Hearing loss and ovarian dysfunction are key features of Perrault syndrome (PRLTS) but the clinical and pathophysiological features of hearing impairment in PRLTS individuals have not been addressed. Mutations in one of five different genes HSD17B4, HARS2, LARS2, CLPP or TWNK (previous symbol C10orf2) cause the autosomal recessive disorder but they are found only in about half of the patients. We report on two siblings with a clinical picture resembling a severe, neurological type of PRLTS. For an exhaustive characterisation of the phenotype neuroimaging with volumetric measurements and objective measures of cochlear hair cell and auditory nerve function (otoacustic emissions and auditory brainstem responses) were used. Whole exome sequencing was applied to identify the genetic cause of the disorder. Co-segregation of the detected mutations with the phenotype was confirmed by Sanger sequencing. In silico analysis including 3D protein structure modelling was used to predict the deleterious effects of the detected variants on protein function. We found two rare biallelic mutations in TWNK, encoding Twinkle, an essential mitochondrial helicase. Mutation c.1196A>G (p.Asn399Ser) recurred for the first time in a patient with PRLTS and the second mutation c.1802G>A (p.Arg601Gln) was novel for the disorder. In both patients neuroimaging studies showed diminished cervical enlargement of the spinal cord and for the first time in PRLTS partial atrophy of the vestibulocochlear nerves and decreased grey and increased white matter volumes of the cerebellum. Morphological changes in the auditory nerves, their desynchronized activity and partial cochlear dysfunction underlay the complex mechanism of hearing impairment in the patients. Our study unveils novel features on the phenotypic landscape of PRLTS and provides further evidence that the newly identified for PRLTS TWNK gene is involved in its pathogenesis.

A synchronized spontaneous otoacoustic emission paradigm was used to measure the response in time intervals of 80 ms following a click stimulus. The responses obtained were decomposed into basic waveforms by means of adaptive approximations using a matching pursuit algorithm. High-resolution time-frequency distributions of signal energy were calculated and showed three types of component: (1) purely evoked of duration less than 5 ms, (2) longer lasting and decaying, with exponentially decreasing amplitude, and (3) long lasting and stable. The distributions of the frequencies of components of different durations were similar, with most components falling within the 1-2 kHz interval. It is shown that the presence of long-lasting components may influence the estimation of the latency of evoked emissions, especially at higher frequencies where the evoked part has a very short duration.

Testing of ChatGPT has recently been performed over a diverse range of topics. However, most of these assessments have been based on broad domains of knowledge. Here, we test ChatGPT’s knowledge of tinnitus, an important but specialized aspect of audiology and otolaryngology. Testing involved evaluating ChatGPT’s answers to a defined set of 10 questions on tinnitus. Furthermore, given the technology is advancing quickly, we re-evaluated the responses to the same 10 questions 3 and 6 months later. The accuracy of the responses was rated by 6 experts (the authors) using a Likert scale ranging from 1 to 5. Most of ChatGPT’s responses were rated as satisfactory or better. However, we did detect a few instances where the responses were not accurate and might be considered somewhat misleading. Over the first 3 months, the ratings generally improved, but there was no more significant improvement at 6 months. In our judgment, ChatGPT provided unexpectedly good responses, given that the questions were quite specific. Although no potentially harmful errors were identified, some mistakes could be seen as somewhat misleading. ChatGPT shows great potential if further developed by experts in specific areas, but for now, it is not yet ready for serious application.

Although the tonotopic organisation of the human primary auditory cortex (PAC) has already been studied, the question how its responses are affected in sensorineural hearing loss remains open. Twenty six patients (aged 38.1 ± 9.1 years; 12 men) with symmetrical sloping sensorineural hearing loss (SNHL) and 32 age- and gender-matched controls (NH) participated in an fMRI study using a sparse protocol. The stimuli were binaural 8s complex tones with central frequencies of 400 HzCF, 800 HzCF, 1600 HzCF, 3200 HzCF, or 6400 HzCF, presented at 80 dB(C). In NH responses to all frequency ranges were found in bilateral auditory cortices. The outcomes of a winnermap approach, showing a relative arrangement of active frequency-specific areas, was in line with the existing literature and revealed a V-shape high-frequency gradient surrounding areas that responded to low frequencies in the auditory cortex. In SNHL frequency-specific auditory cortex responses were observed only for sounds from 400 HzCF to 1600 HzCF, due to the severe or profound hearing loss in higher frequency ranges. Using a stringent statistical threshold (p < 0.05; FWE) significant differences between NH and SNHL were only revealed for mid and high-frequency sounds. At a more lenient statistical threshold (p < 0.001, FDRc), however, the size of activation induced by 400 HzCF in PAC was found statistically larger in patients with a prelingual, as compared to a postlingual onset of hearing loss. In addition, this low-frequency range was more extensively represented in the auditory cortex when outcomes obtained in all patients were contrasted with those revealed in normal hearing individuals (although statistically significant only for the secondary auditory cortex). The outcomes of the study suggest preserved patterns of large-scale tonotopic organisation in SNHL which can be further refined following auditory experience, especially when the hearing loss occurs prelingually. SNHL can induce both enlargement and reduction of the extent of responses in the topically organized auditory cortex.

Several studies have suggested that distortion product otoacoustic emissions (DPOAEs) may be an early marker not only of hearing loss (HL) but also of tinnitus. The purpose of this study was to investigate whether DPOAEs measured up to 16 kHz are affected by the presence of tinnitus. Pure tone thresholds and DPOAEs were measured in two groups: 55 patients with tinnitus and 63 subjects without tinnitus. The subjects were divided into three groups according to their audiometric results—better than 25 dB HL at all tested frequencies from 0.125 to 16 kHz, better than 25 dB up to 8 kHz, and hearing impaired. Receiver operator characteristics (ROCs) were used to test whether DPOAEs could differentiate between normal hearing, hearing loss, and tinnitus. Comparison of tinnitus subjects with the control group, matched accurately according to thresholds, did not yield any significant difference in DPOAEs. However, in both these groups hearing loss was accompanied by a decrease in DPOAEs, specifically, at 2–6 kHz and 16 kHz. The results suggest that any decrease in DPOAEs seems to be related only to hearing loss and there is no additional effect from tinnitus.

Transiently evoked otoacoustic emissions (TEOAEs) are normally modeled as the sum of asymmetric waveforms. However, some previous studies of TEOAEs used time-frequency (TF) methods to decompose the signals into symmetric waveforms. This approach was justified mainly as a means to reduce the complexity of the calculations. The present study extended the dictionary of numeric functions to incorporate asymmetric waveforms into the analysis. The necessary calculations were carried out using an adaptive approximation algorithm based on the matching pursuit (MP) numerical technique. The classic MP dictionary uses Gabor functions and consists of waveforms described by five parameters, namely, frequency, latency, time span, amplitude, and phase. In the present investigation, a sixth parameter, the degree of asymmetry, was added in order to enhance the flexibility of this approach. The effects of expanding the available functions were evaluated by means of both simulations using synthetic signals and authentic TEOAEs. The resulting analyses showed that the contributions of asymmetric components in the OAE signal are appreciable. In short, the expanded analysis method brought about important improvements in identifying TEOAE components including the correct detection of components with long decays, which are often related to spontaneous OAE activity, the elimination of a "dark energy" effect in TF distributions, and more reliable estimates of latency-frequency relationships. The latter feature is especially important for correct estimation of latency-frequency data, which is a crucial factor in investigations of OAE-generation mechanisms.