Comparison of fMRI motion correction software tools

Oakes, Terrence R.; Johnstone, Tom; Walsh, Kathleen S. Ores; Greischar, Lawrence L.; Alexander, Andrew L.; Fox, Andrew S.; Davidson, Richard J.

doi:10.1016/j.neuroimage.2005.05.058

Cited by 164 publications

(141 citation statements)

References 51 publications

(49 reference statements)

Supporting

Mentioning

137

Contrasting

Order By: Relevance

“…We would also like to increase the bandwidth of the biofeedback loop by including a larger number of different simultaneous activities to make the subject able to control a more advanced dynamical system. We also want to make our motion compensation algorithm to run in real-time and compare it to existing techniques [13].…”

Section: Discussionmentioning

confidence: 99%

Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification

Eklund

Andersson

Rydell

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

View full text Add to dashboard Cite

Abstract. We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverted pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. The state of the inverted pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverted pendulum during several minutes, both with real activity and imagined activity. In each classification 9000 brain voxels were used and the response time for the system to detect a change of activity was on average 2-4 seconds. The developments here have a potential to aid people with communication disabilities, such as locked in people. Another future potential application can be to serve as a tool for stroke and Parkinson patients to be able to train the damaged brain area and get real-time feedback for more efficient training.

show abstract

Section: Discussionmentioning

confidence: 99%

Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification

Eklund

Andersson

Rydell

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

View full text Add to dashboard Cite

show abstract

“…The studies from which these data were drawn and associated preprocessing steps have been described in detail elsewhere Kalin et al, 2005;Oakes et al, 2005;Johnstone et al, 2006]. For both data sets, a similar approach was used to prepare the anatomical and functional data.…”

Section: Iic Exemplar Data Setsmentioning

confidence: 99%

Integrating VBM into the General Linear Model with voxelwise anatomical covariates

et al. 2007

View full text Add to dashboard Cite

A current limitation for imaging of brain function is the potential confound of anatomical differences or registration error, which may manifest via apparent functional "activation" for between-subject analyses. With respect to functional activations, underlying tissue mismatches can be regarded as a nuisance variable. We propose adding the probability of gray matter at a given voxel as a covariate (nuisance variable) in the analysis of voxelwise multisubject functional data using standard statistical techniques. A method is presented to assess the extent to which a functional activation can reliably be explained by underlying anatomical differences, and simultaneously, to assess the component of the functional activation which cannot be attributed to anatomical difference and thus is likely due to functional difference alone. Extension of the method to other intermodal imaging applications is discussed. Two exemplary data sets, one PET and one fMRI, are used to demonstrate the implementation and utility of this method, which apportions the relative contributions of anatomy and function for an apparent functional activation. The examples show two distinct types of results. First, a so-called functional activation may actually be caused by a systematic anatomical difference which, when modeled, diminishes the functional effect. In the second result type, including the anatomical differences in the model can account for a large component of otherwise unmodeled variance, yielding an increase in the functional effect cluster size and/or magnitude. In either case, ignoring the readily available structural information can lead to misinterpretation of functional results.

show abstract

“…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

View full text Add to dashboard Cite

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 motion correction software tools

Cited by 164 publications

References 51 publications

Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification

Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification

Integrating VBM into the General Linear Model with voxelwise anatomical covariates

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

Contact Info

Product

Resources

About