Max Hinne

Currently, I am a post-doctoral researcher at the Computational Cognitive Neuroscience Lab, at the Donders Centre for Cognition, as well as a lecturer at the Cognitive Artificial Intelligence department, both at the Radboud University in Nijmegen. My research is aimed at modeling macro-scale brain connectivity and its dynamics.

There are few objects more interesting than the brain. It has just the right structure and dynamics that results in large-scale information processing, from which our complex cognition emerges and using which it ultimately studies itself.

Using neuroimaging techniques such as MRI and EEG, we can probe into the brain without disturbing it (inasmuch as lying an fMRI scanner can be non-disturbing), hoping to make sense of how this clump of wetware produces such a rich repertoire of behaviour. This is a daunting task for two reasons. On the one hand, the resolution of our data is (typically) far from ideal and often we measure only indirectly. On the other hand, our computational resources are not yet up to par with the demands of our analyses. Therefore, we need statistical techniques that make sense of neuroimaging data, inferring latent structure where we cannot directly measure it, that are at the same time efficient enough to allow for practical application.

My contact information is listed below.

“All stable processes we shall predict. All unstable processes we shall control.”

John von Neumann


PhD thesis

On June 9th 2017, I defended my doctoral thesis entitled Bayesian Connectomics: a probabilistic perspective on brain networks with a cum laude distinction.

Brain connectivity

Gaussian graphical models

Network Analysis

Information Retrieval



The Bayesian Connectomics Toolbox contains Matlab/MEX scripts that implement MCMC approximations for:

Basic usage of each of the scripts is demonstrated in demo.m. Feel free to email me with any questions or comments.


Code for the effective connectivity estimation using GP CaKe is available here.

Data sets

Two data sets are currently available:

  1. Probabilistic streamline counts based on diffusion-weighted MRI, for 162 regions (FreeSurfer parcellation: 74 cortical regions per hemisphere, 7 subcortical regions per hemisphere). These data were used in the paper regarding the clustered connectome.
  2. Functional MRI BOLD response timeseries, as well as probabilistic streamline counts based on diffusion-weighted MRI, for 14 subcortical regions (7 per hemisphere). These data were used in the paper on conditional independence and data fusion.
Credit for collecting these data goes to Erik van Oort.

Contact information






Google Scholar

Max Hinne




Visiting Address DCC

Room B.03.45
Donders Institute for Brain, Cognition and Behaviour
Montessorilaan 3
6525 HR Nijmegen