Comparison of fMRI statistical software packages and strategies for analysis of images containing random and stimulus-correlated motion

Morgan, Victoria L.; Dawant, Benoît M.; Li, Yong; Pickens, David R.

doi:10.1016/j.compmedimag.2007.04.002

Cited by 86 publications

(35 citation statements)

References 42 publications

(65 reference statements)

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“…Several fMRI software packages now allow motion parameters to be included as regressors in the analysis. Morgan et al [72] compared the sensitivity of four commonly used fMRI software packages such as SPM2 and FSL with simulated data. They found that the most sensitive analysis technique was to perform motion correction and include the realignment parameters as regressors in the GLM, which was most beneficial when stimulus-correlated motion was present.…”

Section: Evaluation Of the Preprocessing Stepsmentioning

confidence: 99%

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA

Zhang

Anderson

Liang

et al. 2009

Magnetic Resonance Imaging

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Section: Evaluation Of the Preprocessing Stepsmentioning

confidence: 99%

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA

Zhang

Anderson

Liang

et al. 2009

Magnetic Resonance Imaging

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“…The following preprocessing steps were performed. (1) Head motion between scans was removed by rigid body registration to the first functional run (2) Within-scan motion correction and slice time correction was performed using standard, validated procedures (e.g., 3dvolreg) within AFNI (Cox and Jesmanowicz, 1999), whose validity and reliability has been stress-tested and confirmed in phantom data (Morgan, et al, 2007;Oakes et al, 2005). TRs with significant head motion ( > 0.3 mm) were excluded from further analysis using a 1-d TR filter, which only removed an average of 34.8 TRs of the 342 collected per subject.…”

Section: Task-regressed Hippocampal Functional Connectivitymentioning

confidence: 99%

Functional Connectivity of the Hippocampus in Temporal Lobe Epilepsy: Feasibility of a Task-Regressed Seed-Based Approach

et al. 2013

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Objective: Resting-state functional connectivity (FC) has revealed marked network dysfunction in patients with temporal lobe epilepsy (TLE) compared to healthy controls. However, the nature and the location of these changes have not been fully elucidated nor confirmed by other methodologies. We assessed the presence of hippocampal FC changes in TLE based on the low frequency residuals of task-related functional magnetic resonance imaging data after the removal of task-related activation [i.e., task-regressed functional connectivity MRI (fcMRI)]. Method: We employed a novel, task-regressed approach to quantify hippocampal FC, and compare hippocampal FC in 17 patients with unilateral TLE (9 left) with 17 healthy controls. Results: Our results suggest widespread FC reductions in the mesial cortex associated with the default mode network (DMN), and some local FC increases in the lateral portions of the right hemisphere. We found more pronounced FC decreases in the left hemisphere than in the right, and these FC decreases were greatest in patients with left TLE. Moreover, the FC reductions observed between the hippocampus and posterior cingulate, inferior parietal, paracentral regions are in agreement with previous resting state studies. Conclusions: Consistent with the existing literature, FC reductions in TLE appear widespread with prominent reductions in the medial portion of the DMN. Our data expand the literature by demonstrating that reductions in FC may be greatest in the left hemisphere and in patients with left TLE. Overall, our findings suggest that task-regressed FC is a viable alternative to resting state and that future studies may extract similar information on network connectivity from already existing datasets.

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“…the preprocessing pipeline), or that the default settings in established software packages give near-optimal results. In recent years, though, it has been repeatedly shown that both standard and non-standard data preprocessing choices may have significant effects (negative or positive) on the quality of extracted results (e.g., [Della-Maggiore et al, 2002;Kay et al, 2007;Morgan et al, 2007;Murphy et al, 2009;Poline et al, 2006;Sarty, 2007;Strother et al, 2004;Tanabe et al, 2002;Zhang et al, 2009]. To draw reliable conclusions from fMRI results, it is thus necessary to evaluate the interactions of preprocessing and data analysis choices in a rigorous, systematic manner.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies demonstrate the importance of standard motion correction (MC) algorithms [Ardekani et al., 2001;Friston et al, 1995a,b;Jiang et al, 1995;Morgan et al, 2007;Oakes et al, 2005]. Nonetheless, standard rigid-body alignment may introduce significant artifacts, which may be corrected by non-rigid alignment methods Kim et al, 1999].…”

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

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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.

show abstract

Comparison of fMRI statistical software packages and strategies for analysis of images containing random and stimulus-correlated motion

Cited by 86 publications

References 42 publications

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA

Functional Connectivity of the Hippocampus in Temporal Lobe Epilepsy: Feasibility of a Task-Regressed Seed-Based Approach

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

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