Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.

Dopamine underlies important aspects of cognition, and has been suggested to boost cognitive performance.However, how dopamine modulates the large-scale cortical dynamics during cognitive performance has remained elusive. Using functional MRI during a working memory task in healthy young human listeners (N=22), we investigated the effect of levodopa (L-dopa) on two aspects of cortical dynamics, blood oxygen-leveldependent (BOLD) signal variability and the functional connectome of large-scale cortical networks. We here show that enhanced dopaminergic signaling modulates the two potentially interrelated aspects of large-scale cortical dynamics during cognitive performance, and the degree of these modulations is able to explain interindividual differences in L-dopa-induced behavioral benefits. Relative to placebo, L-dopa increased BOLD signal variability in task-relevant temporal, inferior frontal, parietal and cingulate regions. On the connectome level, however, L-dopa diminished functional integration across temporal and cingulo-opercular regions. This hypointegration was expressed as a reduction in network efficiency and modularity in more than two thirds of the participants and to different degrees. Hypo-integration co-occurred with relative hyper-connectivity in paracentral lobule and precuneus, as well as posterior putamen. Both, L-dopa-induced BOLD signal variability modulation and functional connectome modulations proved predictive of an individual's L-dopa-induced gain in behavioral performance, namely response speed and perceptual sensitivity. Lastly, L-dopa-induced modulations of BOLD signal variability were correlated with L-dopa-induced modulation of nodal connectivity and network efficiency. Our findings underline the role of dopamine in maintaining the dynamic range of, and communication between, cortical systems, and their explanatory power for inter-individual differences in benefits from dopamine during cognitive performance.Dopaminergic neurotransmission supports cognitive functions, such as flexible updating and stable maintenance of working memory (Goldman-Rakic, 1995;Wang et al., 2004;Vijayraghavan et al., 2007;Cools and D'Esposito, 2011;Kobayashi et al., 2017). Dopamine (DA) plays an important role in modulating synaptic strengths of cortico-striatal pathways that subserve a wide range of cognitive functions (Reynolds and Wickens, 2002). Interestingly, a mere increase of DA level is not beneficial in every individual, and can even be detrimental to task performance depending on individuals' baseline DA and cognitive performance (Cools and Robbins, 2004; for a review, see Cools and D'Esposito, 2011).How changing the amounts of DA impacts the cortical dynamics at large scale, and importantly, its relation to individuals' cognitive performance, has remained unclear. Here, using a double-blind Ldopa vs. placebo intervention during functional MRI, we investigate how DA increase modulates large-scale cortical dynamics during cognitive performance, and whether this modulation can explain intermittent...

The default mode network (DMN), a network centered around the cortical midline, shows deactivation during most cognitive tasks and pronounced resting-state connectivity, but is actively engaged in self-reference and social cognition. It is, however, yet unclear how information reaches the DMN during social cognitive processing. Here, we addressed this question using dynamic causal modeling (DCM) of functional magnetic resonance imaging (fMRI) data acquired during self-reference (SR) and reference to others (OR). Both conditions engaged the left inferior frontal gyrus (LIFG), most likely reflecting semantic processing. Within the DMN, self-reference preferentially elicited rostral anterior cingulate and ventromedial prefrontal cortex (rACC/vmPFC) activity, whereas OR engaged posterior cingulate and precuneus (PCC/PreCun). DCM revealed that the regulation of information flow to the DMN was primarily inhibitory. Most prominently, SR elicited inhibited information flow from the LIFG to the PCC/PreCun, while OR was associated with suppression of the connectivity from the LIFG to the rACC/vmPFC. These results suggest that task-related DMN activation is enabled by inhibitory down-regulation of task-irrelevant information flow when switching from rest to stimulus-specific processing.