Enhanced identification of BOLD-like components with multi-echo simultaneous multi-slice (MESMS) fMRI and multi-echo ICA

The recent introduction of simultaneous multi-slice (SMS) acquisitions has enabled the acquisition of blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) data with significantly higher temporal sampling rates. In a parallel development, the use of multi-echo fMRI acquisitions in conjunction with a multi-echo independent component analysis (ME-ICA) approach has been introduced as a means to automatically distinguish functionally-related BOLD signal components from signal artifacts, with significant gains in sensitivity, statistical power, and specificity. In this work, we examine the gains that can be achieved with a combined approach in which data obtained with a multi-echo simultaneous multi-slice (MESMS) acquisition are analyzed with ME-ICA. We find that ME-ICA identifies significantly more BOLD-like components in the MESMS data as compared to data acquired with a conventional multi-echo single-slice acquisition. We demonstrate that the improved performance of MESMS derives from both an increase in the number of temporal samples and the enhanced ability to filter out high-frequency artifacts.

[1]  Bharat B. Biswal,et al.  Functional Integration Between Brain Regions at Rest Occurs in Multiple-Frequency Bands , 2015, Brain Connect..

[2]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[3]  J. Polimeni,et al.  Blipped‐controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g‐factor penalty , 2012, Magnetic resonance in medicine.

[4]  Noah D. Brenowitz,et al.  Integrated strategy for improving functional connectivity mapping using multiecho fMRI , 2013, Proceedings of the National Academy of Sciences.

[5]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[6]  Edward T. Bullmore,et al.  Differentiating BOLD and non-BOLD signals in fMRI time series from anesthetized rats using multi-echo EPI at 11.7T , 2014, NeuroImage.

[7]  Michael Erb,et al.  Comparison of longitudinal metabolite relaxation times in different regions of the human brain at 1.5 and 3 Tesla , 2003, Magnetic resonance in medicine.

[8]  Roland N. Boubela,et al.  Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest , 2013, Front. Hum. Neurosci..

[9]  S. Posse,et al.  Enhancement of BOLD‐contrast sensitivity by single‐shot multi‐echo functional MR imaging , 1999, Magnetic resonance in medicine.

[10]  Jürgen Hennig,et al.  Tracking dynamic resting-state networks at higher frequencies using MR-encephalography , 2013, NeuroImage.

[11]  Hugo R. Shi,et al.  Toeplitz-based iterative image reconstruction for MRI with correction for magnetic field inhomogeneity , 2005, IEEE Transactions on Signal Processing.

[12]  I Daubechies,et al.  Independent component analysis for brain fMRI does not select for independence , 2009 .

[13]  Chi Wah Wong,et al.  Improved detection of subcortical resting state networks in functional MRI using multi-echo simultaneous multi-slice acquisition , 2012 .

[14]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[15]  Roland N. Boubela,et al.  The Spectral Diversity of Resting-State Fluctuations in the Human Brain , 2014, PloS one.

[16]  P. Boesiger,et al.  SENSE: Sensitivity encoding for fast MRI , 1999, Magnetic resonance in medicine.

[17]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[18]  Chi Wah Wong,et al.  Improved Detection of BOLD-like Independent Components with Multi-Echo Simultaneous Multi-Slice Acquisitions and Multi-echo ICA , 2013 .

[19]  Wen-Ming Luh,et al.  Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI , 2012, NeuroImage.

[20]  P. Boesiger,et al.  Advances in sensitivity encoding with arbitrary k‐space trajectories , 2001, Magnetic resonance in medicine.

[21]  Wen-Ming Luh,et al.  High spatial and temporal resolution fcMRI with BOLD selectivity using multiecho simultaneous multislice EPI , 2011 .

[22]  Markus Barth,et al.  TR RESTING STATE DATA ACQUIRED WITH A SIMULTANEOUS MULTISLICE MULTI-ECHO SEQUENCE AT 7 T , A COMPARISON WITH MULTI-ECHO , 2012 .

[23]  Mark W. Woolrich,et al.  Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.

[24]  Stephen M. Smith,et al.  Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.

[25]  Biyu J. He.  Scale-Free Properties of the Functional Magnetic Resonance Imaging Signal during Rest and Task , 2011, The Journal of Neuroscience.

[26]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[27]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .