Medical Bionics - Research Publications

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    Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study.
    Paranawithana, I ; Mao, D ; Wong, YT ; McKay, CM (SPIE-Intl Soc Optical Eng, 2022-01)
    Significance: Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool that can measure resting-state functional connectivity; however, non-neuronal components present in fNIRS signals introduce false discoveries in connectivity, which can impact interpretation of functional networks. Aim: We investigated the effect of short channel correction on resting-state connectivity by removing non-neuronal signals from fNIRS long channel data. We hypothesized that false discoveries in connectivity can be reduced, hence improving the discriminability of functional networks of known, different connectivity strengths. Approach: A principal component analysis-based short channel correction technique was applied to resting-state data of 10 healthy adult subjects. Connectivity was analyzed using magnitude-squared coherence of channel pairs in connectivity groups of homologous and control brain regions, which are known to differ in connectivity. Results: By removing non-neuronal components using short channel correction, significant reduction of coherence was observed for oxy-hemoglobin concentration changes in frequency bands associated with resting-state connectivity that overlap with the Mayer wave frequencies. The results showed that short channel correction reduced spurious correlations in connectivity measures and improved the discriminability between homologous and control groups. Conclusions: Resting-state functional connectivity analysis with short channel correction performs better than without correction in its ability to distinguish functional networks with distinct connectivity characteristics.
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    Speech token detection and discrimination in individual infants using functional near-infrared spectroscopy
    Mao, D ; Wunderlich, J ; Savkovic, B ; Jeffreys, E ; Nicholls, N ; Lee, OW ; Eager, M ; McKay, CM (NATURE PORTFOLIO, 2021-12-14)
    Speech detection and discrimination ability are important measures of hearing ability that may inform crucial audiological intervention decisions for individuals with a hearing impairment. However, behavioral assessment of speech discrimination can be difficult and inaccurate in infants, prompting the need for an objective measure of speech detection and discrimination ability. In this study, the authors used functional near-infrared spectroscopy (fNIRS) as the objective measure. Twenty-three infants, 2 to 10 months of age participated, all of whom had passed newborn hearing screening or diagnostic audiology testing. They were presented with speech tokens at a comfortable listening level in a natural sleep state using a habituation/dishabituation paradigm. The authors hypothesized that fNIRS responses to speech token detection as well as speech token contrast discrimination could be measured in individual infants. The authors found significant fNIRS responses to speech detection in 87% of tested infants (false positive rate 0%), as well as to speech discrimination in 35% of tested infants (false positive rate 9%). The results show initial promise for the use of fNIRS as an objective clinical tool for measuring infant speech detection and discrimination ability; the authors highlight the further optimizations of test procedures and analysis techniques that would be required to improve accuracy and reliability to levels needed for clinical decision-making.
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    No Evidence That Music Training Benefits Speech Perception in Hearing-Impaired Listeners: A Systematic Review
    McKay, CM (SAGE PUBLICATIONS INC, 2021-02-01)
    As musicians have been shown to have a range of superior auditory skills to non-musicians (e.g., pitch discrimination ability), it has been hypothesized by many researchers that music training can have a beneficial effect on speech perception in populations with hearing impairment. This hypothesis relies on an assumption that the benefits seen in musicians are due to their training and not due to innate skills that may support successful musicianship. This systematic review examined the evidence from 13 longitudinal training studies that tested the hypothesis that music training has a causal effect on speech perception ability in hearing-impaired listeners. The papers were evaluated for quality of research design and appropriate analysis techniques. Only 4 of the 13 papers used a research design that allowed a causal relation between music training and outcome benefits to be validly tested, and none of those 4 papers with a better quality study design demonstrated a benefit of music training for speech perception. In spite of the lack of valid evidence in support of the hypothesis, 10 of the 13 papers made claims of benefits of music training, showing a propensity for confirmation bias in this area of research. It is recommended that future studies that aim to evaluate the association of speech perception ability and music training use a study design that differentiates the effects of training from those of innate perceptual and cognitive skills in the participants.
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    Applications of Phenomenological Loudness Models to Cochlear Implants
    McKay, CM (FRONTIERS MEDIA SA, 2021-01-13)
    Cochlear implants electrically stimulate surviving auditory neurons in the cochlea to provide severely or profoundly deaf people with access to hearing. Signal processing strategies derive frequency-specific information from the acoustic signal and code amplitude changes in frequency bands onto amplitude changes of current pulses emitted by the tonotopically arranged intracochlear electrodes. This article first describes how parameters of the electrical stimulation influence the loudness evoked and then summarizes two different phenomenological models developed by McKay and colleagues that have been used to explain psychophysical effects of stimulus parameters on loudness, detection, and modulation detection. The Temporal Model is applied to single-electrode stimuli and integrates cochlear neural excitation using a central temporal integration window analogous to that used in models of normal hearing. Perceptual decisions are made using decision criteria applied to the output of the integrator. By fitting the model parameters to a variety of psychophysical data, inferences can be made about how electrical stimulus parameters influence neural excitation in the cochlea. The Detailed Model is applied to multi-electrode stimuli, and includes effects of electrode interaction at a cochlear level and a transform between integrated excitation and specific loudness. The Practical Method of loudness estimation is a simplification of the Detailed Model and can be used to estimate the relative loudness of any multi-electrode pulsatile stimuli without the need to model excitation at the cochlear level. Clinical applications of these models to novel sound processing strategies are described.
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    Intensity Discrimination and Speech Recognition of Cochlear Implant Users
    McKay, CM ; Rickard, N ; Henshall, K (SPRINGER, 2018-10-01)
    The relation between speech recognition and within-channel or across-channel (i.e., spectral tilt) intensity discrimination was measured in nine CI users (11 ears). Within-channel intensity difference limens (IDLs) were measured at four electrode locations across the electrode array. Spectral tilt difference limens were measured with (XIDL-J) and without (XIDL) level jitter. Only three subjects could perform the XIDL-J task with the amount of jitter required to limit use of within-channel cues. XIDLs (normalized to %DR) were correlated with speech recognition (r = 0.67, P = 0.019) and were highly correlated with IDLs. XIDLs were on average nearly 3 times larger than IDLs and did not vary consistently with the spatial separation of the two component electrodes. The overall pattern of results was consistent with a common underlying subject-dependent limitation in the two difference limen tasks, hypothesized to be perceptual variance (how the perception of a sound differs on different presentations), which may also underlie the correlation of XIDLs with speech recognition. Evidence that spectral tilt discrimination is more important for speech recognition than within-channel intensity discrimination was not unequivocally shown in this study. However, the results tended to support this proposition, with XIDLs more correlated with speech performance than IDLs, and the ratio XIDL/IDL also being correlated with speech recognition. If supported by further research, the importance of perceptual variance as a limiting factor in speech understanding for CI users has important implications for efforts to improve outcomes for those with poor speech recognition.
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    Comparing fNIRS signal qualities between approaches with and without short channels
    Zhou, X ; Sobczak, G ; McKay, CM ; Litovsky, RY ; Sakakibara, M (PUBLIC LIBRARY SCIENCE, 2020-12-23)
    Functional near-infrared spectroscopy (fNIRS) is a non-invasive technique used to measure changes in oxygenated (HbO) and deoxygenated (HbR) hemoglobin, related to neuronal activity. fNIRS signals are contaminated by the systemic responses in the extracerebral tissue (superficial layer) of the head, as fNIRS uses a back-reflection measurement. Using shorter channels that are only sensitive to responses in the extracerebral tissue but not in the deeper layers where target neuronal activity occurs has been a 'gold standard' to reduce the systemic responses in the fNIRS data from adults. When shorter channels are not available or feasible for implementation, an alternative, i.e., anti-correlation (Anti-Corr) method has been adopted. To date, there has not been a study that directly assesses the outcomes from the two approaches. In this study, we compared the Anti-Corr method with the 'gold standard' in reducing systemic responses to improve fNIRS neural signal qualities. We used eight short channels (8-mm) in a group of adults, and conducted a principal component analysis (PCA) to extract two components that contributed the most to responses in the 8 short channels, which were assumed to contain the global components in the extracerebral tissue. We then used a general linear model (GLM), with and without including event-related regressors, to regress out the 2 principal components from regular fNIRS channels (30 mm), i.e., two GLM-PCA methods. Our results found that, the two GLM-PCA methods showed similar performance, both GLM-PCA methods and the Anti-Corr method improved fNIRS signal qualities, and the two GLM-PCA methods had better performance than the Anti-Corr method.
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    Interpreting the Effect of Stimulus Parameters on the Electrically Evoked Compound Action Potential and on Neural Health Estimates.
    Brochier, T ; McKay, CM ; Carlyon, RP (Springer Science and Business Media LLC, 2021-02)
    Variations in the condition of the neural population along the length of the cochlea can degrade the spectral and temporal representation of sounds conveyed by CIs, thereby limiting speech perception. One measurement that has been proposed as an estimate of neural survival (the number of remaining functional neurons) or neural health (the health of those remaining neurons) is the effect of stimulation parameters, such as the interphase gap (IPG), on the amplitude growth function (AGF) of the electrically evoked compound action potential (ECAP). The extent to which such measures reflect neural factors, rather than non-neural factors (e.g. electrode orientation, electrode-modiolus distance, and impedance), depends crucially upon how the AGF data are analysed. However, there is currently no consensus in the literature for the correct method to interpret changes in the ECAP AGF due to changes in stimulation parameters. We present a simple theoretical model for the effect of IPG on ECAP AGFs, along with a re-analysis of both animal and human data that measured the IPG effect. Both the theoretical model and the re-analysis of the animal data suggest that the IPG effect on ECAP AGF slope (IPG slope effect), measured using either a linear or logarithmic input-output scale, does not successfully control for the effects of non-neural factors. Both the model and the data suggest that the appropriate method to estimate neural health is by measuring the IPG offset effect, defined as the dB offset between the linear portions of ECAP AGFs for two stimuli differing only in IPG.
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    Objective measurement of tinnitus using functional near-infrared spectroscopy and machine learning
    Shoushtarian, M ; Alizadehsani, R ; Khosravi, A ; Acevedo, N ; McKay, CM ; Nahavandi, S ; Fallon, JB ; Dalla Mora, A (PUBLIC LIBRARY SCIENCE, 2020-11-18)
    Chronic tinnitus is a debilitating condition which affects 10-20% of adults and can severely impact their quality of life. Currently there is no objective measure of tinnitus that can be used clinically. Clinical assessment of the condition uses subjective feedback from individuals which is not always reliable. We investigated the sensitivity of functional near-infrared spectroscopy (fNIRS) to differentiate individuals with and without tinnitus and to identify fNIRS features associated with subjective ratings of tinnitus severity. We recorded fNIRS signals in the resting state and in response to auditory or visual stimuli from 25 individuals with chronic tinnitus and 21 controls matched for age and hearing loss. Severity of tinnitus was rated using the Tinnitus Handicap Inventory and subjective ratings of tinnitus loudness and annoyance were measured on a visual analogue scale. Following statistical group comparisons, machine learning methods including feature extraction and classification were applied to the fNIRS features to classify patients with tinnitus and controls and differentiate tinnitus at different severity levels. Resting state measures of connectivity between temporal regions and frontal and occipital regions were significantly higher in patients with tinnitus compared to controls. In the tinnitus group, temporal-occipital connectivity showed a significant increase with subject ratings of loudness. Also in this group, both visual and auditory evoked responses were significantly reduced in the visual and auditory regions of interest respectively. Naïve Bayes classifiers were able to classify patients with tinnitus from controls with an accuracy of 78.3%. An accuracy of 87.32% was achieved using Neural Networks to differentiate patients with slight/ mild versus moderate/ severe tinnitus. Our findings show the feasibility of using fNIRS and machine learning to develop an objective measure of tinnitus. Such a measure would greatly benefit clinicians and patients by providing a tool to objectively assess new treatments and patients' treatment progress.
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    Temporal Processing in the Auditory System
    McKay, CM ; Lim, HH ; Lenarz, T (SPRINGER, 2013-02-01)
    Central auditory processing in humans was investigated by comparing the perceptual effects of temporal parameters of electrical stimulation in auditory midbrain implant (AMI) and cochlear implant (CI) users. Four experiments were conducted to measure the following: effect of interpulse intervals on detection thresholds and loudness; temporal modulation transfer functions (TMTFs); effect of duration on detection thresholds; and forward masking decay. The CI data were consistent with a phenomenological model that based detection or loudness decisions on the output of a sliding temporal integration window, the input to which was the hypothetical auditory nerve response to each stimulus pulse. To predict the AMI data, the model required changes to both the neural response input (i.e., midbrain activity to AMI stimuli, compared to auditory nerve activity to CI stimuli) and the shape of the integration window. AMI data were consistent with a neural response that decreased more steeply compared to CI stimulation as the pulse rate increased or interpulse interval decreased. For one AMI subject, the data were consistent with a significant adaptation of the neural response for rates above 200 Hz. The AMI model required an integration window that was significantly wider (i.e., decreased temporal resolution) than that for CI data, the latter being well fit using the same integration window shape as derived from normal-hearing data. These models provide a useful way to conceptualize how stimulation of central auditory structures differs from stimulation of the auditory nerve and to better understand why AMI users have difficulty processing temporal cues important for speech understanding.
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    Can ECAP Measures Be Used for Totally Objective Programming of Cochlear Implants?
    McKay, CM ; Chandan, K ; Akhoun, I ; Siciliano, C ; Kluk, K (SPRINGER, 2013-12-01)
    An experiment was conducted with eight cochlear implant subjects to investigate the feasibility of using electrically evoked compound action potential (ECAP) measures other than ECAP thresholds to predict the way that behavioral thresholds change with rate of stimulation, and hence, whether they can be used without combination with behavioral measures to determine program stimulus levels for cochlear implants. Loudness models indicate that two peripheral neural response characteristics contribute to the slope of the threshold versus rate function: the way that neural activity to each stimulus pulse decreases as rate increases and the slope of the neural response versus stimulus current function. ECAP measures related to these two characteristics were measured: the way that ECAP amplitude decreases with stimulus rate and the ECAP amplitude growth function, respectively. A loudness model (incorporating temporal integration and the two neural response characteristics) and regression analyses were used to evaluate whether the ECAP measures could predict the average slope of the behavioral threshold versus current function and whether individual variation in the measures could predict individual variation in the slope of the threshold function. The average change of behavioral threshold with increasing rate was well predicted by the model when using the average ECAP data. However, the individual variations in the slope of the thresholds versus rate functions were not well predicted by individual variations in ECAP data. It was concluded that these ECAP measures are not useful for fully objective programming, possibly because they do not accurately reflect the neural response characteristics assumed by the model, or are measured at current levels much higher than threshold currents.