We are writing up a project that explored the potential benefits of using neural network algorithms to model relationships between white matter tract profiles and phenotypes. One of the main questions we are asking in this work is whether NNs would be able to capitalize on the structure that exists in the data and discover non-linear relationships between white matter tissue properties and chronological age of the participants. It turned out that the benefits in model accuracy where rather small (although we also know of use cases where the benefits are much more dramatic).

When presenting this work at the eScience Institute core staff meeting a few months ago, Anissa Tanweer raised the interesting question of whether we could reason about the data in advance of the rather extensive empirical evaluation that we did to tell us whether this data would benefit from the additional flexibility and power of an NN algorithm. My current hunch is that this is not possible, because it’s hard to discover a specific non-linear relationship in high dimensional data without the non-linear model. However, I think that there could be a way to reason about data that would bring us closer to this interesting silver bullet. This could have to do with the fact that different types of data have different characteristics. In the paper that we are writing, we touch on the fact that tract profiles are derived from images, but they are no longer images. In fact, when they are naively observed, they are tabular data. But we also know that they have group structure, and that they contain spatially contiguous data in neighboring nodes within a tract, but not across tracts. In addition, there could be other kinds of structure. For example, some tissue properties that we calculate could be correlated because of physical relationships between them (for example, diffusion FA and estimates of axonal water fraction are related to each other). Another example is that there are known correlations between the same tract in the two hemispheres (a fact beautifully exploited by Lerma et al.).

One way to analyze and discover this structure may be through unsupervised learning approaches (linear and non-linear). If this analysis reveals the kind of structure that would benefit from the strengths of the NN, then it might be worth pursuing.

This also relates to a typology of different kinds of data that we might encounter with more or less of this kind of structure. For example, things that are more “image-like” vs. things that are more “table-like”. It also relates to findings about the relative disadvantage of CNNs in analysis of large tabular data, where other kinds of non-linear models may be better suited.