To overcome limitations with neuroimaging work in small clinical samples, large-scale data collection and data pooling initiatives have been established and are ongoing. These “big data” initiatives aim to maximize sample size and are anticipated to transform our neurobiological understanding of psychiatric disorders. A complementary—yet much less appreciated in the context of clinical neuroimaging—form of big data is the acquisition of large amounts of data over time for a small(er) number of individuals, or dense sampling. We argue such dense sampling of neuroimaging data is needed in psychiatry to provide essential information about the neurobiological foundations of what are highly dynamic disorders of brain function. As with densely sampled psychological data [1, 2], tying densely sampled neuroimaging data collection to clinically meaningful transitions, such as standard phases of clinical care of an individual, can, we suggest, help maximize mechanistic insight and lead to improved translation of findings to treatment development, as highlighted here within the specific context of addiction.

While informative and efficient, the necessary focus of case-control studies on static, group-average deviations from a presumed “normative” population may preclude their real-world clinical utility. For example, recent machine learning studies of addictions indicate that brain networks which distinguish patients from controls are often distinct from those that predict specific clinical outcomes within-group [3, 4]. This striking distinction suggests person-specific neurobiology is dissociable from group-specific patterns; as such, group-specific findings may not translate to improved understanding of person-specific pathophysiology or to treatment.

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