"Individual risk attitudes are correlated with the grey matter volume in the posterior parietal cortex suggesting existence of an anatomical biomarker for financial risk-attitude," said Dr Tymula.



This means tolerance of risk "could potentially be measured in billions of existing medical brain scans." 1



-Gray matter matters when measuring risk tolerance

The results will also provide a simple measurement of risk attitudes that could be easily extracted from abundance of existing medical brain scans, and could potentially provide a characteristic distribution of these attitudes for policy makers.

Our finding suggests the existence of a simple biomarker for risk attitude, at least in the midlife [sic] population we examined in the northeastern United States. ... If generalized to other groups, this finding will also imply that individual risk attitudes could, at least to some extent, be measured in many existing medical brain scans, potentially offering a tool for policy makers seeking to characterize the risk attitudes of populations.

1A

2

We do not know precisely how GM volume translates to the neural level. It is possible that volume differences reflect synaptogenesis and dendritic arborization (Kanai and Rees, 2011), but to-date there is no clear evidence of correlation between GM volume measured by VBM and any histological measure, including neuronal density (Eriksson et al., 2009).





Figure 1 (Levy et al., 2012). Risky and ambiguous stimuli. A) In risky stimuli the red and blue areas of each image are proportional to the number of red and blue chips. Three outcome probabilities were used: 13, 25 and 38%. B) In ambiguous stimuli the central part of the image is obscured with a gray occluder. In the gray area the number of chips of each color is unknown, and thus the probability of drawing a chip of a certain color is not precisely known. Three levels of ambiguity were used, where 25, 50 or 75% of the image is occluded. Inthe red and blue areas of each image are proportional to the number of red and blue chips. Three outcome probabilities were used: 13, 25 and 38%.Inthe central part of the image is obscured with a gray occluder. In the gray area the number of chips of each color is unknown, and thus the probability of drawing a chip of a certain color is not precisely known. Three levels of ambiguity were used, where 25, 50 or 75% of the image is occluded.

3

4

5

ADDENDUM (Sept 28 2014): The first author, Dr. Gilaie-Dotan, has not used in the paper. I have added the legend for the correlation plot in Fig. 2 at the bottom of the post, which states that it is shown for illustrative purposes only and should not be used for inference. She also explains additional aspects of the data presented in Fig. 4 of the paper (not shown here). : The first author, Dr. Gilaie-Dotan, has commented to clarify that voodoo correlations wereused in the paper. I have added the legend for the correlation plot in Fig. 2 at the bottom of the post, which states that it is shown for illustrative purposes only and should not be used for inference. She also explains additional aspects of the data presented in Fig. 4 of the paper (not shown here).

1

"Based on our findings, we could, in principle, use millions of existing medical brains scans to assess risk attitudes in populations," said Levy. "It could also help us explain differences in risk attitudes based in part on structural brain differences."

1A

ADDENDUM (Sept 16 2014): The billions [i.e. millions] of existing medical brain scans are not all high-resolution T1-weighted anatomical images (1 × 1 × 1 mm3) acquired using a 3T Siemens Allegra scanner equipped with a custom RF coil. In other words, most may not have the anatomical resolution to measure such a small brain area.

2

3

The age and gender of the participants and global GM volume (following ANCOVA normalization) were included in the design matrix as covariates of no interest, and were thus regressed out. F contrasts were applied first with p < 0.001 uncorrected as the criterion to detect voxels with significant correlation to individual’s risk attitudes. Whole-brain correction procedures were then applied...

4

5

Journal of Neuroscience, 34 (37), 12394-12401 DOI: Gilaie-Dotan, S., Tymula, A., Cooper, N., Kable, J., Glimcher, P., & Levy, I. (2014). Neuroanatomy Predicts Individual Risk Attitudes.(37), 12394-12401 DOI: 10.1523/JNEUROSCI.1600-14.2014

Journal of Visualized Experiments (67) DOI: Levy, I., Rosenberg Belmaker, L., Manson, K., Tymula, A., & Glimcher, P. (2012). Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods.(67) DOI: 10.3791/3724

ADDENDUM (Sept 28 2014): Here is the legend for Fig 2 (Bottom).

To demonstrate that the observed correlations were not driven by outliers, for each individual, GM volume of the PPC cluster (top) is plotted on the x-axis against risk attitude on the y-axis. Note that this should not be used for inference as it is not independent of the whole-brain analysis and is presented for visualization purposes only. No other regions were found to be correlated with risk attitudes.