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The brain atlas concordance problem: quantitative comparison of anatomical parcellations.

Bohland JW, Bokil H, Allen CB, Mitra PP - PLoS ONE (2009)

Bottom Line: These analyses result in conditional probabilities that enable mapping between regions across atlases, which also form the input to graph-based methods for extracting higher-order relationships between sets of regions and to procedures for assessing the global similarity between different parcellations of the same brain.At a global scale, the overall results demonstrate a considerable lack of concordance between available parcellation schemes, falling within chance levels for some atlas pairs.At a finer level, this study reveals spatial relationships between sets of defined regions that are not obviously apparent; these are of high potential interest to researchers faced with the challenge of comparing results that were based on these different anatomical models, particularly when coordinate-based data are not available.

View Article: PubMed Central - PubMed

Affiliation: Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America. jbohland@bu.edu

ABSTRACT
Many neuroscientific reports reference discrete macro-anatomical regions of the brain which were delineated according to a brain atlas or parcellation protocol. Currently, however, no widely accepted standards exist for partitioning the cortex and subcortical structures, or for assigning labels to the resulting regions, and many procedures are being actively used. Previous attempts to reconcile neuroanatomical nomenclatures have been largely qualitative, focusing on the development of thesauri or simple semantic mappings between terms. Here we take a fundamentally different approach, discounting the names of regions and instead comparing their definitions as spatial entities in an effort to provide more precise quantitative mappings between anatomical entities as defined by different atlases. We develop an analytical framework for studying this brain atlas concordance problem, and apply these methods in a comparison of eight diverse labeling methods used by the neuroimaging community. These analyses result in conditional probabilities that enable mapping between regions across atlases, which also form the input to graph-based methods for extracting higher-order relationships between sets of regions and to procedures for assessing the global similarity between different parcellations of the same brain. At a global scale, the overall results demonstrate a considerable lack of concordance between available parcellation schemes, falling within chance levels for some atlas pairs. At a finer level, this study reveals spatial relationships between sets of defined regions that are not obviously apparent; these are of high potential interest to researchers faced with the challenge of comparing results that were based on these different anatomical models, particularly when coordinate-based data are not available. The complexity of the spatial overlap patterns revealed points to problems for attempts to reconcile anatomical parcellations and nomenclatures using strictly qualitative and/or categorical methods. Detailed results from this study are made available via an interactive web site at http://obart.info.

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Visualization of region-level concordance results.A: Pij matrix after permuting the indices (region labels) independently within each block in order to reduce matrix bandwidth. The blocks can not be completely diagonalized because of the lack of one-to-one correspondence between regions in pairs of parcellations. B: Visualization of region labels using multi-dimensional scaling. Top: 2-D landscape of computed coordinates for each anatomical region, with the parcellation from which each region is drawn indicated by the marker type. Bottom: magnified portion of the 2-D landscape above revealing the anatomical regions and their labels that occupy this segment of the space (the highlighted rectangular area in top).
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pone-0007200-g005: Visualization of region-level concordance results.A: Pij matrix after permuting the indices (region labels) independently within each block in order to reduce matrix bandwidth. The blocks can not be completely diagonalized because of the lack of one-to-one correspondence between regions in pairs of parcellations. B: Visualization of region labels using multi-dimensional scaling. Top: 2-D landscape of computed coordinates for each anatomical region, with the parcellation from which each region is drawn indicated by the marker type. Bottom: magnified portion of the 2-D landscape above revealing the anatomical regions and their labels that occupy this segment of the space (the highlighted rectangular area in top).

Mentions: The ordering of regions in each parcellation as depicted in Figure 3(A) is somewhat arbitrary, and thus visually determining the degree of correspondence between two parcellations is difficult. By rearranging the rows and columns (the ordering of regions), in the matrix, the interpretation of results is made easier. A heuristic based on the singular value decomposition (SVD; see Materials and Methods) was applied to permute the rows and columns of each rectangular block in C independently in order to minimize the matrix bandwidth (the maximum distance of non-zero entries from the diagonal) for that block. The result of applying this reordering algorithm is shown in Figure 5(A). It can be seen that, although non-zero conditional probability values have been moved toward the diagonal, there remains considerable bandwidth for each block, which is due to the general lack of one-to-one correspondences between regions. Still, this procedure provides a one-dimensional embedding for region labels; i.e. regions with similar spatial definitions appear nearby in this space. This is useful to impose a meaningful order on the sets of region labels when comparing two parcellations.


The brain atlas concordance problem: quantitative comparison of anatomical parcellations.

Bohland JW, Bokil H, Allen CB, Mitra PP - PLoS ONE (2009)

Visualization of region-level concordance results.A: Pij matrix after permuting the indices (region labels) independently within each block in order to reduce matrix bandwidth. The blocks can not be completely diagonalized because of the lack of one-to-one correspondence between regions in pairs of parcellations. B: Visualization of region labels using multi-dimensional scaling. Top: 2-D landscape of computed coordinates for each anatomical region, with the parcellation from which each region is drawn indicated by the marker type. Bottom: magnified portion of the 2-D landscape above revealing the anatomical regions and their labels that occupy this segment of the space (the highlighted rectangular area in top).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2748707&req=5

pone-0007200-g005: Visualization of region-level concordance results.A: Pij matrix after permuting the indices (region labels) independently within each block in order to reduce matrix bandwidth. The blocks can not be completely diagonalized because of the lack of one-to-one correspondence between regions in pairs of parcellations. B: Visualization of region labels using multi-dimensional scaling. Top: 2-D landscape of computed coordinates for each anatomical region, with the parcellation from which each region is drawn indicated by the marker type. Bottom: magnified portion of the 2-D landscape above revealing the anatomical regions and their labels that occupy this segment of the space (the highlighted rectangular area in top).
Mentions: The ordering of regions in each parcellation as depicted in Figure 3(A) is somewhat arbitrary, and thus visually determining the degree of correspondence between two parcellations is difficult. By rearranging the rows and columns (the ordering of regions), in the matrix, the interpretation of results is made easier. A heuristic based on the singular value decomposition (SVD; see Materials and Methods) was applied to permute the rows and columns of each rectangular block in C independently in order to minimize the matrix bandwidth (the maximum distance of non-zero entries from the diagonal) for that block. The result of applying this reordering algorithm is shown in Figure 5(A). It can be seen that, although non-zero conditional probability values have been moved toward the diagonal, there remains considerable bandwidth for each block, which is due to the general lack of one-to-one correspondences between regions. Still, this procedure provides a one-dimensional embedding for region labels; i.e. regions with similar spatial definitions appear nearby in this space. This is useful to impose a meaningful order on the sets of region labels when comparing two parcellations.

Bottom Line: These analyses result in conditional probabilities that enable mapping between regions across atlases, which also form the input to graph-based methods for extracting higher-order relationships between sets of regions and to procedures for assessing the global similarity between different parcellations of the same brain.At a global scale, the overall results demonstrate a considerable lack of concordance between available parcellation schemes, falling within chance levels for some atlas pairs.At a finer level, this study reveals spatial relationships between sets of defined regions that are not obviously apparent; these are of high potential interest to researchers faced with the challenge of comparing results that were based on these different anatomical models, particularly when coordinate-based data are not available.

View Article: PubMed Central - PubMed

Affiliation: Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America. jbohland@bu.edu

ABSTRACT
Many neuroscientific reports reference discrete macro-anatomical regions of the brain which were delineated according to a brain atlas or parcellation protocol. Currently, however, no widely accepted standards exist for partitioning the cortex and subcortical structures, or for assigning labels to the resulting regions, and many procedures are being actively used. Previous attempts to reconcile neuroanatomical nomenclatures have been largely qualitative, focusing on the development of thesauri or simple semantic mappings between terms. Here we take a fundamentally different approach, discounting the names of regions and instead comparing their definitions as spatial entities in an effort to provide more precise quantitative mappings between anatomical entities as defined by different atlases. We develop an analytical framework for studying this brain atlas concordance problem, and apply these methods in a comparison of eight diverse labeling methods used by the neuroimaging community. These analyses result in conditional probabilities that enable mapping between regions across atlases, which also form the input to graph-based methods for extracting higher-order relationships between sets of regions and to procedures for assessing the global similarity between different parcellations of the same brain. At a global scale, the overall results demonstrate a considerable lack of concordance between available parcellation schemes, falling within chance levels for some atlas pairs. At a finer level, this study reveals spatial relationships between sets of defined regions that are not obviously apparent; these are of high potential interest to researchers faced with the challenge of comparing results that were based on these different anatomical models, particularly when coordinate-based data are not available. The complexity of the spatial overlap patterns revealed points to problems for attempts to reconcile anatomical parcellations and nomenclatures using strictly qualitative and/or categorical methods. Detailed results from this study are made available via an interactive web site at http://obart.info.

Show MeSH
Related in: MedlinePlus