Prefrontal cortical mechanisms underlying individual differences in cognitive flexibility and stability

Contains fulltext : 102813-OA.pdf (publisher's version ) (Open Access)The pFC is critical for cognitive flexibility (i.e., our ability to flexibly adjust behavior to changing environmental demands), but also for cognitive stability (i.e., our ability to follow behavioral plans in the face of distraction). Behavioral research suggests that individuals differ in their cognitive flexibility and stability, and neurocomputational theories of working memory relate this variability to the concept of attractor stability in recurrently connected neural networks. We introduce a novel task paradigm to simultaneously assess flexible switching between task rules (cognitive flexibility) and task performance in the presence of irrelevant distractors (cognitive stability) and to furthermore assess the individual "spontaneous switching rate" in response to ambiguous stimuli to quantify the individual dispositional cognitive flexibility in a theoretically motivated way (i.e., as a proxy for attractor stability). Using fMRI in healthy human participants, a common network consisting of parietal and frontal areas was found for task switching and distractor inhibition. More flexible persons showed reduced activation and reduced functional coupling in frontal areas, including the inferior frontal junction, during task switching. Most importantly, the individual spontaneous switching rate antagonistically affected the functional coupling between inferior frontal junction and the superior frontal gyrus during task switching and distractor inhibition, respectively, indicating that individual differences in cognitive flexibility and stability are indeed related to a common prefrontal neural mechanism. We suggest that the concept of attractor stability of prefrontal working memory networks is a meaningful model for individual differences in cognitive stability versus flexibility.15 p

Stochastic dynamics underlying cognitive stability and flexibility

Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences

High-resolution variations in size, number and arrangement of air bubbles in the EPICA DML (Antarcitca) ice core

ABSTRACT. We investigated the large-scale (10–1000m) and small-scale (mm–cm) variations in size, number and arrangement of air bubbles in the EPICA Dronning Maud Land (EDML) (Antarctica) ice core, down to the end of the bubble/hydrate transition (BHT) zone. On the large scale, the bubble number density shows a general correlation with the palaeo-temperature proxy, d18O, and the dust concentration, which means that in Holocene ice there are fewer bubbles than in glacial ice. Small-scale variations in bubble number and size were identified and compared. Above the BHT zone there exists a strong anticorrelation between bubble number density and mean bubble size. In glacial ice, layers of high number density and small bubble size are linked with layers with high impurity content, identified as cloudy bands. Therefore, we regard impurities as a controlling factor for the formation and distribution of bubbles in glacial ice. The anticorrelation inverts in the middle of the BHT zone. In the lower part of the BHT zone, bubble-free layers exist that are also associated with cloudy bands. The high contrast in bubble number density in glacial ice, induced by the impurities, indicates a much more pronounced layering in glacial firn than in modern firn

Fitted parameter set.

<p>Fitted parameter set.</p

Localization of the rule module.

<p>Using the predicted BOLD time courses for the modeled rule module, we were able to localize it to the left inferior frontal junction (IFJ) and the left intraparietal sulcus (IPS), both known to be crucially involved in task switching and distractor inhibition. Weaker activity is observed in the superior frontal gyrus (SFG). The single-voxel threshold of <i>p</i> = 10^–7 corresponds to <i>p</i> = 0.05 Bonferroni corrected for the total number of voxels.</p

Relationship between physiological parameters and dynamic potential landscape of the rule module.

<p>(A) Potential landscape of the rule module on the phase space spanned by the synaptic gating variables (S1, S2) of the two rule-selective populations with standard parameters <i>s</i>_<i>NMDA</i> = 1.0,<i>s</i>_<i>GABA</i> = 1.0,<i>σ</i>_<i>rule</i> = 0.1. (B-D) Changes in the potential landscape of the rule module relative to that standard parameters, i.e., depending on individual increases of (B) <i>s</i>_<i>NMDA</i> = 1.005, (C) <i>s</i>_<i>GABA</i> = 1.005, and (D) <i>σ</i>_<i>rule</i> = 0.15. (E) Potential along the paths corresponding to the minimal action transition from the spontaneous state to a high-activity state for the parameter combinations from A-D. The minimal actions required for the transitions are given in the legend. (F) Potential along the paths corresponding to the minimal action transition from a high-activity state to the spontaneous state for the parameter combinations from A-D. The minimal actions required for the transitions are given in the legend. (G) Potential along the paths corresponding to the minimal action transition between the two high-activity states for the parameter combinations from A-D. The minimal actions required for the transitions are given in the legend.</p

Generation of fMRI BOLD timecourses from fitted models.

<p>(A) Simulated energy consumption of the rule module. Simulated trials were sorted by condition and decision taken, and the average integral of the sum of the spiking rates <i>r</i>₁+<i>r</i>₂ over the course of a trial was calculated. This estimate of the energy consumption was normalized relative to the average spiking rate integral of a correct baseline trial. (B) Depending on the condition and decision recorded in the individual participants’ behavioral logs, the corresponding energy estimates were placed on a timeline. The resulting time course was z-scored. (C) The normalized timecourse estimating the neural energy consumption was then convolved with a canonical hemodynamic response function (as derived from the SPM8 software package), resulting in (D) a predictor timeseries representing the activation of the rule module over the course of the experiment, individually for each participant. (E) This regressor was entered into a multi-univariate GLM of the functional MRI data, together with regressors for each individual ambiguous trial and with six motion regressors derived from the coregistration of the functional MRI volumes (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004331#sec018" target="_blank">Methods</a> section, “Generation and Localization of fMRI Timeseries from Fitted Models”).</p

Reconstructed potential landscapes.

<p>(A/B) Color map of the reconstructed potential <i>U</i> = -ln<i>P</i>_<i>ss</i> on the phase space spanned by the synaptic gating variables (S1, S2) of the rule selective populations for two representative subjects (subjects 26 and 9). The green line indicates the transition from the rule 1 to the rule 2 attractor that minimizes the path-integral action. The red line corresponds to a transition in the opposite direction. (C/D) Surface plot of the reconstructed potential landscapes. Note the deeper and steeper basins of attraction for subject 9. (E/F) Plot of the potential along the transition from the rule 1 to the rule 2 attractor that minimizes the path-integral action (green) and back (red). The individually fitted noise parameters (<i>σ</i>_<i>rule</i>) for each subject were scaled by a factor of 10 for easier visualization.</p