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Beyond Increasing Sample Sizes: Optimizing Effect Sizes in Neuroimaging Research on Individual Differences.

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Title: Beyond Increasing Sample Sizes: Optimizing Effect Sizes in Neuroimaging Research on Individual Differences.
Authors: DeYoung, Colin G. (AUTHOR), Hilger, Kirsten (AUTHOR), Hanson, Jamie L. (AUTHOR), Abend, Rany (AUTHOR), Allen, Timothy A. (AUTHOR), Beaty, Roger E. (AUTHOR), Blain, Scott D. (AUTHOR), Chavez, Robert S. (AUTHOR), Engel, Stephen A. (AUTHOR), Feilong, Ma (AUTHOR), Fornito, Alex (AUTHOR), Genç, Erhan (AUTHOR), Goghari, Vina (AUTHOR), Grazioplene, Rachael G. (AUTHOR), Homan, Philipp (AUTHOR), Joyner, Keanan (AUTHOR), Kaczkurkin, Antonia N. (AUTHOR), Latzman, Robert D. (AUTHOR), Martin, Elizabeth A. (AUTHOR), Nikolaidis, Aki (AUTHOR)
Source: Journal of Cognitive Neuroscience. Jun2025, Vol. 37 Issue 6, p1023-1034. 12p.
Subjects: Psychometrics, Individual differences, Sample size (Statistics), Brain imaging, Neurobiology
Abstract: Linking neurobiology to relatively stable individual differences in cognition, emotion, motivation, and behavior can require large sample sizes to yield replicable results. Given the nature of between-person research, sample sizes at least in the hundreds are likely to be necessary in most neuroimaging studies of individual differences, regardless of whether they are investigating the whole brain or more focal hypotheses. However, the appropriate sample size depends on the expected effect size. Therefore, we propose four strategies to increase effect sizes in neuroimaging research, which may help to enable the detection of replicable between-person effects in samples in the hundreds rather than the thousands: (1) theoretical matching between neuroimaging tasks and behavioral constructs of interest; (2) increasing the reliability of both neural and psychological measurement; (3) individualization of measures for each participant; and (4) using multivariate approaches with cross-validation instead of univariate approaches. We discuss challenges associated with these methods and highlight strategies for improvements that will help the field to move toward a more robust and accessible neuroscience of individual differences. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Cognitive Neuroscience is the property of MIT Press 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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Linking neurobiology to relatively stable individual differences in cognition, emotion, motivation, and behavior can require large sample sizes to yield replicable results. Given the nature of between-person research, sample sizes at least in the hundreds are likely to be necessary in most neuroimaging studies of individual differences, regardless of whether they are investigating the whole brain or more focal hypotheses. However, the appropriate sample size depends on the expected effect size. Therefore, we propose four strategies to increase effect sizes in neuroimaging research, which may help to enable the detection of replicable between-person effects in samples in the hundreds rather than the thousands: (1) theoretical matching between neuroimaging tasks and behavioral constructs of interest; (2) increasing the reliability of both neural and psychological measurement; (3) individualization of measures for each participant; and (4) using multivariate approaches with cross-validation instead of univariate approaches. We discuss challenges associated with these methods and highlight strategies for improvements that will help the field to move toward a more robust and accessible neuroscience of individual differences. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Cognitive Neuroscience is the property of MIT Press 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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        Value: 10.1162/jocn_a_02297
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      – SubjectFull: Sample size (Statistics)
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