Rapid cortical auditory evoked potentials audiometry

OBJECTIVE

The current 'gold standard' of audiometry relies on subjective behavioural responses, which is impractical and unreliable for certain groups such as children, individuals with severe disabilities, or the disabled elderly. This study aims to validating blindly a novel electroencephalography (EEG) technique to estimate audiometric thresholds quickly.

DESIGN

Air-conduction audiometric thresholds at 250, 500, 1000, 2000, 4000, and 8000 Hz at 5 dB resolution were measured using three different systems: the novel EEG system, conventional pure-tone audiometry (PTA), and an automated behavioural test with the same stimulus properties as in the EEG test. EEG data were collected for 15 min from 32 semi-dry EEG electrodes. Later, the EEG system was trimmed to 8 electrodes and 7.5 min of data with satisfactory results. EEG and PTA thresholds were estimated blindly.

STUDY SAMPLE

Audiometric thresholds were estimated from 10 elderly patients with asymmetric sensorineural hearing loss and five normal hearing young adults at both ears.

RESULTS

Correlation and regression analysis validated the hearing thresholds derived from both EEG configurations relative to the behavioural hearing thresholds - Spearman's correlation of 0.78 between PTA and 8-electrode 7.5-min EEG data.

CONCLUSIONS

The results of this study open the door to rapid and objective hearing threshold estimation with EEG.

A parcellation scheme of mouse isocortex based on reversals in connectivity gradients

The brain is composed of several anatomically clearly separated structures. This parcellation is often extended into the isocortex, based on anatomical, physiological, or functional differences. Here, we derive a parcellation scheme based purely on the spatial structure of long-range synaptic connections within the cortex. To that end, we analyzed a publicly available dataset of average mouse brain connectivity, and split the isocortex into disjunct regions. Instead of clustering connectivity based on modularity, our scheme is inspired by methods that split sensory cortices into subregions where gradients of neuronal response properties, such as the location of the receptive field, reverse. We calculated comparable gradients from voxelized brain connectivity data and automatically detected reversals in them. This approach better respects the known presence of functional gradients within brain regions than clustering-based approaches. Placing borders at the reversals resulted in a parcellation into 41 subregions that differs significantly from an established scheme in nonrandom ways, but is comparable in terms of the modularity of connectivity between regions. It reveals unexpected trends of connectivity, such as a tripartite split of somatomotor regions along an anterior to posterior gradient. The method can be readily adapted to other organisms and data sources, such as human functional connectivity.