A new method to measure complexity in binary or weighted networks and applications to functional connectivity in the human brain.

Background: Networks or graphs play an important role in the biological sciences. Protein interaction networks and metabolic networks support the understanding of basic cellular mechanisms. In the human brain, networks of functional or structural connectivity model the information-flow between cortex regions.

Selectively and progressively disrupted structural connectivity of functional brain networks in Alzheimer's disease - revealed by a novel framework to analyze edge distributions of networks detecting disruptions with strong statistical evidence.

Alzheimer's disease (AD) disrupts selectively and progressively (increasing with severity) functional connectivity of intrinsic brain networks (IBNs), most prominent in the default mode network. Given that IBNs' functional connectivity depends on structural connectivity, we hypothesize for our study selective and progressive changes of IBN based structural connectivity in AD. To achieve strong statistical evidence, we introduce a novel statistical method based on the edge frequency distributions of structural connectivity networks.

Fitting of two-tensor models without ad hoc assumptions to detect crossing fibers using clinical DWI data.

Analysis of crossing fibers is a challenging topic in recent diffusion-weighted imaging (DWI). Resolving crossing fibers is expected to bring major changes to present tractography results based on the standard tensor model. Model free approaches, like Q-ball or diffusion spectrum imaging, as well as multi-tensor models are used to unfold the different diffusion directions mixed in a voxel of DWI data.