Our project has made important progress in both furthering our understanding of the nature of ongoing thought, as well as understanding the underlying mechanisms. Our study has highlighted that patterns of neural activity in the default mode network can dissociate individuals in terms of their patterns of ongoing thought. Critically, we found that these patterns of individual difference made unique predictions for individuals associated functional outcomes. This observations suggests that the way that neural processing occurs in the default mode network may be important in the ability to discriminate different types of ongoing thought, providing a potential neural mechanism that may underpin the different types of experience. This work is published in Psychological Science.
In a similar vein, our work on the macroscale patterns of neural function has highlighted that the default mode network, rather than being a task negative network, can instead be understood as a set of regions that sit at the top of a cognitive hierarchy. This observation, published in the Proceedings of the National Academy of Sciences, not only provides a mechanistic account for why this system is involved in patterns of ongoing thought, but explains why this system has a complex heterogenous role in many different cognitive states. This observation therefore provides a neurcognitive description that goes beyond the state of the art.
Moving forward, we anticipate that we will make important headway in understanding the pattern of neural activity that explains why ongoing thought can have both costs and benefits, focusing on the hypothesis that problems in attention describe why this state can impact on the performance of complex external tasks, and we will also explore the hypothesis that problems in emotional regulation explain the associations between ongoing thought and affective disturbance. Data collection and analysis for these strands of the project are currently underway. We also expect to make important advances in understanding the dynamics of ongoing thought by applying hidden markov models to understand the temporal dynamics of ongoing thought. This project is at the point of writing up. Finally, we expect to make important headway in understanding how patterns of ongoing thought can discriminate between psychiatric conditions (such as ADHD, ASD and epilepsy). These projects are currently at the point of data collection, but we anticipate that they will be completed within the next 24 months.
