Prospective motion correction in functional MRI

Zaitsev, Maxim; Akin, Burak; LeVan, Pierre; Knowles, Benjamin R.

doi:10.1016/j.neuroimage.2016.11.014

Cited by 109 publications

(89 citation statements)

References 99 publications

(127 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For robust activations and simple analysis, such as fCNR of the primary visual cortex, there would appear to be little discernible benefit of PMC, in line with the results obtained by Zaitsev et al (2016). For high‐resolution multivariate pattern, decoding analyses where accurate voxel registration across time are essential, there was a clear benefit of PMC.…”

Section: Discussionsupporting

confidence: 80%

“…The motion parameters are used to update the position of the acquisition box within the participant's head just before each radiofrequency (RF) pulse. Maclaren, Herbst, Speck, & Zaitsev, 2013 and Zaitsev, Akin, LeVan, & Knowles, 2016 provide a good overview on the current state of the field and list the most promising techniques, some of which have demonstrated significant benefits to data quality relative to RMC (Muraskin et al, 2013; Stucht et al, 2015; Todd, Josephs, Callaghan, Lutti, & Weiskopf, 2015). The estimation of PMC parameters can be done by either using the internal MR data or external tracking modules.…”

Section: Introductionmentioning

confidence: 99%

“…In the same article, they also demonstrated an increase in significant voxels for a motor and visual task, albeit only in a single subject. Another group utilized the same system for 2D EPI and did not observe any improvement in tSNR values for a finger tapping task (Zaitsev et al, 2016). The authors noted that this is likely due to the poor adhesion of the marker as the marker was attached to the nose instead of via a mouthpiece.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prospective motion correction improves the sensitivity of fMRI pattern decoding

Huang

Carlin

Alink

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

We evaluated the effectiveness of prospective motion correction (PMC) on a simple visual task when no deliberate subject motion was present. The PMC system utilizes an in‐bore optical camera to track an external marker attached to the participant via a custom‐molded mouthpiece. The study was conducted at two resolutions (1.5 mm vs 3 mm) and under three conditions (PMC On and Mouthpiece On vs PMC Off and Mouthpiece On vs PMC Off and Mouthpiece Off). Multiple data analysis methods were conducted, including univariate and multivariate approaches, and we demonstrated that the benefit of PMC is most apparent for multi‐voxel pattern decoding at higher resolutions. Additional testing on two participants showed that our inexpensive, commercially available mouthpiece solution produced comparable results to a dentist‐molded mouthpiece. Our results showed that PMC is increasingly important at higher resolutions for analyses that require accurate voxel registration across time.

show abstract

Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prospective motion correction improves the sensitivity of fMRI pattern decoding

Huang

Carlin

Alink

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…PACE is the only technique for fMRI applications that allows for adequate correction of spin‐history effects (Yancey et al, ) and intra‐volume distortions (Speck, Henning, & Zaitsev, ). The use of prospective motion correction via PACE together with retrospective motion correction is superior in fMRI analyses to either alone (see Zaitsev, Akin, LeVan, & Knowles, ). The functional data were collected in thirty four 3 mm‐thick slices that were aligned approximately 30° perpendicular to the hippocampus, which is effective for maximizing signal‐to‐noise ratios in the medial temporal lobes (Whalen et al, ).…”

Section: Methodsmentioning

confidence: 99%

Evaluating the organizational structure and specificity of network topology within the face processing system

Elbich

Molenaar

Scherf

2019

Human Brain Mapping

View full text Add to dashboard Cite

There is increasing appreciation that network‐level interactions among regions produce components of face processing previously ascribed to individual regions. Our goals were to use an exhaustive data‐driven approach to derive and quantify the topology of directed functional connections within a priori defined nodes of the face processing network and evaluate whether the topology is category‐specific. Young adults were scanned with fMRI as they viewed movies of faces, objects, and scenes. We employed GIMME to model effective connectivity among core and extended face processing regions, which allowed us to evaluate all possible directional connections, under each viewing condition (face, object, place). During face processing, we observed directional connections from the right posterior superior temporal sulcus to both the right occipital face area and right fusiform face area (FFA), which does not reflect the topology reported in prior studies. We observed connectivity between core and extended regions during face processing, but this limited to a feed‐forward connection from the FFA to the amygdala. Finally, the topology of connections was unique to face processing. These findings suggest that the pattern of directed functional connections within the face processing network, particularly in the right core regions, may not be as hierarchical and feed‐forward as described previously. Our findings support the notion that topologies of network connections are specialized, emergent, and dynamically responsive to task demands.

show abstract

“…Otherwise, compensation of effects due to head motions occurring at shorter time scales that may only impact a subset of slices would be limited. In that sense, the high temporal resolution of external optical tracking systems (Schulz et al, 2014; Speck et al, 2006; Todd et al, 2015) or the use of dedicated sequences with navigators echoes or active markers (Hu and Kim, 1994; Lee et al, 1996; Muraskin et al, 2013; Pfeuffer et al, 2002; White et al, 2010) can be useful for prospective motion correction (Zaitsev et al, 2016). The extra information can also be used retrospectively.…”

Section: Denoising Motion-related Noisementioning

confidence: 99%

Methods for cleaning the BOLD fMRI signal

2017

View full text Add to dashboard Cite

Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

show abstract

Prospective motion correction in functional MRI

Cited by 109 publications

References 99 publications

Prospective motion correction improves the sensitivity of fMRI pattern decoding

Prospective motion correction improves the sensitivity of fMRI pattern decoding

Evaluating the organizational structure and specificity of network topology within the face processing system

Methods for cleaning the BOLD fMRI signal

Contact Info

Product

Resources

About