Unsupervised stratification in neuroimaging through deep latent embeddings

There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly `brain sciences' as neurology and psychiatry) is an oversimplification. Although clear diagnostic boundaries remain useful for patients, families, and their access to dedicated NHS and health care services, the traditional dichotomic categories are not helpful to describe the complexity and large heterogeneity of symptoms across many and overlapping clinical phenotypes. With the advent of 'big' multimodal neuroimaging databases, data-driven stratification of the wide spectrum of healthy human physiology or disease based on neuroimages is theoretically become possible. However, this conceptual framework is hampered by severe computational constraints. In this paper we present a novel, deep learning based encode-decode architecture which leverages several parameter efficiency techniques generate latent deep embedding which compress the information contained in a full 3D neuroimaging volume by a factor 1000 while still retaining anatomical detail and hence rendering the subsequent stratification problem tractable. We train our architecture on 1003 brain scan derived from the human connectome project and demonstrate the faithfulness of the obtained reconstructions. Further, we employ a data driven clustering technique driven by a grid search in hyperparameter space to identify six different strata within the 1003 healthy community dwelling individuals which turn out to correspond to highly significant group differences in both physiological and cognitive data. Indicating that the well-known relationships between such variables and brain structure can be probed in an unsupervised manner through our novel architecture and pipeline. This opens the door to a variety of previously inaccessible applications in the realm of data driven stratification of large cohorts based on neuroimaging data.