ORIGINAL RESEARCH
published: 30 March 2015
doi: 10.3389/fnhum.2015.00160

Functional MRI neurofeedback
training on connectivity between two
regions induces long-lasting changes
in intrinsic functional network
Fukuda Megumi 1, 2, 3*, Ayumu Yamashita 1, 4 , Mitsuo Kawato 1, 2 and Hiroshi Imamizu 1, 5*
1

Edited by:
Daniel S. Margulies,
Max Planck Institute for Human
Cognitive and Brain Sciences,
Germany
Reviewed by:
Marco Taubert,
Max-Planck-Institute for Human
Cognitive and Brain Sciences,
Germany
Michelle Hampson,
Yale University, USA
Megan Teresa DeBettencourt,
Princeton University, USA*Correspondence:
Fukuda Megumi,
Institute of Cognitive Neuroscience,
University College London, 17 Queen
Square, London WC1N 3AR, UK
megumi.fukuda.11@ucl.ac.uk;
Hiroshi Imamizu,
Cognitive Mechanisms Laboratories,
Advanced Telecommunications
Research Institutes International,
2-2-2 Hikaridai, Keihanna Science
City, Kyoto 6190288, Japan
imamizu@gmail.com
Received: 22 November 2014
Accepted: 07 March 2015
Published: 30 March 2015
Citation:
Megumi F, Yamashita A, Kawato M
and Imamizu H (2015) Functional MRI
neurofeedback training on connectivity
between two regions induces
long-lasting changes in intrinsic
functional network.
Front. Hum. Neurosci. 9:160.
doi: 10.3389/fnhum.2015.00160

Advanced Telecommunications Research Institutes International, Kyoto, Japan, 2 Graduate School of Information Science,
Nara Institute of Science and Technology, Ikoma, Japan, 3 Institute of Cognitive Neuroscience, University College London,
London, UK, 4 Department of Systems Science, Graduate School of Informatics, Kyoto University, Sakyo-ku, Japan, 5 Center
for Information and Neural Networks, National Institute of Information and Communications Technology and Osaka University,
Suita, Japan

Motor or perceptual learning is known to influence functional connectivity between brain
regions and induce short-term changes in the intrinsic functional networks revealed ascorrelations in slow blood-oxygen-level dependent (BOLD) signal fluctuations. However,no cause-and-effect relationship has been elucidated between a specific change
in connectivity and a long-term change in global networks. Here, we examine thehypothesis that functional connectivity (i.e., temporal correlation between two regions) is
increased and preserved for a long time when two regions are simultaneously activatedor deactivated. Using the connectivity-neurofeedback training paradigm, subjects
successfully learned to increase the correlation of activity between the lateral parietal andprimary motor areas, regions that belong to different intrinsic networks and negatively
correlated before training under the resting conditions. Furthermore, whole-brain
hypothesis-free analysis as well as functional network analyses demonstrated that thecorrelation in the resting state between these areas as well as the correlation between the
intrinsic networks that include the areas increased for at least 2 months. These findings
indicate that the connectivity-neurofeedback training can cause long-term changes inintrinsic connectivity and that intrinsic networks can be shaped by experience-driven
modulation of regional correlation.
Keywords: functional MRI neurofeedback, intrinsic functional network, resting state functional connectivity,
long-lasting changes, default mode network

Introduction
Spatial and temporal correlations in spontaneous brain activity are generated by the underlying
connectivity of brain networks (Ringach, 2009). An increasing number of functional neuroimaging studies have used resting-state functional connectivity magnetic resonance imaging (rs-fcMRI), which quantifies correlations in low-frequency fluctuations of spontaneous
blood oxygen level-dependent (BOLD) signals during rest (Biswal et al., 1995; Raichle et al.,
2001; Fox and Raichle, 2007). Independent component analysis (ICA) of data in thousands of task-driven activation studies has demonstrated co-activation networks that were

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strikingly similar to networks estimated by spatial ICA of theresting state fMRI (Smith et al., 2009; Laird et al., 2011). Thesestudies indicate that the repertoire of functional networks iscontinuously and dynamically activated even during “rest” andthat the dynamics represent the brain architecture. It has beensuggested that intrinsic connectivity investigated by rs-fcMRI
reflects closely, though not exactly, the structural or anatomical
connectivity (Vincent et al., 2007; Greicius et al., 2008; Honeyet al., 2009) and that abnormality in rs-fcMRI is related to many
psychiatric and neurological disorders (Broyd et al., 2009).
An interesting aspect of functional connectivity is that experiences in daily life (Fair et al., 2007) and behavioral training,
including motor learning (Albert et al., 2009; Taubert et al., 2011;
Vahdat et al., 2011) and perceptual learning (Lewis et al., 2009),
lead to changes in intrinsic connectivity (Fair et al., 2007; Lewis
et al., 2009) and functional networks. However, little is known
about a possible cause-and-effect relationship between a specific change in regional-activation correlations and a long-termchange in intrinsic networks. Here, we propose and directly test
the hypothesis that spontaneous brain activity is shaped in anexperience-driven manner, where the temporal correlation at rest
between two brain regions is increased when they are simultaneously activated or deactivated. In the current study, “functional
connectivity” is defined as a temporal correlation between activations in two brain regions, which is calculated from BOLD
time courses. We assume that an absolute value of correlationreflects the strength of functional connectivity, e.g., a large negative (r ≈ −1.0) or positive (r ≈ +1.0) correlation means a strong
connectivity while a correlation near zero (r ≈ 0.0) means a weakconnectivity.
To examine our hypothesis, we developed a straightforward
method for inducing regionally specific correlated activations
based on online fMRI neurofeedback (see reviews: Weiskopf
et al., 2003; deCharms, 2007; Sulzer et al., 2013). Previous
fMRI neurofeedback methods successfully up- or down- regulated single region-of-interest (ROI) activation (deCharms et al.,2005; Subramanian et al., 2011); furthermore, these studies wereextended to control voxel patterns of spatial activity (Shibata
et al., 2011) and multiple brain regions (Robineau et al., 2014).
These studies have proven that online neurofeedback training canbe an effective method to manipulate brain activation at various
levels. In our connectivity-neurofeedback training, the temporalcorrelation of activation in two specific regions (see below) during a motor imagery task for 14 s was calculated. Subjects were
informed of the correlation immediately after each trial as anintermittent feedback. A monetary reward was given in proportion to the increment of the correlation. Through repetition of afew hundreds of trials over 4 days, subjects learned to increase
the correlation between the regions in a trial-and-error manner
while guided by the reward without conscious understanding of
the meaning of feedback signals (Weiskopf et al., 2004; Bray et al.,2007; Shibata et al., 2011).
Recently, the effect of single-ROI regulation was investigated on functional connectivity and networks including rsfcMRI (Ruiz et al., 2011; Harmelech et al., 2013; Scheinost et al.,2013; Scharnowski et al., 2014). Furthermore, the importanceof connectivity-neurofeedback for improvement of cognitive

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functions and psychiatric disorders was suggested in a review
article (Ruiz et al., 2014), and manipulation of local network
dynamics was examined through an online evaluation of hypothesized connectivity models (Koush et al., 2013). These studies
suggest that neurofeedback training induces changes in functional connectivity. We were specifically interested in the changes
in correlation across intrinsic networks, and thus we selected two
regions from distinct intrinsic networks.
Specifically, we selected the left primary motor cortex (lM1)from motor/visuospatial network group (MVN) and the left lateral parietal cortex (lLP) from default mode network (DMN)(Callard and Margulies, 2014) according to a meta-analysis study(Laird et al., 2011). A number of rs-fcMRI studies have consistently shown that the DMN and some of networks related
to MVN (such as the dorsal attention network) are negatively correlated (Fox et al., 2005; Fransson, 2005; Biswal et al.,2010). The genotype of the dopamine transporter affects thedegree of the negative correlation (Gordon et al., 2012). The
existence of consistent DMN across mammalian species indicates an evolutionary organization of the negative correlation(Rilling et al., 2007; Vincent et al., 2007; Lu et al., 2012). If
our method can change this correlation, it suggests that trainings and experiences modify connectivity largely determinedby genetic and evolutional factors, and that the connectivityneurofeedback training will contribute to educations and medical
treatments.

Materials and Methods
Participants
Thirty-three healthy subjects (23 males and 10 females, aged
19–43 years) participated in this experiment. All subjects wereright-handed according to the Edinburgh inventory (Oldfield,
1971). Following a previous neurofeedback study that uses
a neurofeedback-training group and several control groups(deCharms et al., 2005), we randomly assigned subjects toa test group (n = 12) or one of two control groups (asham-feedback group: n = 12 and a tapping-imagery group:n = 9; see below), each of which had more than eight subjects.
There was no significant difference in age [F(2, 30) = 0.34,
p = 0.72, n.s] and gender balance [F(2, 30) = 2.03, p =0.15, n.s.] across groups. The Institutional Review Board ofAdvanced Telecommunications Research Institute International(ATR) approved this study. All subjects gave written informed
consent.

MR Image Acquisition
Images were obtained using a Siemens MAGNETOM Trio (3Tesla) scanner. BOLD signals were measured using echo planar
imaging (EPI) sequence (volume repetition time, 2 s; echo time,
30 ms; flip angle, 80◦ ). The entire brain was covered in 33 axial
slices (3.5-mm thickness; no gap), voxel size was 3 × 3 × 3.5 mm,
and field of view was 192 × 192 mm. T1-weighted structural
images were acquired with 1×1 × 1-mm resolution. T2-weighted
structural images were acquired on each day with 1×1 × 3.5-mm
resolution.

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Neurofeedback Training Protocol

numerical score was presented on the screen in the feedback
period (right panel of Figure 1B). The score indicated an increment of the correlation from that in the initial block on thefirst day (see below). We used MATLAB (The MathWorks,
Inc.) for online processing of fMRI data. We also used realignment modules of SPM8 (Wellcome Department of Cognitive
Neurology, London, UK; http://www.fil.ion.ucl.ac.uk/spm). The
software ran on a computer that accessed data files in the MRI
system. Each volume of the functional image (EPI) was realigned
in real time to the first volume obtained on the first training
day. Seven volumes were obtained during an imagery period
in each trial, but the first volume was discarded and one volume from the feedback period was added. One may argue thata one-volume shift (2 s) is not enough to fully compensate thehemodynamic delay (4–8 s). However, in a previous fMRI neurofeedback study with an intermittent feedback paradigm (Brayet al., 2007), the first 2 s of each imagery period was eliminatedfrom the feedback signal computation, and participants were able
to learn to regulate their own brain activity. We followed theirapproach and this enabled us to minimize the delay of feedback to participants. BOLD signal time courses were extracted
from the lM1 and lLP ROIs in these volumes. To remove
low-frequency trends in the BOLD signal, a high-pass temporal filter (cutoff frequency of 0.06 Hz) was applied to the timecourses.

Subjects in the test group received neurofeedback training toincrease temporal correlation of BOLD signals between two target ROIs: lM1 and lLP (see Figure 4A). Each subject received
training for 4 days (white boxes in Figure 1A). Subjects underwent 5.0 blocks of training per day on average (SD: 1.32), witheach block consisting of 10 trials, for a total of 20.0 (SD: 1.73)
blocks (see below for the other groups). A trial in each block
began with a rest period of 14 s, during which the “=” sign waspresented on the screen (Figure 1B). When the sign changed
to “+,” subjects performed the tapping imagery task for 14 s
(imagery period). Subjects were instructed to imagine tapping
their thumbs with their fingers randomly as fast as possible during the imagery period. They were instructed to produce kinetic
imagery related to tapping, rather than attempt visual imageryof tapping fingers, and not to overtly move their hands during
the task. After the imagery period, a feedback score calculatedby the online MRI system (see below for calculation of feedback
score) was presented on the screen (feedback period). As noted
below, the score was determined from the correlation between
two regions.

Online Calculation of Feedback Score
Temporal correlations between BOLD signals were averaged
within the target ROIs during the imagery period, and a

FIGURE 1 | Experimental procedures. (A) Measurement of resting state
activity and neurofeedback training. Resting state activity measurement(pre-REST) was followed by neurofeedback training for 4 days. Resting state
activity was also measured at the end of the fourth day of training(post-REST) and 2 months after the training (REST after 2 months).
(B) Timeline and displays for subjects in a trial during training in theneurofeedback-training group. After a rest period while the “=” sign waspresented on the screen, subjects were instructed to produce tapping
imagery during the imagery period while the “+” sign was presented. They

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received a numerical feedback score (e.g., 20) that represented an increased
correlation of activation in the left motor cortex (lM1) and the left lateralparietal region (lLP) during the imagery period (see Materials and Methods).
(C) Displays for control groups. Subjects were also required to produce
tapping imagery. The sham-feedback group received feedback, which was
calculated from the activity of another subject. For the tapping-imagery
group, the score value was not presented to the subjects; instead, threeasterisks (***) were presented on the screen. Subjects were required to fix
their eyes to a circle at the center of the screen.

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Using the filtered time courses, the feedback score in theimagery period of the i-th trial was calculated as:
Scorei =(Correlationi − CorrelationInitial )


× 100
1 + CorrelationInitial (Figure 1C). A sham-feedback group received scores and reward,
but the scores’ time series was taken from another subject randomly chosen from the neurofeedback-training group. We used
the score time-series of all subjects in the neurofeedback-training
group (n = 12) for sham-scores in the sham-feedback group(n = 12). Each subject in the sham-feedback group saw atime-series from one randomly selected different subject in theneurofeedback-training group. No other subject in the shamgroup was given the same feedback time-series of that subject in
the neurofeedback-training group. As explained later, the scoresin the neurofeedback-training group increased as training proceeded. Thus, the sham-feedback control group was used to
examining whether a spurious increase in score/reward, which
does not correctly reflect the actual correlation between the two
ROIs in one’s own brain, could induce increased correlation
between the ROIs. If the sham-feedback group does not showchanges in functional connectivity, we can exclude any mechanism that explains the changes in functional connectivity solely
by a combination of mental motor imagery and slowly increasingscore/reward. A tapping-imagery group (n = 9) was instructedto perform the imagery task without the feedback score. In thisgroup, we examined whether the repetition of mere tapping
imagery could increase the correlation between the ROIs. Subjects in the sham-feedback and tapping-imagery groups underwent a total of 19.5 (SD 1.85) and 20.56 (SD: 1.07) blocks,
respectively.

(1)Here, Correlationi represents the correlation of time courses averaged within the ROIs. CorrelationInitial is the correlation averaged
over trials in the initial block of the first training day. We added“1” in the denominator to keep the score range between −100
and +100. Thus, the score represents increments of the correlation value in each trial compared to the value in the first training
block. The purpose of using initial score as a baseline was tocompensate individual differences in the initial correlation andto keep participants motivated. For instance, if the correlation
changes from r = −0.6 to −0.2, the above score increases from 0
to 25. We note that the score was designed so that positive reward
is given in all of the following three cases: (i) a positive correlation
increases in its magnitude (e.g., from 0.2 to 0.6); (ii) a negative
correlation changes to a positive correlation (from −0.2 to 0.2);
and (iii) a negative correlation increases but stays negative, thus
decreasing in absolute value (e.g., from −0.6 to −0.2). We notethat the objective of our connectivity neurofeedback training is toincrease the correlation and not to increase the absolute value ofthe correlation, which has sometimes been termed “connectivity”
elsewhere. The score was calculated immediately after the acquisition of the first volume in a feedback period (2 s). Preprocessing
and score calculation were completed within 2 s. Thus, subjects
received the score within 4 s after the end of the imagery periods.
Subjects were instructed to imagine tapping their thumbs with
their fingers randomly as fast as possible during the imagery
period, with the aim of increasing the score that reflects theirimagery performance. During the initial block of the first day, noscore was provided to the participants. They were informed that
we compute the baseline of their brain-training performance inthe first training session. The post-experiment debriefing (Supplementary Text S1) indicated that all subjects performed themental motor imagery task during training. A standard Statistical Parametric Mapping (SPM) analysis revealed activities inmotor-related areas, especially in the left supplementary motor
area (SMA) and the premotor area (PM; see Supplementary Figure S1), thus supporting the subjects’ reports. We confirmedthat subjects had no knowledge of ROIs or how the score wascalculated.
Subjects received additional monetary reward in proportion
to their total score on each day. Subjects were instructed toincrease their score by producing tapping imagery, and they were
informed that a monetary reward (up to 2000 JPY) would be
paid in proportion to their score. At the end of each block, theaveraged score within the block was presented on the screen. We
showed a negative score when the temporal correlation between
the ROIs dropped in relation to the baseline. We did not decrease
monetary reimbursement when negative scores were shown toparticipants.

ROI definition for Neurofeedback Training
We determined two ROIs for calculation of the feedback score.
We selected the lM1 as one of the two ROIs. This is because consistent temporal changes in BOLD time courses with significant
amplitudes are necessary for calculation of a reliable temporalcorrelation of the two regions during the 14-s training periods.
We followed previous neurofeedback studies (deCharms et al.,
2004; Bray et al., 2007) that successfully induced large BOLD
modulation in the lM1 by asking subjects to imagine finger movements. The lM1-ROI was defined as Brodmann area 4 according to the anatomical map in PickAtlas (http://fmri.wfubmc.edu/
software/PickAtlas) (Lancaster et al., 1997; Maldjian et al., 2003).
As the other ROI, we selected a region in the DMN because
activity in DMN regions is expected to negatively correlate withthat in the lM1 during the motor imagery task. We found a negative correlation before the neurofeedback training during rest
as shown in the Results section. We aimed to induce a marked
change in the correlation. We adopted a region in the left lateral parietal cortex (lLP) as the nearest DMN region to lM1. Thisis because functional connectivity between closer regions is on
average higher than that between distant regions (Bullmore andSporns, 2012), and a strong negative correlation is required tocompute a reliable feedback score within a short time interval(14 s) at least for the initial stage. Another reason for selectionof close regions is to minimize the temporal gaps between activation in the two regions. MRI cannot acquire BOLD signals
simultaneously over the entire brain but instead acquires signals
sequentially slice by slice. Thus, the longer distance between thetwo regions may increase the greater temporal gap. Anatomically,lLP is a part of Brodmann’s area 39 and the posterior-lateral part

Control Groups
Subjects in the two control groups were trained using a similar procedure but without the proper neurofeedback information

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Connectivity-neurofeedbackof the intraparietal-sulcus region (see Table 1 for coordinates).
Previous studies reported lLP as a part of the DMN; in particular, rs-fcMRI studies indicated anti-correlation with regions
such as the dorsal attention networks (Fox et al., 2005; Van Dijk
et al., 2010), and a meta-analysis of attention-demanding experiments reported a consistent decrease in activity during task performance (Shulman et al., 1997). In accordance with previous
studies of the DMN (Fox et al., 2005; Fox and Raichle, 2007),
we refer to this region as the “lateral parietal (LP).” The lLPROI was defined as a sphere with a 7.5-mm radius centered at(x, y, z) = (−45, −67, 36) in the Montreal Neurological Institutestandard brain coordinates (MNI; Montreal, Quebec, Canada)according to a previous study on brain networks (Biswal et al.,2010).
Because these ROIs were defined in the standard brain, we
identified corresponding voxels in the functional images of individual subjects’ brains using a deformation module in SPM8. We
took several volumes of functional images for this purpose at the
beginning of the experiment on the first training day, and weused the identified voxels as ROIs for calculating scores in thesubsequent training blocks.

for spectrum density analysis). This indicates that the recall or
rehearsal unlikely affects rs-fcMRI (see Supplementary Text S2for effects of rehearsal on rs-fcMRI). We did not find any significant difference in head movements estimated by realignment
parameters among the three REST measurements (see Supplementary Text S3).

Whole Brain Analysis of rs-fcMRI
We first conducted a whole-brain and hypothesis-free analysis of
the degree of connectivity (Buckner et al., 2009; Hampson et al.,2012; Scheinost et al., 2012, 2013), which was defined for eachvoxel as the number of voxels to which the voxel was connectedwith a correlation coefficient r above or below a threshold. Preprocessing of rs-fcMRI and calculation of the degree of connectivity followed a previous study (Scheinost et al., 2012). Previous
studies on positive connectivity used a threshold of r > 0.25
(Buckner et al., 2009; Hampson et al., 2012). These studies examined functional connectivities with positive correlations and set
a positive threshold value (r > 0.25). By contrast, we aimed tochange normally negative correlation toward zero or positive correlation. Therefore, we changed the sign of the threshold and the
direction of the inequality sign (r < −0.25) to investigate regions
where negative correlation changed toward zero or positive values. We examined statistically significant decreases in degree ofconnectivity between pre-REST and post-REST. Let us illustrate
the relationship between the change of degree of connectivity andthe change of correlation between lM1 and lLP when the correlation between two voxels in lM1 and lLP changed from −0.4 to 0.3
by the neurofeedback training. This connection is counted withthe threshold of r < −0.25 (−0.4 < −0.25) before neurofeedback training, but it is not counted after training (0.3 > −0.25),
and thus the degrees of connectivity of lM1 and lLP decreaseby 1. If there exist 300 voxel pairs between lM1 and lLP whose
correlations increase across the threshold −0.25 (e.g., from −0.3
to 0.1, from −0.4 to −0.2), then the degree of connectivity oflM1 and lLP decrease by 300. In summary, if the change in the

Measurement of rs-fcMRI
For all subjects, we measured rs-fcMRI before (pre-REST), immediately after (post-REST), and more than 2 months (REST after
2 months) after the training (gray/black boxes in Figure 1A) toinvestigate the effects of training on intrinsic functional connectivity at rest. Rs-fcMRI was recorded for 5 min (152 volumes) ina run with the standard protocol (Van Dijk et al., 2010). Subjects were instructed to gaze at a fixation point on a screen, notto move during the measurements, and not to recall or rehearse
what they did during neurofeedback training. The major frequency of rs-fcMRI was below 0.05 Hz while a 0.06-Hz high-passfilter was applied to signals in neurofeedback training. Therefore, the rs-fcMRI provided an index of training effect that was
independent of the feedback score (see Supplementary Figure S2
TABLE 1 | Sixteen regions of interest for analysis of resting state fMRI.
Network

Label Anatomical region

Brodmann area/MNI coordinates Averaged volume in individual space (mm3 )Motor/visuospatial network

lM1

Left primary motor cortex

Area 4

rM1

Right primary motor cortex

Area 43906

lSMA

Left supplementary motor area

Area 6

13,302rSMA

Right supplementary motor area Area 6

14,662lIPS

Left intra-parietal sulcus(−25, −57, 46)

1475

rIPS

Right intra-parietal sulcus(25, −57, 46)

1489

lFEF

Left frontal eye field(−25, −13, 50)

1566

rFEF

Right frontal eye field(25, −13, 50)

1607

lLP

Left lateral parietal region(−45, −67, 36)

1533

rLP

Right lateral parietal region(45, −67, 36)

1608

Default-mode network

3307

PCC

Posterior cingulate cortex(−5, −49, 40)

1533

MPF

Medial prefrontal cortex(−1, 47, −4)1418

Control (visual and auditory networks) lV1

Left primary visual cortex

Area 17

1575

rV1

Right primary visual cortex

Area 17

1701

lA1

Left auditory cortex

Areas 41 and 42

2014

rA1

Right auditory cortex

Areas 41 and 42

2043

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degree of connectivity is a decrease, the result is in accordancewith our prediction that a negative correlation between lM1 andlLP increases (e.g., from −0.4 to −0.2 or 0.1). First, we applieda threshold for P < 0.05 corrected for multiple comparisons atcluster level to the connectivity map, as well as a liberal threshold of P < 0.005 without correction, averaged across subjects
separately for the three groups.

studies of resting-state fMRI (Efron and Tibshirani, 1986; Bellec
et al., 2010) and analyzed the effect of training and its influence on combinations of networks under rest. The z-transformed
correlation value during pre-REST was first subtracted from thevalue during post-REST for each region pair. We subtracted
Fisher’s z-transformed correlation matrix values before the training (pre-REST) from that immediately (Figure 5A: post-REST)
or 2 months (Figure 5B: REST after 2 months) after the trainingof individual subjects and calculated within-group averages. To
examine the differences in the training effect among six types of
network pairs (MVN-MVN, MVN-DMN, DMN-DMN, MVNcontrol, DMN-control, and control-control), we divided the subtracted and then averaged correlation matrix into six network
pairs corresponding to the types, and then we investigated which
network pairs exhibited a significant number of region pairs inwhich the correlation values increased by more than the summation of mean and standard deviation across all region pairs andsubject groups (colored cells in Figures 5A,B). Specifically, we
computed confidence intervals (CI) for the number of coloredcells for each network pair using a bootstrapping technique, as
follows. We created 3000 resampled matrices by randomly sampling cells in the three matrices corresponding to the three subject
groups, each of which was obtained by averaging the subtracted
matrices within the subject group. Based on these matrices, weestimated the distribution of the numbers of colored cells for each
network pair and calculated the (100 − α)% CI corresponding
to a significance level at α (two-sided). To address the problem
of multiple comparisons using Bonferroni correction, the upper
range of the CI was raised from (100 − α)% to (100 − α/(6×3))%
while taking into account the number of comparisons (i.e., number of network pairs × subject groups = 6 × 3). We determinedwhich network pairs exhibited a larger number of colored cells
than the corrected (100 − α)% CI.

ROI-Based Network Analysis of rs-fcMRI
As we noted in the Introduction section, we adopted the definition of intrinsic networks in Laird et al. (2011) and investigated the changes in correlations between the MVN and DMN.
Our network analysis was based on the correlation between ROIs
from the two networks (Figure 4A and Table 1), including thetarget ROIs (M1 and LP). We included the SMA, which is related
to a motor-imagery task (Grezes and Decety, 2001), in the ROIsof the MVN. Other regions in the MVN and DMN were determined according to a previous study on brain networks (Biswal
et al., 2010). This study examined reproducibility of networks
in rs-fcMRI across 1414 volunteers collected at 35 centers. Oneof the examined features was a negative correlation between the
DMN and a group of regions named “task-positive” network (Foxet al., 2005) in a ROI-based correlation analysis. The ROIs for the
DMN were located in the lateral parietal region (LP), the posterior cingulate cortex (PCC) and the medial prefrontal cortex(MPFC). We used these ROIs for the DMN. The ROIs for thetask-positive network were located in the intra-parietal sulcus(IPS), the frontal eye field (FEF), and the middle temporal region(MT) (see Supporting Information of the study Biswal et al.,2010). We used the IPS- and FEF-ROIs since they are includedin the MVN. As control networks besides the MVN and DMN,
we defined ROIs in the visual and audition/speech networks.
Because the previous study (Biswal et al., 2010) determined ROIs
as spherical regions whose center was defined in MMN coordinates (Table 1), we followed these definitions. The other regions
were anatomically defined according to the Brodmann area
map in PickAtlas.
Concretely, data were analyzed using SPM8. The first two volumes of images in each run were discarded to allow for T1 equilibration. Functional images were temporally realigned to correct
for the sequence of slice acquisition and then spatially aligned
to the first remaining volume in a run with a six-parameter
rigid-body transformation. Data were smoothed spatially with aGaussian kernel of 6 mm full-width at half-maximum. To remove
several sources of spurious variance unlikely to reflect spatially
specific functional correlations, we applied regression analysis
to the extracted time courses using explanatory variables: six
realignment parameters, averaged signal over gray matter, whitematter, and cerebrospinal fluid. We averaged the resultant residual time courses within each ROI. After these preprocessing steps,
we calculated Fisher’s z-transformed Pearson correlation coefficients (r) between the averaged time courses and then produced
correlation matrices of all ROI pairs (Figure 4B).

Results
Changes in Score during Training
Figure 2 shows the change in feedback score, representing theincrement of the correlation, averaged across blocks and subjects

FIGURE 2 | Change in feedback score during neurofeedback training.
Neurofeedback scores averaged across subjects as a function of training days
(error bars: standard errors). Broken lines indicate score values averaged
across days for subject groups. Right asterisks indicate results of post-hoc
t-tests on the averaged scores (see main text). P-values were corrected using
the Bonferroni method.

Bootstrapping Method for Analysis of Network
Correlation
To investigate changes across subjects in correlations between
networks, we adopted the bootstrapping method used in previous
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as a function of training day. For comparison, we calculated thescore from individual BOLD signals for the two control groups,
although this score was not presented to the control groups.
We applied a One-Way ANOVA to scores averaged across subjects separately for each day and found a significant effect ofgroup [F(2, 9) = 13.8, P = 0.0018]. Post-hoc t-tests indicatedthat the score of the neurofeedback training group is significantly higher than that of the sham-feedback group [t(6) =4.25, P = 0.0054; P < 0.02 after Bonferroni correction for
two comparisons] or the tapping-imagery group [t(6) = 3.04,
P = 0.023; P < 0.05 after correction]. Note that the score
represents the increment in the correlation compared to the initial block on day 1. Consequently, these results indicate that
only the neurofeedback-training group exhibited a training effectaveraged across the days, thus successfully increasing the correlation between the two areas by neurofeedback training.

training to −0.1 or 0.2 after the training decreases the degreeof connectivity. Such change in correlation was in accordancewith our objective of applying connectivity-neurofeedback, i.e.,
to increase the correlation from negative toward zero or positive correlation. Changes in the degree of connectivity were
found only in the neurofeedback group, which was the only
one among the three groups to show the effect of neurofeedback training; this suggests a causal link between neurofeedback
training and rs-fcMRI changes. We examined the robustness ofthese results with respect to the threshold setting for correlation
values: the clusters in LP and PCC were consistently found at
threshold ranges of −0.3 ≤ r ≤ −0.1 only in the neurofeedback training group, while no significant cluster could be foundat a threshold of r < −0.25 in either the sham-feedback or thetapping-imagery group (several clusters appeared at much lower
threshold than −0.25 in the sham-feedback [a cluster at r < −0.2
and three clusters at r < −0.1] and tapping-imagery [no cluster
at r < −0.2 and three clusters at r < −0.1] groups). We applied aquite liberal threshold of P < 0.005 without correction for multiple comparisons to the above data. The decrease in connectivity
was observed in the left SMA, lM1, the posterior cingulate cortex(PCC), lLP, the auditory cortex and other areas (see Supplementary Figure S3). Clusters having more than five voxels are listed in
Supplementary Table S1 for the three subject-groups.

Changes in whole-Brain Connectivity
Our whole-brain connectivity analysis found clusters with significant decreases in degree of connectivity only in the neurofeedback group but no clusters in the two control groups (Figure 3:
P < 0.05 corrected for multiple comparisons at cluster level[cluster size > 186 voxels]). These clusters were found in thelLP and the posterior cingulate cortex (PCC). A decrease in the
degree of connectivity means a decrease in the number of voxels to which each voxel in these clusters was connected withhighly negative correlation (r < −0.25, see Materials and Methods). See section Whole Brain Analysis of rs-fcMRI for a detailed
explanation on the consistent relationships between a decreasein the degree of connectivity and an increase in correlation. For
instance, the change in correlation from r = −0.3 before theTraining Effect in Intrinsic Networks
We conducted a ROI-based correlation analysis between the
MVN and DMN using 16 ROIs (Figure 4A and Table 1). Notethat these ROIs were not specifically located at regions wherea significant change in degree of connectivity was identified in
the neurofeedback-training group but were the target ROIs (M1

FIGURE 3 | Regions of the brain where degree of negative
connectivity decreased from pre-REST to post-REST inneurofeedback training (A), sham-feedback (B), andtapping-imagery (C) groups. We followed the methods of previous
studies (Scheinost et al., 2012, 2013) for preprocessing of rs-fcMRI andcomputing degree of connectivity maps (Scheinost et al., 2012, 2013)
except that we counted for each voxel the number of voxels to which
it was correlated with r < −0.25 (see text). The difference in degree ofconnectivity between pre-REST and post-REST was examinedvoxel-by-voxel by using a paired t-test separately for the three subject

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groups. Regions with a significant decrease in degree of connectivity(red blobs) were identified by a cluster larger than 186 voxels (P < 0.05
corrected for multiple comparisons across the gray matter). Suchregions were found only in the neurofeedback-training group. We used
the AlphaSim program in Analysis of Functional Neuroimages (AFNI:
http://afni.nimh.nih.gov/afni) for calculation of the cluster-level threshold.
Note that functional images were smoothed spatially with a Gaussian
kernel of 10 mm full-width at half-maximum in the preprocessing. x, y,
and z indicate slice levels in NMI coordinates. LP, Lateral parietal region;
PPC, Posterior parietal cortex.

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negative values (blue) were found in many region pairs between
MVN and DMN (red rectangle), confirming the negative correlation. However, these values increased immediately after training(post-REST; upper-right triangle area; yellow/red).
To investigate consistent changes across subjects, we first subtracted the z-transformed correlation value during pre-REST
from the value during post-REST for each region pair. The colors in Figure 5A indicate the subtracted values averaged acrosssubjects separately for the groups. To indicate pairs of regionsin which correlation markedly increased, cells (region pairs) in
Figure 5A are colored if their values are higher than the summation of mean value and standard deviation (mean + SD: −0.02 +0.09) across all region pairs and subject groups. We examined which network pairs in the correlation matrix exhibiteda significantly larger number of colored cells (compared to thenumber generated by a random process) when the matrix waspartitioned into six areas according to the type of network combination (MVN-MVN, MVN-DMN, DMN-DMN, MVN-control,
DMN-control, and control-control; gray or red border lines in
Figure 5). The bootstrap sampling approach (see Materials andMethods for bootstrapping method) was used to estimate theprobability of the number of colored cells, if they were generated by a random process, while it took into account the total
number of cells (region pairs) in each network pair. The analysis
revealed that only the network pair of the MVN and DMN in theneurofeedback-training group exhibited a significant number ofcolored cells (P = 0.0003, computed using bootstrap samples;
P < 0.01 corrected by the Bonferroni method across six network pairs and three experimental groups). This finding suggeststhat the neurofeedback training significantly increased the correlation in intrinsic activity only between the MVN and DMN, andonly for the neurofeedback-training group. Note that 16 ROIs
are small representations of the brain’s functional networks, so
to verify our conclusions we increased the number of ROIs from
16 to 33 to cover a broader range of regions in the cerebral cortex
and found similar results to those in Figure 5 (see Supplementary
Table S2 and Supplementary Figure S5A).
Furthermore, highly similar patterns were found in matrices
obtained by the subtraction of correlations in pre-REST fromthose in REST after 2 months (Figure 5B), suggesting that thetraining effect was stably maintained for 2 months. It should be
noted that the increase in correlation value was not restricted to
the region pair used for neurofeedback (lM1—lLP) but was also
found in the other region pairs between the MVN and DMN. In
an additional, more extensive network analysis, we applied thesame analyses to a larger number of ROIs and obtained similar
results (Supplementary Figure S5B).
Regarding the control groups, no significant increase in correlation for any combination of network types was found, either
in post-REST (middle and right panels of Figure 5A) or REST
after 2 months (those of Figure 5B). Consequently, a significant
increase in correlation between the networks was found only inthe neurofeedback-training group.

FIGURE 4 | Regions of interest (ROIs) and their functional connectivity.
(A) ROIs from which BOLD signal time courses were extracted. Circles
indicate center positions of ROIs projected on the cortical surface. Orange,
cyan and magenta circles correspond to ROIs in the motor/visuospatial
network (MVN), the default mode network (DMN), and the control regions,
respectively. The double-sided arrow indicates ROIs used for calculation of the
feedback score in the neurofeedback training. This figure was created withmulti_color software (http://www.cns.atr.jp/multi_color). (B) The z-transformed
correlation matrix of all ROI pairs from a subject in the neurofeedback training
group before (left-lower triangle area) and immediately after (right-upper
rectangle area) training. Color bar indicates z-transformed correlation value.
Red rectangles indicate region pairs between MVN and DMN. r, right; l, left.
See Table 1 for abbreviations of region names.

and LP), regions related to motor-imagery (SMA), or those fromprevious studies on brain networks (see Materials and Methods).
We calculated, separately for each of the 16 ROIs, the ratio of
the number of voxels at which the degree of connectivity significantly changed (P < 0.005 uncorrected in group-level statistics, see above) across subjects to the number of voxels includedin the ROI used for the network analysis. Ratios of significantvoxels averaged across 16 ROIs were 0.8% (SD: 2.1) for theneurofeedback-training group, 1.3% (4.8) for the sham-feedback
group, and 0.6% (1.8) for the tapping-imagery group. We applieda One-Way ANOVA to the ratios but could not identify a significant effect of subject group [F(2, 45) = 0.23, P = 0.79]. Thus,
the results of the degree-of-connectivity analysis did not bias ourselection of 16 ROIs for a particular group.
The temporal correlations between BOLD signal time courses
averaged within each ROI were computed. Figure 4B shows
z-transformed correlation matrices calculated from a representative subject in the neurofeedback-training group. The cell color
indicates the z-transformed correlation value in each pair ofregions. Before the training (pre-REST; lower-left triangle area),

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Changes in Correlation-Values
Figure 5C indicates an increase in the z-transformed correlationfrom pre- to post-training between the MVN and DMN, which

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FIGURE 5 | Increase in correlation from pre- to post-training. (A)Increase from pre-REST to post-REST. Yellow/red cells indicate ROI pairs
whose correlation markedly increased (i.e., the increase was greater than themean + SD [−0.02 + 0.09] across all pairs and groups). A red rectangle
indicates a network pair having a significantly larger number of yellow/red
cells than that generated by a random process according to a bootstrap
sampling approach (P = 0.0001; P < 0.01 after the Bonferroni correction).
(B) Increase from pre-REST to “REST after 2 months.” Colored cells indicatethat the increase is greater than the mean + SD (0.00 + 0.07). Color bars
indicate increments from pre- to post-training. (C) Increase in correlation
averaged across subjects and region pairs between the MVN and DMN (error
bars: standard errors). Filled bars indicate the increase from pre-REST to

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post-REST, and open bars indicate the increase from pre-REST to REST
after 2 months. Asterisks indicate results of post-hoc comparisons on the
averaged increase (see main text). (D) Z-transformed correlation in pre- andpost-training for the neurofeedback training group. BOLD signal time courses
were averaged within individual ROIs: lM1, lLP, sensorimotor network (SMN)and DMN. Correlations were calculated using the averaged time courses
between lM1 and lLP (left), SMN and DMN (middle), and MVN and DMN(right). They were averaged across subjects during the three resting state
activity measurements (error bars: standard errors). Asterisks indicate theresults of one-sample t-tests that examined whether the z-transformed
correlation is significantly smaller than zero after P-values were corrected forthree comparisons using the Bonferroni method (see main text).

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Discussion

were averaged across subjects and shown separately for the threesubject groups. A Two-Way ANOVA (subject group x measurement [Post—Pre or 2 m—Pre]) identified a significant effect ofsubject group [F(2, 60) = 5.32, P = 0.007] but no significant
effect of measurement [F(1, 60) = 0.02, P = 0.88] or interaction[F(2, 60) = 0.12, P = 0.89]. This suggests significant differencesin the increase across the groups. Post-hoc comparisons found asignificant difference between neurofeedback-training and shamfeedback groups (P = 0.011; P < 0.03 after correction for two
comparisons), and neurofeedback-training vs. tapping-imagery(P = 0.010; P < 0.03 after correction). However, there wasno significant difference in sham-feedback and tapping-imagery(P = 1.00, n.s.). We applied a One-Way ANOVA to correlations
between M1 and LP during the pre-REST (i.e., before neurofeedback training) across the three subject groups but did not find asignificant difference [F(2, 30) = 2.39, P = 0.11, n.s.]. Becauseof non-significant difference in correlation before the training,
the significant difference in the correlation increase found acrossthe three groups was caused by the difference in the training
conditions.
The above subtraction of correlation values in the pre-training
from those in the post-training indicated a significant increase incorrelation between the MVN and DMN in the neurofeedback
group. To investigate changes in correlation at different levels ofregional sizes, we further calculated correlation values by changing pairs of ROIs and networks: lM1—lLP, sensorimotor network(Laird et al., 2011) (SMN including M1)—DMN, and VMN—
DMN. We applied a Three-Way ANOVA (group × measurement[Pre, Post or 2 m] × ROI pair) to the z-transformed correlation values before subtraction, and found a significant effect of
the group [F(2, 270) = 3.59, P = 0.029]. Therefore, we applied
Two-Way ANOVA (measurement × ROI pair) to the correlation values separately for the three groups, and found a significant
effect of measurement only for the neurofeedback-training group[F(2, 99) = 6.56, P = 0.002; P < 0.01 after correction for threecomparisons, see Supplementary Table S3 for other effects andgroups].
Figure 5D shows correlation values averaged across subjects of the neurofeedback-training group separately for pairsof ROIs. We applied one-sample t-tests to examine whether
z-transformed correlation is significantly smaller than zero. Consequently, for the lM1-lLP connectivity (left panel), the correlation was significantly negative in the pre-REST (t(11) = −5.18,
P < 0.001 [P < 0.01 corrected for three comparisons]) but not in
the post-REST (t(11) = −1.89, P = 0.26 [n.s.]) or the REST after
2 months (t(11) = −1.81, P = 0.29 [n.s.]). For the SMN-DMN
connectivity (middle), the correlation was significantly negativein the pre-REST (t(11) = −9.73, P < 0.001 [P < 0.01]) andthe post-REST (t(11) = −3.47, P = 0.02 [P < 0.05]) but notin the REST after 2 months (t(11) = −2.45, P = 0.10 [n.s.]).
For the MVN-DMN connectivity (right), the correlation was significantly negative in the pre-REST (t(11) = −8.83, P < 0.001[P < 0.01]) and the post-REST (t(11) = −3.26, P = 0.02
[P < 0.05]) but not in the REST after 2 months (t(11) = −2.40,
P = 0.11 [n.s.]). Therefore, the negative correlation in the preREST consistently increased from negative toward zero after theneurofeedback training for all three ROI and network pairs.

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We hypothesized that positively correlated activations of two
regions evoked by repeated experiences induces a long-term
increase in their functional connectivity, which is the temporal correlation values within intrinsic networks. To directly
examine this hypothesis, we induced an experimentally traceable change in the correlation between specific networks, theMVN and DMN, by evoking correlated activation between
two specific regions based on the connectivity-neurofeedback
training (Fukuda et al., 2011). The training led to a significant increase in correlation between the two networks
under rest, and this increase was preserved for more than 2months. Consistent and significant changes in the feedback
score, changes in degree of connectivity from pre- to posttraining, and changes in network connectivity revealed by the
ROI-based analysis from pre- to post-training were all identified only in the neurofeedback-training group. This suggests
a causal link between neurofeedback training and rs-fcMRIchanges.

Instruction to Subjects
We instructed subjects to perform a mental imagery task during
the training session. This instruction, however, is unlikely directly
related to modulation of temporal correlation between lM1 andlLP because a meta-analysis of more than 3000 experiments indicated that lM1 and lLP do not co-activate under known tasks
including motor imagery tasks (Toro et al., 2008). There is no task
strategy known to be efficient for increasing correlation between
M1 and LP, so we chose the reinforcement-leaning method, inwhich the desired response is reinforced by reward during a trialand-error search without an effective instruction of strategies.
This method has been adopted in previous fMRI neurofeedback
experiments (Bray et al., 2007; Shibata et al., 2011). However,
the motor imagery should have certainly contributed to consistent temporal changes in BOLD time courses in M1 (deCharms
et al., 2004; Bray et al., 2007) as well as in LP at the initial stage oflearning.

Control Groups and Decrease in Network
Correlation
There was no significant change in score during training
or rs-fcMRI in the sham-feedback group. This indicates thatthe spurious increase in score/reward, which does not correctly reflect the actual correlation between the two ROIs in
one’s own brain, even combined with mental motor imagery,cannot induce increases in correlation between the ROIs orchanges in the intrinsic network. No significant change in thetapping-imagery group suggests that the repetition of tapping
imagery cannot change correlation between the ROIs or the
network.
We could not identify a consistent decrease from pre-REST
to either post-REST or REST after 2 months (see Supplementary
Figures S4, S6). This suggests that the long-lasting increase was
not due to daily fluctuations in rs-fcMRI but to the effect of theconnectivity-neurofeedback training.

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Change in Functional Connectivity during
Neurofeedback Training

Extension of fMRI-Based Neurofeedback
Therapeutics

We used a neurofeedback training method to induce comodulation between two specific regions. However, these tworegions also exhibit connectivity with many other regions. Therefore, many regions may have been simultaneously modulated,
and the correlations among various regions may have increased
simultaneously during training. To examine this possibility, weinvestigated the change in correlated activity during neurofeedback training in regions other than the lM1 and lLP.
Supplementary Figure S7 shows changes in patterns of correlation during the imagery period from day 1 to day 4 following the conventions of Figure 5. The bootstrap sampling method(see legend of Supplementary Figure S7 for details) revealed that
only the network pair of MVN and DMN contained a significant number of colored cells (P = 0.0017; P < 0.05, correctedby Bonferroni method across six network pairs). This suggeststhat a significant increase in correlation was restricted to the network pair in which the correlation was manipulated. Moreover,the most prominent increase was found in region pairs between
motor regions (M1 and SMA) and the DMN (blue rectangle in
Supplementary Figure S7). No significant increment in correlation was found for any network combination in either of thecontrol groups. Thus, an effect of experimental manipulation
during training on connectivity was found only in the neurofeedback training group, and this effect was restricted to the correlation between the MVN and DMN, most prominently in thecorrelation between motor regions and the DMN.
Correlations among BOLD-signal time series are known to bevulnerable to the artifact, such as scanner-dependent drift andhead movement (Power et al., 2012). We applied motion realignment and temporal filtering but could not remove motion effect
explicitly during online feedback computation (e.g., correct signal using realign parameters). Thus, we cannot completely rejectsuch an artifact during training in the current design. However,
we confirmed that the functional connectivity changes inducedby training in the independent resting-state data remained whilehead movement effects were removed, suggesting changes infunctional connectivity is not a mere reflection of a movementrelated artifact. It is critical to improve online motion correction
algorithms and artifact removal techniques (e.g., Zaitsev et al.,2006; Koush et al., 2012) for future studies.

Analyses of rs-fcMRI in large-scale brain networks have foundabnormality with many psychiatric and neurological disorders(for reviews: Fox and Raichle, 2007; Broyd et al., 2009). Specific abnormal connectivity between limited brain regions has
been identified in many psychiatric disorders, for instance, theconnectivity between orbitofrontal cortex and ventral striatum
in obsessive-compulsive disorder (OCD) (Harrison et al., 2009)
and connectivity between left dorsolateral prefrontal cortex andsubgenual cingulate in major depressive disorder (Fox et al.,2012). Many clinical studies have demonstrated that various typesof treatments normalized pathological functional connectivity.
Such treatments include pharmacotherapy [depressive disorder(Anand et al., 2005), schizophrenia (Abbott et al., 2013a), attention deficit/hyperactivity disorder (Wong and Stevens, 2012),
and Alzheimer’s disease (Goveas et al., 2011)], repetitive transcranial magnetic stimulation (depressive disorder, Liston et al.,2014), electroconvulsive therapy (depressive disorder, Perrin
et al., 2012), deep brain stimulation (OCD, Figee et al., 2013),
and ROI-based fMRI neurofeedback training (OCD, Scheinost
et al., 2013). Moreover, some of these studies indicate that themagnitude of change in connectivity is significantly correlatedwith curing effect (Figee et al., 2013; Scheinost et al., 2013; Liston et al., 2014). FMRI neurofeedback equipped with high spatial resolution has the potential for direct normalization of thefunctional connectivity between specific regions and may provide entirely new therapeutic methods for psychiatric disorders.
Recently, fMRI neurofeedback training (Scheinost et al., 2013) on
regulation of activity in a single ROI in the orbitofrontal cortex,
for subclinical contamination anxiety, was found to normalize
the functional connectivity of that ROI. Furthermore, changes in
resting-state global connectivity of the orbitofrontal ROI measured by degree of connectivity were correlated with the alleviation of anxiety. However, no existing treatment, including
fMRI neurofeedback, can selectively change specified functional
connectivity between two selected brain areas, which is essential for selective and effective therapeutic treatment of psychiatric disorders. Here, as a proof of concept, we demonstrated
that fMRI “connectivity” neurofeedback training could change
functional connectivity, as revealed by a correlation in rs-fcMRI
between two selected regions, and that the effect of training
could be preserved for a long time, which is crucial for clinicalapplications.
DMN has been frequently found in aberrant connectivity
in many psychiatric disorders (for reviews: Fox and Raichle,
2007; Broyd et al., 2009). It has been shown that the negative
correlation between parts of DMN and the dorsolateral prefrontal cortex is stronger in patients with major depressive disorder than healthy controls, and that electroconvulsive therapy
reduces this correlation (Perrin et al., 2012; Abbott et al., 2013b).
Our method can be applied to therapeutics for major depressive disorder by weakening the negative correlation. By contrast, the negative correlation between the DMN and regionsin the task-positive network is known to have positive effects
on cognitive functions: for instance, strength of the negative
correlation is positively correlated with performance in working memory (Hampson et al., 2010; Keller et al., 2015) or

Increase in Negative Correlation
Our results do not exclude the possibility that changes in otherregions may mediate or facilitate changes in correlation between
target regions because we calculated correlation in BOLD timecourses between two regions during the training. Furthermore,
there should be a number of pathways between the two regions.
These complicate the relationships between “correlation” and“connectivity,” especially for the change in negative correlation.
When the correlation increases for example from r = −0.5 tor = −0.1, (1) the strength of inhibitory connection may be weakened at some connections, and/or (2) an excitatory connection
may emerge and increase at other connections. The current rsfcMRI method cannot discriminate these different neural mechanisms, and in the future we need to utilize higher temporal
resolution techniques and/or depend on animal models.
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small variability in reaction time in a conflict task (Kelly et al.,2008). Thus, weakening the negative correlation may cause negative effects on higher order cognitive functions. In our current
study, the effect of neurofeedback training was found on correlation between the DMN and MVN but not between the DMN
and the executive network (Supplementary Figure S5), whichis the most likely related to higher order functions. However,when considering the long-term effects and potential ability tochange connectivity determined by genetic and evolutional factors, as we mentioned in the introduction part, future studies
need careful experimental deigns from an ethical perspective if
the connectivity includes the DMN regions. For instance, anexperiment should include monitoring cognitive functions andbehavioral variables, which could be influenced by the training, and the training should be terminated if aversive effects are
identified.

Research Program for Brain Sciences by the Ministry of Education, Culture, Sports, Science and Technology of Japan.
This research was partially supported by a contract with
the Ministry of Internal Affairs and Communications entitled “Novel and Innovative R&D making Use of Brain Structures.” HI was partially supported by JSPS KAKENHI Grant
Number 26120002. We thank Y. Aramaki, M. Gamez, M.
Kimura, A. Kanemura, and N. Sugimoto for advice and help
with the experimental setup and analysis, R. Hashimoto, Y.
Ikegaya, K. Sakai, Y. Sasaki, B. Seymour, K. Shibata, N. Yahata,
and T. Watanabe for their comments on the manuscript,
Y. Furukawa for subject recruitment, and members of theBrain Activity Imaging Center of ATR-Promotions for technicalsupport.

Supplementary Material
Acknowledgments

The Supplementary Material for this article can be foundonline at: http://www.frontiersin.org/journal/10.3389/fnhum.
2015.00160/abstract

This study is the result of “Development of BMI Technologies for Clinical Application” carried out under the Strategic

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Conflict of Interest Statement: Japanese patent (“”) 2013-259554 (pending)on the use of connectivity-neurofeedback. The authors declare that the research
was conducted in the absence of any commercial or financial relationships that
could be construed as a potential conflict of interest.
Copyright © 2015 Megumi, Yamashita, Kawato and Imamizu. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CC
BY). The use, distribution or reproduction in other forums is permitted, provided theoriginal author(s) or licensor are credited and that the original publication in thisjournal is cited, in accordance with accepted academic practice. No use, distribution
or reproduction is permitted which does not comply with these terms.

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