Distinguishing resilience from susceptibility: Brain network alterations during fear learning in response to...

Not all individuals exposed to childhood unpredictability develop psychopathology. This study breaks new ground by identifying the neural network signatures that distinguish resilience from susceptibility following such adversity. Using fear learning paradigms, we acquired fMRI data from 213 participants and constructed whole-brain functional connectivity networks. Innovatively, we employed dual resilience metrics-psychological (depressive symptoms) and biological (inflammatory TNF-alpha levels)-to classify participants into resilient and susceptible groups. Graph theory analysis found that the resilient group exhibited a sparser global network structure compared to the susceptible group. At the nodal level, the susceptible group showed increased nodal degree centrality in eight brain regions across multiple functional networks, compared to both the resilient and control groups. Furthermore, network-based statistic revealed that the resilient group demonstrated stronger connectivity between the default mode network (DMN) and both the frontoparietal and attention networks during fear learning. In contrast, susceptibility was associated with stronger connectivity between the visual network (VN), dorsal attention network (DAN), DMN, and frontoparietal network (FPN), alongside weaker connectivity within and between the DMN, DAN, and FPN. Importantly, network connectivity-based models predicted an individual's classification into the resilient or susceptible group with 89 % accuracy. Our findings uncover abnormal connectome organization within the DMN, DAN, FPN, and VN, shedding light on the neural mechanisms underlying vulnerability and resilience in the context of childhood unpredictability. By identifying these neurobiological markers of resilience, our work opens new avenues for early identification of vulnerability and development of targeted interventions to promote adaptive brain functioning in at-risk populations.