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Trajectories of the Late Positive Potential across Childhood and Adolescence: A 9-Year Longitudinal Study

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Title: Trajectories of the Late Positive Potential across Childhood and Adolescence: A 9-Year Longitudinal Study
Language: English
Authors: Alison E. Calentino (ORCID 0000-0002-9051-8043), Nathan M. Hager, Elise M. Adams, Aline K. Szenczy, Lindsay Dickey, Autumn Kujawa, Greg Hajcak, Brady D. Nelson, Daniel N. Klein
Source: Child Development. 2025 96(3):1088-1097.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 10
Publication Date: 2025
Sponsoring Agency: National Institute of Mental Health (NIMH) (DHHS/NIH)
Contract Number: R01MH069942
Document Type: Journal Articles
Reports - Research
Descriptors: Cognitive Processes, Brain Hemisphere Functions, Children, Adolescents, Longitudinal Studies, Growth Models, Cognitive Development
DOI: 10.1111/cdev.14223
ISSN: 0009-3920
1467-8624
Abstract: The late positive potential (LPP), an event-related potential reflecting affective processing, may exhibit developmental shifts in magnitude and scalp location. In the present longitudinal study, 501 youth (47.3% female; 89.4% White; 12.0% Hispanic) completed the emotion interrupt task to elicit the LPP to neutral, positive, and negative images at approximately 9, 12, 15, and 18 years old (data collected 2010-2022). Multilevel growth models indicated an initial decrease in the occipital LPP and an increase in the parietal and central LPP during late childhood, with rates of change leveling off across adolescence. Trial condition (i.e., valence) significantly impacted trajectories only when the LPP was measured over occipital sites. Results provide novel evidence of stability and change in the LPP across development.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1469339
Database: ERIC
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  Value: <anid>AN0184767928;cdv01may.25;2025Apr29.08:28;v2.2.500</anid> <title id="AN0184767928-1">Trajectories of the Late Positive Potential Across Childhood and Adolescence: A 9‐Year Longitudinal Study </title> <p>The late positive potential (LPP), an event‐related potential reflecting affective processing, may exhibit developmental shifts in magnitude and scalp location. In the present longitudinal study, 501 youth (47.3% female; 89.4% White; 12.0% Hispanic) completed the emotion interrupt task to elicit the LPP to neutral, positive, and negative images at approximately 9, 12, 15, and 18 years old (data collected 2010–2022). Multilevel growth models indicated an initial decrease in the occipital LPP and an increase in the parietal and central LPP during late childhood, with rates of change leveling off across adolescence. Trial condition (i.e., valence) significantly impacted trajectories only when the LPP was measured over occipital sites. Results provide novel evidence of stability and change in the LPP across development.</p> <p>Keywords: adolescence; emotion; event‐related potential; late positive potential</p> <p>The neural circuitry involved in emotion processing, including emotion reactivity and regulation, shifts across childhood and adolescence. These normative developmental changes have been shown to follow a hierarchical pattern in which connectivity within subcortical regions gives way to increased connectivity between subcortical and prefrontal regions, and finally within prefrontal regions as individuals approach early adulthood. These cascading changes support the development of top‐down emotion regulation capabilities, such as reappraisal of affective stimuli and the ability to direct attention to or away from emotionally salient stimuli (Casey et al. [<reflink idref="bib4" id="ref1">4</reflink>]). One neurophysiological index of emotion processing is the late positive potential (LPP), which is an event‐related potential (ERP) associated with attention to, and elaborative processing of, affective stimuli—reflecting both automatic processing of emotional stimuli and volitional regulation efforts (Cuthbert et al. [<reflink idref="bib7" id="ref2">7</reflink>]; Dickey, Politte‐Corn, and Kujawa [<reflink idref="bib8" id="ref3">8</reflink>]; Hajcak, Dunning, and Foti [<reflink idref="bib11" id="ref4">11</reflink>]). Deviations from normative activation patterns have been investigated as potential neural markers of, or risk factors for, emotional and behavior problems in both youth and adults. For example, heightened reactivity to negative relative to neutral stimuli is associated with anxiety symptoms, and blunted reactivity to positive and negative stimuli relative to neutral stimuli is associated with depressive symptoms (Burkhouse et al. [<reflink idref="bib3" id="ref5">3</reflink>]; Granros et al. [<reflink idref="bib9" id="ref6">9</reflink>]; Kinney, Burkhouse, and Klumpp [<reflink idref="bib12" id="ref7">12</reflink>]; Kujawa et al. [<reflink idref="bib15" id="ref8">15</reflink>]).</p> <p>The LPP is maximal over occipital and parietal regions in early childhood, but is often maximal over parietal and central sites in adolescence, possibly reflecting a shift in the neural circuitry involved in affective processing during development (Dickey, Politte‐Corn, and Kujawa [<reflink idref="bib8" id="ref9">8</reflink>]). While ERPs record the summation of electrical impulses at the electrode site, precluding the ability to localize signals to specific brain regions, there is some evidence that the LPP is associated with posterior–anterior connectivity. Indeed, research has shown that the emotion‐modulated LPP mirrors underlying functional connectivity between bilateral occipitoparietal sites and the right prefrontal cortex (Moratti, Saugar, and Strange [<reflink idref="bib19" id="ref10">19</reflink>]). Together, this literature suggests that the shift in scalp distribution of the emotion‐modulated LPP may reflect the maturation and normative development of affective brain networks. However, findings on the change in LPP magnitude across development are inconsistent (Dickey, Politte‐Corn, and Kujawa [<reflink idref="bib8" id="ref11">8</reflink>]): some studies report a normative decrease in overall LPP magnitude across late childhood (roughly ages 9–12) and adolescence (roughly ages 13–18; MacNamara et al. [<reflink idref="bib18" id="ref12">18</reflink>]; Pegg et al. [<reflink idref="bib21" id="ref13">21</reflink>]) and others suggest that the overall LPP increases with age (Cheng, Chen, and Decety [<reflink idref="bib5" id="ref14">5</reflink>]; Chronaki et al. [<reflink idref="bib6" id="ref15">6</reflink>]; Zhang et al. [<reflink idref="bib25" id="ref16">25</reflink>]). Studies investigating emotion‐modulated LPP changes have shown a decrease in magnitude across time (MacNamara et al. [<reflink idref="bib18" id="ref17">18</reflink>]; Pegg et al. [<reflink idref="bib21" id="ref18">21</reflink>]) or no significant emotion‐age interaction (Kujawa, Klein, and Proudfit [<reflink idref="bib14" id="ref19">14</reflink>]; Zhang et al. [<reflink idref="bib25" id="ref20">25</reflink>]).</p> <p>A majority of studies that have examined developmental shifts in the LPP are based on cross‐sectional designs that compare age groups of varying ranges, precluding the ability to investigate within‐person developmental changes (Cheng, Chen, and Decety [<reflink idref="bib5" id="ref21">5</reflink>]; Chronaki et al. [<reflink idref="bib6" id="ref22">6</reflink>]; Kujawa, Klein, and Hajcak [<reflink idref="bib13" id="ref23">13</reflink>]; Kujawa, Klein, and Proudfit [<reflink idref="bib14" id="ref24">14</reflink>]; MacNamara et al. [<reflink idref="bib18" id="ref25">18</reflink>]; Zhang et al. [<reflink idref="bib25" id="ref26">25</reflink>]). Those that do utilize longitudinal data contain a limited number of assessments which do not span the entirety of adolescence (Mulligan et al. [<reflink idref="bib20" id="ref27">20</reflink>]; Pegg et al. [<reflink idref="bib21" id="ref28">21</reflink>]). We are unaware of any longitudinal studies with at least three repeated measures of the LPP and sample sizes large enough to investigate within‐person effects. Thus, the present understanding of developmental shifts in the LPP, including normative changes in amplitude and spatial shifts towards centroparietal sites, has been pieced together from studies with a limited ability to examine such relationships. Moreover, these prior studies are inconsistent in their selection of scalp sites in which to score the LPP, with some restricting analyses to a pooling of occipital and parietal sites (Kujawa, Klein, and Hajcak [<reflink idref="bib13" id="ref29">13</reflink>]; Kujawa, Klein, and Proudfit [<reflink idref="bib14" id="ref30">14</reflink>]; MacNamara et al. [<reflink idref="bib18" id="ref31">18</reflink>]) and others examining a wider range of sites (Cheng, Chen, and Decety [<reflink idref="bib5" id="ref32">5</reflink>]; Zhang et al. [<reflink idref="bib25" id="ref33">25</reflink>]; Mulligan et al. [<reflink idref="bib20" id="ref34">20</reflink>]), thus contributing to discrepant findings and a lack of consensus around normative LPP trajectories.</p> <p>The LPP is a useful marker of affective processing; it provides insight into emotion‐modulated reactivity with high temporal resolution, and the EEG protocols used to elicit the LPP are well tolerated by individuals of all ages. Given that adolescence is characterized by significant development in the neural circuits that support emotion regulation, gaining a more thorough understanding of developmental changes in the LPP across adolescence is essential (Ahmed, Bittencourt‐Hewitt, and Sebastian [<reflink idref="bib1" id="ref35">1</reflink>]). This longitudinal view of the LPP is a necessary step in discerning how deviations from normative developmental LPP trajectories relate to psychopathology risk and resilience, and also provides important context for studies examining the LPP within this timeframe.</p> <p>The present study is the first to employ a multilevel growth modeling framework to investigate within‐person trajectories of the LPP across four assessments from ages 9, 12, 15, and 18. We model trajectories of the LPP when scored at three different scalp sites (occipital, parietal, central) in order to better understand the normative changes of the LPP across development. This design allows us to characterize LPP trajectories at scalp sites that are often combined or left out in other studies. We also investigate how trial condition (positive vs. neutral, negative vs. neutral) impacts trajectories. Consistent with prior research on developmental changes in the LPP, we hypothesize that individuals will show an initial decrease in the average LPP across conditions when scored at a pooling of occipital electrodes, and will show an initial increase when scored at poolings of parietal or central electrodes, with all models exhibiting a quadratic effect such that these initial rates of change level off in late adolescence. We do not have specific hypotheses about interaction effects of condition and age. Developing hypotheses about such interaction effects is difficult, as we expect normative anterior shifts in the spatial location of the maximal LPP to overlap with normative changes in magnitude in both the overall and emotion‐modulated LPP—overlapping patterns of change that may obscure each other. Due to these complexities, analyses including condition predictors are exploratory.</p> <hd id="AN0184767928-2">Methods</hd> <p></p> <hd id="AN0184767928-3">Participants</hd> <p>Participants were a community sample of 501 youth (47.3% female; 89.4% White, 7.8% Black, 2.4% Asian, 0.2% Native American, 0.2% other; 12.0% Hispanic) who were initially recruited via commercial mailing lists at 3 or 6 years of age as part of a longitudinal study on the development of psychopathology which took place in a Northeastern suburb. Participants had to live with at least one English‐speaking biological parent and have no significant medical illnesses or developmental disabilities. Participants completed the study protocol at approximately ages 9, 12, 15, and 18 between the years 2010 and 2022 and were included in the present sample if they had EEG data from at least one assessment. At each wave, individual EEG data was removed from the overall dataset due to the following exclusions: less than 15 usable trials for each EEG task condition (positive, negative, neutral) at midline electrode sites (Oz, Pz, Cz), < 65% accuracy on the EEG task, or excessively noisy EEG data based on subjective quality ratings (e.g., motion artifact, electrode connectivity issues). Participants were excluded from the present study if they were missing EEG data at all four waves.</p> <hd id="AN0184767928-4">Procedure</hd> <p>The study protocol was approved by the Institutional Review Board at the home institution. All parents provided informed consent and youth provided verbal assent.</p> <hd id="AN0184767928-5">Emotion Processing Task</hd> <p>The LPP was measured using the emotional interrupt task (Kujawa, Klein, and Hajcak [<reflink idref="bib13" id="ref36">13</reflink>]), which is a computerized paradigm in which participants are presented with neutral, pleasant (positive), or unpleasant (negative) images from the International Affective Picture System (Lang, Bradley, and Cuthbert [<reflink idref="bib17" id="ref37">17</reflink>]). The images were selected to be developmentally appropriate at the age 9 assessment, and the same images were used in the task at each timepoint. In order to engage participants in the task, they are asked to press either the left or right mouse button in response to a target (left or right arrow) shown between two presentations of the image on each trial. A total of 60 images were presented: 20 pleasant (e.g., children playing, cute animals, babies), 20 unpleasant (e.g., sad or angry people, weapons, scary animals), and 20 neutral (e.g., outdoor scenes, household objects). The task included 120 total trials, and each image was randomly displayed once in each of two blocks. Each trial began with an 800‐ms fixation (+), and then an image was presented for 1000 ms followed by a target (< or >) for 150 ms, and the same image was presented for an additional 400 ms. The intertrial interval varied randomly between 1500 and 2000 ms. Participants were instructed to respond as quickly as possible to the target by clicking the corresponding left or right mouse button.</p> <hd id="AN0184767928-6">EEG Recording and Analysis</hd> <p>Continuous EEG was recorded using a 34‐channel BioSemi system based on the 10/20 system. Two electrodes were placed on the left and right mastoids. Electrooculogram was generated from eye blinks and movements and was recorded from two pairs of facial electrodes placed approximately 1 cm from the outer corner of both the left and right eye and 1 cm above and below the right eye. The Common Mode Sense active electrode and the Driven Right Leg passive electrode formed the ground electrode. The data were digitized using ActiView software (BioSemi, Amsterdam, Netherlands) at 24‐bit resolutions with an LSB value of 31.25 nV and a sampling rate of 1024 Hz, using a low‐pass filter of 204.8 Hz.</p> <p>Offline analyses were performed using Brain Vision Analyzer 2.2 (Brain Products; Gilching, Germany). All data were converted to a mastoid reference and band‐pass filtered with cutoffs of 0.1 and 30 Hz. The EEG was segmented for each trial, beginning 200 ms before stimulus onset and continuing for 1000 ms after stimulus presentation. The EEG was corrected for eye blinks (Gratton, Coles, and Donchin [<reflink idref="bib10" id="ref38">10</reflink>]), and semi‐automated artifact rejection was used to remove artifacts with a voltage step of more than 50 μV between sample points, a voltage difference of 300 μV within a trial, or a maximum voltage difference of less than 0.5 μV within 100 ms intervals. Visual inspection was then used to identify artifacts, and individual segments containing artifacts were removed.</p> <p>ERPs were constructed by averaging the responses to neutral, positive, or negative images for each electrode site of interest. ERPs were baseline corrected to the 200 ms interval prior to stimulus onset. The LPP was scored as the mean activity 400–1000 ms after stimulus onset at an occipital pooling (O1, Oz, O2), a parietal pooling (P3, Pz, P4), and a central pooling (C3, Cz, C4) at each of the four waves (ages 9, 12, 15, and 18). Poolings were created by taking the average of the mean activity at the included electrodes. Means and standard deviations of the number of segments included in LPP averages, broken down by trial condition, electrode site, and wave, are presented in the Supporting Information. Waveforms and scalp distributions of the LPPs by age and electrode pooling are also presented in the Supporting Information.</p> <p>In addition to the 400–1000 ms time window, the LPP was also scored at early (400–700 ms) and late (700–1000 ms) time windows for exploratory analyses (Schupp et al. [<reflink idref="bib23" id="ref39">23</reflink>]). Scalp distributions for the early and late LPP windows are presented in the Supporting Information.</p> <hd id="AN0184767928-7">Data Analysis</hd> <p>Participants had a mean of 2.85 assessments with usable LPP data. Of the 501 participants, 151 had usable LPP data for all four waves, 196 had three waves, 83 participants had two waves, and 71 participants had one wave. The number of missing waves did not significantly differ by sex, race, or ethnicity. In all models, missing data was estimated using restricted maximum likelihood (REML).</p> <p>To examine the reliability of the LPP, data within each wave, scalp site, and condition (e.g., age 9, occipital, positive), were split into odd and even trials. Split‐half averages were computed per individual, and a Pearson correlation was calculated to examine the correlation between the two averages across the full sample. Correlations were then corrected by the Spearman‐Brown prophecy formula. Reliability coefficients were considered acceptable at 0.60 or greater, and substantial at 0.80 or greater (Shrout [<reflink idref="bib24" id="ref40">24</reflink>]).</p> <p>To examine LPP trajectories, a series of nested unconditional multilevel models were fit for each scalp site (occipital, parietal, central). Analyses were conducted using R Statistical Software (R Core Team [<reflink idref="bib22" id="ref41">22</reflink>]) using the packages lme4 version 1.1‐34 (Bates et al. [<reflink idref="bib2" id="ref42">2</reflink>]), and lmerTest version 3.1‐3 (Kuznetsova, Brockhoff, and Christensen [<reflink idref="bib16" id="ref43">16</reflink>]). First, an intercept‐only model (model 0) was fit, from which the intraclass correlation coefficient (ICC) was derived. While the ICC typically represents an estimate of the variability at the between‐ versus within‐person level, here the within‐person variability does not just include within‐person variability between timepoints but also within timepoints, within‐person. This is because each participant has three values for the LPP, corresponding to the three trial conditions, at each wave. This intercept‐only model included random intercepts, allowing individuals to vary in their intercept estimates. Second, a random intercept, fixed linear slope model (Model 1) was fit, in which age was centered at age 9, and the fixed effect of age represented the average linear change in LPP from ages 9–18. Next, a random intercept, random linear slope model (Model 2) was fit, which builds on model 1 by including a random effect for age to allow for interindividual variability in linear change in LPP. Next, a random intercept, random linear slope, and fixed quadratic model (Model 3) was fit, which builds on Model 2 by including fixed quadratic curvature, allowing for nonlinear LPP trajectories. The inclusion of the quadratic effect influences the interpretation of the linear slope, which in the quadratic models represents the initial rate of change at age 9, with the quadratic effect representing the change in the slope across time. Data were not sufficient to model a random quadratic effect.</p> <p>Finally, a conditional growth model with a random intercept, random slope, fixed quadratic slope, and trial condition as a predictor, was fit (Model 4). Condition, a categorical predictor, was dummy coded, with neutral set as the reference category and positive and negative compared to the reference. This yielded two predictors in the model: one reflected the difference between positive and neutral LPP (pos vs. neut), and one reflected the difference between negative and neutral LPP (neg vs. neut). The main intercept and slope estimates in this conditional model reflect the neutral condition (i.e., when the condition predictors have the value of "0"). This model also included interactions between each of these contrasts and the linear slope, as well as between the contrasts and the quadratic curvature.</p> <p>Models 0–4 were fit for each scalp site (occipital, parietal, central) separately. Likelihood ratio testing (LRT) was utilized to compare model fit (i.e., Model 2 was compared to Model 1, Model 3 was compared to Model 2, and Model 4 was compared to Model 3). In order to calculate LRT statistics, models were refit using Maximum Likelihood (ML) estimation. A decrease in Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), and a significant chi‐square difference test were used as indicators of improved model fit and justification for proceeding with the more complex model. To investigate whether trajectory patterns differed as a function of sex, all final models were tested again with the inclusion of sex as a main time‐invariant predictor; the three‐way interaction between sex, linear slope (i.e., time), and condition; and the two‐way interactions contained therein. These models were conducted in order to determine whether any significant sex interaction effects were observable, which would indicate that LPP trajectories differ by sex in some way. For example, the sex × linear slope interaction reflects differences between males and females in initial slope for the neutral condition.</p> <p>In order to test model robustness, all final models were examined for outliers in the form of influential data points by calculating DFBETA weights for each model estimate, for each individual. If an individual had a DFBETA for any of the model estimates that was above 3 standard deviations from the mean DFBETA for that model estimate, they were removed from the sample. Models were then refitted without the influential participants.</p> <hd id="AN0184767928-8">Results</hd> <p></p> <hd id="AN0184767928-9">LPP Split‐Half Reliability</hd> <p>Spearman‐Brown corrected reliability estimates are presented in Table 1. All reliability estimates showed acceptable to substantial reliability, ranging from 0.66 (age 9, central, negative) to 0.88 (age 9, occipital, positive, and negative). Reliability coefficients for the early and late time windows are presented in the Supporting Information.</p> <p>1 TABLE Split‐half reliability estimates for the LPP.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Condition</th><th align="center">Age 9</th><th align="center">Age 12</th><th align="center">Age 15</th><th align="center">Age 18</th></tr><tr><th align="center">Occ</th><th align="center">Par</th><th align="center">Cen</th><th align="center">Occ</th><th align="center">Par</th><th align="center">Cen</th><th align="center">Occ</th><th align="center">Par</th><th align="center">Cen</th><th align="center">Occ</th><th align="center">Par</th><th align="center">Cen</th></tr></thead><tbody valign="top"><tr><td align="left">Neutral</td><td align="center">0.85</td><td align="center">0.77</td><td align="center">0.71</td><td align="center">0.86</td><td align="center">0.82</td><td align="center">0.80</td><td align="center">0.84</td><td align="center">0.74</td><td align="center">0.76</td><td align="center">0.73</td><td align="center">0.76</td><td align="center">0.75</td></tr><tr><td align="left">Positive</td><td align="center">0.88</td><td align="center">0.82</td><td align="center">0.76</td><td align="center">0.86</td><td align="center">0.81</td><td align="center">0.82</td><td align="center">0.79</td><td align="center">0.72</td><td align="center">0.74</td><td align="center">0.74</td><td align="center">0.73</td><td align="center">0.73</td></tr><tr><td align="left">Negative</td><td align="center">0.88</td><td align="center">0.77</td><td align="center">0.66</td><td align="center">0.86</td><td align="center">0.79</td><td align="center">0.79</td><td align="center">0.80</td><td align="center">0.68</td><td align="center">0.67</td><td align="center">0.73</td><td align="center">0.69</td><td align="center">0.73</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note:</emph> Spearman‐Brown corrected split‐half correlations between even and odd trials for each condition, within each electrode pooling, within each wave.</p> <p>2 Abbreviations: Cen = central, Occ = occipital, Par = parietal.</p> <hd id="AN0184767928-10">Model Results</hd> <p>Summaries of the results of four nested unconditional models can be found in Table 2; the conditional models in Table 3; and interaction trajectory plots of conditional growth models in Figure 1. Results of models tested with the early and late LPP timeframes are presented in the Supporting Information.</p> <p>2 TABLE Unconditional model results.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center">Model</th><th align="center">Intercept</th><th align="center">Intercept variance</th><th align="center">Linear slope</th><th align="center">Linear slope variance</th><th align="center">Intercept‐slope correlation</th><th align="center">Quadratic slope</th><th align="center">Quadratic slope variance</th><th align="center">AIC</th><th align="center">BIC</th><th align="center"><italic>χ</italic><sup>2</sup><sub>Diff</sub></th><th align="center">ICC</th></tr></thead><tbody valign="top"><tr><td align="left">LPP(Occ)</td><td align="center">0</td><td align="center">11.47</td><td align="center">0.30</td><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center">30121</td><td align="center">30140</td><td align="center" /><td align="center">0.414</td></tr><tr><td align="center">1</td><td align="center">17.58</td><td align="center">0.30</td><td align="center">−1.46</td><td align="center">0.03</td><td align="center">−0.41</td><td align="center" /><td align="center" /><td align="center">28256</td><td align="center">28282</td><td align="center">1866.88</td><td align="center" /></tr><tr><td align="center">2</td><td align="center">17.47</td><td align="center">0.45</td><td align="center">−1.47</td><td align="center">0.05</td><td align="center">−0.90</td><td align="center" /><td align="center" /><td align="center">27391</td><td align="center">27429</td><td align="center">869.10</td><td align="center" /></tr><tr><td align="center">3</td><td align="center">18.64</td><td align="center">0.46</td><td align="center">−2.45</td><td align="center">0.10</td><td align="center">−0.65</td><td align="center">0.11</td><td align="center">0.01</td><td align="center">27258</td><td align="center">27302</td><td align="center">135.62</td><td align="center" /></tr><tr><td align="left">LPP(Par)</td><td align="center">0</td><td align="center">−0.54</td><td align="center">0.22</td><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center">27436</td><td align="center">27455</td><td align="center" /><td align="center">0.413</td></tr><tr><td align="center">1</td><td align="center">−1.78</td><td align="center">0.25</td><td align="center">0.30</td><td align="center">0.03</td><td align="center">−0.46</td><td align="center" /><td align="center" /><td align="center">27320</td><td align="center">27346</td><td align="center">118.14</td><td align="center" /></tr><tr><td align="center">2</td><td align="center">−1.66</td><td align="center">0.34</td><td align="center">0.27</td><td align="center">0.04</td><td align="center">−0.85</td><td align="center" /><td align="center" /><td align="center">26909</td><td align="center">26947</td><td align="center">415.18</td><td align="center" /></tr><tr><td align="center">3</td><td align="center">−1.84</td><td align="center">0.36</td><td align="center">0.43</td><td align="center">0.09</td><td align="center">−0.61</td><td align="center">−0.02</td><td align="center">< 0.01</td><td align="center">26907</td><td align="center">26951</td><td align="center">3.98</td><td align="center" /></tr><tr><td align="left">LPP(Cen)</td><td align="center">0</td><td align="center">−9.83</td><td align="center">0.23</td><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center">28347</td><td align="center">28366</td><td align="center" /><td align="center">0.373</td></tr><tr><td align="center">1</td><td align="center">−14.42</td><td align="center">0.24</td><td align="center">1.09</td><td align="center">0.03</td><td align="center">−0.45</td><td align="center" /><td align="center" /><td align="center">26879</td><td align="center">26905</td><td align="center">1469.72</td><td align="center" /></tr><tr><td align="center">2</td><td align="center">−14.21</td><td align="center">0.34</td><td align="center">1.07</td><td align="center">0.04</td><td align="center">−0.82</td><td align="center" /><td align="center" /><td align="center">26511</td><td align="center">26549</td><td align="center">372.73</td><td align="center" /></tr><tr><td align="center">3</td><td align="center">−14.99</td><td align="center">0.34</td><td align="center">1.73</td><td align="center">0.09</td><td align="center">−0.60</td><td align="center">−0.07</td><td align="center">0.01</td><td align="center">26439</td><td align="center">26483</td><td align="center">73.87</td><td align="center" /></tr></tbody></table> </ephtml> </p> <ulist> <item>3 <emph>Note:</emph> Model 0: intercept‐only model, Model 1: random intercept, fixed slope growth model, Model 2: random intercept, random slope growth model, Model 3: random intercept, random slope, fixed quadratic slope model. Bolded values represent significant effects.</item> <item>4 Abbreviations: AIC = akaike information criteria, BIC = Bayesian information criteria, ICC = intraclass correlation, LPP = late positive potential, <emph>χ</emph><sups>2</sups><subs>Diff</subs> = likelihood ratio test comparing model fit of nested models.</item> <item>3 TABLE Conditional model results.</item> </ulist> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center">LPP (Occipital)</th><th align="center">LPP (Parietal)</th><th align="center">LPP (Central)</th></tr><tr><th align="center">Estimate</th><th align="center">SE</th><th align="center"><italic>p</italic></th><th align="center">Estimate</th><th align="center">SE</th><th align="center"><italic>p</italic></th><th align="center">Estimate</th><th align="center">SE</th><th align="center"><italic>p</italic></th></tr></thead><tbody valign="top"><tr><td align="left">Intercept</td><td align="center">15.90</td><td align="center">0.50</td><td align="center">< 0.001</td><td align="center">−3.89</td><td align="center">0.40</td><td align="center">< 0.001</td><td align="center">−16.5</td><td align="center">0.39</td><td align="center">< 0.001</td></tr><tr><td align="left">Linear Slope</td><td align="center">−1.96</td><td align="center">0.14</td><td align="center">< 0.001</td><td align="center">0.44</td><td align="center">0.13</td><td align="center">< 0.001</td><td align="center">1.84</td><td align="center">0.12</td><td align="center">< 0.001</td></tr><tr><td align="left">Neg vs. Neut</td><td align="center">5.37</td><td align="center">0.33</td><td align="center">< 0.001</td><td align="center">4.74</td><td align="center">0.32</td><td align="center">< 0.001</td><td align="center">3.91</td><td align="center">0.31</td><td align="center">< 0.001</td></tr><tr><td align="left">Pos vs. Neut</td><td align="center">2.83</td><td align="center">0.33</td><td align="center">< 0.001</td><td align="center">1.43</td><td align="center">0.32</td><td align="center">< 0.001</td><td align="center">0.65</td><td align="center">0.31</td><td align="center">0.033</td></tr><tr><td align="left">Quadratic Slope</td><td align="center">0.08</td><td align="center">0.01</td><td align="center">< 0.001</td><td align="center">−1.44</td><td align="center">0.01</td><td align="center">0.289</td><td align="center">−0.09</td><td align="center">0.01</td><td align="center">< 0.001</td></tr><tr><td align="left">Neg vs. Neut × Linear Slope</td><td align="center">−0.84</td><td align="center">0.18</td><td align="center">< 0.001</td><td align="center">−0.09</td><td align="center">0.17</td><td align="center">0.577</td><td align="center">−0.27</td><td align="center">0.16</td><td align="center">0.096</td></tr><tr><td align="left">Pos vs. Neut × Linear Slope</td><td align="center">−0.62</td><td align="center">0.18</td><td align="center">< 0.001</td><td align="center">0.07</td><td align="center">0.17</td><td align="center">0.662</td><td align="center">−0.07</td><td align="center">0.16</td><td align="center">0.676</td></tr><tr><td align="left">Neg vs. Neut × Quadratic Slope</td><td align="center">0.04</td><td align="center">0.02</td><td align="center">0.024</td><td align="center">−0.002</td><td align="center">0.02</td><td align="center">0.914</td><td align="center">0.03</td><td align="center">0.02</td><td align="center">0.117</td></tr><tr><td align="left">Pos vs. Neut × Quadratic Slope</td><td align="center">0.04</td><td align="center">0.02</td><td align="center">0.062</td><td align="center">−0.01</td><td align="center">0.02</td><td align="center">0.560</td><td align="center">0.01</td><td align="center">0.02</td><td align="center">0.531</td></tr><tr><td align="left">∆AIC<xref ref-type="fn" rid="tfn6" /></td><td align="center">−362</td><td align="center" /><td align="center" /><td align="center">−639</td><td align="center" /><td align="center" /><td align="center">−525</td><td align="center" /><td align="center" /></tr><tr><td align="left">∆BIC<xref ref-type="fn" rid="tfn6" /></td><td align="center">−324</td><td align="center" /><td align="center" /><td align="center">−601</td><td align="center" /><td align="center" /><td align="center">−487</td><td align="center" /><td align="center" /></tr><tr><td align="left">χ<sup>2</sup><sub>Diff</sub><xref ref-type="fn" rid="tfn6" /></td><td align="center">373.81</td><td align="center" /><td align="center">< 0.001</td><td align="center">651.24</td><td align="center" /><td align="center">< 0.001</td><td align="center">536.89</td><td align="center" /><td align="center">< 0.001</td></tr></tbody></table> </ephtml> </p> <ulist> <item>5 <emph>Note:</emph> Condition predictors are coded with neutral as the reference category (0 = neutral), such that Neg versus Neutral represents the difference between LPP to negative stimuli and LPP to neutral stimuli, and Pos versus Neutral represents the difference between LPP to positive stimuli and LPP to neutral stimuli. Bolded values represent significant effects.</item> <item>6 * Tests of model fit comparing Model 3 (unconditional model with random intercept, random slope, and fixed quadratic curvature) and Model 4 (conditional model).</item> </ulist> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01may25/cdev14223-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14223-fig-0001.jpg" title="1 N = 501. Interaction plots for conditional models, including separate average trajectories for LPP to positive, negative, and neutral stimuli. (a) occipital conditional model; (b) parietal conditional model; (c) central conditional model." /> </p> <p></p> <hd id="AN0184767928-12">Models Testing Change in the LPP Over the Occipital Site</hd> <p></p> <hd id="AN0184767928-13">Unconditional Models</hd> <p>The intercept‐only model (model 0) yielded an ICC of 0.41, suggesting that 41% of the variance in occipital LPP, averaged across conditions, is at the between‐person level and the remaining 59% is either at the within‐person level or is error.</p> <p>Of the nested unconditional models (Models 0–3, Table 2), the random intercept, random linear slope, fixed quadratic model (Model 3) was the best fitting model for the occipital LPP averaged across trial conditions. This model indicated a significant average initial decrease in LPP at age 9 (<emph>p</emph> < 0.001), and a significant quadratic effect (<emph>p</emph> < 0.001), together indicating an initial reduction in occipital LPP with a decrease in the rate of change across time, yielding a concave curvature.</p> <hd id="AN0184767928-14">Conditional Model</hd> <p>Because Model 3 was the best fitting unconditional model for the occipital LPP, the conditional model (Model 4, Table 3) built on Model 3 by including trial condition as a predictor of the LPP. This model yielded significant trial condition effects such that age 9 occipital LPPs to positive and negative stimuli were greater than to neutral stimuli (<emph>p</emph>s < 0.001). The occipital LPP for the neutral condition showed an initial linear decrease at age 9 (<emph>p</emph> < 0.001), which was qualified by a significant positive quadratic effect of age (<emph>p</emph> < 0.001). Together these age effects indicated that the occipital LPP to neutral stimuli initially decreased but that the rate of decrease slowed significantly across adolescence, yielding a concave curvature. The neg versus neut × linear slope interaction and the pos versus neut × linear slope interaction both indicated that the initial rate of change in LPP differed between the occipital LPP to emotional compared to neutral stimuli (<emph>p</emph>s < 0.001). The neg versus neut × quadratic curvature effect indicated that the rate of change in the slope (i.e., the rate of change of the rate of change) of the occipital LPP differed significantly between the negative and neutral trial conditions (neg versus neut × quadratic slope; <emph>p</emph> = 0.024), yielding a more pronounced curvature for the occipital LPP to negative, relative to neutral, stimuli.</p> <hd id="AN0184767928-15">Models Testing Change in the LPP Over the Parietal Site</hd> <p></p> <hd id="AN0184767928-16">Unconditional Models</hd> <p>The intercept‐only model (Model 0) yielded an ICC of 0.41, suggesting that 41% of the variance in parietal LPP, averaged across conditions, is at the between‐person level, and the remaining 59% is either at the within‐person level or is error.</p> <p>Of the nested unconditional models (Models 0–3, Table 2), the random intercept, random linear slope, fixed quadratic model (Model 3) was the best fitting model for the parietal LPP averaged across trial conditions. This model revealed an initial increase in the LPP at age 9 (<emph>p</emph> < 0.001) and a significant negative quadratic effect (<emph>p</emph> = 0.046), together indicating an initial increase in parietal LPP with a decrease in the rate of change across time, yielding a convex curvature.</p> <hd id="AN0184767928-17">Conditional Model</hd> <p>Because Model 3 was the best fitting unconditional model for the parietal LPP, the conditional model (Model 4, Table 3) built on Model 3 by including trial condition as a predictor of the LPP. This model yielded a significant neg versus neut effect and pos versus neut effect such that the age 9 parietal LPP to either emotional set of stimuli was greater than to neutral stimuli (<emph>p</emph>s < 0.001). The parietal LPP for the neutral condition initially increased at age 9 (<emph>p</emph> < 0.001), with no significant quadratic effect of age, together indicating linear increase in parietal LPP to neutral stimuli across waves. The neg versus neut × linear slope and pos versus neut × linear slope interactions indicated that the rates of change did not differ as a function of condition. There were no significant condition × quadratic slope effects, indicating that the rate of change in the slope of the parietal LPP did not differ as a function of trial condition.</p> <hd id="AN0184767928-18">Models Testing Change in the LPP Over the Central Site</hd> <p></p> <hd id="AN0184767928-19">Unconditional Models</hd> <p>The intercept‐only model (model 0) yielded an ICC of 0.37, suggesting that 37% of the variance in central LPP, averaged across conditions, is at the between‐person level, and the remaining 63% is either at the within‐person level or is error.</p> <p>Of the nested unconditional models (Models 0–3, Table 2), the random intercept, random linear slope, fixed quadratic model (Model 3) was the best fitting model for the central LPP averaged across trial conditions. This model revealed an initial increase in the LPP at age 9 (<emph>p</emph> < 0.001) and a significant quadratic effect (<emph>p</emph> < 0.001), together indicating an initial increase in central LPP across waves with a decrease in the rate of change across time, yielding a convex curvature.</p> <hd id="AN0184767928-20">Conditional Model</hd> <p>Because Model 3 was the best fitting unconditional model for the central LPP, the conditional model (Model 4, Table 3) built on Model 3 by including trial condition as a predictor of the central LPP. This model indicated that the age 9 central LPP to both negative and positive stimuli were greater than to neutral stimuli (neg vs. neut <emph>p</emph> < 0.001; pos vs. neut <emph>p</emph> = 0.033). The central LPP for the neutral condition initially increased at age 9 (<emph>p</emph> < 0.001), with a significant negative quadratic effect (<emph>p</emph> < 0.001), together indicating initial growth in the central LPP to neutral stimuli with a decrease in the rate of change across time, yielding a convex curvature. The model showed no significant neg versus neut × linear slope interaction nor a pos versus neut × linear slope interaction, indicating that the initial rates of change did not differ between the emotional and neutral conditions. There were no significant condition × quadratic slope effects, indicating that the rate of change in the slope of the central LPP did not differ as a function of trial condition.</p> <hd id="AN0184767928-21">Sex Effects</hd> <p>As indicated in the data analytic plan, all final models were tested again with the inclusion of sex as a predictor along with its interactions with linear slope and condition. These models yielded no significant interactions between sex and linear slope, sex and condition, or the three‐way interaction of sex, linear slope, and condition at any of the three scalp sites, indicating that the above trajectory patterns do not differ between males and females. The inclusion of sex also did not improve model fit for any of the models. The interaction between sex and quadratic slope was not included in these analyses as we already lacked sufficient data to model random quadratic slope effects.</p> <hd id="AN0184767928-22">Outlier Analyses</hd> <p>All effects that were present in the full‐sample models were observed in refitted models without outliers as well, indicating that these effects are robust. Due to the stability of the full‐sample effects, we chose to retain the full sample for the results reported above.</p> <hd id="AN0184767928-23">Discussion</hd> <p>The present study examined within‐person trajectories of the LPP across four assessments spanning childhood to late adolescence, incorporating a study design that allowed for investigation of site‐specific developmental changes and how stimulus valence influenced these trajectories within a community sample. Reliability analyses showed that the LPP exhibits acceptable to very good split‐half reliability across all ages and scalp sites. The occipital LPP was characterized by an initial decrease in magnitude in late childhood (i.e., around age 9), and the parietal and central LPPs were characterized by initial increases in late childhood. These trajectories exhibited significant variability between individuals and were curvilinear in nature at the occipital and central sites, with rates of change leveling off across time. When considering trial condition (negative, positive, or neutral), we observed significant differences between the initial rate of change in the negative, compared to neutral, LPP, as well as in the positive, compared to neutral, LPP when measured in the occipital region. In addition, we observed a significant difference in degree of curvature, or slowing of the initial linear slope effect, for the occipital LPP to negative stimuli compared to neutral stimuli.</p> <p>The results from the unconditional models, which investigate normative changes in the LPP averaged across trial conditions, were consistent with our hypotheses that the LPP would exhibit an initial decrease at the occipital site and an initial increase at parietal and central sites. This pattern may reflect hierarchical shifts in emotion processing circuitry which increasingly recruit prefrontal regions across adolescent development (Casey et al. [<reflink idref="bib4" id="ref44">4</reflink>]). It may also reflect an overall decrease in amplitude of the LPP across time, with the LPP scored at occipital sites becoming less positive over time and the LPP scored at parietal and central sites becoming less negative across time, both coming closer to 0, as has been shown in previous studies (MacNamara et al. [<reflink idref="bib18" id="ref45">18</reflink>]; Pegg et al. [<reflink idref="bib21" id="ref46">21</reflink>]). Also consistent with our hypotheses, the fit of these trajectories was significantly improved by the inclusion of a nonlinear age effect across all three scalp sites, showing a consistent pattern of faster rates of change in childhood and slower rates of change in mid‐adolescence as youth approach early adulthood. Such curvilinear patterns align with the expectation that adolescent neural circuitry will more closely resemble that of adults as they approach adulthood (Casey et al. [<reflink idref="bib4" id="ref47">4</reflink>]).</p> <p>The results from the conditional models provide insight into emotion modulation of these shifts in brain activity. The linear slopes of the occipital LPP trajectories differed by trial condition, such that the LPP exhibited a steeper decrease for negative relative to neutral images as well as for positive relative to neutral images in late childhood. Previous work has similarly found a decrease in reactivity to emotional (relative to neutral) stimuli across childhood and adolescence (MacNamara et al. [<reflink idref="bib18" id="ref48">18</reflink>]). However, the present findings suggest that normative decreases in emotional (relative to neutral) stimuli may be more evident in the LPP measured over the occipital region. Similarly, differences in the quadratic effect in the LPP to negative relative to neutral stimuli were only observed when the LPP was measured over the occipital region. Thus, researchers must be cognizant of how electrode sites impact the developmental patterns that are observed in future longitudinal investigations of the LPP.</p> <p>Results of the present study indicate the need to consider developmental changes in scalp topographies of ERP components in longitudinal research. The scalp distributions in Figure S1 suggest that the negative minus neutral LPP shifts towards parietal and central sites across development. However, model results indicate that the normative decrease in LPP to negative relative to neutral stimuli is only statistically significant at occipital sites. Neglecting to investigate a range of electrode sites may obscure developmental effects. We recommend that researchers approach longitudinal LPP analyses with a clear sense of the underlying processes most relevant to their research question to select electrode sites in an intentional, hypothesis‐driven manner. Investigators may also consider multiple scoring approaches in order to derive a more complete picture of developmental changes in the LPP across development. The task used to elicit the LPP may also influence trajectories. For example, the task demands involved in the emotion interrupt task (attending to a target stimulus and making mouse clicks) may influence the scalp distribution of the LPPs in the present study. Use of a task involving explicit emotion modulation, passive viewing, or even different kinds of stimuli (i.e., faces) may impact observed trajectories. It is also important to note that LPP scalp distributions may be influenced by processing parameters and choice of reference used. We encourage researchers to examine their own electrode‐specific, emotion‐modulated trajectories to further inform their work.</p> <p>The present study exhibits multiple strengths: it utilized the same EEG task to elicit the LPP across four assessments spanning childhood and adolescent development in a large community sample of youth and utilized modeling that allows for the mapping of within‐person trajectories and inclusion of random effects. While the present study is a significant contribution to the LPP and affective processing literature, there are also important limitations. The distance between assessments was 3 years, precluding us from observing more fine‐grained changes in the LPP across time. While we investigated sex as a potential moderator and found no interactions between sex and other model predictors, we did not investigate the role of pubertal development on LPP trajectories. Future researchers may consider including measures of pubertal development in their investigations. Additionally, it is possible that the negative stimuli used in the present study were more arousing than the positive stimuli, as some of the most arousing positive images in the IAPS (e.g., erotic scenes) could not be included due to the developmental nature of the sample. The IAPS images included in the present study were determined to be developmentally appropriate at the age 9 wave, and then were used in all subsequent waves to facilitate longitudinal analyses and ensure that any changes observed were not due to changes in stimuli. While this was necessary for the study design, it likely impacts the emotion modulation observed, particularly for the positive stimuli. Future studies could address this by ensuring stimuli are matched in valence ratings across waves. Another limitation related to the overall study design is that data missingness increased across waves, such that the age 18 wave has the greatest amount of missing data. This is expected in longitudinal studies, however it may have been exacerbated by two other practical factors: study communications (emails, scheduling, etc.) shifted from the parents to the youth once the youth turned 18, and the onset of the COVID‐19 pandemic occurred during the age 18 wave which impacted our ability to collect in‐person data such as EEG assessments for a few months. While the number of missing waves did not differ significantly by sex, race, or ethnicity, the pattern of missing data across the study may introduce bias in our findings.</p> <p>Lastly, the present discussion utilizes the term "normative" to refer to the trajectories derived from this middle‐ and working‐class suburban community sample; however, it should be noted that the sample is largely homogenous in racial, ethnic, and socioeconomic background, limiting the generalizability of these findings to other populations. The term "normative," as it is used in this paper, is meant to denote typical patterns of development for the present sample, however we can only reasonably posit that the presented trajectories are typical of White, non‐Hispanic youth born in the early aughts and raised in a suburban Northeastern community in the United States. Even so, the present findings still provide a significant improvement in our understanding of developmental shifts in the LPP and provide a basis for future investigations of LPP development.</p> <p>Researchers can build on the present findings by investigating how different trajectory patterns may contribute to risk and resilience, or how a variety of environmental, genetic, and individual‐difference characteristics influence LPP trajectories. Importantly, differences in both magnitude and scalp location may be associated with subsequent outcomes. An individual who exhibits a slower decrease in occipital LPP to emotional stimuli may be at higher risk for developing emotional and behavior problems due to weaker top‐down regulation of affective processing. Children and adolescents with genetic or psychosocial vulnerabilities may also go on to exhibit LPP trajectories that deviate from the norm. These inquiries may provide further insight into the relationship between affective processing and risk and resilience across development.</p> <p>In conclusion, the present study provides novel evidence regarding the normative within‐person development of the LPP across childhood and adolescence. These findings also highlight the importance of scalp site in investigating LPP trajectories.</p> <hd id="AN0184767928-24">Acknowledgments</hd> <p>Support for this research was provided through NIMH R01 MH069942 (Klein).</p> <hd id="AN0184767928-25">Conflicts of Interest</hd> <p>Dr. Hajcak consults for Universal Brain and Alto Neuroscience.</p> <hd id="AN0184767928-26">Data Availability Statement</hd> <p>The data, materials, and code necessary to reproduce the analyses presented here are not publicly accessible, however they are available from the first author upon reasonable request. Analyses were not pre‐registered.</p> <p>GRAPH: Data S1.</p> <ref id="AN0184767928-27"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref35" type="bt">1</bibl> <bibtext> Funding: This work was supported by National Institute of Mental Health, NIMH R01 MH069942.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref42" type="bt">2</bibl> <bibtext> Brady D. Nelson and Daniel N. Klein have equal authorship in this study.</bibtext> </blist> </ref> <ref id="AN0184767928-28"> <title> References </title> <blist> <bibtext> Ahmed, S. P., A. Bittencourt‐Hewitt, and C. L. Sebastian. 2015. " Neurocognitive Bases of Emotion Regulation Development in Adolescence." Developmental Cognitive Neuroscience 15 : 11 – 25. https://doi.org/10.1016/j.dcn.2015.07.006.</bibtext> </blist> <blist> <bibtext> Bates, D., M. Mächler, B. Bolker, and S. Walker. 2015. " Fitting Linear Mixed‐Effects Models Using lme4." 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  Data: <searchLink fieldCode="AR" term="%22Alison+E%2E+Calentino%22">Alison E. Calentino</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9051-8043">0000-0002-9051-8043</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nathan+M%2E+Hager%22">Nathan M. Hager</searchLink><br /><searchLink fieldCode="AR" term="%22Elise+M%2E+Adams%22">Elise M. Adams</searchLink><br /><searchLink fieldCode="AR" term="%22Aline+K%2E+Szenczy%22">Aline K. Szenczy</searchLink><br /><searchLink fieldCode="AR" term="%22Lindsay+Dickey%22">Lindsay Dickey</searchLink><br /><searchLink fieldCode="AR" term="%22Autumn+Kujawa%22">Autumn Kujawa</searchLink><br /><searchLink fieldCode="AR" term="%22Greg+Hajcak%22">Greg Hajcak</searchLink><br /><searchLink fieldCode="AR" term="%22Brady+D%2E+Nelson%22">Brady D. Nelson</searchLink><br /><searchLink fieldCode="AR" term="%22Daniel+N%2E+Klein%22">Daniel N. Klein</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Child+Development%22"><i>Child Development</i></searchLink>. 2025 96(3):1088-1097.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: The late positive potential (LPP), an event-related potential reflecting affective processing, may exhibit developmental shifts in magnitude and scalp location. In the present longitudinal study, 501 youth (47.3% female; 89.4% White; 12.0% Hispanic) completed the emotion interrupt task to elicit the LPP to neutral, positive, and negative images at approximately 9, 12, 15, and 18 years old (data collected 2010-2022). Multilevel growth models indicated an initial decrease in the occipital LPP and an increase in the parietal and central LPP during late childhood, with rates of change leveling off across adolescence. Trial condition (i.e., valence) significantly impacted trajectories only when the LPP was measured over occipital sites. Results provide novel evidence of stability and change in the LPP across development.
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