Register for an account

X

Enter your name and email address below.

Your email address is used to log in and will not be shared or sold. Read our privacy policy.

X

Website access code

Enter your access code into the form field below.

If you are a Zinio, Nook, Kindle, Apple, or Google Play subscriber, you can enter your website access code to gain subscriber access. Your website access code is located in the upper right corner of the Table of Contents page of your digital edition.

Mind

How Well Does Brain Structure Predict Behaviour?

NeuroskepticBy NeuroskepticSeptember 13, 2016 9:39 PM

Newsletter

Sign up for our email newsletter for the latest science news

To what extent does brain structure correlate with different psychological traits? An interesting new paper from Massachusetts General Hospital researchers Mert R. Sabuncu and colleagues uses a new method to examine what the authors call the 'morphometricity' of various behaviours and mental disorders. Sabuncu et al. define morphometricity as "the proportion of phenotypic variation that can be explained by macroscopic brain morphology" - in other words, the degree to which people with similar brains tend to be similar in a particular behaviour. Morphometricity is somewhat analagous to the concept of heritability from genetics. Using FreeSurfer software and the statistical technique of linear mixed-effects (LME) modelling, the authors examined over 3,800 structural MRI scans, pooled from 9 studies. Sabuncu et al.'s analysis was based on calculating an anatomical similarity matrix (ASM) across the individual brains. The ASM represents the "global morphological resemblance between pairs of individuals in the sample". Essentially, the ASM represents how similar two individuals are in overall brain structure. Sabuncu then calculated the morphometricity of each trait by comparing the structural similarity to behavioural similarity. The results showed that Alzheimer's disease is almost perfectly morphometric, with an estimated value of 0.94–1.00 (where possible values range from 0 to 1). Schizophrenia was moderately morphometric (estimate 0.55), with autism coming in slightly lower at 0.38. Perhaps surprisingly, Parkinson's disease had a much lower morphometric value of just 0.20.

sabuncu.png

Other, non-disease-related traits, such as IQ and level of education, were highly morphometric too, with values above 0.8. In fact, IQ was slightly more morphometric than sex (IQ 0.95 vs. sex 0.93), while age was perfectly morphometric (1.00). The authors conclude that

In the dawning era of large-scale datasets comprising traits across a broad phenotypic spectrum, morphometricity will be critical in prioritizing and characterizing behavioral, cognitive, and clinical phenotypes based on their neuroanatomical signatures. Furthermore, the proposed framework will be significant in dissecting the functional, morphological, and molecular underpinnings of different traits.

This is an important paper, but we shouldn't rush to over-interpret the results. For instance, whereas Sabuncu et al. say that the high morphometricity estimates for disorders such as autism and schizophrenia "unequivocally point to a neuroanatomical substrate for these clinical conditions", this really doesn't follow. Consider the schizophrenia data in this study. The MRI scans came from a database called MCIC. The problem is that 86% of the patients in this study were taking antipsychotic medication, which might well effect brain structure. 29% of the schizophrenia patients also had a history of alcohol or drug abuse, which could leave an impact on the brain as well. So we can't say whether the high morphometricity of schizophrenia is driven by the syndrome itself, or by exposure to various substances. We should not conclude that high morphometricity means that brain structure causes a particular behaviour. In terms of the morphometricity measure itself, I note that the authors say that it "does not require cross-validation, which is often the technique used in machine learning to gauge prediction accuracy". That's because their method "exploits the entire dataset to fit the model and estimate the unknown variance component parameters, and in turn morphometricity, in an unbiased fashion." I would prefer if some validation had been performed, e.g. by calculating the morphometricity of a randomly generated 'trait' or by permuting the trait data.

rb2_large_white.png

Sabuncu MR, Ge T, Holmes AJ, Smoller JW, Buckner RL, Fischl B, & Alzheimer's Disease Neuroimaging Initiative (2016). Morphometricity as a measure of the neuroanatomical signature of a trait. Proceedings of the National Academy of Sciences of the United States of America PMID: 27613854

    2 Free Articles Left

    Want it all? Get unlimited access when you subscribe.

    Subscribe

    Already a subscriber? Register or Log In

    Want unlimited access?

    Subscribe today and save 70%

    Subscribe

    Already a subscriber? Register or Log In