Cortical analysis of speech has long been considered the domain of left-hemisphere auditory areas. A recent hypothesis poses that cortical processing of acoustic signals, including speech, is mediated bilaterally based on the component rates inherent to the speech signal. In support of this hypothesis, previous studies have shown that slow temporal features (3-5 Hz) in nonspeech acoustic signals lateralize to right-hemisphere auditory areas, whereas rapid temporal features (20 -50 Hz) lateralize to the left hemisphere. These results were obtained using nonspeech stimuli, and it is not known whether right-hemisphere auditory cortex is dominant for coding the slow temporal features in speech known as the speech envelope. Here we show strong right-hemisphere dominance for coding the speech envelope, which represents syllable patterns and is critical for normal speech perception. Right-hemisphere auditory cortex was 100% more accurate in following contours of the speech envelope and had a 33% larger response magnitude while following the envelope compared with the left hemisphere. Asymmetries were evident regardless of the ear of stimulation despite dominance of contralateral connections in ascending auditory pathways. Results provide evidence that the right hemisphere plays a specific and important role in speech processing and support the hypothesis that acoustic processing of speech involves the decomposition of the signal into constituent temporal features by rate-specialized neurons in right-and left-hemisphere auditory cortex.

Multivariate pattern recognition methods are increasingly being used to identify multiregional brain activity patterns that collectively discriminate one cognitive condition or experimental group from another, using fMRI data. The performance of these methods is often limited because the number of regions considered in the analysis of fMRI data is large compared to the number of observations (trials or participants). Existing methods that aim to tackle this dimensionality problem are less than optimal because they either over-fit the data or are computationally intractable. Here, we describe a novel method based on logistic regression using a combination of L1 and L2 norm regularization that more accurately estimates discriminative brain regions across multiple conditions or groups. The L1 norm, computed using a fast estimation procedure, ensures a fast, sparse and generalizable solution; the L2 norm ensures that correlated brain regions are included in the resulting solution, a critical aspect of fMRI data analysis often overlooked by existing methods. We first evaluate the performance of our method on simulated data and then examine its effectiveness in discriminating between well-matched music and speech stimuli. We also compared our procedures with other methods which use either L1-norm regularization alone or support vector machine based feature elimination. On simulated data, our methods performed significantly better than existing methods across a wide-range of contrast-to-noise ratios and feature prevalence rates. On experimental fMRI data, our methods were more effective in selectively isolating a distributed fronto-temporal network that distinguished between brain regions known to be involved in speech and music processing. These findings suggest that our method is not only computationally efficient, but it also achieves the twin objectives of identifying relevant discriminative brain regions and accurately classifying fMRI data.

Individuals with autism spectrum disorders (ASDs) often show insensitivity to the human voice, a deficit that is thought to play a key role in communication deficits in this population. The social motivation theory of ASD predicts that impaired function of reward and emotional systems impedes children with ASD from actively engaging with speech. Here we explore this theory by investigating distributed brain systems underlying human voice perception in children with ASD. Using resting-state functional MRI data acquired from 20 children with ASD and 19 age-and intelligence quotientmatched typically developing children, we examined intrinsic functional connectivity of voice-selective bilateral posterior superior temporal sulcus (pSTS). Children with ASD showed a striking pattern of underconnectivity between left-hemisphere pSTS and distributed nodes of the dopaminergic reward pathway, including bilateral ventral tegmental areas and nucleus accumbens, left-hemisphere insula, orbitofrontal cortex, and ventromedial prefrontal cortex. Children with ASD also showed underconnectivity between righthemisphere pSTS, a region known for processing speech prosody, and the orbitofrontal cortex and amygdala, brain regions critical for emotion-related associative learning. The degree of underconnectivity between voice-selective cortex and reward pathways predicted symptom severity for communication deficits in children with ASD. Our results suggest that weak connectivity of voiceselective cortex and brain structures involved in reward and emotion may impair the ability of children with ASD to experience speech as a pleasurable stimulus, thereby impacting language and social skill development in this population. Our study provides support for the social motivation theory of ASD.auditory cortex | nucleus accumbens

Children with reading impairments have long been associated with impaired perception for rapidly presented acoustic stimuli and recently have shown deficits for slower features. It is not known whether impairments for low-frequency acoustic features negatively impact processing of speech in reading-impaired individuals. Here we provide neurophysiological evidence that poor readers have impaired representation of the speech envelope, the acoustical cue that provides syllable pattern information in speech. We measured cortical-evoked potentials in response to sentence stimuli and found that good readers indicated consistent right-hemisphere dominance in auditory cortex for all measures of speech envelope representation, including the precision, timing, and magnitude of cortical responses. Poor readers showed abnormal patterns of cerebral asymmetry for all measures of speech envelope representation. Moreover, cortical measures of speech envelope representation predicted up to 41% of the variability in standardized reading scores and 50% in measures of phonological processing across a wide range of abilities. Our findings strongly support a relationship between acoustic-level processing and higher-level language abilities, and are the first to link reading ability with cortical processing of low-frequency acoustic features in the speech signal. Our results also support the hypothesis that asymmetric routing between cerebral hemispheres represents an important mechanism for temporal encoding in the human auditory system, and the need for an expansion of the temporal processing hypothesis for reading disabilities to encompass impairments for a wider range of speech features than previously acknowledged.

The way in which normal variations in human neuroanatomy relate to brain function remains largely uninvestigated. This study addresses the question by relating anatomical measurements of Heschl’s gyrus (HG), the structure containing human primary auditory cortex, to how this region processes temporal and spectral acoustic information. In this study, subjects’ right and left HG were identified and manually indicated on anatomical MRI scans. Volumes of gray matter, white matter and total gyrus were recorded, and asymmetry indices were calculated. Additionally, cortical auditory activity in response to noise stimuli varying orthogonally in temporal and spectral dimensions was assessed and related to the volumetric measurements. A high degree of anatomical variability was seen, consistent with other reports in the literature. The auditory cortical responses showed the expected leftward lateralization to varying rates of stimulus change and rightward lateralization of increasing spectral information. However, the present data are the first to explicitly link anatomical variability of auditory cortex to individual differences in the way that cortex processes acoustic information. Specifically, larger volumes of left HG were associated with larger extents of rate-related cortex on the left, and larger volumes of right HG related to larger extents of spectral-related cortex on the right. This finding is discussed in relation to known microanatomical asymmetries of HG, including increased myelination of its fibers, and implications for language learning are considered.

Music and speech are complex sound streams with hierarchical rules of temporal organization that become elaborated over time. Here, we use functional magnetic resonance imaging to measure brain activity patterns in 20 right-handed nonmusicians as they listened to natural and temporally reordered musical and speech stimuli matched for familiarity, emotion, and valence. Heart rate variability and mean respiration rates were simultaneously measured and were found not to differ between musical and speech stimuli. Although the same manipulation of temporal structure elicited brain activation level differences of similar magnitude for both music and speech stimuli, multivariate classification analysis revealed distinct spatial patterns of brain responses in the 2 domains. Distributed neuronal populations that included the inferior frontal cortex, the posterior and anterior superior and middle temporal gyri, and the auditory brainstem classified temporal structure manipulations in music and speech with significant levels of accuracy. While agreeing with previous findings that music and speech processing share neural substrates, this work shows that temporal structure in the 2 domains is encoded differently, highlighting a fundamental dissimilarity in how the same neural resources are deployed.

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While predominant models of visual word form area (VWFA) function argue for its specific role in decoding written language, other accounts propose a more general role of VWFA in complex visual processing. However, a comprehensive examination of structural and functional VWFA circuits and their relationship to behavior has been missing. Here, using high-resolution multimodal imaging data from a large Human Connectome Project cohort (N = 313), we demonstrate robust patterns of VWFA connectivity with both canonical language and attentional networks. Brain-behavior relationships revealed a striking pattern of double dissociation: structural connectivity of VWFA with lateral temporal language network predicted language, but not visuo-spatial attention abilities, while VWFA connectivity with dorsal fronto-parietal attention network predicted visuo-spatial attention, but not language abilities. Our findings support a multiplex model of VWFA function characterized by distinct circuits for integrating language and attention, and point to connectivity-constrained cognition as a key principle of human brain organization.