“Though Troubled Be My Brain:” Madness in Early Modern England, 1603-1714

This dissertation is a study of madness in Stuart-era England. Madness was pervasive in early modern England. it was in the streets, performed on stage, discussed in political pamphlets and legal treatises, and physically housed in Bethlehem Hospital. Madness, therefore, serves as a significant lens because in differentiating between madness and sanity, contemporaries regularly drew clear boundaries between acceptable, or “normal” behavior, and unacceptable, or “abnormal” behavior, that was particular to seventeenth-century English culture and society. Specifically, I argue that madness serves as a channel to examine the diagnoses and treatment of mental disorders that contemporaries believed altered the body and mind, the legal repercussions of abnormal behavior at the state and local level, gender relations and stereotypes, and the use of corporeal rhetoric in political culture. Before public institutions for the insane were founded specifically for that purpose, family or community-based care was the norm for the mad (in addition to the few private madhouses that were founded by private entrepreneurs during the last half of the seventeenth century). This dissertation therefore draws on a wide variety of sources, including manuscripts, parish records, land commissions, autobiographies, spiritual biographies, criminal cases, political pamphlets, doctors’ notes, medical guidebooks, and more

Neural networks for genetic epidemiology: past, present, and future

During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN) are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes