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Neural Correlates of Fluid Intelligence

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Neural Correlates of Fluid Intelligence
via Functional and Structural Network Connectivity Measures

https://philoneuro.files.wordpress.com/2016/12/vuong_purc_2016_final.pdf

We used 264 unique regions-of-interest (parcellated into 14 cognitive networks) that
were identified in a meta-study as belonging to 14 distinct cognitive regions.
These results demonstrate that individual differences in the connectivity within and between
brain networks can explain variance in fluid intelligence abilities.
• When examining intra-network effects, we found that structural connectivity was a better
predictor of fluid intelligence than functional connectivity, with reliable effects seen in the DMN
and FPN.
• When examining inter-network effects, we found that structural connectivity between nodes of
the DMN and CON/SAL networks was particularly informative. Functional connectivity was
also informative, but only when constrained by high structural connectivity values.
• Most interestingly, the interaction feature set of both functional and structural values provided
the highest predictive power for our model. Using both types of connectivity was notably more
informative than just using either structural or functional connectivity alone.
Animekitty:

The topology of each region (14 of them) would explain separate abilities and the topology of connecting all 14 (Structured networks) would predict coordination between them. An example is that 5 or so of the 14 in a typology may make it so that these regions fail to coordinate with the rest of the 9 other networks. I am pretty sure this is the case in my brain. That only a few regions in my brain work well together because of the structural connectivity in my brain.

 
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