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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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Region-level concordance analysis across eight anatomical parcellations.A: Overall non-symmetric concordance matrix P. Entry Pij gives P(i/j), the probability that a voxel is in region i given that it is in region j in another parcellation scheme. Each row and column corresponds to a particular anatomical region, and regions are grouped by parcellation method (separated by the gray horizontal and vertical lines). B: The column (top) and row (bottom) from the matrix P corresponding to the Superior Temporal region as delineated by the ICBM atlas (see arrows in A) were extracted and the corresponding conditional probability values rendered as the heights of bars. The orange bars give the fraction of the ICBM Superior Temporal region contained in other regions, and the blue bars (below) give the fraction of other regions contained in the ICBM region. The names of the example overlapping regions corresponding to the annotated bars are as follows: 1. AAL superior temporal gyrus; 2. AAL middle temporal gyrus; 3. ICBM superior temporal; 4. LPBA40 superior temporal gyrus; 5. TALg superior temporal gyrus; 6. CYTO TE1.2; 7. H-O superior temporal gyrus, anterior division; 8. T&G anterior superior temporal gyrus; 9. T&G posterior dorsal superior temporal sulcus; 10. TALc Brodmann Area 41; 11. TALg transverse temporal gyrus. C: Histogram of the mean number of regions from any parcellation R′ that overlap a single region drawn from a different parcellation R.
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pone-0007200-g003: Region-level concordance analysis across eight anatomical parcellations.A: Overall non-symmetric concordance matrix P. Entry Pij gives P(i/j), the probability that a voxel is in region i given that it is in region j in another parcellation scheme. Each row and column corresponds to a particular anatomical region, and regions are grouped by parcellation method (separated by the gray horizontal and vertical lines). B: The column (top) and row (bottom) from the matrix P corresponding to the Superior Temporal region as delineated by the ICBM atlas (see arrows in A) were extracted and the corresponding conditional probability values rendered as the heights of bars. The orange bars give the fraction of the ICBM Superior Temporal region contained in other regions, and the blue bars (below) give the fraction of other regions contained in the ICBM region. The names of the example overlapping regions corresponding to the annotated bars are as follows: 1. AAL superior temporal gyrus; 2. AAL middle temporal gyrus; 3. ICBM superior temporal; 4. LPBA40 superior temporal gyrus; 5. TALg superior temporal gyrus; 6. CYTO TE1.2; 7. H-O superior temporal gyrus, anterior division; 8. T&G anterior superior temporal gyrus; 9. T&G posterior dorsal superior temporal sulcus; 10. TALc Brodmann Area 41; 11. TALg transverse temporal gyrus. C: Histogram of the mean number of regions from any parcellation R′ that overlap a single region drawn from a different parcellation R.

Mentions: Figure 3(A) shows the overall results of this region-level analysis across the eight parcellations. The matrix of conditional probabilities P is depicted as an image, with each pixel's color indicating the value of that matrix entry (on a logarithmic scale). Each row and column corresponds to one particular anatomical region in a parcellation, and regions are grouped by parcellation method (the rows or columns between sets of grey lines). Non-zero (non-black) entries indicate that two regions exhibit some degree of spatial overlap, and it is apparent that overlap is often partial between region pairs. While the results contained in matrix P are too numerous to describe individually here, an annotated software tool is available online (http://www.obart.info) that allows the interested reader to view the findings interactively and in full detail. Presently, we provide further explication for a single illustrative brain region, continuing to expand on the example from Figure 1.


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

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

Region-level concordance analysis across eight anatomical parcellations.A: Overall non-symmetric concordance matrix P. Entry Pij gives P(i/j), the probability that a voxel is in region i given that it is in region j in another parcellation scheme. Each row and column corresponds to a particular anatomical region, and regions are grouped by parcellation method (separated by the gray horizontal and vertical lines). B: The column (top) and row (bottom) from the matrix P corresponding to the Superior Temporal region as delineated by the ICBM atlas (see arrows in A) were extracted and the corresponding conditional probability values rendered as the heights of bars. The orange bars give the fraction of the ICBM Superior Temporal region contained in other regions, and the blue bars (below) give the fraction of other regions contained in the ICBM region. The names of the example overlapping regions corresponding to the annotated bars are as follows: 1. AAL superior temporal gyrus; 2. AAL middle temporal gyrus; 3. ICBM superior temporal; 4. LPBA40 superior temporal gyrus; 5. TALg superior temporal gyrus; 6. CYTO TE1.2; 7. H-O superior temporal gyrus, anterior division; 8. T&G anterior superior temporal gyrus; 9. T&G posterior dorsal superior temporal sulcus; 10. TALc Brodmann Area 41; 11. TALg transverse temporal gyrus. C: Histogram of the mean number of regions from any parcellation R′ that overlap a single region drawn from a different parcellation R.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2748707&req=5

pone-0007200-g003: Region-level concordance analysis across eight anatomical parcellations.A: Overall non-symmetric concordance matrix P. Entry Pij gives P(i/j), the probability that a voxel is in region i given that it is in region j in another parcellation scheme. Each row and column corresponds to a particular anatomical region, and regions are grouped by parcellation method (separated by the gray horizontal and vertical lines). B: The column (top) and row (bottom) from the matrix P corresponding to the Superior Temporal region as delineated by the ICBM atlas (see arrows in A) were extracted and the corresponding conditional probability values rendered as the heights of bars. The orange bars give the fraction of the ICBM Superior Temporal region contained in other regions, and the blue bars (below) give the fraction of other regions contained in the ICBM region. The names of the example overlapping regions corresponding to the annotated bars are as follows: 1. AAL superior temporal gyrus; 2. AAL middle temporal gyrus; 3. ICBM superior temporal; 4. LPBA40 superior temporal gyrus; 5. TALg superior temporal gyrus; 6. CYTO TE1.2; 7. H-O superior temporal gyrus, anterior division; 8. T&G anterior superior temporal gyrus; 9. T&G posterior dorsal superior temporal sulcus; 10. TALc Brodmann Area 41; 11. TALg transverse temporal gyrus. C: Histogram of the mean number of regions from any parcellation R′ that overlap a single region drawn from a different parcellation R.
Mentions: Figure 3(A) shows the overall results of this region-level analysis across the eight parcellations. The matrix of conditional probabilities P is depicted as an image, with each pixel's color indicating the value of that matrix entry (on a logarithmic scale). Each row and column corresponds to one particular anatomical region in a parcellation, and regions are grouped by parcellation method (the rows or columns between sets of grey lines). Non-zero (non-black) entries indicate that two regions exhibit some degree of spatial overlap, and it is apparent that overlap is often partial between region pairs. While the results contained in matrix P are too numerous to describe individually here, an annotated software tool is available online (http://www.obart.info) that allows the interested reader to view the findings interactively and in full detail. Presently, we provide further explication for a single illustrative brain region, continuing to expand on the example from Figure 1.

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