The new hybrid-BCI-DNI atlas is a high-resolution MPRAGE, single-subject atlas, constructed using both anatomical
and functional information to guide the parcellation of the cerebral cortex. Anatomical labeling was performed
manually on coronal single-slice images guided by sulcal and gyral landmarks to generate the original (non-hybrid)
BCI-DNI atlas. Functional sub-parcellations of the gyral ROIs were then generated from 40 minimally preprocessed
resting fMRI datasets from the HCP database. Gyral ROIs were transferred from the BCI-DNI atlas to the 40 subjects
using the HCP grayordinate space as a reference. For each subject, each gyral ROI was subdivided using the fMRI
data by applying spectral clustering to a similarity matrix computed from the fMRI time-series correlations between
each vertex pair. The sub-parcellations were then transferred back to the original cortical mesh to create the subparcellated
hBCI-DNI atlas with a total of 67 cortical regions per hemisphere. To assess the stability of the gyral
subdivisons, a separate set of 60 HCP datasets were processed as follows: 1) coregistration of the structural scans to
the hBCI-DNI atlas; 2) coregistration of the anatomical BCI-DNI atlas without functional subdivisions, followed by
sub-parcellation of each subject’s resting fMRI data as described above. We then computed consistency between the
anatomically-driven delineation of each gyral subdivision and that obtained per subject using individual fMRI data.
The gyral sub-parcellations generated by atlas-based registration show variable but generally good overlap of the
confidence intervals with the resting fMRI-based subdivisions. These consistency measures will provide a quantitative
measure of reliability of each subdivision to users of the atlas.
Brain connectivity patterns are useful in understanding brain function and organization. Anatomical brain
connectivity is largely determined using the physical synaptic connections between neurons. In contrast statistical
brain connectivity in a given brain population refers to the interaction and interdependencies of statistics of
multitudes of brain features including cortical area, volume, thickness etc. Traditionally, this dependence has
been studied by statistical correlations of cortical features. In this paper, we propose the use of Bayesian network
modeling for inferring statistical brain connectivity patterns that relate to causal (directed) as well as non-causal
(undirected) relationships between cortical surface areas. We argue that for multivariate cortical data, the
Bayesian model provides for a more accurate representation by removing the effect of confounding correlations
that get introduced due to canonical dependence between the data. Results are presented for a population of 466
brains, where a SEM (structural equation modeling) approach is used to generate a Bayesian network model, as
well as a dependency graph for the joint distribution of cortical areas.
Inter-subject analysis of anatomical and functional brain imaging data requires the images to be registered to
a common coordinate system in which anatomical features are aligned. Intensity-based volume registration
methods can align subcortical structures well, but the variability in sulcal folding patterns typically results
in misalignment of the cortical surface. Conversely, surface-based registration using sulcal features can produce
excellent cortical alignment but the mapping between brains is restricted to the cortical surface. Here we describe
a method for volumetric registration that also produces a one-to-one point correspondence between cortical
surfaces. This is achieved by first parameterizing and aligning the cortical surfaces. We then use a constrained
harmonic mapping to define a volumetric correspondence between brains. Finally, the correspondence is refined
using an intensity-based warp. We evaluate the performance of our proposed method in terms of the inter-subject
alignment of expert-labeled sub-cortical structures after registration.
We present a new technique for segmentation of skull in human T1-weighted magnetic resonance (MR) images that generates realistic models of the head for EEG and MEG source modeling. Our method performs skull segmentation using a sequence of mathematical morphological operations. Prior to the segmentation of skull, we segment the scalp and the brain from the MR image. The scalp mask allows us to quickly exclude background voxels with intensities similar to those of the skull, while the brain mask obtained from our Brain Surface Extractor algorithm ensures that the brain does not intersect our skull segmentation. We find the inner and the outer skull boundaries using thresholding and morphological closing and opening operations. We then mask the results with the scalp and brain volumes to ensure closed and nonintersecting skull boundaries. We applied our scalp and skull segmentation algorithm to several MR images and validated our method using coregistered CT-MR image data sets. We observe that our method is capable of producing scalp and skull segmentations suitable for MEG and EEG source modeling in 3D T1-weighted human MR images.
We present a semi-automated method for constraining the topology of inner cerebral cortex volumes generated from T1- weighted magnetic resonance images (MRI). An initial tissue classification is generated based on a measurement model that accounts for partial volume effects and the presence of image inhomogeneities due to field non-uniformities. This classification is used to generate an interior cerebral white matter brain volume. This volume is processed by our algorithm to ensure that the topology of the cortical surface, that of a two-dimensional sheet, is represented in tessellations of the volume. We extend our previous work on topological constraints, which introduced an automated correction procedure that creates graph representations of volumetric objects in order to identify topological defects in their segmentation. We improve this method by performing an iterative correction process that limits the severity of changes made to the initial segmentation. The previous method also requires the duplication of slices of data wherever a change is required, which significantly increases the size of the volume. In the new method we identify a set of corrections that can be made without slice duplication. The key benefits to the improvements in our method are improved localization and correction of topological defects resulting in increased accuracy of the resulting cortical surface representation and a decrease in the size of the resulting volume.
We present a method for enforcing a topological constraint, homeomorphism to a sphere, on a set of volumetric data. A graph-based topological representation is created from voxel connectivity within the volume and automatically edited to have the desired topology. The volume is forced to match this structure, resulting in a topologically spherical surface. The method is fully automated and has the advantage of operating on the volume data prior to tessellation, significantly reducing computational costs compared to mesh- based methods. We demonstrate the method on a simple test volume and on the surface of a cerebral cortex obtained from a magnetic resonance image volume.