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Proceedings Paper

Proposal for a biometrics of the cortical surface: a statistical method for relative surface distance metrics
Author(s): Fred L. Bookstein
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Paper Abstract

Recent advances in computational geometry have greatly extended the range of neuroanatomical questions that can be approached by rigorous quantitative methods. One of the major current challenges in this area is to describe the variability of human cortical surface form and its implications for individual differences in neurophysiological functioning. Existing techniques for representation of stochastically invaginated surfaces do not conduce to the necessary parametric statistical summaries. In this paper, following a hint from David Van Essen and Heather Drury, I sketch a statistical method customized for the constraints of this complex data type. Cortical surface form is represented by its Riemannian metric tensor and averaged according to parameters of a smooth averaged surface. Sulci are represented by integral trajectories of the smaller principal strains of this metric, and their statistics follow the statistics of that relative metric. The diagrams visualizing this tensor analysis look like alligator leather but summarize all aspects of cortical surface form in between the principal sulci, the reliable ones; no flattening is required.

Paper Details

Date Published: 11 August 1995
PDF: 12 pages
Proc. SPIE 2573, Vision Geometry IV, (11 August 1995); doi: 10.1117/12.216423
Show Author Affiliations
Fred L. Bookstein, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 2573:
Vision Geometry IV
Robert A. Melter; Angela Y. Wu; Fred L. Bookstein; William D. K. Green, Editor(s)

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