Biology subjects

Zuk, N. J.

Publications and source records attributed to Zuk, N. J..

5 recordsLinked to original sources

bioRxiv2024

Reduced monitoring of task performance is an effective biomarker of autism

Can reduced flexibility in autistics behavior be a reliable classifier of autism? We asked this question in the context of pitch discrimination, which is performed with similar accuracy by autistic and non-autistic participants, matched for cognitive skills. We found that three reliable, uncorrelated characteristics quantified reduced behavioral flexibility. Most non-autistic participants showed a clear feedback-related negativity (FRN) in their EEG when incorrect, larger EEG responses during feedback following difficult versus easy trials, and increased perceptual bias following correct and easy trials versus difficult ones. All three measures were weaker in autistic participants. To assess reliability, we replicated all three measures in an experiment approximately a year later. These measures were not correlated within participants, indicating that several mechanisms, associated with anterior cingulate cortex, are atypical in autism. Together, these three measures provide group classification with above 80% accuracy, showing promise for their utility as a neurocognitive biomarker of autism. Significance statementA characteristic of autism is cognitive "slowness" in updating expectations in new contexts. Our study shows that this cognitive difference is related to a reduced use of feedback, linked to atypical activity in a brain area involved in error detection and behavioral adjustment. Using EEG, we found that autistic individuals have weaker feedback-related signals and do not adjust their behavior as effectively as non-autistic individuals. Together, these neurocognitive signals could serve as a biomarker for autism. This is the first time such brain responses have been measured in a perceptual task requiring continuous adjustments. Understanding these brain mechanisms can inform strategies to enhance learning and adaptability in autism, providing new insights into the neural basis of behavioral flexibility.

neuroscience↗

bioRxiv2021

The integration of continuous audio and visual speech in a cocktail-party environment depends on attention

In noisy, complex environments, our ability to understand audio speech benefits greatly from seeing the speakers face. This is attributed to the brains ability to integrate audio and visual information, a process known as multisensory integration. In addition, selective attention to speech in complex environments plays an enormous role in what we understand, the so-called cocktail-party phenomenon. But how attention and multisensory integration interact remains incompletely understood. While considerable progress has been made on this issue using simple, and often illusory (e.g., McGurk) stimuli, relatively little is known about how attention and multisensory integration interact in the case of natural, continuous speech. Here, we addressed this issue by analyzing EEG data recorded from subjects who undertook a multisensory cocktail-party attention task using natural speech. To assess multisensory integration, we modeled the EEG responses to the speech in two ways. The first assumed that audiovisual speech processing is simply a linear combination of audio speech processing and visual speech processing (i.e., an A+V model), while the second allows for the possibility of audiovisual interactions (i.e., an AV model). Applying these models to the data revealed that EEG responses to attended audiovisual speech were better explained by an AV model than an A+V model, providing evidence for multisensory integration. In contrast, unattended audiovisual speech responses were best captured using an A+V model, suggesting that multisensory integration is suppressed for unattended speech. Follow up analyses revealed some limited evidence for early multisensory integration of unattended AV speech, with no integration occurring at later levels of processing. We take these findings as evidence that the integration of natural audio and visual speech occurs at multiple levels of processing in the brain, each of which can be differentially affected by attention.

neuroscience↗

bioRxiv2021

Envelope reconstruction of speech and music highlights unique tracking of speech at low frequencies

The human brain tracks amplitude fluctuations of both speech and music, which reflects acoustic processing in addition to the processing of higher-order features and ones cognitive state. Comparing neural tracking of speech and music envelopes can elucidate stimulus-general mechanisms, but direct comparisons are confounded by differences in their envelope spectra. Here, we use a novel method of frequency-constrained reconstruction of stimulus envelopes using EEG recorded during passive listening. We expected to see music reconstruction match speech in a narrow range of frequencies, but instead we found that speech was reconstructed better than music for all frequencies we examined. Additionally, speech envelope tracking at low frequencies, below 1 Hz, was uniquely associated with increased weighting over parietal channels. Our results highlight the importance of low-frequency speech tracking and its origin from speech-specific processing in the brain.

neuroscience↗

bioRxiv2020

More than Words: Neurophysiological Correlates of Semantic Dissimilarity Depend on Comprehension of the Speech Narrative

Speech comprehension relies on the ability to understand the meaning of words within a coherent context. Recent studies have attempted to obtain electrophysiological indices of this process by modelling how brain activity is affected by a words semantic dissimilarity to preceding words. While the resulting indices appear robust and are strongly modulated by attention, it remains possible that, rather than capturing the contextual understanding of words, they may actually reflect word-to-word changes in semantic content without the need for a narrative-level understanding on the part of the listener. To test this possibility, we recorded EEG from subjects who listened to speech presented in either its original, narrative form, or after scrambling the word order by varying amounts. This manipulation affected the ability of subjects to comprehend the narrative content of the speech, but not the ability to recognize the individual words. Neural indices of semantic understanding and low-level acoustic processing were derived for each scrambling condition using the temporal response function (TRF) approach. Signatures of semantic processing were observed for conditions where speech was unscrambled or minimally scrambled and subjects were able to understand the speech. The same markers were absent for higher levels of scrambling when speech comprehension dropped below chance. In contrast, word recognition remained high and neural measures related to envelope tracking did not vary significantly across the different scrambling conditions. This supports the previous claim that electrophysiological indices based on the semantic dissimilarity of words to their context reflect a listeners understanding of those words relative to that context. It also highlights the relative insensitivity of neural measures of low-level speech processing to speech comprehension.

neuroscience↗

bioRxiv2019

EEG-based classification of natural sounds reveals specialized responses to speech and music

Humans can easily distinguish many sounds in the environment, but speech and music are uniquely important. Previous studies, mostly using fMRI, have identified separate regions of the brain that respond selectively for speech and music. Yet there is little evidence that brain responses are larger and more temporally precise for human-specific sounds like speech and music, as has been found for responses to species-specific sounds in other animals. We recorded EEG as healthy, adult subjects listened to various types of two-second-long natural sounds. By classifying each sound based on the EEG response, we found that speech, music, and impact sounds were classified better than other natural sounds. But unlike impact sounds, the classification accuracy for speech and music dropped for synthesized sounds that have identical \"low-level\" acoustic statistics based on a subcortical model, indicating a selectivity for higher-order features in these sounds. Lastly, the trends in average power and phase consistency of the two-second EEG responses to each sound replicated the patterns of speech and music selectivity observed with classification accuracy. Together with the classification results, this suggests that the brain produces temporally individualized responses to speech and music sounds that are stronger than the responses to other natural sounds. In addition to highlighting the importance of speech and music for the human brain, the techniques used here could be a cost-effective and efficient way to study the human brains selectivity for speech and music in other populations.\n\nHighlightsO_LIEEG responses are stronger to speech and music than to other natural sounds\nC_LIO_LIThis selectivity was not replicated using stimuli with the same acoustic statistics\nC_LIO_LIThese techniques can be a cost-effective way to study speech and music selectivity\nC_LI

neuroscience↗