Neurofeedback for Tinnitus Treatment – Review and Current Concepts

Abstract

An effective treatment to completely alleviate chronic tinnitus symptoms has not yet been discovered. However, recent developments suggest that neurofeedback (NFB), a method already popular in the treatment of other psychological and neurological disorders, may provide a suitable alternative. NFB is a non-invasive method generally based on electrophysiological recordings and visualizing of certain aspects of brain activity as positive or negative feedback that enables patients to voluntarily control their brain activity and thus triggers them to unlearn typical neural activity patterns related to tinnitus. The purpose of this review is to summarize and discuss previous findings of neurofeedback treatment studies in the field of chronic tinnitus. In doing so, also an overview about the under-lying theories of tinnitus emergence is presented and results of resting-state EEG and MEG studies summarized and critically discussed. To date, neurofeedback as well as electrophysiological tinnitus studies lack general guidelines that are crucial to produce more comparable and consistent results. Even though neurofeedback has already shown promising results for chronic tinnitus treatment, further research is needed in order to develop more sophisticated protocols that are able to tackle the individual needs of tinnitus patients more specifically.

Full text

fnagi-09-00386 November 30, 2017 Time: 17:17 # 1
REVIEW
published: 01 December 2017
doi: 10.3389/fnagi.2017.00386
Edited by:
Berthold Langguth,
University of Regensburg, Germany
Reviewed by:
Daniel Llano,
University of Illinois
at Urbana–Champaign, United States
Andrea Crocetti,
Independent Consultant, Milan, Italy
*Correspondence:
Tobias Kleinjung
tobias.kleinjung@usz.ch
Received: 31 July 2017
Accepted: 09 November 2017
Published: 01 December 2017
Citation:
Güntensperger D, Thüring C,
Meyer M, Neff P and Kleinjung T
(2017) Neurofeedback for Tinnitus
Treatment – Review and Current
Concepts.
Front. Aging Neurosci. 9:386.
doi: 10.3389/fnagi.2017.00386
Neurofeedback for Tinnitus
Treatment – Review and Current
Concepts
Dominik Güntensperger1,2 , Christian Thüring3, Martin Meyer1,2 , Patrick Neff1,2 and
Tobias Kleinjung3*
1Neuroplasticity and Learning in the Healthy Aging Brain (HAB LAB), Department of Psychology, University of Zurich, Zurich,
Switzerland, 2University Research Priority Program ‘Dynamics of Healthy Aging’, University of Zurich, Zurich, Switzerland,
3Department of Otorhinolaryngology, University Hospital of Zurich, Zurich, Switzerland
An effective treatment to completely alleviate chronic tinnitus symptoms has not yet
been discovered. However, recent developments suggest that neurofeedback (NFB),
a method already popular in the treatment of other psychological and neurological
disorders, may provide a suitable alternative. NFB is a non-invasive method generally
based on electrophysiological recordings and visualizing of certain aspects of brain
activity as positive or negative feedback that enables patients to voluntarily control their
brain activity and thus triggers them to unlearn typical neural activity patterns related
to tinnitus. The purpose of this review is to summarize and discuss previous findings
of neurofeedback treatment studies in the field of chronic tinnitus. In doing so, also an
overview about the underlying theories of tinnitus emergence is presented and results
of resting-state EEG and MEG studies summarized and critically discussed. To date,
neurofeedback as well as electrophysiological tinnitus studies lack general guidelines
that are crucial to produce more comparable and consistent results. Even though
neurofeedback has already shown promising results for chronic tinnitus treatment,
further research is needed in order to develop more sophisticated protocols that are
able to tackle the individual needs of tinnitus patients more specifically.
Keywords: tinnitus, phantom perception, EEG, plasticity, heterogeneity, neurofeedback, frequency bands, alpha
band
INTRODUCTION
Subjective tinnitus has been described as the constant perception of an auditory sensation that
does not correlate to any external acoustic stimulus (Stouffer and Tyler, 1990). It can be perceived
as either pitch or noise-like sound and its perception may be unilateral, bilateral or spread
out in the whole head (De Ridder et al., 2014b). In industrialized countries, roughly 10% of
the population is affected by this stressful condition and many people suffer from sleeping or
concentration problems, affected social interactions and psychological distress that can also lead
to severe depression or anxiety impairments (Heller, 2003;Henry et al., 2005). The relatively large
percentage of affected people, recently developed neuropsychological models, and the fact that, to
date, no satisfactory potent treatment has been discovered may explain the increasing interest in
tinnitus research. New findings on the pathophysiology of tinnitus have led to the development
of several promising neuromodulatory techniques that have been shown to relieve symptoms of
the chronic acoustic sensation and significantly increase quality of life for tinnitus sufferers (e.g.,
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Eggermont and Roberts, 2004;Weisz et al., 2007a). One
of them is neurofeedback, an already well-established form
of neuropsychological treatment that recently enjoys great
popularity due to its non-invasive nature, its long-lasting effects,
its easy-handling and relatively low cost, as well as its rapid
technological improvements. The purpose of this review is
to summarize and discuss findings of neurofeedback studies
for the treatment of chronic tinnitus. The focus is hereby
laid on neurofeedback based on electrophysiological recordings
with electroencephalography (EEG) or magnetoencephalography
(MEG) but also a short summary of new innovative methods
(e.g., real-time functional Magnetic Resonance Imaging, rt-fMRI)
will be given. In a first step, an overview about popular models
of tinnitus genesis will be provided, and studies investigating
chronic tinnitus with EEG or MEG will be presented and critically
discussed. Next, the development and history of neurofeedback
will be briefly introduced and the different neurofeedback
protocols used in tinnitus treatment summarized and evaluated.
Finally, limitations of existing treatment studies will be discussed,
and implications for future studies will be given.
TINNITUS MODELS AND
ELECTROPHYSIOLOGICAL STUDIES
Tinnitus was first assumed to be solely generated in the ear or by
a dysfunction of the auditory nerve (Møller, 1984;Eggermont,
1990), but the focus of attention quickly shifted to the human
brain after Jastreboff (1990) proposed what is nowadays known as
the neurophysiological model of tinnitus. Even though some form
of inner ear damage indeed seems to be a necessary prerequisite,
Jastreboff (1990) suggested central processes in the auditory
cortex, the limbic system, and prefrontal areas to be crucial
for tinnitus genesis. Later models picked up this idea and tried
to specify the neuroplastic alterations emerging after auditory
deafferentation. In this context, an increase in central gain in
subcortical structures of the auditory pathway (Noreña, 2011),
reorganization of tonotopic maps in the primary auditory cortex
(Mühlnickel et al., 1998), a thalamocortical dysrhythmia (Llinás
et al., 1998, 1999, 2005;Weisz et al., 2007a) and changes in neural
synchrony (Noreña and Eggermont, 2003;Seki and Eggermont,
2003;Eggermont and Roberts, 2004;Weisz et al., 2005), or
a failing top-down noise-canceling mechanism (Rauschecker
et al., 2010, 2015) have been discussed. Furthermore, global
workspace models emphasize the importance of networks beyond
the auditory system (De Ridder et al., 2014b), and frameworks of
filling-in missing auditory information have been suggested in a
Bayesian way (De Ridder et al., 2006, 2011, 2014a) or based on
predictive coding (Sedley et al., 2016).
First Wave of Electrophysiological
Studies
Apart from animal experiments, brain imaging and
morphometry studies, the investigation of resting-state brain
activity with electrophysiological methods, such as EEG or
MEG, enjoys great popularity in tinnitus research (Adjamian,
2014). In order to pinpoint neural correlates of the ongoing
tinnitus sensation, first studies compared spontaneous brain
activity of tinnitus patients at rest with the one of healthy
controls. In this context, most investigations focused on the
analysis of neuronal oscillations separated into distinct frequency
bands: delta (0.5–4 Hz), theta (4.5–8 Hz), alpha (8.5–12 Hz),
beta (12.5–35 Hz), and gamma (35.5–80 Hz). Following this
approach, early studies (Weisz et al., 2005, 2007b;Ashton et al.,
2007;Kahlbrock and Weisz, 2008;Lorenz et al., 2009) found a
relatively consistent pattern of enhanced activity in delta- and
gamma frequencies, alongside with reduced amounts of alpha
oscillations over temporal areas of tinnitus patients (for a review,
see Schlee et al., 2008;Adjamian et al., 2009). These findings
have been interpreted in the framework of the thalamocortical
dysrhythmia model (TCD), originally proposed by Llinás et al.’s
(1998,1999,2005) and later significantly refined by Weisz et al.
(2007a) to the synchronization by loss of inhibition modulation
(SLIM) model. Both models aim at sketching tinnitus genesis as
the result of an imbalance between inhibition and excitation in
thalamocortical circuits. Loss of sensory input (deafferentation)
gives raise to low frequent self-oscillations of thalamic cells
which activate the auditory cortex and can thus be measured as
oscillations in a slow delta rhythm on the scalp. At the same time,
input deprivation also leads to a downregulation of inhibitory
mechanisms which is reflected in alpha desynchronization in
the resting-state EEG or MEG. This decrease of inhibition is
then proposed to lead to spontaneous synchronization of firing
reflected in increasing activity in fast gamma oscillations. This
pattern of increased resting-state delta and gamma and decreased
alpha has thus been termed the neural signature of tinnitus, and
gamma has been interpreted as the neuronal substrate of the
sound percept itself.
Limitations of the Early Studies
One of the major flaws of these early studies, however, was
that they did not consider that chronic tinnitus is a very
heterogeneous phenomenon and can differ substantially between
individuals. It has clearly been shown that the subjective
experience of the chronic sound (intensity, pitch, location) as well
as the related distress and comorbid symptoms vary considerably
among sufferers (Landgrebe et al., 2010;Langguth et al., 2013;
Weidt et al., 2016;van den Berge et al., 2017). In addition, the
underlying neuroanatomical and neurophysiological alterations
may be far from homogenous in the population of tinnitus
patients. Instead of comparing tinnitus patients with healthy
controls, more recent studies thus focused on differences within
the tinnitus sample with the ultimate goal of identifying distinct
subtypes of tinnitus and finding different forms of treatment for
each of these subtypes.
Another issue that the earlier studies had to deal with is
the fact that electrophysiological methods suffer from rather
poor spatial resolution. In terms of neuroscience, the inverse
problem describes the fact that signal as measured by electrodes
or magnetometers on the scalp could be generated by infinite
combinations of neuronal sources (Scherg and Berg, 1991).
The described pattern of tinnitus-specific oscillations found in
the earlier studies, even though measured over temporal areas,
could therefore have been generated in (or significantly altered
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by) cell assemblies outside of the primary auditory cortex.
Different source estimation algorithms have been developed
in the recent past to solve this inverse problem as well as
possible by applying different a priori assumptions. With these
algorithms the source of a measured signal can be estimated and
spatial resolution of resting-state EEG and MEG measurements
significantly increased (Michel et al., 2004). Standardized Low
Resolution Electromagnetic Tomography (sLORETA) (Pascual-
Marqui, 2002) or beamformer algorithms (van Veen et al., 1997;
Hillebrand et al., 2005;Grosse-Wentrup et al., 2009) are examples
of fairly precise and therefore relatively popular source estimation
techniques.
The new focus on differences within the tinnitus population
and the improvements in electrophysiological analysis methods
have led to a veritable boom of resting-state tinnitus studies.
Some investigations have confirmed the neuronal tinnitus code
and auditory gamma as its major brain correlate by applying
sLORETA (van der Loo et al., 2009;Moazami-Goudarzi et al.,
2010;Vanneste et al., 2011a) or beamformer (Ortmann et al.,
2011) source estimations to the measured signal, reporting
correlations between tinnitus loudness and auditory gamma (van
der Loo et al., 2009) or by performing intervention studies with
acoustic coordinated reset (Tass et al., 2012;Adamchic et al.,
2014a,b, 2017). Schlee et al. (2014), on the other hand, found
decreased power (and variability) only for the lower (8–10 Hz)
but not for the upper alpha band (10–12 Hz) and other studies
failed completely to find the expected pattern in the auditory
areas (Vanneste et al., 2011b, 2012;Song et al., 2013;Meyer et al.,
2014;Zobay and Adjamian, 2015). Furthermore, two studies
(Sedley et al., 2012;Sedley and Cunningham, 2013) discussed the
possibility that auditory gamma oscillations could emerge as an
attempt of the brain to suppress the tinnitus percept rather than
causing it.
Tinnitus Network(s) and Areas Beyond
the Auditory Cortex
In neuroscience, the gamma frequency range has also been
debated as a binding medium connecting activity of various
circuits to form a unified percept (Singer, 1993). Already
Schlee et al. (2009) reported gamma-related abnormalities in
a network with core regions in prefrontal, orbitofrontal, and
parieto-occipital areas. Later the different parallel networks that
may differentially contribute to the various tinnitus symptoms
were described in more detail (De Ridder et al., 2011, 2014b;
Vanneste and De Ridder, 2012). A tinnitus core network was
proposed to generate the sound per se and code its intensity
and location (holocranial, uni- or bilateral). Other networks
were introduced as modulating the sound type (sine wave tone,
hissing, ringing) as well as aversive states and feelings (e.g.,
distress or mood) of tinnitus (De Ridder et al., 2014b). An
increased and persisting amount of gamma oscillations and
coupling with slow-waves could thus suggest that activity of these
widely-distributed brain networks is constantly bound together
(synchronized), and a unified tinnitus percept is formed with its
very own characteristics for each individual coded in the relevant
sub-networks. In order to capture the tinnitus phenomenon
in its entirety, areas outside of the central auditory regions
therefore have to be considered. Furthermore, the specificity
of the measured EEG-patterns has to be carefully validated
as related disorders might produce similar findings (e.g., Joos
et al., 2012;Meyer et al., 2017). These considerations are
also relevant with regard to the development of neurofeedback
protocols.
Apart from investigations comparing brain networks of
tinnitus patients and healthy controls based on analyses with
graph theory or machine learning algorithms (Mohan et al.,
2016a,b, 2017a,b), a multitude of recent electrophysiological
studies attempt to find specific correlates in neural networks
for the different aspects of tinnitus (Adjamian, 2014;De Ridder
et al., 2015;Eggermont, 2015;Elgoyhen et al., 2015). These
studies mainly investigated tinnitus-related distress or loudness,
but also covered tinnitus type, pitch, location/laterality, duration,
age of onset, day-time awareness, or related problems such
as hearing loss, hyperacusis, depression, or general quality of
life (a detailed summary is provided in the Supplementary
Materials). The most consistent findings are reported for
tinnitus-related distress, which seems to be represented in a
network ranging from structures of the limbic system (e.g.,
anterior cingulate cortex and amygdala) to prefrontal areas (e.g.,
dorsolateral prefrontal cortex), and also includes the insula.
Altogether, however, the results of these studies are rather
heterogeneous, and attempts of replication are scarce and partly
fail to confirm previous findings (Pierzycki et al., 2015;Meyer
et al., 2017). This can partially be explained by different EEG
or MEG hardware used for resting-state recordings, different
paradigms during the measurement [e.g., length of measurement,
operationalization of tinnitus symptoms, or condition of resting-
state (eyes open/closed) used for the analysis], different source
estimation algorithms and data analysis procedures. To resolve
this issue, scholars of the European research network TINNET1
are channeling their efforts to establishing general guidelines for
(electrophysiological) tinnitus studies and collecting comparable
data in a large database2. In order to tackle the problem of
tinnitus heterogeneity, it is thus of utmost importance that future
studies take these guidelines into consideration, report also null-
or conflicting results and further also extend their focus to
replicating previous findings.
NEUROFEEDBACK
Applying neurophysiological methods, neurofeedback is a non-
invasive neuromodulation technique which records a subject’s
neuronal activity, extracts relevant aspects of brain processes by
means of real time signal processing and returns feedback to the
subject as visual or auditory stimuli. The aim of neurofeedback
is to change behavioral traits or medical conditions associated
with altered neural activity as demonstrated for chronic
tinnitus in the previous section. This is generally done by
means of operant conditioning (i.e., rewarding of wanted,
1http://tinnet.tinnitusresearch.net/
2https://www.tinnitus-database.de/
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inhibiting of unwanted changes) whereby the subjects learn
to voluntarily change their own brain activity in the desired
direction.
A Brief History of Neurofeedback
In the early 1930’s and 1940’s, human studies already suggested
the capability of the central nervous system to alter neural
activity patterns by means of conditioning methods (Loomis
et al., 1936;Jasper and Shagass, 1941). Later, Wyrwicka and
Sterman (1968) were able to train cats to change their brain
activity in a specific direction, and, shortly after that, the
first study with human subjects in this context was published
(Sterman and Friar, 1972). In the following years, neurofeedback
was intensively tested and showed promising results mainly in
treatment studies with epilepsy and attention deficit hyperactivity
disorder (ADHD) (Lubar and Bahler, 1976;Lubar and Lubar,
1984). For ADHD, neurofeedback already found acceptance as
alternative to established medication based treatment, due to
its non-invasive character, the almost complete absence of any
side-effects and high self-efficacy experienced by the subjects
(Lubar et al., 1995;Lévesque et al., 2006;Arns et al., 2009;
Gevensleben et al., 2009;Strehl et al., 2017). Apart from that,
effectiveness and feasibility of neurofeedback are more and more
investigated in the context of many other psychological disorders
and neurological conditions ranging from the treatment of
depression (Kelley et al., 2017), anxiety (Mennella et al., 2017), or
autism (Datko et al., 2017) to stroke patients (Kober et al., 2017)
and prevention of Alzheimer’s disease (Jiang et al., 2017). Today,
quality control is an important aspect in the neurofeedback field.
The Biofeedback Certification International Alliance (BCIA)3
certifies bio- and neurofeedback practitioners who meet certain
requirements and the Association for Applied Psychophysiology
and Biofeedback (AAPB)4recently released the 3rd edition
of Evidence-Based Practice in Biofeedback and Neurofeedback,
a document that summarizes treatment efficacy for various
disorders (Tan et al., 2016).
Common Neurofeedback Paradigms
Neurofeedback training of classical definitions of distinct
frequency bands (i.e., delta, theta, alpha, beta, and gamma) are
the most commonly used protocols in the current literature. The
main field of frequency band neurofeedback is the treatment
of ADHD, where often a combination of different frequencies
is trained (Lofthouse et al., 2012). However, classic frequency
band training has also been adapted for other disorders, most
prominently anxiety or affective problems (Hammond, 2005).
Importantly, neurofeedback training based on this paradigm
ultimately depends on findings of fundamental research about
disorder-specific neural alterations and can even be used to
confirm or disprove these findings.
Sensorimotor rhythms (SMR) are defined as EEG oscillations
in the lower beta range (12 – 20 Hz). They are generally measured
over the sensorimotor cortex and proposed to originate from
the ventrobasal nucleus in the thalamus (Howe and Sterman,
3http://www.bcia.org
4https://www.aapb.org
1972, 1973). Neurofeedback training based on SMR mainly found
application in the treatment of epilepsy (Sterman and Egner,
2006) or ADHD (Monastra et al., 2002;Fuchs et al., 2003).
Slow cortical potentials (SCP’s) describe very slow oscillations
in a range of 0.3–1.5 Hz. They describe slow, discrete, and
continuous shifts (up to seconds) of the overall cortical
distribution of electrical activity representing increased or
decreased excitability of underlying neuronal structures. SCP’s
are usually recorded with a single electrode in a central position
(Cz) and are proposed to reflect cognitive or motor preparation
(Hammond, 2011). Initially, SCP training was exclusively applied
in trials with patients suffering from epilepsy (Rockstroh et al.,
1993) but later also found application in the treatment of ADHD
(Strehl et al., 2017).
Infra-low neurofeedback (ILN) relies on training of even
slower brain oscillations, ranging from 0.001 to 1.5 Hz (Vanhatalo
et al., 2004). Infra-low oscillations were shown to correlate with
other frequency bands as well (Monastra et al., 2002). There is
an overlap with SCP-based neurofeedback, which mainly differs
in the recording of SCP’s with a single central electrode and thus
a training of a more summarized potential over the whole head.
Positive effects of ILN on different neurological conditions were
reported in case reports (Legarda et al., 2011).
In z-score neurofeedback, the training protocol for an
individual patient is based on previous recordings of EEG data
and comparison to a healthy age-matched normative database
(Thatcher, 2010). During the neurofeedback training, patients
try to normalize their EEG patterns and minimize deviations
from this control group. This NFB alternative is a rather data-
driven technique, and some studies report successful treatment
of various disorders (e.g., schizophrenia, addiction, ADHD,
or personality, anxiety, and affective disorders) with z-score
neurofeedback (Surmeli and Ertem, 2009;Surmeli et al., 2012;
Simkin et al., 2014).
Functional magnetic resonance imaging (fMRI) was
introduced to the field of neurofeedback to obtain a better
spatial resolution. Real-time acquisition of blood oxygenation
level dependent (BOLD) signals demonstrates increased neural
activity according to higher oxygen supply to active neurons
(Ogawa et al., 1990). Although newer to the field, a large quantity
of clinical treatment studies already focused on the use of
real-time fMRI neurofeedback (Sulzer et al., 2013). The higher
spatial resolution of fMRI neurofeedback, however, does not
come without limitations. Increased blood oxygenation can
be measured only after a delay of several seconds and is an
indirect correlate of underlying neuronal processes. Compared
to electrophysiological methods, the temporal resolution of
fMRI is thus rather poor, and fast fluctuations cannot be
captured accordingly and used for the feedback. Additionally,
it is questionable if an MRI-scanner is a favorable setting to
perform neurofeedback because of the limited space and the
loud constant background noise. For tinnitus patients, this is a
huge drawback, in particular in those individuals suffering from
additional hyperacusis.
To address the poor spatial resolution of single- or multi-
electrode EEG and MEG recordings, neurofeedback techniques
have also been combined with source estimation algorithms.
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Congedo et al. (2004) introduced the first tomographic
neurofeedback protocol based on the inverse solution technique
LORETA (Pascual-Marqui et al., 1994). This approach has
subsequently been intensely tested mainly in the context of
ADHD treatment (Cannon et al., 2006, 2007, 2009, 2014;Koberda
et al., 2012, 2013) and has recently been further refined (Congedo,
2006;Pllana and Bauer, 2011;Kopˇ
rivová et al., 2013;Bauer and
Pllana, 2014;White et al., 2014).
Neurofeedback and Tinnitus: Existing
Studies
Presently, only a handful of studies investigated the efficacy of
neurofeedback in the treatment of chronic tinnitus according
to standard searching tools such as PubMed5. An overview is
provided in Table 1.
In the first study in this context published by Gosepath
et al. (2001), 40 patients suffering from chronic tinnitus and 15
control subjects underwent neurofeedback training. The training
protocol included alpha training (8–13 Hz) alongside with a
reduction of beta oscillations (14–30 Hz). While one group of
patients (n=24) was able to only increase their alpha activity,
the effects of the other group (n=16) were limited to the
decrease of beta oscillations. All patients, however, reported to
be less disturbed by their tinnitus after the training, indicated
by significant decrement in scores of the tinnitus questionnaire
(TQ) (Goebel and Hiller, 1994). Control subjects underwent
identical trainings but without real-time feedback and did thus
not show any changes in alpha or beta activity. Schenk et al.
(2005) aimed at replicating the findings from Gosepath et al.
(2001) with the aforementioned protocol. Before assigning them
to different study groups, participants underwent baseline EEG-
recordings at rest and during a stress test. Participants (n=40)
were assigned to three different groups according to their results.
Twenty-three subjects showing decreased alpha activity under
stress were allocated to a first group and set to train alpha
activity (8–13 Hz) in the subsequent neurofeedback training.
The second group consisted of 13 patients with increased beta
activity in the stress condition and their treatment protocol thus
aimed at the decreasing of beta oscillations (14–30 Hz). Four
patients could not be assigned to either of the aforementioned
groups according to their spontaneous brain activity and hence
were allocated in a third group that had to increase alpha and
decrease beta activity simultaneously. Subjects of the first group
were able to increase their alpha activity, whereas subjects of
the second group failed to significantly decrease their amount
of beta oscillations. Surprisingly, also subjects of the second
group showed increases in alpha activity even though it was not
intended with the feedback. Reduced subjective tinnitus distress
in terms of a reduction of TQ scores was reported for both groups.
The third group was excluded from data analysis due to its small
size.
A third rather explorative study shall briefly be mentioned.
In a case report, Weiler et al. (2002) used z-score neurofeedback
for one patient with bilateral tinnitus. The feedback protocol was
based on EEG recordings prior to the training where decreased
5https://www.ncbi.nlm.nih.gov/pubmed/
delta, theta, alpha and beta activities compared to 20 control
subjects had been observed. The results indicated a normalization
of depressive and anxiety symptoms and the patient reported that
tinnitus was only occasionally present. However, no comparisons
of pre–post changes in EEG patterns have been drawn in this
study.
Even though these three first attempts to treat tinnitus with
neurofeedback seemed to be promising, they should not be
over-interpreted. First, the training-protocols were chosen rather
arbitrarily and not based on previous findings of tinnitus-specific
neural abnormalities. Moreover, the fact that patients of all
groups reported significant improvements in tinnitus-related
distress, regardless of their actual alterations of neural activity,
speaks in favor of unspecific effects of the neurofeedback training.
Especially the unintended increase of alpha activity in the second
group of the study by Schenk et al. (2005) suggests that a general
relaxation effect might have had a bigger impact than the actual
neurofeedback protocol. In general, these first three studies rather
aimed at helping their patients relax and reduce their general level
of stress, and it is thus not surprising that reduced distress was
reported after the training. However, since knowledge about the
origins of tinnitus was still rare at this time, these studies can
clearly be seen as pioneering works in the treatment of tinnitus
with neurofeedback.
The TCD-model by Llinás et al. (1999, 2005) and the
proposition of the neural signature of tinnitus (Weisz et al.,
2007a) gave rise to new and potentially more appropriate
neurofeedback protocols. Dohrmann et al. (2007a,b) developed
their neurofeedback protocols by reference to these findings
and aimed at an increasing of alpha and a decreasing of delta
activity. Twenty-one patients suffering from chronic tinnitus
were included into their study and further assigned to three
different treatment groups (see Table 1). For the neurofeedback
application 4 fronto-central electrodes (F3, F4, Fc1, and Fc2)
were chosen because the recorded signal is most likely generated
in the auditory cortex according to the authors. For a forth
group of tinnitus patients (n=27) frequency discrimination
training (FDT) was applied aiming at a change of hearing-loss
induced cortical map reorganization. Data analysis showed a
significantly increased ratio between alpha and delta activity
for the three neurofeedback groups suggesting an increase of
alpha alongside with a decrease of delta over temporal auditory
regions. These alterations were also correlated with a significant
decline of tinnitus loudness for tinnitus patients. Subjects who
were able to modify both bands simultaneously in the desired
way showed the strongest relief from tinnitus compared to other
groups (i.e., subgroups of patients with only alpha-, only delta-,
or no change). Furthermore, the training generally resulted in a
reduction of tinnitus related distress that was still notable even
6 months after the termination of the training. No statistically
meaningful effects regarding tinnitus loudness or distress were
found in the FDT group. In order to replicate these findings,
Crocetti et al. (2011) conducted a study with 15 normal hearing
tinnitus patients and tried to train them in decreasing delta and
increasing alpha frequency bands. Even though no significant
differences between pre- and post-training EEG patterns have
been found, the results suggested an obvious trend toward an
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TABLE 1 | Summary of studies investigating neurofeedback for treatment of tinnitus.
Authors Tinnitus patients Neurofeedback Electrodes/Sources Feedback Behavioral findings Neuronal findings
Crocetti et al., 2011 N=15 α↑δ↓
12 sessions
F3, F4, Fc1, Fc2 Plane moving up and down
(with audio-visual
reinforcement)
Distress ↓
Loudness ↓
α/δ-ratio ↑
(not all participants were
able to manipulate αand δ
successfully)
Dohrmann et al., 2007a,b Group 1 (n=11)
Group 2 (n=5)
Group 3 (n=5)
Controls (n=27)
Group 1: α↑δ↓
Group 2: α↑
Group 3: δ↓
Control: FDT
10 sessions
F3, F4, Fc1, Fc2 Fish moving up and down All groups:
Distress ↓
Loudness↓
Group 1: strongest relief
Controls: no reduction
All groups:
α↑and δ↓
Correlation with decrease in
loudness
Gosepath et al., 2001 N=40
Controls (n=15)
α↑β↓
15 sessions
P4 Auditory and visual (not
further explained)
Distress ↓Group 1 (n=24): α↑
Group 2 (n=16): β↓
Controls: no effect
Hartmann et al., 2013 N=8
Controls (n=9)
α↑
10 sessions
Controls: rTMS
Source space projection on
two temporal sources
Smiley Distress ↓
Controls: no reductions
α↑estimated over r PAC
Schenk et al., 2005 Group 1 (n=23)
Group 2 (n=13)
Group 1: α↑
Group 2: β↓
Group 3: α↑β↓
Group 1: P4
Group 2: C3
Floating ball and melody Distress ↓Both groups: α↑
Vanneste et al., 2016 Group 1 (n=23)
Controls 1 (n=17)
Controls 2 (n=22)
Group 1: α↑β↓γ↓
15 sessions
Controls 1: α↑β↓γ↓
Controls 2: passive
sLORETA
Group 1: PCC
Controls 1: LG
Green bar moving up and
down
Group 1: distress ↓
Controls: no reduction
No alterations in target
areas for α,βand γ
Changes in functional and
effectivity connectivity
Weiler et al., 2002 N=1α↑β↑δ↑θ↑19 electrodes Varying Depression ↓
Anxiety ↓
Tinnitus ↓
No analysis
↑, increase; ↓, decrease; r PAC, right primary auditory cortex; PCC, posterior cingulate cortex; LG, lingual gyrus.
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Güntensperger et al. Neurofeedback for Tinnitus Treatment
increasing alpha/delta ratio. In addition, scores evaluated with the
Tinnitus Handicap Questionnaire (THI) (Newman et al., 1996)
indicated significant improvements, which were maintained after
the end of the training period.
All in all, these two studies suggested the protocol of
upregulating alpha and downregulating delta to be a highly
promising approach in tinnitus treatment. However, the surface-
based nature of the neurofeedback application by simply using
four electrodes on the scalp could not ensure that the brain
activity used for the feedback indeed originated in the auditory
areas. To address this problem, Hartmann et al. (2013) used
a 32-channel EEG system and projected the recorded activity
on the surface to eight regional dipole-sources, of which two
were situated in the temporal cortex. Eight subjects of this
investigation received neurofeedback treatment to train an
increase of alpha power and nine subjects were treated with
repetitive transcranial magnetic stimulation (rTMS). With the
completion of the training, only patients of the neurofeedback
group showed improved tinnitus distress scores. In comparison
to the control group with rTMS treatment, they achieved
significantly ameliorated scores in the TQ. Additionally, a
comparison of MEG resting-state activity before and after
treatment combined with spatial filtering based on a LCMV
beamformer algorithm (van Veen et al., 1997) revealed a
significant increase of alpha activity over the right primary
auditory cortex. According to Hartmann et al. (2013) this proves
that alpha activity can be systematically altered in the primary
auditory cortex which helps restore the disturbed excitatory–
inhibitory balance of tinnitus patients.
Finally, two recently published neurofeedback studies shall be
mentioned. Milner et al. (2015) used SCP neurofeedback training
in a case report and could show decreased tinnitus pitch and
loudness as well as a reduction of delta and theta frequencies
over left hemispheric fronto-temporal and temporo-occipital
electrodes which they interpret as a normalization of tinnitus-
specific activity. Vanneste et al. (2016) applied neurofeedback
combined with sLORETA source estimation to a group of 58
tinnitus patients. A first group (n=23) of this study received
alpha-up training, and beta- and gamma-down training whereby
the feedback was limited on the activity that was estimated to
originate over the posterior cingulate cortex (PCC). A second
group of 17 tinnitus patients received the same training but for
activity over the lingual gyrus and a third group (n=18) did
not receive any treatment at all. Decreased tinnitus distress was
only found for the PCC-group but no significant changes in
any frequency bands were found in the trained areas. However,
decreased cross-frequency coupling (i.e., alpha to beta and alpha
to gamma power nesting) in the PCC and changes in functional
and effective connectivity between PCC and different areas of the
distress network suggest a specific effect of this training.
Finally, even though this review mainly focuses on
neurofeedback based on electrophysiological recordings, it
shall be noted that also real-time fMRI protocols are currently
being developed and tested for tinnitus treatment with promising
results (Haller et al., 2010, 2013;Emmert et al., 2017). In their
investigations, the auditory cortex of tinnitus patients is first
precisely localized thanks to the good spatial resolution of fMRI,
and, subsequently, neurofeedback training aiming at reducing
auditory BOLD activity provided. Even though this protocol
leads to the intended neuronal alterations, no significant effects
on tinnitus symptoms have been reported (Emmert et al., 2017).
Limitations of Neurofeedback Training
Studies
Currently, the AAPB rates the efficacy of chronic tinnitus
treatment with neurofeedback as possibly efficacious (level 2)
(Tan et al., 2016). Although various neurofeedback training
protocols showed promising results in treatment of several
neurological disorders, there still remain limitations and open
issues which need to be addressed. In particular, EEG- and
MEG-based neurofeedback studies are often criticized about the
low spatial resolution of electrophysiological recordings. Despite
more refined source estimation algorithms, an uncertainty about
the precision of the estimation remains, which is especially
important when changes in frequency bands are considered as
primary outcome measures. Studies that are able to verify specific
effects in the brain areas of interest are still scarce and successful
improvements of certain symptoms are thus often criticized
to be the mere result of unspecific placebo effects (Thibault
et al., 2016, 2017). Expectations of researcher and participant,
the treatment condition in general (e.g., taking time off from a
busy work schedule) and interactions with the practitioner (such
as, the simple meeting with a clinician) can contribute greatly
to the improvement of psychological symptoms. This problem is
especially predominant in the context of chronic tinnitus therapy
where most participants turn to neurofeedback hopefully after
repeatedly being told by their doctors that nothing can be done to
treat tinnitus and having undergone a wide variety of (sometimes
rather questionable) treatments on their own.
One way to resolve this issue is to improve study designs and
conduct double-blind trials with control groups using a form
of sham neurofeedback. In this context, Thibault et al. (2016)
suggest the use of prerecorded feedback of other participants,
feedback of another disease-unrelated brain area, or inverse
feedback protocols that reward unwanted and inhibit wanted
changes of brain activity. The use of sham-control is, however,
difficult to establish in clinical neurofeedback trials because of
several reasons. First, participation in neurofeedback treatment
studies requires considerable investments in time and energy
on the part of participants as they generally have to attend
multiple training sessions over the course of several weeks.
Furthermore, in sham-controlled clinical studies, participants
always enter a trial with some form of expectation and hope
to be part of the treatment group. Absent success after the
first training sessions may lead to a misleading belief that they
instead have been assigned to the control group which negatively
affects their motivation and further success in the training process
(Strehl et al., 2017). These drawbacks of placebo-controlled trials
have to be considered and alleviated with appropriate designs,
such as a cross-over approach where one group of participants
receives sham training first while the other starts with verum
treatment. In a second step the protocols are swapped so that
both groups undergo sham- as well as verum-neurofeedback.
In this context several authors point to the importance of a
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systematic investigation of non-specific factors in neurofeedback
studies (Friedrich et al., 2014;Sitaram et al., 2017;Thibault et al.,
2017). Appropriate knowledge about the factors favoring and the
ones hindering success in neurofeedback treatment can indeed
lead to a better understanding of the actual mode of action of
neurofeedback as well as help improve the treatment setting in
order to optimize therapy outcomes for patients.
A major flaw of previous neurofeedback studies is that most of
them settle for reporting positive effects of their trained protocol.
It is known, however, that there is a wide variability among the
efficacy of neurofeedback treatment for different subjects. While
some are able to successfully self-regulate their neural activity
in the desired way and show improvements of corresponding
symptoms (responders), others fail to do so (non-responders)
(Friedrich et al., 2014). This issue was described as neurofeedback
inefficacy by Alkoby et al. (2017) who provide a thorough review
about this currently existing topic. In their publication, they
chose 20 papers published after 2010 at random and found that
only two of them reported the actual number of responders
and non-responders in their studies. This, of course, hampers
a proper evaluation of the feasibility of a given neurofeedback
protocol for the treatment of a certain disorder. For one thing,
positive effects of the training might be concealed or confounded
by the negative results of non-responders in the clinical trial.
Furthermore, information provided about responder and non-
responder groups helps define and analyze factors for success
or failure of the protocol. That is, by means of a thorough
investigation of the attributes of responders and non-responders,
predictors for (un-) successful neurofeedback can be identified,
which can be used to improve training protocols for future
patients.
Another issue in this context is the high heterogeneity among
outcome measures and definitions to appropriately measure
success or failure used in previous neurofeedback studies. On
the one hand, it can be useful to use a wide variety of outcome
measures in a clinical study in order to account for changes
which might not be anticipated in the first place. For instance,
it can be important to measure the general level of stress of
tinnitus patients as the positive effects of neurofeedback could
also be explained by a decrease of the general stress condition
of the patient. However, guidelines need to be established
which suggest the use of certain questionnaires or tests for
a given field of interest to which scholars can relate when
planning an investigation [substantial work in the tinnitus field
is currently being done by Hall et al. (2016) in this context].
This will limit the amount of different outcome measures in
clinical trials, promote the use of well-established and validated
questionnaires, and foster direct comparability between findings
of different investigations. Additionally, guidelines in the context
of neurofeedback treatment need to answer the question as to
what can be regarded as successful or unsuccessful training and
how to distinguish responders from non-responders. Is it already
sufficient that a given symptom simply changes over the course
of a training in a positive way or does it have to improve
by a certain amount (e.g., an increase by certain points in a
questionnaire score)? What, on the other hand, needs to happen
to and in between brain circuits? How and how much does neural
activity have to be altered by the neurofeedback treatment so
that an individual can be labeled as a responder? Even though
some publications already tried to postulate criteria or guidelines
(Gruzelier, 2014;Rogala et al., 2016;Enriquez-Geppert et al.,
2017), many open issues remain in this regard.
CONCLUSION
In this review, we summarized and discussed the current state of
electrophysiological brain research in the field of chronic tinnitus
as well as recent advances of neurofeedback treatment. Up to
date, only a handful of studies exist that investigated feasibility of
neurofeedback protocols for chronic tinnitus patients. While the
first studies in this context rather focused on creating a general
state of relaxation for the subject, later trials considered tinnitus-
specific alterations in brain activity based on comparisons of
EEG or MEG resting-state recordings between tinnitus patients
and healthy controls. The main region of interest in these
studies was the auditory cortex, and fairly good results have been
achieved following this approach. With the newer developments
in tinnitus research and the numerous investigations dealing
with differences within the tinnitus population, which take
into account the substantial amount of heterogeneity amongst
tinnitus sufferers, also other potential tinnitus-related brain areas
can be targeted in future neurofeedback studies. A good example
in this regard is the recent publication by Vanneste et al. (2016)
where the posterior cingulate cortex as part of the tinnitus distress
network has been targeted. Furthermore, this investigation is
the only neurofeedback study in the context of chronic tinnitus
treatment to date that included a control group with training of a
tinnitus-irrelevant brain area in its design.
To sum up, even though often criticized in the recent past,
results of current studies suggest that neurofeedback seems to
be a promising method for efficient tinnitus treatment and may
enjoy great popularity in the future. The ultimate goal may
be to develop different neurofeedback alternatives for a given
subgroup of tinnitus sufferers or even establish neurofeedback on
an individualized basis for each patient. In this context, multi-
location and multi-frequency neurofeedback protocols with
adequate source estimation algorithms, which are able to train
multiple brain networks in power and maybe even connectivity
changes simultaneously, can be seen as the gold standard
for future neurofeedback protocols. At the moment, however,
there still exist several challenges that need to be overcome.
A general issue are technological aspects of electrophysiological
measurements (e.g., the limited spatial precision of resting-
state EEG recordings) and neurofeedback applications (e.g., the
implementation of connectivity-based neurofeedback protocols)
that need to be improved. Regarding the treatment of chronic
tinnitus in particular, results of existing fundamental studies are
still too heterogeneous in order to suffice for the development of
more sophisticated neurofeedback protocols. One possibility to
resolve this latter issue is by means of the establishment of general
guidelines about adequate symptom assessment, measurement
paradigms, and analysis methods. In this way, more coherent
and comparable results should be published in order to lead
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Güntensperger et al. Neurofeedback for Tinnitus Treatment
to a better understanding of tinnitus heterogeneity and its
underlying alterations in brain networks that could be tackled by
future neurofeedback protocols. Additionally, this urgent need
for guidelines has been shown to be an open issue in the field
of clinical neurofeedback research in general. Clarity is needed
about how to separate responders from non-responders, and
which outcome domains and measurements are best suited to
do so. Furthermore, also non-specific effects of the training have
to be taken into account and systematic investigations about the
most (or least) favorable neurofeedback settings and treatment
conditions are needed.
AUTHOR CONTRIBUTIONS
Each author has provided substantial contributions to warrant
authorship. Contributions are as follows: DG and CT equally
contributed to the conception, draft and revision of the paper
and are sharing first-authorship. MM, PN, and TK contributed
to conception, critically revising and final approval of the
manuscript.
FUNDING
The authors disclose the following financial support for
research, authorship, and/or publication of this article: ‘Velux
Stiftung’, ‘Zürcher Stiftung für das Hören (ZSFH)’, ‘Fonds zur
Förderung des akademischen Nachwuchses (FAN) des Zürcher
Universitätsvereins (ZUNIV)’, University Research Priority
Program ‘Dynamics of Healthy Aging’ of the University of
Zurich.
ACKNOWLEDGMENTS
The authors are further indebted to the TINNET - COST Action
BM1306 ‘Better Understanding the Heterogeneity of Tinnitus
to Improve and Develop New Treatments’ for providing a
network, which allows exchange of knowledge among tinnitus
researchers in Europe. During the work on his dissertation,
DG was a pre-doctoral fellow of LIFE (International Max
Planck Research School on the Life Course; participating
institutions: MPI for Human Development, Humboldt-
Universität zu Berlin, Freie Universität Berlin, University
of Michigan, University of Virginia, University of Zurich).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fnagi.
2017.00386/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
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be construed as a potential conflict of interest.
The handling Editor declared a past co-authorship with the authors MM and TK.
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