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Automated Analysis Of (2003)
| Citations: | 1 - 0 self |
BibTeX
@MISC{Data03automatedanalysis,
author = {Structural Mri Data and Paul Thompson Phd and Judith L. Rapoport Md and Tyrone D. Cannon Phd and Arthur W. Toga Phd and Stephen Lawrie and Eve C. Johnstone and Daniel Weinberger and Paul M. Thompson and Judith L. Rapoport and Tyrone D. Cannon and Arthur W. Toga},
title = {Automated Analysis Of},
year = {2003}
}
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Abstract
UCLA School of Medicine Introduction Recent advances in medical imaging have revolutionized our ability to investigate disease. Current brain mapping initiatives are charting brain structure and function in thousands of subjects (e.g. Mazziotta et al., 2001; N=7000, including 5800 genotyped subjects and 342 mono- and dizygotic twins; Rapoport et al., 1999; N=1000+ children and adolescents). An urgent goal of these projects is to analyze patterns of altered brain structure and function in disorders such as schizophrenia, Alzheimer's disease, and abnormal childhood development. The near-exponential pace of data collection (Fox, 1997) has stimulated the development of image analysis algorithms that compare, pool and average brain data across whole populations. Even so, brain structure is complex and varies dramatically across normal subjects, so systematic patterns of altered structure are hard to detect. This statistical challenge has ignited the rapidly growing field of computationa
Keyphrases
brain structure average brain data statistical challenge data collection ucla school altered structure dizygotic twin whole population urgent goal image analysis near-exponential pace medical imaging genotyped subject medicine introduction recent advance abnormal childhood development systematic pattern current brain mapping initiative altered brain structure normal subject
