Pre- and post-target cortical processes predict speech-in-noise performance.

Understanding speech in noise (SiN) is a complex task that recruits multiple cortical subsystems. There is a variance in individuals' ability to understand SiN that cannot be explained by simple hearing profiles, which suggests that central factors may underlie the variance in SiN ability. Here, we elucidated a few cortical functions involved during a SiN task and their contributions to individual variance using both within- and across-subject approaches. Through our within-subject analysis of source-localized electroencephalography, we investigated how acoustic signal-to-noise ratio (SNR) alters cortical evoked responses to a target word across the speech recognition areas, finding stronger responses in left supramarginal gyrus (SMG, BA40 the dorsal lexicon area) with quieter noise. Through an individual differences approach, we found that listeners show different neural sensitivity to the background noise and target speech, reflected in the amplitude ratio of earlier auditory-cortical responses to speech and noise, named as an internal SNR. Listeners with better internal SNR showed better SiN performance. Further, we found that the post-speech time SMG activity explains a further amount of variance in SiN performance that is not accounted for by internal SNR. This result demonstrates that at least two cortical processes contribute to SiN performance independently: pre-target time processing to attenuate neural representation of background noise and post-target time processing to extract information from speech sounds.

Causal contribution of primate auditory cortex to auditory perceptual decision-making.

Auditory perceptual decisions are thought to be mediated by the ventral auditory pathway. However, the specific and causal contributions of different brain regions in this pathway, including the middle-lateral (ML) and anterolateral (AL) belt regions of the auditory cortex, to auditory decisions have not been fully identified. To identify these contributions, we recorded from and microstimulated ML and AL sites while monkeys decided whether an auditory stimulus contained more low-frequency or high-frequency tone bursts. Both ML and AL neural activity was modulated by the frequency content of the stimulus. But, only the responses of the most stimulus-sensitive AL neurons were systematically modulated by the monkeys' choices. Consistent with this observation, microstimulation of AL, but not ML, systematically biased the monkeys' behavior toward the choice associated with the preferred frequency of the stimulated site. Together, these findings suggest that AL directly and causally contributes sensory evidence to form this auditory decision.

Temporal Integration of Auditory Information Is Invariant to Temporal Grouping Cues

Auditory perception depends on the temporal structure of incoming acoustic stimuli. Here, we examined whether a temporal manipulation that affects the perceptual grouping also affects the time dependence of decisions regarding those stimuli. We designed a novel discrimination task that required human listeners to decide whether a sequence of tone bursts was increasing or decreasing in frequency. We manipulated temporal perceptual-grouping cues by changing the time interval between the tone bursts, which led to listeners hearing the sequences as a single sound for short intervals or discrete sounds for longer intervals. Despite these strong perceptual differences, this manipulation did not affect the efficiency of how auditory information was integrated over time to form a decision. Instead, the grouping manipulation affected subjects' speed-accuracy trade-offs. These results indicate that the temporal dynamics of evidence accumulation for auditory perceptual decisions can be invariant to manipulations that affect the perceptual grouping of the evidence.

A high-density, high-channel count, multiplexed µECoG array for auditory-cortex recordings.

Our understanding of the large-scale population dynamics of neural activity is limited, in part, by our inability to record simultaneously from large regions of the cortex. Here, we validated the use of a large-scale active microelectrode array that simultaneously records 196 multiplexed micro-electrocortigraphical (µECoG) signals from the cortical surface at a very high density (1,600 electrodes/cm(2)). We compared µECoG measurements in auditory cortex using a custom "active" electrode array to those recorded using a conventional "passive" µECoG array. Both of these array responses were also compared with data recorded via intrinsic optical imaging, which is a standard methodology for recording sound-evoked cortical activity. Custom active µECoG arrays generated more veridical representations of the tonotopic organization of the auditory cortex than current commercially available passive µECoG arrays. Furthermore, the cortical representation could be measured efficiently with the active arrays, requiring as little as 13.5 s of neural data acquisition. Next, we generated spectrotemporal receptive fields from the recorded neural activity on the active µECoG array and identified functional organizational principles comparable to those observed using intrinsic metabolic imaging and single-neuron recordings. This new electrode array technology has the potential for large-scale, temporally precise monitoring and mapping of the cortex, without the use of invasive penetrating electrodes.