Establishing the clinical utility of sensorimotor adaptation for speech rehabilitation
- Institution
- University of Wisconsin-Madison, WI
- Principal investigator
- Caroline Niziolek
- Funding
- $389K
- Duration
- 2021–2026
Moonbase score
Still being scored
Abstract
Project Summary & Abstract Individuals with brain injuries or disorders that affect movement (such as Parkinson’s disease, cerebral palsy, amyotrophic lateral sclerosis, and many others) often have difficulties in being understood when they speak. While treatments exist, they often require substantial conscious attention to the way speech is produced, or require increased breath support to speak louder. Many individuals with speech disorders have cognitive or respiratory difficulty that renders these treatments ineffective. These individuals will benefit from alternative strategies that promote motor learning: the ability to alter motor actions through practice. One type of motor learning, sensorimotor adaptation, is a particularly promising pathway for alternative rehabilitation. In this paradigm, the auditory feedback people receive while speaking is externally perturbed, causing them to quickly change their speech to oppose these perturbations. Because of its ability to rapidly induce changes in speech production without conscious control, sensorimotor adaptation holds unique promise for rehabilitation. However, its potential clinical applicability is limited by poor understanding of key clinically-relevant features. First, existing sensorimotor adaptation paradigms do not affect speech in a way that facilitates communication. To improve rehabilitation outcomes, sensorimotor learning must target clinically-relevant speech parameters such as intelligibility. We address this barrier through a novel auditory perturbation that artificially decreases the perceived space between vowels, causing speakers to produce more vowel contrast. Critically, reduced vowel contrast is a hallmark of motor speech disorders and significantly contributes to decreased intelligibility. We determine the effectiveness of this paradigm to increase intelligibility and test how these increases are retained across multiple training sessions, how they generalize to untrained words, and how they can be elicited in complex sentences—characteristics which are key for potential clinical applications. Second, while sensorimotor adaptation is a robust effect on average, not all individuals learn to the same degree. This variability limits the potential impact to only those who show a large degree of learning. This proposal uses behavioral interventions and brain stimulation that target the hypothesized causes of this variability. By directly manipulating these factors, we can determine, for the first time, the mechanisms that underlie speech motor learning. Additionally, establishing how these factors can be modulated to increase learning would allow treatment to benefit a wider range of individuals. Although sensorimotor adaptation can quickly induce changes in speech, its current clinical applicability is limited by substantial gaps in our understanding of its mechanisms. By establishing the capacity of sensorimotor adaptation to increase speech intelligibility, characterizing retention and transfer of learning, and identifying the mechanisms underlying variability between individuals, this work lays a critical foundation for future treatments that optimize the clinical impact of motor learning.
Research team
- Caroline NiziolekPI
- Benjamin ParrellPI
LOADING MOONBASE SCORE
Questions for University of Wisconsin-Madison
Answered by the team behind this research.
No questions yet. Sign in to ask the first one
Related grants
Same researchers, same institution, same topic, scored first.
- NIH · Yale UniversityNot yet scored
Fundamental predictive computations in upper-limb and speech adaptation
Samuel David Mcdougle · $441K · 2024–2029
- NIH · University of California, San FranciscoNot yet scored
The role of the cerebellum in speech
John Francis Houde · $691K · 2018–2030
- NSF · University of Wisconsin-Madison69
Next-Generation Safe and Dexterous Robot Hands using High-Torque Direct-Drive Actuation
Lei Zhou · $566K · 2023–2026