Review

Advances in fMRI Real-Time
Neurofeedback
Takeo Watanabe,1,2,4 Yuka Sasaki,1,2,4 Kazuhisa Shibata,2,3,4
and Mitsuo Kawato2,*
Functional magnetic resonance imaging (fMRI) neurofeedback is a type of
biofeedback in which real-time online fMRI signals are used to self-regulate
brain function. Since its advent in 2003 signiﬁcant progress has been made in
fMRI neurofeedback techniques. Speciﬁcally, the use of implicit protocols,
external rewards, multivariate analysis, and connectivity analysis has allowed
neuroscientists to explore a possible causal involvement of modiﬁed brain
activity in modiﬁed behavior. These techniques have also been integrated into
groundbreaking new neurofeedback technologies, speciﬁcally decoded neurofeedback (DecNef) and functional connectivity-based neurofeedback (FCNef).
By modulating neural activity and behavior, DecNef and FCNef have substantially advanced both basic and clinical research.

TrendsAdvanced fMRI neurofeedback can be
conducted without participant awareness of what is manipulated.
Advanced fMRI neurofeedback techniques use multivariate analysis of a particular brain region to induce a speciﬁc
activation pattern in the targeted
region, rather than simply increasingor decreasing the mean activation level
throughout the region.
Advanced neurofeedback fMRI techniques can modify connectivity between
different brain regions and could leadto amelioration of aberrant connectivity
in clinical populations.

Recent Advances in fMRI Neurofeedback
Functional magnetic resonance imaging (fMRI; see Glossary) neurofeedback is a type of
biofeedback in which real-time online fMRI signals are used to self-regulate brain function [1–9].
Studies using fMRI neurofeedback have shown improvements or changes in speciﬁc brain
functions and/or behavior associated with changes in the activity of localized brain areas [10–20]. For example, fMRI neurofeedback changed the activation of the visual cortex and improved
performance on a visual task [20].

DecNef integrates aspects such as
implicitness, reinforcement schedulewith external reward, and multivariate
analyses.
FCNef integrates aspects such as
implicitness, reinforcement schedulewith external reward, and connectivity
analyses.

Since its initial development in 2003 [9] fMRI neurofeedback research has grown rapidly inpopularity. This is demonstrated through the accelerated number of publications on fMRI
neurofeedback over the past decades (Figure 1).
Such an increase in attention has been accompanied by signiﬁcant progress in fMRI neurofeedback techniques. We focus on four of these advances. First, the use of implicit protocols
allows the participants to be kept unaware of the purpose of neurofeedback training [10–12,15,19–21] or even the fact that they are being trained [22]. Second, the use of external
rewards, such as money, has been reported to facilitate neurofeedback learning [20,23]. Third,
the development of multivariate analysis techniques, which allows more sensitive neurofeedback [24–28], has been incorporated into neurofeedback [13,20,29]. Fourth, changes ofconnectivity in a targeted brain network [30,31], which could be a cause of mental abnormalities[32–34], have been incorporated into neurofeedback techniques. Combined, the use of thesetechniques has provided insights into a causal relationship between the modiﬁed neural change
and modiﬁed behavior.
Recently, these four fMRI neurofeedback technique advances have been further integrated into
new technological developments, speciﬁcally decoded neurofeedback (DecNef), which isusually applied to speciﬁc brain regions, and functional connectivity-based neurofeedback (FCNef), which is applied to connectivity strength between different brain regions. Both1

Department of Cognitive, Linguistic,
and Psychological Sciences, Brown
University, 190 Thayer Street,
Providence, RI 02912, USA
2
Brain Information Communication
Research Laboratory Group,
Advanced Telecommunications
Research Institutes International, 2-22 Hikaridai, Seika-cho, Soraku-gun,Kyoto 619-0288, Japan
3
Graduate School of Informatics,
Nagoya University, Furo-cho,
Chikusa-ku, Nagoya-shi, Nagoya 4640814, Japan
4
Equal contributions*Correspondence:
kawato@atr.jp (M. Kawato).

Trends in Cognitive Sciences, December 2017, Vol. 21, No. 12 http://dx.doi.org/10.1016/j.tics.2017.09.010© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Glossary

Number of papers

200

150

100

50

0
1990

1995

2000

2005

2010

2015

Year
fMRI, convenonal methods, non-clinicalfMRI, convenonal methods, clinicalfMRI, advanced methods, non-clinicalfMRI, advanced methods, clinicalNon-fMRI neurofeedback

Figure 1. The Number of Publications of Neurofeedback Studies in the Past Three Decades (1990–2016).
Each color represents the number of publications searched with a different set of words on PubMed. Red and magenta:
studies with the terms ‘neurofeedback AND fMRI’; non-clinical populations are shown in red and clinical populations inmagenta. Blue and cyan: studies with the terms ‘neurofeedback AND fMRI AND (multi-voxel OR decoding OR connectivity)’; non-clinical populations are in blue, and clinical populations in cyan. Grey: studies with the term ‘neurofeedback’
in which studies using fMRI were excluded, without overlapping with other color bars.

DecNef and FCNef are recent developments in fMRI neurofeedback techniques with uniquefeatures (see below). These technologies have helped uncover the relationships between basic
brain functions and behavior, and have successfully been applied in clinical settings (Table 1).
Although the results of individual studies using DecNef and FCNef have been promising [10–12,19–21,35–37], several fundamental questions about these technologies have been left
unanswered.
In this review, after brieﬂy discussing each of the four aspects of the recent advances of fMRI
neurofeedback, we address the following four questions about recent neurofeedback technologies, speciﬁcally DecNef and FCNef. First, how have the four advances (implicitness,reward, multivariate analysis, and connectivity) been integrated into DecNef and FCNef?
Second, how do these new technologies control fMRI voxel patterns resulting in speciﬁc
activity changes at the neuronal level? Third, how is a speciﬁc fMRI voxel pattern determinedfrom a huge number of possible combinations of fMRI voxel patterns in a brain region? Fourth,
how have these new technologies advanced basic and clinical research?
Note that this paper does not aim to comprehensively survey fMRI neurofeedback studies ordiscuss general models of fMRI neurofeedback. Readers interested in a more comprehensive
review of neurofeedback work are encouraged to read other recently published reviews [1,38,39].

Four Signiﬁcant Aspects of Progress of fMRI Neurofeedback
We discuss here the characteristics as well as representative basic and clinical studies of eachof the above-mentioned four aspects. We indicate how each of these characteristics has an

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Correlation-basedneurofeedback: one of the mostfrequently used types of connectivitybased neurofeedback. Feedback
scores reﬂect temporal correlations
between fMRI signals in two brain
regions.
Curse of dimensionality: this refers
to a problem that is caused by an
exponential increase in volume inassociation with adding extra
dimensions to Euclidean space [76].
The curse of dimensionality canoccur wherever a high-dimensional
dataset is handled. For instance, MRI
datasets are considered to be highdimensional. One MR image contains
many voxels (see below). If one MRimage contains 100 voxels, and eachvoxel has one of two values (0 or 1),
the number of possible voxel
patterns (see below) in the MR image
should be 2100, which exceeds one
nonillion. One effective method tocircumvent the curse ofdimensionality is to reduce thenumber of dimensions.
Decoded neurofeedback(DecNef): DecNef was introduced in2011 [20]. This advanced
neurofeedback technique has beendeveloped by the integration ofrecent technological advances
including implicit protocols, external
rewards, and multivariate analysis(main text for details). The integrationof these aspects enables DecNef toinduce a speciﬁc pattern (see below)
of fMRI signals in the targeted brain
region without the participants being
aware of the meaning of theneurofeedback provided to them.
Dynamic causal modeling (DCM):
this method infers causality between
different brain regions, or mutualinﬂuences of different regions in a
network. In a DCM-based
connectivity neurofeedback, thedegrees of ﬁtness of two or morecompeting models to neuroimaging
data are calculated. The model withthe largest degree of ﬁtness isselected as the right model.
Functional magnetic resonanceimaging (fMRI): fMRI measures
blood oxygen level-dependent(BOLD) signals, which are thought toreﬂect neuronal activation. fMRI is an
MRI technique that employs highmagnetic ﬁeld strengths.
Functional connectivity-basedneurofeedback (FCNef): a
neurofeedback technique that wasadvantage over earlier fMRI neurofeedback studies. Finally, we point out controversies andunclear characteristics of each of these aspects.
Implicit Neurofeedback
In conventional fMRI neurofeedback methods, participants are informed of the purpose of
training, what the neurofeedback signal represents, what brain function is to be trained byneurofeedback, and/or what behavioral changes are expected to occur [4,14,18]. However,recent studies have demonstrated that neurofeedback training can be highly effective evenwhen participants do not know what behavior is being trained, or indeed that they are being
trained at all [10–12,15,19–22]. We have termed this type of fMRI neurofeedback implicit
neurofeedback.
For example, participants might be presented with a disk that represents their feedback score,
and asked to try to make the disk as large as possible without being informed of any additional
information. Unknown to the participants, the size of the disk actually reﬂects the degree of‘similarity’ of an fMRI voxel pattern in a speciﬁc brain region measured on a real-time basis to thefMRI voxel pattern based on a predetermined targeted neural activity pattern. After repetitive
trials with this procedure, participants learn to signiﬁcantly enhance the degree of the similarity[10–12,15,19–21]. In other words, they learn to induce neural activity patterns similar to thetargeted predetermined activity pattern. Importantly, results of post-experiment questionnaires
showed that participants are indeed unaware of what brain functions were being trained[10–12,15,19–21].
Implicit neurofeedback has several advantages and/or novel features compared to traditional
neurofeedback. First, implicit neurofeedback reduces or eliminates the possibility that thechanges in brain function or behavior associated with neurofeedback training are due to neuralpattern changes involved in the speciﬁc intention of the participants to improve the function.
This is because, using implicit proposals, participants are aware of the presence of feedback
scores, but they are unaware of what the feedback scores represent. Thus, the modiﬁed
behavior can be more conﬁdently attributed to the brain region that was targeted by theneurofeedback. Second, implicit neurofeedback decreases the possibility of the so-called
experimenter effect in which participants consciously or unconsciously learn how to produce
results that they think would meet the expectations of the experimenter [40] (Box 1). Third,
implicit neurofeedback may be applied to clinical interventions where conventional methods do
not effectively work. For example, in conventional methodology for extinguishing fear
responses to traumatic memories, a participant is repeatedly presented with an aversive
stimulus associated with fear [41]. The repeated presentation of the aversive stimulus cancause overwhelming distress in the participant, and can therefore lead to a high dropout ratefrom the extinction therapy [42]. However, the implicitness of neurofeedback could eliminate or
greatly reduce the possibility of patients developing such distress during training. Anotheradvantage of implicit feedback in clinical interventions is that it can be used for patients whose
cognitive ability does not allow them to understand complicated training instructions. Fourth, if
implicit neurofeedback successfully improves a brain function or behavior without the awareness of the participant, this suggests that conscious processing is not greatly involved in the
improved function or behavior.
Although studies that use implicit neurofeedback have demonstrated very robust effects[10–12,15,19–21], it remains unclear whether implicit or explicit neurofeedback is more
effective. One study did demonstrate a case in which implicit neurofeedback was more
effective than explicit neurofeedback in changing fMRI activity in the supplementary motor
area [23], but this study did not investigate resultant behavioral measures. Systematicinvestigations of comparisons between effectiveness between implicit and explicit

ﬁrst introduced in 2015 [35]. FCNef
integrates correlation-basedneurofeedback with the recent
technological advances including
implicit protocols and external
rewards (main text for details). FCNefchanges the temporal correlation offMRI signals between two brain
regions in a network without theparticipants being aware of themeaning of neurofeedback given to
them.
Implicit neurofeedback: a type ofneurofeedback in which participants
are not aware of the meaning of the
feedback provided to them or of the
purpose of the neurofeedback
training in which they participate.
Implicit neurofeedback is opposed to
explicit neurofeedback in which
participants are informed of themeaning of feedback, what brainarea(s) are intended to be changed,
and/or the purpose of theneurofeedback training.
Neurofeedback: a generic term formethods to provide participants withinformation that reﬂects their own
physiological state (e.g., brain
activation or blood pressure) as afeedback signal for possible selfregulation.
One-to-many relationship: therelationship between two entities (Aand B) in which an element in A islinked to multiple elements in B,
while an element in B is linked toonly one element in A.
Resting-state functional
connectivity: this is characterized
as temporal correlations between
fMRI signals in multiple brain regions.
fMRI signals are measured during a
resting state in which participants
merely ﬁxate on a blank visualdisplay without performing any othertask.
Voxel pattern: a pattern of multiple
voxel values. A voxel refers to a 3D
volumetric unit in an MR image. A
voxel is analogous to a pixel in a 2D
picture or computer screen, althougha voxel is 3D. In fMRI measurements,
one voxel is typically cubic of a few
millimeters in size (e.g.,3  3  3 mm3). One voxel consistsof one BOLD signal located at one
point in the gradation of intensity
between black and white.

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999Table 1. Development and Applications of DecNef and FCNef in Chronological Order

a

Year

Refs

Population

MethodTarget brainarea/connectivity

Purpose of neurofeedback
training

Increase inneurofeedback
scores?
(effect size ofmajor results)

Behavioral change?
(effect size of major
results)

Correlation
betweenneural andbehavioral
changes?

2011

Shibata et al.
[20]

NormalDecNef

Early visualcortexTo test if inductions ofactivations in the early visualcortex lead to visual perceptual
learning of an orientationYes(1.06)

Perceptual learningof an orientation
occurred (1.77)

Signiﬁcant(r = 0.87)

2015

Megumi et al.
[35]

NormalFCNef

Parietal andmotor cortices

To test if FCNef is capable ofinducing a long-term increasein target connectivity

Yes(0.74)

N/Aa

N/A

2016

Amano et al.
[10]

NormalDecNef

Early visualcortexTo test if the early visual cortex
is capable of associative
learning of an orientation and
color

Yes
(1.88)

Associative learningof an orientation andred color occurred(0.87)

N/A

2016

Shibata et al.
[19]

NormalDecNefCingulate cortex

To test if induction activationsin the cingulate cortex increase
and decrease preferences for
faces

Yes forincrease (1.17)
and decrease(0.70) groups

Preferences forfaces increased(1.38) anddecreased (0.96)

Signiﬁcant(r = 0.78)

2016

Koizumi et al.
[15]

NormalDecNef

Early visualcortexTo test if pairings of monetary
reward and activations of theearly visual cortex lead tocounter-conditioning of fear
memoryYes(0.53)

Skin conductance
response to a fearassociated stimuli
decreased (0.46)

N/A

2016

Cortese et al.b[11]

NormalDecNefParietal andfrontal cortices

To test if inductions ofactivations in the parietal andfrontal cortices increase anddecrease perceptual
conﬁdence

Yes forincrease (2.40)
and decrease(2.65) groups

Conﬁdence in avisual task increased(1.60) anddecreased (0.37)

Signiﬁcant(r = 0.68)

2017

TaschereauDumouchel
et al. [21]PhobiaDecNefVentrotemporal
cortexTo test if pairing of monetary
reward and activation of theventrotemporal cortex reduces
fear to a speciﬁc object
category

Yes(0.62)

Skin conductance
response to a fearful
category decreased(0.57)N/A

2017

Yamada et al.
[36]

Major
depression

FCNef

Middle frontal
gyrus andprecuneus

To test if FCNef on abnormal
connectivity for patients withmajor depression reduced theseverity of depression

Yes(1.85)

Hamiltondepression rating
scale improved(1.83)

Signiﬁcant(r = 0.87)

2017

Yamashita
et al. [37]NormalFCNef

Parietal andmotor cortices

To test if changes in target
connectivity lead to changes inreaction times in a visual task

Yes
(0.22)

Changes in reaction
times in a color/
word Stroop task
(0.38)

Signiﬁcant(adjusted
R2 = 0.22)

N/A, not available.
The authors published another paper [12] using a different method of data analysis with a different purpose.

bneurofeedback will be necessary to clarify this issue. One possibility is that implicit feedback ismore effective on passive types of learning, such as exposure-based learning [43] and classicalconditioning [44], which do not require much conscious effort for learning acquisition.
Role of External Reward
In most conventional fMRI neurofeedback studies, feedback scores are provided to participants in the form of a visual or auditory stimulus without external reward [4,9,13,14,17].
Feedback scores basically reﬂect how close the brain activation is to the predeterminedtargeted measure. If the brain activation becomes closer to the predetermined target, a larger

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Box 1. Artifact Possibilities
In fMRI neurofeedback there are two possible sources of contamination: (i) experimenter effects, and (ii) explicit
consciousness strategy.
(i) Experimenter effects refer to an experimental artifact in which participants consciously or unconsciously aim to
produce the results to meet what they think of as the expectation of the experimenter [40]. In conventional neurofeedback methods participants are given an explicit instruction, which may increase the possibility of contamination withexperimenter effects because participants are aware of what is expected. By contrast, in DecNef and FCNef it is difﬁcultfor participants to guess what is expected because they do not know what the feedback represents. Some might thinkthat participants in implicit neurofeedback also learn to induce voxel patterns similar to the predetermined targeted voxel
pattern by trying to learn what is expected by the experimenters. However, this possibility has been ruled out by DecNef
training with a double-blind method [21], an extension of [15], and by DecNef training in a totally automated and modernmonkey experimental system [77], which replicated the human study [19].
(ii) Explicit consciousness strategy refers to various voluntary and active efforts of the participants to improve feedback
scores. It has been assumed that DecNef is accomplished without the participants being aware of what neurofeedback
signals represent, and therefore no explicit consciousness strategy is used. However, one may raise the possibility that,to obtain higher feedback scores during neurofeedback training, participants intentionally generate and hold an image
related to the trained feature as an explicit consciousness strategy. For example, DecNef led to higher sensitivity to thetrained orientation [20]. This sensitivity enhancement could have occurred because subjects intentionally caused avisual image of the trained orientation. However, this possibility is highly unlikely. After a DecNef experiment was over,participants were asked to report what they were doing during training. If subjects had intentionally used an explicit
conscious strategy, such as having an image of the trained feature, and thereby obtained higher scores, subjects shouldhave a vivid memory of the strategy and/or the image they had during the training. However, none of the participants
reported anything close to the strategy or image.

feedback score is provided to participants. In this case, feedback scores may work as areinforcement signal, which provides (i) cues to making activations closer to the targeted
activation measure as a supervising signal, and also gives (ii) an internal sense of achievement
or internal reward [45].
In addition to feedback scores, some recent fMRI neurofeedback studies provide participants
with external reward such as money [10–12,15,19–21,35,37], an approach inspired by humanand animal reward studies [46–48]. These experiments have robustly shown that external
rewards enhance learning. Indeed, a combination of feedback scores and external rewards
enhanced neurofeedback effects to a greater degree than feedback scores or external reward
alone. A previous study [23] recently reported that feedback scores combined with monetary
reward are more effective on fMRI self-regulation in the supplementary motor area (SMA) thanfeedback scores alone. Conversely, at least in simple human instrumental conditioning,
external reward alone without feedback scores did not seem to produce a large effect [49].
Although feedback scores play both roles in inducing goal-directed neural activity and inproviding participants with internal sense of achievement, which reinforces neural activity
induction, it is possible that external reward merely acts as an additional reinforcing factor.
One interesting question concerns how the timing of the presentation of external rewards andfeedback scores affects neurofeedback training. In general, feedback scores are presented in two
different ways during the fMRI neurofeedback training; continuously and intermittently [50–52].
Continuous feedback refers to feedback that is presented continuously during neurofeedback
training, and the feedback score changes every time brain activation is measured by fMRI, forinstance, every 2 s. Intermittent feedback is deﬁned as feedback that is not provided continuously,
and is provided only intermittently, for instance at the end of a block that spans 40 s. Theintermittent feedback score is based on the brain activation averaged across the block, which
includes several fMRI measurements [50,51]. Which type of feedback is more effective remains
controversial. A recent computational study [52] has indicated that, for successful fMRI neurofeedback learning, the temporal characteristics of the fMRI signal need to be considered because

Trends in Cognitive Sciences, December 2017, Vol. 21, No. 12

1001

the fMRI signal is delayed and blurred in time relative to underlying neural activities because
hemodynamics is slower than neuronal ﬁring. These characteristics form a temporal credit
assignment problem [53] in which participants need to associate their neural activities at aparticular timepoint with a feedback score that reﬂects the delayed and blurred fMRI signals.
The results of this study [52] suggest that continuous feedback is more effective when participants
are provided with explicit knowledge of how the feedback is calculated because this knowledge
helps participants to solve the temporal credit assignment problem. On the other hand, intermittent feedback may lead to better neurofeedback learning without such knowledge (i.e., implicit
neurofeedback training) because intermittent feedback largely reduces the temporal credit
assignment problem. It is yet to be revealed how often feedback scores and external reward
should be given to achieve maximum neurofeedback training.
Multivariate Methods
A new trend in fMRI neurofeedback [20,29] is to employ multivariate analyses or decoded fMRI
signals [24–28,54]. Conventional fMRI neurofeedback methods increase or decrease the 1D
amplitude of fMRI signals averaged across a region of interest (ROI) in the brain. However,neurofeedback based on multivariate analysis can change fMRI voxel patterns in the ROI rather
than the mean amplitude of fMRI signals of the ROI. Figure 2 (Key Figure) and Box 2 show atypical procedure with a decoding technique. For example, ﬁrst a decoder is constructed toclassify an fMRI voxel pattern into one of different states for a participant (different tasks,
exposure to different stimuli) in advance. Second, one of these states is selected as the target
state for neurofeedback training. Third, in each training trial (i) the fMRI voxel pattern in the ROIof the participant is measured on a real-time basis, and (ii) the fMRI voxel pattern is input into thedecoder, which then computes the likelihood of the targeted state. (iii) A feedback score is given
to the participant, which is proportional to the likelihood of the targeted state [20]. A new
sequence then starts for a new trial.
One advantage of using a multivariate analysis for neurofeedback over a general linear model(GLM)-based method is that feedback based on multivariate analysis reﬂects increased and more
spatially sensitive information from the fMRI signal patterns than does feedback based on the GLM
method. Neurofeedback from the multivariate analysis is based on an fMRI voxel pattern, whereas
GLM neurofeedback is based on the averaged amplitude of fMRI voxels in an ROI.
Connectivity-Based Neurofeedback
Previous neurofeedback approaches have focused on modifying brain activity within a targeted
ROI [4,9,10,13,15,17,19,20]. However, important brain functions are also formed through a
network of interacting brain regions that are highly correlated with each other [55–57]. Recent
technological developments, known as connectivity-based neurofeedback [31], have enabled
us to modify the connectivity of a targeted network.
There are two main methods for connectivity-based neurofeedback. One is based on dynamic
causal modeling (DCM) [30,31]. In DCM-based connectivity neurofeedback, participants are
asked to try to increase feedback scores that are based on the comparison between two or
more predeﬁned alternative models to a targeted model. That is, positive feedback is given to aparticipant when connectivity strength estimated from measured activity ﬁts better with thetargeted model than with an alternative model, while negative feedback is given when connectivity strength ﬁts better with the alternative model than with the targeted model [30,31]. This
DCM-based connectivity approach can strengthen unidirectional connectivity where the direction of information ﬂow is hypothesized.
The other method is correlation-based neurofeedback in which the feedback score isbased on Pearson’s correlation coefﬁcients of activity time-courses between two ROIs

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Key Figure

A Typical Procedure of One Cycle (Trial) of Decoded Neurofeedback(DecNef) Training
fMRI signal paern
in targeted region

Decoder

Likelihood of thetargeted funconor feature

Figure 2. First, while in the scanner, theparticipant is presented with a ﬁxation
mark on which (s)he is asked to maintainhis/her gaze (top left). Second, an fMRI
voxel pattern in a targeted brain region ismeasured and input to the decoder thathas been constructed before DecNef
training (top right). Third, based on themeasured fMRI pattern and decoder,
the likelihood of the targeted function orfeature is computed (bottom right).
Finally, the participant is presented witha disk whose size is proportional to thelikelihood (bottom left). This cycle is thenrepeated (Box 2 for more details).

[35,37,58]. No model of connectivity is presumed in this method. Unlike the DCM-based
approach, the correlation-based approach is not capable of manipulating the direction ofconnectivity. However, the correlation-based approach has taken advantage of the development of biomarkers of psychiatric disorders (see below) based on fMRI resting-state functional connectivity (rs-fc).
Connectivity-based feedback has been developed to change the degree of interactions
between different brain areas. DCM-based neurofeedback is based on predeﬁned models,
whereas correlation-based neurofeedback does not assume a model. Because a feedback
Box 2. Typical Procedure of DecNef Experiments
A typical DecNef procedure is described here. First, the function or feature that is to be trained by DecNef is determined.
We call this the predetermined targeted function or feature. We also term the underlying fMRI and neural activity patterns
associated with the targeted function or feature as the predetermined targeted voxel and neural activity patterns,
respectively. The targeted brain region in which neural activity processing of the predetermined function or feature that is
to be trained by DecNef is also predetermined. Second, fMRI voxel patterns that correspond to the targeted function orfeature in the targeted brain region are measured. Third, using a multivariate analysis based on the measured fMRI signal
patterns, a decoder is constructed to decode the targeted function or feature from the fMRI voxel patterns. A decoder
may be based on various techniques. For DecNef, sparse logistic regression [20] or sparse linear regression [19] is used,
and a support vector machine algorithm is employed in most cases [29]. Fourth, a few days of DecNef training areconducted as illustrated in Figure 2 in main text. Participants in an MRI scanner are presented with a ﬁxation mark where
they are asked to somehow regulate their brain activity (Figure 2, top left). They are told that they will be given an amountof bonus money, provided as the external reward, in proportion to the size of the disk which is presented several
seconds later (Figure 2, bottom left). No further information is divulged to the participants. Fifth, participant fMRI voxel
patterns in the targeted brain regions are measured on a real-time basis and are input into the decoder (Figure 2, top
right). The decoder provides the likelihood of the predetermined targeted function or feature together with the likelihoodof irrelevant functions or features (Figure 2, bottom right). Sixth, the size of the disk displayed to participants is changed
according to the likelihood of the predetermined targeted function or feature provided by the decoder (Figure 2, bottom
left). Seventh, this cycle of 20 s (regulation of brain activity ! fMRI measurement ! calculation of the likelihood of thetargeted function or feature ! presentation of the disk) is repeated for about 1 h per day.

Trends in Cognitive Sciences, December 2017, Vol. 21, No. 121003

score is computed more rapidly based on the correlation-based approach, this may be a morepractical choice for connectivity-based neurofeedback in clinical populations than DCM-basedneurofeedback (see below).

New Neurofeedback Techniques Resulting from the Integration of Multiple
New Aspects
Thus far we have reviewed the four key aspects of recent progress in fMRI neurofeedback:
implicit neurofeedback, external reward, multivariate methods, and connectivity-based neurofeedback. Recently these aspects have been integrated into DecNef and FCNef for further
improvements. In this section we mainly discuss these two new fMRI neurofeedback techniques. The goal of DecNef is for participants to learn to induce a speciﬁc activity pattern within atarget brain region. To achieve this goal, implicit neurofeedback, external reward, and multivariate analysis are introduced into DecNef training. The goal of FCNef is to change speciﬁcfunctional connectivity between two brain regions. For this goal, FCNef consists of implicit andcorrelation-based neurofeedback which is accompanied with external reward.
We ﬁrst outline DecNef and FCNef and indicate their conceptual advantages as neuroscientiﬁc
and clinical tools. We then discuss how DecNef and FCNef circumvent potential problems.
Finally, we discuss clinical applications of DecNef and FCNef.
Conceptual Advantages of DecNef and FCNef
A long-term goal of neuroscience is to establish a causal relationship between speciﬁc neuralactivity and speciﬁc behavior. To attain this goal, it is necessary to develop a method by which
neural processing, including spatiotemporal brain activity patterns and network-level functional
connectivity, are solely and precisely manipulated. We believe that DecNef and FCNef havecome closer to this goal than have previous neurofeedback technologies. This is largely
because both techniques have incorporated some of the four new aspects discussed above.
Speciﬁcally, during neurofeedback training both DecNef and FCNef utilize implicit protocols aswell as external reward, but DecNef utilizes multivariate analysis whereas FCNef utilizes
correlation-based connectivity.
The combination of multivariate analysis and implicitness in DecNef may change brain processing in a more speciﬁc manner than other neurofeedback methods. This is because
multivariate analysis is sensitive to patterns of activation instead of averaged signals withina brain area. This sensitivity can allow neurofeedback signals to carry more detailed and ﬁnergrained information, and can cause activity patterns that are more speciﬁc and closer to thetargeted pattern in the targeted brain area. Furthermore, implicitness in DecNef may eliminate
or reduce the possibility of contamination from verbal instruction, participant’s knowledge ofwhat brain function is being trained, and the use of explicit cognitive strategies to achieve thepurpose of training. Contamination by these factors may prevent neurofeedback from precisely
producing targeted neural processing, and therefore may produce uncontrolled neural processing. Thus, implicitness in neurofeedback may also better specify the targeted patterns andbrain area(s).
Several reports suggest that DecNef causes participants to induce speciﬁc activation patterns
in the targeted brain region as well as targeted behavioral changes [10–12,15,19–21] (Table 1).
For example, DecNef successfully increased the rating of the facial preferences of the participants by presumably repeatedly inducing a speciﬁc brain activation pattern within the cingulate
cortex and also decreased the rating by inducing another brain activation pattern within thesame brain area [19]. In this study, analyses of brain activity during neurofeedback training
indicated that the area modulated by DecNef was largely conﬁned to the cingulate cortex, thepredetermined target area. Preference changes occurred in the two groups of participants

1004

Trends in Cognitive Sciences, December 2017, Vol. 21, No. 12

whose preference was set to be changed in either the positive or negative direction. These
results suggest that DecNef changed activity patterns in a speciﬁc way (preference in thepositive or negative direction) within the same targeted area [19]. Such speciﬁcation in changed
activity patterns in the speciﬁc brain area resulting from multivariate analysis and implicitness in
DecNef could narrow down the possible causes of changing behaviors in a particular manner,and therefore better explore the causal relationship than neurofeedback without multivariate
analysis and implicitness. Similarly, implicitness in FCNef can also narrow down the number ofconnectivity patterns that are affected by FCNef training. The recent ﬁnding that neurofeedback
is effective even when participants do not know that they are being trained [22] could allow
implicit neurofeedback to even more greatly narrow down the effective areas.
Potential Problems and Solutions of DecNef and FCNef
Although DecNef and FCNef have the potential to narrow down the possible causal neuralactivity and resultant behavioral changes, they face two potential problems. The ﬁrst problem
involves the one-to-many relationship from a voxel pattern to neuronal patterns (Figure 3).
Many different neural activity patterns can cause the same targeted fMRI voxel pattern in the
ROI. If everything is randomly determined, and if participants successfully induce the predetermined voxel pattern the likelihood of the targeted neural activity could be the same as thelikelihood of other neural activity patterns. Whether and how DecNef solves this indeterminacy
in mapping from a voxel pattern to a neuronal activity pattern is an outstanding question.
We propose here a model of how DecNef handles this problem. The indeterminacy in mappinga voxel pattern to neural activity patterns should occur in a system in which other information
processing is randomly determined. However, we suggest that, owing to the regularity of brain
processing and the logistic regression analysis used in DecNef, the number of possible neuralactivity patterns that cause a targeted fMRI voxel pattern may be reduced to one. First, several
studies have shown that spontaneous activities in a sensory cortex of animals are not random
but are constrained by responses to stimuli that frequently occur in natural scenes, and also by
neural circuits that are well organized [59–64]. Such regularities in spontaneous activities largely
limit the number of possible neural activity patterns that underlie a function or feature. Second,
because the logistic regression analysis used in DecNef can achieve much higher multivariate
resolution than voxel size [27,28], DecNef should generate correspondingly hyper-resolution
signals, which could make mapping an fMRI voxel level pattern to a neural activity level a oneto-one correspondence or at least mapping an fMRI voxel level pattern to a constrained small
number of neuronal activity patterns as in Figure 3. Finally, neurofeedback with external reward
may strongly reinforce the component in spontaneous activity, which is stochastically equal tothe predetermined neural activity pattern.
The second problem involves the curse of dimensionality. In DecNef training the goal for theparticipant is to learn to induce the targeted voxel pattern in a ROI. The curse of dimensionality
in DecNef is the problem that dimensional space of potential voxel activity patterns in a ROI is
too large for the brain to discover an efﬁcient solution within as few as hundreds of trials
because the search space is excessively high-dimensional [65]. However, there are two
reasons that the curse of dimensionality should not apply to DecNef. First, the curse ofdimensionality would arise if each voxel pattern is randomly searched in a standard reinforcement learning paradigm. However, this may not be the case [66]. Because numerous neurons
are connected to many other neurons, and these connections are not random, the number ofneural activity patterns should be limited. Therefore, the number of possible voxel patterns
should also be limited [66]. Second, functions used for decoders in DecNef are either pseudolinear [28] or linear [54] and monotonically increasing functions. In this case, reinforcement
learning may become almost equal to supervised learning with stochastic gradient ascent [67].
Therefore, the curse of dimensionality should not apply to DecNef.

Trends in Cognitive Sciences, December 2017, Vol. 21, No. 121005

(B) Neural acvity paern= Orientaon 1

(C) Neural acvity paern≠ Orientaon 1

Figure 3. Schematic Illustration to
Explain Indeterminacy in Mappinga Voxel Pattern to a Neuronal Pattern (One-to-Many Problem). Red
cells and neurons represent activationand grey/white cells no activation. (A)The targeted predetermined voxel pattern. If the majority of the nine neurons
in a neural activity pattern are activated,the voxel that receives activity from thenine neurons in a neural activity pattern is
excited. (B) Neural activity pattern causedby the presentation of orientation 1 andthat leads to the targeted voxel pattern asin (A). (C) Neural activity pattern which isirrelevant to orientation 1 but still leads tothe same targeted voxel pattern as in (A).

(A) Targeted voxel paern

FCNef may have the same mathematical problems of the one-to-many relationship and thecurse of dimensionality as DecNef, although the level at which problems occur could be
different because DecNef concerns voxel patterns while FCNef concerns connectivity. However, we suggest that similar solutions and principles for DecNef also apply to FCNef.
In summary, there appear to be two potential problems that might prevent DecNef and FCNef
from narrowing down the possible causal neural activity and resultant behavioral changes. One
potential problem is that one voxel activity reﬂects so many neural activities that inducing thetargeted voxel activity pattern does not guarantee that the targeted neural activity pattern has
occurred. The other problem is the curse of dimensionality, where the dimensional space ofpotential voxel activity patterns is too large for the brain to discover an efﬁcient solution withinhundreds of trials. These problems are serious when a system consists of random processing.
However, regularity in brain processing and logistic regression analysis in DecNef may work as
powerful constraints to signiﬁcantly reduce the number of possible neural activity patterns.
Clinical Applications of FCNef and DecNef
A growing body of evidence has indicated that fMRI neurofeedback is a promising tool toameliorate brain dysfunctions [18,39]. While it is argued that placebo-controlled and doubleblind trials on fMRI neurofeedback are necessary to conﬁrm clinical effects [68], recent studies
have started to document signiﬁcant clinical effects of neurofeedback with exactly these typesof study design [21,69].
FCNef is characterized as (i) implicitness and external-reward driven and as (ii) rs-fcMRI-based.
The combination of these characteristics makes FCNef a powerful tool. Implicitness narrows
down targeted networks and connectivity pairs. Changes of the targeted network and1006

Trends in Cognitive Sciences, December 2017, Vol. 21, No. 12

connectivity are reinforced by external reward in FCNef. Such strong reinforcement may render
FCNef training long-lasting with a relatively short training time. For example, one study [35]applied FCNef to targeted functional connectivity with a 4 day training period, and found thatthe modiﬁed state of connectivity lasted for more than 2 months.
FCNef has two advantages because it is rs-fc-based. One is that it can be directly applied to rsfc-based abnormal connectivity, or to a biomarker, in networks [32–34]. For clinical purposes,
connectivity-based neurofeedback techniques including FCNef need to be combined withbiomarkers that characterize psychiatric disorders based on rs-fcMRI. Therefore, success inclinical applications of connectivity-based neurofeedback largely depends on the developmentof biomarkers. Regrettably, early studies of biomarkers suffered from overﬁtting of machinelearning algorithms and did not exhibit generalization to completely independent validation
cohorts [70–74]. However, the recent development of sophisticated machine-learning algorithms has overcome these technical problems and has led to the production of rs-fcMRIbased biomarkers which are capable of distinguishing patients from controls with high accuracy, and can be generalized to completely independent cohorts [32–34,75]. Development ofthese robust rs-fcMRI-based biomarkers have allowed FCNef, which is also rs-fcMRI-based, todirectly modify them. The second advantage of rs-fc in FCNef is computational efﬁciency. Thatis, rs-fc can be estimated easily and quickly during a single-trial period lasting for tens ofseconds to 1 minute of an fMRI session without any sensory stimuli presentation, movement, or
performance of cognitive tasks [55,56].
Considering these advantages, FCNef has produced preliminary but encouraging therapeutic
results in patients with depression as well as in adults with high-functioning autism spectrum
disorder (ASD) [34,36,75]. With regards to depression, abnormally more positive functional
connectivity between the left dorsolateral prefrontal cortex and left precuneus/posterior cingulate cortex was found to play the most crucial role in patients with high depression scoresand melancholic depression [32]. This suggests the possibility that this abnormality may be oneof the causes of the depression. Thus, this network was identiﬁed as a target of FCNef. FCNef
was then applied only to this network to change the connectivity in the negative direction [36].
Both subclinical and patient groups successfully changed the connectivity towards morenegative and normal connectivity after 4 days of FCNef training. The subclinical group showed asigniﬁcant correlation between the amount of decrease of the connection by FCNef and thedegree of improvement in the Beck depression inventory, a self-report inventory that is one of
the most widely used psychometric tests for measuring the severity of depression. For thepatient group, FCNef training was associated with signiﬁcant improvement in scores on the
Hamilton depression rating scale, the most widely adopted clinician-administered depression
assessment scale [36]. This not only indicates the clinical effectiveness of this method but also
supports the idea that network connectivity can be a cause of mental disorder because thestrength of connectivity of the speciﬁc network predicts the severity of depression.
In contrast to the above depression study [36] in which FCNef was applied to only one
connectivity, in a study exploring the application of FCNef to ASD patients all 16 functional
connections that had been detected as abnormal by the rs-fcMRI biomarker of ASD [34] wereselected as FCNef targets [36]. Because there were multiple targeted connections, theweighted linear summation of ASD biomarkers was used as the FCNef neurofeedback score.
Intriguingly, several adults with high-functioning ASD successfully changed their neurofeedback scores to a typically-developed level, and retained the typically-developed-like connectivity on a long-term basis [36]. These results indicate that FCNef can be successfully applied toabnormal connectivities that have been identiﬁed as biomarkers of depression or ASD.

Trends in Cognitive Sciences, December 2017, Vol. 21, No. 121007

Although DecNef is less extensively used in clinical applications than FCNef, it can be also astrong tool to change activity patterns that underlie mental disorders or abnormalities, forexample phobia [21].

Concluding Remarks
Implicitness, external reward, multivariate analysis, and connectivity in fMRI neurofeedback
have recently been integrated to produce ground-breaking methods such as DecNef and
FCNef. These methods have the unique ability to explore causal relationships between targeted
brain functions and resultant behavioral changes, particularly via implicit protocols. DecNef and
FCNef have great potential for interventions in psychiatric disorders which may be caused byabnormal brain functions and/or connectivity. However, the basic principles of fMRI neurofeedback including DecNef and FCNef have yet to be completely clariﬁed (see Outstanding
Questions). It is vital to strive to understand these basic principles and address these outstanding issues.
Acknowledgments
This research is conducted as the ‘Application of DecNef for development of diagnostic and cure system for mental
disorders and construction of clinical application bases’ of the Strategic Research Program for Brain Sciences from Japan
Agency for Medical Research and Development (AMED). T.W. is also supported in part by the National Institutes of Health(R01EY019466), Y.S. by the National Science Foundation (BCS 1539717), K.S. by the Japan Society for the Promotion of
Science (KAKENHI grant 17H04789), and M.K. by the ImPACT Program of Council for Science, Technology, andInnovation (Cabinet Ofﬁce, Government of Japan). We appreciate comments from Aaron Berard on an early draft.

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