Functional imaging of cerebral oxygenation with intrinsic optical contrast and phosphorescent probes

Microscopic in vivo measurements of cerebral oxygenation are of key importance for understanding normal cerebral energy metabolism and its dysregulation in a wide range of clinical conditions. Relevant cerebral pathologies include compromised blood perfusion following stroke and a decrease in efficiency of single-cell respiratory processes that occurs in neurodegenerative diseases such as Alzheimer's and Parkinson's disease. In this chapter we review a number of quantitative optical approaches to measuring oxygenation of blood and cerebral tissue. These methods can be applied to map the hemodynamic response and study neurovascular and neurometabolic coupling, and can provide microscopic imaging of biomarkers in animal models of human disease, which would be useful for screening potential therapeutic approaches. © 2014 Springer Science+Business Media New York

Discriminative extended canonical correlation analysis for pattern set matching

In this paper we address the problem of matching sets of vectors embedded in the same input space. We propose an approach which is motivated by canonical correlation analysis (CCA), a statistical technique which has proven successful in a wide variety of pattern recognition problems. Like CCA when applied to the matching of sets, our extended canonical correlation analysis (E-CCA) aims to extract the most similar modes of variability within two sets. Our first major contribution is the formulation of a principled framework for robust inference of such modes from data in the presence of uncertainty associated with noise and sampling randomness. E-CCA retains the efficiency and closed form computability of CCA, but unlike it, does not possess free parameters which cannot be inferred directly from data (inherent data dimensionality, and the number of canonical correlations used for set similarity computation). Our second major contribution is to show that in contrast to CCA, E-CCA is readily adapted to match sets in a discriminative learning scheme which we call discriminative extended canonical correlation analysis (DE-CCA). Theoretical contributions of this paper are followed by an empirical evaluation of its premises on the task of face recognition from sets of rasterized appearance images. The results demonstrate that our approach, E-CCA, already outperforms both CCA and its quasi-discriminative counterpart constrained CCA (C-CCA), for all values of their free parameters. An even greater improvement is achieved with the discriminative variant, DE-CCA.Comment: Machine Learning, 201