Functional–Anatomical Validation and Individual Variation of Diffusion Tractography-based Segmentation of the Human Thalamus

Abstract

Parcellation of the human thalamus based on cortical connectivity information inferred from non-invasive diffusion-weighted images identifies sub-regions that we have proposed correspond to nuclei. Here we test the functional and anatomical validity of this proposal by comparing data from diffusion tractography, cytoarchitecture and functional imaging. We acquired diffusion imaging data in eleven healthy subjects and performed probabilistic tractography from voxels within the thalamus. Cortical connectivity information was used to divide the thalamus into sub-regions with highest probability of connectivity to distinct cortical areas. The relative volumes of these connectivity-defined sub-regions correlate well with volumetric predictions based on a histological atlas. Previously reported centres of functional activation within the thalamus during motor or executive tasks co-localize within atlas regions showing high probabilities of connection to motor or prefrontal cortices, respectively. This work provides a powerful validation of quantitative grey matter segmentation using diffusion tractography in humans. Co-registering thalamic sub-regions from 11 healthy individuals characterizes inter-individual variation in segmentation and results in a population-based atlas of the human thalamus that can be used to assign likely anatomical labels to thalamic locations in standard brain space. This provides a tool for specific localization of functional activations or lesions to putative thalamic nuclei.

Grapheme-color synesthetes show peculiarities in their emotional brain: cortical and subcortical evidence from VBM analysis of 3D-T1 and DTI data

“…Anatomical identification was performed via XjView 8 (http://www.alivelearn.net/xjview8/) and the anatomy toolbox by Eickhoff et al (2005). The Oxford Thalamic Connectivity Probability Atlas was used for the identification of areas of the cortex where thalamic nucleus project (Johansen-Berg et al 2005). In addition, for the labeling of anatomical results, the neuroanatomy atlases by Haines (2011) and Nolte and Angevine (2007) were consulted.…”

Defining functional areas in individual human brains using resting functional connectivity MRI

“…The maturation of the above methods (Johansen-Berg et al, 2004;Johansen-Berg et al, 2005;Margulies et al, 2007;Nir et al, 2006) and those used in this manuscript, could radically change the way functional neuroimaging data are analyzed in basic, translational, and clinical settings. If functional areas could be reliably identified within individual subjects, spatially normalizing individual brains using probabilistic atlases could be supplanted by the individual's own functional area locations as constraints on the registration process.…”

“…Recently, the use of areal connections to define areas has been employed in humans. Diffusion tensor imaging (DTI) tractography, which measures the directional diffusion of water within a voxel, can reveal local anisotropic differences in fiber bundles in neighboring regions of cortex in living humans, and was recently used to delineate some human cortical areas Croxson et al, 2005;Johansen-Berg et al, 2004;Johansen-Berg et al, 2005;Klein et al, 2007). The use of probabilistic fiber bundle differences in diffusion tractography is in some respects analogous to the use of blunt dissection to identify major fiber bundles in humans.…”

A mean three-dimensional atlas of the human thalamus: Generation from multiple histological data

“…For the intrathalamic structures, such as the ventral lateral posterior (or VIM) nucleus, routine clinical imaging methods provide insufficient contrast for visual detection (Patil et al, 1999;Nowinski et al, 2006;Mercado et al, 2006). To surpass these limitations, segmentation of thalamic nuclei with a clustering approach based on a specific MRI-sequence (Deoni et al, 2007) or on the connectivity pattern of the individual constituents (Behrens et al, 2003;Johansen-Berg et al, 2005) has been used. Jonasson et al (2005) and Wiegell et al (2003) employed the characteristic fiber orientations provided by DTI-MRI to differentiate between individual areas.…”