Changes in connectivity profiles define functionally-distinct regions in human medial frontal cortex H Johansen-Berg*1, TEJ Behrens*1, MD Robson2, I Drobnjak1, MFS Rushworth1,3, JM Brady4, SM Smith1, DJ Higham5and PM Matthews1
5Department of Mathematics, University of Strathclyde, Glasgow, G1 1HX, Scotland, UK
*These authors contributed equally to this work
Correspondence should be addressed to H.J-B. (email: firstname.lastname@example.org; tel: 44 1865 222782, fax: 44 1865 222717, address: Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK)
Submission date: April 20
A fundamental issue in neuroscience is the relation between structure and function. However, gross landmarks do not correspond well to micro-structural borders and cytoarchitecture cannot be visualised in a living brain used for functional studies. Here we used diffusion-weighted and functional MRI to test structure-function relations directly. Distinct neocortical regions were defined as volumes having similar connectivity profiles and borders identified where connectivity changed. Without using prior information, we found an abrupt profile change where the border between supplementary motor area (SMA) and pre-SMA is expected. Consistent with this anatomical assignment, putative SMA and pre-SMA connected to motor and prefrontal regions, respectively. Excellent spatial correlations were found between volumes defined using connectivity alone and volumes activated during tasks designed to involve SMA or pre-SMA selectively. This demonstrates a strong relationship between structure and function in medial frontal cortex and offers a strategy for testing such correspondences elsewhere in the brain. Since early attempts to parcellate human and non-human cortex into structurally distinct subdivisions, the hypothesis that structural borders correspond to functional borders has been widely held 1,2,3. However, this hypothesis has been tested only rarely. Structural features such as sulci and gyri are commonly used to define anatomical regions in functional imaging, neurophysiology and lesion studies, yet they have only a limited correspondence to more fine-grained structural organisation such as cytoarchitecture4,5,6. Micro-structural borders based, for example, on measurements of cyto-, myelo- or receptor architecture7,8,9, can only be defined post mortem and the methodological demands of such studies preclude investigation of the regional functional specialisations in the same animals. Detailed testing of the relationship between these anatomically-based measures and function based on comparisons between subjects is limited by the apparently substantial inter-individual variations in microstructural anatomical boundaries6,5,4.
A structural feature which has not previously been utilised to define areal boundaries in the human neocortex is connectivity to other brain regions. While features such as cytoarchitecture, myeloarchitecture and receptor distributions distinguish the processing capabilities of a region, connectional anatomy constrains the nature of the information available to a region and the influence that it can exert over other regions in a distributed network. Therefore, not only does structural variation reflect functional organisation, but local structural organisation also determines local functional specialisation. Data on brain connectivity in macaque monkeys shows that cytoarchitectonically and functionally distinct regions of prefrontal cortex have distinct connectivity ‘fingerprints’10. Differences in connectivity that parallel differences in cytoarchitecture, have been used to define subdivisions in macaque cortex within regions previously thought to be homogenous11.
Previously, we have shown that the human thalamus can be subdivided using non-invasive diffusion imaging data on the basis of its connectivity to specific cortical targets12. However, this approach was limited by the need to define potentially connected cortical target regions a priori. Here we develop a fundamentally different strategy for inferring structural parcellation from diffusion data that allows “blind” discrimination of regions with different patterns of connection. Probabilistic diffusion tractography is used to derive connectivity profiles for points along cortical regions of interest. By calculating the cross-correlation between these profiles it is possible to define regions with similar connections and to identify points where connectivity profiles change.
Our focus here is the medial frontal cortex. In the macaque monkey, the medial part of the homologue of Brodmann’s area 6 consists of two cytoarchitectonically distinct regions: F3 or SMA proper and F6 or pre-SMA2,13. These two regions exhibit different functional responses14,15,16 and have distinct connections17,18. The precise anatomical homologues of SMA and pre-SMA in humans are not clear as different studies have identified two19 or three20 cytoarchitectonically distinct regions within human area 6. There is consistent evidence for a functional distinction, at least between anterior and posterior parts of human medial area 6, as functional imaging studies have found differential involvement of these regions in tasks engaging distinct cognitive or motor domains21,22,23. While the arcuate sulcus corresponds with the border between SMA and pre-SMA in macaque16,14, there is no local landmark that differentiates functionally-defined SMA and pre-SMA in the human brain24; the vertical line from the anterior commissure (VCA line) provides the best approximation19. Here, we use novel diffusion tractography methods and fMRI to test directly whether boundaries defined by differences in connectivity can discriminate between functionally-defined SMA and pre-SMA in humans.
For each subject, diffusion-weighted imaging data were used to perform probabilistic tractography12,25 from voxels within large medial frontal cortex ‘seed’ masks. Probabilities of connection from each seed voxel to every other voxel in the brain were binarised and stored in a matrix, A, whose cross correlation matrix, B, was found. Elements in B therefore express the correlation in connectivity profile between medial frontal seed points. The nodes in B were permuted using a spectral reordering algorithm26 (DJH, submitted) that forces large values towards the diagonal (see Methods). If the data contain clusters (representing seed voxels with similar connectivity), then these clusters will be apparent in the reordered matrix and break points between clusters will represent locations where connectivity patterns change. Note that if such structure is not present in the original data then the reordered matrix will not have a clustered organisation.
Connectivity-based division of medial frontal cortex
We first defined single slice orthogonal seed masks on the medial frontal cortex in the axial (MNI Z=58) or sagittal (MNI X=-2) plane on the group average T1-weighted anatomical MR image (Figure 1). These seed masks were registered to each subject’s diffusion-weighted data for generation of connectivity matrices. Reordered connectivity cross-correlation matrices contained clearly identifiable clusters in all nine subjects (Figure 2 and Supplementary Information). Note that such structure will only be apparent in the reordered matrices if there is clustered organisation in the data. The reordered matrices were divided into two or three clusters. When these clusters were mapped back onto the brain they corresponded to discrete regions situated along the anterior-posterior axis of the medial frontal cortex (Figure 2 and Supplementary Information). The border between the most anterior and most posterior cluster was located close to the vertical line extending from the anterior commissure (VCA line, Y=0) suggesting that the regions correspond to SMA and pre-SMA. In order to test this hypothesis directly we compared subregions defined on the basis of connectivity to functional activation sites during tasks designed to involve SMA or pre-SMA selectively.
Figure 1: Medial frontal cortex maskshown in axial (left, Z=58) and sagittal (right, X=-2). The vertical line indicates the position of Y=0 (VCA line). The two slices shown are those used for the initial, single slice parcellations of medial frontal cortex.