Neural correlates of sine-wave speech intelligibility in human frontal and temporal cortex

Abstract

Auditory speech comprehension is the result of neural computations that occur in a broad network that includes the temporal lobe auditory cortex and the left inferior frontal cortex. It remains unclear how representations in this network differentially contribute to speech comprehension. Here, we recorded high-density direct cortical activity during a sine-wave speech (SWS) listening task to examine detailed neural speech representations when the exact same acoustic input is comprehended versus not comprehended. Listeners heard SWS sentences (pre-exposure), followed by clear versions of the same sentences, which revealed the content of the sounds (exposure), and then the same SWS sentences again (post-exposure). Across all three task phases, high-gamma neural activity in the superior temporal gyrus was similar, distinguishing different words based on bottom-up acoustic features. In contrast, frontal regions showed a more pronounced and sudden increase in activity only when the input was comprehended, which corresponded with stronger representational separability among spatiotemporal activity patterns evoked by different words. We observed this effect only in participants who were not able to comprehend the stimuli during the pre-exposure phase, indicating a relationship between frontal high-gamma activity and speech understanding. Together, these results demonstrate that both frontal and temporal cortical networks are involved in spoken language understanding, and that under certain listening conditions, frontal regions are involved in discriminating speech sounds.

Neural indices of listening effort in noisy environments

“…These findings therefore suggest that correct digit identification is associated with left frontal speech-brain coherence driving auditory cortex. Left frontal speech-brain coherence is also consistent with previous fMRI/ECoG (electrocorticography) noise vocoding and sine-wave speech learning studies demonstrating increased left IFG activity after successful speech vocoder training 64–67 .…”

The revolution will not be controlled: natural stimuli in speech neuroscience

“…Linearised models have demonstrated the existence of strong spectrotemporal and phonetic feature representations in superior temporal gyrus (de Heer et al, 2017;Di Liberto et al, 2015;Hullett et al, 2016;Mesgarani et al, 2014) and motor cortex (Cheung et al, 2016), independent representations of pitch intonation information, speaker identity, and sentence identity in bilateral superior temporal gyrus (Tang et al, 2017), and semantic representations across a wide swath of cortex (Huth et al, 2016). They have also shown how spectrotemporal, articulatory, and semantic information contribute to the generation of neural signals in different cortical areas (de Heer et al, 2017), and how feature representations may be modulated by attention (Fritz, Elhilali, David, & Shamma, 2007;Mesgarani & Chang, 2012;O'Sullivan, Reilly, & Lalor, 2015), intelligibility (Holdgraf et al, 2016;Khoshkhoo, Leonard, Mesgarani, & Chang, 2018), or behavioural context (David, 2017).…”

“…While using completely naturalistic stimuli has some disadvantages as described above, researchers have also made significant progress by taking naturalistic stimuli and manipulating them in specific ways in order to address specific questions. These questions have included the relative separability of acoustic-phonetic and prosodic information (Tang et al, 2017), how comprehension affects language representation (Adank & Devlin, 2010;Broderick, Anderson, Di Liberto, Crosse, & Lalor, 2018;Peelle, Gross, & Davis, 2013), how degrading stimuli by adding noise influences specific feature representations (Di Ding & Simon, 2013), how natural stimulus statistics influence the ability to segregate simultaneous speech streams (Popham, Boebinger, Ellis, Kawahara, & McDermott, 2018), how temporal structure affects speech processing (Lerner et al, 2011;Overath, McDermott, Zarate, & Poeppel, 2015), and how prior knowledge affects representations of previously incomprehensible stimuli (Davis & Johnsrude, 2007;Di Liberto, Lalor, & Millman, 2018;Holdgraf et al, 2016;Khoshkhoo et al, 2018), among many others. In each of these studies, natural language stimuli were systematically manipulated in order to address a specific question.…”

Reframing PTSD for computational psychiatry with the active inference framework

“…This would preclude sensitive responses to ascending prediction errors, in that it would uncouple long-range signal propagation to higher cortical regions in exteroceptive hierarchies. Crucially, the latter are instrumental in stimulus comprehension, rather than mere detection (Khoshkhoo, Leonard, Mesgarani, & Chang, 2018).…”