1996
DOI: 10.1002/mrm.1910350312
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Movement‐Related effects in fMRI time‐series

Abstract: This paper concerns the spatial and intensity transformations that are required to adjust for the confounding effects of subject movement during functional MRI (fMRI) activation studies. An approach is presented that models, and removes, movement-related artifacts from fMRI time-series. This approach is predicated on the observation that movement-related effects are extant even after perfect realignment. Movement-related effects can be divided into those that are a function of position of the object in the fra… Show more

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Cited by 3,308 publications
(2,331 citation statements)
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References 4 publications
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“…The first‐level model specified 6 regressors of interest for each session, comprising Right, Left, and No‐Go and their first‐order temporal derivative. The influence of head movement artifacts was modeled by including 24 nuisance regressors derived from realignment 20. We also included recordings of respiration and cardiac pulsation as nuisance covariates 21…”
Section: Methodsmentioning
confidence: 99%
“…The first‐level model specified 6 regressors of interest for each session, comprising Right, Left, and No‐Go and their first‐order temporal derivative. The influence of head movement artifacts was modeled by including 24 nuisance regressors derived from realignment 20. We also included recordings of respiration and cardiac pulsation as nuisance covariates 21…”
Section: Methodsmentioning
confidence: 99%
“…Additionally, we performed a first-level conjunction analysis (Friston et al 1999) with implicit modeling of resting periods, contrasting the activations detected in both groups. Because of the described effects of motion on individual and group data (Hajnal et al 1994;Friston et al 1996), we carefully investigated the presence of motion at the individual and group level and concluded that only an insignificant drift of less than 0.4 mm, progressive and unrelated to task periods, was present in our samples. When a significant activation in the group data was identified, we investigated its variability in voxel intensity, cluster extent, and time-course across all subjects within our design matrix, in which intersubject variability is normalized via proportional scaling.…”
Section: Imaging Protocolsmentioning
confidence: 99%
“…Signi®cantly activated voxels were searched for by using the`General Linear Model' approach for timeseries data suggested by Friston and colleagues [12,15,57]. For this we de®ned a design matrix comprising contrasts modeling the alternating periods of baseline' and`activation' using a delayed box-car reference vector accounting for the delayed cerebral blood¯ow after stimulus presentation.…”
Section: Statistical Parametric Mappingmentioning
confidence: 99%