Lateralization is specialization of the brain hemispheres in certain tasks, such as language, mathematics, cognition and motor skills. It is one of the most queried topics related to the human brain. After the invention of modern medical imaging techniques including functional magnetic resonance imaging (fMRI), scientific research about the human brain, including lateralization, gained huge momentum. There have been a remarkable numbers of studies about lateralization and most of these studies focused on investigating which part of the brain dominates in which tasks. However, there have been very few lateralization studies on brain intrinsic activity, i.e., resting state activity where subjects are asked to stay awake while resting without performing any specific tasks.
Independent component analysis (ICA), a data-driven blind source separation method, has become one of the conventional data analysis tools for brain imaging data. ICA can separate the brain imaging data into functional regions that are temporally coherent, and functional network connectivity (FNC) of these regions can be computed. FNC is a measure that captures the temporal covariance of the brain networks.
In this dissertation, we focus on the lateralization during the resting state and assess hemispheric differences during the resting state. The lateralization of the resting state networks and their association with age and gender is presented using a large resting state fMRI dataset. A novel approach for generating hemisphere specific time-courses and computing FNC inside the hemispheres and between hemispheres is proposed and the relationship of these FNC values with age, gender and mental illness, schizophrenia is reported. Finally, a new framework to estimate power spectral density of 4D brain imaging data and a dimension reduction method to reduce dimensionality from 4D frequency domain to 2D frequency domain has been proposed. This framework helps us to reveal spatiotemporal organization differences between hemispheres. In summary, our work has made several contributions to advance lateralization analysis and has improved our understanding of various aspects of hemispheric differences during the resting state
laterality, fMRI, independent component analysis, schizophrenia
Level of Degree
Electrical and Computer Engineering
First Committee Member (Chair)
Second Committee Member
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Fourth Committee Member
Agcaoglu, Oktay. "NEW APPROACHES FOR ESTIMATING HEMISPHERIC LATERALIZATION FROM RESTING STATE FMRI DATA WITH RELATIONSHIP TO AGE, GENDER AND MENTAL DISORDERS." (2016). https://digitalrepository.unm.edu/ece_etds/306
Available for download on Monday, December 17, 2018