A neural population selective for song in human auditory cortex

Norman-Haignere, Sam V; Feather, Jenelle; Boebinger, Dana; Brunner, Peter; Ritaccio, Anthony L.; McDermott, Josh H.; Schalk, Gerwin; Kanwisher, Nancy

doi:10.1016/j.cub.2022.01.069

Cited by 77 publications

(77 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Indeed, when we obtained the visually matched pairs of food and nonfood textures that had produced similar responses in Downing and Kanwisher's (1999) study 17 , leading them to argue against food selectivity, we found that our predictive model for Component 3 produced a higher response to food than nonfood. A similar pattern was observed previously with our voxel decomposition analysis of responses in auditory cortex 8 , where we found only weak selectivity for music in raw voxels, but strong selectivity for music in the inferred music component, which was later validated by clear music selectivity in the responses of individual intracranial electrodes 30 .…”

Section: Discussionsupporting

confidence: 90%

A Highly Selective Response to Food in Human Visual Cortex Revealed by Hypothesis-Free Voxel Decomposition

Khosla

Murty

Kanwisher

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Prior work has identified cortical regions selectively responsive to specific categories of visual stimuli. However, this hypothesis-driven work cannot reveal how prominent these category selectivities are in the overall functional organization of visual cortex, or what others might exist that scientists have not thought to look for. Further, standard voxel-wise tests cannot detect distinct neural selectivities that coexist within voxels. To overcome these limitations, we used data-driven voxel decomposition methods to identify the main components underlying fMRI responses to thousands of complex photographic images (Allen et al 2021). Our hypothesis-neutral analysis rediscovered components selective for faces, places, bodies, and words, validating our method and showing that these selectivities are dominant features of the ventral visual pathway. The analysis also revealed an unexpected component with a distinct anatomical distribution that responded highly selectively to images of food. Alternative accounts based on low to mid-level visual features like color, shape or texture failed to account for the food selectivity of this component. High-throughput testing and control experiments with matched stimuli on a highly accurate computational model of this component confirm its selectivity for food. We registered our methods and hypotheses before replicating them on held-out participants and in a novel dataset. These findings demonstrate the power of data-driven methods, and show that the dominant neural responses of the ventral visual pathway include not only selectivities for faces, scenes, bodies, and words, but also the visually heterogeneous category of food, thus constraining accounts of when and why functional specialization arises in the cortex.

show abstract

Section: Discussionsupporting

confidence: 90%

A Highly Selective Response to Food in Human Visual Cortex Revealed by Hypothesis-Free Voxel Decomposition

Khosla

Murty

Kanwisher

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This stands in contrast to functional neuroimaging work that used the NatS stimulus set to show that temporal voice regions may not exist [ 20 ]. Notably, Norman-Haignere and colleagues [ 27 ] recently published on intracranial recordings to the same NatS stimulus set and showed voice selectivity not driven by speech. They suggested that the disparity between these results and previous fMRI work could be related to the relatively reduced granularity of fMRI approaches compared to sEEG.…”

Section: Discussionmentioning

confidence: 99%

“…However, other studies have shown voice selectivity persists when controlling for the unique acoustic properties of voice stimuli [12]. At a broader level, it remains unknown to what extent processing of complex auditory stimuli, such as speech, music, or naturally occurring environmental sounds, rely on shared or unique neural mechanisms [18,20,[25][26][27] and how these mechanisms are organized across the auditory cortical hierarchy [28][29][30][31][32]. Together, support for TVAs suggests that populations of neurons in STG/ STS exhibit specialization for the rich information carried by vocalizations and substantiates the hypothesis that cortical representations of vocalizations represent categorical encoding (voice versus nonvoice) beyond the contribution of encoding of vocal acoustics.…”

Section: Introductionmentioning

confidence: 99%

Neural responses in human superior temporal cortex support coding of voice representations

et al. 2022

View full text Add to dashboard Cite

The ability to recognize abstract features of voice during auditory perception is an intricate feat of human audition. For the listener, this occurs in near-automatic fashion to seamlessly extract complex cues from a highly variable auditory signal. Voice perception depends on specialized regions of auditory cortex, including superior temporal gyrus (STG) and superior temporal sulcus (STS). However, the nature of voice encoding at the cortical level remains poorly understood. We leverage intracerebral recordings across human auditory cortex during presentation of voice and nonvoice acoustic stimuli to examine voice encoding at the cortical level in 8 patient-participants undergoing epilepsy surgery evaluation. We show that voice selectivity increases along the auditory hierarchy from supratemporal plane (STP) to the STG and STS. Results show accurate decoding of vocalizations from human auditory cortical activity even in the complete absence of linguistic content. These findings show an early, less-selective temporal window of neural activity in the STG and STS followed by a sustained, strongly voice-selective window. Encoding models demonstrate divergence in the encoding of acoustic features along the auditory hierarchy, wherein STG/STS responses are best explained by voice category and acoustics, as opposed to acoustic features of voice stimuli alone. This is in contrast to neural activity recorded from STP, in which responses were accounted for by acoustic features. These findings support a model of voice perception that engages categorical encoding mechanisms within STG and STS to facilitate feature extraction.

show abstract

“…An important open question is whether there exist brain regions and networks that are specific to music, or whether music-related information is processed in input agnostic auditory pathways 50,62,63 . While this study links musical elements to STRF components and precise anatomical locations, it is unlikely that these regions respond specifically to music.…”

Section: Discussionmentioning

confidence: 99%

Encoding and decoding analysis of music perception using intracranial EEG

Bellier

Llorens

Marciano

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Music is core to human experience across cultures. Multiple brain regions are engaged in music perception, but the neural dynamics of music processing are not well defined. We applied predictive modeling tools to intracranial EEG data recorded from 29 patients who passively listened to a song (Another Brick in the Wall, Part 1, Pink Floyd), focusing on high-frequency activity (HFA; 70-150Hz) as a marker of local neural population activity. Encoding models characterized the spectrotemporal receptive fields (STRFs) of each electrode and decoding models investigated the population-level song representation. With the STRFs, we confirmed a central role of bilateral superior temporal gyri (STG) in music perception with additional involvement of bilateral sensory-motor cortices (SMC) and inferior frontal gyri (IFG). We also observed a right hemispheric preference for music perception. Using both an independent component analysis (ICA) and temporal modulations, we observed cortical regions tuned to specific musical elements including the vocals, lead guitar notes and rhythm guitar patterns. An ablation analysis selectively removed music-responsive electrodes from decoding models to assess the contribution of anatomical and functional regions to representing the song's acoustics. Ablating electrodes from either left and right hemispheres sites deteriorated decoding accuracy, and we confirmed a right STG preference for music perception. We also report, to our knowledge, the first attempt at reconstructing the song from neural activity using both linear and non-linear decoding models, and discuss methodological factors impacting decoding accuracy.

show abstract

A neural population selective for song in human auditory cortex

Cited by 77 publications

References 74 publications

A Highly Selective Response to Food in Human Visual Cortex Revealed by Hypothesis-Free Voxel Decomposition

A Highly Selective Response to Food in Human Visual Cortex Revealed by Hypothesis-Free Voxel Decomposition

Neural responses in human superior temporal cortex support coding of voice representations

Encoding and decoding analysis of music perception using intracranial EEG

Contact Info

Product

Resources

About