Donnerstag, 09. Januar 2020, 15:00 - 16:30 iCal

Talk of Dr. Romy Lorenz

Towards a neurobiologically-derived cognitive taxonomy

Faculty of Psychology, Lecture hall G, 2nd floor (left wing of the building)
Liebigasse 5, 1010 Wien


The classic taxonomy of cognitive processes was developed largely blind to the functional organization of the brain; therefore, classic cognitive tasks tend to tap multiple cognitive processes that involve multiple brain networks. Resolving this many-to-many mapping problem between cognitive tasks and brain networks is practically intractable with standard fMRI methodology as only a small subset of all possible cognitive tasks can be tested. This is problematic, as studying only a fraction from the large task space can result in over-specified inferences about functional-anatomical mappings with a misleadingly narrow function being proposed as the definitive role of a network, concealing the broader role each network may play in cognition.

In this talk, I present an alternative approach that resolves these problems by combining real-time fMRI with a branch of machine learning, Bayesian optimization. Neuroadaptive Bayesian optimization is a powerful strategy to efficiently explore more experimental conditions than is currently possible with standard methodology. I will present results from a study where we used this method to identify the exact cognitive task conditions that optimally dissociate frontoparietal brain networks. Our findings deviate from previous meta-analyses and hypothesized functional labels for these frontoparietal brain networks. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy.

In addition, I will touch on the potential of the approach for accelerated biomarker discovery by presenting a small pilot study involving aphasic stroke patients. Finally, I will show how the approach can be used to tailor non-invasive brain stimulation (e.g., tACS) protocols to particular research questions or individual patients. I will conclude my talk in discussing how Bayesian optimization can be combined with preregistration to cover exploration, mitigating researcher bias more broadly and improving reproducibility.



Department of Basic Psychological Research and Research Methods (Unit of Research Methods)


Abla Marie-José Bedi
Department of Basic Psychological Research and Research Methods