Abstract
This paper describes novel methods for constructing the intrinsic geometry of the human brain connectome using dimensionality-reduction techniques. We posit that the high-dimensional, complex geometry that represents this intrinsic topology can be mathematically embedded into lower dimensions using coupling patterns encoded in the corresponding brain connectivity graphs. We tested both linear and nonlinear dimensionality-reduction techniques using the diffusion-weighted structural connectome data acquired from a sample of healthy subjects. Results supported the nonlinearity of brain connectivity data, as linear reduction techniques such as the multidimensional scaling yielded inferior lower-dimensional embeddings. To further validate our results, we demonstrated that for tractography-derived structural connectome more influential regions such as rich-club members of the brain are more centrally mapped or embedded. Further, abnormal brain connectivity can be visually understood by inspecting the altered geometry of these three-dimensional (3D) embeddings that represent the topology of the human brain, as illustrated using simulated lesion studies of both targeted and random removal. Last, in order to visualize brain’s intrinsic topology we have developed software that is compatible with virtual reality technologies, thus allowing researchers to collaboratively and interactively explore and manipulate brain connectome data.
Highlights
Magnetic resonance imaging (MRI) techniques have allowed us to noninvasively study the human brain both anatomically and functionally
Current connectome visualization methods typically represent anatomic and functional connectivity data using somewhat arbitrary or heuristic methods [2]. We address this shortcoming by developing a framework that realizes, constructs, and visually represents the complex intrinsic geometry or topology of the entire brain network
Sixty seven contiguous axial slices aligned to the anterior commissure–posterior commissure (AC-PC) line were collected in 32 gradient directions with b = 700 s/mm2 and one acquisition without diffusion sensitization (B0 image)
Summary
Magnetic resonance imaging (MRI) techniques have allowed us to noninvasively study the human brain both anatomically and functionally. The complex interactions between different regions of the brain have necessitated the development and growth of the field of connectomics. A brain connectome at the macroscale is typically mathematically represented with connectivity matrices that describe the interaction between the different brain regions. Most current connectome study designs are based on brain connectivity matrices, which involve the computation of summary statistics on a global or nodal level [1]. Current connectome visualization methods typically represent anatomic and functional connectivity data using somewhat arbitrary or heuristic methods [2]. We address this shortcoming by developing a framework that realizes, constructs, and visually represents the complex intrinsic geometry or topology of the entire brain network
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