Multiscale integration organizes hierarchical computation in human auditory cortex

Norman-Haignere, Sam V; Lk, Long; Devinsky, Orrin; Doyle, Werner; Irobunda, Ifeoma; Em, Merricks; Na, Feldstein; Gm, McKhann; Ca, Schevon; Flinker, Adeen; Mesgarani, Nima

doi:10.1101/2020.09.30.321687

Cited by 9 publications

(10 citation statements)

References 58 publications

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“…In line with previous studies ( Puvvada and Simon, 2017 ; O’Sullivan et al, 2019 ; Brodbeck et al, 2020 ; Norman-Haignere et al, 2020 ), our results showed significant differences in reconstruction accuracy between early and late integration windows, indicating enhanced envelope representation and significant effect of attention in the late EEG responses. This can be seen by comparing the reconstruction values across Figures 4 , 5 , or observing the data replotted in Figure 6 .…”

Section: Discussionsupporting

confidence: 92%

“…Therefore, hypothesized experimental effects can be found in the EEG at different latencies depending on the type of task or experimental manipulation. In addition, the analytic methods used to extract the EEG response perform differently depending on the duration of the analysis window used (Bouchard et al, 2013;Presacco et al, 2016Presacco et al, , 2019Brodbeck et al, 2018;Broderick et al, 2020b;Norman-Haignere et al, 2020). A few recent studies investigating differences in cortical tracking of speech across different groups of listeners reported the decline of reconstruction accuracy with narrowing the time window (from 500 ms down to 150 ms) in older adults, meaning that older adults require more time to process the information in the speech signal (Presacco et al, 2016(Presacco et al, , 2019.…”

Section: Relationships Among Early Responses Late Responses Overallmentioning

confidence: 99%

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Effects of Hearing Aid Noise Reduction on Early and Late Cortical Representations of Competing Talkers in Noise

Aličković

Fiedler

et al. 2021

Front. Neurosci.

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ObjectivesPrevious research using non-invasive (magnetoencephalography, MEG) and invasive (electrocorticography, ECoG) neural recordings has demonstrated the progressive and hierarchical representation and processing of complex multi-talker auditory scenes in the auditory cortex. Early responses (<85 ms) in primary-like areas appear to represent the individual talkers with almost equal fidelity and are independent of attention in normal-hearing (NH) listeners. However, late responses (>85 ms) in higher-order non-primary areas selectively represent the attended talker with significantly higher fidelity than unattended talkers in NH and hearing–impaired (HI) listeners. Motivated by these findings, the objective of this study was to investigate the effect of a noise reduction scheme (NR) in a commercial hearing aid (HA) on the representation of complex multi-talker auditory scenes in distinct hierarchical stages of the auditory cortex by using high-density electroencephalography (EEG).DesignWe addressed this issue by investigating early (<85 ms) and late (>85 ms) EEG responses recorded in 34 HI subjects fitted with HAs. The HA noise reduction (NR) was either on or off while the participants listened to a complex auditory scene. Participants were instructed to attend to one of two simultaneous talkers in the foreground while multi-talker babble noise played in the background (+3 dB SNR). After each trial, a two-choice question about the content of the attended speech was presented.ResultsUsing a stimulus reconstruction approach, our results suggest that the attention-related enhancement of neural representations of target and masker talkers located in the foreground, as well as suppression of the background noise in distinct hierarchical stages is significantly affected by the NR scheme. We found that the NR scheme contributed to the enhancement of the foreground and of the entire acoustic scene in the early responses, and that this enhancement was driven by better representation of the target speech. We found that the target talker in HI listeners was selectively represented in late responses. We found that use of the NR scheme resulted in enhanced representations of the target and masker speech in the foreground and a suppressed representation of the noise in the background in late responses. We found a significant effect of EEG time window on the strengths of the cortical representation of the target and masker.ConclusionTogether, our analyses of the early and late responses obtained from HI listeners support the existing view of hierarchical processing in the auditory cortex. Our findings demonstrate the benefits of a NR scheme on the representation of complex multi-talker auditory scenes in different areas of the auditory cortex in HI listeners.

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Section: Discussionsupporting

confidence: 92%

Section: Relationships Among Early Responses Late Responses Overallmentioning

confidence: 99%

Effects of Hearing Aid Noise Reduction on Early and Late Cortical Representations of Competing Talkers in Noise

Aličković

Fiedler

et al. 2021

Front. Neurosci.

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“…All other non-music and non-speech sounds produced weak responses. C10 also showed the most delayed response of all of the inferred components (708 ms; measured as the time-to-half maximum of the first principal component of the time x sound response matrix for each component), perhaps suggesting a longer integration window (Norman-Haignere et al, 2020). The weights for this component showed three hotspots in posterior, middle and anterior STG.…”

Section: Resultsmentioning

confidence: 99%

Intracranial recordings from human auditory cortex reveal a neural population selective for song

Norman-Haignere

Feather²,

Brunner

et al. 2019

Preprint

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24What is the neural basis of the human capacity for music? Neuroimaging has suggested some 25 segregation between responses to music and other sounds, like speech. But it remains unclear 26 whether finer-grained neural organization exists within the domain of music. Here, using intracranial 27 recordings from the surface of the human brain, we demonstrate a selective response to music with 28 vocals, distinct from responses to speech and to music more generally. Song selectivity was evident 29 using both data-driven component modeling and single-electrode analyses, and could not be 30 explained by standard acoustic features. These results suggest that music is represented by multiple 31 neural populations selective for different aspects of music, at least one of which is specialized for 32 the analysis of song. 33 3 Music is a quintessentially human capacity that is present in some form in nearly every society 34 (Savage et al., 2015;Lomax, 2017; Mehr et al., 2018), and that differs substantially from its closest 35 analogues in non-human animals (Patel, 2019). Researchers have long debated whether the human 36 brain has neural mechanisms dedicated to music, and if so, what computations those mechanisms 37 perform (Patel, 2012;Peretz et al., 2015). These questions have important implications for 38 understanding the organization of auditory cortex (Leaver and Rauschecker, 2010; Norman-39 Haignere et al., 2015), the neural basis of sensory deficits such as amusia (Peterson and 40 Pennington, 2015;Norman-Haignere et al., 2016;Peretz, 2016), the consequences of auditory 41 expertise (Herholz and Zatorre, 2012), and the computational underpinnings of auditory behavior 42 (Casey, 2017;Kell et al., 2018). 43 44Neuroimaging studies have suggested that representations of music diverge from those of other 45 sound categories in non-primary human auditory cortex: some non-primary voxels show partial 46 selectivity for music compared with other categories (Leaver and Rauschecker, 2010; Fedorenko et 47 al., 2012;Angulo-Perkins et al., 2014), and a recent study from our lab, which modeled voxels as 48 weighted sums of multiple response profiles, inferred a component of the fMRI response with clear 49 selectivity for music (Norman-Haignere et al., 2015). However, there are few reports of finer-grained 50 organization within the domain of music (Casey, 2017), potentially due to the coarse resolution of 51 fMRI. As a consequence, we know little about the neural code for music. 52 53Here, we tested for finer-grained selectivity for music using intracranial recordings from the human 54 brain (electrocorticography or ECoG) ( Fig 1A). We measured ECoG responses to a diverse set of 55 165 natural sounds, and submitted these responses to a novel decomposition method that is well-56 suited to the statistical structure of ECoG to reveal dominant response components of auditory 57cortex. This data-driven method revealed multiple music-and speech-selective response 58 components. Our key finding is that one of these components re...

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“…Our results demonstrate cortical regions in the STG and STS, the putative sites of TVAs, have strong vocal category preference across 2 distinct voice localizer tasks, NatS and VL. Notably, the data show that voice selectivity strengthens along the auditory cortical hierarchy from STP to STG/STS in a temporally dynamic fashion, with an initial less specific onset response followed by a sustained response with pronounced voice category preference in STG/STS [ 45 ]. Auditory sEEG experimental data presented here demonstrate category selectivity to voice even in participants as young as 9, suggesting functional specialization of the STG/STS arises prior to adulthood.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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Multiscale integration organizes hierarchical computation in human auditory cortex

Cited by 9 publications

References 58 publications

Effects of Hearing Aid Noise Reduction on Early and Late Cortical Representations of Competing Talkers in Noise

Effects of Hearing Aid Noise Reduction on Early and Late Cortical Representations of Competing Talkers in Noise

Intracranial recordings from human auditory cortex reveal a neural population selective for song

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

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