Microstructural grey matter parcellation and its relevance for connectome analyses - PubMed (original) (raw)
Microstructural grey matter parcellation and its relevance for connectome analyses
Svenja Caspers et al. Neuroimage. 2013.
Abstract
The human brain connectome is closely linked to the anatomical framework provided by the structural segregation of the cortex into distinct cortical areas. Therefore, a thorough anatomical reference for the analysis and interpretation of connectome data is indispensable to understand the structure and function of different regions of the cortex, the white matter fibre architecture connecting them, and the interplay between these different entities. This article focuses on parcellation schemes of the cerebral grey matter and their relevance for connectome analyses. In particular, benefits and limitations of using different available atlases and parcellation schemes are reviewed. It is furthermore discussed how atlas information is currently used in connectivity analyses with major focus on seed-based and seed-target analyses, connectivity-based parcellation results, and the robust anatomical interpretation of connectivity data. Particularly this last aspect opens the possibility of integrating connectivity information into given anatomical frameworks, paving the way to multi-modal atlases of the human brain for a thorough understanding of structure-function relationships.
Copyright © 2013 Elsevier Inc. All rights reserved.
Conflict of interest statement
Conflict of interest
The authors have no conflict of interest to declare.
Figures
Fig. 1.
The JuBrain Cytoarchitectonic Atlas (Zilles and Amunts, 2010) as accessible via the JuBrain WebTool (
http://www.fz-juelich.de/luBrain/EN/\_node.html
), visualized on the MNI single subject template. (A) Cytoarchitectonic areas mapped until now (coloured) as maximum probability maps (MPMs) from left (top left), right (bottom left), occipital (top middle) and medial views (bottom middle). Note the pink and purple coloured areas as visible from the occipital and medial view, depicting primary visual area hOc1 (pink, probability map shown in B) and secondary visual area hOc2 (purple) (Amunts et al., 2000). (B) Probability map of area 17/hOc1 (Amunts et al., 2000) from occipital (top right) and medial right hemisphere view (bottom right), projected onto the grey matter/white matter interface for better visualization of the sulcal patterns. Colours from blue to red decode lower and higher probabilities, respectively, of finding this area in that voxel.
Fig. 2.
The JuBrain Cytoarchitectonic Atlas (Zilles and Amunts, 2010) as implemented in the SPM Anatomy Toolbox (Eickhoff et al., 2005;
), depicted on sections of the MNI single subject template. Grey colours depict different cytoarchitectonic areas. (A) Cytoarchitectonic probabilities at defined MNI coordinates. Cross hair positioned within parietal opercular area OP4 (Eickhoff et al., 2006a,c). (B) Visualization of cytoarchitectonic probability maps, here for inferior parietal area PF (Caspers et al., 2006, 2008). (C) Example of assigning functional activations (here meta-analysis results) to cytoarchitectonic areas with different probabilities (Schilbach et al., 2012): Convergence of default-mode and affective processing within left amygdala (laterobasal nuclei group, LB; Amunts et al., 2005).
Fig. 3.
Seed-based probabilistic tractography (performed with FSL) on diffusion-imaging data of 39 subjects (for details: Caspers et al., 2011), visualized on the MNI single subject template. (A) Two seeds within the inferior parietal lobule, areas PFt (green) and PF (red), which are located next to each other on the supramarginal gyrus. (B) Tracts running from the seeds towards the external capsule. (C) Course of the tracts through the external capsule. The tract of PFt (green) is displayed in transparent fashion to allow for visualization of the tract of PF (red). (D) Transcallosal connections of the seeds, clearly distinct from each other.
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References
- Amunts K, Schleicher A, Bürgel U, Mohlberg H, Uylings HBM, Zilles K, 1999. Broca's region revisited: cytoarchitecture and intersubject variability. J. Comp. Neurol 412, 319–341. - PubMed
- Amunts K, Malikovic A, Mohlberg H, Schormann T, Zilles K, 2000. Brodmann’s areas 17 and 18 brought into stereotaxic space — where and how variable? NeuroImage 11, 66–84. - PubMed
- Amunts K, Kedo O, Kindler M, Pieperhoff P, Mohlberg H, Shah NJ, Habel U, Schneider F, Zilles K, 2005. Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat. Embryol. (Berl.) 210 (5–6), 343–352. - PubMed
- Amunts K, Schleicher A, Zilles K, 2007. Cytoarchitecture of the cerebral cortex — more than localization. NeuroImage 37,1061–1065. - PubMed
- Anwander A, Tittgemeyer M, von Cramon DY, Friederici AD, Knösche TR, 2007. Connectivity-based parcellation of Broca's area. Cereb. Cortex 17 (4), 816–825. - PubMed
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