Thursday, February 10, 2011

Research Bytes: Neuro-imaging research--brain networks and public interest

Beck, D. M. (2010). The Appeal of the Brain in the Popular Press. Perspectives on Psychological Science, 5(6), 762-766.

Since the advent of human neuroimaging, and of functional magnetic resonance imaging (fMRI) in particular, the popular press has shown an increasing interest in brain-related findings. In this article, I explore possible reasons behind this interest, including recent data suggesting that people find brain images and neuroscience language more convincing than results that make no reference to the brain (McCabe & Castel, 2008; Weisberg, Keil, Goodstein, Rawson, & Gray, 2008). I suggest that part of the allure of these data are the deceptively simply messages they afford, as well as general, but sometimes misguided, confidence in biological data. In addition to cataloging some misunderstandings by the press and public, I highlight the responsibilities of the research scientist in carefully conveying their work to the general public.

Gonsalves, B. D., & Cohen, N. J. (2010). Brain Imaging, Cognitive Processes, and Brain Networks. Perspectives on Psychological Science, 5(6), 744-752.

McDonald, R. P. (2010). Structural Models and the Art of Approximation. Perspectives on Psychological Science, 5(6), 675-686

Structural equation models have provided a seemingly rigorous method for investigating causal relations in nonexperimental data in the presence of measurement error or multiple measures of putative causes or effects. Methods have been developed for fitting these very complex models globally and obtaining global fit statistics or global measures of their approximation to sample data. Structural equation models are idealizations that can serve only as approximations to real multivariate data. Further, these models are multidimensional, and the approximation is itself multidimensional. Tests of “significance” and global indices of approximation do not provide an adequate basis for judging the acceptability of the approximation. Standard applications of structural models use a composite of two models—a measurement (path) model and a path (causal) model. Separate analyses of the measurement model and the path model provide an informed judgment, whereas the composite global analysis can easily yield unreasonable conclusions. Separating the component models enables a careful assessment of the actual constraints implied by the path model, using recently developed methods. An empirical example shows how the conventional global treatment yields unacceptable conclusions

Poldrack, R. A. (2010). Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed? Perspectives on Psychological Science, 5(6), 753-761

The goal of cognitive neuroscience is to identify the mapping between brain function and mental processing. In this article, I examine the strategies that have been used to identify such mappings and argue that they may be fundamentally unable to identify selective structure–function mappings. To understand the functional anatomy of mental processes, it will be necessary for researchers to move from the brain-mapping strategies that the field has employed toward a search for selective associations. This will require a greater focus on the structure of cognitive processes, which can be achieved through the development of formal ontologies that describe the structure of mental processes. In this article, I outline the Cognitive Atlas Project, which is developing such ontologies, and show how this knowledge could be used in conjunction with data-mining approaches to more directly relate mental processes and brain function.

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