Article

Smith, S. M. et al. Variability in fMRI: a re-examination of inter-session differences. Hum. Brain Mapp. 24, 248?257

Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Department of Clinical Neurology, Oxford University, John Radcliffe Hospital, Headington, Oxford, United Kingdom.
Human Brain Mapping (Impact Factor: 5.97). 03/2005; 24(3):248-57. DOI: 10.1002/hbm.20080
Source: PubMed

ABSTRACT

We revisit a previous study on inter-session variability (McGonigle et al. [2000]: Neuroimage 11:708-734), showing that contrary to one popular interpretation of the original article, inter-session variability is not necessarily high. We also highlight how evaluating variability based on thresholded single-session images alone can be misleading. Finally, we show that the use of different first-level preprocessing, time-series statistics, and registration analysis methodologies can give significantly different inter-session analysis results.

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    • "Another important issue is the variability in fMRI analysis and group-wise methods. In other words, there is remarkable variability of activation magnitudes for the corresponding brain regions across individual subjects and imaging sessions (Smith et al., 2005; Thirion et al., 2007), due to physiological noises, head/body motion, resting-state activity and other factors. This variability imposes additional challenges to the robust and reliable inference of group-wise consistent functional networks. "
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    • "Another important issue is the variability in fMRI analysis and group-wise methods. In other words, there is remarkable variability of activation magnitudes for the corresponding brain regions across individual subjects and imaging sessions (Smith et al., 2005; Thirion et al., 2007), due to physiological noises, head/body motion, resting-state activity and other factors. This variability imposes additional challenges to the robust and reliable inference of group-wise consistent functional networks. "
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    ABSTRACT: Task-based fMRI activation mapping has been widely used in clinical neuroscience in order to assess different functional activity patterns in conditions such as prenatal alcohol exposure (PAE) affected brains and healthy controls. In this paper, we propose a novel, alternative approach of group-wise sparse representation of the fMRI data of multiple groups of subjects (healthy control, exposed non-dysmorphic PAE and exposed dysmorphic PAE) and assess the systematic functional activity differences among these three populations. Specifically, a common time series signal dictionary is learned from the aggregated fMRI signals of all three groups of subjects, and then the weight coefficient matrices (named statistical coefficient map (SCM)) associated with each common dictionary were statistically assessed for each group separately. Through inter-group comparisons based on the correspondence established by the common dictionary, our experimental results have demonstrated that the group-wise sparse coding strategy and the SCM can effectively reveal a collection of brain networks/regions that were affected by different levels of severity of PAE. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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    • "In other cases a separation of a steady signal pulsation time course into two complementary ICs with time courses that displayed decreasing and increasing pulsation amplitude was observed. Several studies have shown that optimization of the data analysis methodology, such as using back-projection methods, reduces inter-session variability (Smith et al., 2005; Chen et al., 2008). Further work across larger groups of subjects is thus necessary to assess the reproducibility of source separation in single subjects. "
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