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.  

Lab News

April 2024: Mandy is teaching longitudinal and Bayesian modeling at the two-week NIH-funded course in Advanced Statistics in Neuroimaging and Genetics at the University of Utah 

Spring 2024: Mandy is on sabbatical visiting the Computational Brain Imaging Group at the Center for Sleep and Cognition at the National University of Singapore

July 2023: PhD student Fatma Parlak successfully defends her thesis on robust statistical methods for fMRI.  Congratulations, Fatma!!

July 2023: Mandy promoted to Associate Professor with tenure at Indiana University

June 2023: Mandy selected as the global 2023 J&J WiSTEM2D Mathematics Fellow 

May 2023: Fatma Parlak named a student paper award finalist for the 2023 New England Statistics Symposium (NESS) in recognition of her work in robust outlier detection for scrubbing in fMRI.  Congratulations, Fatma!

April 2023: Mandy named IU Outstanding Junior Faculty  https://today.iu.edu/live/news/3155-6-named-outstanding-junior-faculty 

March 2023: Damon Pham's paper on data-driven scrubbing in fMRI to remove noise while maximizing signal retention is published in NeuroImage. Congratulations, Damon!

February 2023: Fatma Parlak awarded first place for outstanding graduate student research in physical and mathematical sciences in 2023 IU Women's Poster Competition.  Congratulations, Fatma!

January 2023: Our paper on dealing with residual autocorrelation in multiband task fMRI studies is published in Frontiers in Neuroscience

December 2022: Lab postdoc Dan Spencer accepts a position in climate forecasting at Verisk in Boston.  We wish you all the best, Dan!

October 2022: Paper with Johns Hopkins collaborators Frederick Barret, Mary Beth Nebel and others on the effects of psilocybin on thalamic organization and connectivity using lab methods is published in Neuroimage

September 2022: Mandy's paper on using cortical surface-based spatial priors to enhance accuracy and power in independent component analysis for fMRI is published in the Journal of Computational and Graphical Statistics

July 2022: Mandy's paper with collaborators Robert Welsh and Vincent Koppelmans examining neurodegeneration in ALS using spatial Bayesian task fMRI modeling is published in NeuroImage

April 2022: Damon Pham’s paper describing the R package ciftiTools, which facilitates analysis and statistical method development for surface/grayordinate neuroimaging data, published in NeuroImage

April 2022: Our paper validating surface-based spatial Bayesian modeling for task fMRI analysis using test-retest data from the Human Connectome Project is published in NeuroImage

May 2021: Mandy and University of Utah site PI Robert Welsh and co-I’s Vincent Koppelmans and Kevin Duff are awarded NIA funding to develop fMRI-based brain biomarkers for Alzheimer’s and MCI

September 2020: After a long team effort, our paper titled “Open Science, Communal Culture, and Women’s Participation in the Movement to Improve Science” has been published in PNAS

June 2020: New preprint describing our work building a spatial Bayesian template ICA model for fMRI analysis. This model provides efficient estimation and powerful inference for subject-level brain networks. “A spatial template independent component analysis model for subject-level brain network estimation and inference

October 2019: Our new paper on fast, reliable estimation of subject-level brain networks using a hierarchical Bayesian framework with empirical “big data” population priors is now in press in Journal of the American Statistical Association! “Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors

September 2019: New paper in Nature Communications with Jorge Mejia and Franco Pestilli! “Open data on industry payments to healthcare providers reveals potential hidden costs to the public”

June 2019: New paper describing our method for estimating task activation on the cortical surface of the brain using spatial Bayesian modeling with INLA just published in Journal of the American Statistical Association! “A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis”

January 2019: Mandy is awarded an NIH grant 1R01EB027119-01 on Bayesian methods for cortical surface neuroimaging data with co-investigators Martin Lindquist and Mary Beth Nebel

December 2018: Mandy is awarded a ASA Biometrics Section JSM 2019 travel award for the paper “A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis,” joint work with Ryan Yue, David Bolin, Finn Lindgren and Martin Lindquist

 Logo and sticker design by Damon Pham.