DOI: https://doi.org/10.21203/rs.2.12838/v1
Tinnitus, the perception of sound in the absence of external acoustic stimuli (1), is a rather common condition. However, getting a precise prevalence estimate poses a challenge. This is mainly due to the heterogeneity of tinnitus definitions in research (2). Nevertheless, prevalence research points to an estimated 10 to 20 percent of the general population experiencing tinnitus (2, 3). Although most manage to live well with and tolerate the condition, for two to three percent of the population, the condition is so severe, that it seriously interferes with the quality of life (2, 4). Feelings of despair, hopelessness, anxiety, and depression are commonly reported by distressed tinnitus patients, as are concentration difficulties and insomnia (5, 6).
Early models on the pathophysiology of tinnitus emphasized peripheral auditory structures as the locus of tinnitus generation (e.g. 7). However, more recent neuroscientific advances have led to a broader explanatory framework of the pathogenesis of tinnitus. Although the condition is likely precipitated by damage to the cochlea and/or auditory structures, current theories and models hold as basic assumptions, that tinnitus is generated and perpetuated in the brain (8), through a complex interplay between cortical -and subcortical networks (9). At the cortical level, this is evident in reduced alpha and increased delta brain wave activity over temporal regions in individuals with tinnitus compared with normal hearing controls (10). These observations have given rise to the Synchronization by Loss of Inhibition Model (SLIM; 11). This model makes two propositions about how alpha and delta oscillatory activity might be causally implicated, both in the emergence of the tinnitus percept and in the distress often experienced by people with tinnitus. First, in the normal functioning brain, alpha activity is hypothesized to regulate the excitatory/inhibitory balance of neurons, with strong alpha being indicative of relative inhibition of neurons (12, 13). In tinnitus however, where alpha activity is reduced in the temporal cortex, inhibitory drive is no longer strong enough to prevent neuronal cell assemblies from spontaneously synchronizing their activity. At the behavioral level, this manifests as an ongoing tinnitus percept (11).
The enhanced delta activity in tinnitus is likely associated with the distress aspect of the condition. Slow wave oscillations are generally involved in the synchronization and coordination of the activity of spatially distinct neural networks (14). Therefore, enhanced delta activity in individuals with tinnitus is suggested to be the neuronal signature of an ongoing activation and coupling of a tinnitus distress network, involving temporal, limbic and frontal regions specifically. This is then at the behavioral level expressed as tinnitus distress (15).
This advanced understanding of the neurophysiological signature of tinnitus, has led to the development of several promising neuromodulation techniques, aimed at normalizing the aberrant EEG frequency pattern described above, one of which is neurofeedback (16).
Based on the principle of operant conditioning, neurofeedback is suggested to facilitate volitional control of the activity of selected EEG frequency bands (17, 18). Normally, EEG activity occurs outside conscious awareness. However, transforming the EEG activity to an auditory and/or visual presentation format, brings it within the realm of consciousness, and with it the possibility of volitional control. Through practice and with time, it is then suggested, that a normalized activity pattern can be retrained and reconditioned (17).
Informed by the assumptions of SLIM outlined above, Dohrmann, Weisz (15) developed a specific neurofeedback training protocol, aimed at reversing the abnormal EEG pattern seen in individuals with tinnitus (15). More specifically, it does so through enhancing alpha while simultaneously reducing delta EEG activity. If, as SLIM suggests, there is a distinct association between sub-optimal alpha/delta EEG activity and tinnitus symptoms, then training them with neurofeedback to approximate normalized pattern, should result in symptom improvement. This is exactly what two studies, using an alpha/delta ratio (ADR) training protocol have found. In Dohrmann, Elbert (19), chronic tinnitus participants underwent ten neurofeedback training sessions. The study found improvement, not only in the subjectively reported distress, but also in the perception of the tinnitus sound. However, the training had differential effects. Only participants with post-training altered alpha and delta EEG activity, showed the respective behavioral changes. Consequently, this finding supports the hypothesized neural signature of tinnitus, that is, symptom alleviation appears dependent on normalization of both alpha and delta EEG activity. Using a similar neurofeedback training protocol, Crocetti, Forti (20) also reported positive effects of the ADR neurofeedback training protocol in their study, where participants found both, significant relief from tinnitus distress and a subjectively rated lowering of the sound intensity.
Although the results from previous studies are encouraging, what can be inferred from them is rather limited, due to their explorative nature, small sample sizes and lack of adequate sham neurofeedback control conditions. The lack of a neurofeedback control condition is particularly critical. Generally, in neurofeedback studies, there is insufficient evidence of an association between trained frequency bands and behavioral outcome measures (18). Therefore, in studies without neurofeedback control condition(s) (20, 21), it remains unclear if improvements were caused by the specific modification of alpha and/or delta EEG frequencies per se, or if an alternative explanation exists, why participants in these studies found neurofeedback beneficial. One plausible explanation is, that improvements were mediated by powerful placebo effects, to which neurofeedback is particularly vulnerable (21). Not only do participants in neurofeedback invest a lot of time. They may also come to overestimate the effect of the intervention, because they are enchanted with the technology and environment. It has been suggested, that neuroimaging techniques and neuroscience in general results in the abolishment of critical thinking and that it clouds judgments, even in individuals with some familiarity with the subject matter (22). Thus, despite the use of an auditory frequency discrimination task as comparator in Dohrmann, Elbert (19), it may have been an inadequate control, when taking the neuro-enchanting effect of the neurofeedback intervention into account. Therefore, it cannot be ruled out, that placebo effects may have influenced participant’s post-assessment of the neurofeedback interventions in previous studies (19, 20). Despite their shortcomings, these studies are promising enough that their training protocol will be replicated in the present trial, with one crucial difference however. To examine the assumptions of SLIM and the specificity of alpha/delta neurofeedback training in alleviating tinnitus intensity and distress, it is compared to a beta/theta ratio (BTR) training protocol, chosen as neither beta nor theta frequency activity has been linked to the genesis, the maintenance or the distress of tinnitus. Studies involving beta/theta neurofeedback training have aimed at enhancing attention (23) in clinical (e.g. 24, 25, 26) and in healthy populations (e.g. 23). In addition to the two neurofeedback treatment groups, a diary group comprises the third arm of the study. The rationale for including this group is to control for 1) the passage of time and 2) for support from interaction with a member of the research team.
Based on the assumptions of SLIM and results from previous studies, it is predicted that participants undergoing ADR neurofeedback training will report significantly stronger reductions in tinnitus intensity and distress, compared to participants undertaking BTR neurofeedback training. Moreover, given the particularly powerful placebo effects of neurofeedback, it is predicted that both groups will improve significantly more on measures of tinnitus intensity and distress than the diary control group.
The study, a three-armed, single-blind randomized clinical trial, will be conducted at the Philipps University in Marburg, Germany. The study has been approved by the respective ethics committees of the Department of Psychology and the Department of Medicine, Philipps-University Marburg, with registration numbers 2018–4k (Additional file 2) and 162/18 (Additional file 3) respectively. Furthermore, the trial is registered with clinicaltrials.gov (ClinicalTrials.gov Identifier: NCT03550430).
Sample size calculations were based on a repeated-measures multivariate analysis of variance (MANOVA) and conducted with G*Power 3.1.9.2. (27). The total sample size needed to detect a medium assessment-by-treatment interaction effect (f = 0.25, Beta = 0.80, Alpha =.05, Groups = 3, measurements = 4) is 171. However, to compensate for an estimated drop-out in treatment of 20%, the aim is to recruit 204 participants, i.e. 68 participants per group. Post-hoc analyses are carried out between ADR vs. BTR, ADR vs. diary control and BTR vs. diary control, respectively.
Inclusion and exclusion criteria
Participants are eligible for inclusion if they meet the following criteria:
The study exclusion criteria are as follows:
Procedure/ Trial Protocol
Recruitment for the trial is done primarily through advertisements placed in local and regional media. In addition, a referral program is set up to entice ear-nose-throat physicians (ENT’s) to forward participants to the study. Interested individuals can read more about the study on a dedicated website, where they can also register their interest in trial participation. The website as well as the questionnaires are provided via the iterapi platform (29).
The enrollment period (t0, see figure 1 above) is a four-stage process. In the first stage, participants complete a battery of online screening questionnaires. These are mainly designed to sort individuals into principally eligible and non-eligible categories, based on the inclusion/exclusion criteria. Prior to taking the screening questionnaires, participants give informed consent for questionnaire completion only. For principally eligible individuals, the second stage in the screening process is a telephone interview. The purpose of this is to validate questionnaire responses and to assess motivation for participation in a trial. The third stage in t0 is a visit to the department of Psychology. The first part of this visit provides participants with detailed information about the study, about procedures and how to complete baseline assessments, including primary and secondary outcome measures on the secure online platform. Following this, participants sign the informed consent (see Additional file 4), before partaking in the first of three electrophysiological assessments scheduled across the study period. In the electrophysiological assessments, resting EEG is recorded for a total of ten minutes, with five minutes eyes open and five minutes eyes closed, respectively. This is followed by completion of the State Anxiety Inventory online (STAI, 30), immediately before taking two attention tests, the Attention Network Task (ANT, 31) and the Sustained Attention to Response Task (SART, 32) respectively.
The fourth and final stage in t0 is a standard audiological examination at the ENT-clinic of the University Hospital of Gießen and Marburg. There are two main purposes of this visit: The first is to rule out possible non-subjective causes of tinnitus and second, to acquire audiometric data. At the clinic, participants first learn about the components of the audiological examination, before signing the informed consent (see Additional file 5). Subsequently, the audiological examination, in which the below information is acquired, takes place:
Participants who are included after the visit to the ENT clinic, take the baseline assessment in the pre-allocation (t1) stage. Upon completion of this assessment, participants move to the allocation (t1) stage and are randomly assigned to one of three arms in the study: Group 1, who receives alpha/delta neurofeedback training; Group 2, who receives beta/theta neurofeedback; Group 3, the diary control group.
The post-allocation (t2) intervention period extends over four weeks. Within this time-frame, ten neurofeedback training sessions are undertaken for the two neurofeedback groups, with a minimum of two and maximum of three sessions per week. Mid-point assessments are completed online after the first five of ten neurofeedback training sessions. For the diary control group, two face-to face visits to the department and two telephone calls are scheduled within the four-week period. Mid-point assessments are completed online after two weeks. In the post-allocation (t3) stage, the second electrophysiological assessment is recorded at the department of psychology and end-point assessments are completed online.
The follow-up (t4) stage is three months after the post-allocation (t3) period. Here, participants complete follow-up assessments online and are invited to the department of psychology for a third and final electrophysiological assessment.
Randomization and blinding
When, as in our study, specific prognostic factors may influence outcome, the best way to ensure a balanced distribution of participants across groups, is to use an allocation by minimization process (33). Accordingly, participants are stratified to one of the three study groups, based on their age and tinnitus severity (34). Age is included as a prognostic factor. This is based on the assumption, that neurofeedback utilizes the brain’s ability to reorganize itself functionally and/or structurally (35, 36). With the current knowledge, evidence favors a hypothesis, that younger individuals compared to older, have greater neuroplastic potential (37). This is partially because of the association between increasing age, functional and structural brain deterioration (38, 39). However, it would be a falsehood to believe, that this deterioration affects all individuals to the same degree. With increased age come large individual differences in brain health (37). These are apparent already, when people are in their mid-fifties (38). Taken together, these observations have led to the following stratification categories for age in the present study: (1) ≤ 55 years; (2) > 55 ≤ 64; (3) > 64 ≤ 69; (4) > 69.
Recent neuroimaging studies have found the presence of distinct distress networks in tinnitus (40, 41). In severely distressed individuals, a neuronal network encompassing areas of the brain processing emotions is activated. This is not observable in individuals with lower levels of tinnitus distress (41). Therefore, based on the classification by McCombe, Baguley (42), participants are stratified to one of four levels of tinnitus distress: 1) Mild (THI 18–36); 2) moderate (THI 38—56); 3) severe (THI 58–76); 4) catastrophic (THI 78–100).
Allocating participants to one of the three groups is done by a researcher independent of the study. This individual, trained in the minimization program, Minim (43), will subsequently inform the research team about group allocation of each participant. A group allocation list is kept in a separate and password secured excel file on the local drive of the independent researchers’ PC.
To ensure blinding of trial participants, all instructions, electrode positions, number and duration of training sessions, trial runs and feedback mode used in training are identical between the two groups.
Interventions
Neurofeedback training groups
The neurofeedback training sessions were designed identically for the ADR and the BTR training. Both trainings will applied unidirectionally, with the aim to a) decrease the ADR by an increase of alpha (8–12Hz) and/or a reduction of delta (2–4 Hz) and b) decrease the BTR by inhibiting theta (4–8 Hz) and/or reinforcing beta (13–20 Hz) activity bilaterally over the fronto-central cortex (FC1, FC2, F3 and F4), respectively.
During each training session EEG, electromyography (EMG) as well as vertical and horizontal electrooculogram (EOG) are recorded from Ag/AgCl sintered ring electrodes with a 13-channel DC-amplifier (THERA PRAX ®MOBILE, Neurocare GmbH, Ilmenau, Germany; > 10 GOhm input impedance) and a sampling rate of 256 Hz. EEG electrodes are placed at F3, F4, FC1 and FC2 in accordance with the 10/10 electrode placement system (44) using an elastic electrode cap (Easy Cap, Germany). Those electrode sites are identical to those used in previous neurofeedback studies in tinnitus (15, 20) to guarantee comparability with former research. Reference and ground electrodes are attached to right and left mastoid, respectively. EMG is recorded with two electrodes placed at the upper descending part of the M. trapezius. EOG is obtained with two electrodes at external canthi, and two electrodes at infra‐ and supraorbital sides. An online ocular correction is applied as described in Schlegelmilch, Markert (45) during the training. In short, an online artifact correction for eye movements will be performed using an eye movement calibration file. Before each session, this is created, through three phases of systematic eye movements, more specifically vertical, horizontal and eye blinks respectively. However, all signals acquired during neurofeedback training are stored as raw signals (no ocular correction, no filtering), without any calibration or filtering. Those EEG-recordings will be analyzed with Brain Vision Analyzer v2.0 (Brain Products GmbH, Germany) as an outcome measure.
Furthermore, a 50 Hz notch filter is applied to EEG signals before direct feedback. Depending on the respective training group, BTR [theta(μV2/Hz)‐beta(μV2/Hz)/theta(μV2/Hz) + beta(μV2/Hz)] or ADR [delta(μV2/Hz)‐alpha(μV2/Hz)/delta(μV2/Hz) + alpha(μV2/Hz)] are computed with a short‐time‐Fourier transformed moving average across the four training electrodes and fed back to the patients’ monitor, using graphical objects (e.g. fish, moon etc.): horizontal movements of the object from the left to the right with constant speed represents the temporal proceeding of the trial (i.e., sampling rate), while vertical movements of the object, indicates the targeted changes in the feedback parameter (ADR or BTR), either by moving up (targeted change) or down (non-targeted change). Successful changes in cortical activity (i.e., keeping the ADR or BTR above the training threshold for at least 250 msec.) is rewarded with the symbol of a sun after each trial, as the only performance-dependent reinforcement.
Neurofeedback training increases in intensity across the intervention period. Session 1—5 consists of four training blocks, each with 10 training trials. In session 6—10, an additional training block is added (see figure 2). One trial consists of 30 seconds of active neurofeedback training, followed by a 10 second inter trial interval, in which the last 2 seconds are used to determine the current baseline for ADR or BTR. Therefore, each trial during training is individually baseline-corrected and the feedback object always starts moving from the same position (Please see https://youtu.be/K9MWTjHjMU8 for an illustration of the individual trial run). The scale of the feedback monitor is set to 2μV. For the first training trial in the first session, the training threshold is set to 20%, corresponding to reward reinforcement when the ADR or BTR is greater than 0,2 μV for at least 250 msec. If a reinforcement rate of at least 70% is reached for a training block, the threshold is increased by 3%. After blocks with less than 30% successful trials, the threshold is lowered by 2%. The following training session is started with the thresholds of the last block of the preceding session.
Transfer runs without immediate visible feedback were included in an additional 5th training block from the 6th training session onwards. The percentage of transfer trials increased gradually with session (20%, 40%, 60%. 80% and finally 100%). However, as in the training sessions a reward symbol appears after successful targeted changes during the transfer condition (Please see https://youtu.be/shcR8Ilq3mo for an example of a transfer trial).
To further transfer learned behaviors, participants are instructed to retrieve their neurofeedback experiences by designing personalized cues (i.e., printed graphics representing the mental strategy used during the neurofeedback training) and to use those cues both during within-session transfer trials and during daily life. Compliance is verified by questioning the participants after each session in a session evaluation, whether they have used the transfer cards over the intervention period.
A neurofeedback therapist manual has been developed with standardized instructions of what can be said to participants both prior to -and between training blocks and sessions. The instructions and comments are mainly designed, first to aid the understanding of the learning process and second, to support motivation. In terms of the former, the emphasis is on conveying the implicitness of the learning process in neurofeedback. Therefore, making too much conscious effort in an attempt to influence the feedback animation is discouraged. In terms of supporting participant motivation, small encouragements in-between training blocks are given more frequently within the first two to three training sessions than in the remaining sessions.
Diary control group
In the diary control group, Group 3, an eight-item diary is completed each evening for seven consecutive days, in week one and week three of the four-week intervention period. The items are purposefully designed to make participants reflect more positively about their experience with tinnitus (e.g. The tinnitus didn’t disturb me today). As adherence to diary completion is a concern, participants have two supportive face-to-face meetings. In these, participants are informed about the content and structure of the diary and instructed on its completion. In addition, there is an element of psychoeducation on tinnitus etiology, the content of which is based mainly on recognized self-help sources (46, 47). In addition, two follow-up telephone calls are held throughout the four-week period. The telephone follow-up calls are supportive in nature and give participants an opportunity to share their experience with the diary exercise.
To maximize treatment adherence in clinical trials in general, several suggestions have been proposed (e.g. 48, 49, 50). Of these suggestions, particularly three are relevant to our study. First, participants will find it relatively easy to complete questionnaires in the study, since all primary and secondary outcome measures are collected via an easy to navigate online platform. For people without access to the internet, pen and paper versions of questionnaires can be collected at the department, or sent via mail upon specific request. Second, for participants in both neurofeedback groups and in the diary control group, there is frequent contact with members of the research team throughout the intervention period. Third, participants in the diary control group are given an incentive, a self-help book (46), upon completion of all outcome measures.
Primary outcome measures
There are two primary outcome measures in the study. First, change in tinnitus distress following neurofeedback training is measured with the Tinnitus Handicap Inventory (THI; 28). The THI is a 25-item self-report instrument. Each item is rated on a three-point Likert scale with responses “Yes” = 4 points, “Sometimes” = 2 points and “No” = 0 points, thus yielding a total score between 0 and 100. The instrument has three subscales, assessing functional, emotional and catastrophic reactions to tinnitus respectively. The overall test-retest reliability of the scale is 0.92 (51). It has been translated and validated into a German version (52, 53), which is used in the present study.
To assess the effectiveness of neurofeedback training in reducing the perceived intensity, i.e. the sound percept of tinnitus, the Tinnitus Magnitude Index (TMI) is used (54). It is a three-item measure, designed to assess the individual perception of tinnitus sound intensity, without overlapping significantly with cognitive, behavioral and emotional reactions to tinnitus. The internal consistency of the scale is excellent with Cronbach’s α = 0.86 and it has satisfactory discriminant validity (correlation of r =.62 with the THI). One obstacle in the development of the TMI, noted by Schmidt, Kerns (54), is the differential scaling of its three items. This has, according to the authors, the potential to increase measurement error. Schmidt, Kerns (54) suggested converting all three items to a standardized scale. Therefore, in the present study, the response scales for all three items are standardized to a range from 0 to 100.
Secondary outcome measures
To detect responsiveness to the intervention, the German version of the Tinnitus Functional Index (TFI; 55, 56) is used in the present study. It assesses treatment related changes in different areas of functionality, e.g. sleep or sense of control. It consists of 25 items, with 23 responses being rated on a Likert scale from 0 to 10 and two items between 0 to 100. When scoring the TFI, the two items ranging from 0 to 100 are divided by 10, thus yielding a total TFI score of the 25 items between 0 and 250. There are eight subscales associated with the TFI (intrusiveness, sense of control, cognitive interference, sleep, auditory difficulties, relaxation, quality of life, and emotional distress). Overall, the TFI has a good test-retest reliability of 0.78, convergent (r = 0.86 with the Tinnitus Handicap Inventory) and discriminant validity (r = 0.56 with Beck Depression Inventory Primary Care). (55).
To assess changes in sleep quality following neurofeedback training, the Insomnia Severity Index (ISI; 57) is used. The ISI consists of seven items, with Likert responses ranging from 0 to 4. Thus, a person can score between 0 to 28 on the total scale, with higher values indicating greater sleep disturbances. The ISI has a good internal consistency (Cronbach’s α = 0.91, 57) and convergent validity (r = 0.80 with the Pittsburg Quality Sleep Index, 58).
Changes in depressive symptoms following neurofeedback are assessed with the Personal Health Questionnaire–9 (PHQ–9; 59), a nine-item self-report instrument with Likert scale responses ranging from 0 to 3. The total score of the scale thus ranges from 0 to 27. The internal consistency of the PHQ–9 is good (Cronbach’s α = 0.89) and a test-retest reliability of r = 0.84 has been evidenced (60). Moreover, the PHQ–9 has strong convergent validity with other scores of depression, e.g. Beck Depression Inventory, r = 0.73 or General Health Questionnaire–12, r = 0.59 (61).
Further secondary exploratory research questions of the current trial focus on the moderating role of pre-treatment expectancy, perceived treatment credibility and somatic self-efficacy on treatment outcome. These moderator analyses are assessed with the following instruments: an adapted form of the treatment credibility and expectancy questionnaire (62); a translated to German version of the somatic self-efficacy questionnaire (63); an expectancy questionnaire, developed specifically for this study.
Training Outcomes
In order to keep track of individual learning curves during training, we will analyze ADR and BTR for each participant within and across sessions. However, session 1 will be discarded, because it is assumed that participants still have to habituate to the setting. Monitoring and encouraging learning is, one the one hand, done by the trainers. On a patient monitor, the trainer can keep track of training progression, as the percentage decrease in the BTR or ADR, compared to baseline for each training block separately. On the other hand, the total number of earned suns (positive reinforcement signal) per training block is used to guide, either upwards or downward adjustment of training thresholds. Dependent measures included the mean training level over training blocks within a session (%), the best run of each session (maximum achieved training level), and the total number of obtained rewards per session.
Furthermore, electrophysiological training data will be analyzed offline with Brain Vision Analyzer v2.0 (Brain Products GmbH, Germany). A high-pass filter of 0.1 Hz and a low-pass filter of 30 Hz, 24dB/octave will be applied. Ocular correction will use the algorithm of Gratton, Coles (64) as implemented in Brain Vision Analyzer software. Using a semi-automatic raw data inspection procedure, the recorded data from the training blocks will be screened for artifacts. Planned criteria for artifact screening will be as follows: maximal voltage step of 50 μV/ms, maximal amplitude of±100 μV, maximum allowed difference of 150 μV in each segment, values greater than 200 μV per 200 ms interval, activity below 0.5 μV in a 50-msec. period as criteria. Before and after detected artifacts, 100 msec. of data will be removed. Fast Fourier Transformation (FFT) with a 10% Hamming window will be applied for tapering, and averages over the artifact-free 2s-epochs will be calculated. We will analyze absolute and relative power for delta (2–4 Hz) theta (4–8 Hz), alpha (8–12 Hz) and beta (13–20 Hz) frequency bands, as well as the BTR and ADR.
Resting EEG
Resting EEG is recorded from 19 electrodes using a 32-channel NeXus 32 amplifier (24 bit A/D conversion, sampling rate 2.048 kHZ with Ag/AgCl sintered ring-electrodes with carbon coating and active shielding (Mind Media B. V., Herten, Holland) according to the 10–20 system (Jasper, 1958). DC offset was kept below 25 μV during the recording. Reference electrodes were placed at the right and left mastoids. Two channels of the NeXus–32 will be dedicated to detecting horizontal eye movements and are attached 1.5 cm lateral to the outer canthus of each eye. The resting state assessment will consist of a 10-min alternating eyes open (EO, 5 min)/eyes closed (EC, 5min) recording. The sequence of EO and EC was not counterbalanced but always in the EO/EC order.
Off‐line analysis will be performed with Brain Vision Analyzer 2 software (Brain Products, Gilching, Germany). The sampling rate will be down‐sampled to 512 Hz and scalp electrodes re-referenced to the average. Data will be band‐pass filtered (0.1–30 Hz at 24 dB/oct; 50‐Hz notch filter). For ocular artifact control, we will use the algorithm of Gratton, Coles (64). The continuous EEG will be segmented in 2‐sec. intervals and semi-automatically screened for artifacts by the following criteria: maximal voltage step of 50 μV/msec., maximal amplitude of±100 μV, values greater than 200 μV per 200 msec. interval, activity below 0.5 μV in a 100-msec. period. At least 30 artifact‐free segments are required for EC and EO conditions for further analysis. The remaining segments will be fast Fourier transformed using a 10% Hamming window and averaged. We will analyze absolute and relative power for delta (2–4 Hz) theta (4–8 Hz), alpha (8–12 Hz) and beta (13–20 Hz) frequency bands, coherence, alpha peak frequency and long range temporal correlations.
The neurophysiological parameters will be used to assess differences between the two neurofeedback training groups and healthy control participants at baseline, as well as to compare the pre- and post-training effects between the two neurofeedback training groups and the diary control group.
Data management and monitoring
Upon registration on the web platform, participants are assigned a computer-generated study code, which will follow them throughout the trial. All collected data are stored pseudonymized in locked cabinets or as computer files. The pseudonymization allocation list is kept separate from the pseudonymized data and deleted upon completion of the trial. Participants are informed about this. Only people who have signed a confidentiality agreement, and who are part of the research team, have access to the data collected in the study.
The study’s assessment questionnaires are answered online. All necessary precautions have been taken to ensure data protection and security. All data exchanged between participants and the online system are encrypted prior to transmission and storage. The online system is managed and hosted by the IT-department of Linköping University (Sweden). No entries in the form of video or sound recordings, which could make it possible for third parties to identify the participants, are registered. Participants can request their data deleted at any time upon stating their assigned code, until the pseudonymization allocation list is deleted. Lastly, data collected during the study are kept for ten years before being deleted.
Adverse events
Neurofeedback is generally considered safe and involving no risk to participants (65). This is perhaps best reflected in the fact, that it has been used extensively in studies involving children with ADHD. Thus, participants in our trial should not experience more serious side-effects than perhaps mild headaches, stemming mainly from the prolonged period of sustaining attention during training. Nevertheless, adverse effects of neurofeedback training will be routinely monitored as part of the ongoing dialogue between data collectors and trial participants. In the unlikely event, that a trial participant complains about serious adverse effects of training, data collectors will, as a first step, reduce the frequency of training sessions in the week of the complaint, and if inadequate, reduce the number of training segments per training session, until the trial participant no longer complains about adverse effects.
Ethics and dissemination
Should any future modification to the protocol occur, which changes the study objective, design or procedures, they will be decided upon by the study’s principal investigator. Substantial protocol amendments are submitted to and approved by the responsible ethics committee, prior to implementation.
Dissemination of results is expected to follow completion of data collection, scheduled to last until late spring of 2020. Results of primary outcome measures are reported and disseminated, regardless of the direction and magnitude of effect(s). No restrictions are imposed on which results can be disseminated from neither trial sponsor nor other interested parties.
Statistical analysis
Data will be analyzed primarily in the intention-to-treat (mITT) population. Supportive analyses are planned in the per-protocol (PP) population. mITT comprises all randomized patients, while PP analysis assesses mITT patients who do not meet any of the following criteria: violation of inclusion and/or exclusion criteria, major deviations from the visit schedule, and poor compliance during feedback sessions. For all outcome measures we will probe the longitudinal course across all assessments using a linear mixed model for repeated measures (MMRM; see 66). The MMRM model includes fixed effects for group (ADR vs. TBR vs. diary control), time and group-by-time interaction and a random intercept for subject specific effects using maximum likelihood estimation, adding sex, age, baseline tinnitus severity scores and patients’ expectations as covariates. The error variance-covariance matrices for the repeated factor will be specified in accordance to the data. The pattern of missing data is assumed to be random.
Our advanced understanding of the pathogenesis of tinnitus as a neurodegenerative disorder has led to the development of several promising neuromodulation techniques, including neurofeedback. Two neurofeedback studies in particular have shown, that the modulation of alpha and delta frequency spectra have apparent benefit in individuals with chronic tinnitus (19, 20). However, given their exploratory nature, the findings from these studies need to be replicated in a sufficiently powered randomized controlled clinical trial. In particular, the inclusion of a semi-active control group is arguably of primary importance, given the powerful (super) placebo effects involved in neurofeedback treatments (21). In the present study, neurofeedback training of alpha/delta is compared to beta/theta. To our knowledge, no previous studies have causally implicated neural oscillations in the beta/theta frequency bands with tinnitus pathogenesis, nor used a comparator in general, when assessing the efficacy of alpha/delta neurofeedback training on tinnitus distress and sound intensity. Our study thus is the first, seeking to disentangle the efficacy of a specific neurofeedback training protocol, by comparing it to what Arns, Heinrich (25) refer to as a semi-active control condition. Characterizing this is that it controls for non-specific effects without having a clinically meaningful impact on the condition under investigation.
The present study is, in many respects, similar to the studies by Crocetti, Forti (20) and Dohrmann, Elbert (19). There are however also differences. In the former studies, the training protocol utilized ratio training. Consequently, for reward to be elicited, only one condition had to be met, namely either upregulation of alpha amplitude or downregulation of delta amplitude. In contrast, in the present study, reward is contingent on two conditions being met simultaneously, more specifically the enhancement in one frequency band (i.e. alpha or beta), and a decrease in the other (i.e. theta or delta) frequency band. The decision to utilize this particular type of training is based on the finding, that successful outcome appears related to the simultaneous modification of both alpha and delta amplitude (Dohrmann et al. 2007). It is therefore suggested, that the approach in the present study, increases the likelihood of a positive study outcome.
Limitations
The intent-to-treat statistical method applied in the study is particularly vulnerable in relation to treatment adherence and drop-out. The willingness of participants to complete all outcome measures, independent of their status in the trial, is crucial to avoid distortion of study results. In an effort to maximize treatment adherence and minimize drop-out in the present study, based on recommendations in the literature (e.g. 48, 49, 50), several steps have been taken to minimize the risk.
The present study is designed as a double-blinded trial. However, according to Sherlin, Arns (67), double-blinding in neurofeedback poses a significant challenge. The goal in neurofeedback is to use rewards to promote shaping of behavior. Blinding however, necessitates the use of auto-thresholding procedures, which, according to Sherlin et al. (2011), violates this principle of shaping. When using auto-thresholding procedures, thresholds are adjusted moment by moment, to ensure reward a certain percentage of time, based on the previous averaged period, e.g. 15 seconds. Because of this, performance can deteriorate, but reward is still elicited, because of the indiscriminate nature of auto-thresholds. This problem naturally poses a challenge for the present study. Therefore, a major psychological component of neurofeedback training is behavioral cognitive therapy that assures by using standard intervention techniques to motivate the trainees and strengthen their efforts and self-efficiency—especially if training gets rough and boring.
We hope our study will further contribute to the understanding of the neural underpinnings of tinnitus and that this can be used, to further advance treatments for this, as of yet, non-curable phantom sensation.
Issue date: 6. June 2019. Protocol version no. 1
Recruitment commenced on 1st November 2018 and is expected to be completed in April 2020.
ADHD: Attention Deficit Hyperactivity Disorder; ANOVA: Analysis of Variance; EEG: Electroencephalography; ENT: Ear Nose Throat; Hz: Hertz; ISI: Insomnia Sleep Index; MANOVA: Multivariate Analysis of Variance; OAE: Otoacoustic emission; PHQ–9: Personal Health Questionnaire–9; TFI: Tinnitus Functional Index; THI: Tinnitus Handicap Inventory; TMI: Tinnitus Magnitude Index
As described in an earlier section, informed consent will be obtained from all study participants, both at their first visit to the department of Psychology and the UKGM. The study was approved by the ethics committees at the department of Psychology (approval reference 2018–4K, additional file 2) and the department of Medicine (approval reference Az. 162/18, additional file 3), Philipps-University Marburg.
Not applicable
De-identified, limited data will be made available upon reasonable request from the corresponding author.
Apart from being registered as PhD student at Philipps-University Marburg, MJ is an employee at Eriksholm Research Centre. Patent applications may be sought during the PhD period, based on the content of the study. The other authors included in the study have no competing interests.
The authors would like to thank Professor Dr. Boris A. Stuck, Dr. Jochen Müller-Mazzotta, Dr. Kristina Sinemus and other participating staff at the ENT department, University Hospital Gieβen and Marburg for assisting with the conduct of this trial. We thank George Vlaescu for excellent IT support.
The project is funded by the Oticon Foundation. It is however Philipps-University, Marburg, who owns the data collected in the present study. The funding source had no role in the design of the study, and will not have any role during its execution, analyses, interpretation of the data or decision to submit results.
MJ and CW are responsible for conceptualization of the study. CW, MJ, EH, MLC and JS all contributed to the design of the study. MJ, EH are principally responsible for conducting the study. MJ and CW drafted the manuscript. CW, JS, MLC and GA advised on methodological issues and drafting the manuscript. MJ, CW, MLC, JS, EH and GA all contributed to- and read and approved the final manuscript.
CW is the corresponding author of the article.
1.Lockwood AH, Salvi RJ, Burkard RF. Tinnitus. New England Journal of Medicine. 2002;347(12):904–10.
2.McCormack A, Edmondson-Jones M, Somerset S, Hall D. A systematic review of the reporting of tinnitus prevalence and severity. Hearing Research. 2016;337(Supplement C):70–9.
3.Baguley D, Andersson G, McFerran D, McKenna L. Prevalence and Natural History. Tinnitus: A Multidisciplinary Approach: John Wiley & Sons, Ltd.; 2013. p. 7–17.
4.Davis A, El Refaie A. Epidemiology of tinnitus. Tyler RS, editor. San Diego: Singular, Thomson Learning; 2000.
5.Andersson G. Tinnitus patients with cognitive problems: causes and possible treatments. Hearing Journal 2009;62(11):27 - 30.
6.Manchaiah V, Beukes EW, Granberg S, Durisala N, Baguley DM, Allen PM, et al. Problems and Life Effects Experienced by Tinnitus Research Study Volunteers: An Exploratory Study using the ICF Classification. Journal of the American Academy of Audiology. 2018.
7.Møller AR. Pathophysiology of Tinnitus. Annals of Otology, Rhinology & Laryngology. 1984;93(1):39–44.
8.Meyer M, Neff P, Grest A, Hemsley C, Weidt S, Kleinjung T. EEG oscillatory power dissociates between distress- and depression-related psychopathology in subjective tinnitus. Brain Research. 2017;1663:194–204.
9.De Ridder D, Vanneste S, Weisz N, Londero A, Schlee W, Elgoyhen AB, et al. An integrative model of auditory phantom perception: Tinnitus as a unified percept of interacting separable subnetworks. Neuroscience & Biobehavioral Reviews. 2014;44:16–32.
10.Weisz N, Moratti S, Meinzer M, Dohrmann K, Elbert T. Tinnitus Perception and Distress Is Related to Abnormal Spontaneous Brain Activity as Measured by Magnetoencephalography. PLOS Medicine. 2005;2(6):e153.
11.Weisz N, Hartmann T, Müller N, Lorenz I, Obleser J. Alpha rhythms in audition: cognitive and clinical perspectives. Frontiers in Psychology. 2011;2:1–15.
12.Hartmann T, Lorenz I, Müller N, Langguth B, Weisz N. The effects of neurofeedback on oscillatory processes related to tinnitus. Brain Topography. 2014;27:149–57.
13.Klimesch W, Sauseng P, Hanslmayr S. EEG alpha oscillations: The inhibition–timing hypothesis. Brain Research Reviews. 2007;53(1):63–88.
14.Sauseng P, Klimesch W, Schabus M, Doppelmayr M. Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. International Journal of Psychophysiology. 2005;57(2):97–103.
15.Dohrmann K, Weisz N, Schlee W, Hartmann T, Elbert T. Neurofedback for treating tinnitus. Progress in brain research. 2007;166:473 - 85.
16.Güntensperger D, Thüring C, Meyer M, Neff P, Kleinjung T. Neurofeedback for Tinnitus Treatment—Review and Current Concepts. Frontiers in Aging Neuroscience. 2017;9:386.
17.Hammond DC. What is neurofeedback: an update. Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience. 2011;15(4):305 - 36.
18.Rogala J, Jurewicz K, Paluch K, Kublik E, Cetnarski R, Wróbel A. The Do’s and Don’ts of Neurofeedback Training: A Review of the Controlled Studies Using Healthy Adults. Frontiers in Human Neuroscience. 2016;10(301).
19.Dohrmann K, Elbert T, Schlee W, Weisz N. Tuning the tinnitus percept by modification of synchronous brain activity. Restorative Neurology and Neuroscience. 2007;25:371–8.
20.Crocetti A, Forti S, Bo LD. Neurofeedback for subjective tinnitus. auris Nasus Larynx. 2011;38:735–8.
21.Thibault RT, Lifshitz M, Raz A. Neurofeedback or neuroplacebo? Brain. 2017;140(4):862–4.
22.Ali SS, Lifshitz M, Raz A. Empirical neuroenchantment: from reading minds to thinking critically. Frontiers in Human Neuroscience. 2014;8(357).
23.Studer P, Kratz O, Gevensleben H, Rothenberger A, Moll GH, Hautzinger M, et al. Slow cortical potential and theta/beta neurofeedback training in adults: effects on attentional processes and motor system excitability. Frontiers in Human Neuroscience. 2014;8(555).
24.Arns M, Conners CK, Kraemer HC. A Decade of EEG Theta/Beta Ratio Research in ADHD: A Meta-Analysis. Journal of Attention Disorders. 2012;17(5):374–83.
25.Arns M, Heinrich H, Strehl U. Evaluation of neurofeedback in ADHD: The long and winding road. Biological Psychology. 2014;95:108–15.
26.Van Doren J, Arns M, Heinrich H, Vollebregt MA, Strehl U, K. Loo S. Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis. European Child & Adolescent Psychiatry. 2018.
27.Faul F, Erdfelder E, Lang A-G, Buchner A. G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods. 2007;39:175–91.
28.Newman CW, Jacobson GP, Spitzer JB. Development of the tinnitus handicap inventory. Archives of Otolaryngology–Head & Neck Surgery. 1996;122(2):143–8.
29.Vlaescu G, Alasjö A, Miloff A, Carlbring P, Andersson G. Features and functionality of the Iterapi platform for internet-based psychological treatment. Internet Interventions. 2016;6:107–14.
30.Laux L, Glanzmann P, Schaffner P, Spielberger CD. Das State-Trait Angstinventar. Wennheim: Beltz Testgesellschaft; 1981.
31.Fan J, McCandliss BD, Sommer T, Raz A, Posner MI. Testing the Efficiency and Independence of Attentional Networks. Journal of Cognitive Neuroscience. 2002;14(3):340–7.
32.Robertson IH, Manly T, Andrade J, Baddeley BT, Yiend J. ‘Oops!’: Performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologia. 1997;35(6):747–58.
33.Altman DG, Bland JM. Treatment allocation by minimisation. BMJ (Clinical research ed). 2005;330(7495):843-.
34.McCombe A, Baguley D, Coles R, McKenna L, McKinney C, Windle-Taylor P. Guidelines for the grading of tinnitus severity: the results of a working group commissioned by the British Association of Otolaryngologists, Head and Neck Surgeons, 1999. Clin Otolaryngol Allied Sci. 2001;26(5):388–93.
35.Ros T, Munneke MAM, Ruge D, Gruzelier JH, Rothwell JC. Endogenous control of waking brain rhythms induces neuroplasticity in humans. European Journal of Neuroscience. 2010;31(4):770–8.
36.Ros T, Théberge J, Frewen PA, Kluetsch R, Densmore M, Calhoun VD, et al. Mind over chatter: Plastic up-regulation of the fMRI salience network directly after EEG neurofeedback. NeuroImage. 2013;65:324–35.
37.Park DC, Bischof GN. The aging mind: neuroplasticity in response to cognitive training. Dialogues in clinical neuroscience. 2013;15(1):109–19.
38.Raz N, Lindenberger U, Rodrigue KM, Kennedy KM, Head D, Williamson A, et al. Regional Brain Changes in Aging Healthy Adults: General Trends, Individual Differences and Modifiers. Cerebral Cortex. 2005;15(11):1676–89.
39.Raz N, Rodrigue KM. Differential aging of the brain: Patterns, cognitive correlates and modifiers. Neuroscience & Biobehavioral Reviews. 2006;30(6):730–48.
40.De Ridder D, Vanneste S, Congedo M. The Distressed Brain: A Group Blind Source Separation Analysis on Tinnitus. PLOS ONE. 2011;6(10):e24273.
41.Vanneste S, Plazier M, der Loo Ev, de Heyning PV, Congedo M, De Ridder D. The neural correlates of tinnitus-related distress. NeuroImage. 2010;52(2):470–80.
42.McCombe A, Baguley D, Coles R, McKenna L, McKinney C, Windle-Taylor P. Guidelines for the grading of tinnitus severity: the results of a working group commissioned by the British Association of Otolaryngologists, Head and Neck Surgeons, 1999. Clinical Otolaryngology & Allied Sciences. 2001;26(5):388–93.
43.Saunders JC. The role of central nervous system plasticity in tinnitus. Journal of Communication Disorders. 2007;40(4):313–34.
44.The Electrode Position Nomenclature C. Guideline thirteen: guidelines for standard electrode position nomenclature. J Clin Neurophysiol. 1994;11:111–3.
45.Schlegelmilch F, Markert S, Berkes S, Schellhorn K. Online ocular artifact removal for dc-EEG-signals: estimation of dc-level. Biomed Tech 2004;49:340 - 1.
46.Weise C, Kleinstauber M, Kaldo V, Andersson G. Mit Tinnitus leben lernen. Ein manual für Therapeuten und Betroffene. Berlin: Heidelberg: Springer; 2016.
47.Kröner-Herwig B. Psychologische Behandlung des chronischen Tinnitus. Weinheim: BeltzPVU; 1997.
48.O’Neill RT, Temple R. The prevention and treatment of missing data in clinical trials: an FDA perspective on the importance of dealing with it. Clinical pharmacology and therapeutics. 2012;91(3):550–4.
49.Dziura JD, Post LA, Zhao Q, Fu Z, Peduzzi P. Strategies for dealing with missing data in clinical trials: from design to analysis. The Yale journal of biology and medicine. 2013;86(3):343–58.
50.Little RJ, D’Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, et al. The Prevention and Treatment of Missing Data in Clinical Trials. New England Journal of Medicine. 2012;367(14):1355–60.
51.Newman CW, Sandridge SA, Jacobson GP. Psychometric adequacy of the Tinnitus Handicap Inventory (THI) for evaluating treatment outcome. Journal of the American Academy of Audiology. 1998;9(2):153–60.
52.Kleinstäuber M, Frank I, Weise C. A confirmatory factor analytic validation of the Tinnitus Handicap Inventory. Journal of Psychosomatic Research. 2015;78(3):277–84.
53.Kleinjung T, Fischer B, Langguth B, Sand PG, Hajak G, Dvorakova J, et al. Validierung einer deutschsprachigen Version des Tinnitus Handicap Inventory. Psychiat Prax. 2007;34(1):140 - 2.
54.Schmidt CJ, Kerns RD, Griest S, Theodoroff SM, Pietrzak RH, Henry JA. Toward Development of a Tinnitus Magnitude Index. Ear & Hearing. 2014;35(4):476 - 84.
55.Meikle MB, Henry JA, Griest SE, Stewart BJ, Abrams HB, McArdle R, et al. The Tinnitus Functional Index: Development of a New Clinical Measure for Chronic, Intrusive Tinnitus. Ear & Hearing. 2012;33(2):153–76.
56.Brüggemann P, Szczepek AJ, Kleinjung T, Ojo M, Mazurek B. Validierung der deutschen Version des Tinnitus Functional Index (TFI). Laryngo-Rhino-Otol. 2017;96(09):615–9.
57.Bastien CH, Vallières A, Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Medicine. 2001;2(4):297–307.
58.Morin CM, Belleville G, Bélanger L, Ivers H. The Insomnia Severity Index: Psychometric Indicators to Detect Insomnia Cases and Evaluate Treatment Response. Sleep. 2011;34(5):601–8.
59.Kroenke K, Spitzer RL. The PHQ–9: A new depression diagnostic and severity measure. Psychiatric Annals. 2002;32(9):509 - 15.
60.Kroenke K, Spitzer RL, Williams JBW. Journal of General Internal Medicine. 2001;16(9):606–13.
61.Martin A, Rief W, Klaiberg A, Braehler E. Validity of the Brief Patient Health Questionnaire Mood Scale (PHQ–9) in the general population. General hospital psychiatry. 2006;28(1):71–7.
62.Devilly GJ, Borkovec TD. Psychometric properties of the credibility/expectancy questionnaire. Journal of Behavior Therapy and Experimental Psychiatry. 2000;31(2):73–86.
63.Schmidt J. Neurofeedback as a psychophysiological treatment for disinhibited eating—An analysis of efficacy and mechanisms. Germany: University of Wuppertal; 2016.
64.Gratton G, Coles MGH, Donchin E. A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology. 1983;55(4):468–84.
65.Begemann MJH, Florisse EJR, Van Lutterveld R, Kooyman M, Sommer IE. Efficacy of EEG neurofeedback in psychiatry: a comprehensive overview and meta-analysis. Transl Brain Rhythmicity. 2016;1:19–29.
66.Detry MA, Ma Y. Analyzing Repeated Measurements Using Mixed ModelsAnalyzing Repeated Measurements Using Mixed ModelsAnalyzing Repeated Measurements Using Mixed Models. JAMA. 2016;315(4):407–8.
67.Sherlin LH, Arns M, Lubar J, Heinrich H, Kerson C, Strehl U, et al. Neurofeedback and basic learning theory: Implications for research and practice. Journal of Neurotherapy. 2011;15:292–304.