Literature DB >> 34492308

Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI.

Logan T Dowdle1, Geoffrey Ghose2, Clark C C Chen3, Kamil Ugurbil4, Essa Yacoub4, Luca Vizioli5.   

Abstract

Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.
Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Fast fMRI; Neuroscience; Rapid sampling; Spatiotemporal dynamics; Statistics; Temporal dynamics

Mesh:

Year:  2021        PMID: 34492308      PMCID: PMC8688272          DOI: 10.1016/j.pneurobio.2021.102171

Source DB:  PubMed          Journal:  Prog Neurobiol        ISSN: 0301-0082            Impact factor:   11.685


  183 in total

1.  Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses.

Authors:  Daniel A Handwerker; John M Ollinger; Mark D'Esposito
Journal:  Neuroimage       Date:  2004-04       Impact factor: 6.556

Review 2.  Simultaneous EEG-fMRI acquisition at low, high and ultra-high magnetic fields up to 9.4 T: perspectives and challenges.

Authors:  Irene Neuner; Jorge Arrubla; Jörg Felder; N Jon Shah
Journal:  Neuroimage       Date:  2013-06-22       Impact factor: 6.556

3.  Dynamic spatiotemporal variability of alpha-BOLD relationships during the resting-state and task-evoked responses.

Authors:  S D Mayhew; A P Bagshaw
Journal:  Neuroimage       Date:  2017-04-25       Impact factor: 6.556

4.  Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI.

Authors:  Prantik Kundu; Souheil J Inati; Jennifer W Evans; Wen-Ming Luh; Peter A Bandettini
Journal:  Neuroimage       Date:  2011-12-23       Impact factor: 6.556

5.  Processing strategies for time-course data sets in functional MRI of the human brain.

Authors:  P A Bandettini; A Jesmanowicz; E C Wong; J S Hyde
Journal:  Magn Reson Med       Date:  1993-08       Impact factor: 4.668

6.  Simultaneous EEG-fMRI at ultra-high field: artifact prevention and safety assessment.

Authors:  João Jorge; Frédéric Grouiller; Özlem Ipek; Robert Stoermer; Christoph M Michel; Patrícia Figueiredo; Wietske van der Zwaag; Rolf Gruetter
Journal:  Neuroimage       Date:  2014-10-29       Impact factor: 6.556

Review 7.  In vivo B0 field shimming methods for MRI at 7T.

Authors:  Jason P Stockmann; Lawrence L Wald
Journal:  Neuroimage       Date:  2017-06-07       Impact factor: 6.556

Review 8.  Vascular and neural basis of the BOLD signal.

Authors:  Patrick J Drew
Journal:  Curr Opin Neurobiol       Date:  2019-07-21       Impact factor: 6.627

9.  Layer-specific fMRI reflects different neuronal computations at different depths in human V1.

Authors:  Cheryl A Olman; Noam Harel; David A Feinberg; Sheng He; Peng Zhang; Kamil Ugurbil; Essa Yacoub
Journal:  PLoS One       Date:  2012-03-20       Impact factor: 3.240

Review 10.  Misconceptions in the use of the General Linear Model applied to functional MRI: a tutorial for junior neuro-imagers.

Authors:  Cyril R Pernet
Journal:  Front Neurosci       Date:  2014-01-21       Impact factor: 4.677

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