Leveraging mixed-effects location scale models to assess the ERP mismatch negativity's psychometric properties and trial-by-trial neural variability in toddler-mother dyads

Serena K. Mon; Brittany L. Manning; Lauren S. Wakschlag; Elizabeth S. Norton

doi:10.1016/j.dcn.2024.101459

Leveraging mixed-effects location scale models to assess the ERP mismatch negativity's psychometric properties and trial-by-trial neural variability in toddler-mother dyads

Serena K. Mon, Brittany L. Manning, Lauren S. Wakschlag, Elizabeth S. Norton^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Trial-by-trial neural variability, a measure of neural response stability, has been examined in relation to behavioral indicators using summary measures, but these methods do not characterize meaningful processes underlying variability. Mixed-effects location scale models (MELSMs) overcome these limitations by accounting for predictors and covariates of variability but have been rarely used in developmental studies. Here, we applied MELSMs to the ERP auditory mismatch negativity (MMN), a neural measure often related to language and psychopathology. 84 toddlers and 76 mothers completed a speech-syllable MMN paradigm. We extracted early and late MMN mean amplitudes from trial-level waveforms. We first characterized our sample's psychometric properties using MELSMs and found a wide range of subject-level internal consistency. Next, we examined the relation between toddler MMNs with theoretically relevant child behavioral and maternal variables. MELSMs offered better model fit than analyses that assumed constant variability. We found significant individual differences in trial-by-trial variability but no significant associations between toddler variability and their language, irritability, or mother variability indices. Overall, we illustrate how MELSMs can characterize psychometric properties and answer questions about individual differences in variability. We provide recommendations and resources as well as example code for analyzing trial-by-trial neural variability in future studies.

Original language	English (US)
Article number	101459
Journal	Developmental Cognitive Neuroscience
Volume	70
DOIs	https://doi.org/10.1016/j.dcn.2024.101459
State	Published - Dec 2024

Funding

Funding for this study was provided by NIH grants R01DC016273 (MPIs Norton & Wakschlag) and R01MH107652 (PI Wakschlag). NIMH grant T32MH126368 (MPIs Wakschlag & Shankman) supported a fellowship for author BLM. We thank Emily Harriott, Jessica Page, Yudong Zhang, Gina Giase, Julia Raven, Anne Zola, Ewa Gut, Kaitlyn Fredian, Kiera Cook, and Alex Harpole for their assistance with recruitment, testing, and data coordination. We also thank Kevin Sitek, Adriana Weisleder, Julia Nikolaeva, Jinnie Choi, Hudi Licht, Jiyoon Kim, Ania Holubecki, and Ananya Mittal for helpful feedback. Some data for this study were collected and managed using REDCap, which is supported by a Clinical and Translational Science Award (CTSA) grant UL1TR001422 from the National Institutes of Health (NIH) to Northwestern University. In addition, this research was supported in part through the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University which is jointly supported by the Northwestern University Information Technology, Office of the Provost, and the Office for Research. We also warmly thank the participating families. Funding for this study was provided by NIH grants R01DC016273 (MPIs Norton & Wakschlag) and R01MH107652 (PI Wakschlag). NIMH grant T32MH126368 (MPIs Wakschlag & Shankman) supported a fellowship for author BLM. We thank Emily Harriott, Jessica Page, Yudong Zhang, Gina Giase, Julia Raven, Anne Zola, Ewa Gut, Kaitlyn Fredian, Kiera Cook, and Alex Harpole for their assistance with recruitment, testing, and data coordination. We also thank Kevin Sitek, Adriana Weisleder, Julia Nikolaeva, Jinnie Choi, Hudi Licht, Jiyoon Kim, Ania Holubecki, and Ananya Mittal for helpful feedback. Some data for this study were collected and managed using REDCap, which is supported by an NIH Clinical and Translational Science Award (CTSA) grant UL1TR001422 from the NIH to Northwestern University. This content is solely the responsibility of the authors and does not represent the official views of the NIH. In addition, this research was supported in part through the computational resources and staff contributions provided for the Quest high performance computing facility at Northwestern University which is jointly supported by the Northwestern University Information Technology, Office of the Provost, and the Office for Research. We also warmly thank the participating families.

Keywords

Event-related potentials (ERPs)
Individual differences
Mismatch negativity
Mixed-effects location scale models
Neural variability
Psychometric reliability

ASJC Scopus subject areas

Cognitive Neuroscience

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10.1016/j.dcn.2024.101459

Cite this

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title = "Leveraging mixed-effects location scale models to assess the ERP mismatch negativity's psychometric properties and trial-by-trial neural variability in toddler-mother dyads",

abstract = "Trial-by-trial neural variability, a measure of neural response stability, has been examined in relation to behavioral indicators using summary measures, but these methods do not characterize meaningful processes underlying variability. Mixed-effects location scale models (MELSMs) overcome these limitations by accounting for predictors and covariates of variability but have been rarely used in developmental studies. Here, we applied MELSMs to the ERP auditory mismatch negativity (MMN), a neural measure often related to language and psychopathology. 84 toddlers and 76 mothers completed a speech-syllable MMN paradigm. We extracted early and late MMN mean amplitudes from trial-level waveforms. We first characterized our sample's psychometric properties using MELSMs and found a wide range of subject-level internal consistency. Next, we examined the relation between toddler MMNs with theoretically relevant child behavioral and maternal variables. MELSMs offered better model fit than analyses that assumed constant variability. We found significant individual differences in trial-by-trial variability but no significant associations between toddler variability and their language, irritability, or mother variability indices. Overall, we illustrate how MELSMs can characterize psychometric properties and answer questions about individual differences in variability. We provide recommendations and resources as well as example code for analyzing trial-by-trial neural variability in future studies.",

keywords = "Event-related potentials (ERPs), Individual differences, Mismatch negativity, Mixed-effects location scale models, Neural variability, Psychometric reliability",

author = "Mon, {Serena K.} and Manning, {Brittany L.} and Wakschlag, {Lauren S.} and Norton, {Elizabeth S.}",

note = "Publisher Copyright: {\textcopyright} 2024 The Authors",

year = "2024",

month = dec,

doi = "10.1016/j.dcn.2024.101459",

language = "English (US)",

volume = "70",

journal = "Developmental Cognitive Neuroscience",

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Leveraging mixed-effects location scale models to assess the ERP mismatch negativity's psychometric properties and trial-by-trial neural variability in toddler-mother dyads. / Mon, Serena K.; Manning, Brittany L.; Wakschlag, Lauren S. et al.
In: Developmental Cognitive Neuroscience, Vol. 70, 101459, 12.2024.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Leveraging mixed-effects location scale models to assess the ERP mismatch negativity's psychometric properties and trial-by-trial neural variability in toddler-mother dyads

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AU - Manning, Brittany L.

AU - Wakschlag, Lauren S.

AU - Norton, Elizabeth S.

PY - 2024/12

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AB - Trial-by-trial neural variability, a measure of neural response stability, has been examined in relation to behavioral indicators using summary measures, but these methods do not characterize meaningful processes underlying variability. Mixed-effects location scale models (MELSMs) overcome these limitations by accounting for predictors and covariates of variability but have been rarely used in developmental studies. Here, we applied MELSMs to the ERP auditory mismatch negativity (MMN), a neural measure often related to language and psychopathology. 84 toddlers and 76 mothers completed a speech-syllable MMN paradigm. We extracted early and late MMN mean amplitudes from trial-level waveforms. We first characterized our sample's psychometric properties using MELSMs and found a wide range of subject-level internal consistency. Next, we examined the relation between toddler MMNs with theoretically relevant child behavioral and maternal variables. MELSMs offered better model fit than analyses that assumed constant variability. We found significant individual differences in trial-by-trial variability but no significant associations between toddler variability and their language, irritability, or mother variability indices. Overall, we illustrate how MELSMs can characterize psychometric properties and answer questions about individual differences in variability. We provide recommendations and resources as well as example code for analyzing trial-by-trial neural variability in future studies.

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Leveraging mixed-effects location scale models to assess the ERP mismatch negativity's psychometric properties and trial-by-trial neural variability in toddler-mother dyads

Abstract

Funding

Keywords

ASJC Scopus subject areas

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