Perilaryngeal Functional Muscle Network in Patients with Vocal Hyperfunction - A Case Study

Rory O'Keeffe, Seyed Yahya Shirazi, Sarmad Mehrdad, Tyler Crosby, Aaron M. Johnson, S. Farokh Atashzar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Patients with both phonotraumatic and non-phonotraumatic dysphonia commonly present with vocal hyperfunction, defined as excessive perilaryngeal muscle activity and characterized by muscular pain and strain in the neck, increased vocal effort, and vocal fatigue. The inability to reliably measure vocal hyperfunction is a barrier to adequate evaluation and treatment of hyperfunctional voice disorders. We have recently demonstrated that the perilaryngeal functional muscle network can be a novel sensitive neurophysiological window to vocal performance in vocally healthy subjects. In this paper, for the first time, we evaluate the performance and symmetry of functional perilaryngeal muscle networks in three patients with voice disorders. Surface electromyography signals were recorded from twelve sensors (six on each side of the neck) using the wireless Trigno sEMG system (Delsys Inc., Natick, MA). Patient 1 was diagnosed with primary muscle tension dysphonia, Patient 2 was diagnosed with unilateral vocal fold paresis, and Patient 3 was diagnosed with age-related glottal insufficiency. This paper reports altered functional connectivity and asymmetric muscle network scan behavior in all three patients when compared with a cohort of eight healthy subjects. Our approach quantifies synergistic network activity to interrogate coordination of perilaryngeal and surrounding muscles during voicing and potential discoordination of the muscle network for dysphonic conditions. Asymmetry in muscle networks is proposed here as a biomarker for monitoring vocal hyperfunction.

Original languageEnglish (US)
Title of host publication11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 - Proceedings
PublisherIEEE Computer Society
ISBN (Electronic)9781665462921
DOIs
StatePublished - 2023
Event11th International IEEE/EMBS Conference on Neural Engineering, NER 2023 - Baltimore, United States
Duration: Apr 25 2023Apr 27 2023

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2023-April
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference11th International IEEE/EMBS Conference on Neural Engineering, NER 2023
Country/TerritoryUnited States
CityBaltimore
Period4/25/234/27/23

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'Perilaryngeal Functional Muscle Network in Patients with Vocal Hyperfunction - A Case Study'. Together they form a unique fingerprint.

Cite this