The level of cognitive functioning in school-aged children is predicted by resting EEG Directed Phase Lag Index

Quantifying cognitive potential relies on psychometric measures that do not directly reflect cortical activity. While the relationship between cognitive ability and resting state EEG signal dynamics has been extensively studied in children with below-average cognitive performances, there remains a paucity of research focusing on individuals with normal to above-average cognitive functioning. This study aimed to elucidate the resting EEG dynamics in children aged four to 12 years across normal to above-average cognitive potential. Our findings indicate that signal complexity, as measured by Multiscale Entropy (MSE), was not significantly predictive of the level of cognitive functioning. However, utilizing Directed Phase Lag Index (DPLI) as an effective connectivity measure, we observed consistent patterns of information flow between anterior and posterior regions. Fronto-parietal as well as local connectivity patterns were seen across most of the cognitive functions. Moreover, specific connectivity patterns were obtained for each intellectual quotient index (namely verbal comprehension, visuospatial, fluid reasoning, and processing speed indexes as well as full-scale intellectual quotient). These results underscore the presence of long-range connections and support fronto-parietal theories of cognitive abilities within the resting state brain dynamics of children.

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Publication Details

Journal
Scientific Reports
Published
2025-01-09
DOI
https://doi.org/10.1038/s41598-025-85635-6
Citations
1
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
1.43

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article

The level of cognitive functioning in school-aged children is predicted by resting EEG Directed Phase Lag Index

Saeideh Davoudi, Inga Sophia Knoth, Sarah Lippé, Audrey-Rose Charlebois-Poirier et al.
1 citations
Scientific Reports
Functional Brain Connectivity Studies
1.43
article

The level of cognitive functioning in school-aged children is predicted by resting EEG Directed Phase Lag Index

Saeideh Davoudi, Inga Sophia Knoth, Sarah Lippé, Audrey-Rose Charlebois-Poirier, Ève Lalancette
article en
1 citations

Abstract

Quantifying cognitive potential relies on psychometric measures that do not directly reflect cortical activity. While the relationship between cognitive ability and resting state EEG signal dynamics has been extensively studied in children with below-average cognitive performances, there remains a paucity of research focusing on individuals with normal to above-average cognitive functioning. This study aimed to elucidate the resting EEG dynamics in children aged four to 12 years across normal to above-average cognitive potential. Our findings indicate that signal complexity, as measured by Multiscale Entropy (MSE), was not significantly predictive of the level of cognitive functioning. However, utilizing Directed Phase Lag Index (DPLI) as an effective connectivity measure, we observed consistent patterns of information flow between anterior and posterior regions. Fronto-parietal as well as local connectivity patterns were seen across most of the cognitive functions. Moreover, specific connectivity patterns were obtained for each intellectual quotient index (namely verbal comprehension, visuospatial, fluid reasoning, and processing speed indexes as well as full-scale intellectual quotient). These results underscore the presence of long-range connections and support fronto-parietal theories of cognitive abilities within the resting state brain dynamics of children.

Scientific ReportsVol. 15(1)
Centre Hospitalier Universitaire Sainte-Justine (CA), Université de Montréal (CA)
Natural Sciences and Engineering Research Council of Canada, Fonds de recherche du Québec – Nature et technologies
Quality Education
Openalex Percentile: Top 24%
Functional Brain Connectivity Studies
1.43
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The level of cognitive functioning in school-aged children is predicted by resting EEG Directed Phase Lag Index — Saeideh Davoudi, Inga Sophia Knoth, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS