Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

Do Methods Matter In Global Leadership Development? A Mixed-Methods Study Of A U.s.-Based International Mba Program

As world communication, technology, and trade become increasingly integrated through globalization, multinational corporations seek employees with global leadership experience and skills. However, the demand for these skills currently outweighs the supply. Given the rarity of globally ready leaders, global competency development should be emphasized in higher education programs. The reality, however, is that university graduate programs are often outdated and focus mostly on cognitive learning. Global leadership competence requires moving beyond the cognitive domain of learning to create socially responsible and culturally connected global leaders. This requires attention to development methods; however, limited research in global leadership development methods has been conducted. A new conceptual model, the global leadership development ecosystem, was introduced in this study to guide the design and evaluation of global leadership development programs. It was based on three theories of learning and was divided into four development methodologies. This study quantitatively tested the model and used it as a framework for an in-depth examination of the design of one International MBA program. The program was first benchmarked, by means of a qualitative best practices analysis, against the top-ranking IMBA programs in the world. Qualitative data from students, faculty, administrators, and staff was then examined, using descriptive and focused data coding. Quantitative data analysis, using PASW Statistics software, and a hierarchical regression, showed the individual effect of each of the four development methods, as well as their combined effect, on student scores on a global leadership assessment. The analysis revealed that each methodology played a distinct and important role in developing different competencies of global leadership. It also confirmed the critical link between self-efficacy and global leadership development.