The motor act of speaking is the last and crucial step in the process of translating intended messages into the physical processes that convey those messages, i.e. the movements of articulators resulting in producing intended vocal sounds. Surprisingly many aspects of this process remain poorly understood, in particular, the role of feedback processing in controlling speech. Deficits in feedback processing are implicated in major speech impairments including stuttering, conduction aphasia, spasmodic dysphonia, and apraxia of speech. However, due to a paucity of testable models, and a lack of well-established methods, little is understood about the neural mechanisms underlying auditory feedback control in speech. In this grant application, we extend a quantitative model for speech motor control called state-feedback control (SFC) that we have previously developed. SFC posits that the brain controls speech using internal predictions of the state of the vocal tract and of the sensory consequences of speaking. The SFC model accounts for many behavioral and neural phenomena in speech motor control, including two key behavioral responses to unexpectedly altered auditory feedback - compensation and adaptation. Compensation refers to short-term changes in speech output in response to feedback alteration. Adaptation refers to long-term changes in speech output that persist even after the feedback alteration is removed. Here, we propose to use state-of-the-art methods for magnetoencephalographic imaging (MEGI) and electrocorticography (ECOG) in conjunction with cutting-edge methods of quantitative modeling (Bayesian estimation) and behavioral experimentation (real-time speech feedback alteration, audiomotor studies with a touch-screen, speech controlled visual stimulation). Our goals are to further elaborate our SFC model of speech motor control and examine what aspects of speech constrain its adaptation behavior.
The specific aims are to: 1) determine the neural correlates of sensorimotor adaptation in speech;2) determine the role of somatosensory feedback in compensation and adaptation;and 3) isolate perceptual contributions to speech compensation and adaptation. The proposed studies will refine our SFC model of speech motor control, increase its predictive power, and examine what specific aspects of speech constrain its compensation and adaptation behaviors. Such an understanding of the neural basis of speaking has the potential to develop better treatments of dysfunctions of speech motor control such as stuttering, conduction aphasia, spasmodic dysphonia, apraxia of speech, spas and hypophonic dysarthria in Parkinson's disease.
Speaking is a uniquely human ability vital to our functioning socially with oral communication capabilities;its breakdown can have profound social impact and negative effects on one's quality of life. To develop a clear understanding of and effective treatments for speech dysfunctions, we need accurate models of the neural processes controlling speaking that we develop in this grant. Knowledge from proposed studies are expected to lead to improved diagnosis and the development of innovative treatment strategies for patients with speech impairments such as conduction aphasia, stuttering, apraxia of speech, spasmodic dysphonia, and hypophonic dysarthria in Parkinson's disease.
|Subramaniam, Karuna; Kothare, Hardik; Mizuiri, Danielle et al. (2018) Reality Monitoring and Feedback Control of Speech Production Are Related Through Self-Agency. Front Hum Neurosci 12:82|
|Subramaniam, Karuna; Gill, Jeevit; Fisher, Melissa et al. (2018) White matter microstructure predicts cognitive training-induced improvements in attention and executive functioning in schizophrenia. Schizophr Res 193:276-283|
|Cai, Chang; Sekihara, Kensuke; Nagarajan, Srikantan S (2018) Hierarchical multiscale Bayesian algorithm for robust MEG/EEG source reconstruction. Neuroimage 183:698-715|
|Sekihara, Kensuke; Adachi, Yoshiaki; Kubota, Hiroshi K et al. (2018) Beamspace dual signal space projection (bDSSP): a method for selective detection of deep sources in MEG measurements. J Neural Eng 15:036026|
|Parrell, Benjamin; Agnew, Zarinah; Nagarajan, Srikantan et al. (2017) Impaired Feedforward Control and Enhanced Feedback Control of Speech in Patients with Cerebellar Degeneration. J Neurosci 37:9249-9258|
|Ranasinghe, Kamalini G; Gill, Jeevit S; Kothare, Hardik et al. (2017) Abnormal vocal behavior predicts executive and memory deficits in Alzheimer's disease. Neurobiol Aging 52:71-80|
|Ranasinghe, Kamalini G; Hinkley, Leighton B; Beagle, Alexander J et al. (2017) Distinct spatiotemporal patterns of neuronal functional connectivity in primary progressive aphasia variants. Brain 140:2737-2751|
|Subramaniam, Karuna; Ranasinghe, Kamalini G; Mathalon, Daniel et al. (2017) Neural mechanisms of mood-induced modulation of reality monitoring in schizophrenia. Cortex 91:271-286|
|Sekihara, Kensuke; Nagarajan, Srikantan S (2017) Subspace-based interference removal methods for a multichannel biomagnetic sensor array. J Neural Eng 14:051001|
|Schuerman, William L; Nagarajan, Srikantan; McQueen, James M et al. (2017) Sensorimotor adaptation affects perceptual compensation for coarticulation. J Acoust Soc Am 141:2693|
Showing the most recent 10 out of 22 publications