The small world networks theory suggests that all of our neural networks can be categorized into two classes. First, there are neighborhood clusters of neurons with high levels of intraconnectivity that are capable of efficient local information processing. Then, there are several long-distance connections between these neighborhoods which provide for global communication and cross-modular integration.
Researchers van de Heuval et al recently administered fMRI during rest on 19 subjects and gave them the Wechsler IQ test in order to determine whether the properties of the small world network would vary along with IQ scores. IQ scores showed no correlation with the total number of connections in the subject’s brain networks, nor did IQ correlate with levels of local neighborhood clustering. However, the researchers did find a negative correlation with normalized path length between regions and IQ, with either r = -0.54 or r = -0.57, depending on the zero-lag correlation threshold the researchers set between the time series of the two given voxels, p<0.05 for both. A shorter interregion path length suggests a more efficient connectivity pattern, which is a plausible explanation for enhanced cognitive performance.
Previous neuroimaging studies have linked IQ to the structural organization of myelinated brain tissue on the micro scale, the total brain volume/focal brain structure, and the development/functional dynamics of specific higher-order regions. These results may or may not end up being useful to AI researchers, but of all neuroscience topics this seems to be one of the most relevant.
van den Heuvel MP, et al. 2009 Efficiency of functional brain networks and intellectual performance. The Journal of Neuroscience 29: 7619-7624. doi:10.1523/JNEUROSCI.1443-09.2009.