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
  • [21] Factors influencing total bacterial count in tanks: an application of linear mixed-effect models
    Guerra, Mirela Gurgel
    Rangel, Adriano H. Do Nascimento
    Spyrides, Maria H. Constantino
    De lara, Idemauro A. Rodrigues
    De Araujo, Viviane Maia
    De Aguiar, Emerson Moreira
    [J]. ITALIAN JOURNAL OF ANIMAL SCIENCE, 2013, 12 (04) : 468 - 471
  • [22] Visualization of Complex Trial Data in Non-Linear Mixed-Effect Analyses with Covariates
    Lommerse, Jos
    Green, Michelle
    Aliprantis, Antonios
    Finelli, Lynn
    Espeseth, Amy
    Sachs, Jeffrey R.
    [J]. JOURNAL OF PHARMACOKINETICS AND PHARMACODYNAMICS, 2018, 45 : S54 - S54
  • [23] Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data
    Kia, Seyed Mostafa
    Marquand, Andre F.
    [J]. INTERNATIONAL CONFERENCE ON MEDICAL IMAGING WITH DEEP LEARNING, VOL 102, 2019, 102 : 297 - 314
  • [24] Considerations in analyzing single-trough samples using mixed-effect modeling.
    Booth, BP
    Gobburu, JV
    [J]. CLINICAL PHARMACOLOGY & THERAPEUTICS, 2002, 71 (02) : P21 - P21
  • [25] Using a linear mixed-effect model framework to estimate multivariate generalizability theory parameters in R
    Zhehan Jiang
    Mark Raymond
    Dexin Shi
    Christine DiStefano
    [J]. Behavior Research Methods, 2020, 52 : 2383 - 2393
  • [26] Non-concave penalization in linear mixed-effect models and regularized selection of fixed effects
    Abhik Ghosh
    Magne Thoresen
    [J]. AStA Advances in Statistical Analysis, 2018, 102 : 179 - 210
  • [27] Noise reduction in genome-wide perturbation screens using linear mixed-effect models
    Yu, Danni
    Danku, John
    Baxter, Ivan
    Kim, Sungjin
    Vatamaniuk, Olena K.
    Salt, David E.
    Vitek, Olga
    [J]. BIOINFORMATICS, 2011, 27 (16) : 2173 - 2180
  • [28] Using a linear mixed-effect model framework to estimate multivariate generalizability theory parameters in R
    Jiang, Zhehan
    Raymond, Mark
    Shi, Dexin
    DiStefano, Christine
    [J]. BEHAVIOR RESEARCH METHODS, 2020, 52 (06) : 2383 - 2393
  • [29] Non-concave penalization in linear mixed-effect models and regularized selection of fixed effects
    Ghosh, Abhik
    Thoresen, Magne
    [J]. ASTA-ADVANCES IN STATISTICAL ANALYSIS, 2018, 102 (02) : 179 - 210
  • [30] PHARMACOKINETICS OF FELBAMATE, A NOVEL ANTIEPILEPTIC DRUG - APPLICATION OF MIXED-EFFECT MODELING TO CLINICAL-TRIALS
    GRAVES, NM
    LUDDEN, TM
    HOLMES, GB
    FUERST, RH
    LEPPIK, IE
    [J]. PHARMACOTHERAPY, 1989, 9 (06): : 372 - 376