Work Package 1

Individual resilience learning after stress: Insights from behavioral pattern analyses and brain activity measures.

Work Package 1 (WP1) aims to study the effects of stress and develop a more refined classification of behavioral phenotypes beyond the current state-of-the-art. Our research objectives are:

This work will provide high-dimensional data-sets on individual mouse behavior and aims at classifying individuals beyond the current state-of-the-art.

People: Marianne Müller, Beat Lutz, Stefan Remy.

Work Package 2

Promoting plasticity-mediated self-balancing of cortical and hippocampal network states in stress resilience.

The optimal regulation of stress responses is important to prevent stress-related dysfunctions. Recent research suggests that there are distinct states of local ensemble activity in layers II/III of the mouse cortex that represent plasticity-driven, rather discrete, active shifts of the functional state. The ability to transition between these states is intrinsic to the cortical network and is linked to the phenotype of cognitive flexibility.
Based on the knowladge from previous works, we ask, whether resilient behavior - mirrored by a distinct set-point of local cortical, striatal, and hippocampal ensemble activity - is causally linked to cognitive flexibility. In WP2, we aim to:

To achive this, our team will conduct longitudinal 2-photon calcium imaging and electrophysiological recordings using flexible cortical electrode arrays in primary visual cortex and fronto-motor cortex. Our team will then test cognitive flexibility by switching the contingencies of visual or acustic stimuli to reward once they reach criterion and analyze the low dimensional manifold that can explain differences in the network activity of resilient and non-resilient mice using neural network simulations and advanced data analysis techniques.

People: Tatjana Tchumatchenko, Michael Lippert, Frank Ohl, Albrecht Stroh.

Work Package 3

Brain-wide network state transitions associated with cognitive flexibility in the framework of stress resilience.

Brains operate in different states. Within the brain networks, unique functional signatures are thought to give rise to individual response signatures. These brain state signatures are expected to be different in cognitive flexibility and response to social memory and stress. Yet learning may shape them as well. We will examine the global brain network using functional magnetic resonance imaging (MRI), single-photon emission tomography (SPECT) of cerebral blood flow, neuronal population recordings, and immediate early gene (IEG) expression in mice performing cognitive flexibility tasks and processing of social information. Ultimately, we will gain a better understanding how these processes are dynamically modulated by stress resilience of the individual.

People: Sarah Ayash, Renée Hartig, Wolfgang Kelsch, Eike Budinger, Jürgen Goldschmidt.

Work Package 4

Modelling network attractor states of resilience to guide interventions.

In this WP, we explore the hypothesis that attractor states underlying resilient behavior can be encoded in local neuronal ensemble activity. We propose that learning processes enable the network to return to these set-points as a way to rebalance in response to dysregulation. We will identity differences in these attractor states in normal and dysregulated network dynamics from experimentally recorded activity patterns in different brain circuits and across spatial scales. Characterizing the temporal evolution of attractors states will enable us to describe the changing energy landscape as the networks learn and undergo activity-dependent plasticity. We will use our computational network models to determine key network elements which establish and modify network attractor states in response to dysregulation, and identify plasticity mechanisms which return dynamics to normal set-points.

People: Julijana Gjorgjieva, Sebastian Stober.