Non-invasive functional magnetic resonance imaging (fMRI) has revolutionized our understanding of macroscopic functional brain networks. However, inherent constraints of current fMRI methodologies in humans limit our ability to probe the mechanisms underlying these networks. The overarching goal of this project is to shed light on cellular and circuit mechanisms underlying the functional organization of the default-mode network (DMN) – a large-scale brain network that is crucial for a wide range of behaviors. While the new technologies in rodents allows us to experimentally reveal causal control of DMN, rodent DMN topology has only been defined using resting-state fMRI, but not functionally in terms of activation or suppression of brain activity in response to behaviorally relevant salient stimuli. This represents a critical barrier preventing any straightforward translation between rodent and human DMN research findings. To address this, we developed a novel silent zero-echo- time (ZTE) fMRI technique, enabling awake rodent imaging and the use of an auditory oddball paradigm, wherein deviant oddball stimuli presented amongst a sequence of repetitive control stimuli can drive attention and suppress DMN. We also developed an MR-compatible, four-channel, spectrally-resolved fiber-photometry system, allowing concurrent recording of ground-truth neuronal activities during fMRI. To shed light on the circuit mechanisms governing the DMN, we proposed two complementary research Aims building on our rigorous prior research. In Aim 1, we will determine how attention to salient stimuli alters DMN activity and connectivity using the novel ZTE-photometry platform. In Aim 2, we will introduce time-locked optogenetics on defined cell types to causally manipulate the activity of anterior insula – the brain region assumed to be responsible for DMN dynamic switching in numerous fMRI causal modeling studies. Functionally dissecting the rodent DMN architecture is critical to the understanding of DMN transition mechanisms, which will enable us to causally model, and make predictions about brain states, bringing insight into the network basis of human behavior and neuropsychiatric/neurological disorders.
