Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

The effect of dual -language instruction on academic achievement as measured by the SAT: An evaluation of the "International Studies Program" of Miami -Dade County Public Schools

This study investigated the influence that receiving instruction in two languages, English and Spanish, had on the performance of students enrolled in the International Studies Program (delayed partial immersion model) of Miami Dade County Public Schools on a standardized test in English, the Stanford Achievement Test, eighth edition, for three of its sections, Reading Comprehension, Mathematics Computations, and Mathematics Applications.^ The performance of the selected IS program/Spanish section cohort of students (N = 55) on the SAT Reading Comprehension, Mathematics Computation, and Mathematics Application along four consecutive years was contrasted with that of a control group of comparable students selected within the same feeder pattern where the IS program is implemented (N = 21). The performance of the group was also compared to the cross-sectional achievement patterns of the school's corresponding feeder pattern, region, and district.^ The research model for the study was a variation of the "causal-comparative" or "ex post facto design" sometimes referred to as "prospective". After data were collected from MDCPS, t-tests were performed to compare IS-Spanish students SAT performance for grades 3 to 6 for years 1994 to 1997 to control group, feeder pattern, region and district norms for each year for Reading Comprehension, Mathematics Computation, and Mathematics Applications. Repeated measures ANOVA and Tukey's tests were calculated to compare the mean percentiles of the groups under study and the possible interactions of the different variables. All tests were performed at the 5% significance level.^ From the analyses of the tests it was deduced that the IS group performed significantly better than the control group for all the three measures along the four years. The IS group mean percentiles on the three measures were also significantly higher than those of the feeder pattern, region, and district. The null hypotheses were rejected and it was concluded that receiving instruction in two languages did not negatively affect the performance of IS program students on tests taken in English. It was also concluded that the particular design the IS program enhances the general performance of participant students on Standardized tests.^ The quantitative analyses were coupled with interviews from teachers and administrators of the IS program to gain additional insight about different aspects of the implementation of the program at each particular school. ^

Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.