Evaluating subject specific preprocessing choices in multisubject fMRI data sets using data-driven performance metrics

Shaw, Marnie; Strother, Stephen C.; Gavrilescu, Maria; Podzebenko, Katherine; Waites, Anthony B.; Watson, J. D. G.; Anderson, Jon R.; Jackson, Graeme D.; Egan, Gary F.

doi:10.1016/s1053-8119(03)00116-2

Cited by 44 publications

(50 citation statements)

References 23 publications

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“…Intra-subject variability was decreased when larger smoothing kernels were used (Rombouts et al, 1998) or when the four nearest in plane neighbours were included in the analysis (Yetkin et al, 1996). These results are in agreement with what has also been observed for inter-subject variability (White et al, 2001;Shaw et al, 2003). Unfortunately reduction of variability by smoothing comes at the cost of reduced spatial resolution.…”

Section: Introductionsupporting

confidence: 92%

Motion or activity: their role in intra- and inter-subject variation in fMRI

Lund¹,

Nørgaard²,

Rostrup³

et al. 2005

NeuroImage

212

157

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Functional MRI (fMRI) carries the potential for non-invasive measurements of brain activity. Typically what are referred to as activation images are actually thresholded statistical parametric maps. These maps possess large inter-session variability. This is especially problematic when applying fMRI to pre-surgical planning because of a higher requirement for intra-subject precision. The purpose of this study was to investigate the impact of residual movement artefacts on intra-subject and intersubject variability in the observed fMRI activation. Ten subjects were examined using three different word-generation tasks. Two of the subjects were examined 10 times on 10 different days using the same paradigms. We systematically investigated one approach of correcting for residual movement effects: the inclusion of regressors describing movement-related effects in the design matrix of a General Linear Model (GLM). The data were analysed with and without modeling the residual movement artefacts and the impact on inter-session variance was assessed using F-contrasts. Inclusion of motion parameters in the analysis significantly reduced both the intrasubject as well as the inter-subject-variance.

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Section: Introductionsupporting

confidence: 92%

Motion or activity: their role in intra- and inter-subject variation in fMRI

Lund¹,

Nørgaard²,

Rostrup³

et al. 2005

NeuroImage

212

157

View full text Add to dashboard Cite

show abstract

“…The present work extends the results of LaConte et al [2003], Shaw et al [2003], Strother et al [2004], and Zhang et al [2008Zhang et al [ , 2009, among others, who showed that spatial smoothing, temporal filtering and motion correction have significant impacts on fMRI results, as measured with NPAIRS metrics. These results also revealed measurable subject heterogeneity, in which different sets of preprocessing choices performed better for different subjects.…”

Section: Introductionsupporting

confidence: 88%

“…Additionally, Jones et al [2008] have shown that the effectiveness of PNC depends on its order in the preprocessing pipeline. Low-order temporal detrending has also been found to significantly affect results, depending on choice of detrending basis [Kay et al, 2007;Tanabe et al, 2002] and interactions with other parameters, such as spatial smoothing [Shaw et al, 2003]. A number of studies have also investigated basis decomposition techniques using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for data denoising [e.g.…”

Section: Introductionmentioning

confidence: 99%

Optimizing preprocessing and analysis pipelines for single‐subject fMRI. I. Standard temporal motion and physiological noise correction methods

Churchill

Oder

Abdi

et al. 2011

Human Brain Mapping

Self Cite

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Subject-specific artifacts caused by head motion and physiological noise are major confounds in BOLD fMRI analyses. However, there is little consensus on the optimal choice of data preprocessing steps to minimize these effects. To evaluate the effects of various preprocessing strategies, we present a framework which comprises a combination of (1) nonparametric testing including reproducibility and prediction metrics of the data-driven NPAIRS framework (Strother et al. [2002]: NeuroImage 15:747-771), and (2) intersubject comparison of SPM effects, using DISTATIS (a three-way version of metric multidimensional scaling (Abdi et al. [2009]: NeuroImage 45:89-95). It is shown that the quality of brain activation maps may be significantly limited by sub-optimal choices of data preprocessing steps (or "pipeline") in a clinical task-design, an fMRI adaptation of the widely used Trail-Making Test. The relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending were examined for fMRI data acquired in young, healthy adults. Analysis performance and the quality of activation maps were evaluated based on Penalized Discriminant Analysis (PDA). The relative importance of different preprocessing steps was assessed by (1) a nonparametric Friedman rank test for fixed sets of preprocessing steps, applied to all subjects; and (2) evaluating pipelines chosen specifically for each subject. Results demonstrate that preprocessing choices have significant, but subject-dependant effects, and that individually-optimized pipelines may significantly improve the reproducibility of fMRI results over fixed pipelines. This was CIHR Author Manuscript CIHR Author Manuscript CIHR Author Manuscriptdemonstrated by the detection of a significant interaction with motion parameter regression and physiological noise correction, even though the range of subject head motion was small across the group (≪ 1 voxel). Optimizing pipelines on an individual-subject basis also revealed brain activation patterns either weak or absent under fixed pipelines, which has implications for the overall interpretation of fMRI data, and the relative importance of preprocessing methods.

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“…As noted in their original article, "it is possible that spatial preprocessing (for example) may affect inter-session variance quite independently of underlying physical or physiological variability." This view is supported by Shaw et al [2003], where analysis methodology is shown to affect apparent inter-session variance. In the present study, we revisit the analysis of data from McGonigle et al [2000] and consider session variability in the light of the effects that different first-level processing methods can have.…”

Section: Introductionmentioning

confidence: 85%

Variability in fMRI: A re‐examination of inter‐session differences

Smith

Beckmann

Ramnani

et al. 2005

Human Brain Mapping

168

116

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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. Hum Brain Mapp 24:248 -257, 2005.

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Evaluating subject specific preprocessing choices in multisubject fMRI data sets using data-driven performance metrics

Cited by 44 publications

References 23 publications

Motion or activity: their role in intra- and inter-subject variation in fMRI

Motion or activity: their role in intra- and inter-subject variation in fMRI

Optimizing preprocessing and analysis pipelines for single‐subject fMRI. I. Standard temporal motion and physiological noise correction methods

Variability in fMRI: A re‐examination of inter‐session differences

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