A Translational Neural Network Mechanism of Resilience: Top-Down Control and Plasticity of the Visual Cortex Relates to Resilient Outcome and Performance

Abstract

To reduce mental disorder prevalence, the understanding of resilience to stress-related disorder and its neurobiological mechanisms has come into the focus of biomedical research to develop both biologically rooted prevention and innovative therapeutic approaches for stress-related disorder. While some resilience mechanisms have been exemplified on the molecular, cellular, and brain-regional level, evidence on the neural systems level is rather sparse. We present the first translational evidence of adaptive plasticity in visual microcircuits and top-down modulation onto the visual system as a neurobiological resilience mechanism at the neural systems level in both humans and mice. In humans, we demonstrate that this adaptive microcircuit plasticity is linked to interactions between neurocognitive domains-executive and perceptual-and between brain regions-frontal and occipital-in specific oscillatory frequencies (beta band in frontal inferior frontal gyrus and gamma band in occipital V2). Additionally, expanding upon prior resilience research, our findings offer further evidence that phenotypic resilience is associated not only with macro- and microcircuit plasticity but also with better performance in neurocognitive functions central to resilience, i.e., perceptual discrimination in mice and cognitive control in humans. In mice, using awake 2-photon calcium imaging, we observed distinct resilient and susceptible network phenotypes in mouse visual cortex. Resilient animals surpassed both susceptible animals and nonstressed controls in their ability to encode visual afferents. This suggests an improved performance supporting the concepts of posttraumatic growth and stress inoculation on a neurobiological level. Resilience at the neural systems level involves active, dynamic processes rather than being merely passive responses to stress and constitutes a first example that neural network states of resilience are metastable, self-stabilizing, and noncontinuous entities that could serve as a target for new neural network interventions for fostering resilience.