Abstract
Previous structural brain connectivity studies have mainly focussed on the macroscopic scale of around 1,000 or fewer brain areas (network nodes). However, it has recently been demonstrated that high resolution structural connectomes of around 50,000 nodes can be generated reproducibly. In this study, we infer high resolution brain connectivity matrices using diffusion imaging data from the Human Connectome Project. With such high resolution we are able to analyse networks within brain areas in a single subject. We show that the global network has a scale invariant topological organisation, which means there is a hierarchical organisation of the modular architecture. Specifically, modules within brain areas are spatially localised. We find that long range connections terminate between specific modules, whilst short range connections via highly curved association fibers terminate within modules. We suggest that spatial locations of white matter modules overlap with cytoarchitecturally distinct grey matter areas and may serve as the structural basis for function specialisation within brain areas. Future studies might elucidate how brain diseases change this modular architecture within brain areas.
Highlights
Question of reliability applies to all DW-MRI studies
We attempt a pioneering investigation using high resolution connectomics to investigate 1) measures of scale-invariance, and 2) localised properties of brain areas in a single subject. Both of these two properties can only be investigated using networks of sufficiently high resolution. We demonstrate that this approach may have potential utility for analysing local properties of subsets of brain networks whilst acknowledging potential biases at such high resolution
As an example we show a subset of the connectivity matrix with connectivity between superior frontal (SF) and superior parietal (SP) areas in the left hemisphere
Summary
Question of reliability applies to all DW-MRI studies. DW-MRI based connectomics has been shown to be useful in various situations[5,6,7]. We attempt a pioneering investigation using high resolution connectomics to investigate 1) measures of scale-invariance, and 2) localised properties of brain areas in a single subject. Both of these two properties can only be investigated using networks of sufficiently high resolution. We demonstrate that this approach may have potential utility for analysing local properties of subsets of brain networks whilst acknowledging potential biases at such high resolution (see discussion for further details)
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