Evaluating the external validity of comorbid disorders in youth using rating scales and behavior observations

To evaluate the theoretical underpinnings of current categorical approaches to classify childhood psychopathological conditions, this dissertation examined whether children with a single diagnosis of an anxiety disorder (ANX only) and children with an anxiety diagnosis comorbid with other diagnoses (i.e., anxiety + anxiety disorder [ANX + ANX], anxiety + depressive disorder [ANX + DEP], and anxiety + disruptive disorder [ANX + EXT]) could be differentiated using external validation criteria of clinical phenomenology (i.e., levels of anxiety, depression, and internalizing, externalizing and total behavior problems). This study further examined whether the four groups could be differentiated in terms of their interaction patterns with their parents and peers, respectively. The sample consisted of 129 youth and their parents who presented to the Child Anxiety and Phobia Program (CAPP) housed within the Child and Family Psychosocial Research Center at Florida International University, Miami. Youth were between the ages of 8 and 14 years old. A battery of questionnaires was used to assess participants' clinical presentation in terms of levels of anxiety, depression, and internalizing and externalizing symptoms. Family and peer interaction were evaluated through rating scales and through behavior observation tasks. Statistics based on the parameter estimates of the structured equation models indicated that all the comorbid groups were significantly different from the pure anxiety disorder group when it came to depression indices of clinical phenomenology. Further, significant differences appeared mainly in terms of the ANX + DEP comorbid group relative to the other comorbid groups. In terms of Parent-child interaction the ANX + EXT and the ANX + DEP comorbid groups were differentiated from the pure anxiety disorder and ANX + ANX comorbid group when it came to the appraisal of the parent/child relationship by the parent, and the acceptance subscale according to the mother report. In terms of peer-child interaction the ANX + EXT and the ANX + DEP comorbid groups were statistically significantly different from the pure anxiety disorder only when it came to the positive interactions and the social skills as rated by mother. Limitations and future research recommendations are discussed.

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.

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.