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

Area of Science:

• Neuroscience
• Cognitive Science
• Individual Differences Research

Background:

• Linking neurobiology to stable individual differences in cognition, emotion, motivation, and behavior often necessitates large sample sizes for replicable results.
• Neuroimaging studies investigating individual differences typically require sample sizes in the hundreds, whether examining whole-brain or focal hypotheses.

Purpose of the Study:

• To propose strategies for increasing effect sizes in neuroimaging research.
• To enable the detection of replicable between-person effects in smaller sample sizes (hundreds instead of thousands).

Main Methods:

• Theoretical matching between neuroimaging tasks and behavioral constructs.
• Enhancing the reliability of neural and psychological measurements.
• Individualizing measures for each participant.
• Employing multivariate approaches with cross-validation over univariate methods.

Main Results:

• The proposed strategies aim to increase effect sizes in neuroimaging studies.
• These methods can facilitate the detection of replicable individual differences with smaller sample sizes.
• Challenges associated with these methods are discussed, with strategies for improvement.

Conclusions:

• Implementing these strategies can lead to a more robust and accessible neuroscience of individual differences.
• Increased effect sizes are crucial for detecting reliable between-person effects in neuroimaging research.
• The field can advance by adopting these methods to improve the quality and efficiency of individual differences research.