Evidence map›Paper›PMID 38836001›Full record

ArticleFrontiers in neurology2024

A multimodal approach to automated hierarchical assessment of bulbar involvement in amyotrophic lateral sclerosis.

Panying Rong, Lindsey Heidrick, Gary L Pattee

Abstract read
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Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Panying RongDepartment of Speech-Language-Hearing: Sciences and Disorders, University of Kansas, Lawrence, KS, United States.
Lindsey HeidrickDepartment of Hearing and Speech, University of Kansas Medical Center, Kansas City, KS, United States.
Gary L PatteeNeurology Associate P.C., Lincoln, NE, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: As a hallmark feature of amyotrophic lateral sclerosis (ALS), bulbar involvement leads to progressive declines of speech and swallowing functions, significantly impacting social, emotional, and physical health, and quality of life. Standard clinical tools for bulbar assessment focus primarily on clinical symptoms and functional outcomes. However, ALS is known to have a long, clinically silent prodromal stage characterized by complex subclinical changes at various levels of the bulbar motor system. These changes accumulate over time and eventually culminate in clinical symptoms and functional declines. Detection of these subclinical changes is critical, both for mechanistic understanding of bulbar neuromuscular pathology and for optimal clinical management of bulbar dysfunction in ALS. To this end, we developed a novel multimodal measurement tool based on two clinically readily available, noninvasive instruments-facial surface electromyography (sEMG) and acoustic techniques-to hierarchically assess seven constructs of bulbar/speech motor control at the neuromuscular and acoustic levels. These constructs, including prosody, pause, functional connectivity, amplitude, rhythm, complexity, and regularity, are both mechanically and clinically relevant to bulbar involvement. Methods: Using a custom-developed, fully automated data analytic algorithm, a variety of features were extracted from the sEMG and acoustic recordings of a speech task performed by 13 individuals with ALS and 10 neurologically healthy controls. These features were then factorized into 10 composite outcome measures using confirmatory factor analysis. Statistical and machine learning techniques were applied to these composite outcome measures to evaluate their reliability (internal consistency), validity (concurrent and construct), and efficacy for early detection and progress monitoring of bulbar involvement in ALS. Results: The composite outcome measures were demonstrated to (1) be internally consistent and structurally valid in measuring the targeted constructs; (2) hold concurrent validity with the existing clinical and functional criteria for bulbar assessment; and (3) outperform the outcome measures obtained from each constituent modality in differentiating individuals with ALS from healthy controls. Moreover, the composite outcome measures combined demonstrated high efficacy for detecting subclinical changes in the targeted constructs, both during the prodromal stage and during the transition from prodromal to symptomatic stages. Discussion: The findings provided compelling initial evidence for the utility of the multimodal measurement tool for improving early detection and progress monitoring of bulbar involvement in ALS, which have important implications in facilitating timely access to and delivery of optimal clinical care of bulbar dysfunction.

Indexed as

bulbar involvementcomputational techniqueinstrumental measurementmachine learningmultimodal assessmentneurodegenerative diseasespeechsurface electromyography

Identifiers

PMID38836001
PMCPMC11148322

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.