Our paper on multi-modal dimensionality reduction was recently accepted by Methods. It shows how to use our sparse dimensionality reduction techniques to form interpretable predictive models from neuroimaging data. All data and code are open-source. Paper is available here.
We designed a method to construct structural cortical graphs in a manner similar to BOLD-based functional connectivity graphs (paper, poster at MICCAI 2014).
Linking structural neuroimaging data from multiple modalities to cognitive performance is an important challenge for cognitive neuroscience. In this study we examined the relationship between verbal fluency performance and neuroanatomy in 54 patients with frontotemporal degeneration (FTD) and 15 age-matched … Continue reading