Grasp, Speech, and Internal Speech Representation in the Human Cortical Grasp Circuit

Citation

Wandelt, Sarah Kim (2023) Grasp, Speech, and Internal Speech Representation in the Human Cortical Grasp Circuit. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/0461-g107. https://resolver.caltech.edu/CaltechTHESIS:05122023-085319625

Abstract

The ability to move freely and to connect with others through communication is invaluable for human independence. In this thesis, we explore how brain-machine interfaces (BMIs) can help patients affected by movement or speech deficits to recover lost human experiences. This work builds on previous findings indicating that premotor and posterior parietal areas are involved in movement generation and language processes. These higher-level brain areas do not only engage in movement execution, but also during planning, representing rich behavioral patterns that can be leveraged for BMI applications. In this work, we investigated how the ventral premotor cortex (PMv), the posterior parietal cortex (PPC), and the sensorimotor cortex (S1) represent grasp and speech processes at a single-neuron level. Using multielectrode Utah arrays, neuronal populations were recorded in tetraplegic human participants. We found that the supramarginal gyrus (SMG), PMv and S1 significantly encode motor imagery of grasping. By studying the cognitive processes underlying neural activity during the cue phase of grasping, we found a transition from cue-modality dependent to cue-modality independent grasp representation in SMG, the anterior intraparietal cortex (AIP) and PMv. Our findings suggest SMG integrates audio, written, and image cue modalities, but more similarly represents audio and written cues, indicating language involvement. We confirmed this hypothesis by demonstrating that SMG encodes spoken words, engaging different motor plans for speech compared to grasping even when the semantic content remained unchanged. These results suggest a BMI could be trained to decode both grasp motor imagery and speech from one brain area. Lastly, we showed that SMG is highly involved in language processes, modulating for written word recognition, auditory tones, vocalized speech, and internal speech. As a proof-of-concept, we built a real-time internal speech BMI from signals recorded in SMG that can decode eight words with high accuracy. This work is the first of its kind, demonstrating internal speech can be robustly decoded from an implant in a single brain area. We find high neural SMG generalization between seeing a written word, saying it internally and vocalizing the word, suggesting shared cognitive functions between different language processes. Furthermore, words in different languages are represented in SMG. This thesis advances the BMI field by providing a better understanding of the neural processes that underly grasp motor imagery and language. To summarize, our findings suggest that studying higher-level brain areas can lead to the development of more effective and versatile brain-machine interfaces.

Thesis Files