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DETECTING DYNAMIC AND GENETIC EFFECTS ON BRAIN STRUCTURE USING HIGH-DIMENSIONAL CORTICAL PATTERN MATCHING.

Paul M Thompson¹, Kiralee M Hayashi, Greig de Zubicaray

¹Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles.

Proceedings. IEEE International Symposium on Biomedical Imaging

|September 28, 2011

Summary

This summary is machine-generated.

This study introduces novel algorithms for brain imaging analysis, enabling precise mapping of disease and genetic effects on cortical anatomy. These methods improve our understanding of brain structure variations in populations.

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Area of Science:

Neuroimaging
Computational Anatomy
Statistical Genetics

Background:

Cortical anatomy exhibits significant variation, complicating the detection of systematic effects in human populations.
Accurate mapping of disease and genetic influences on brain structure is essential for understanding neurological disorders and heritability.

Purpose of the Study:

To develop and illustrate algorithms for detecting and visualizing the effects of disease and genetic factors on brain structure.
To address challenges in mapping systematic effects on brain anatomy due to natural variations in cortical patterns.

Main Methods:

A two-stage approach involving cortical pattern matching using metrically covariant partial differential equations (PDEs) on MRI scans (N=102).
Utilizing high-dimensional deformation maps to transfer within-subject cortical signals (gray matter distribution, shape, degeneration rates) to a common template.
Extending behavioral genetics statistics to cortical manifolds, including h-squared distributed random fields for mapping hereditary influences.

Main Results:

Demonstrated the ability to map dynamic patterns of gray matter loss in Alzheimer's disease patients.
Successfully mapped genetic influences on brain structure, extending statistical methods to cortical manifolds.

Conclusions:

The developed algorithms provide a robust framework for analyzing brain structure in populations, accounting for anatomical variability.
These techniques facilitate the study of both disease progression and genetic contributions to brain morphology.