Another one on fear, this time acrophobia. Normally I assume that everything on TV is staged for your entertainment. The wonderful Isabella Purkart who interviewed me was really, really terrified of heights. The whole show was quite an ordeal for her. At the end, she managed to climb all the 183 of the Vienna Jubiläumswarte. Not only was she rewarded with a fantastic view over our city and the Wienerwald, she also got a first hand experience how confrontation helped her to overcome her fear. Great job!
The lovely people from the brainstorms group invited me to give an online talk about predictive processing. Predictive processing theory suggests that all brain functions depend on comparisons between ongoing actual experiences and the brain’s expectations. It suggests that expectations and predictions about reality are, probably, more important than the direct live sensory evidence that the brain receives. But is it just a new data modelling method? Does it describe just one aspect of cognition? No, there is much more potential. Let us look at the loose ends of present day cognitive (neuro)science to see why we can be really excited about predictive processing.
Using our brain we predict the future based on what we have learnt in the past. But what if there is no precedence to learn from? How can we cope with lock-downs, isolation, existential crises, and fear of an unknown infection?
Covid-19 brings most of us out of their comfort zone but we are built for enduring hardships and uncertainty.
I was invited to an afternoon TV talk show to discuss my outlook on the future with social economics Prof. Marina Fischer-Kowalski, psychologist Irina Nalis, behavioral biologist Gregor Fauma and Matthias & Tristan Horx.
The brain, the cause of – and solution to – all of life’s problems. According to our brains it is the most fascinating structure in the known universe. Consisting of about 86 billion neurons of which each can form thousands of connections to other neurons it is also the most complex structure in the known universe. In this course we would like to give you a rough guide and introduction to the basic principles, fundamental theories, and methods of neuroscience.
We will demonstrate that neuroscience can be seen as a multi-modal, multi-level, multi-disciplinary research framework that aims at addressing the challenges of this megalomaniac scientific endeavor. We will see that different frameworks and methods can lead to conflicting empirical evidence, theoretical assumptions, and heated debates. However, we argue that this might be the only way to uncover the mysteries of our brain.Read more about our course.
Finally, I was asked for an interview in the children’s television show okidoki. A childhood’s dream come true :) I was asked to explain what happens in the brain and the body when we are afraid. A very suitable topic for Halloween, I guess.
Anna-Lisa Schuler has been working on our CREAM data and found out that people with stronger brain connectivity of the dopaminergic midbrain also show higher levels of creativity. She did her analysis on resting state data, this means, at that time our participants did not perform any specific task. Instead, while lying inside our comfortable MRI scanner, they engaged in daydreaming. Karl Friston has called this state of mind unconstrained cognition because people are not distracted by our experimental stimuli.
Trends are not always a good thing. Sometimes trends can obscure the things that are really important. A common problem in signal processing is that measured data can be affected by signal drifts – for example, due to temperature changes in your sensor or the thing that you try to measure. To get rid of these drifts the signal can be detrended. This filtering is a standard data processing step for many applications.
However, in real-time fMRI we need to perform this detrending online, that is, while we acquire the data. This is not so trivial, so Rotem Kopel, Frank Scharnowski, and I wrote a paper about it.
Introduction. A ubiquitous challenge for neuroimaging and other forms of empirical research is the organization and quality assessment of collected data. Nowadays there are excellent standards for organizing datasets, such as BIDS (Gorgolewski et al., 2017)and the OpenfMRI format (Poldrack et al., 2013). These structured datasets are optimized for machine-processability, enabling the use of automated scripts for safe and secure data storage, efficient data processing using computational pipelines, and intuitive collaboration between labs. However, before data actually conforms with the a prioridefined syntax and semantics, data needs to be manually transferred and rearranged in an unstructured and insecure fashion across different systems and air-bridged devices, such as an MRI scanner, lab bench, and online questionnaire servers (Figure 1A). Typical solutions for this problem are (a) relying that all lab members are able to responsibly organize their data independently (the hoping for the bestapproach), (b) all data is handed over to one or a few data science experts who are responsible for data management and assessment (the hoping that they will never leave the labapproach), and (c) hybrid approaches where data experts define strict policies on how to organize the data and expect all members (including those with different project requirements) to follow these guidelines (the creating lots of frustration on both sidesapproach). To a different degree, all of these options entail problems such as potential data loss, hard to manage data security and backup strategies, and storage policies that are too inflexible to be applicable for all types of studies. In interdisciplinary teams, it cannot be expected that the required data management skills and coding competences are present in all lab members. This applies in particular for, e.g., the new lab member with a background in molecular psychiatry or the neurophenomenology full professor who cannot be expected to run obscure bash scripts on the lab server to monitor the project’s progress. This was the motivation for developing sweetDatathat provides a user-friendly, efficient, modular, and open framework for management of raw source data.
Results. Currently, sweetData supports the management of text, DICOM, and NIFTI files. A development snapshot of sweetData is provided online (http://www.sweetneuron.at/wp/sweetdata/) and collaboration in this project, in particular to add new data formats (e.g., EEG data, MAT files) is highly encouraged.
Conclusions. With an ever-increasing number of files and heterogeneous data sources, robust and practicable solutions for project data management are highly relevant. Standardized fMRI reporting formats and international collaborations require the use of structuralized and reproducible forms of data management. While software exists for validating if a project conforms to BIDS, a more general form of data validation to customized schemas optimized for source data has been missing. sweetData can provide an interface to enable translating heterogenous forms of source data into a self-defined, well-ordered, structured project format. Finally, these datasets can be converted to other standardized data management schemas.
Figure 1A. sweetData workflow.Typically, source data is manually collected (e.g., via USB drives) from heterogeneous data sources. Then, data can be ordered and validated using sweetData. If successfully validated, this data can be converted easily to other data organization formats, such as BIDS, using re-usable scripts. B. sweetData architecture.sweetData can run within a modern web browser or as an electron stand-alone application. Modularity of user interface, client logic, server, and data storage allow for distributed implementations, if needed. The client software requests information, such as a representation of the project’s file/object tree, via the server’s API. The result is delivered as a JSON object that can be parsed by the client.
Gorgolewski, K.J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capota, M., Chakravarty, M.M., Churchill, N.W., Cohen, A.L., Craddock, R.C., Devenyi, G.A., Eklund, A., Esteban, O., Flandin, G., Ghosh, S.S., Guntupalli, J.S., Jenkinson, M., Keshavan, A., Kiar, G., Liem, F., Raamana, P.R., Raffelt, D., Steele, C.J., Quirion, P.O., Smith, R.E., Strother, S.C., Varoquaux, G., Wang, Y., Yarkoni, T., Poldrack, R.A., 2017. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput Biol 13, e1005209.
Poldrack, R.A., Barch, D.M., Mitchell, J.P., Wager, T.D., Wagner, A.D., Devlin, J.T., Cumba, C., Koyejo, O., Milham, M.P., 2013. Toward open sharing of task-based fMRI data: the OpenfMRI project. Front Neuroinform 7, 12.