Figure 1.

Topic Modeling of Multimodal Features in ADHD: a conceptual illustration. The structural MRI, functional MRI, and phenotypic observations are all generated by latent topics, which in turn generate each subject's multimodal dataset. By learning the topics, we get a mapping across multimodal features and a generative model behind the observed data. The data matrix V has n feature rows and m observation columns. If V contained a collection of multimodal features (total features by patients), then NMF would decompose the data into a set of “basis images” and encodings, such that $V_{i\mu }\approx {\left(WH\right)}_{i\mu }={\sum }_{k=1}^K{W}_{ik}{H}_{k\mu }$ where the W matrix contains the basis set of multimodal features (topics) and is of dimension n × k, and the “encoding matrix” H is of dimensions k × m, for row i and column μ.