Max Hinne

Currently, I am a post-doctoral researcher at the Department of Psychology at the University of Amsterdam, in the labs of both Eric-Jan Wagenmakers and Denny Borsboom. Furthermore, I teach the course 'Bayesian Statistics' at the Cognitive Artificial Intelligence department, at the Radboud University of Nijmegen.

My research is aimed at the development of (Bayesian) statistical methods for inference of network structure, with applications in cognitive and clinical psychology as well as in neuroscience. In general, I'm interested in network modeling, (Gaussian) graphical models, Gaussian processes, causal inference and connectomics & brain dynamics.

My contact information is listed below.


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

John von Neumann

Publications

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

Software

BaCon

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.

Latent space modeling

Matlab code and Stan models are available for the latent space model that was used for link prediction. Running the sample requires that you have Stan and the MatlabStan interface installed.

GP CaKe

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.
    [doi]
  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.
    [doi]
Credit for collecting these data goes to Erik van Oort.

Contact information

Online

Email
m.hinne@donders.ru.nl & m.hinne@uva.nl
LinkedIn
http://nl.linkedin.com/in/mhinne
Skype
max.hinne
Google Scholar
Max Hinne

Offline

Amsterdam

Visiting Address PM

Room G.0.33
Nieuwe Achtergracht 129B
1018 WS Amsterdam

Nijmegen

Phone

+31-24-3612554

Visiting Address AI

Room B.00.67B
Montessorilaan 3
6525 HR Nijmegen