Combining neurofeedback with source estimation: Evaluation of an sLORETA neurofeedback protocol for chronic tinnitus treatment

Finding tau rhythms in EEG: An independent component analysis approach

Tau rhythms are largely defined by sound responsive alpha band (~8-13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high-pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low-pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi-model AMICA [mAMICA]). Tau-related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8-13 Hz alpha band. These "tau ICs" showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high-pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high-pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high-pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.

Self-Directed Neurofeedback Treatment for Subjective Tinnitus Patients Evaluated by Multimodal Functional Imaging

Neurofeedback (NFB) is a relatively novel approach to the treatment of tinnitus, and prior studies have demonstrated that the increases in alpha activity rather than reduced delta power seem to drive these NFB-related improvements in tinnitus symptoms. The present study was therefore designed to explore whether the implementation of an alpha training protocol with a portable neurofeedback apparatus would achieve improvements in tinnitus patient symptoms. In this study, 38 tinnitus patients underwent NFB training while 18 were enrolled in a control group. The study was single-blinded such that only participants were not aware of their group assignments. Those in the NFB group underwent 15 NFB training sessions over 5 weeks, in addition to pre- and posttraining tests including the Tinnitus Handicap Inventory (THI), Tinnitus Handicap Questionnaire (THQ), visual analog scales (VAS), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). Our result find that when the THI, THQ, and VAS scores of patients in the two groups were assessed after a 5-week training period, these scores were unchanged in control patients whereas they had significantly improved in the NFB group patients. EEG analyses revealed that the alpha band was increased in the occipital lobe following NFB treatment, while fMRI indicated an increase in regional homogeneity (ReHo) in the right frontal lobe of patients in the NFB group after treatment that was negatively correlated with THI and VAS scores. The results of this analysis indicate that alpha NFB training can be effectively used to reduce tinnitus-related distress and sound perception in patients.

Migrainous Vertigo, Tinnitus, and Ear Symptoms and Alternatives

Migraine headaches frequently coexist with vestibular symptoms such as vertigo, motion sickness, and gait instability. Migraine-related vasospasm can also damage the inner ear, which results in symptoms such as sudden sensorineural hearing loss and resultant tinnitus. The pathophysiology of these symptoms is not yet fully understood, and despite their prevalence, there is no universally approved management. This review summarizes the data on complementary and integrative medicine in treating patients with migrainous ear disorders.

Recovering Hidden Responder Groups in Individuals Receiving Neurofeedback for Tinnitus

The widespread understanding that chronic tinnitus is a heterogeneous phenomenon with various neural oscillatory profiles has spurred investigations into individualized approaches in its treatment. Neurofeedback, as a non-invasive tool for altering neural activity, has become increasingly popular in the personalized treatment of a wide range of neuropsychological disorders. Despite the success of neurofeedback on the group level, the variability in the treatment efficacy on the individual level is high, and evidence from recent studies shows that only a small number of people can effectively modulate the desired aspects of neural activity. To reveal who may be more suitable, and hence benefit most from neurofeedback treatment, we classified individuals into unobserved subgroups with similar oscillatory trajectories during the treatment and investigated how subgroup membership was predicted by a series of characteristics. Growth mixture modeling was used to identify distinct latent subgroups with similar oscillatory trajectories among 50 individuals suffering from chronic subjective tinnitus (38 male, 12 female, mean age = 47.1 ± 12.84) across 15 neurofeedback training sessions. Further, the impact of characteristics and how they predicted the affiliation in the identified subgroups was evaluated by including measures of demographics, tinnitus-specific (Tinnitus Handicap Inventory) and depression variables, as well as subjective quality of life subscales (World Health Organization—Quality of Life Questionnaire), and health-related quality of life subscales (Short Form-36) in a logistic regression analysis. A latent class model could be fitted to the longitudinal data with a high probability of correctly classifying distinct oscillatory patterns into 3 different groups: non-responder (80%), responder (16%), and decliner (4%). Further, our results show that the health-related wellbeing subscale of the Short Form-36 questionnaire was differentially associated with the groups. However, due to the small sample size in the Responder group, we are not able to provide sufficient evidence for a distinct responder profile. Nevertheless, the identification of oscillatory change-rate differences across distinct groups of individuals provides the groundwork from which to tease apart the complex and heterogeneous oscillatory processes underlying tinnitus and the attempts to modify these through neurofeedback. While more research is needed, our results and the analytical approach presented may bring clarity to contradictory past findings in the field of tinnitus research, and eventually influence clinical practice.

Transcranial electric and acoustic stimulation for tinnitus: study protocol for a randomized double-blind controlled trial assessing the influence of combined transcranial random noise and acoustic stimulation on tinnitus loudness and distress

Background

Tinnitus is the result of aberrant neuronal activity. As a novel treatment form, neuromodulation is used to modify neuronal activity of brain areas involved in tinnitus generation. Among the different forms of electric stimulation, transcranial random noise stimulation (tRNS) has been shown to be a promising treatment option for tinnitus. In addition, recent studies indicate that the reduction in tinnitus can be more pronounced when different modalities of stimulation techniques are combined (“bimodal stimulation”). TRNS can be used in combination with acoustic stimulation (AS), a further treatment option recognized in the literature. The aim of the proposed study is to investigate whether simultaneous tRNS and AS improve levels of tinnitus loudness and distress.

Methods

The intervention consists of bilateral high-definition tRNS (HD-tRNS) over the auditory cortex combined with the application of AS which is studied in a crossover design. The visits will be performed in 26 sessions. There will be 20 treatment sessions, divided into two blocks: active and sham HD-tRNS. Within the blocks, the interventions are divided into group A: HD-tRNS and AS, and group B: HD-tRNS alone. Furthermore, in addition to the assessments directly following the intervention sessions, there will be six extra sessions performed subsequently at the end of each block, after a period of some days (follow-ups 1 and 2) and a month after the last intervention (C). Primary outcome measures are analog scales for evaluation of subjective tinnitus loudness and distress, and the audiological measurement of minimum masking level (MML). Secondary outcome measures are brain activity as measured by electroencephalography and standardized questionnaires for evaluating tinnitus distress and severity.

Discussion

To the best of our knowledge, this is the first study which uses HD-tRNS combined with AS for tinnitus treatment. The crossover design permits the comparison between HD-tRNS active vs. sham and with vs. without AS. Thus, it will be possible to evaluate the efficacy of the combined approach to HD-tRNS alone. In addition, the use of different objective and subjective evaluations for tinnitus enable more reliable and valid results.

Trial registration

Swiss Ethics Committee (BASEC-Nr. 2020-02027); Swiss Federal Complementary Database (kofam.ch: SNCTP000004051); and ClinicalTrials.gov (clinicaltrials.gov: NCT04551404).