What can we do with functional MRI data? For many years, fMRI has been used to discover population-level patterns of brain function, organization and connectivity, and to understand differences in those patterns across populations or due to treatment, disease, aging or development. Yet there are many things fMRI has yet to achieve in a widespread way. Biomarker development, clinical care, and therapeutical trials are all contexts where the promise of fMRI tends to exceed its real impact. One of the reasons for this gap is the inability of many conventional statistical methods to overcome the high noise levels of fMRI and produce accurate functional brain measures in individuals. At the same time, technological advances in fMRI acquisition and processing have been enormous in recent years, along with large-scale data sharing initiatives. The result is an "embarrassment of riches" in terms of fMRI data quantity and quality. There is a need for scientifically appropriate statistical methods to make the most of this rich data landscape.

The focus of the StatMIND lab is to develop advanced yet practical statistical techniques for fMRI data (implemented in user-friendly software) that are optimized to enhance accuracy and power to extract reliable and relevant functional brain features in individuals. We ultimately aim to advance the generalizability of fMRI studies to more diverse populations. We pursue this aim through close collaborations with scientists working in a range of domains, including neurodegenerative disease, neonatal development, and psilocybin therapy.

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