Detecting and characterizing large-scale human brain networks
- Kaustubh Satyendra Supekar.
- Aug. 2010.
- Physical description
- online resource (xiii, 172 pages) : illustrations, (some color)
- Supekar, Kaustubh Satyendra.
- Greicius, Michael D. thesis advisor.
- Menon, Vinod, 1961- thesis advisor.
- Musen, Mark A. thesis advisor (primary).
- Rubin, Daniel (Daniel L.). thesis advisor.
- Stanford University. Program in Medical Information Sciences. degree grantor.
- Stanford University. Committee on Graduate Studies.
- Includes bibliographical references (p. 148-172).
- Understanding human brain function is one of the most important endeavors in modern science. There is growing evidence that cognitive functions are executed by large-scale networks, comprising multiple interacting anatomically-connected brain areas. Although considerable progress has been made in understanding which specific brain areas are involved in particular cognitive functions, very little is known about the integrative functioning of large-scale brain networks. This is due in part to the lack of methods to pursue this line of research. This dissertation describes computational methods for detecting and characterizing large-scale human brain networks, combining data from task-free functional magnetic resonance imaging (fMRI) and structural diffusion tensor imaging (DTI), two complementary brain imaging modalities. Application of our methods to task-free fMRI and DTI data obtained from a wide range of subject populations provided new insights into how large-scale human brain networks develop, mature, and get disrupted in psychiatric and neurological disorders. More generally, this work demonstrates the power of our multimodal network-analytic approach to obtain a system-level understanding of brain function across the human lifespan.
- Brain > physiology
- Brain Mapping > methods
- Magnetic Resonance Imaging > methods
- Neural Pathways > physiology
- Image Processing, Computer-Assisted
- Publication date
- Submitted to the Department of Biomedical Informatics and the Committee on Graduate Studies of Stanford University.
- Thesis (Ph.D.)--Stanford University, 2010.