A speech envelope landmark for syllable encoding in human superior temporal gyrus.

The most salient acoustic features in speech are the modulations in its intensity, captured by the amplitude envelope. Perceptually, the envelope is necessary for speech comprehension. Yet, the neural computations that represent the envelope and their linguistic implications are heavily debated. We used high-density intracranial recordings, while participants listened to speech, to determine how the envelope is represented in human speech cortical areas on the superior temporal gyrus (STG). We found that a well-defined zone in middle STG detects acoustic onset edges (local maxima in the envelope rate of change). Acoustic analyses demonstrated that timing of acoustic onset edges cues syllabic nucleus onsets, while their slope cues syllabic stress. Synthesized amplitude-modulated tone stimuli showed that steeper slopes elicited greater responses, confirming cortical encoding of amplitude change, not absolute amplitude. Overall, STG encoding of the timing and magnitude of acoustic onset edges underlies the perception of speech temporal structure

TRANDESNF: A computer program for transonic airfoil design and analysis in nonuniform flow

The use of a transonic airfoil code for analysis, inverse design, and direct optimization of an airfoil immersed in propfan slipstream is described. A summary of the theoretical method, program capabilities, input format, output variables, and program execution are described. Input data of sample test cases and the corresponding output are given

Cost-Effectiveness of Advanced Imaging Technologies in the Presurgical Workup of Epilepsy.

The cost-effectiveness and benefit of many diagnostic tests used in the presurgical evaluation for persons with epilepsy is for the most part uncertain as is their influence on decision-making. The options we have at our disposal are ever increasing. Advanced imaging modalities aim to improve surgical candidacy by helping us better define the epileptogenic zone and optimize surgical planning. However, judicious use is important. Randomized controlled trials delineating which mode of investigation is superior are lacking. Presurgical tests do have incremental value by increasing surgical candidacy and refining surgical planning. The yield of additional imaging will increase with complex localization. However, every case must be tailored by hypothesis, cost, and accessibility. Future studies using a quantitative cost-benefit framework are needed to determine the cost-effectiveness of advanced diagnostic tests (beyond magnetic resonance imaging) in the presurgical evaluation of those with epilepsy

Functional organization of human sensorimotor cortex for speech articulation.

Speaking is one of the most complex actions that we perform, but nearly all of us learn to do it effortlessly. Production of fluent speech requires the precise, coordinated movement of multiple articulators (for example, the lips, jaw, tongue and larynx) over rapid time scales. Here we used high-resolution, multi-electrode cortical recordings during the production of consonant-vowel syllables to determine the organization of speech sensorimotor cortex in humans. We found speech-articulator representations that are arranged somatotopically on ventral pre- and post-central gyri, and that partially overlap at individual electrodes. These representations were coordinated temporally as sequences during syllable production. Spatial patterns of cortical activity showed an emergent, population-level representation, which was organized by phonetic features. Over tens of milliseconds, the spatial patterns transitioned between distinct representations for different consonants and vowels. These results reveal the dynamic organization of speech sensorimotor cortex during the generation of multi-articulator movements that underlies our ability to speak

Real-time decoding of question-and-answer speech dialogue using human cortical activity.

Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate

Effect of neurostimulation on cognition and mood in refractory epilepsy.

Epilepsy is a common, debilitating neurological disorder characterized by recurrent seizures. Mood disorders and cognitive deficits are common comorbidities in epilepsy that, like seizures, profoundly influence quality of life and can be difficult to treat. For patients with refractory epilepsy who are not candidates for resection, neurostimulation, the electrical modulation of epileptogenic brain tissue, is an emerging treatment alternative. Several forms of neurostimulation are currently available, and therapy selection hinges on relative efficacy for seizure control and amelioration of neuropsychiatric comorbidities. Here, we review the current evidence for how invasive and noninvasive neurostimulation therapies affect mood and cognition in persons with epilepsy. Invasive therapies include vagus nerve stimulation (VNS), deep brain stimulation (DBS), and responsive neurostimulation (RNS). Noninvasive therapies include trigeminal nerve stimulation (TNS), repetitive transcranial magnetic stimulation (rTMS), and transcranial direct current stimulation (tDCS). Overall, current evidence supports stable cognition and mood with all neurostimulation therapies, although there is some evidence that cognition and mood may improve with invasive forms of neurostimulation. More research is required to optimize the effects of neurostimulation for improvements in cognition and mood

Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex.

A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from neural data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a data analysis tool for neuroscience. We first show that deep network's confusions revealed hierarchical latent structure in the neural data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience

Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data

Understanding the neural implementation of complex human behaviors is one of the major goals in neuroscience. To this end, it is crucial to find a true representation of the neural data, which is challenging due to the high complexity of behaviors and the low signal-to-ratio (SNR) of the signals. Here, we propose a novel unsupervised learning framework, Neural Latent Aligner (NLA), to find well-constrained, behaviorally relevant neural representations of complex behaviors. The key idea is to align representations across repeated trials to learn cross-trial consistent information. Furthermore, we propose a novel, fully differentiable time warping model (TWM) to resolve the temporal misalignment of trials. When applied to intracranial electrocorticography (ECoG) of natural speaking, our model learns better representations for decoding behaviors than the baseline models, especially in lower dimensional space. The TWM is empirically validated by measuring behavioral coherence between aligned trials. The proposed framework learns more cross-trial consistent representations than the baselines, and when visualized, the manifold reveals shared neural trajectories across trials.Comment: Accepted at ICML 202