The aim of the pilot study was to evaluate the effect of Vagus Nerve Stimulation (VNS) paired with sounds in chronic tinnitus patients. All participants were implanted and randomized to a paired VNS (n = 16) or control (n = 14) group. After 6 weeks of home therapy, all participants received paired VNS. The device was used on 96% of days with good compliance. After 6 weeks, the paired VNS group improved on the Tinnitus Handicap Inventory (THI) (p = 0.0012) compared to controls (p = 0.1561). The between-group difference was 10.3% (p = 0.3393). Fifty percent of the participants in the paired VNS group showed clinically meaningful improvements compared to 28% in controls. At one year, 50% of participants had a clinically meaningful response. The therapy had greater benefits for participants with tonal and non-blast induced tinnitus at the end of 6 (24.3% vs. 2%, p = 0.05) and 12 weeks (34% vs. 2%, p = 0.004) compared to controls with 80% and 70% responding at 6 months and 1 year, respectively. Adverse effects were mild and well-tolerated and the therapy had a similar safety profile to VNS for epilepsy. VNS paired with tones may be effective for a subgroup of tinnitus patients and provides impetus for a larger pivotal study.
The N1 peak in the late auditory evoked potential (LAEP) decreases in amplitude following stimulus repetition, displaying an adaptive pattern. The present study explored the functional neural substrates that may underlie the N1 adaptive pattern using standardized Low Resolution Electromagnetic Tomography (sLORETA). Fourteen young normal hearing (NH) listeners participated in the study. Tone bursts (80 dB SPL) were binaurally presented via insert earphones in trains of ten; the inter-stimulus interval was 0.7 s and the inter-train interval was 15 s. Current source density analysis was performed for the N1 evoked by the 1st, 2nd and 10th stimuli (S1, S2 and S10) at three different timeframes that corresponded to the latency ranges of the N1 waveform subcomponents (70–100, 100–130 and 130–160 ms). The data showed that S1 activated broad regions in different cortical lobes and the activation was much smaller for S2 and S10. Response differences in the LAEP waveform and sLORETA were observed between S1 and S2, but not between the S2 and S10. The sLORETA comparison map between S1 and S2 response showed the activation was located in the parietal lobe for the 70–100 ms timeframe, the frontal and limbic lobes for the 100–130 ms timeframe, and the frontal lobe for the 130–160 ms timeframe. These sLORETA comparison results suggest a parieto-frontal network that might help to sensitize the brain to novel stimuli by filtering out repetitive and irrelevant stimuli. This study demonstrates that sLORETA may be useful for identifying generators of scalp-recorded event related potentials and for examining the physiological features of these generators. This technique could be especially useful for cortical source localization in individuals who cannot be examined with functional magnetic resonance imaging or magnetoencephalography (e.g., cochlear implant users).
Four brain regions related to language function and attention were identified that correlated with CELF-P2. Children with better oral language performance postimplant displayed greater activation in these regions preimplant. The results suggest that despite auditory deprivation, these regions are more receptive to gains in oral language development performance of children with hearing loss who receive early intervention via cochlear implantation. The present study suggests that oral language outcome following cochlear implant may be predicted by preimplant fMRI with auditory stimulation using natural speech.
IntroductionWe developed a machine learning model to predict whether or not a cochlear implant (CI) candidate will develop effective language skills within 2 years after the CI surgery by using the pre‐implant brain fMRI data from the candidate.MethodsThe language performance was measured 2 years after the CI surgery by the Clinical Evaluation of Language Fundamentals‐Preschool, Second Edition (CELF‐P2). Based on the CELF‐P2 scores, the CI recipients were designated as either effective or ineffective CI users. For feature extraction from the fMRI data, we constructed contrast maps using the general linear model, and then utilized the Bag‐of‐Words (BoW) approach that we previously published to convert the contrast maps into feature vectors. We trained both supervised models and semi‐supervised models to classify CI users as effective or ineffective.ResultsCompared with the conventional feature extraction approach, which used each single voxel as a feature, our BoW approach gave rise to much better performance for the classification of effective versus ineffective CI users. The semi‐supervised model with the feature set extracted by the BoW approach from the contrast of speech versus silence achieved a leave‐one‐out cross‐validation AUC as high as 0.97. Recursive feature elimination unexpectedly revealed that two features were sufficient to provide highly accurate classification of effective versus ineffective CI users based on our current dataset.ConclusionWe have validated the hypothesis that pre‐implant cortical activation patterns revealed by fMRI during infancy correlate with language performance 2 years after cochlear implantation. The two brain regions highlighted by our classifier are potential biomarkers for the prediction of CI outcomes. Our study also demonstrated the superiority of the semi‐supervised model over the supervised model. It is always worthwhile to try a semi‐supervised model when unlabeled data are available.
Purpose Background sounds provided by a wearable sound playback device were mixed with the acoustical input picked up by a cochlear implant speech processor in an attempt to suppress tinnitus. Method First, patients were allowed to listen to several sounds and to select up to 4 sounds that they thought might be effective. These stimuli were programmed to loop continuously in the wearable playback device. Second, subjects were instructed to use 1 background sound each day on the wearable device, and they sequenced the selected background sounds during a 28-day trial. Patients were instructed to go to a website at the end of each day and rate the loudness and annoyance of the tinnitus as well as the acceptability of the background sound. Patients completed the Tinnitus Primary Function Questionnaire (Tyler, Stocking, Secor, & Slattery, 2014) at the beginning of the trial. Results Results indicated that background sounds were very effective at suppressing tinnitus. There was considerable variability in sounds preferred by the subjects. Conclusion The study shows that a background sound mixed with the microphone input can be effective for suppressing tinnitus during daily use of the sound processor in selected cochlear implant users.
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