Onni: An online experiment platform
Onni (“Bliss” in Finnish) is Emotion Lab’s toolbox for online experiments. You can think it as a merger of super simple E-prime or Presentation and hassle-free Webropol / Google docs that can also handle bodily sensation mapping (emBODY measurements). It is geared towards getting fast ratings for video / audio / image / text stimuli and setting up simple questionnaires, but is easy to customise towards other uses too. Setting up experiments is really fast. Web-based user interface with simple formatting options and batch stimulus upload & questionnaire generation allows deploying studies in minutes. Data are stored centrally and accessible in R and Excel-friendly formats. You can try experiments done with the release candidate at onni.utu.fi. Onni is developed by Timo Heikkilä and Ossi Laine. If you use Onni in your research, please cite it as follows: Heikkilä, T.T., Laine, O., Savela, J., & Nummenmaa, L. (2020). Onni: An online experiment platform for research (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.4305573
MAGIA pipeline for PET neuroinformatics
The MAGIA toolbox is designed for swift automated preprocessing of PET data with multiple ligands and models. he MAGIA implements automatic image retrieval from PACS combined with functions from FreeSurfer and SPM image preprocessing pipelines coupled with in-house PET modelling and metadata annotation code. Quality control is performed with in-house code and MRIQC toolbox. The compilation of MAGIA code is site-independent and can be installed anywhere. If you use MAGIA in your work, please cite the following paper: Karjalainen, T., Tuisku, J., Santavirta, S., Kantonen, T., Bucci, M., Tuominen, L., Hirvonen, J., Hietala, J., Rinne J., & Nummenmaa, L. (2020). Magia: Robust automated image processing and kinetic modeling toolbox for PET neuroinformatics. Frontiers in Neuroinformatics. MAGIA is developed by Tomi Karjalainen.
Emotions and other sensations are often experienced in the body. The emBODY tool can be used for mapping emotion-dependent sensations (or any other feature such as pain!) in distinct bodily regions. If you use emBODY, please cite this study where the online data acquisition and analysis suite was originally used in: Nummenmaa, L., Glerean, E., Hari, R., & Hietanen, J.K. (2014). Bodily maps of emotions. Proceedings of the National Academy of Sciences of the United States of America, 111, 646-651. The software was developed by Enrico Glerean.
Using emBODY with Gorilla
It is also possible to use the body mapping tools with the popular Gorilla online experiment laboratory using Gorilla’s Canvas Painting zone. This feature is still a beta release but to our knowledge it is perfectly stable. The main difference to our own tools are that data are stored as binary rather than integer values, and that the data format (bitmap images) is somewhat cumbersome. The Canvas Painting Zone however works well and you can see our data from art-evoked emotions acquired with Gorilla in this paper. To use body mapping in Gorilla, generate a Canvas Painting zone for your experimental display and use the desired body shape as the background image. You may download out original versions from the emotion, social touch, and sexual touch studies from this link. Vesa Putkinen from our laboratory (with a bit of help from Severi Santavirta and Birgitta Paranko) has written code for analysing the data acquired with Gorilla tools that you can download from GIT. It is based on the original written by Enrico Glerean, but accommodates to the data format in Gorilla taking care of data scaling and cleanup.
Eye movements provide an unobtrusive measure for quantifying cognition, attention and emotion. Model-based analysis of eye movements recorded during a complex high-dimesional stimulus such as a movie is however complicated. To resolve this issue, we have developed Eye Movement Intersubject Correlation Toolbox (e-ISC), which provides a straightforward way for model-free analysis of eye movements. This toolbox computes time course of the similarity of eye movements across individuals, based on sliding window heatmaps of fixation positions. The time course can be further averaged for indexing total similarity of eye movements. When using e-ISC toolbox, please cite this paper: Nummenmaa, L., Smirnov, D., Lahnakoski, J., Glerean, E., Jääskeläinen, I.P., Sams, M., & Hari, R. (2014). Mental action simulation synchronizes action-observation circuits across individuals. The Journal of Neuroscience, 34,748 –757. The e-ISC toolbox was developed by Juha Lahnakoski.
Fast Ridge Regression Toolbox
Multicolinearity poses a major problem to analysing fMRI data with high-dimensional stimulus model. Traditionally used orthogonalisation techniques are oftentimes problematic, as they yield suboptimal solutions, moreover the orthogonalised regressors and their relations are difficult to interpret. Our proposed solution to this problem is to use Ridge Regression that penalises the regressors based on their multicolinearity structure, yielding accurate and unbiased models for the predictors. This code was written by Jonatan Ropponen.
Cluster-based Bayesian Hierarchical Modelling
Bayesian hierarchical modelling (BHM) can be used for robust analysis of neuroimaging data at region-of-interest level, yet the technique is computationally prohibitive at full-volume (voxelwise) level. This toolbox allows generation of anatomically constrained, molecularly (or functionally) homogenous regions of interest. These can be used in BHM, and the results can be subsequently be back-projected to original anatomical space to yield “pseudo-full-volume” maps of the results. The GIT also contains nifty scripts for general-purpose BHM analysis. The package was written by Tomi Karjalainen.
Sorting tool for fast similarity ratings
Many psychophysical, neuroimaging, and analytic (e.g. RSA) techniques necessitate obtaining the similarity structure of the stimuli, traits or phenomena under scrutiny. Direct pairwise ratings are the usual gold standard, but this becomes prohibitive as the number of tokens increases even slightly over 10. This can be circumvented using Q-sort like arrangement task, where the tokens are spatially arranged in a grid based on their similarity. This is an online implementation of the arrangement task as originally used in Nummenmaa, L., Hari, R., Hietanen, J.K., & Glerean, E. (in press). Maps of subjective feelings. Proceedings of the National Academy of Sciences of the United States of America. This toolbox was written by Enrico Glerean.
Dynamic annotation web-tool
When working with high-dimensional stimuli such as movies or speech, the stimulus model has to account for intensity and temporal fluctuations. This is a web implementation of dynamic stimulus annotation toolbox originally used in Nummenmaa, L., Glerean, E., Viinikainen, M., Jääskeläinen, I.P., Hari, R., & Sams, M. (2012) Emotions promote social interaction by synchronizing brain activity across individuals. Proceedings of the National Academy of Sciences of the United States of America, 109, 9599–9604. This toolbox was written by Enrico Glerean.
Aggressive Behaviour Stimuli
We have generated a standardised set of affective pictures displaying visually matched aggressive and neutral scenes for psychological and neuroimaging research. The set has been validated in both fMRI, behavioural, and eye tracking studies. The stimuli are available for download from the link below, please cite this paper if you use them in your work: Nummenmaa, L., Hirvonen, J., Parkkola, R., & Hietanen, J. K. (2008). Is emotional contagion special? An fMRI study on neural systems for affective and cognitive empathy. NeuroImage, 43, 571–580.
Categorical emotion vignettes
Our group has developed a set of short vignettes for evoking basic emotions. The set contains brief scenarios that help subjects in framing their emotional imagery, but they can be used as “bottom-up” stimuli. The vignettes have been successfully used in both fMRI and behavioural studies. Please note that we have only validated the Finnish translations as our studies using these materials have been conducted in Finnish-speaking subjects. The stimuli are available for download from the link below, please cite this paper if you use them in your work: Nummenmaa, L., Glerean, E., Hari, R., & Hietanen, J.K. (2014). Bodily maps of emotions. Proceedings of the National Academy of Sciences of the United States of America, 111, 646-651.
Download: Emotional vignettes
We have also developed a set of vignettes that cover both basic and complex emotions. For this set, please refer to this paper: Saarimäki, H., Ejthedian, L.F., Glerean, E., Jääskeläinen, I.P., Vuilleumier, P., Sams, M., & Nummenmaa, L. (2018). Distributed affective space represents multiple emotion categories across the brain. Social Cognitive and Affective Neuroscience, 13, 471-482.
Download: Complex emotional vignettes
Dynamic emotional stories
Prolonged and variable affective stimuli are well optimised for naturalistic stimulation models. We have generated a set of ~30-s long affective stories that are suited for e.g. fMRI, psychophysiological and eye tracking experiments on semantic processing of emotions. The stories have been rated for affective valence and arousal and are currently available in Finnish. The stimuli are available for download from the link below, please cite this paper if you use them in your work:Nummenmaa, L., Saarimäki, H., Glerean, E., Gotsopoulos, A., Hari, R., & Sams, M. (2014). Emotional speech synchronizes brains across listeners and engages large-scale dynamic brain networks. NeuroImage, 102, 498-509.