithm that provides an estimate of motion with essentially no processing delay as well as a modular rt-fMRI system design. Using empirical data from rt-fMRI scans, we assessed the quality of motion correction in this new system. The present algorithm performed comparably to standard (non real-time) offline methods and outperformed other real-time methods based on zero order interpolation of motion parameters. The modular approach to the rt-fMRI system allows the system to be flexible to the experiment and feedback design, a valuable feature for many applications. We illustrate the flexibility of the system by describing several of our ongoing studies. Our hope is that continuing development of open-source rt-fMRI algorithms and software will make this new technology more accessible and adaptable, and will thereby accelerate its application in the clinical and cognitive neurosciences.
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Numerous research groups are now using analysis of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) results and relaying back information about regional activity in their brains to participants in the scanner in 'real time'. In this study, we explored the feasibility of self-regulation of frontal cortical activation using real-time fMRI (rtfMRI) neurofeedback in nicotine-dependent cigarette smokers during exposure to smoking cues. Ten cigarette smokers were shown smoking-related visual cues in a 3 Tesla MRI scanner to induce their nicotine craving. Participants were instructed to modify their craving using rtfMRI feedback with two different approaches. In a 'reduce craving' paradigm, participants were instructed to 'reduce' their craving, and decrease the anterior cingulate cortex (ACC) activity. In a separate 'increase resistance' paradigm, participants were asked to increase their resistance to craving and to increase middle prefrontal cortex (mPFC) activity. We found that participants were able to significantly reduce the BOLD signal in the ACC during the 'reduce craving' task (P = 0.028). There was a significant correlation between decreased ACC activation and reduced craving ratings during the 'reduce craving' session (P = 0.011). In contrast, there was no modulation of the BOLD signal in mPFC during the 'increase resistance' session. These preliminary results suggest that some smokers may be able to use neurofeedback via rtfMRI to voluntarily regulate ACC activation and temporarily reduce smoking cue-induced craving. Further research is needed to determine the optimal parameters of neurofeedback rtfMRI, and whether it might eventually become a therapeutic tool for nicotine dependence.