Speech prosody enhances the neural processing of syntax.

Human language relies on the correct processing of syntactic information, as it is essential for successful communication between speakers. As an abstract level of language, syntax has often been studied separately from the physical form of the speech signal, thus often masking the interactions that can promote better syntactic processing in the human brain. However, behavioral and neural evidence from adults suggests the idea that prosody and syntax interact, and studies in infants support the notion that prosody assists language learning. Here we analyze a MEG dataset to investigate how acoustic cues, specifically prosody, interact with syntactic representations in the brains of native English speakers. More specifically, to examine whether prosody enhances the cortical encoding of syntactic representations, we decode syntactic phrase boundaries directly from brain activity, and evaluate possible modulations of this decoding by the prosodic boundaries. Our findings demonstrate that the presence of prosodic boundaries improves the neural representation of phrase boundaries, indicating the facilitative role of prosodic cues in processing abstract linguistic features. This work has implications for interactive models of how the brain processes different linguistic features. Future research is needed to establish the neural underpinnings of prosody-syntax interactions in languages with different typological characteristics.

Aberrant neurophysiological signaling associated with speech impairments in Parkinson's disease.

Difficulty producing intelligible speech is a debilitating symptom of Parkinson's disease (PD). Yet, both the robust evaluation of speech impairments and the identification of the affected brain systems are challenging. Using task-free magnetoencephalography, we examine the spectral and spatial definitions of the functional neuropathology underlying reduced speech quality in patients with PD using a new approach to characterize speech impairments and a novel brain-imaging marker. We found that the interactive scoring of speech impairments in PD (N = 59) is reliable across non-expert raters, and better related to the hallmark motor and cognitive impairments of PD than automatically-extracted acoustical features. By relating these speech impairment ratings to neurophysiological deviations from healthy adults (N = 65), we show that articulation impairments in patients with PD are associated with aberrant activity in the left inferior frontal cortex, and that functional connectivity of this region with somatomotor cortices mediates the influence of cognitive decline on speech deficits.

Audio-Tokens: A toolbox for rating, sorting and comparing audio samples in the browser.

Here we describe a JavaScript toolbox to perform online rating studies with auditory material. The main feature of the toolbox is that audio samples are associated with visual tokens on the screen that control audio playback and can be manipulated depending on the type of rating. This allows the collection of single- and multidimensional feature ratings, as well as categorical and similarity ratings. The toolbox ( github.com/pwdonh/audio_tokens ) can be used via a plugin for the widely used jsPsych, as well as using plain JavaScript for custom applications. We expect the toolbox to be useful in psychological research on speech and music perception, as well as for the curation and annotation of datasets in machine learning.

Two Distinct Neural Timescales for Predictive Speech Processing.

During speech listening, the brain could use contextual predictions to optimize sensory sampling and processing. We asked if such predictive processing is organized dynamically into separate oscillatory timescales. We trained a neural network that uses context to predict speech at the phoneme level. Using this model, we estimated contextual uncertainty and surprise of natural speech as factors to explain neurophysiological activity in human listeners. We show, first, that speech-related activity is hierarchically organized into two timescales: fast responses (theta: 4-10 Hz), restricted to early auditory regions, and slow responses (delta: 0.5-4 Hz), dominating in downstream auditory regions. Neural activity in these bands is selectively modulated by predictions: the gain of early theta responses varies according to the contextual uncertainty of speech, while later delta responses are selective to surprising speech inputs. We conclude that theta sensory sampling is tuned to maximize expected information gain, while delta encodes only non-redundant information. VIDEO ABSTRACT.

Imaging of neural oscillations with embedded inferential and group prevalence statistics.

Magnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. For that reason, imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be adjusted to many experimental questions in systems neuroscience.

Biasing the perception of ambiguous vocal affect: a TMS study on frontal asymmetry.

Several sources of evidence point toward a link between asymmetry of prefrontal brain activity and approach-withdrawal tendencies. Here, we tested the causal nature of this link and examined if the categorization of an ambiguous approach- or withdrawal-related vocal signal can be biased by manipulating left and right frontal neural activity. We used voice morphing of affective non-verbal vocalizations to create individually tailored affectively ambiguous stimuli on an Anger-Fear continuum-two emotions that represent extremes on the approach-withdrawal dimension. We tested perception of these stimuli after 10 min of low-frequency repetitive transcranial magnetic stimulation over left or right dorsolateral prefrontal cortex or over the vertex (control), a technique that has transient inhibitory effects on the targeted brain region. As expected, ambiguous stimuli were more likely perceived as expressing Anger (approach) than Fear (withdrawal) after right prefrontal compared with left prefrontal or control stimulation. These results provide the first evidence that the manipulation of asymmetrical activity in prefrontal cortex can change the explicit categorization of ambiguous emotional signals.