Long-term untreated vision and hearing impairments can negatively impact physical and mental wellbeing. We investigated the association of vision and hearing status with depressive symptoms among middle-aged and older Chinese adults.This was a prospective cohort study of 9,492 participants from the China Health and Retirement Longitudinal Study (CHARLS) carried out in 2011, 2013, 2015, and 2018. This study used self-reported vision and hearing status to determine the degree of impairment. Depressive symptoms were examined using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10), with a total score of ≥ 12 indicating depressive symptoms. A Cox proportional hazards model adjusted for age, sex, residence, marital status, educational level, smoking history, alcohol consumption, hypertension, diabetes, heart disease, digestive disease, arthritis, wearing glasses, and hearing aids was used to estimate the association of vision and hearing status with depressive symptoms among middle-aged and older Chinese adults.Of the 9,492 participants , 3,238 (34.11%) participants reported incident depressive symptoms during the 7-year follow-up period. Participants who self-reported only vision impairment , only hearing impairment , and both vision and hearing impairments  were independently associated with a greater increase in the hazard risk of incident depressive symptoms compared to those without vision and hearing impairments. An increase in participants' vision and hearing scores was associated with a significant increase in the hazard risk of incident depressive symptoms .Vision and hearing status was associated with increased depressive symptoms among middle-aged and older Chinese adults during the 7-year follow-up period. Participants' use of glasses and hearing aids did not improve their depressive symptoms. Our findings may facilitate the development of effective treatments to prevent and treat vision and hearing impairments, thereby enhancing the physical and mental wellbeing of middle-aged and older adults. Vision and hearing impairments are common among middle-aged and older adults but are typically neglected or dismissed as a natural part of aging . The preMental health among middle-aged and older adults often includes depressive symptoms, the risk of which increases with age . Indeed,Most previous studies that focused on the effects of vision and hearing impairments on depressive symptoms were conducted in developed countries and yielded mixed results. A systematic review reported the inability to perform a meta-analysis or establish a clear association between vision impairments and depressive symptoms in elderly individuals . SeveralThe CHARLS data utilized in this study were obtained in 2011 (Wave 1), 2013 (Wave 2), 2015 (Wave 3), and 2018 (Wave 4) . The CHAThe CHARLS project was approved by the Peking University Biomedical Ethics Review Committee (IRB00001052-11015) . All parAssessment of vision and hearing impairments included three major questions based on participants' self-reported vision and hearing status from the CHARLS 2011 baseline. To evaluate participants' vision and hearing , 16, parDepressive symptoms were treated as primary outcome events during follow-up. Participants were assessed using a 10-item short form of the Center for Epidemiologic Studies Depression Scale (CESD-10) in each CHARLS wave survey . Using tParticipants' covariate information at CHARLS 2011 baseline include  self-asst-test or a chi-square test. A Cox proportional hazards model was used to calculate the hazard risk between vision and/or hearing status and depressive symptoms. Stratified analysis according to different characteristics was conducted to calculate the hazard risks between vision and hearing scores and depressive symptoms among participants, and the significance of all risk factor group interactions between vision and hearing scores and depressive symptoms was also calculated. The following four models were estimated in this study: Model 0 was unadjusted; Model 1 was adjusted for age, sex, residence, marital status, and educational level; Model 2 was adjusted for factors in Model 1 plus smoking history, alcohol consumption history, hypertension, diabetes, heart disease, digestive disease, and arthritis; and Model 3 was adjusted for factors in Model 2 plus wearing glasses and hearing aids. Covariate information was assumed to be missing at random in this study, and we performed a complete case analysis. The level of statistical significance was set at 95% . Associations are presented as hazard ratios (HRs) with 95% confidence intervals (CIs). All data analyses were conducted using R statistical software version 4.1.2.For dichotomous covariate information at the CHARLS 2011 baseline, we compared the differences among participants with and without depressive symptoms using a We performed two additional analyses to evaluate the robustness of our findings:  the FineOf the 17,708 participants at CHARLS 2011 baseline, we excluded 114 participants with incomplete information on health status and functioning, 2,323 participants with incomplete information on depressive symptoms, 103 participants with missing vision and/or hearing status information, 294 participants with incomplete information on covariates, 307 participants younger than 45 years, 928 participants who were lost to follow-up, 562 participants without answers to the questions on depressive symptoms during the 7-year follow-up period, and 3,585 participants with depressive symptoms at CHARLS 2011 baseline. A final total of 9,492 participants were included in the analysis .P-value = 0.033), and significant differences were observed in gender , residence , education level , marital status , smoking history , alcohol consumption , heart disease , digestive disease , arthritis , and wearing glasses  among participants with incident depressive symptoms. The average age of the 9,492 participants was 58.12 years (SD: 9.00 years). Of participants, 4,930 (51.94%) were men and 4,562 (48.06%) were women. The significance of the differences between participants with and without depressive symptoms stratified by different characteristics is presented in At CHARLS 2011 baseline, 6,463 (68.09%) participants had no vision and hearing impairments, 2,154 (22.69%) had only vision impairments, 407 (4.29%) had only hearing impairments, and 468 (4.93%) had both vision and hearing impairments. After a 7-year follow-up period, 3,238 (34.11%) participants reported incident depressive symptoms. 2-value = 7.30, P-value = 0.026)    and a po= 0.545) .There was no evidence that our results lacked robustness in the two additional analyses. P-values for interaction > 0.05). The association of vision and hearing scores with depressive symptoms differed among participants stratified according to different risk factors at baseline. The analysis stratified according to different risk factors revealed that participants with depressive symptoms were more likely to be younger, had a higher level of education, did not consume alcohol, and did not wear glasses or hearing aids.This study established the association between vision and hearing status and depressive symptoms in middle-aged and older Chinese adults. Our findings provide evidence based on a nationally representative cohort study sample of 9,492 Chinese community-dwelling elderly individuals and longitudinal follow-up to support our hypotheses. This study had four major findings based on the cohort study data. First, participants with only vision impairments, only hearing impairments, and both vision and hearing impairments exhibited incident depressive symptoms during the 7-year follow-up period. Second, the fact that all risk factors  were obtained using CHARLS self-reported questionnaires does not diminish the interpretation of the association between vision and/or hearing status and depressive symptoms. Third, participants with higher vision and hearing scores had a greater risk of depressive symptoms. Finally, the use of glasses and hearing aids did not improve depressive symptoms. In addition, this study excluded 8,416 participants from the CHARLS baseline, which may have contributed to the non-representativeness of the 9,492 participants included in this study and the acquisition of erroneous estimates. Therefore, this study used the IPW method to adjust for bias from non-random selection and missingness to obtain valid estimates of the association.Our findings identified a total of 2,154 22.69%) participants with only vision impairments, 407 (4.29%) with only hearing impairments, and 468 (4.93%) with both vision and hearing impairments at the CHARLS 2011 baseline. After the 7-year follow-up period, 3,238 (34.11%) participants reported incident depressive symptoms. Our findings revealed that higher vision and hearing scores were indicative of more depressive symptoms. As reported by Armenia .69% part. Our finOur results revealed that participants with only vision impairments, only hearing impairments, and both vision and hearing impairments presented with depressive symptoms but with a different degree of risk. Several population-based longitudinal studies in Canada , the SalConsistent with previous studies, our findings indicated that participants with depressive symptoms stratified by potential risk factors were more likely to be younger, have a high level of education, not consume alcohol, and not wear glasses and hearing aids. Our data revealed that age was not a significant risk factor for this association. Depression is a serious public health concern among elderly individuals; it causes disability, medical illnesses, mental health problems, and increased mortality . PreviouIn this study, vision and hearing status was self-reported in CHARLS. In this regard, objective clinical vision and hearing screening are more appropriate than subjective self-reported vision and hearing status, although a previous study reported the opposite result . ConsistIn this study, only a small number of participants wore glasses and hearing aids. Moreover, the use of glasses and hearing aids did not attenuate the association of vision and hearing impairments with depressive symptoms. The use of glasses and hearing aids is lower in older Chinese adults, which may be modulated by outdated beliefs  . FurtherThe strengths of our research design based on a large population in China and distinct vision and hearing analyses revealed a significant connection between vision and hearing status and depressive symptoms, which was based on the CHARLS design of a prospective cohort study with a 7-year follow-up period. However, this study has several limitations. First, additional confounding factors linked with depressive symptoms, such as income, life satisfaction, and social support, were not included as covariates for model adjustment. Second, vision and hearing status was self-reported and was not based on medical records in CHARLS. Third, only a small number of participants wore glasses and hearing aids; hence, any improvements could not be conclusively determined. Participants were only asked about their vision and hearing impairments and use of glasses and hearing aids at baseline but not during the 7-year follow-up period, during which we focused only on depressive symptoms. Finally, the self-reported variables may have introduced recall bias when the study included older adults in CHARLS.In conclusion, this study demonstrated that vision and hearing impairments were associated with depressive symptoms among middle-aged and older Chinese adults. Furthermore, our findings revealed that participants with only vision impairments, only hearing impairments, and both vision and hearing impairments were at a higher risk of incident depressive symptoms. Based on our findings, individuals with both vision and hearing impairments had the highest risk of developing depressive symptoms, followed by those with only hearing or vision impairments. The presence of other risk factors did not affect the interpretation of the association of vision and hearing impairments with depressive symptoms. Moreover, an increase in participants' vision and hearing scores was associated with an increase in the hazard risk of incident depressive symptoms among middle-aged and older Chinese adults. In addition, participants' use of glasses and hearing aids did not improve depressive symptoms. Despite the growing burden of vision and hearing impairments in China's aging society, these conditions have been overlooked. Accordingly, effective treatments to prevent and treat vision and hearing impairments are warranted to improve the mental health and quality of life of middle-aged and older individuals with vision and hearing impairments.http://charls.pku.edu.cn/.Publicly available datasets were analyzed in this study. This data can be found here: The studies involving human participants were reviewed and approved by Peking University Biomedical Ethics Review Committee (IRB00001052-11015). The patients/participants provided their written informed consent to participate in this study.Y-GL performed data analysis, interpreted the data, and drafted the manuscript. C-CW interpreted the data and reviewed the manuscript. QH and LZ performed data collection and data cleaning. YL designed the study, reviewed, and critically revised the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by the Qinghai province high-end innovative talents thousand talents program (No. 2020-12) and Graduate Program Construction Project of Qinghai University (Project Number: qdyk-200301). The funders played no role in designing or conducting the study or in the collection, management, analysis, or interpretation of the data, nor did they have any input into preparation, review, or approval of this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Diabetic retinopathy (DR) is one of the major complications of diabetes mellitus and is a significant cause of blindness worldwide. In Uganda, the prevalence of diabetes is approximately 2.7% of the urban population and 1% in rural areas. Many diabetics cannot access an eye exam due to the lack of less costly and user-friendly equipment that primary eye workers can use. Smartphone-based fundus photography allows for a cheap and mobile fundus examination. The study aimed to determine the sensitivity and specificity of the Portable Eye Examination Kit (PEEK) retina compared to a standard ophthalmic fundus camera (Zeiss Visucam 200) for the diagnosis of DR.From January-March 2020, 286 people with diabetes (type 1 & 2) patients were seen at Kiruddu National referral hospital diabetes clinic. All participants had funduscopy with PEEK retina and the standard ophthalmic fundus camera following ophthalmic examination and pupillary dilation. The PEEK retina’s sensitivity, specificity and reliability were determined using an ophthalmic fundus camera as the gold standard.The participants’ mean age was 51 with a standard deviation of ±11years, 213 (74.5%) were females, and the majority (93.4%) had Type 2 diabetes. The overall Sensitivity of PEEK retina for DR was 84% (95% CI 70.9–83.5), while the specificity was 79.9% (95% CI 76–83.5) with a positive predictive value (PPV) of 30.9% (95% CI 23.2–39.4) and a negative predictive value (NPV) of 97.9% (95% CI 95.9–99.1).PEEK retina has high sensitivity and specificity, making it suitable for screening and diagnostic purposes. Therefore, we recommend the integration of the PEEK retina in the screening and diagnosis of DR in resource-limited settings. Diabetic retinopathy (DR), a complication of diabetes mellitus, is a significant cause of avoidable blindness worldwide . Its preWith the advance in age and increased therapeutics, there has been an increase in the life expectancy of diabetic individuals. This increase is expected to cause a rise in the burden of DR since most of these patients may not be able to receive a timely eye examination or treatment for diabetes . FurtherLMICs need a unique modality that combines affordability, availability and a short learning curve to screen for DR. Furthermore, they need training and expertise, limiting their use to the ophthalmologists who are only 40 in Uganda. Therefore, many diabetic patients may not access proper fundus examination for DR despite the adequate numbers of ophthalmic clinical officers in the peripheral health centres. The smartphone-based camera can bridge this gap in LIMCs; however, there is no published data to support its use in our setting.The International Council of Ophthalmology (ICO) and Uganda Ministry of Health guidelines recommend screening, follow-up, referral, and early diagnosis and treatment of DR –16. ThisIn the last decade, there has been a trend to develop cost-effective, portable, and easy-to-use devices by minimally trained personnel , 19, 20.Therefore, this study aimed to determine the sensitivity and specificity of the smartphone-based device (PEEK retina) compared to the standard tabletop fundus camera (Zeiss Visucam 200) as the gold standard for diagnosing diabetic retinopathy. We also tested the reliability of the Peek retina by grading the diabetic retinopathy and comparing it with the Zeiss Visucam 200.This descriptive hospital-based cross-sectional study was conducted in the Diabetic Clinic of the Department of Medicine, Kiruddu National Referral Hospital, between January and March 2020. The hospital is located in Kampala, Uganda and serves as the primary inpatient and outpatient hospital for Kampala and its surrounding districts. The Diabetes clinic operates weekly and reviews approximately 80 to 110 patients daily. It is run by one ophthalmologist and five ophthalmic clinical officers. The clinic equipment used to examine the posterior segment of the eye includes a slit lamp, 90D and78D lenses, a direct ophthalmoscope and an indirect ophthalmoscope.All known diabetic patients (Type 1 or Type 2) previously diagnosed and taking medicine from the diabetes clinic who were 18 years and above, had given written informed consent and were willing to undergo the study procedure were recruited. We excluded participants with the following: a mental disorder, contraindication to mydriasis, allergy to tropicamide eye drops, hazy optical media and those too sick to undergo the study procedure.We collected data from the patient’s medical records card using a standardised pretested questionnaire after the patient had been reviewed by the physician in the diabetes clinic that day. This included the demographics, clinical presentation, diagnosis, duration and family history of diabetes mellitus, fasting blood glucose measurement, blood pressure measurements of the hypertensive patients, and history of kidney and sickle diseases. We measured the participants’ visual acuity using a Snellen’s chart and examined them with portable slit-lamp biomicroscopy. Findings of the anterior segment were recorded for each patient. Tonometry was done using a handheld ICARE tonometer , and Visual fields were tested by the confrontational method. Eye movements were tested in all gazes seeking any paresis or paralysis of extraocular muscles. Each patient then underwent mydriatic dilation with Tropicamide 1% eye drops.The PEEK is a comprehensive and integrated smartphone-based tool kit that contains a set of core tests needed for eye screening. It is designed for use by practitioners with minimal to no training. It comprises a smartphone application and a low-cost adapter for retinal imaging. Both are optimised for ease of use, and it allows operators to test for the core vision problems that are visual acuity, colour, and contrast sensitivity, image grading of cataracts and photos of the back of the eye to be taken, saved, and sent to experts for diagnosis, follow-up, and arranging treatment.The PEEK retina is a plug-in imaging system with adjustable light and power to illuminate the retina. Users can change the amount of light to illuminate the retina by choosing one level out of three. Since it has its light source, Peek Retina has a universal clip to attach to any smartphone. It also has an Android application named Peek Retina Camera that enables it to capture photos and record videos. However, any camera application with iPhone and Windows Phone models can be used to capture images by adjusting manual settings in terms of autofocusing, clarity, and brightness.It also has a synthetic eye model box for synthetic data capture, thus providing a package with eight different retina images with different eye disease conditions ranging from the normal eye to proliferative diabetic retinopathy. The merits of the PEEK retina include its portability, ease of use with a short learning curve, cost-effectiveness, availability, and ability to make a remote diagnosis , 30.With the eye fully dilated, each participant’s fundus image was recorded as a video by the PEEK retina using a Samsung S8 edge plus(model SM-G955FD) mobile digital camera. The peek retina adapter was attached to the phone’s native camera, aligned and tightened with a knob to hold the device in the correct position. Since the PEEK retina has its light source, the smartphone flashlight was disabled, providing the Peek retina software with a retinal imaging function.The participant was given a target, and the video was acquired from the central posterior 45 degrees of the retina. The video length depended on the participant’s cooperation and the ability to acquire the required images from the posterior pole. After that, the participant’s fundus picture of the posterior pole was then taken by the ZEISS VISUCAM 200 ophthalmic fundus camera. The principal investigator reviewed the videos and pictures from both devices to diagnose DR and graded them using ICDRDS classification . If the Using Bruderer’s sample size estimation formula for determining sensitivity and specificity , a samplWe used systematic random sampling with a sampling interval, k = 4, which was obtained by dividing the sampling frame size by the study sample size. According to the time of arrival at the clinic, every fourth patient was enrolled in the study. If they declined to join the study, the subsequent fourth patients’ in the series were selected. The first patient was picked from the first four patients using random number tables, and all patients were seen after being reviewed by the physician.For descriptive statistics, continuous variables were summarised as mean and standard deviation if normally distributed or median if not normally distributed. The categorical variables were summarised into frequencies and percentages. Participants whose images were recorded as unclear were not included in the calculation for sensitivity and specificity. Considering the Zeiss Visucam 200 as the gold standard, sensitivity and its 95% Confidence interval were determined by getting the number of diabetic patients diagnosed with DR by the smartphone-based retinal photography (PEEK-retina) divided by all those that were diagnosed as positive by the gold standard. Specificity and its 95% Confidence interval were determined by getting the number of diabetic patients who were correctly diagnosed as not having diabetic retinopathy by PEEK retina divided by all those diagnosed as negative by the ZEISS VISUCAM 200. We determined the reliability and the concordance of the PEEK retina and ZEISS VISUCAM 200 ophthalmic fundus camera in DR staging. The proportion of concordant and discordant pairs was obtained as a percentage of all the pairs of observations. All analyses were done in STATA version 14.0.We obtained ethical approval from the School of Medicine Research and Ethics Committee (SOMREC) of Makerere University and granted an approval number of # REC REF 2020–011. We also sought voluntary written informed consent from the participants before they joined the study.We recruited 286 participants into the study who had a mean age of 51 and a standard deviation of ± 11 years. Most study participants were female, 213/286 (74.5%). More than half of the participants had primary education, 168/286 (58.8%) and 93.4% (267/286) of the participants had type 2 DM. We excluded unclear images from four patients during the result analysis, as shown in " From "In "Overall the agreement in "In this hospital-based cross-sectional study, we tested the validity of the Portable Eye Examination Kit (PEEK) retina for the diagnosis of DR in the Kiruddu National referral hospital diabetes clinic. We compared it to a standard ophthalmic fundus camera (Zeiss Visucam 200). The PEEK retina uses a low-cost adapter attached to the phone camera. Peek retina software downloaded on the smartphone provides a small-sized, portable, low-power, and affordable biomedical imaging device with a retinal imaging function . This caThe PEEK retina had an overall high sensitivity (84.0%) and specificity (79.9%) compared to the Zeiss Visucam 200. Both devices had a high agreement in detecting the different grades of DR. The rate of recorded ungradable (unclear) images was acceptably low, approximately 9% and these were excluded from the above calculations.Our participants had a mean age of 51 years, which is no different from other studies , 33. ThiSensitivity and specificity are the main assessment factors to validate a screening tool or device. Since sensitivity is the actual positive rate of a device, this means that the PEEK retina would approximately correctly identify 84 out of 100 participants that indeed had DR as having DR. This value is very high; hence the device can be used as a screening tool for DR since it would not misclassify most of the participants as not having DR yet they do. The findings of our study are similar to those of other studies done in Africa and elsewhere in the world regarding smartphone-based retina photography . RamachaFindings in our study showed a specificity of 79.9% for the PEEK retina; this means that the PEEK retina would approximately correctly identify 80 out of 100 participants without DR as not having DR. This value is high, which means the device is excellent as a diagnostic device for DR because of the few false negatives thus, reducing misclassification bias. This result is consistent with the other smartphone-based fundus camera studies from India that reported 98.4%, 94.9% and 94.3% , 29, 37.In our study, the PEEK retina also had an NPV of 97.9%, while the PPV was 30.9%. Therefore, the probability of a positive test for DR to be positive is 0.309, which is low for determining positive tests. However, the prevalence affects the above two parameters, which can explain the difference from other smartphone-based fundus camera studies –41. In aThe agreement of PEEK retina in the diagnosis of Diabetic Retinopathy had most percentage agreements more significant than 90% for the right eye and the left eye in severe NPDR and PDR; however, the agreements for no apparent DR were below 80%. Similarly, an agreement of 0.91 was reported by Toy et al.  after exIn this study, video recording was used for the PEEK retina instead of photography because of the small field of view that the peek retina gives. Smartphone-based fundus video recording has also been used to extract images in a study done in retinopathy of prematurity with an outstanding image quality of 98%; however, no studies show its use in screening DR . FurtherThe strengths of this study included the following: The study used the gold standard to reliably evaluate the PEEK retina as a cheap alternative for the screening of DR. Optic disc assessment was easier with the PEEK retina, and this was also demonstrated in other studies . This coWhen using the PEEK retina in this study, several limitations were encountered. Firstly, this study was hospital-based; therefore, results from this study can only be generalised to tertiary health centres in resource-limited settings with diabetic patients but not the entire population. Secondly, there were white artefacts at the images’ edge, which led to missing some signs like microaneurysms. This could be because the Peek Retina imaging system includes light to illuminate the retina, and a smartphone captures the image; therefore, it does not reflect the flashlight. In addition, this device could not be used on an undilated pupil and had a limited field of view. This was minimised by asking the patient to look into different fields of gaze to get a periphery image and record videos from which the images were captured. Furthermore, the device also needed good cooperation from the patient to be able to obtain interpretable images.Thirdly, using a single mobile model may have also affected the image quality. In this study, the Samsung model was used, while other studies have used the i phone and HTC phones . In addiPEEK retina has very high sensitivity and specificity, making it suitable for screening DR in a resource-limited setting. It can reliably diagnose no apparent DR properly most of the time. That being the majority of the population, PEEK retina helps by saving them the stress and money to undergo gold standard ophthalmoscopy, which is expensive and not easily accessible.We, therefore, recommend the PEEK retina to be used in the screening of diabetic patients for DR, especially in lower health units with poor infrastructure, given its high overall sensitivity and specificity, low cost, portability, and ease of use, cloud storage and the availability of smartphones. These advantages of a PEEK retina smartphone-based system can be harnessed to improve telescreens’ penetration of DR, especially in resource-poor settings of the developing world. In addition, more research is needed in the application of the PEEK retina while using the different types of smartphones to obtain quality retina images and to incorporate smartphone-based systems that offer a way to analyse and evaluate eye disease using the PEEK retina by using image processing techniques for grading retinal images, especially in resource-limited settings.S1 Fig(TIF)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.


Hearing aid users reject their own hearing aid because of annoyance with background noise. The reason for dissatisfaction is located anywhere from the hearing aid microphone to the integrity of neurons along the auditory pathway. In this preview, the output of hearing aid was recorded at the level of ear canal and at auditory cortex in good and poor hearing aid users, who were classified using acceptable noise level.To study the representation of amplified speech in good and poor hearing aid performers.n = 35) and poor (n = 25) hearing aid performers. Gap detection test and aided SNR 50 were administered. In addition, ear canal acoustic measures and cortical auditory evoked potentials were recorded in unaided and aided conditions at 65 dB SPL.A total of 60 participants (age ranged 15–65 years) with moderate bilateral sensorineural hearing impairment grouped into good (Hearing aid minimally alters temporal contrast of speech reflected in envelope difference index. Although having similar temporal impairment, acoustic characteristics of amplified speech sounds and SNR 50 scores from both groups, the aided cortical auditory evoked potentials surprisingly showed significant earlier latencies and higher amplitudes in good performers than poor performers. In addition, good and poor performers classified based on annoyance level was predicted by latencies of 2N1 and 2P2 components of acoustic change complex. Further, a follow-up revealed hearing aid use has relation with acceptance towards noise.Participants who are willing to accept noise from those who are not willing to accept noise have subtle physiological changes evident at the auditory cortex, which supports the hearing aid usage. Older adults with hearing impairment find it difficult to process vowel-formant discrimination due to a deficit in temporal encoding.18Thus, it is hypothesized that though study participants have similar degree of hearing loss, temporal ability, minimally altered temporal envelope after processing through the hearing aid, an annoyance level toward noise in them may show difference in representation of amplified speech at cortical level in good and poor hearing aid performers. The following objectives were formulated: (1) to evaluate gap detection threshold, temporal alteration induced by hearing aid using EDI, SNR 50 and encoding of speech stimuli at cortical level using late latency response and acoustic change complex in good and poor hearing aid performers; (2) to find the relationship between signal to noise ratio of recorded output and latency; and amplitude of CAEPs and (3) to investigate which of these measures predicts good and poor hearing aid performers.A factorial research design was utilized as the assigned independent variable was group (with two levels) made based on annoyance level toward noise. The hearing aid outcome was the dependent variable, which was assessed through behavioral, psychoacoustic and electrophysiological approaches.n = 35) and a score of ≥13 were considered as poor hearing aid performers (n = 25). The participants were excluded if their ANL was between 7 and 13.Sixty patients who had bilateral moderate sensorineural hearing loss with flat configuration were recruited for the study. Flat configuration was operationally defined as the difference between the least and highest air conduction thresholds of the test ear being less than 20 dB from 250 Hz to 8000 Hz.Informed consent was obtained from all individual participants included in the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the AIISH institutional research committee (SH/AEC/HN/2013-14).The recorded Kannada passage was routed to the loudspeaker through the auxiliary input of the audiometer at the level of speech recognition threshold (SRT). Gradually, the level was adjusted in 5 dB-steps up to the level of most comfortable level (MCL) and then in smaller steps size of +1 and −2 dB, until the MCL of the participant was established reliably. After the MCL was established, a speech noise was introduced at 30 dB HL. The level of the speech noise was increased in 5 dB-steps initially, and then in 2 dB-steps, to a point at which the participant was willing to accept the noise without becoming tired or fatigued while listening to and following the words in the story. The maximum level at which he/she could accept or put up with the noise without becoming tired was considered as the (BNL). ANL is calculated by subtracting BNL (dB HL) from MCL (dB HL) and based on the scores of ANL, each participant of clinical group was classified as good (≤7) and poor (≥13) hearing aid performers.Temporal processing ability from each of the study participant was obtained binaurally from the gap detection test (GDT). Maximum likelihood procedure (MLP) m-code stored in MATLAB (2009B) was used to deliver the gap in three-interval force choice method. The output of a personal laptop was routed through the audiometer. Output of the audiometer was presented through the headphone at participant's most comfortable level. The paradigm comprised of three blocks of broad band noise having a duration of 500 ms each and having inter block intervals of 200 ms. In one among three blocks of noise the gap used to be present at center. Further, the occurrence of gap in one of the block will be randomized in each trial. The step size in gap detection test utilized was 0.5 ms. The duration of gap in succeeding trial will be decided based on participant response in the previous trial. Bracketing method (+0.5 ms and −1 ms) was used to detect the minimum gap will be noted down.The Microtech radius ‘2’ behind the ear hearing aids was used. According to manufacturer's specification the frequency range of each of this hearing aid extended from 210 to 5400 Hz. The peak full-on gain was 58 dB and 56 dB and high-frequency average full-on gain was 49 dB was 50 dB, respectively. The equivalent input noise was 10 Db SPL and 9 dB SPL. The signal processing delay of the hearing aids was 4.4 ms and 4 ms. Further, the attack and release times were 10–15 ms and 30–35 ms, respectively for an input of 2000 Hz tone. The functioning of both the hearing aids were ensured at the beginning of the data collection and repeated every 3 months till the completion of data collection.Each participant was fitted with the digital BTE test hearing aids binaurally using a custom-made soft shell mold. The hearing aid was programmed using NAL-NL1 prescriptive formula with noise reduction circuit being activated. The real ear measurement was carried out using FONIX 8000 hearing aid analyser to optimize the gain of the each hearing aid to match the target gain objectively. In addition, it was ensured that from each ear the aided thresholds from 500 Hz to 4 kHz in octaves were within aided speech spectrum. Each participant was scheduled to visit the clinic after one month of their hearing aid usage. A data logging option in the software was utilized to assess an average number of hours in a month the hearing aid being used by the client was documented.The output of the hearing aid for /da/ and /si/ stimuli at the ear canal was measured, in both unaided and aided conditions. The output at the ear canal from the test ear was chosen equally from either right or left ear in random order. The output of the laptop computer 2 was connected to the auxiliary input of audiometer to present the recorded CV syllables /da/and /si/ stimuli at 65 dB SPL. The speech output from the audiometer was delivered through a loudspeaker. The output of these stimuli  was subsequently digitally recorded using a laptop computer 1 installed with Praat software (version 5.1.29), using a sampling frequency of 44.1 kHz and 16 bit resolution. It is calculated by the ratio between the signal power and the noise power: SNR = P(C)/P(A). SNR is also usually represented in decibels Sound pressure level (dB SPL): SNR = 20 log (P(C)P(A))(dB).Each participant fitted with binaural hearing aids was seated comfortably in an armrest reclining chair. The electrode sites were cleaned with skin preparing gel. Disk type silver coated electrodes were placed using conduction gel at the test sites. The non-inverting electrodes (+) were placed on the Cz, Fpz, C3, and C4, the ground electrode was on upper forehead (Fpz) and the inverting electrode (–) was placed on nose. Horizontal (electrodes placed left and right outer canthi of eyes) and vertical (electrodes placed above and below eye) electro ocular responses were eliminated from the recoding. It was ensured that the electrode impedance was less than 5k Ω for each of the electrodes and that the inter-electrode impedance was less than 2k Ω.The loudspeaker was placed at 0° Azimuth located at the calibrated position of 1 m distance. The height of the loudspeaker was at the level of ear of the participant. The participant was instructed to ignore the stimulus and watch a movie that was muted and played through a battery-operated laptop computer. He/she was also asked to minimize the head movement. The PC-based evoked potential system, Neuroscan 4.4 (Stim 2-version 4.4), controlled the timing of stimulus presentation and delivered an external trigger to the evoked potential recording system, Neuroscan (Scan 2-version 4.4). To allow for a sufficient refractory period within the stimulus sweep, while minimizing the total recording time, an inter-stimulus interval (ISI) of 700 ms from offset to onset of the next stimulus was included for recording LLR to/da/ stimulus. However, for recording unaided and aided acoustic change complex, /si/ stimulus was used with an ISI of 700 ms.The recording was initiated once a stable EEG was obtained. The ongoing EEG was online band-pass filtered from 0.1 to 100 Hz with 12 dB/octave roll-off and continuously recorded. This was stored to disk for offline analysis. LLR recorded for 250 sweeps each in alternative polarities, delivered in a homogeneous train using stimulus presentation software Neuroscan 4.4 (Stim 2-version 4.4). A similar procedure was repeated to record the unaided and aided Acoustic Change Complex (ACC) for /si/ stimulus. The order of stimuli while testing on each participant was counter-balanced. Each potential (LLR and ACC) was recorded twice for reliability. The continuous EEG data were epoched over a window of 800 ms for each stimulus (including a −100 ms pre-stimulus period and a 700 ms post-stimulus time). The epoched waveforms were corrected for baseline. After eye-blink rejection, the remaining sweeps were averaged and filtered off-line from 1.0 Hz  to 30 Hz . All artifacts exceeding ± 75 μV were rejected while averaging the response for alternative polarity. It was found that unaided response was absent as the stimulus intensity of 65 dB SPL failed to elicit the response. Thus, aided response was analyzed from the good and poor hearing aid performers. At the cortical level, the response was recorded from four electrode sites ‘C3’, ‘Cz’, ‘C4’ and ‘Fpz’. The electrode site from which higher amplitude was recorded was utilized to investigate the representation of speech syllables.Each participant was fitted with the digital BTE test hearing aids binaurally. The speech stimuli used were standardized Kannada sentences developed by Geetha et al.L) and number of correctly recognized target words in each sentence was noted down. The total number of target words from all sentences was added (T). Also, the total number of words per decrement (W) and SNR decrement step size in each sentence (d) were noted down. The obtained values were substituted to the given equation adapted by Spearman–Karber to determine SNR 50%. The equation below was used to calculate the SNR 50 (50 point = L + (0.5*d) − d (T)/W).Ten sentences embedded at different SNRs were randomized. Each sentence was presented at 65 dB SPL in aided condition. The participants were instructed to repeat the sentence heard. The SNR level at which the testing started (t(58) = 1.07, p = 0.305]. This infers temporal processing ability is similar in both groups. In addition, the data of EDI were analyzed for each stimulus, which was recorded from participants’ ear canal of good and poor hearing aid performers. The Mean (M) and Standard Deviation (SD) of EDI for /da/ stimulus was 0.31 ± 0.04 in GHP and 0.30 ± 0.36 in PHP. For /si/ stimulus the value of EDI was 0.36 ± 0.06 in GHP and 0.34 ± 0.26 in PHP. An independent samples t test revealed that there was no significant difference on EDI between GHP and PHP for /da/  and /si/  stimuli. This infers though a difference in EDI was not observed between groups the output of hearing aid induces a minimal temporal alteration on each stimulus. Further, the aided representation at cortical response for two stimuli /da/ (slope of LLR) and /si/ (ACC) were analyzed. It was noted that though the slope of N1-P2 was steeper in GHP (0.057 ± 0.03) than in PHP (0.047 ± 0.027) (t(58) = 0.270, p = 0.788].The mean and Standard Deviation (SD) of gap detection threshold (in ms) was less in good hearing aid performers (12.95 ± 0.43) than poor hearing aid performers (12.18 ± 0.53). To examine the effect of groups on gap detection threshold, an independent samples t test revealed no significant difference between groups on GDT ; noise level ; and signal to noise ratio . These results suggest that signal characteristics of amplified speech are same in both groups  between the SNR and each component of the latency, the amplitude of ACC  and PHP  on aided SNR 50. The test result revealed that there was no statistically significant  between groups on SNR 50.To ascertain difference in representation of amplified speech at auditory cortical level from GHP and PHP has any influences on speech perception, the aided SNR 50 was evaluated from them. The aided SNR 50 in dB was obtained from participants of each group. It was subjected to an independent samples r (60) = 0.738, p = 0.000) and 2P2 (r (60) = 0.704, p = 0.000) with increase in raw ANL scores. Before performing multiple regressions, multicollinearity was assessed between independent variables i.e., latencies of 2N1 and 2P2. These independent variables are significantly correlated and its strength found to be strong with the values of r = 0.700 and p = 0.000. Thus, a linear regression was performed to predict acceptable noise level from latencies of 2N1 and 2P2. The regression line was fitted in the scatter plot as shown in y = β (x) − β0  and y = β (x) − β0  were obtained for the latencies of 2N1 and 2P2 respectively to predict the raw ANL score. The results indicate that with an increase in the latency of 2N1 or 2P2, the raw ANL score increases linearly.Further, to assess the correlation between raw ANL scores of the study participants (dependent variable) and the latency and amplitude of ACC, EDI for each stimulus, SNR 50, and GDT, a series of Pearson product moment correlation were carried out. The results showed a significant strong positive correlation for latency of 2N1 (n = 35) and 48% (n = 25) respectively. Using data logging option, the hearing aid usage was assessed on eleven participants of GHP (32%) and 16 participants of PHP (64%) who were visited the clinic for a follow up. Data logging analysis revealed that GHP participants have used their hearing aid on an average of 7.2 h per day. Whereas, PHP participants used their hearing aid less than 3 h in a day  and 32% (11) of GHP participants visited the clinic. The participants of PHP have reported unsatisfied with the hearing aid because of background noise. On data logging analysis revealed that PHP participants have used their hearing aid less than 3 h in a day. Whereas GHP participants reported a satisfactory benefit from their aid and data logging analysis found that they have used their hearing aid on an average of 7.2 h per day. It infers that annoyance toward noise have restricted to use their hearing aid for a long hour in PHP participants. Thus, ANL measurement should be performed at least before procuring a hearing aid or at the time of fitting the hearing aid such that appropriate well fitted hearing aid with suitable options can be selected.The findings of the study accept alternate hypothesis. Though study participants have similar temporal ability with minimally altered temporal contrasts in speech stimuli after processed through hearing aid, the annoyance level reflected subtle difference in the representation of amplified speech at cortical level from good and poor hearing aid performers. To conclude for those individuals who have a high ANL score tend to have likelihood chance of either reject their own hearing aid or become a part time hearing aid users even though their speech perception in noise is good.Though the hearing loss and temporal resolution are same between good and poor hearing aid performers a subtle difference on aided representation of amplified speech at auditory cortical level is mediated by annoyance toward noise, which decides usage of hearing aid.All procedures performed in studies involving human participants were in accordance with the ethical standards of the AIISH institutional research committee.Informed consent was obtained from all individual participants included in the study.The authors declare no conflicts of interest.


Cleft palates cause alterations in palate and lip structures, and it may also cause hearing loss because of recurrent otitis media. The appropriate treatment is controversial. It may include the prescription of antibiotics and insertion of a ventilation tube, or even otorhinolaryngological and audiological assistance, and hearing rehabilitation, with the use of an individual sound amplifier aid (ISAA).To characterize the profile of individuals with cleft palate and hearing loss, users of ISAA are assisted by the center of otorhinolaryngology and speech therapy of a hospital specialized in craniofacial anomalies and hearing impairment. Retrospective Study.Retrospective analysis of 131 charts of patients with corrected cleft palate and hearing loss, fitted with ISAA by the center abovementioned.The sample (n=131) was characterized by a prevalence of females (53%), unilateral incisive transforaminal cleft (27%), presence of associated anomalies (51%), history of alterations of the middle ear (56%) and surgery intervention (56%).The general profile of the individuals with cleft palate and hearing loss, fitted with ISAA, was characterized by the predominance of cleft lip and palate, positive history of middle ear alterations, surgery intervention and bilateral sensorineural hearing loss. Cleft lip and palate is a malformation that involves the lip and palate structures leading to possible oral communication disorders, principally due to audiological changes and alterations in the speech-related joints, apart from introducing feeding, psychosocial, educational, dental, and cosmetic disorders.Cleft lip and palate patients present middle ear ventilation problems resulting from changes in the movement of the Eustachian tube caused by inadequate insertion of the palate tensor and elevator muscles, thus producing a functional obstruction on the Eustachian tube and negative pressure in the middle ear, leading to otitis media.The significant degree of association between cleft lip and palate and changes in the middle ear has been profusely described in the literatureVarious studies have supported the importance of using antibiotics and inserting the Eustachian tube during palate repair surgery to prevent the occurrence of middle ear disorders and their consequences.The use of hearing aids as a mode of aural rehabilitation in cleft lip and palate patients with hearing loss is a viable and relevant alternative for such individuals. It is worth mentioning that this indication requires a complex process through which the right device is chosen and validated for each patientIn such context, given the relevance of the association between cleft lip and palate and hearing loss, this study aims to characterize the profile of patients with cleft lip and palate and hearing loss using hearing aids.This study was approved by the Ethics Committee under permit 314/2005UEPCEP. A retrospective study was carried out on the charts of 131 patients operated for cleft lip and palateThis research was carried out between August of 2005 and August of 2007.Patient charts were checked for data on patient identification , otologic history , hearing  and hearing aid (type and model).The classification developed by Spina et al. and modified by Silva Filho et al. was used to describe the cleft lip and palate types. This method uses the incisive foramen - the point where lip and palate are joined together - as an anatomic landmark; for clarification purposes, however, as results are presented in our study, the chosen nomenclature was cleft lip for pre-incisive foramen manifestations, cleft palate for post-incisive foramen occurrences, and cleft lip and palate for trans-incisive foramen alterations.Results were submitted to a descriptive analysis after data collection and summarized in the form of tables to facilitate analysis and interpretation. The chi-square test was performed with a significance level of 5% (p<0.05) to check for the association between the analyzed variables.All patients in our sample are being treated and followed specifically for their craniofacial malformations and systematically followed by the Otorhinolaryngology and Speech and Hearing Therapy service at a hospital specialized in craniofacial anomalies and hearing loss.Patients were aged between 2 and 72 years .The presence of anomalies associated with cleft lip and palate was analyzed in all charts, and 67 (51%) patients were found to have other anomalies associated with cleft lip and palate.The otologic history of the studied population is shown in Use of hearing AID per type is described in Our study looked into the charts of 131 patients with cleft lip and palate fitted with hearing aids, with ages ranging between 2 and 72 years, most of which females. According to the literature, isolated cleft palates are more common in women, as also found in our study , in whicAs vastly described in the literature,Most patients with cleft lip (67%) do not have a history of otitis; in contrast, 52% of the patients with cleft palate have a history of otitis , althougAnother study reported worse air tone thresholds in patients with cleft palates when compared to cleft lip patients, indicating a greater probability of hearing loss in cleft palate patients due to middle ear disorders.When looking at hearing , all typThe prevalence of otitis media was reduced as a result of palate repair surgery, but it is possible that middle ear disorders linger on for a long time, until the adulthood.Fifty six percent of the patients enrolled in our study underwent surgery to treat recurring otitis media, showing a greater tendency towards surgical treatment as also seen in the literature as a preventive approach to middle ear disorders.17However, many discuss the possibility of offering patients a less aggressive therapy that does not introduce late complications, provided in the form of otorhinolaryngological and audiological follow-up combined with indication of hearing aids.42Some authors state that the use of hearing aids is an effective tool in treating persistent and recurring otitis media after the ventilation tube has been removed. Additionally, they found that when the justification to treat otitis is hearing loss and the ensuing disabilities, hearing aids can be used as an effective non-invasive therapeutic measure.Our sample of patients with cleft lip and palate and hearing loss fitted with hearing aids may be characterized mainly as patients with cleft lip and palate with a history of middle ear disorders treated surgically in cases of recurring otitis media.


Severe and profound childhood hearing loss is a medical condition that can affect the functional development of the brain  is an electronic medical device that provides auditory access to speech sounds that cannot be supplied by sound amplification through conventional hearing aids in individuals with severe and profound hearing loss. Early application of CI facilitates spoken language acquisition  and spoken language skills Huber, , and aca“Quality of life” refers to different aspects of a person's life, such as economic status, rights, culture, and health The technical progress of cochlear implants is beneficial but also may have limitations. Rijke et al. informs about the experience of Dutch adolescents and young adults with CIs and with conventional hearing aids. The participants reported that they could participate in hearing society; however, they reported challenges such as dependency on the technical device (compare Huber) and feeling often misunderstood and sometimes stigmatized when comparing themselves to typical-hearing peers.The qualitative study of Schweinberger and von Eiff points to the importance of new methods for training vocal emotion recognition, morphing and caricaturing. These methods use “digitally modified stimuli with extended diagnostic information” (Schweinberger and von Eiff). The authors suggested that this training will have a comprehensive positive impact on the QoL of children with CI. From a socio-emotional point of view, recognizing the emotional timbre of the other person's voice is likely to be of great importance for young CI users.The perspective paper of This small volume provides some novel insights in QoL of young CI recipients. Improved communication, including vocal emotion recognition, and social participation seem to be important factors for a good quality of life in many cultures. There seem to be cultural differences in how hearing loss in childhood is perceived and experienced and how it affects QoL, at least from the perspective of parents. In addition, it seems to be important to be aware of device-specific (CI) risks in the context of QoL.However, there are also limitations. Not all regions have access to cochlear implants and rehabilitation that is affordable for all people. This can make it difficult to compare studies addressing QoL of young individuals with CI. Furthermore, two out of four contributions provide information on perspectives. Further studies are needed.So, the papers in this small volume raise more questions than answers. Studies that provide possible answers to these questions would come from a wide variety of disciplines, including clinical psychology, educational science, audiology, otology-neurotology, neuroscience, computer science, electrical engineering, and sociology.All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


Children who are hard of hearing are at risk for developmental language and academic delays compared with children with normal hearing. Some work suggests that high-order cognitive function, including fluid intelligence, may relate to language and academic outcomes in children with hearing loss, but findings in these studies have been mixed and to date, there have been no studies of the whole-brain neural dynamics serving fluid intelligence in the context of hearing loss. To this end, this study sought to identify the impact of hearing loss and subsequent hearing aid use on the neural dynamics serving abstract reasoning in children who are hard of hearing relative to children with normal hearing using magnetoencephalography. We found significant elevations in occipital and parietal theta activity during early stimulus evaluation in children who are hard of hearing relative to normal-hearing peers. In addition, we found that greater hearing aid use was significantly related to reduced activity throughout the fronto-parietal network. Notably, there were no differences in alpha dynamics between groups during later-stage processing nor did alpha activity correlate with hearing aid use. These cross-sectional data suggest that differences in auditory experience lead to widespread alterations in the neural dynamics serving initial stimulus processing in fluid intelligence in children. et al. report elevations in parieto-occipital theta activity during abstract reasoning in children with hearing loss relative to children with normal hearing. Theta activity throughout the fronto-parietal network was also related to the amount of hearing aid use, underscoring the importance of consistent hearing aid use on cognitive and brain development.Heinrichs-Graham   While fluid intelligence is a component of overall intelligence, the developmental trajectories of the components that make up overall intelligence  can be dissociated. There is a consensus that abstract reasoning emerges between 2 and 3 years of age and develops rapidly from early childhood into adolescence before reaching maturity in early adulthood.9–14 This includes children who are hard of hearing  as well as children who are deaf. Two recent large-scale studies of children and adolescents suggest that bilateral hearing loss leads to reduced performance on non-verbal intelligence tasks, including fluid intelligence.16 Most of the children in these studies had mild-to-moderate hearing loss, which suggests that even CHH are at risk of non-verbal cognitive delays compared to children with normal hearing (CNH). Nonetheless, these large-scale analyses did not take into account the presence of other comorbidities prevalent in children with hearing loss, so the impact of hearing loss on non-verbal intelligence over-and-above additional developmental disabilities remains unclear. Furthermore, we calculated effect sizes using the non-verbal intelligence data from these studies and found them to be relatively modest (e.g. Cohen’s d = 0.5),16 and thus smaller studies are less likely to have the sample sizes required to detect these subtle behavioural differences in non-verbal intelligence at the group level. Indeed, findings in other studies are mixed; some show specific non-verbal cognitive decrements , whereas others show no difference groupwise between CHH and CNH.19 However, numerous studies have shown that non-verbal intelligence predicts language ability in children with and without hearing loss, suggesting that fluid intelligence may moderate academic and language outcomes in this population.20–24 Because language issues pose the biggest risk to CHH, an understanding of how these cognitive processes are linked and affected by auditory experience is important for improving outcomes.One such developmental risk factor is congenital hearing loss. Children with bilateral hearing loss are at increased risk of developmental language and cognitive delays.25 However, 30–40% of early identified CHH experience persistent deficits in language,9 executive function18 and academic outcomes.26 Recent studies have found that CHH who have more consistent hearing aid use have better academic and language outcomes than CHH who have fewer hours of hearing aid use per day.26 Crucially, we recently reported that the amount of hearing aid use also significantly relates to neurophysiological markers of cognitive processing in CHH, such that greater hearing aid use  served to significantly normalize patterns of neural activity during working memory processing in CHH.27 Nonetheless, the effects of individual differences in auditory experience on non-verbal intelligence have rarely been described in previous studies, and to date, there has been no investigation of the underlying neurophysiology.As alluded to above, the variability across studies between CHH and CNH and among CHH may be related to differences in auditory experience  and thus, alterations in auditory experience may be able to explain differences in behaviour and neurophysiology between CHH and CNH on the group level, as well as the variability in outcomes within the hard-of-hearing population. CHH is increasingly identified with hearing loss via newborn hearing screening programmes and then fitted with hearing aids and enroled in early intervention within months of birth.28 Jung and Haier28 performed a meta-analysis of 37 structural and functional neuroimaging studies of various intelligence constructs and found that grey matter volumes in the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortices, as well as temporal and occipital regions, reliably predicted intelligence and reasoning performance in humans. Importantly, longitudinal studies of development also showed that increases in grey matter in these regions in early childhood, as well as proportional decreases in cortical thickness in later adolescence  positively correlated with performance on intelligence tests in children.28 These results have largely been corroborated in later studies of intelligence in children and adults.30 Most recently, Taylor et al.31 sought to identify the oscillatory neural dynamics that underlie abstract reasoning performance in children and adolescents. They found significant correlations between age and theta event-related synchronization (ERS) activity in the medial occipital and cerebellar cortices. They also found robust age-by-sex relationships throughout the fronto-parietal network, such that theta ERS activity in the dorsolateral lateral and superior prefrontal, posterior parietal, and occipital cortices increased as a function of age in males, whereas activity in these regions showed no change or a decrease as a function of age in females, suggesting differential maturation in these regions by sex.31 Nonetheless, the impact of auditory experience on these neural responses remains completely unknown. This is an important avenue, as it is possible that CHH show differential neural patterns despite having comparable performance, and this may give insight into the locus of variability in this population.Researchers have long attempted to determine the neural correlates of intelligence more broadly, as well as fluid intelligence as its own construct. The prevailing theory is the parietal-frontal intelligence theory (P-FIT).The present study sought to identify the impact of mild-to-severe hearing loss and subsequent hearing intervention on the neural dynamics serving abstract reasoning performance in children and adolescents. We recorded the neural oscillatory responses from a group of 7- to 15-year-old CHH and CNH using magnetoencephalography (MEG) while they performed an abstract reasoning task adapted from Raven’s Progressive Matrices. Given our sample size, we hypothesized that we would not detect significant decrements in abstract reasoning performance at the group level between CHH and CNH. However, we hypothesized that they would exhibit altered neural responses in fronto-parietal executive function regions during task performance. We also hypothesized that the amount of hearing aid use would significantly correlate with the neural responses serving abstract reasoning performance within CHH.32 All data collection occurred at the University of Nebraska Medical Center. Exclusionary criteria included any medical illness affecting CNS function, current or previous major neurological or psychiatric disorder, history of head trauma, current substance abuse, and/or the presence of irremovable ferromagnetic material in or on the body . After a complete description of the study was given to participants, written informed consent was obtained from the parent/guardian of the participant, and informed assent was obtained from the participant following the guidelines of the University of Nebraska Medical Center’s Institutional Review Board, which approved the study protocol. Exclusionary criteria were discovered in three CNH and four CHH after consent, and these seven participants were not included in the sample. Two additional participants (one CNH and one CHH) were not included in the analysis due to technical errors in MEG data acquisition, and one additional CHH chose to withdraw from the study before MEG recording. Finally, three CNH and five CHH were excluded due to an inability to perform the task  or irremovable movement artefacts during recording (one CHH). Thus, 19 CNH and 16 CHH were included in the analysis. The final sample demographics were as follows: CNH average age = 11.58 (SD = 1.97) years, range = 7.50–14.83 years, 8 females; CHH average age = 11.59 (SD = 2.02) years, range = 8.31–15.64 years, 6 females. There were no between-group differences in age, t(33) = 0.015, P = 0.988, or sex, X2  = 0.077, P = 0.782. Researchers were blinded to group membership during all preprocessing and processing of MEG and behavioural data until statistically necessary to avoid any potential bias.A total of 52 participants, including 27 CHH and 25 CNH, were initially consented from the local community to participate in this study. This sample size was selected based on our prior work showing differences in brain activity between CHH and CNH.33) to characterize their level of verbal and non-verbal cognitive function. The WASI-II consists of the following subtests: Vocabulary, Similarities, Block Design and Matrix Reasoning, which can be used to calculate an individual’s verbal, non-verbal and overall IQ. Scores on the Vocabulary and Similarities subtests are combined to create the Verbal Composite Index (VCI), which is a metric of verbal intelligence, whereas the Block Design and Matrix Reasoning scores are combined to create a Perceptual Reasoning Index (PRI), which is a measure of non-verbal intelligence. In addition, we calculated the degree of hearing loss [i.e. better ear pure-tone average (BEPTA)] for all CHH from the participants’ most recent clinical audiogram, which was completed within the past year and provided with parent consent. Audiograms consisted of air-conduction audiometric thresholds that had been measured at octave frequencies from 250 to 8000 Hz. The thresholds at 500, 1000, 2000 and 4000 Hz were averaged to calculate the pure-tone average (PTA) for each ear, and the PTA for the better ear was used to represent degree of hearing loss in the statistical analyses. Finally, for the CHH only, parents or guardians filled out questionnaires regarding their child’s hearing aid use during the school year, summer and weekends . We then calculated hearing aid use in a total number of hours per week, Monday through Sunday. For all analyses, we used the number of hours participants wore their hearing aids during the school year. The final sample of CHH had an average BEPTA of 42.80 dB  and wore their hearing aids at an average of 70.50 h/week .All participants completed all four subtests of the Wechsler Abbreviated Scale of Intelligence . All recordings were conducted in a one-layer magnetically shielded room with active shielding engaged. Before MEG measurement, four continuous head position indicator coils were attached to the subject’s head and localized, along with the three fiducial points and scalp surface, with a 3D digitizer . Once the subject was positioned for MEG recording, an electric current with a unique frequency label (e.g. 322 Hz) was fed to each of the coils. This induced a measurable magnetic field and allowed each coil to be localized relative to the sensors throughout the recording session, which by proxy allowed the head position to be continuously monitored relative to the sensor array. Offline, MEG data from each subject were then individually corrected for head motion and subjected to noise reduction using the signal space separation method with a temporal extension 1-weighted MRI data before source space analyses using Brain Electrical Source Analysis (BESA) MRI (Version 2.0). Structural MRI data were aligned parallel to the anterior and posterior commissures and transformed into the Talairach coordinate system.36 Following source analysis (i.e. beamforming), each subject’s functional images were transformed into standardized space using the transform applied to the structural MRI volume.Because head position indicator coil locations were also known in head coordinates, all MEG measurements could be transformed into a common coordinate system. With this coordinate system, each participant’s MEG data were coregistered with structural T37 The continuous magnetic time series was divided into epochs of 6.5 s duration, with the baseline being defined as −1.8 to −0.8 s before initial stimulus onset. Epochs containing artefacts were rejected based on a fixed threshold method, supplemented with a visual inspection. Artefact-free epochs were transformed into the time–frequency domain using complex demodulation38 , and the resulting spectral power estimations per sensor were averaged over trials to generate time–frequency plots of mean spectral density. These sensor-level data were normalized by dividing the power value of each time–frequency bin by the respective bin’s baseline power, which was calculated as the mean power during the −1.8 to −0.8 s time period. This normalization allowed task-related power fluctuations to be visualized in sensor space.Cardiac, eye blink and excessive eye movement artefacts were removed from the data using signal-space projection, which was accounted for during source reconstruction.39 Each data point in the spectrogram was initially evaluated using a mass univariate approach based on the general linear model. To reduce the risk of false-positive results while maintaining reasonable sensitivity, a two-stage procedure was followed to control for Type 1 error. In the first stage, one-sample t-tests were conducted on each data point, and the output spectrogram of t-values was thresholded at P < 0.05 to define time–frequency bins containing potentially significant oscillatory deviations across all participants. In Stage 2, time–frequency bins that survived the threshold were clustered with temporally and/or spectrally neighbouring bins that were also above the (P < 0.05) threshold on sensors within 4 cm of each other , and a cluster value was derived by summing all of the t-values of all data points in the cluster. Non-parametric permutation testing was then used to derive a distribution of cluster values, and the significance level of the observed clusters (from Stage 1) was tested directly using this distribution41. For each comparison, at least 10 000 permutations were computed to build a distribution of cluster values. Based on these analyses, the time–frequency windows that contained significant oscillatory events across all participants during the encoding and maintenance phases were subjected to beamforming.The time–frequency windows subjected to beamforming (i.e. imaging) in this study were derived through a two-stage statistical analysis of the sensor-level spectrograms across the entire array of gradiometers  during the 4 s stimulus presentation window, in line with our previous studies.42–44 The single images are derived from the cross-spectral densities of all combinations of MEG gradiometers averaged over the time–frequency range of interest and the solution of the forward problem for each location on a grid specified by input voxel space. Following convention, we computed noise-normalized, differential source power per voxel in each participant using active (i.e. task) and passive (i.e. baseline) periods of equal duration and bandwidth.44 Such images are typically referred to as pseudo-t maps, with units (i.e. pseudo-t) that reflect noise-normalized power differences per voxel. Magnetoencephalography preprocessing and imaging used the BESA (version 6.0) software.Cortical networks were imaged through an extension of the linearly constrained minimum variance vector beamformer, which employs spatial filters in the frequency domain to calculate source power for the entire brain volume.t-tests were performed to dissociate the impact of hearing loss on the neural dynamics serving abstract reasoning. In addition, whole-brain Pearson correlations were performed to dissociate the impact of hearing aid use on abstract reasoning-related neural activity. We controlled for the degree of hearing loss in all hearing aid use analyses. All output statistical maps were adjusted for multiple comparisons using a spatial extent threshold  based on the theory of Gaussian random fields.45–47 Basically, statistical maps were initially thresholded at P < 0.005 and then a cluster-based correction method was applied such that at least six contiguous voxels must be significant at P < 0.005 in order for a cluster to be considered significant.Normalized differential source power was computed for the selected time–frequency bands, using a baseline of equal length, over the entire brain volume per participant at 4.0 × 4.0 × 4.0 mm resolution. Each participant’s functional images were then transformed into standardized space using the transform that was previously applied to the structural images. Then, whole-brain independent samples The data that support the findings of this study are available from the corresponding author, upon reasonable request.t-test with equal variance not assumed. Behaviourally, there were no differences in task performance between groups, accuracy: t(25.4) = 0.478, P = 0.636, CNH: 75.1% (SD = 9.6%), CHH: 73.1% (SD = 14.3%); reaction time: t(33) = −0.276, P = .785, CNH: 2117 ms (SD = 271 ms), CHH: 2141 ms (SD = 217 ms). r(35) = 0.443, P = 0.008 and r(35) = 0.439, P = 0.009, respectively)] suggesting that this task likely tapped the same cognitive constructs as these well-studied abstract reasoning and non-verbal intelligence metrics. Both correct and incorrect trials were subject to preprocessing. Following artefact rejection, an average of 99.79 (SD = 8.92) trials for CNH and 103.44 (SD = 4.47) trials for CHH were included in analysis; this difference was not significant, t(33) = 1.485, P = 0.147.Accuracy data did not pass Levene’s test for equality of variances (CHH had significantly more variability than CNH), so we report the results of that P < 0.0001, corrected). This response was followed by a significant sustained alpha (8–13 Hz) event-related desynchronization (ERD) in a large number of posterior sensors that was sustained until after the average response onset . Because we sought to determine the impact of hearing loss on abstract reasoning processing, we focused our analysis of alpha dynamics to the time period before the earliest response onset (i.e. before 1200 ms). Time–frequency bins of oscillatory activity to be imaged were identified across both groups. The two-stage statistical analysis of the sensor-level time–frequency spectrograms resulted in two significant time–frequency bins. There was a significant theta (3–7 Hz) ERS that was identified in a large number of posterior and medial sensors from 50 to 400 ms after stimulus onset (t-tests (CHH vs. CNH) were performed on the theta ERS and alpha ERD images separately. Resultant images were thresholded at P < 0.005 and corrected for multiple comparisons . We found significant differences in theta ERS activity between CNH and CHH in the medial occipital cortex, as well as the right supramarginal gyrus; CHH showed stronger ERS responses than CNH in both regions  were entered into a voxel-wise whole-brain correlation analysis with theta ERS and alpha ERD maps separately, with BEPTA acting as a covariate. As described previously, the resultant correlation maps were thresholded at P < 0.005 and corrected for multiple comparisons using a cluster threshold of k = 6 contiguous 4 mm3 voxels.Given the increasing evidence that consistent hearing aid use moderates language, executive function, and academic outcomes in CHHP < 0.005, corrected). Specifically, there were significant negative correlations in the left DLPFC; (peak r2 = 0.640), medial parieto-occipital cortex (peak r2 = 0.629) and right supramarginal gyrus (peak r2 = 0.682). There was also a significant negative correlation between hearing aid use and theta activity in the right postcentral gyrus (peak r2 = 0.686). In all of these regions, there was less theta activity with more hearing aid use , or audiovisual stimuli. The authors showed that overall, if the speech is accompanied by a visual cue (i.e. either a static face or a face uttering the speech sound), then both CNH and CHH responded more quickly to the stimulus. However, CHH responded more quickly when the visual cue was present relative to auditory-only, both when the face was dynamic (i.e. uttering the speech sound) and when it was static, while CNH only responded more quickly than auditory-only if the visual cue was dynamic.49 This suggests that CHH utilize greater visual attention resources overall to understand speech sounds. Extending this to the current study, it is possible that greater attention to visual input is a domain-general mechanism that applies not only to speech perception but also attention more broadly in CHH. Future studies should investigate the role of visual neural activity in moderating behavioural outcomes in CHH, especially using multimodal methods such as neuroimaging with simultaneous eye-tracking.CHH showed increased theta ERS activity relative to CNH in the medial occipital cortex along the calcarine fissure during early stimulus processing. This increased theta ERS activity is likely indicative of a heightened response during initial bottom-up visual processing, as the calcarine fissure hosts the primary visual cortex. This visual theta response has been shown to be enhanced with increased attention to a visual stimulus.50 emotion processing51 and spatial awareness,52 recent work suggests that the supramarginal gyrus and lateral inferior parietal cortex are also crucial to object categorization and perception–action sequences,53 as well as visual attention.54 Thus, greater theta activity in these regions during the early stages of processing in CHH relative to CNH may be related to visual processing and categorization of the stimuli, as is necessary in this task before making a decision regarding the pattern compliance of the target. It is possible that this increased activity is compensatory, such that there may be decreased efficiency in the neural network and/or an increased allocation of the neural resources serving pattern recognition and visual attention, concurrent with the increased visual attention resources found in occipital regions as described previously. Taken together, the results of the present study suggest that, at least at the group level, CHH utilizes increased neural resources during the initial stages of visual processing and pattern recognition than CNH. The lack of difference in later-stage alpha ERD dynamics or overall task behaviour between CHH and CNH suggests that this increased initial theta activity serves as a successful compensatory mechanism with which CHH can adequately perform the task at levels similar to CNH.In addition to elevated theta ERS activity in the primary visual cortices, CHH showed increased theta ERS activity in the supramarginal gyrus relative to CNH. While the supramarginal gyrus is known to be associated with a variety of high-order cognitive tasks including expressive language function,within the CHH. We found a significant modulation of theta activity throughout the fronto-parietal executive function network with hearing aid use within the CHH, such that CHH who wore their hearing aids more consistently had less activity in the fronto-parietal network, while those who did not wear their hearing aids consistently had elevated activity throughout this network. As described previously, P-FIT suggests that the structure and function of the bilateral DLPFC, posterior parietal cortices and occipital cortices are causally related to fluid intelligence in children and adults.28–30 Thus, an increase in activity in this network with decreased hearing aid use could reflect the greater allocation of neuronal resources to perform the task (i.e. less efficiency within the network). This interpretation has widespread implication for real-life behaviour in CHH. For example, it is possible that CHH performed at or near levels of CNH on this task at the group level in a controlled, decontextualized laboratory setting, but the neural systems serving these processes were taxed to a greater extent in CHH who did not wear their hearing aids consistently. If this is the case, real-world settings in which there are distractions and/or the need to multitask could result in an overburden of the neural networks serving these cognitive processes in CHH and may lead to subsequent behavioural decrements. This proposed dose–response relationship  may explain the variability in previous behavioural findings across the literature and underscores the importance of consistent hearing aid use for brain and behavioural health in CHH. In sum, these data are the first to suggest that hearing aid use is directly related to the neural dynamics serving non-verbal executive function, above and beyond any effects of the degree of hearing loss, and suggests that neuromaging may be helpful in quantifying the impact of therapeutic intervention parameters on cognitive and neural development in CHH. In addition, hearing aid use is not the only audiological metric of importance in the context of hearing intervention. Future studies should investigate the added impacts of hearing aid fit (e.g. audibility) and age of intervention.Perhaps, our most striking pattern of results was found In conclusion, this study is the first to determine the impact of hearing loss and subsequent hearing aid use on the spatiotemporal dynamics underlying abstract reasoning in children. We found that CHH showed increased theta activity in the right supramarginal gyrus and medial occipital regions during the early stages of stimulus processing, but no significant changes in neural activity during later stages of the task. Thus, it is possible that any behavioural differences in fluid intelligence found in other studies are due to the differences in the initial processing of the stimuli, which may also have cascading cumulative effects on later-stage cognitive demands . In addition, we found significant correlations between hearing aid use and early theta activity throughout the fronto-parietal network, such that there was decreased recruitment of these regions with increased hearing aid use in CHH. This pattern of results suggests dissociable impacts of hearing loss and hearing aid use on the oscillatory dynamics serving fluid intelligence and that CHH may tax their neural systems to a greater extent than CNH to perform at the same behavioural level. Finally, increased recruitment of neural resources, especially in the fronto-parietal executive function network, is exacerbated in CHH who do not wear their hearing aids consistently. More broadly, these data underscore the importance of looking at both groupwise comparisons and individual differences in the context of CHH. Future studies should enroll a larger, longitudinal cohort of CHH and CNH to investigate the developmental trajectory of performance and related brain dynamics with regard to non-verbal intelligence.