A test-retest reliability analysis of diffusion measures of white matter tracts relevant for cognitive control

Recent efforts to replicate structural brain-behavior correlations have called into question the replicability of structural brain measures used in cognitive neuroscience. Here, we report an evaluation of test-retest reliability of diffusion tensor imaging (DTI) measures, including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, in several white matter tracts previously shown to be involved in cognitive control. In a data set consisting of 34 healthy participants scanned twice on a single day, we observe overall stability of DTI measures. This stability remained in a subset of participants who were also scanned a third time on the same day as well as in a 2-week follow-up session. We conclude that DTI measures in these tracts show relative stability, and that alternative explanations for the recent failures of replication must be considered

A perspective on terra incognita: uncovering the neuroanatomy of the human subcortex

Recent exciting advancements in the field of in vivo neuroimaging allow for visualization of the living human brain with unprecedented anatomical detail. Large consortium studies will provide us with novel insights in the function and connectivity of the human brain. However, it is unlikely that the spatial resolution obtained using in vivo imaging will, in the near future, approximate the level of detail obtained in post-mortem anatomical studies. Initiatives such as the recently published Big Brain project (Amunts et al., 2013) herald a novel approach in post-mortem brain research. We feel that linking data from histological observations with in vivo imaging studies will greatly advance our understanding of the functional neuroanatomy of the human brain

Modelling decision-making biases

Biases are a fundamental aspect of everyday life decision-making. A variety of modelling approaches have been suggested to capture decision-making biases. Statistical models are a means to describe the data, but the results are usually interpreted according to a verbal theory. This can lead to an ambiguous interpretation of the data. Mathematical cognitive models of decision-making outline the structure of the decision process with formal assumptions, providing advantages in terms of prediction, simulation, and interpretability compared to statistical models. We compare studies that used both signal detection theory and evidence accumulation models as models of decision-making biases, concluding that the latter provides a more comprehensive account of the decision-making phenomena by including response time behavior. We conclude by reviewing recent studies investigating attention and expectation biases with evidence accumulation models. Previous findings, reporting an exclusive influence of attention on the speed of evidence accumulation and prior probability on starting point, are challenged by novel results suggesting an additional effect of attention on non-decision time and prior probability on drift rate