Using functional neuroimaging combined with a think-aloud protocol to explore clinical reasoning expertise in internal medicine.

BACKGROUND: Clinical reasoning is essential to medical practice, but because it entails internal mental processes, it is difficult to assess. Functional magnetic resonance imaging (fMRI) and think-aloud protocols may improve understanding of clinical reasoning as these methods can more directly assess these processes. The objective of our study was to use a combination of fMRI and think-aloud procedures to examine fMRI correlates of a leading theoretical model in clinical reasoning based on experimental findings to date: analytic (i.e., actively comparing and contrasting diagnostic entities) and nonanalytic (i.e., pattern recognition) reasoning. We hypothesized that there would be functional neuroimaging differences between analytic and nonanalytic reasoning theory. METHODS: 17 board-certified experts in internal medicine answered and reflected on validated U.S. Medical Licensing Exam and American Board of Internal Medicine multiple-choice questions (easy and difficult) during an fMRI scan. This procedure was followed by completion of a formal think-aloud procedure. RESULTS: fMRI findings provide some support for the presence of analytic and nonanalytic reasoning systems. Statistically significant activation of prefrontal cortex distinguished answering incorrectly versus correctly (p < 0.01), whereas activation of precuneus and midtemporal gyrus distinguished not guessing from guessing (p < 0.01). CONCLUSIONS: We found limited fMRI evidence to support analytic and nonanalytic reasoning theory, as our results indicate functional differences with correct vs. incorrect answers and guessing vs. not guessing. However, our findings did not suggest one consistent fMRI activation pattern of internal medicine expertise. This model of employing fMRI correlates offers opportunities to enhance our understanding of theory, as well as improve our teaching and assessment of clinical reasoning, a key outcome of medical education.

An experimental evaluation of diffusion tensor image segmentation using graph-cuts.

The segmentation of diffusion tensor imaging (DTI) data is a challenging problem due to the high variation and overlap of the distributions induced by individual DTI measures (e.g., fractional anisotropy). Accurate tissue segmentation from DTI data is important for characterizing the mi-crostructural properties of white matter (WM) in a subsequent analysis. This step may also be useful for generating a mask to constrain the results of WM tractography. In this study, a graph-cuts segmentation method was applied to the problem of extracting WM, gray matter (GM) and cerebral spinal fluid (CSF) from brain DTI data. A two-phase segmentation method was adopted by first segmenting CSF signal from the DTI data using the third eigenvalue (lambda(3)) maps, and then extracting WM regions from the fractional anisotropy (FA) maps. The algorithm was evaluated on ten real DTI data sets obtained from in vivo human brains and the results were compared against manual segmentation by an expert. Overall, the graph cuts method performed well, giving an average segmentation accuracy of about 0.90, 0.77 and 0.88 for WM, GM and CSF respectively in terms of volume overlap(VO).

A population-average MRI-based atlas collection of the rhesus macaque.

Magnetic resonance imaging (MRI) studies of non-human primates are becoming increasingly common; however, the well-developed voxel-based methodologies used in human studies are not readily applied to non-human primates. In the present study, we create a population-average MRI-based atlas collection for the rhesus macaque (Macaca mulatta) that can be used with common brain mapping packages such as SPM or FSL. In addition to creating a publicly available T1-weighted atlas (http://www.brainmap.wisc.edu/monkey.html), probabilistic tissue classification maps and T2-weighted atlases were also created. Theses atlases are aligned to the MRI volume from the Saleem, K.S. and Logothetis, N.K. (2006) atlas providing an explicit link to histological sections. Additionally, we have created a transform to integrate these atlases with the F99 surface-based atlas in CARET. It is anticipated that these tools will help facilitate voxel-based imaging methodologies in non-human primate species, which in turn may increase our understanding of brain function, development, and evolution.