I posted on this study a while back, but here is the actual abstract to the study (full paper here). Researchers tried to guess IQ scores of children based upon multiple MRI images of the children’s brains. A correlation of .071 was found between estimator’s guesses and the children’s actual measured IQ’s. The guesses had an average error of ~8 IQ points. That is, the guesses tended to be within 8 IQ points of the actual IQ. This shows that there is indeed a high correlation between brain size and IQ, although it is often hard to measure given today’s technology.
Previous studies had found lower correlations of ~.41, but the recent study used many newer techniques utilizing multiple images that allow better estimation of brain size than the previous techniques.
MRI-Based Intelligence Quotient (IQ) Estimation with Sparse Learning
In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity.
Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets.
To address this issue, we design a two-step procedure for
1) first identifying the possible scanning site for each testing subject and
2) then estimating the testing subject’s IQ by using a specific estimator designed for that scanning site.
We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old.
In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones.
In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.
In this paper, we proposed a novel framework for the estimation of a subject’s IQ score based on the neuroimaging features. Methodologically, since the number of features in neuroimaging data usually overwhelms the number of available samples, feature selection has been always an important role in the field.
To this end, considering the strong relationship between GM and WM features in MR images, we devised a feature selection method based on a dirty model  that efficiently considered the coupling of different feature types, but still alleviated the strong parameter overlap across features. Specifically, we penalized an objective function with a squared Frobenius norm of the element-wise sparsity matrix.
Using the MR Images acquired at different scanning sites with their own scanning parameters and protocols, we designed a two-step procedure, by which we first identified the scanning site of a test image and then estimated the test subject’s IQ by using the respective estimator. Also, we performed comparison between multi-kernel SVR and single-kernel SVR by two sets of experiments.
From a practical point of view, although the current framework is not limited to apply for the MR images obtained from only a predefined site, it would be our forthcoming research issue to develop a more generalized method for efficiently handling the inter-site variability and thus constructing a single generalized estimator model for all subjects by skipping the scanning site identification step.
Furthermore, thanks to the availability of various imaging modalities, it would be beneficiary to integrate their complementary information for more precise IQ score estimation. It should be emphasized again that our work paves a new way for a research on predicting an infant’s future IQ score by using neuroimaging data, which can be a potential indicator for parents to prepare their child’s education if needed.