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Associations between the resting EEG aperiodic slope and broad domains of cognitive ability.

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Title: Associations between the resting EEG aperiodic slope and broad domains of cognitive ability.
Authors: Euler, Matthew J. (AUTHOR), Vehar, Julia V. (AUTHOR), Guevara, Jasmin E. (AUTHOR), Geiger, Allie R. (AUTHOR), Deboeck, Pascal R. (AUTHOR), Lohse, Keith R. (AUTHOR)
Source: Psychophysiology. Jun2024, Vol. 61 Issue 6, p1-21. 21p.
Subjects: Cognitive ability, Cognition, Cognitive processing speed, Verbal ability, Electroencephalography, Structural equation modeling
Abstract: Recent studies suggest that the EEG aperiodic exponent (often represented as a slope in log–log space) is sensitive to individual differences in momentary cognitive skills such as selective attention and information processing speed. However, findings are mixed, and most of the studies have focused on just a narrow range of cognitive domains. This study used an archival dataset to help clarify associations between resting aperiodic features and broad domains of cognitive ability, which vary in their demands on momentary processing. Undergraduates (N = 166) of age 18–52 years completed a resting EEG session as well as a standardized, individually administered assessment of cognitive ability that included measures of processing speed, working memory, and higher‐order visuospatial and verbal skills. A subsample (n = 110) also completed a computerized reaction time task with three difficulty levels. Data reduction analyses revealed strong correlations between the aperiodic offset and slope across electrodes, and a single component accounted for ~60% of variance in slopes across the scalp, in both eyes‐closed and eyes‐open conditions. Structural equation models did not support relations between the slope and specific domains tapping momentary processes. However, secondary analyses indicated that the eyes‐open slope was related to higher overall performance, as represented by a single general ability factor. A latent reaction time variable was significantly inversely related to both eyes‐closed and eyes‐open resting exponents, such that faster reaction times were associated with steeper slopes. These findings support and help clarify the relation of the resting EEG exponent to individual differences in cognitive skills. This study is among the first to examine associations between the EEG aperiodic slope and cognitive performance as assessed by both validated clinical assessments and laboratory tasks in the same cohort. Results replicated prior findings that higher resting slopes are linked to faster reaction times. This association did not extend to individually‐administered tasks of clerical speed, working memory, or higher order visuospatial or verbal skills; however, the slope was related to a single general ability factor in the eyes‐open resting condition. [ABSTRACT FROM AUTHOR]
Copyright of Psychophysiology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Associations between the resting EEG aperiodic slope and broad domains of cognitive ability.
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  Data: Recent studies suggest that the EEG aperiodic exponent (often represented as a slope in log–log space) is sensitive to individual differences in momentary cognitive skills such as selective attention and information processing speed. However, findings are mixed, and most of the studies have focused on just a narrow range of cognitive domains. This study used an archival dataset to help clarify associations between resting aperiodic features and broad domains of cognitive ability, which vary in their demands on momentary processing. Undergraduates (N = 166) of age 18–52 years completed a resting EEG session as well as a standardized, individually administered assessment of cognitive ability that included measures of processing speed, working memory, and higher‐order visuospatial and verbal skills. A subsample (n = 110) also completed a computerized reaction time task with three difficulty levels. Data reduction analyses revealed strong correlations between the aperiodic offset and slope across electrodes, and a single component accounted for ~60% of variance in slopes across the scalp, in both eyes‐closed and eyes‐open conditions. Structural equation models did not support relations between the slope and specific domains tapping momentary processes. However, secondary analyses indicated that the eyes‐open slope was related to higher overall performance, as represented by a single general ability factor. A latent reaction time variable was significantly inversely related to both eyes‐closed and eyes‐open resting exponents, such that faster reaction times were associated with steeper slopes. These findings support and help clarify the relation of the resting EEG exponent to individual differences in cognitive skills. This study is among the first to examine associations between the EEG aperiodic slope and cognitive performance as assessed by both validated clinical assessments and laboratory tasks in the same cohort. Results replicated prior findings that higher resting slopes are linked to faster reaction times. This association did not extend to individually‐administered tasks of clerical speed, working memory, or higher order visuospatial or verbal skills; however, the slope was related to a single general ability factor in the eyes‐open resting condition. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychophysiology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2024
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