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.

Thermal imaging as a biometrics approach to facial signature authentication

This dissertation develops an image processing framework with unique feature extraction and similarity measurements for human face recognition in the thermal mid-wave infrared portion of the electromagnetic spectrum. The goals of this research is to design specialized algorithms that would extract facial vasculature information, create a thermal facial signature and identify the individual. The objective is to use such findings in support of a biometrics system for human identification with a high degree of accuracy and a high degree of reliability. This last assertion is due to the minimal to no risk for potential alteration of the intrinsic physiological characteristics seen through thermal infrared imaging. The proposed thermal facial signature recognition is fully integrated and consolidates the main and critical steps of feature extraction, registration, matching through similarity measures, and validation through testing our algorithm on a database, referred to as C-X1, provided by the Computer Vision Research Laboratory at the University of Notre Dame. Feature extraction was accomplished by first registering the infrared images to a reference image using the functional MRI of the Brain’s (FMRIB’s) Linear Image Registration Tool (FLIRT) modified to suit thermal infrared images. This was followed by segmentation of the facial region using an advanced localized contouring algorithm applied on anisotropically diffused thermal images. Thermal feature extraction from facial images was attained by performing morphological operations such as opening and top-hat segmentation to yield thermal signatures for each subject. Four thermal images taken over a period of six months were used to generate thermal signatures and a thermal template for each subject, the thermal template contains only the most prevalent and consistent features. Finally a similarity measure technique was used to match signatures to templates and the Principal Component Analysis (PCA) was used to validate the results of the matching process. Thirteen subjects were used for testing the developed technique on an in-house thermal imaging system. The matching using an Euclidean-based similarity measure showed 88% accuracy in the case of skeletonized signatures and templates, we obtained 90% accuracy for anisotropically diffused signatures and templates. We also employed the Manhattan-based similarity measure and obtained an accuracy of 90.39% for skeletonized and diffused templates and signatures. It was found that an average 18.9% improvement in the similarity measure was obtained when using diffused templates. The Euclidean- and Manhattan-based similarity measure was also applied to skeletonized signatures and templates of 25 subjects in the C-X1 database. The highly accurate results obtained in the matching process along with the generalized design process clearly demonstrate the ability of the thermal infrared system to be used on other thermal imaging based systems and related databases. A novel user-initialization registration of thermal facial images has been successfully implemented. Furthermore, the novel approach at developing a thermal signature template using four images taken at various times ensured that unforeseen changes in the vasculature did not affect the biometric matching process as it relied on consistent thermal features.

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.

Proteomic Analysis of Wolbachia Symbiosis within the Drosophila

Wolbachia pipientis are bacterial endosymbionts of arthropods and in some filarial nematodes. Wolbachia are of particular interest because nematodeWolbachia have been shown to cause the diseases African river blindness and Lymphatic Filariasis. Doxycycline can be used to eliminate nematode Wolbachia, however, more efficient treatments are needed. Ideally, we would like to repurpose another FDA approved drug that helps to shorten treatment duration. Vitamins are one of the best classes of FDA approved compounds, generally recognized as safe. Interestingly, prior work by Serbus and colleagues found that dietary yeast, which is highly enriched in vitamins, dramatically reducesWolbachia titer in Drosophila melanogaster ovarian tissue. Imaging data indicated that the Wolbachia nucleoids were disrupted in response to yeast. This raised the possibility that yeast cells contain a bio-reactive, anti-Wolbachiacompound. Our close examination of yeast nutritional information identified which vitamins are most highly enriched in yeast. We then administered several of these to D. melanogaster, and saw that two of these led to reduced ovarianWolbachia titers, analogous to yeast-fed flies. This was especially interesting, as both vitamins are critical for functioning of the same biochemical pathway. We used retested effect of one of these vitamins in oogenesis by performing a dilution series, and achieved positive correlation from this dilution series. This opens up the avenue for clarifying the mechanism of how vitamins suppressWolbachia titer, and for testing enhancement of Doxycycline, to hopefully provide faster, more affordable treatment for millions of patients.

Proteomic Analysis of Wolbachia Symbiosis within the Drosophila

Wolbachia pipientis are bacterial endosymbionts of arthropods and in some filarial nematodes. Wolbachia are of particular interest because nematodeWolbachia have been shown to cause the diseases African river blindness and Lymphatic Filariasis. Doxycycline can be used to eliminate nematode Wolbachia, however, more efficient treatments are needed. Ideally, we would like to repurpose another FDA approved drug that helps to shorten treatment duration. Vitamins are one of the best classes of FDA approved compounds, generally recognized as safe. Interestingly, prior work by Serbus and colleagues found that dietary yeast, which is highly enriched in vitamins, dramatically reducesWolbachia titer in Drosophila melanogaster ovarian tissue. Imaging data indicated that the Wolbachia nucleoids were disrupted in response to yeast. This raised the possibility that yeast cells contain a bio-reactive, anti-Wolbachiacompound. Our close examination of yeast nutritional information identified which vitamins are most highly enriched in yeast. We then administered several of these to D. melanogaster, and saw that two of these led to reduced ovarianWolbachia titers, analogous to yeast-fed flies. This was especially interesting, as both vitamins are critical for functioning of the same biochemical pathway. We used retested effect of one of these vitamins in oogenesis by performing a dilution series, and achieved positive correlation from this dilution series. This opens up the avenue for clarifying the mechanism of how vitamins suppressWolbachia titer, and for testing enhancement of Doxycycline, to hopefully provide faster, more affordable treatment for millions of patients.

Determining magmatic processes from analysis of phenocrysts and gabbroic xenoliths contained in Calbuco andesites

Calbuco Volcano, in Southern Chile, has eruptive products of predominantly andesitic hornblende-bearing lava. A purpose of this work is to understand magmatic processes and how Calbuco magma chemistry is related to the explosive volcanic character. Calbuco lava has a mineral assemblage of plagioclase, hornblende, orthopyroxene, clinopyroxene, olivine, and magnetite and entrained gabbroic xenoliths with the same mineral assemblage. The presence of hornblende is evidence for dissolved water in the magma. Detailed petrographic/textural analysis has been done using petrographic microscopy and back-scattered electron imaging (BSE); geochemical analysis by electron microprobe (EPMA). Major findings include 1) that hornblende and hornblende-bearing gabbroic cumulates crystallize from Calbuco magma, 2) that plagioclase grains are compositionally zoned, recording evidence of temperature, chemical, and water content fluctuations in the magma, and 3) that hornblende is unstable under upper magma chamber conditions at Calbuco, and is breaking down into plagioclase, olivine, orthopyroxene, clinopyroxene, and magnetite in the magma.

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.