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the structural–functional connectome and the default mode network of the human brain

• Andreas Horn^a, b, , , 
• Dirk Ostwald^a, 
• Marco Reisert^c, 
• Felix Blankenburg^a, d

• ^a Center for Adaptive Rationality (ARC), Max Planck Institute for Human Development, Berlin, Germany
• ^b Department of Neurology, Charité, University Medicine Berlin, Berlin, Germany
• ^c Department of Radiology, Medical Physics, University Hospital Freiburg, Germany
• ^d Dahlem Institute for Neuroimaging of Emotion, Freie Universität Berlin, Berlin, Germany

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Highlights

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Structure–function connectivity relationship

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Multi-modal data fusion

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Voxel-wise connectivity analysis

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Default mode network

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Global fiber-tracking

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Abstract

An emerging field of human brain imaging deals with the characterization of the connectome, a comprehensive global description of structural and functional connectivity within the human brain. However, the question of how functional and structural connectivity are related has not been fully answered yet. Here, we used different methods to estimate the connectivity between each voxel of the cerebral cortex based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data in order to obtain observer-independent functional–structural connectomes of the human brain. Probabilistic fiber-tracking and a novel global fiber-tracking technique were used to measure structural connectivity whereas for functional connectivity, full and partial correlations between each voxel pair's fMRI-timecourses were calculated. For every voxel, two vectors consisting of functional and structural connectivity estimates to all other voxels in the cortex were correlated with each other. In this way, voxels structurally and functionally connected to similar regions within the rest of the brain could be identified. Areas forming parts of the ‘default mode network’ (DMN) showed the highest agreement of structure–function connectivity. Bilateral precuneal and inferior parietal regions were found using all applied techniques, whereas the global tracking algorithm additionally revealed bilateral medial prefrontal cortices and early visual areas. There were no significant differences between the results obtained from full and partial correlations. Our data suggests that the DMN is the functional brain network, which uses the most direct structural connections. Thus, the anatomical profile of the brain seems to shape its functional repertoire and the computation of the whole-brain functional–structural connectome appears to be a valuable method to characterize global brain connectivity within and between populations.

Keywords

• Connectome; 
• Structure–function relationship; 
• Functional magnetic resonance imaging (fMRI); 
• Diffusion tensor imaging (DTI); 
• Resting-state fMRI; 
• Default mode network (DMN)

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Figures and tables from this article:

Fig. 1. 

Processing of functional time signals and structural data obtained from functional MRI and diffusion based imaging. Panel A: A group template as a best mean agreement between the individual anatomical features is parcellated into 40,000 voxels. Panel B: Structural and functional connectivity is obtained by using diffusion-weighted and fMRI data. Probabilistic and global tracking is performed between each point defined in panel A. Functional time courses are extracted from the same points, accordingly. Panel C: For each connectivity method, an adjacency matrix is obtained for each subject, showing the connectivity between each pair of the 40,000 voxels. Panel D: Each voxel's connectivity to the rest of the brain is represented in a 1 × 40,000 vector (i.e. one row or column extracted from the matrices in panel C). Agreement between vectors obtained from different methods within subjects can be combined by spatially correlating vectors for each voxel and by mapping resulting values back to the group-template space (panel E). Results can then be transformed to standard MNI space by applying the normalization transformation of the template to the resulting images.

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Fig. 2. 

Group results of correlations between functional and structural connectivity measures. Voxel-wise comparisons between each voxel's functional and structural connectivity to the rest of the brain were analyzed for each subject and analyzed in a t-test group analysis. Findings from spatial correlation analyses between one of the structural connectivity measures (probabilistic global fiber-tracking) and one of the functional connectivity measures (full or partial correlations) are shown. Areas in this figure are thresholded at p < 0.001 and corrected for multiple comparisons on a cluster-level (FWE < 0.05). 1: Precuneus and adjacent posterior cingulate/retrosplenial cortex (PCC/Rsp), 2. Bilateral Inferior parietal lobe/angular gyrus (IPL/AG), 3. Right Supramarginal gyrus (SMG), 4. Bilateral Medial prefrontal gyrus (MPFC), 5. Left middle/inferior frontal gyrus, 6. Occipital pole.

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Fig. 3. 

Contrasts between results from Fig. 2 further show the effect of the different structural tracking methods and the similarity between functional methods. Differential contrast for probabilistic > global tracking similarity with full and partial correlations show higher t-values in the middle temporal gyrus and precuneus. Global > probabilistic tracking similarity with full and partial correlations show higher t-values in frontal areas. Areas in this figure are thresholded at p < 0.001 and corrected for multiple comparisons on the cluster-level (FWE < 0.05). 1: Precuneus and adjacent posterior cingulate/retrosplenial cortex (PCC/Rsp), 2. Bilateral inferior parietal lobe/angular gyrus (IPL/AG), 3. Bilateral Medial prefrontal gyrus (MPFC), 4. Occipital pole.

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Fig. 4. 

Unthresholded maps of mean effect size from spatial correlation analyses for the different connectivity measures. Voxel-wise comparisons between each voxel's functional and structural connectivity to the rest of the brain were analyzed for each subject and then averaged over the group of subjects. Note that Pearson's R-values were Fisher-transformed to a gaussian distribution prior to averaging over subjects.

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Fig. 5. 

Influence of the identified regions as shown by their connectivity in tractography. Regions that showed the highest structure–function agreement in between probabilistic trackings and full correlations are visualized as the solid regions in yellow. Only fibers that connect to these regions are displayed and color-coded for their traversing direction (xyz mapping to rgb). Fibers of the cingulum bundle connect precuneal regions to the medial prefrontal cortex, inferior longitudinal fascicle to the temporal lobes. Interhemispheric connectivity is mediated via the corpus callosum.

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Table 1.

Connectivity agreement as measured by correlating whole connectivity matrices for different connectivity measures. Mean Pearson's R-values, as well as t- and p-values of a one-sample t-test over the group are given for the analyses with and without prior regression of Euclidean distance.

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Table 2.

Results from spatial correlations between different connectivity measures. Coordinates and t-values refer to peak-voxels in significant clusters.

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Table 3.

Intensity values in brain-maps showing regions with high cross-modal connectivity agreement were correlated to intensity values in a template of the default mode network obtained from a classical independent component analysis study (Garrity et al., 2007). Mean correlation strength of single-subject images with the template and their standard deviation are displayed for the combinations between the two functional and structural methods applied.

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