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Cortical surface-based analysis II: Inflation, flattening, and a surface-based coordinate system

by Bruce Fischl, Martin I. Sereno, Anders M. Dale - NEUROIMAGE , 1999
"... The surface of the human cerebral cortex is a highly folded sheet with the majority of its surface area buried within folds. As such, it is a difficult domain for computational as well as visualization purposes. We have therefore designed a set of procedures for modifying the representation of the c ..."
Abstract - Cited by 738 (57 self) - Add to MetaCart
The surface of the human cerebral cortex is a highly folded sheet with the majority of its surface area buried within folds. As such, it is a difficult domain for computational as well as visualization purposes. We have therefore designed a set of procedures for modifying the representation of the cortical surface to (i) inflate it so that activity buried inside sulci may be visualized, (ii) cut and flatten an entire hemisphere, and (iii) transform a hemisphere into a simple parameterizable surface such as a sphere for the purpose of establishing a surface-based coordinate system.

Cortical surface-based analysis. I. Segmentation and surface reconstruction

by Anders M. Dale, Bruce Fischl, Martin I. Sereno - Neuroimage , 1999
"... Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical pr ..."
Abstract - Cited by 450 (42 self) - Add to MetaCart
Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging.
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Removing Electroencephalographic Artifacts: Comparison between ICA and PCA

by Tzyy-Ping Jung, Colin Humphries, Te-won Lee, Scott Makeig, Martin J. Mckeown, Vicente Iragui, Terrence J. Sejnowski , 1998
"... Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records ..."
Abstract - Cited by 240 (22 self) - Add to MetaCart
Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records based on an extended version of an Independent Component Analysis (ICA) algorithm [2, 12] for performing blind source separation on linear mixtures of independent source signals. Our results show that ICA can effectively separate and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably to those obtained using Principal Component Analysis. 1 INTRODUCTION Since the landmark development of electroencephalography (EEG) in 1928 by Berger, scalp EEG has been used as a clinical tool for the diagnosis and treatment of brain diseases, and used as a non-invasive approach for research in the quantitative study of human neurophysiology. Ironic...

Retinotopic organization in human visual cortex and the spatial precision of functional MRI

by Stephen A. Engel , Gary H. Glover, Brian A. Wandell , 1997
"... A method of using functional magnetic resonance imaging (fMRI) to measure retinotopic organization within human cortex is described. The method is based on a visual stimulus that creates a traveling wave of neural activity within retinotopically organized visual areas. We measured the fMRI signal ca ..."
Abstract - Cited by 197 (9 self) - Add to MetaCart
A method of using functional magnetic resonance imaging (fMRI) to measure retinotopic organization within human cortex is described. The method is based on a visual stimulus that creates a traveling wave of neural activity within retinotopically organized visual areas. We measured the fMRI signal caused by this stimulus in visual cortex and represented the results on images of the #attened cortical sheet. We used the method to measure visual areas and to evaluate the spatial precision of fMRI. Specifically, we: 1) identified the borders between several retinotopically organized visual areas in the posterior occipital lobe, 2) measured the function relating cortical position to visual field eccentricity within area V1, 3) localized activity to within 1.1 mm of visual cortex, and 4) estimated the spatial resolution of the fMRI signal and found that signal falls to 60 percent at a spatial frequency of 1 cycle per 9 mm of visual cortex. This spatial resolution is consistent with a linespread w...

Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI

by David MacDonald, Noor Kabani, David Avis, Alan C. Evans - NEUROIMAGE , 2000
"... Automatic computer processing of large multidimensional images such as those produced by magnetic resonance imaging (MRI) is greatly aided by deformable models, which are used to extract, identify, and quantify specific neuroanatomic structures. A general method of deforming polyhedra is presented h ..."
Abstract - Cited by 181 (17 self) - Add to MetaCart
Automatic computer processing of large multidimensional images such as those produced by magnetic resonance imaging (MRI) is greatly aided by deformable models, which are used to extract, identify, and quantify specific neuroanatomic structures. A general method of deforming polyhedra is presented here, with two novel features. First, explicit prevention of self-intersecting surface geometries is provided, unlike conventional deformable models, which use regularization constraints to discourage but not necessarily prevent such behavior. Second, deformation of multiple surfaces with intersurface proximity constraints allows each surface to help guide other surfaces into place using model-based constraints such as expected thickness of an anatomic surface. These two features are used advantageously to identify automatically the total surface of the outer and inner boundaries of cerebral cortical gray matter from normal human MR images, accurately locating the depths of the sulci, even where noise and partial volume artifacts in the image obscure the visibility of sulci. The extracted surfaces are enforced to be simple two-dimensional manifolds (having the topology of a sphere), even though the data may have topological holes. This automatic 3-D cortex segmentation technique has been applied to 150 normal subjects, simultaneously extracting both the gray/white and gray/cerebrospinal fluid interface from each individual. The collection of surfaces has been used to create a spatial map of the mean and standard deviation for the location and the thickness of cortical gray matter. Three alternative criteria for defining cortical thickness at each cortical location were developed and compared. These results are shown to corroborate published postmortem and in vivo measurements of cortical thickness.

Automated Manifold Surgery: Constructing Geometrically Accurate and Topologically Correct Models of the Human Cerebral Cortex

by Bruce Fischl, Arthur Liu, Anders M. Dale , 2001
"... Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. E ..."
Abstract - Cited by 167 (25 self) - Add to MetaCart
Highly accurate surface models of the cerebral cortex are becoming increasingly important as tools in the investigation of the functional organization of the human brain. The construction of such models is difficult using current neuroimaging technology due to the high degree of cortical folding. Even single voxel misclassifications can result in erroneous connections being created between adjacent banks of a sulcus, resulting in a topologically inaccurate model. These topological defects cause the cortical model to no longer be homeomorphic to a sheet, preventing the accurate inflation, flattening, or spherical morphing of the reconstructed cortex. Surface deformation techniques can guarantee the topological correctness of a model, but are time-consuming and may result in geometrically inaccurate models. In order to address this need we have developed a technique for taking a model of the cortex, detecting and fixing the topological defects while leaving that majority of the model intact, resulting in a surface that is both geometrically accurate and topologically correct.
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Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

by Barry D. Van Veen, Wim Van Drongelen, Moshe Yuchtman, Akifumi Suzuki - IEEE Trans. Biomed. Eng , 1997
"... Abstract—A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed. The spatial filters are implemented as a weighted sum of the data recorded at different sites. The weights are chosen to minimize the filter output power subject ..."
Abstract - Cited by 148 (4 self) - Add to MetaCart
Abstract—A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed. The spatial filters are implemented as a weighted sum of the data recorded at different sites. The weights are chosen to minimize the filter output power subject to a linear constraint. The linear constraint forces the filter to pass brain electrical activity from a specified location, while the power minimization attenuates activity originating at other locations. The estimated output power as a function of location is normalized by the estimated noise power as a function of location to obtain a neural activity index map. Locations of source activity correspond to maxima in the neural activity index map. The method does not require any prior assumptions about the number of active sources of their geometry because it exploits the spatial covariance of the source electrical activity. This paper presents a development and analysis of the method and explores its sensitivity to deviations between actual and assumed data models. The effect on the algorithm of covariance matrix estimation, correlation between sources, and choice of reference is discussed. Simulated and measured data is used to illustrate the efficacy of the approach. Index Terms — Dipole localization, EEG localization, linearly constrained minimum variance filter, MEG localization, reference
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Magnetic resonance image tissue classification using a partial volume model

by David W. Shattuck, Stephanie R. Sandor-Leahy, Kirt A. Schaper, David A. Rottenberg, Richard M. Leahy - NEUROIMAGE , 2001
"... We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for imag ..."
Abstract - Cited by 137 (6 self) - Add to MetaCart
We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average � indices of ��0.746 � 0.114 for gray matter (GM) and ��0.798 � 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average � indices �� 0.893 � 0.041 for GM and ��0.928 � 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute’s BrainWeb phantom.

A hybrid approach to the skull stripping problem in MRI

by F. Ségonne, A. M. Dale, B E. Busa, B M. Glessner, B D. Salat, B H. K. Hahn, B. Fischl A - NeuroImage , 2004
"... We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single whit ..."
Abstract - Cited by 127 (11 self) - Add to MetaCart
We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skullstripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skullstripping tools.
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Creating connected representations of cortical gray matter for functional MRI visualization

by Patrick C. Teo, Guillermo Sapiro, Brian A. W - IEEE Transactions on Medical Imaging , 1997
"... Abstract—We describe a system that is being used to segment gray matter from magnetic resonance imaging (MRI) and to create connected cortical representations for functional MRI visualization (fMRI). The method exploits knowledge of the anatomy of the cortex and incorporates structural constraints i ..."
Abstract - Cited by 127 (7 self) - Add to MetaCart
Abstract—We describe a system that is being used to segment gray matter from magnetic resonance imaging (MRI) and to create connected cortical representations for functional MRI visualization (fMRI). The method exploits knowledge of the anatomy of the cortex and incorporates structural constraints into the segmentation. First, the white matter and cerebral spinal fluid (CSF) regions in the MR volume are segmented using a novel techniques of posterior anisotropic diffusion. Then, the user selects the cortical white matter component of interest, and its structure is verified by checking for cavities and handles. After this, a connected representation of the gray matter is created by a constrained growing-out from the white matter boundary. Because the connectivity is computed, the segmentation can be used as input to several methods of visualizing the spatial pattern of cortical activity within gray matter. In our case, the connected representation of gray matter is used to create a flattened representation of the cortex. Then, fMRI measurements are overlaid on the flattened representation, yielding a representation of the volumetric data within a single image. The software is freely available to the research community. Index Terms — Functional MRI, human cortex, segmentation, structural MRI, visualization.
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