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Spontaneous thoughts are heterogeneous and inherently dynamic. Despite their time-variant properties, studies exploring spontaneous thoughts have identified thematic patterns that exhibit trait-like characteristics and are stable across time. Concurrently, structural and functional neuroimaging studies have shown unique and stable whole-brain network configurations linked to behaviour either through the static and dynamic intrinsic communication and activity of their core regions or through informational exchange with each other. This thesis aimed to explore how these within and between network interactions at different temporal scales might relate to variations in ongoing experience. We utilised different neuroimaging modalities (diffusion weighted and functional magnetic resonance imaging) and applied both static and dynamic analyses techniques. We found evidence of inter-individual variation in all cases associated with different patterns of spontaneous thoughts. Experiment 1 found that variation in white matter architecture projecting to the hippocampus, as well as the stable functional interaction of the hippocampus with the medial prefrontal cortex were linked to the tendency of experiencing thoughts related to the future or the past. Experiment 2 found that static functional connectivity of the precuneus and a lateral fronto-temporal network was related to visual imagery. Furthermore, we found that coupling of a lateral visual network with regions of the brainstem and cerebellum was associated with ruminative thinking, self-consciousness and attentional problems. Importantly, our results highlighted an interaction among these associations, where the brainstem visual network coupling moderated the relationship between parietal-frontal regions and reports of visual imagery. Finally, Experiment 3 used hidden Markov modelling to identify dynamic neural states linked to thoughts related to problem-solving and less intrusive thinking, as well as better physical and mental health. Collectively, these studies highlight the utility of using both static and dynamic measures of neural function to understand patterns of ongoing experience.