CAS: Cognitive & Neural Systems: Scholarly Articles
Permanent URI for this collection
Browse
Recent Submissions
Item Engaging the articulators enhances perception of concordant visible speech movements(American Speech-Language-Hearing Association, 2019-10-25) Masapollo, Matthew; Guenther, Frank H.PURPOSE This study aimed to test whether (and how) somatosensory feedback signals from the vocal tract affect concurrent unimodal visual speech perception. METHOD Participants discriminated pairs of silent visual utterances of vowels under 3 experimental conditions: (a) normal (baseline) and while holding either (b) a bite block or (c) a lip tube in their mouths. To test the specificity of somatosensory-visual interactions during perception, we assessed discrimination of vowel contrasts optically distinguished based on their mandibular (English /ɛ/-/æ/) or labial (English /u/-French /u/) postures. In addition, we assessed perception of each contrast using dynamically articulating videos and static (single-frame) images of each gesture (at vowel midpoint). RESULTS Engaging the jaw selectively facilitated perception of the dynamic gestures optically distinct in terms of jaw height, whereas engaging the lips selectively facilitated perception of the dynamic gestures optically distinct in terms of their degree of lip compression and protrusion. Thus, participants perceived visible speech movements in relation to the configuration and shape of their own vocal tract (and possibly their ability to produce covert vowel production-like movements). In contrast, engaging the articulators had no effect when the speaking faces did not move, suggesting that the somatosensory inputs affected perception of time-varying kinematic information rather than changes in target (movement end point) mouth shapes. CONCLUSIONS These findings suggest that orofacial somatosensory inputs associated with speech production prime premotor and somatosensory brain regions involved in the sensorimotor control of speech, thereby facilitating perception of concordant visible speech movements. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.9911846Item A Wireless Brain-Machine Interface for Real-Time Speech Synthesis(Public Library of Science, 2009-12-9) Guenther, Frank H.; Brumberg, Jonathan S.; Wright, E. Joseph; Nieto-Castanon, Alfonso; Tourville, Jason A.; Panko, Mikhail; Law, Robert; Siebert, Steven A.; Bartels, Jess L.; Andreasen, Dinal S.; Ehirim, Princewill; Mao, Hui; Kennedy, Philip R.BACKGROUND. Brain-machine interfaces (BMIs) involving electrodes implanted into the human cerebral cortex have recently been developed in an attempt to restore function to profoundly paralyzed individuals. Current BMIs for restoring communication can provide important capabilities via a typing process, but unfortunately they are only capable of slow communication rates. In the current study we use a novel approach to speech restoration in which we decode continuous auditory parameters for a real-time speech synthesizer from neuronal activity in motor cortex during attempted speech. METHODOLOGY/PRINCIPAL FINDINGS. Neural signals recorded by a Neurotrophic Electrode implanted in a speech-related region of the left precentral gyrus of a human volunteer suffering from locked-in syndrome, characterized by near-total paralysis with spared cognition, were transmitted wirelessly across the scalp and used to drive a speech synthesizer. A Kalman filter-based decoder translated the neural signals generated during attempted speech into continuous parameters for controlling a synthesizer that provided immediate (within 50 ms) auditory feedback of the decoded sound. Accuracy of the volunteer's vowel productions with the synthesizer improved quickly with practice, with a 25% improvement in average hit rate (from 45% to 70%) and 46% decrease in average endpoint error from the first to the last block of a three-vowel task. CONCLUSIONS/SIGNIFICANCE. Our results support the feasibility of neural prostheses that may have the potential to provide near-conversational synthetic speech output for individuals with severely impaired speech motor control. They also provide an initial glimpse into the functional properties of neurons in speech motor cortical areas.Item Hippocampal Conceptual Representations and Their Reward Value(Frontiers Research Foundation, 2010-02-03) Okatan, MuratItem Mindboggle: Automated Brain Labeling with Multiple Atlases(BioMed Central, 2005-10-5) Klein, Arno; Mensh, Brett; Ghosh, Satrajit; Tourville, Jason; Hirsch, JoyBACKGROUND: To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural correspondences between labeled atlases and unlabeled image data, where an atlas consists of a set of labels manually assigned to a single brain image. In the present work, we study the influence of using variable numbers of individual atlases to nonlinearly label human brain image data. METHODS: Each brain image voxel of each of 20 human subjects is assigned a label by each of the remaining 19 atlases using Mindboggle. The most common label is selected and is given a confidence rating based on the number of atlases that assigned that label. The automatically assigned labels for each subject brain are compared with the manual labels for that subject (its atlas). Unlike recent approaches that transform subject data to a labeled, probabilistic atlas space (constructed from a database of atlases), Mindboggle labels a subject by each atlas in a database independently. RESULTS: When Mindboggle labels a human subject's brain image with at least four atlases, the resulting label agreement with coregistered manual labels is significantly higher than when only a single atlas is used. Different numbers of atlases provide significantly higher label agreements for individual brain regions. CONCLUSION: Increasing the number of reference brains used to automatically label a human subject brain improves labeling accuracy with respect to manually assigned labels. Mindboggle software can provide confidence measures for labels based on probabilistic assignment of labels and could be applied to large databases of brain images.