Integrated strategy for improving functional connectivity mapping using multiecho fMRI

By Prantik Kundu, Noah D. Brenowitz, Valerie Voon, Y. Worbe, Petra E. Vértes, Souheil J. Inati, Ziad S. Saad, Peter A. Bandettini, E. Bullmore. Proceedings of the National Academy of Sciences of the United States of America, 2013

411 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/61efe43f549e13f7a32b9fb2c13906d49990a44a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Recent progress and outstanding issues in motion correction in resting state fMRI (2014, positive): “In another study (Kundu et al., 2013), data were acquired at 4 TEs (12, 28, 44, and 60 ms) in order to partition the data into ICA components whose signal reflects artifactual S0 changes (such as those caused by motion) versus relaxation rate changes (such as those caused by neural activity or chang”
Methods for cleaning the BOLD fMRI signal (2016, neutral): “Similar to other ICA-based methods, the final step of ME-ICA is to regress out from the data those ICs that are classified as noise (Kundu et al., 2012; 2013; Olafsson et al., 2015).”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, background): “…be noted that while improvements in image acquisition (including multi-echo techniques) may not salvage existing motion-contaminated data, it is likely that they will change the methodological landscape of connectivity research moving forward (Kundu et al., 2012, 2013; Bright and Murphy, 2013).”
Multi‐echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals (2017, methodology): “In the PCA step in the popular probabilistic ICA method, it is assumed that BOLD signals have autocorrelated (e.g. repeating or self-similar) fluctuations over time (Beckmann and Smith, 2004).”
An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI (2017, methodology): “Alternative, prospective methods for motion correction that we did not consider include acquiring multi-echo data (Kundu et al., 2013) and actual monitoring of head motion in the scanner (Herbst et al.”
Graph analysis of functional brain networks: practical issues in translational neuroscience (2014, background): “In the latter cases, referring to “graphs”, which do not make explicit assumptions on the nature of the links but rather emphasize the aspect of mathematical modeling, seems to be more appropriate and can be used to improve the interdisciplinary exchange.”
Inscapes: a movie paradigm to improve compliance in functional magnetic resonance imaging (2015, methodology): “…approach that would include the use of optimized MRIacquisition sequences to decrease the effects of headmovement on signal (Bright and Murphy, 2013; Kundu et al., 2013, 2015) and advanced post-processing techniques for motion correction (Murphy et al., 2013; Yan et al., 2013; Pruim et al., 2015).”