Making movies of children's cortical electrical potentials: A practical procedure for dynamic source localization analysis with validating simulation

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Date
2023-03
Authors
Amedeo D'Angiulli
Matthew F. Kirby
Dao Anh Thu Thu Pham
Gary S Goldfield
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Abstract
Dipole source localization analysis (DSLA) of brain's event-related electrical potentials (ERPs) often presumes time constraints potentially too rigid to capture complex neural dynamics. We present a practical procedure (dynamically-guided DSLA) combining in a novel way well-established off-the-shelf modeling (Independent Component Analysis, and proprietary software modules running on MATLAB, such as FASTICA and EEG-Lab DIPFIT) with the cognitive modeling simulation framework tool known as Adaptive Control of Thought-Rational (ACT-R). The integration of these multiple methods can narrow down the time-windows of interest for DSLA more flexibly. As a demonstration, we used dynamically-guided DSLA to re-analyze cluster-level ERPs from a visual target detection task involving the participation of 26 preschool children. The key analytic features were dynamic ERP movies vis-à-vis validating ACT-R simulation of comparison adult data for the same task. Spatial topography for the six estimated sources did not differ significantly in children's and adult simulated data, which generally showed high fit (predicted R2 > 0.97). A control comparison using the static DSLA showed discrepant fits for two sources, suggesting that dynamic DSLA may offer higher discriminant reliability. Given its high validity, flexibility and relative user-friendliness, dynamically-guided DSLA seems useful for assessing developmental homology and may be suitable for a variety of clinical and experimental applications specifically involving neurodevelopmental data.
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D'Angiulli, Amedeo & Kirby, Matthew & Pham, Dao Anh Thu & Goldfield, Gary. (2023). Making movies of children's cortical electrical potentials: A practical procedure for dynamic source localization analysis with validating simulation. Brain Multiphysics. 4. 100064. 10.1016/j.brain.2023.100064.