There is a large amount of variability in performance in masked-speech reception tasks, as well as in psychophysical auditory temporal processing tasks, between listeners with normal or relatively normal low-frequency hearing. In this study we used a cross-sectional dataset collected on 102 listeners (34 young, 34 middle-aged, 34 older) to assess whether variance in these tasks could be explained by variance in subcortical electrophysiological measures of auditory function (auditory brainstem responses and frequency following responses), and whether variance in speech-reception performance could be explained by variance in auditory temporal processing tasks. The potential confounding effect of high-frequency sensitivity was strictly controlled for by using highpass masking noise. Because each high-level construct (masked-speech reception, auditory temporal processing, and subcortical electrophysiological function) was indexed by several variables, we used principal component analyses to reduce the dimensionality of the dataset. Multiple-regression models were then used to assess the associations between the extracted principal components while controlling for a range of possible confounders including age and audiometric thresholds. We found that masked-speech reception was credibly associated with psychophysical auditory temporal processing abilities. No credible associations were found between masked-speech reception and electrophysiological measures of subcortical auditory function, or between psychophysical measures of auditory temporal processing and electrophysiological measures of subcortical auditory function. These results suggest that either the electrophysiological measures of subcortical auditory function used were not sufficiently sensitive to the subcortical neural processes limiting performance in the speech-reception and psychophysical auditory temporal-processing tasks, or that variance in these tasks is largely unrelated to variance in subcortical neural processes in listeners with near-normal hearing.
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http://dx.doi.org/10.1016/j.heares.2022.108456 | DOI Listing |
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Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, NJ, United States.
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Neuro-Cognitive Psychology, Department of Psychology, Bielefeld University, Bielefeld, Germany.
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View Article and Find Full Text PDFData Brief
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Department of Ecology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Incorporating ecological connectivity into spatial conservation planning is increasingly recognized as a key strategy to facilitate species movements, especially under changing environmental conditions. However, obtaining connectivity data is challenging, especially in the marine realm. Sea currents are essential for exploring marine structural connectivity, but transforming sea current data into spatial connectivity matrices involves complex and resource-intensive processing steps to ensure accuracy and usability.
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Graduate Program in Electrical Engineering, Federal University of Pará - UFPA, Belém, Brazil.
Introduction: Wavelet thresholding techniques are crucial in mitigating noise in data communication and storage systems. In image processing, particularly in medical imaging like MRI, noise reduction is vital for improving visual quality and accurate analysis. While existing methods offer noise reduction, they often suffer from limitations like edge and texture loss, poor smoothness, and the need for manual parameter tuning.
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