Searching for the majority: algorithms of voluntary control.

Voluntary control of information processing is crucial to allocate resources and prioritize the processes that are most important under a given situation; the algorithms underlying such control, however, are often not clear. We investigated possible algorithms of control for the performance of the majority function, in which participants searched for and identified one of two alternative categories (left or right pointing arrows) as composing the majority in each stimulus set. We manipulated the amount (set size of 1, 3, and 5) and content (ratio of left and right pointing arrows within a set) of the inputs to test competing hypotheses regarding mental operations for information processing. Using a novel measure based on computational load, we found that reaction time was best predicted by a grouping search algorithm as compared to alternative algorithms (i.e., exhaustive or self-terminating search). The grouping search algorithm involves sampling and resampling of the inputs before a decision is reached. These findings highlight the importance of investigating the implications of voluntary control via algorithms of mental operations.

Multiple gradient echo propeller (MGREP) MRI: Technical development and potential applications

The PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction) magnetic resonance imaging (MRI) technique has inherent advantages over other fast imaging methods, including robust motion correction, reduced image distortion, and resistance to off-resonance effects. These features make PROPELLER highly desirable for T2*-sensitive imaging, high-resolution diffusion imaging, and many other applications. However, PROPELLER has been predominantly implemented as a fast spin-echo (FSE) technique, which is insensitive to T2* contrast, and requires time-inefficient signal averaging to achieve adequate signal-to-noise ratio (SNR) for many applications. These issues presently constrain the potential clinical utility of FSE-based PROPELLER. ^ In this research, our aim was to extend and enhance the potential applications of PROPELLER MRI by developing a novel multiple gradient echo PROPELLER (MGREP) technique that can overcome the aforementioned limitations. The MGREP pulse sequence was designed to acquire multiple gradient-echo images simultaneously, without any increase in total scan time or RF energy deposition relative to FSE-based PROPELLER. A new parameter was also introduced for direct user-control over gradient echo spacing, to allow variable sensitivity to T2* contrast. In parallel to pulse sequence development, an improved algorithm for motion correction was also developed and evaluated against the established method through extensive simulations. The potential advantages of MGREP over FSE-based PROPELLER were illustrated via three specific applications: (1) quantitative T2* measurement, (2) time-efficient signal averaging, and (3) high-resolution diffusion imaging. Relative to the FSE-PROPELLER method, the MGREP sequence was found to yield quantitative T2* values, increase SNR by ∼40% without any increase in acquisition time or RF energy deposition, and noticeably improve image quality in high-resolution diffusion maps. In addition, the new motion algorithm was found to improve the performance considerably in motion-artifact reduction. ^ Overall, this work demonstrated a number of enhancements and extensions to existing PROPELLER techniques. The new technical capabilities of PROPELLER imaging, developed in this thesis research, are expected to serve as the foundation for further expanding the scope of PROPELLER applications. ^

An empirical evaluation of the Random Forests classifier models for variable selection in a large-scale lung cancer case-control study

Random Forests™ is reported to be one of the most accurate classification algorithms in complex data analysis. It shows excellent performance even when most predictors are noisy and the number of variables is much larger than the number of observations. In this thesis Random Forests was applied to a large-scale lung cancer case-control study. A novel way of automatically selecting prognostic factors was proposed. Also, synthetic positive control was used to validate Random Forests method. Throughout this study we showed that Random Forests can deal with large number of weak input variables without overfitting. It can account for non-additive interactions between these input variables. Random Forests can also be used for variable selection without being adversely affected by collinearities. ^ Random Forests can deal with the large-scale data sets without rigorous data preprocessing. It has robust variable importance ranking measure. Proposed is a novel variable selection method in context of Random Forests that uses the data noise level as the cut-off value to determine the subset of the important predictors. This new approach enhanced the ability of the Random Forests algorithm to automatically identify important predictors for complex data. The cut-off value can also be adjusted based on the results of the synthetic positive control experiments. ^ When the data set had high variables to observations ratio, Random Forests complemented the established logistic regression. This study suggested that Random Forests is recommended for such high dimensionality data. One can use Random Forests to select the important variables and then use logistic regression or Random Forests itself to estimate the effect size of the predictors and to classify new observations. ^ We also found that the mean decrease of accuracy is a more reliable variable ranking measurement than mean decrease of Gini. ^