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
收藏
页数:12
相关论文
共 50 条
  • [1] Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling
    Cobigo, Yann
    Goh, Matthew S.
    Wolf, Amy
    Staffaroni, Adam M.
    Kornak, John
    Miller, Bruce L.
    Rabinovici, Gil D.
    Seeley, William W.
    Spina, Salvatore
    Boxer, Adam L.
    Boeve, Bradley F.
    Wang, Lei
    Allegri, Ricardo
    Farlow, Marty
    Mori, Hiroshi
    Perrin, Richard J.
    Kramer, Joel
    Rosen, Howard J.
    [J]. NEUROIMAGE-CLINICAL, 2022, 36
  • [2] Model selection in linear mixed-effect models
    Buscemi, Simona
    Plaia, Antonella
    [J]. ASTA-ADVANCES IN STATISTICAL ANALYSIS, 2020, 104 (04) : 529 - 575
  • [3] Model selection in linear mixed-effect models
    Simona Buscemi
    Antonella Plaia
    [J]. AStA Advances in Statistical Analysis, 2020, 104 : 529 - 575
  • [4] Application of Linear Mixed-Effect Modeling for the Analysis of Human-in-the-Loop Simulation Experiments
    Lorenz, Bernd
    Chalon-Morgan, Catherine
    De Visscher, Ivan
    Feuerle, Thomas
    [J]. Journal of Air Transportation, 2024, 32 (02): : 71 - 83
  • [5] Estimation of epipolar geometry by linear mixed-effect modelling
    Zhou, Huiyu
    Green, Patrick R.
    Wallace, Andrew M.
    [J]. NEUROCOMPUTING, 2009, 72 (16-18) : 3881 - 3890
  • [6] Gender differences in pharmacokinetics of perfluoropentanoic acid using non-linear mixed-effect modeling in rats
    Choi, Go-Wun
    Choi, Eun-Jeong
    Kim, Ju Hee
    Kang, Dong Wook
    Lee, Yong-Bok
    Cho, Hea-Young
    [J]. ARCHIVES OF TOXICOLOGY, 2020, 94 (05) : 1601 - 1612
  • [7] Gender differences in pharmacokinetics of perfluoropentanoic acid using non-linear mixed-effect modeling in rats
    Go-Wun Choi
    Eun-Jeong Choi
    Ju Hee Kim
    Dong Wook Kang
    Yong-Bok Lee
    Hea-Young Cho
    [J]. Archives of Toxicology, 2020, 94 : 1601 - 1612
  • [8] Linear mixed-effect models for correlated response to process electroencephalogram recordings
    Meinardi, Vanesa B.
    Lopez, Juan M. Diaz
    Fajreldines, Hugo Diaz
    Boyallian, Carina
    Balzarini, Monica
    [J]. COGNITIVE NEURODYNAMICS, 2024, 18 (03) : 1197 - 1207
  • [9] Variability explained by covariates in linear mixed-effect models for longitudinal data
    Hu, Bo
    Shao, Jun
    Palta, Mari
    [J]. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2010, 38 (03): : 352 - 368
  • [10] Increased sensitivity in fMRI group analysis using mixed-effect modeling
    Keller, Merlin
    Roche, Alexis
    [J]. 2008 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1-4, 2008, : 548 - +