Academic journal article Cognitive, Affective and Behavioral Neuroscience

How to Produce Personality Neuroscience Research with High Statistical Power and Low Additional Cost

Academic journal article Cognitive, Affective and Behavioral Neuroscience

How to Produce Personality Neuroscience Research with High Statistical Power and Low Additional Cost

Article excerpt

Abstract Personality neuroscience involves examining relations between cognitive or behavioral variability and neural variables like brain structure and function. Such studies have uncovered a number of fascinating associations but require large samples, which are expensive to collect. Here, we propose a system that capitalizes on neuroimaging data commonly collected for separate purposes and combines it with new behavioral data to test novel hypotheses. Specifically, we suggest that groups of researchers compile a database of structural (i.e., anatomical) and resting-state functional scans produced for other task-based investigations and pair these data with contact information for the participants who contributed the data. This contact information can then be used to collect additional cognitive, behavioral, or individual-difference data that are then reassociated with the neuroimaging data for analysis. This would allow for novel hypotheses regarding brain-behavior relations to be tested on the basis of large sample sizes (with adequate statistical power) for low additional cost. This idea can be implemented at small scales at single institutions, among a group of collaborating researchers, or perhaps even within a single lab. It can also be implemented at a large scale across institutions, although doing so would entail a number of additional complications.

Keywords Personality . Individual differences . Neuroscience . Neuroinformatics . Sample size . Statistical power

Small samples and resultant low statistical power are associated with a number of inferential problems in all domains of research, including neuroimaging. Studies with low power increase the likelihood of inflated effect size estimates within studies (Yarkoni, 2009; cf. Jennings & Van Horn, 2012), and using more stringent alpha thresholds in small samples merely lowers power further, thus exacerbating the problem of inflated effect sizes, while increasing the frequency of false negatives (Gonzalez-Castillo et al., 2012; Thyreau et al., 2012; Yarkoni & Braver, 2010). Small samples and inadequate statistical power also produce an increased proportion of false positives relative to true positives (Green et al., 2008, Box 1), increasing the likelihood that falsely positive "statistically significant" results will enter the literature, even after applying appropriate statistical thresholds (Button et al., 2013; Pashler &Harris,2012). Attaining adequate statistical power is even more difficult when one is interested in individual differences, because of the need for larger sample sizes relative to comparisons of mean differences, so power issues are especially important to consider for personality neuroscience. In this article, we outline how low statistical power is particularly difficult to overcome for personality neuroscience investigations and propose a novel database approach that can help to produce personality neuroscience studies with large samples for low additional cost.

Personality neuroscience

Personality neuroscience entails the examination of how variability among individuals on cognitive, emotional, motivational, or behavioral dimensions (e.g., extraversion, intelligence, empathic ability) is related to neural variables. This approach has uncovered a number of interesting phenomena based on a variety of neural variables, including the size of brain structures, functional connectivity between brain regions, and white matter organization. For example, the size and structure of different brain regions have been found to correspond to individual differences in the Big Five personal-ity traits (DeYoung et al., 2010;Huetal.,2011; Kapogiannis, Sutin, Davatzikos, Costa, & Resnick, 2012), empathy and social cognition (Banissy, Kanai, Walsh, & Rees, 2012; Holmes et al., 2012; cf. Mills, Lalonde, Clasen, Giedd, & Blakemore, 2012), self-reported social network size (Bickart, Wright, Dautoff, Dickerson, & Barrett, 2011), online social network size (based on Web sites such as Facebook; Kanai, Bahrami, Roylance, & Rees, 2012),andevenperceptual rivalry for ambiguous drawings (Kanai, Bahrami, & Rees, 2010). …

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