Non-invasive neuroimaging studies have shown that semantic category and attribute information are encoded in neural population activity. Electrocorticography (ECoG) offers several advantages over non-invasive approaches, but the degree to which semantic attribute information is encoded in ECoG responses is not known. We recorded ECoG while patients named objects from 12 semantic categories and then trained high-dimensional encoding models to map semantic attributes to spectral-temporal features of the task-related neural responses. Using these semantic attribute encoding models, untrained objects were decoded with accuracies comparable to whole-brain functional Magnetic Resonance Imaging (fMRI), and we observed that high-gamma activity (70-110Hz) at basal occipitotemporal electrodes was associated with specific semantic dimensions (manmade-animate, canonically large-small, and places-tools). Individual patient results were in close agreement with reports from other imaging modalities on the time course and functional organization of semantic processing along the ventral visual pathway during object recognition. The semantic attribute encoding model approach is critical for decoding objects absent from a training set, as well as for studying complex semantic encodings without artificially restricting stimuli to a small number of semantic categories.

Hypothesis Impairments of the medial olivocochlear system (MOCS) increase the risk of environmentally induced auditory neuropathy spectrum disorder (ANSD). Background ANSD is a problem in the neural transmission of auditory information that accounts for 10 – 15% of the cases of pediatric hearing loss. The underlying mechanisms of the disorder remain poorly understood, but noise exposure is an important risk factor. The goal of this study was to identify environmental conditions and genetic predispositions that lead to ANSD. Our approach was based on the assumption that noise induces ANSD by impeding the functional maturation of the brain’s sound coding pathways. Because the MOCS adjusts the sensitivity of the inner ear to noise, impairments of this feedback are predicted to increase the disruptive effects of environmental exposures. Methods An animal model of ANSD was created by rearing mice in noise. MOCS protection was assessed by comparing the incidence of noise-induced ANSD among knock-out (KO) mice lacking feedback and wild-type (WT) controls. The mice were screened for ANSD with distortion product otoacoustic emissions (DPOAEs), auditory brainstem responses (ABRs), and behavioral measures of gap detection. Single-unit recording procedures were used to link these deficits to impaired synaptic transmission in the ventral cochlear nucleus. Results ANSD manifested in noise-reared mice as intact DPOAEs, abnormal ABRs, and impaired gap detection. The phenotype was not observed among quiet-reared WT mice, but was occasionally noted among noise-reared WT mice. The incidence of ANSD significantly increased among KO mice, especially when they were reared in noise. Conclusion Noise promotes ANSD by altering the functional maturation of the brain’s temporal pathways. Noise-induced impairments are reduced by the sound-attenuating effects of the MOCS. Noise levels do not need to be unnaturally loud to constitute significant risk in MOCS-compromised individuals.

This report introduces a system for the objective physiological classification of single-unit activity in the anteroventral cochlear nucleus (AVCN) of anesthetized CBA/129 and CBA/CaJ mice. As in previous studies, the decision criteria are based on the temporal properties of responses to short tone bursts that are visualized in the form of peri-stimulus time histograms (PSTHs). Individual unit types are defined by the statistical distribution of quantifiable metrics that relate to the onset latency, regularity, and adaptation of sound-driven discharge rates. Variations of these properties reflect the unique synaptic organizations and intrinsic membrane properties that dictate the selective tuning of sound coding in the AVCN. When these metrics are applied to the mouse AVCN, responses to best frequency (BF) tones reproduce the major PSTH patterns that have been previously demonstrated in other mammalian species. The consistency of response types in two genetically diverse strains of laboratory mice suggests that the present classification system is appropriate for additional strains with normal peripheral function. The general agreement of present findings to established classifications validates laboratory mice as an adequate model for general principles of mammalian sound coding. Nevertheless, important differences are noted for the reliability of specialized endbulb transmission within the AVCN, suggesting less secure temporal coding in this high-frequency species.

Neuroscientists are actively pursuing high-precision maps, or graphs consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs, when coupled with physiological and behavioral data, are likely to facilitate greater understanding of how circuits in these networks give rise to complex information processing capabilities. Given that the automated or semi-automated methods required to achieve the acquisition of these graphs are still evolving, we developed a metric for measuring the performance of such methods by comparing their output with those generated by human annotators (“ground truth” data). Whereas classic metrics for comparing annotated neural tissue reconstructions generally do so at the voxel level, the metric proposed here measures the “integrity” of neurons based on the degree to which a collection of synaptic terminals belonging to a single neuron of the reconstruction can be matched to those of a single neuron in the ground truth data. The metric is largely insensitive to small errors in segmentation and more directly measures accuracy of the generated brain graph. It is our hope that use of the metric will facilitate the broader community's efforts to improve upon existing methods for acquiring brain graphs. Herein we describe the metric in detail, provide demonstrative examples of the intuitive scores it generates, and apply it to a synthesized neural network with simulated reconstruction errors. Demonstration code is available.