Neural pathways for visual speech perception

This paper examines the questions, what levels of speech can be perceived visually, and how is visual speech represented by the brain? Review of the literature leads to the conclusions that every level of psycholinguistic speech structure (i.e., phonetic features, phonemes, syllables, words, and prosody) can be perceived visually, although individuals differ in their abilities to do so; and that there are visual modality-specific representations of speech qua speech in higher-level vision brain areas. That is, the visual system represents the modal patterns of visual speech. The suggestion that the auditory speech pathway receives and represents visual speech is examined in light of neuroimaging evidence on the auditory speech pathways. We outline the generally agreed-upon organization of the visual ventral and dorsal pathways and examine several types of visual processing that might be related to speech through those pathways, specifically, face and body, orthography, and sign language processing. In this context, we examine the visual speech processing literature, which reveals widespread diverse patterns activity in posterior temporal cortices in response to visual speech stimuli. We outline a model of the visual and auditory speech pathways and make several suggestions: (1) The visual perception of speech relies on visual pathway representations of speech qua speech. (2) A proposed site of these representations, the temporal visual speech area (TVSA) has been demonstrated in posterior temporal cortex, ventral and posterior to multisensory posterior superior temporal sulcus (pSTS). (3) Given that visual speech has dynamic and configural features, its representations in feedforward visual pathways are expected to integrate these features, possibly in TVSA

Neural Dynamics of Phonological Processing in the Dorsal Auditory Stream

Neuroanatomical models hypothesize a role for the dorsal auditory pathway in phonological processing as a feedforward efferent system (Davis and Johnsrude, 2007; Rauschecker and Scott, 2009; Hickok et al., 2011). But the functional organization of the pathway, in terms of time course of interactions between auditory, somatosensory, and motor regions, and the hemispheric lateralization pattern is largely unknown. Here, ambiguous duplex syllables, with elements presented dichotically at varying interaural asynchronies, were used to parametrically modulate phonological processing and associated neural activity in the human dorsal auditory stream. Subjects performed syllable and chirp identification tasks, while event-related potentials and functional magnetic resonance images were concurrently collected. Joint independent component analysis was applied to fuse the neuroimaging data and study the neural dynamics of brain regions involved in phonological processing with high spatiotemporal resolution. Results revealed a highly interactive neural network associated with phonological processing, composed of functional fields in posterior temporal gyrus (pSTG), inferior parietal lobule (IPL), and ventral central sulcus (vCS) that were engaged early and almost simultaneously (at 80–100 ms), consistent with a direct influence of articulatory somatomotor areas on phonemic perception. Left hemispheric lateralization was observed 250 ms earlier in IPL and vCS than pSTG, suggesting that functional specialization of somatomotor (and not auditory) areas determined lateralization in the dorsal auditory pathway. The temporal dynamics of the dorsal auditory pathway described here offer a new understanding of its functional organization and demonstrate that temporal information is essential to resolve neural circuits underlying complex behaviors

Differential rates of perinatal maturation of human primary and nonprimary auditory cortex

Abstract Primary and nonprimary cerebral cortex mature along different timescales; however, the differences between the rates of maturation of primary and nonprimary cortex are unclear. Cortical maturation can be measured through changes in tissue microstructure detectable by diffusion magnetic resonance imaging (MRI). In this study, diffusion tensor imaging (DTI) was used to characterize the maturation of Heschl’s gyrus (HG), which contains both primary auditory cortex (pAC) and nonprimary auditory cortex (nAC), in 90 preterm infants between 26 and 42 weeks postmenstrual age (PMA). The preterm infants were in different acoustical environments during their hospitalization: 46 in open ward beds and 44 in single rooms. A control group consisted of 15 term-born infants. Diffusion parameters revealed that (1) changes in cortical microstructure that accompany cortical maturation had largely already occurred in pAC by 28 weeks PMA, and (2) rapid changes were taking place in nAC between 26 and 42 weeks PMA. At term equivalent PMA, diffusion parameters for auditory cortex were different between preterm infants and term control infants, reflecting either delayed maturation or injury. No effect of room type was observed. For the preterm group, disturbed maturation of nonprimary (but not primary) auditory cortex was associated with poorer language performance at age two years

Specialization along the Left Superior Temporal Sulcus for Auditory Categorization

The affinity and temporal course of functional fields in middle and posterior superior temporal cortex for the categorization of complex sounds was examined using functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) recorded simultaneously. Data were compared before and after subjects were trained to categorize a continuum of unfamiliar nonphonemic auditory patterns with speech-like properties (NP) and a continuum of familiar phonemic patterns (P). fMRI activation for NP increased after training in left posterior superior temporal sulcus (pSTS). The ERP P2 response to NP also increased with training, and its scalp topography was consistent with left posterior superior temporal generators. In contrast, the left middle superior temporal sulcus (mSTS) showed fMRI activation only for P, and this response was not affected by training. The P2 response to P was also independent of training, and its estimated source was more anterior in left superior temporal cortex. Results are consistent with a role for left pSTS in short-term representation of relevant sound features that provide the basis for identifying newly acquired sound categories. Categorization of highly familiar phonemic patterns is mediated by long-term representations in left mSTS. Results provide new insight regarding the function of ventral and dorsal auditory streams

Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI

Joint independent component analysis (jICA) can be applied within subject for fusion of multi-channel event-related potentials (ERP) and functional magnetic resonance imaging (fMRI), to measure brain function at high spatiotemporal resolution (Mangalathu-Arumana et al., 2012). However, the impact of experimental design choices on jICA performance has not been systematically studied. Here, the sensitivity of jICA for recovering neural sources in individual data was evaluated as a function of imaging SNR, number of independent representations of the ERP/fMRI data, relationship between instantiations of the joint ERP/fMRI activity (linear, non-linear, uncoupled), and type of sources (varying parametrically and non-parametrically across representations of the data), using computer simulations. Neural sources were simulated with spatiotemporal and noise attributes derived from experimental data. The best performance, maximizing both cross-modal data fusion and the separation of brain sources, occurred with a moderate number of representations of the ERP/fMRI data (10–30), as in a mixed block/event related experimental design. Importantly, the type of relationship between instantiations of the ERP/fMRI activity, whether linear, non-linear or uncoupled, did not in itself impact jICA performance, and was accurately recovered in the common profiles (i.e., mixing coefficients). Thus, jICA provides an unbiased way to characterize the relationship between ERP and fMRI activity across brain regions, in individual data, rendering it potentially useful for characterizing pathological conditions in which neurovascular coupling is adversely affected

Neural Mechanisms of Perceptual Categorization as Precursors to Speech Perception

Perceptual categorization is fundamental to the brain’s remarkable ability to process large amounts of sensory information and efficiently recognize objects including speech. Perceptual categorization is the neural bridge between lower-level sensory and higher-level language processing. A long line of research on the physical properties of the speech signal as determined by the anatomy and physiology of the speech production apparatus has led to descriptions of the acoustic information that is used in speech recognition (e.g., stop consonants place and manner of articulation, voice onset time, aspiration). Recent research has also considered what visual cues are relevant to visual speech recognition (i.e., the visual counter-parts used in lipreading or audiovisual speech perception). Much of the theoretical work on speech perception was done in the twentieth century without the benefit of neuroimaging technologies and models of neural representation. Recent progress in understanding the functional organization of sensory and association cortices based on advances in neuroimaging presents the possibility of achieving a comprehensive and far reaching account of perception in the service of language. At the level of cell assemblies, research in animals and humans suggests that neurons in the temporal cortex are important for encoding biological categories. On the cellular level, different classes of neurons (interneurons and pyramidal neurons) have been suggested to play differential roles in the neural computations underlying auditory and visual categorization. The moment is ripe for a research topic focused on neural mechanisms mediating the emergence of speech representations (including auditory, visual and even somatosensory based forms). Important progress can be achieved by juxtaposing within the same research topic the knowledge that currently exists, the identified lacunae, and the theories that can support future investigations. This research topic provides a snapshot and platform for discussion of current understanding of neural mechanisms underlying the formation of perceptual categories and their relationship to language from a multidisciplinary and multisensory perspective. It includes contributions (reviews, original research, methodological developments) pertaining to the neural substrates, dynamics, and mechanisms underlying perceptual categorization and their interaction with neural processes governing speech perception

EEG and fMRI Coupling and Decoupling Based on Joint Independent Component Analysis (jICA)

Background Meaningful integration of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) requires knowing whether these measurements reflect the activity of the same neural sources, i.e., estimating the degree of coupling and decoupling between the neuroimaging modalities. New method This paper proposes a method to quantify the coupling and decoupling of fMRI and EEG signals based on the mixing matrix produced by joint independent component analysis (jICA). The method is termed fMRI/EEG-jICA. Results fMRI and EEG acquired during a syllable detection task with variable syllable presentation rates (0.25–3 Hz) were separated with jICA into two spatiotemporally distinct components, a primary component that increased nonlinearly in amplitude with syllable presentation rate, putatively reflecting an obligatory auditory response, and a secondary component that declined nonlinearly with syllable presentation rate, putatively reflecting an auditory attention orienting response. The two EEG subcomponents were of similar amplitude, but the secondary fMRI subcomponent was ten folds smaller than the primary one. Comparison to existing method FMRI multiple regression analysis yielded a map more consistent with the primary than secondary fMRI subcomponent of jICA, as determined by a greater area under the curve (0.5 versus 0.38) in a sensitivity and specificity analysis of spatial overlap. Conclusion fMRI/EEG-jICA revealed spatiotemporally distinct brain networks with greater sensitivity than fMRI multiple regression analysis, demonstrating how this method can be used for leveraging EEG signals to inform the detection and functional characterization of fMRI signals. fMRI/EEG-jICA may be useful for studying neurovascular coupling at a macro-level, e.g., in neurovascular disorders