A comprehensive evaluation of increasing temporal resolution with multiband-accelerated protocols and effects on statistical outcome measures in fMRI

ABSTRACT Accelerated functional Magnetic Resonance Imaging (fMRI) with ‘multiband’ protocols is now relatively widespread. These protocols can be used to dramatically reduce the repetition time (TR) and produce a time‐series sampled at a higher temporal resolution, which may produce benefits in the statistical methods typically used to analyse fMRI data. We tested the effects of higher temporal resolutions for fMRI on statistical outcome measures in a comprehensive manner on two different MRI scanner platforms. Spatial resolution was maintained at a constant of 3mm isotropic voxels, and an in‐plane acceleration factor of 2 was used for all experiments. Experiment 1 tested a range of acceleration factors (1–6) against a standard EPI protocol on a single composite task that mapped a number of basic sensory, motor, and cognitive networks. Experiment 2 compared the standard protocol with acceleration factors of 2 and 3 on both resting‐state and two task paradigms (an N‐back task, and faces/places task), with a number of different analysis approaches. Results from experiment 1 showed modest but relatively inconsistent effects of the higher sampling rate on statistical outcome measures. Experiment 2 showed strong benefits of the multiband protocols on results derived from resting‐state data, but more varied effects on results from the task paradigms. Notably, the multiband protocols were superior when Multi‐Voxel Pattern Analysis was used to interrogate the faces/places data, but showed less benefit in conventional General Linear Model analyses of the same data. In general, ROI‐derived measures of statistical effects benefitted only modestly from higher sampling resolution, with greater effects seen when using a measure of the top range of statistical values. Across both experiments, results from the two scanner platforms were broadly comparable. The statistical benefits of high temporal resolution fMRI with multiband protocols may therefore depend on a number of factors, including the nature of the investigation (resting‐state vs. task‐based), the experimental design, the particular statistical outcome measure, and the type of analysis used. HIGHLIGHTSComprehensive testing of high temporal‐resolution fMRI using multiband sequencesEffects on statistical outcomes vary depending on experimental and analysis designResting‐state fMRI showed the strongest benefits of higher temporal resolutionsEquivalent effects are seen on two different scanner platforms

[1]  G. Orban,et al.  Parietal Representation of Symbolic and Nonsymbolic Magnitude , 2003, Journal of Cognitive Neuroscience.

[2]  Valentin Riedl,et al.  Evaluation of Multiband EPI Acquisitions for Resting State fMRI , 2015, PloS one.

[3]  Steen Moeller,et al.  Evaluation of highly accelerated simultaneous multi-slice EPI for fMRI , 2015, NeuroImage.

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

[5]  D. Spencer,et al.  Repetition time in echo planar functional MRI , 2001, Magnetic resonance in medicine.

[6]  B. Thirion,et al.  Fast reproducible identification and large-scale databasing of individual functional cognitive networks , 2007, BMC Neuroscience.

[7]  Guy B. Williams,et al.  Accurate autocorrelation modeling substantially improves fMRI reliability , 2018 .

[8]  Peter J. Koopmans,et al.  Improved sensitivity and specificity for resting state and task fMRI with multiband multi-echo EPI compared to multi-echo EPI at 7T , 2015, NeuroImage.

[9]  Jonathan P. McNulty,et al.  The salience network is responsible for switching between the default mode network and the central executive network: Replication from DCM , 2014, NeuroImage.

[10]  Russell A. Epstein,et al.  Common and Unique Representations in pFC for Face and Place Attractiveness , 2015, Journal of Cognitive Neuroscience.

[11]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[13]  J. Sanes,et al.  Improved Detection of Event-Related Functional MRI Signals Using Probability Functions , 2001, NeuroImage.

[14]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[15]  Kathryn M. McMillan,et al.  N‐back working memory paradigm: A meta‐analysis of normative functional neuroimaging studies , 2005, Human brain mapping.

[16]  Felix Breuer,et al.  Simultaneous multislice (SMS) imaging techniques , 2015, Magnetic resonance in medicine.

[17]  Gian Domenico Iannetti,et al.  Regions of interest analysis in pharmacological fMRI: How do the definition criteria influence the inferred result? , 2008, NeuroImage.

[18]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

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

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

[21]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[22]  Essa Yacoub,et al.  The rapid development of high speed, resolution and precision in fMRI , 2012, NeuroImage.

[23]  Guy B. Williams,et al.  Accurate autocorrelation modeling substantially improves fMRI reliability , 2017, Nature Communications.

[24]  V. Walsh,et al.  The parietal cortex and the representation of time, space, number and other magnitudes , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[25]  Klaus Scheffler,et al.  Effect of temporal resolution and serial autocorrelations in fast fMRI , 2016 .

[26]  Steen Moeller,et al.  Evaluation of slice accelerations using multiband echo planar imaging at 3T , 2013, NeuroImage.

[27]  Karla L. Miller,et al.  Simultaneous Multi-Slice Imaging for Resting-State fMRI , 2015 .

[28]  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.

[29]  N. Kanwisher,et al.  Mental Imagery of Faces and Places Activates Corresponding Stimulus-Specific Brain Regions , 2000, Journal of Cognitive Neuroscience.

[30]  Alberto Llera,et al.  ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data , 2015, NeuroImage.

[31]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[32]  Steen Moeller,et al.  Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts , 2016, NeuroImage.

[33]  M. Gatz,et al.  The Alzheimer's disease knowledge test. , 1988, The Gerontologist.

[34]  N. Kanwisher,et al.  Numerical Magnitude in the Human Parietal Lobe Tests of Representational Generality and Domain Specificity , 2004, Neuron.

[35]  Kawin Setsompop,et al.  Interslice leakage artifact reduction technique for simultaneous multislice acquisitions , 2014, Magnetic resonance in medicine.

[36]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[37]  Nancy Kanwisher,et al.  A cortical representation of the local visual environment , 1998, Nature.

[38]  Joshua Correll,et al.  The Chicago face database: A free stimulus set of faces and norming data , 2015, Behavior research methods.

[39]  N. Filippini,et al.  Group comparison of resting-state FMRI data using multi-subject ICA and dual regression , 2009, NeuroImage.

[40]  Jonathan W. Peirce,et al.  PsychoPy—Psychophysics software in Python , 2007, Journal of Neuroscience Methods.

[41]  Robin M. Chan,et al.  Working memory for complex figures: an fMRI comparison of letter and fractal n-back tasks. , 2002, Neuropsychology.

[42]  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.