Decoding Fear or Safety and Approach or Avoidance by Brain-Wide Network Dynamics

Discerning safety from threat and positive or negative outcomes of adversity are fundamental for mental health. Many brain structures have been implicated in both adaptive and maladaptive stress coping, however, how multiple regions function together as a network in the processing of this information is unclear. Here, we recorded local field potentials from seven regions of the mesolimbic-hippocampal-prefrontal cortical network (MLHFC) of male rats during the conditioning of a stimulus (CS) to the absence (safety) and then to the anticipation (fear) of footshocks, and during an approach-avoidance task. We developed a machine learning pipeline to investigate the relevance of specific features of oscillatory activity in the decoding of fear versus safety and approach versus avoidance. We found that decoding performance increased as a function of the number of brain regions included, reaching the best classification if all regions were considered. In addition, the best decoding was obtained from frequencies within the theta range (4-10 Hz). Remarkably, decoder models showed robust generalization within but not between individuals. Nevertheless, we were also able to identify patterns of MLHFC activity that decoded stress coping states from all rats. These patterns were characterized by increased brain-wide theta synchrony during fear and preceding approach. Our results indicate that stress coping information is encoded at the brain-wide level and highlight individual variability in this neural processing. Our findings also suggest that MLHFC network theta activity underlies active stress coping with both aversive and positive motivational valences. SIGNIFICANCE STATEMENTThe appraisal of safety versus threat and positive versus negative valence of adversity are core dimensions of emotional experience and stress coping. We developed a new behavioral protocol that discriminates states of fear, safety, approach, and avoidance in a single subject and a machine learning-based method to investigate how neural oscillations can decode such states. Our work provides evidence that stress coping is processed at multiple regions on a brain-wide level involving network oscillations at the theta frequencies, which especially synchronizes during fear and approach. We highlight the potentials of combining artificial intelligence and multi-site electroencephalography to guide therapeutic decisions in precision psychiatry and theta-boosting stimulation therapies for stress-related disorders, especially related to cognitive and motivational deficits.