The focus of the StatMIND lab is developing and disseminating sophisticated yet practical statistical modeling tools for the analysis of functional MRI data. These tools are designed to optimize accuracy and power to produce robust and reliable functional brain measures in individuals and groups. We work closely with scientific collaborators to deploy these tools in a range of contexts, including neurodegenerative disease, neonatal development, and psilocybin therapy.

Template-based methods for accurate fMRI-based brain measures in individuals

Obtaining accurate functional brain measures in individuals is essential for fMRI-based biomarker development, clinical translation, longitudinal analysis, and more nuanced discovery science. High noise levels in fMRI data make this challenging. We are working to develop analysis frameworks that use empirical population priors or "templates" to guide and constrain estimation of effects in specific individuals based on population information. This empirical Bayesian approach produces estimates that represent an optimal balance between the prior information encoded in the template and the individual's observed data. The result is subject-level functional brain measures that are more reliable and accurate than those produced with naive subject-level analysis.

Spatial Bayesian models to leverage information across the brain and improve power

Across the brain, many locations tend to exhibit similar functional behavior, whether at rest or while performing a task. These may be neighboring locations along the cortex, within a subcortical structure, or distal locations belonging to the same functional brain network. Spatial Bayesian models formally account for this functional similarity through priors encoding the expected correlation between all locations in the brain. In comparison to massive univatiate approaches, these models have much higher power to detect effects and produce more accurate and reliable function brain measures. We focus on cortical surface-based and subcortical parcel-constrained models to leverage spatial dependencies without blurring across tissue classes or distal cortical areas.

Statistically principled data cleaning methods for fMRI

Functional MRI data is subject to high levels of artifacts from a myriad of sources, including subject head motion, scanner instabilities and processing errors. Eliminating or reducing these sources of noise is essential for optimizing accuracy and power in downstream analysis. Data-driven denoising and scrubbing methods detect abnormalities in the data and reduce them while preserving more signal than conventional head motion-based techniques. Our novel projection scrubbing approach utilizes ICA to isolate noise from signal, combined with multivariate outlier detection methods to flag contaminated volumes. We are working to develop robust outlier detection techniques appropriate for fMRI data to maximize sensitivity to artifacts while avoiding false flags.

Advancing research in neurodegenerative disease

Through close collaborations with experts in amyotrophic lateral sclerosis (ALS) and Alzheimer's disease (AD), we deploy sophisticated statistical methods to understand disease impacts on the function, organization and connectivity of the brain. We work to develop functional imaging biomarkers for disease diagnosis and prognosis to inform clinical trials and ultimately advance clinical care. This work is supported by NINDS, NIBIB and NIA.