Bayesian construction of geometrically based cortical thickness metrics

Michael I. Miller; Allan B Massie; J. Tilak Ratnanather; Kelly N. Botteron; John G. Csernansky

doi:10.1006/nimg.2000.0666

Bayesian construction of geometrically based cortical thickness metrics

Michael I. Miller, Allan B Massie, J. Tilak Ratnanather, Kelly N. Botteron, John G. Csernansky

Research output: Contribution to journal › Article › peer-review

94 Scopus citations

Abstract

This paper describes the construction of cortical metrics quantifying the probabilistic occurrence of gray matter, white matter, and cerebrospinal fluid compartments in their correlation to the geometry of the neocortex as measured in 0.5-1.0 mm magnetic resonance imagery. These cortical profiles represent the density of the tissue types as a function of distance to the cortical surface. These metrics are consistent when generated across multiple brains indicating a fundamental property of the neocortex. Methods are proposed for incorporating such metrics into automated Bayes segmentation. (C) 2000 Academic Press.

Original language	English (US)
Pages (from-to)	676-687
Number of pages	12
Journal	NeuroImage
Volume	12
Issue number	6
DOIs	https://doi.org/10.1006/nimg.2000.0666
State	Published - 2000
Externally published	Yes

ASJC Scopus subject areas

Neurology
Cognitive Neuroscience

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title = "Bayesian construction of geometrically based cortical thickness metrics",

abstract = "This paper describes the construction of cortical metrics quantifying the probabilistic occurrence of gray matter, white matter, and cerebrospinal fluid compartments in their correlation to the geometry of the neocortex as measured in 0.5-1.0 mm magnetic resonance imagery. These cortical profiles represent the density of the tissue types as a function of distance to the cortical surface. These metrics are consistent when generated across multiple brains indicating a fundamental property of the neocortex. Methods are proposed for incorporating such metrics into automated Bayes segmentation. (C) 2000 Academic Press.",

author = "Miller, {Michael I.} and Massie, {Allan B} and Ratnanather, {J. Tilak} and Botteron, {Kelly N.} and Csernansky, {John G.}",

note = "Funding Information: We thank Dr. Hanna Damasio and Dr. Michael Vannier of the University of Iowa, for supplying us with the MRI data of the temporal gyrus and Lei Wang of Washington University for assistance with manual segmentations. This work has been supported by the NIH Grants R01-MH525158-01A1, R01-NS35368-02, R01-MH56584, and NSF BIR-9 424264, with partial support from Medtronic, Inc. and from the Mayo Foundation for the use of the Analyze software.",

year = "2000",

doi = "10.1006/nimg.2000.0666",

language = "English (US)",

volume = "12",

pages = "676--687",

journal = "NeuroImage",

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T1 - Bayesian construction of geometrically based cortical thickness metrics

AU - Miller, Michael I.

AU - Massie, Allan B

AU - Ratnanather, J. Tilak

AU - Botteron, Kelly N.

AU - Csernansky, John G.

N1 - Funding Information: We thank Dr. Hanna Damasio and Dr. Michael Vannier of the University of Iowa, for supplying us with the MRI data of the temporal gyrus and Lei Wang of Washington University for assistance with manual segmentations. This work has been supported by the NIH Grants R01-MH525158-01A1, R01-NS35368-02, R01-MH56584, and NSF BIR-9 424264, with partial support from Medtronic, Inc. and from the Mayo Foundation for the use of the Analyze software.

PY - 2000

Y1 - 2000

N2 - This paper describes the construction of cortical metrics quantifying the probabilistic occurrence of gray matter, white matter, and cerebrospinal fluid compartments in their correlation to the geometry of the neocortex as measured in 0.5-1.0 mm magnetic resonance imagery. These cortical profiles represent the density of the tissue types as a function of distance to the cortical surface. These metrics are consistent when generated across multiple brains indicating a fundamental property of the neocortex. Methods are proposed for incorporating such metrics into automated Bayes segmentation. (C) 2000 Academic Press.

AB - This paper describes the construction of cortical metrics quantifying the probabilistic occurrence of gray matter, white matter, and cerebrospinal fluid compartments in their correlation to the geometry of the neocortex as measured in 0.5-1.0 mm magnetic resonance imagery. These cortical profiles represent the density of the tissue types as a function of distance to the cortical surface. These metrics are consistent when generated across multiple brains indicating a fundamental property of the neocortex. Methods are proposed for incorporating such metrics into automated Bayes segmentation. (C) 2000 Academic Press.

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Bayesian construction of geometrically based cortical thickness metrics

Abstract

ASJC Scopus subject areas

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