A stochastic model for studying the laminar structure of cortex from MRI

Barta, Patrick E.; Miller, Michael I.; Qiu, Anqi

doi:10.1109/tmi.2005.846861

Cited by 22 publications

(24 citation statements)

References 49 publications

(52 reference statements)

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“…To enable the study of morphometric properties of the human cerebral cortex and their relationship to cognitive function, disease state, or other behavioral variables, automated methods have been developed for segmenting and measuring the cerebral cortex from MRI data (Dale et al, 1999;Joshi et al, 1999;MacDonald et al, 1999;Xu et al, 1999;Zeng et al, 1999;van Essen et al, 2001;Shattuck and Leahy, 2002;Sowell et al, 2003;Barta et al, 2005;Han et al, 2005). Using such tools, relationships have been identified between regional cortical thickness and intelligence quotient (Narr et al, 2006;Shaw et al, 2006), personality measures (Wright et al, 2006;Wright et al, 2007), and memory (Walhovd et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths

et al. 2008

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In normal humans, relationships between cognitive test performance and cortical structure have received little study, in part, because of the paucity of tools for measuring cortical structure. Computational morphometric methods have recently been developed that enable the measurement of cortical thickness from MRI data, but little data exist on their reliability. We undertook this study to evaluate the reliability of an automated cortical thickness measurement method to detect correlates of interest between thickness and cognitive task performance. Fifteen healthy older participants were scanned four times at two-week intervals on three different scanner platforms. The four MRI datasets were initially treated independently to investigate the reliability of the spatial localization of findings from exploratory whole-cortex analyses of cortical thickness-cognitive performance correlates. Next, the first dataset was used to define cortical ROIs based on the exploratory results that were then applied to the remaining three datasets to determine whether the relationships between cognitive performance and regional cortical thickness were comparable across different scanner platforms and Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access Author ManuscriptNeuroimage. Author manuscript; available in PMC 2009 January 1. Published in final edited form as:Neuroimage. 2008 January 1; 39(1): 10-18. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript field strengths. Verbal memory performance was associated with medial temporal cortical thickness, while visuomotor speed/set-shifting was associated with lateral parietal cortical thickness. These effects were highly reliable-in terms of both spatial localization and magnitude of absolute cortical thickness measurements-across the four scan sessions. Brain-behavior relationships between regional cortical thickness and cognitive task performance can be reliably identified using an automated data analysis system, suggesting that these measures may be useful as imaging biomarkers of disease or performance ability in multi-center studies in which MRI data are pooled.

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Section: Introductionmentioning

confidence: 99%

Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths

et al. 2008

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“…Fundamental work has been done in [306] on active contour models, and in [272] on active ribbon models. Alternatives and extensions to these techniques have been made in modeling the cortical sulci through statistical shape models and active shape models [307]- [314]. In other work, a method based on filling a cortical mesh with gyral labels has been developed [315], and a surface feature extraction algorithm based on Laplacian maps has been developed [285].…”

Section: Cortical Surface Registrationmentioning

confidence: 99%

Brain Functional Localization: A Survey of Image Registration Techniques

Gholipour

Kehtarnavaz

Briggs

et al. 2007

IEEE Trans. Med. Imaging

226

127

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Abstract-Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.Index Terms-Brain functional localization, fMRI image processing, functional imaging, survey of image registration techniques.

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“…To address these issues for thickness measurements for the CC, we draw on methods that were developed for the related problem of cortical thickness measurement [Barta et al, 2005;Fischl and Dale, 2000;Hutton et al, 2008;Jones et al, 2002;MacDonald et al, 2000;Schmitt and Bö hme, 2002;Yezzi and Prince, 2003;Zeng et al, 1998]. The problem of cortical thickness measurement is more difficult at the segmentation stage due to segmentation uncertainties in the location of borders due to partial volume effects.…”

Section: Introductionmentioning

confidence: 99%

“…A subset of the abovementioned methods modeled the cortical thickness using: signed distance functions [Zeng et al, 1998], minimal Euclidean distances between vertices located on mesh-based estimates of each cortical surface [Fischl and Dale, 2000;MacDonald et al, 2000] and orthogonal projection from the inner boundary to the outer boundary [Barta et al, 2005;MacDonald et al, 2000]. In the first and second cases, many-to-one mappings may be produced because a single node or voxel may be minimally distant to many nodes or voxels on the other surface.…”

Section: Introductionmentioning

confidence: 99%

Thickness profile generation for the corpus callosum using Laplace's equation

Adamson¹,

Wood²,

Chen³

et al. 2011

Human Brain Mapping

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The corpus callosum facilitates communication between the cerebral hemispheres. Morphological abnormalities of the corpus callosum have been identified in numerous psychiatric and neurological disorders. To quantitatively analyze the thickness profile of the corpus callosum, we adapted an automatic thickness measurement method, which was originally used on magnetic resonance (MR) images of the cerebral cortex (Hutton et al. [2008]: NeuroImage 40:1701-10; Jones et al. [2002]: Hum Brain Mapp 11:12-32; Schmitt and Böhme [2002]: NeuroImage 16:1103-9; Yezzi and Prince [2003]: IEEE Trans Med Imaging 22:1332-9), to MR images of the corpus callosum. The thickness model was derived by computing a solution to Laplace's equation evaluated on callosal voxels. The streamlines from this solution form non-overlapping, cross-sectional contours the lengths of which are modeled as the callosal thickness. Apart from the semi-automated segmentation and endpoint selection procedures, the method is fully automated, robust, and reproducible. We compared the Laplace method with the orthogonal projection technique previously published (Walterfang et al. [2009a]: Psych Res Neuroimaging 173:77-82; Walterfang et al. [2008a]: Br J Psychiatry 192:429-34; Walterfang et al. [2008b]: Schizophr Res 103:1-10) on a cohort of 296 subjects, composed of 86 patients with chronic schizophrenia (CSZ), 110 individuals with first-episode psychosis, 100 individuals at ultra-high risk for psychosis (UHR; 27 of whom later developed psychosis, UHR-P, and 73 who did not, UHR-NP), and 55 control subjects (CTL). We report similar patterns of statistically significant differences in regional callosal thickness with respect to the comparisons CSZ vs. CTL, UHR vs. CTL, UHR-P vs. UHR-NP, and UHR vs. CTL.

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A stochastic model for studying the laminar structure of cortex from MRI

Cited by 22 publications

References 49 publications

Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths

Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths

Brain Functional Localization: A Survey of Image Registration Techniques

Thickness profile generation for the corpus callosum using Laplace's equation

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