BrainScapes: The possible landscape of primate brain shapes.

Katja Heuer, Marian Kleineberg, Russell Dinnage, Chet C. Sherwood, William Donald, Hopkins, Ernst Schwartz, Georg Langs, Romain Valabregue, Mathieu D. Santin, Marc Herbin, & Roberto Toro

‪The shape of primate brains varies widely from small smooth to profusely folded large brains. Studying morphological diversity across phylogeny allows us to better understand how primate brains adapt, and in particular the evolutionary context of the human brain.‬

‪Recent advances in generative machine learning models have led to algorithms capable of learning shape embeddings and to generate realistic new instances. We explored an autoencoder deep neural network to generate shapes of primate brains including 34 different species.

Our network successfully learnt a landscape of changes in shape. Interestingly, species with brains of comparable volume were close in the learnt space, despite having been size-normalised for the training. This suggests that changes in volume are consistently concomitant with changes in shape, and this disregarding the species’ position in the phylogenetic tree.

Our network is also able to generate new data. We generated possible brain shapes for all ancestral states of the primate phylogenetic tree, and evolutionary trajectories for each of our species back to the brain of the common ancestor.

In conclusion, we successfully trained a deep neural network to learn the space of morphological variation across primates, and to generate new data along the phylogenetic tree. We obtained evolutionary trajectories of extant primate brains all the way back to the common ancestor.