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A holistic approach to identifying functional units of tongue motion during speech

识别言语过程中舌头运动功能单位的整体方法

基本信息

批准号：
10376818
负责人：
Jonghye Woo
金额：
$ 47.01万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-20 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10376818
关键词：
3-Dimensional Acoustics Address Affect Aftercare Anatomy Atlases Behavior Biomechanics Cessation of life Clinical Complex Computing Methodologies Data Death Rate Deglutition Diagnosis Diffusion Magnetic Resonance Imaging Disease Elements Evaluation Excision Exhibits Fiber Geometry Glossectomy Goals Grouping Impairment Incidence Joints Knowledge Learning Link Magnetic Resonance Imaging Malignant Neoplasms Maps Measurement Measures Methods Modeling Motion Muscle Muscle Fibers Operative Surgical Procedures Outcome Pathologic Patients Physiological Postoperative Period Predictive Value Procedures Production Prognosis Proxy Publishing Radiation therapy Rehabilitation therapy Research Resolution Speech Speech Intelligibility Standardization Structure Surface System Techniques Testing Time Tissue Grafts Tissues Tongue Weight Work base biomechanical model clinical practice deep learning functional outcomes holistic approach improved in vivo insight malignant mouth neoplasm malignant tongue neoplasm motor control multimodality muscular structure novel novel therapeutics outcome prediction reconstruction rehabilitation strategy signal processing spatiotemporal success tool treatment planning treatment strategy tumor

项目摘要

PROJECT SUMMARY Oral cancers have the seventh highest incidence, with roughly 51,540 new cases and 10,030 cancer- related deaths expected to occur in 2018. Although a variety of treatment methods are available, the death rate is higher than that for most cancers with five-year rates of about 50 percent. The most frequently used treatment method, glossectomy surgery, involves the surgical removal of tumors and surrounding tissues, and the addition of grafted tissues, often followed by radiotherapy. Although tongue cancer and its treatment have debilitating effects on speech, the impact of varying degrees of resection and reconstruction on the formation of functional units in speech has remained poorly understood. In order to produce intelligible speech, a variety of local muscle groupings of the tongue—i.e., functional units—emerge and recede rapidly and nimbly in a highly coordinated fashion. Therefore, understanding the formation of functional units that are critical for speech production can provide substantial insights into normal, pathological, and adapted motor control strategies in controls and patients with tongue cancer for novel therapeutic, surgical, and rehabilitative strategies. One of the critical challenges in pre-operative surgical and treatment planning, as well as in post- operative evaluation for tongue cancer is the difficulty in developing objective and quantitative measures and in evaluating their functional outcome predictability. To address this, in this proposal, three integrated approaches will be used in in vivo tongue motion during speech to seamlessly identify the functional units and associated quantitative measures: multimodal MRI methods, multimodal deep learning, and biomechanical simulations. This will provide a convergent approach, thereby allowing us to (1) test hypotheses about the spatiotemporal basis of muscle coordination in a consilient way, and (2) develop objective quantitative measures that are required for understanding the complex biomechanical system as well as for predicting the functional outcomes after various reconstruction methods. The first proof of concept study published by the PI and the team identified the functional units of speech tasks using the sparse non-negative matrix factorization framework, in which the magnitude and angle of displacements from tagged MRI were used as our input quantities. With these advances in place, we will further incorporate muscle fiber anatomy from diffusion MRI and motion tracking from tagged MRI into our framework to yield physiologically and anatomically meaningful functional units. In addition, we will create a completely novel and integrated way of directly relating the functional units to tongue muscle anatomy, learning joint representation via a multimodal deep learning technique, and linking them to biomechanical simulations. Furthermore, 3D and 4D atlases will be utilized to identify objective and quantitative measures based on our functional units analysis. Taken together, the successful implementation of our integrated framework will identify functional units that can be used for research on tongue motion, for surgical planning, and for diagnosis, prognosis, and rehabilitation in a range of speech-related disorders.

项目总结