Visual and semantic interpretability of projections of high dimensional data for classification tasks

A number of visual quality measures have been introduced in visual analytics literature in order to automatically select the best views of high dimensional data from a large number of candidate data projections. These methods generally concentrate on the interpretability of the visualization and pay little attention to the interpretability of the projection axes. In this paper, we argue that interpretability of the visualizations and the feature transformation functions are both crucial for visual exploration of high dimensional labeled data. We present a two-part user study to examine these two related but orthogonal aspects of interpretability. We first study how humans judge the quality of 2D scatterplots of various datasets with varying number of classes and provide comparisons with ten automated measures, including a number of visual quality measures and related measures from various machine learning fields. We then investigate how the user perception on interpretability of mathematical expressions relate to various automated measures of complexity that can be used to characterize data projection functions. We conclude with a discussion of how automated measures of visual and semantic interpretability of data projections can be used together for exploratory analysis in classification tasks.Comment: Longer version of the VAST 2011 poster. http://dx.doi.org/10.1109/VAST.2011.610247

Ventral striatum connectivity during reward anticipation in adolescent smokers

Substance misusers, including adolescent smokers, often have reduced reward system activity during processing of non-drug rewards. Using a psychophysiological interaction approach, we examined functional connectivity with the ventral striatum during reward anticipation in a large (N = 206) sample of adolescent smokers. Increased smoking frequency was associated with (1) increased connectivity with regions involved in saliency and valuation, including the orbitofrontal cortex and (2) reduced connectivity between the ventral striatum and regions associated with inhibition and risk aversion, including the right inferior frontal gyrus. These results demonstrate that functional connectivity during reward processing is relevant to adolescent addiction

Machine learning applications for measuring pH using CEST MRI

Non-invasive measurement of pH provides multiple potential benefits in oncology such as better identifying the type of drug that can be more effective in chemotherapy, potentially identifying tumors that are more likely to metastasize and also better assessing the treatment effects. Chemical Exchange Saturation Transfer (CEST) Magnetic Resonance Imaging (MRI) is a versatile non-invasive technique for molecular imaging. AcidoCEST MRI techniques have been developed over the recent years to perform tumor pH measurements by utilizing a contrast agent for which chemical exchange saturation transfer effects depend on the pH of the microenvironment. Quantitative description of CEST MRI signals are generally done via modeling Bloch-McConnell equations by incorporating pH as a parameter or by fitting Lorentzian line shapes to observed z-spectra and then computing a log ratio of the CEST effects from multiple labile protons of the same molecule (ratiometric method). Modeling using Bloch-McConnell equations is complicated and requires careful inclusion of many scan parameters to infer pH. The ratiometric method requires contrast agents that have multiple labile protons, thus making it unsuitable to use for molecules with a single labile proton. Furthermore, depending on the pH, sometimes it might not be possible to numerically compute the ratio due to the inability of detecting signal peaks for certain labile protons. Our aim here is to develop a machine learning based method that learns the CEST signal patterns from observed z-spectra on temperature and concentration-controlled contrast agent phantoms independent of the type of the contrast agent. Our results indicate that the machine learning method provides more general and accurate prediction of pH in comparison to the ratiometric method based on the phantom CEST dataset. Our method is more general in the sense that it does not require explicit modeling of signal peaks that are dependent on the type of contrast agent. We also describe a state of the art variational autoencoder based algorithm extending our machine learning method to measure tumor pH in vivo using AcidoCEST MRI on mouse tumor models