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C4 - Cognitive flexibility vs. stability: Imaging genetics studies of the dual-state model

Principal investigator(s):

Cognitive Neuroscience Lab
Department of Psychology
Goethe University Frankfurt am Main
Theodor W. Adorno-Platz 6
60323 Frankfurt am Main


Projects within the BCCN:

Subproject C4 had the overarching goal of investigating the antagonistic nature of cognitive flexibility and stability, as well as investigating neural bases underlying this antagonism and individual differences in cognitive flexibility, by taking into account differences in D1- vs. D2-dopamine receptor mediated signaling in prefrontal circuits. As a first step, we developed and evaluated a novel paradigm that allows us to systematically investigate flexibility vs. stability within one task (Armbruster et al. 2012). A specific feature of this paradigm is that it also quantifies individual differences in cognitive flexibility/stability through a novel readout, i.e., the spontaneous switching rate. In unpublished behavioral follow-up studies, we could demonstrate that this behavioral phenotype is reliable and therefore very well suited for individual differences research, which for example makes it an interesting candidate for clinical research. This paradigm was particularly closely modeled after the computationally explicit Dual State Theory of Durstewitz and Seamans, which provided an important basis for later computational modeling of the task (see below). In collaboration with project C8, a translational animal version of this task was established (Richter et al. 2014). This translational work is particularly interesting as it replicated some of the critical behavioral findings in a different species, i.e., mice.
Based on a large-scale human fMRI sample (acquired together with projects C5-C7), we conducted mechanistic investigations of the neural bases underlying flexibility/stability, identifying the inferior frontal junction area (IFJ) in posterior prefrontal cortex and its connectivity profile as an important neural determinant of individual differences in cognitive stability vs. flexibility (Armbruster et al., 2012). Specifically, functional coupling between IFJ and superior PFC was antagonistically associated with individual flexibility (spontaneous switching rate) depending on task demands (flexibility vs. stability), which makes the IFJ a candidate region for further investigations of individual differences in flexibility vs. stability. We subsequently developed new data analysis strategies for investigating task-dependent differences in intra-individual brain signal variability. Using this approach, we demonstrated that individual differences in IFJ BOLD signal variability determine the efficiency of flexible vs. stable behavior and cognition (Armbruster-Genc et al., 2016), which may provide a mechanistic account of individual differences in this domain. Analyses of genetic data are currently in progress and will allow us to provide a better understanding of dopaminergic bases of cognitive flexibility and stability.
In parallel, we established a neurocomputational model to explain behavior and brain activation patterns in the flexibility/stability task in biophysical and neurodynamical terms. This model formalizes task sets as attractor-based working memory processes (Ueltzhöffer et al. 2015). In this model, cognitive flexibility vs. stability depends on the dynamics of switching between these memory states. It reproduces the empirically observed pattern of behavior of human participants with respect both to decisions and response time distributions at high precision. Furthermore, by relating model parameters back to the brain imaging (fMRI) data acquired from our human participants, we could also model the time course of the rule-representing network and use it as a basis for model-based analysis of our fMRI data. Using this approach, we could localize the rule-representing module of our computational model to a frontoparietal network that includes the IFJ – which further contributes to a computationally explicit theory of cognitive flexibility and the role of the IFJ. The code of the neurocomputational model is publicly available.
Beyond this, we have furthermore contributed the methodological expertise established in C4 to a number of studies that investigated higher cognitive processes closely related to the mechanisms investigated in C4, which resulted in further publications (Galeano Weber et al., 2017; Hilger et al., 2017). The results of this project will be the basis for further research related to cognitive stability and flexibility. For example, in the context of the DFG-funded Collaborative Research Center (SFB) 1193 “Neurobiology of Resilience to Stress-Related Dysfunction”, we explore the role of cognitive flexibility for mental health in the face of stress.

Participating groups:

Key publications:

Galeano Weber E, Hahn T, Hilger K, Fiebach CJ (2017) Distributed patterns of occipito-parietal functional connectivity predict the precision of visual working memory. NeuroImage, 146, 404-418 .
Hilger K, Ekman M, Fiebach CJ, Basten U (2017) Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence. Intelligence, 60, 10-25 .
(2016) Neural variability differentially affects cognitive flexibility and cognitive stability. Journal of Neuroscience, 36, 3978-3987 .
(2015) Stochastic dynamics underlying cognitive stability and flexibility. PLOS Computational Biology, e1004331 .
(2012) Prefrontal cortical mechanisms underlying individual differences in cognitive flexibility and stability Journal of Cognitive Neuroscience. 24, 2385-2399 .