Structural variability and the usefulness of understanding neural connectivity patterns

Two recent accounts from Jeff Lichtman about the technical progress in neural connectivity research can be found in his interview with Ira Glass, and his article with Winfried Denk. On a more philosophical note, the end of their article notes that:

During the study of the mouse ear muscle described above, it became clear that every instantiation of the wiring diagram was different from every other one. Some will take such variability to mean that nothing can be learned from doing this kind of tedious, data-intensive, and highly expensive work.

It’s not clear if this argument is merely a straw man, but let’s take them on their word that some critics might espouse such a line of reasoning. From the perspective of explanatory power, it is easy to see why this is a flawed argument.

Indeed, if the connectivity patterns were the same between organisms of the same species, it would mean that these connectivity patterns would be unable to explain any differences in their cognition and behavior.

As an analogy, imagine a hypothetical universe in which the DNA of every organism in the same species were exactly the same, and all of the differences between individuals were mediated via epigenetic modifications.

If this were the case, knowledge of an individual’s DNA sequence would have greatly diminished utility. We wouldn’t be able to correlate genetic variability with molecular, cellular, and organismal variability.

To be fair, it is similarly true that if the DNA of every organism were so variable that we could call it totally random, it would also not have any utility in explaining differences between individuals. The same is true for neural connectivity patterns.

So, for both neural connectivity and DNA base pairs, we can loosely think of the relationship between potential explanatory power and structural variability like this:

The shape of this distribution is modeled after the expected surprisal of a coin flip versus the fairness of the coin. That is, I’d expect extreme degrees of variability or non-variability to be especially uninformative.

The great assumption of connectivity research is that the variability patterns will fall in the “sweet spot” of the above distribution. But Lichtman’s point is that this assumption is not just limited to neural connectivity research–it is an overarching theme of biology.

Reference

Lichtman and Denk. 2011 The Big and the Small: Challenges of Imaging the Brain’s Circuits. Science DOI: 10.1126/science.1209168 

Link to Lichtman’s NPR interview.