Paul Thompson's Research Publications

The UCLA Alzheimer Brain Atlas Project:
Structural and Functional Applications

World Alzheimer's Congress, Washington DC, 2000

Michael S. Mega, Paul M. Thompson, Ivo D. Dinov, Arthur W. Toga, and Jeffrey L. Cummings

Laboratory of Neuro Imaging, Dept. Neurology, Division of Brain Mapping,
UCLA School of Medicine, Los Angeles CA 90095, USA,


UCLA Alzheimer's Disease Center

  • (Images of the Alzheimer's Disease Brain Atlas)


    Objective. We develop, evaluate and validate a probabilistic atlas for the elderly and demented brain.

    Background. Developments [1] in brain mapping instigated the construction of probabilistic sub-volume population atlases (SVPA). Disease specific SVPA's overcome the structural mismatch confounding most multisubject imaging studies in aging and dementia. After construction of a continuum mechanical average atlas [2], manually outlined regions on individual brains, registered to that atlas, produce stochastic probabilistic sub-volumes encoding anatomic, registration, and functional variability.

    Methods. SPGR (spoiled GRASS) MRIs of 36 Alzheimer's disease (AD) subjects were used to construct an average AD atlas incorporating 60 manually defined subvolumes of interest (SVIs). Two probabilistic representations were constructed - a linear-SVPA based on 12-parameter registration [3] and a nonlinear-SVPA based on 6th-order warping [3] - reflecting the chance any given subject's SVI occurs at each Atlas voxel location in the elderly and demented populations. Validity of linear and nonlinear SVPAs for automated gray matter, white matter, and CSF tissue counts was evaluated using native SPGR data. Application of the AD SVPA with a statistical mapping technique [4] designed for use in probabilistic atlases is demonstrated for SPECT and PET functional analyses.

    Results. Correlation of manual native space counts to automated Atlas counts was excellent for the linear 12-parameter probability gradients (r2 = 0.983). Statistical mapping of functional differences between AD treatment groups and within MCI (Minimal Cognitive Impairment) subgroups is provided.

    Conclusions. A deformable probabilistic atlas, appropriate for structural and functional imaging analysis of the elderly and demented populations, is now available.


    [1]. Mazziotta JC, Toga AW, Evans AC, Fox P, Lancaster J. A probabilistic atlas of the human brain: theory and rationale for its development. Neuroimage 1995;2:89-101.
    [2]. Thompson PM, Woods RP, Mega MS, Toga AW. Mathematical and computational challenges in creating deformable and probabilistic atlases of the human brain. Human Brain Mapping 2000;9(2):81-92.
    [3]. Woods RP, Grafton ST, Watson JDG, Sicotte NL, Mazziotta JC. Automated image registration: II. Intersubject validation of linear and nonlinear models. J Comput Assist Tomogr 1998;22:153-165.
    [4]. Dinov ID, Mega MS, Thompson PM, et al. Analyzing functional brain images in a probabilistic atlas: a validation of Sub-volume thresholding. J Comput Assist Tomogr 2000;24:128-138.

    Grant Support. This work was supported by a Human Brain Project Grant to the International Consortium for Brain Mapping, funded jointly by NIMH and NIDA (P20 MH/DA52176), by a P41 Resource Grant from the NCRR (RR13642), by NINCDS Grant K08-NS01646, NIA Grant K08-AG100784, and research grants from the National Library of Medicine (LM/MH05639), the National Science Foundation (BIR93-22434), the NCRR (RR05956) and NINCDS/NIMH (NS38753).

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    Contact Information

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    Paul Thompson, Ph.D.
    Assistant Professor of Neurology
    UCLA Lab of Neuro-Imaging and Brain Mapping Division
    Dept. Neurology and Brain Research Institute
    4238 Reed Neurology, UCLA Medical Center
    710 Westwood Plaza
    Westwood, Los Angeles CA 90095-1769, USA.

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