Visual selective attention in individuals with age-related hearing loss.

Evidence from epidemiological studies suggests that hearing loss is associated with an accelerated decline in cognitive function, but the underlying pathophysiological mechanism remains poorly understood. Studies using auditory tasks have suggested that degraded auditory input increases the cognitive load for auditory perceptual processing and thereby reduces the resources available for other cognitive tasks. Attention-related networks are among the systems overrecruited to support degraded auditory perception, but it is unclear how they function when no excessive recruitment of cognitive resources for auditory processing is needed. Here, we implemented an EEG study using a nonauditory visual attentional selection task in 30 individuals with age-related hearing loss (ARHLs, 60-73 years) and compared them with aged (N = 30, 60-70 years) and young (N = 35, 22-29 years) normal-hearing controls. Compared with their normal-hearing peers, ARHLs demonstrated a significant amplitude reduction for the posterior contralateral N2 component, which is a well-validated index of the allocation of selective visual attention, despite the comparable behavioral performance. Furthermore, the amplitudes were observed to correlate significantly with hearing acuities (pure tone audiometry thresholds) and higher-order hearing abilities (speech-in-noise thresholds) in aged individuals. The target-elicited alpha lateralization, another mechanism of visuospatial attention, demonstrated in control groups was not observed in ARHLs. Although behavioral performance is comparable, the significant decrease in N2pc amplitude in ARHLs provides neurophysiologic evidence that may suggest a visual attentional deficit in ARHLs even without extra-recruitment of cognitive resources by auditory processing. It supports the hypothesis that constant degraded auditory input in ARHLs has an adverse impact on the function of cognitive control systems, which is a possible mechanism mediating the relationship between hearing loss and cognitive decline.

Bayesian Adaptive Beamformer for Robust Electromagnetic Brain Imaging of Correlated Sources in High Spatial Resolution.

Reconstructing complex brain source activity at a high spatiotemporal resolution from magnetoencephalography (MEG) or electroencephalography (EEG) remains a challenging problem. Adaptive beamformers are routinely deployed for this imaging domain using the sample data covariance. However adaptive beamformers have long been hindered by 1) high degree of correlation between multiple brain sources, and 2) interference and noise embedded in sensor measurements. This study develops a novel framework for minimum variance adaptive beamformers that uses a model data covariance learned from data using a sparse Bayesian learning algorithm (SBL-BF). The learned model data covariance effectively removes influence from correlated brain sources and is robust to noise and interference without the need for baseline measurements. A multiresolution framework for model data covariance computation and parallelization of the beamformer implementation enables efficient high-resolution reconstruction images. Results with both simulations and real datasets indicate that multiple highly correlated sources can be accurately reconstructed, and that interference and noise can be sufficiently suppressed. Reconstructions at 2-2.5mm resolution (  âˆ¼  150K voxels) are possible with efficient run times of 1-3 minutes. This novel adaptive beamforming algorithm significantly outperforms the state-of-the-art benchmarks. Therefore, SBL-BF provides an effective framework for efficiently reconstructing multiple correlated brain sources with high resolution and robustness to interference and noise.