Linear mixed-effect modeling of organ of Corti vibratory tuning curves

被引:0
|
作者
Oghalai, John S. [1 ,2 ]
机构
[1] Univ Southern Calif, Caruso Dept Otolaryngol Head & Neck Surg, Los Angeles, CA USA
[2] Univ Southern Calif, Caruso Dept Otolaryngol Head & Neck Surg, Healthcare Ctr 4,1450 San Pablo St,Suite 5800, Los Angeles, CA 90033 USA
关键词
hearing; cochlea; tuning curves; optical coherence tomography; statistics; APEX; VIBROMETRY; MEMBRANE;
D O I
10.1016/j.heares.2023.108820
中图分类号
R36 [病理学]; R76 [耳鼻咽喉科学];
学科分类号
100104 ; 100213 ;
摘要
Optical coherence tomography has become the most popular approach to experimental measures of sound-induced vibrations within the mammalian cochlea. Because it is relatively easy to use and works in the unopened cochlea, the measurement of vibratory tuning curves has become highly reliable, and averaging data from multiple animals in different experimental cohorts is now possible. Here I tested a modern statistical approach to compare cohorts for differences in the magnitude and phase of vibra-tion. A linear mixed-effect approach with first, second, third, and fourth-order models to fit the data was tested. The third-order model best fit both the magnitude and phase data without having terms that did not contribute substantively to improving the R 2 or the p-value for the independent variables. It iden-tified a difference between cohorts of mice that were different and no difference between cohorts that should not be different. Thus, this approach provides a way to simply compare a full set of tuning curves between cohorts. While further analyses by the investigator will always be needed to study specific de-tails related to the study hypothesis, this statistical technique provides a simple way for the cochlear physiologist to perform an initial assessment of whether the cohorts are same or different. & COPY; 2023 Elsevier B.V. All rights reserved.
引用
下载
收藏
页数:12
相关论文
共 50 条
  • [31] Nonlinear Mixed-Effect Models to Describe Growth Curves of Pepper Fruits in Eight Cultivars Including Group Effects
    Teixeira, Filipe Ribeiro Formiga
    Cecon, Paulo Roberto
    Suela, Matheus Massariol
    Nascimento, Moyses
    AGRONOMY-BASEL, 2023, 13 (08):
  • [32] Exploring health outcome disparities in African regional economics communities: a multilevel linear mixed-effect analysis
    Ariane Ephemia Ndzignat Mouteyica
    Nicholas Ngepah
    BMC Public Health, 25 (1)
  • [33] Mapping nighttime PM2.5 from VIIRS DNB using a linear mixed-effect model
    Fu, D.
    Xia, X.
    Duan, M.
    Zhang, X.
    Li, X.
    Wang, J.
    Liu, J.
    ATMOSPHERIC ENVIRONMENT, 2018, 178 : 214 - 222
  • [34] Modeling the Progression of Speech Deficits in Cerebellar Ataxia Using a Mixture Mixed-Effect Machine Learning Framework
    Kashyap, Bipasha
    Pathirana, Pubudu N.
    Horne, Malcolm
    Power, Laura
    Szmulewicz, David J.
    IEEE ACCESS, 2021, 9 : 135343 - 135353
  • [35] Sagebrush Steppe Productivity, Environmental Complexity, and Grazing: Insights From Remote Sensing and Mixed-effect Modeling ☆
    Reintsma, Kaitlyn M.
    Szczypinski, Mark
    Running, Steven W.
    Coons, Shea P.
    Dreitz, Victoria J.
    RANGELAND ECOLOGY & MANAGEMENT, 2024, 95 : 20 - 29
  • [36] A population pharmacokinetic model for Cremophor EL using nonlinear mixed-effect modeling:: model building and validation
    Van Den Bongard, HJGD
    Mathôt, RAA
    Van Tellingen, O
    Schellens, JHM
    Beijnen, JH
    BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, 2002, 53 (05) : 552P - 553P
  • [37] Wafer quality monitoring using spatial Dirichlet process based mixed-effect profile modeling scheme
    Liu, Jia Peter
    Jin, Ran
    Kong, Zhenyu James
    JOURNAL OF MANUFACTURING SYSTEMS, 2018, 48 : 21 - 32
  • [38] Stochastic Norton-Simon-Massague Tumor Growth Modeling: Controlled and Mixed-Effect Uncontrolled Analysis
    Belkhatir, Zehor
    Pavon, Michele
    Mathews, James C.
    Pouryahya, Maryam
    Deasy, Joseph O.
    Norton, Larry
    Tannenbaum, Allen R.
    IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2021, 29 (02) : 704 - 717
  • [39] Computationally Stable Estimation Procedure for the Multivariate Linear Mixed-Effect Model and Application to Malaria Public Health Problem
    Adjakossa, Eric Houngla
    Hounkonnou, Norbert Mahouton
    Nuel, Gregory
    INTERNATIONAL JOURNAL OF BIOSTATISTICS, 2019, 15 (02):
  • [40] Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines
    Grajeda L.M.
    Ivanescu A.
    Saito M.
    Crainiceanu C.
    Jaganath D.
    Gilman R.H.
    Crabtree J.E.
    Kelleher D.
    Cabrera L.
    Cama V.
    Checkley W.
    Emerging Themes in Epidemiology, 13 (1):