Reliabity toolbox fmreli

The toolbox was designed to simplify the assessment of reliability and similarity of fMRI data across different sessions and contrasts. It incorporates common measures of global and local reliability. Moreover, it implements the assessment of reliability for cross-sectional designs with only a single run by randomly splitting the trials in half. fmreli offers a graphical user interface (GUI) and incorporates functions provided by SPM and and the Nifti and ANALYZE toolbox. A preprint of the paper detailing the use of the toolbox is available at

When using the toolbox, please cite as follows:

Fröhner, J.H., Teckentrup, V.,  Smolka, M.N. & Kroemer, N.B. (2017). Addressing the reliability fallacy: Similar group effects may arise from unreliable individual effects.


Non-invasive brain stimulation techniques, such as transcutaneous auricular vagus nerve stimulation (taVNS), have considerable potential for clinical use. Beneficial effects of taVNS have been demonstrated on symptoms in patients with mental or neurological disorders as well as transdiagnostic dimensions, including mood and motivation. However, since taVNS research is still an emerging field, the underlying neurophysiological processes are not yet fully understood, and the replicability of findings on biomarkers of taVNS effects has been questioned. Here, we perform a living Bayesian random effects meta-analysis to synthesize the current evidence concerning the effects of taVNS on heart rate variability (HRV), a candidate biomarker that has, so far, received most attention in the field. To keep the synthesis of evidence transparent and up to date as new studies are being published, we developed a Shiny web app that regularly incorporates new results and enables users to modify study selection criteria to evaluate the robustness of the inference across potential confounds. By increasing transparency and timeliness, we believe that the concept of living meta-analyses can lead to transformational benefits in emerging fields such as non-invasive brain stimulation.

Link to the published Psychophysiology paper:

Open neuroMADLAB task repository

Throughout the years, we have used many different paradigms implemented in various software packages. Yes, they did what they were supposed to do most of the time. But often, basic versions of these tasks were extended for new projects by another researcher, who was working on an insufficiently documented/commented code never intended to be interoperable and re-usable. Such problems are quite common in science and not solely impose an unnecessary workload; they also increase the risk of severe error.

Thanks to basic support provided by the Wikimedia foundation, we are currently initiating an open task repository on github that will make several key tasks available in the next years. The code for the paradigms will be thoroughly tested and reviewed by us and adhere to basic quality criteria so that other labs may build on our efforts whenever they find it useful. We envision that future releases will include data and analysis scripts as well. If you are interested in contributing or having our contribution as part of another repository, please get in touch with us. If you are interested in using our tasks, follow the lab webpage for future release notes.

Upcoming releases:

Go/NoGo reinforcement learning task

Food-cue reactivity paradigm with visual analogue scales for liking and wanting

Effort allocation task

MATLAB code and functions

We will regularly bundle potentially useful code to share it with other researchers as we did, for example, by releasing the fmreli toolbox. However, not every helpful bit of code will qualify for a proper release in due time. As an informal guideline, if several other researchers found a code snippet useful in saving some time or hassle, we will seek to share it here in a low-level format before bundling it some day.

Function to compute jitters based on a truncated exponential distribution (perfect for fMRI ISIs and ITIs)

Basic reinforcement learning tutorial