We seek sponsorship from thie NIH to support our innovative and successful program in thie fundamentals and applications of neuroimaging, tiiat is premised on the belief that neuroscientists of tomorrow are likely to require mastery of neuroimaging methods and principles in their work to address the growing burden of neurological disease and to perform the studies that will best advance our understanding of human behavior and cognition this applications includes both T-90 and R-90 components for our pre-doctoral students. The UCLA Comprehensive Neuroimaging Training Program (NITP) trains pre-doctoral students in principles of neuroimaging that are fundamental - common to most or all neuroimaging - in recognition of the rapid changes that have occurred and will continue in imaging technology. They are exposed to an unusually complete range of imaging approaches from cellular to whole brain, from structural to dynamic and inclusive of advanced mutiimodality imaging. The NITP is both complementary to, and participatory in, existing programs in neurosciences and computational biology already well established at UCLA. The students benefit from the large and experience neuroimaging faculty and from courses newly developed for this program. The NITP has become integral to the graduate programs in multiple departments where its core courses in the Principles of Neuroimaging serve to bridge and integrate neuroscience, computation, physics and signal processing. Rounding out the NITP we have developed a summer Short Course consisting of an annual two-week course in Advanced Functional Neuroimaging. This course brings in 30 to 40 carefully selected scholars for an immersive program in advanced topics and methods, including lecture style classroom training, daily computer labs focused on computational tools and MRI physics, and imaging projects developed, run and analyzed by the student participants.
The impact of Neuroimaging on brain studies at the anatomical and functional levels is ovenA^helming, and has changed the manner in which investigators approach the human brain in particular. The methods of neuroimaging are complex and varied. Traditionally, there has been a separation between people expert in imaging technology and those who are expert in neuroscience. This proposal asks for the continuation of an innovative and highly successful training program in Neuroimaging that demands that its students bridge the gap between technology and experimental science and in so doing expose foundational features that cut across the many imaging tools and the biological targets of neuroscience research and that can be treated with similar mathematical and conceptual formalisms.
|Kerr, Wesley T; Hwang, Eric S; Raman, Kaavya R et al. (2014) Multimodal diagnosis of epilepsy using conditional dependence and multiple imputation. Int Workshop Pattern Recognit Neuroimaging :1-4|
|Lebel, Catherine; Warner, Tamara; Colby, John et al. (2013) White matter microstructure abnormalities and executive function in adolescents with prenatal cocaine exposure. Psychiatry Res 213:161-8|
|Colby, John B; Soderberg, Lindsay; Lebel, Catherine et al. (2012) Along-tract statistics allow for enhanced tractography analysis. Neuroimage 59:3227-42|
|Anderson, Ariana; Labus, Jennifer S; Vianna, Eduardo P et al. (2011) Common component classification: what can we learn from machine learning? Neuroimage 56:517-24|
|Colby, John B; Van Horn, John D; Sowell, Elizabeth R (2011) Quantitative in vivo evidence for broad regional gradients in the timing of white matter maturation during adolescence. Neuroimage 54:25-31|
|Douglas, P K; Harris, Sam; Yuille, Alan et al. (2011) Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. Neuroimage 56:544-53|
|Payer, Doris E; Lieberman, Matthew D; Monterosso, John R et al. (2008) Differences in cortical activity between methamphetamine-dependent and healthy individuals performing a facial affect matching task. Drug Alcohol Depend 93:93-102|