What Is a Representative Brain? Neuroscience Meets Population Science

Article (PDF Available)inProceedings of the National Academy of Sciences 110(44) · October 2013with103 Reads
DOI: 10.1073/pnas.1310134110 · Source: PubMed
Abstract
The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience. Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding brain-behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography, sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain, but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we describe and promote a perspective-population neuroscience-that leverages interdisciplinary expertise to (i) emphasize the importance of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this approach across areas.
    • "As touched upon earlier, the vast majority of anatomical imaging studies included relatively few subjects, less than 25 individuals with ASD in many cases (Duerden et al. 2012). Small studies are prone to producing unreliable results; an effect that is compounded by publication bias and the use of samples of convenience in neuroimaging studies (Falk et al. 2013; Ioannidis et al. 2014). In conclusion, the current study leveraged the unprecedented power of the ABIDE to investigate brain structure differences in individuals with ASD. "
    [Show abstract] [Hide abstract] ABSTRACT: Increased brain volume is a consistent finding in young children with autism spectrum disorders (ASD); however, the regional specificity and developmental course of abnormal brain structure are less clear. Small sample sizes, particularly among voxel-based morphometry (VBM) investigations, likely contribute to this difficulty. Recently established large-scale neuroimaging data repositories have helped clarify the neuroanatomy of neuropsychiatric disorders such as schizophrenia and may prove useful in ASD. Structural brain images from the Autism Brain Imaging Database Exchange (ABIDE), which contains over 1100 participants, were analyzing using DARTEL VBM to investigate total brain and tissue volumes, and regional brain structure abnormalities in ASD. Two, overlapping cohorts were analyzed; an ‘All Subjects’ cohort (n = 833) that included all individuals with usable MRI data, and a ‘Matched Samples’ cohort (n = 600) comprised of ASD and TD individuals matched, within each site, on age and sex. Total brain and grey matter volumes were enlarged by approximately 1–2 % in ASD; however, the effect reached statistical significance in only the All Subjects cohort. Within the All Subjects cohort, VBM analysis revealed enlargement of the left anterior superior temporal gyrus in ASD. No significant regional changes were detected in the Matched Samples cohort. There was a non-significant reduction in the correlation between IQ and TBV in ASD compared to TD. Brain structure abnormalities in ASD individuals age 6 and older consists of a subtle increase in total brain volume due to enlargement of grey matter with little evidence of regionally specific effects.
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    • "Concerns about complexity aside, we maintain that discovery in this context is the only road toward individually tailored and maximally effective intervention and prevention. In parallel to emphatic calls for increased longitudinal developmental research, recent commentaries (Falk et al., 2013) and reviews (Di Martino et al., 2014) reveal an emergent widespread sentiment that wellcharacterized , large, representative samples are badly needed in neurodevelopmental research. These topics are not the focus of the current review, but have bearing on what can be discovered about the impacts of trauma in the developing brain. "
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    • "Both types of cost issues (financial and expertise) can be mitigated through collaborations across disciplines. For example, drawing relatively small sub-samples of participants from larger-scale survey samples which have been specifically designed for representativeness in relation to a target larger-scale population has considerable benefits for both generalizability of the neuroscience findings and for the ability to gain a deeper understanding of mechanisms that may contribute to processes observed in the larger population (for a more complete review of methods and considerations for linking smaller neuroimaging samples and larger-scale population outcomes, see: Falk, Hyde, Mitchell, et al., 2013). "
    [Show abstract] [Hide abstract] ABSTRACT: Understanding the mechanisms of effective communication may be advanced by knowledge from social and cognitive neuroscience. We build on prior brain research that mapped mental processes, and describe a brain-as-predictor approach that encompasses studies that treat measures of brain activity in response to communication relevant tasks as: 1) mediators between communication relevant stimuli and outcomes, 2) moderators of the relationship between communication relevant stimuli and outcomes or 3) direct predictors of communication relevant outcomes. In this article, we give a detailed description of the brain-as-predictor approach and provide a guide and checklist for interested authors, reviewers and editors. We discuss how the approach can provide theoretical insights and advance practical applications in communication research. Given its potential for advancing theory and practice, we argue that the brain-as-predictor approach can complement other communication research methods and serve as a valuable addition to the communication science toolbox.
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