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Developmental trajectory of the late positive potential: Using temporal-spatial PCA to characterize within-subject
Elizabeth M Mulligan1, Zachary P Infantolino2, Daniel N Klein2
1Department of Psychology, Florida State University, Tallahassee, Florida.
The late positive potential (LPP) shows developmental shifts in timing and scalp distribution from childhood to adolescence. This study quantifies these changes using temporospatial principal component analysis on longitudinal electroencephalography data.
Area of Science:
- Neuroscience
- Developmental Psychology
- Cognitive Science
Background:
- The late positive potential (LPP) exhibits developmental changes, but prior research often used limited electrodes and averaged data.
- Understanding LPP's temporal and spatial dynamics is crucial for tracking cognitive development.
Purpose of the Study:
- To characterize temporal and spatial changes in the LPP across development using an empirically based approach.
- To quantify within-subject developmental changes in ERP timing and scalp distribution.
Main Methods:
- Utilized longitudinal data from 380 participants, recording LPP to emotional and neutral images at ages 9 and 12.
- Applied temporospatial principal component analysis (PCA) to within-subject subtraction-based ERPs.
- Analyzed changes in LPP amplitude and distribution related to age and pubertal development.
Main Results:
- A significant PCA factor showed increased LPP amplitude for emotional versus neutral pictures between ages 9 and 12.
- This factor was characterized by occipital negativity and frontocentral positivity around 850 ms post-stimulus.
- Factor scores correlated with pubertal development, suggesting a shift from occipital to frontocentral sites.
Conclusions:
- Temporospatial PCA offers a concise method for quantifying developmental changes in ERPs.
- LPP shifts from occipital to frontocentral areas during development, linked to pubertal maturation.
- This approach enhances the characterization of within-subject developmental shifts in ERP timing and scalp distribution.
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