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AUTOMATED ANALYSIS OF STRUCTURAL MRI DATA
Source: .
Author: Paul M. Thompson, Judith L. Rapoport, Tyrone D. Cannon, Arthur W. Toga
Abstract:
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 computational anatomy (Miller et al., 2002; Thompson and Toga, 2001; Fischl et al., 2000; Ashburner et al., 2003). This field combines new approaches in computer vision (Fitzpatrick and Sonka, 2000), anatomical surface modeling (Thompson et al., 2000; Fischl et al., 2000; Gerig et al., 2001), differential geometry (Miller et al., 2002), and statistical field theory (Friston et al., 1995; Worsley et al., 1999; Taylor and Adler, 2000) to capture anatomic variation, encode it, and detect group-specific patterns. Many computational anatomy techniques are highly automated, making studies of brain structure feasible on a scale not previously imaginable, with extraordinary power to explore disease effects.
Source: .
Author: Paul M. Thompson, Judith L. Rapoport, Tyrone D. Cannon, Arthur W. Toga
Abstract:
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 computational anatomy (Miller et al., 2002; Thompson and Toga, 2001; Fischl et al., 2000; Ashburner et al., 2003). This field combines new approaches in computer vision (Fitzpatrick and Sonka, 2000), anatomical surface modeling (Thompson et al., 2000; Fischl et al., 2000; Gerig et al., 2001), differential geometry (Miller et al., 2002), and statistical field theory (Friston et al., 1995; Worsley et al., 1999; Taylor and Adler, 2000) to capture anatomic variation, encode it, and detect group-specific patterns. Many computational anatomy techniques are highly automated, making studies of brain structure feasible on a scale not previously imaginable, with extraordinary power to explore disease effects.