132 citations · 139 across the 5 of their papers we have counts for
5 papers
A Review on MR Based Human Brain Parcellation Methods
Pantea Moghimi, Anh The Dang, Theoden I. Netoff +2
Brain parcellations play a ubiquitous role in the analysis of magnetic resonance imaging (MRI) datasets. Over 100 years of research has been conducted in pursuit of an ideal brain…
Penalized model-based clustering of fMRI data
Andrew DiLernia, Karina Quevedo, Jazmin Camchong +3
Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activit…
A random covariance model for bi-level graphical modeling with application to resting-state fMRI data
Lin Zhang, Andrew DiLernia, Karina Quevedo +3
This paper considers a novel problem, bi-level graphical modeling, in which multiple individual graphical models can be considered as variants of a common group-level graphical mod…
Construction and Evaluation of Hierarchical Parcellation of the Brain using fMRI with Prewhitening
Pantea Moghimi, Kelvin O. Lim, Theoden I. Netoff
Brain atlases are a ubiquitous tool used for analyzing and interpreting brain imaging datasets. Traditionally, brain atlases divided the brain into regions separated by anatomical…
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction
Zitong Zhang, Qawi K. Telesford, Chad Giusti +2
Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals into time series representing neurophysiological activity in fixed frequency bands. Using these time series,…