41 citations · 56 across the 17 of their papers we have counts for
5 papers · 1 filter
Bayesian brain mapping: a population-informed framework for personalized functional network topography and connectivity
Diego Derman, Nohelia Da Silva Sanchez, Damon D. Pham +3
The spatial topography of functional brain organization is increasingly recognized to play an important role in cognition and disease. Accounting for individual differences in func…
Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing
Damon Pham, Daniel McDonald, Lei Ding +2
Artifacts in functional MRI (fMRI) data cause deviations from common distributional assumptions, introduce spatial and temporal outliers, and reduce the signal-to-noise ratio of th…
Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors
Amanda F. Mejia, Mary Beth Nebel, Yikai Wang +2
Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been us…
Effects of Scan Length and Shrinkage on Reliability of Resting-State Functional Connectivity in the Human Connectome Project
Amanda F. Mejia, Mary Beth Nebel, Anita D. Barber +2
In this paper, we use data from the Human Connectome Project (N=461) to investigate the effect of scan length on reliability of resting-state functional connectivity (rsFC) estimat…
Homotopic Group ICA for Multi-Subject Brain Imaging Data
Juemin Yang, Ani Eloyan, Anita Barber +5
Independent Component Analysis (ICA) is a computational technique for revealing latent factors that underlie sets of measurements or signals. It has become a standard technique in…