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Journal of Neurology, Neurosurgery, and Psychiatry logoLink to Journal of Neurology, Neurosurgery, and Psychiatry
. 2001 Jun;70(6):749–760. doi: 10.1136/jnnp.70.6.749

Functional MRI for presurgical planning: problems, artefacts, and solution strategies

T Krings 1, M Reinges 1, S Erberich 1, S Kemeny 1, V Rohde 1, U Spetzger 1, M Korinth 1, K Willmes 1, J Gilsbach 1, A Thron 1
PMCID: PMC1737418  PMID: 11385009

Abstract

OJECTIVES—Presurgical mapping of motor function is a widely used clinical application of functional (f) MRI, employing the blood oxygenation level dependent contrast. The aim of this study was to report on 3 years experience of 194 fMRI studies on the representation of motor function in 103 patients and to describe the problems and artefacts that were typically present.
METHODS—An evaluation was carried out to determine whether the patients' age, type or location of the tumourous lesion, severity of the paresis, or the tasks used during the investigation have an effect on artefacts of fMRI studies and how these artefacts are best overcome.
RESULTS—Functional MRI identified the motor regions in 85% of all investigated paradigms. In 11% of the investigated patients no information at all on functional localisation was obtained. A draining vein within the central sulcus was present in all patients that showed activation within the parenchyma of the precentral gyrus but also in three patients in whom no parenchymal activation was present. Head movement artefacts were the most frequent cause for fMRI failure, followed by low signal to noise ratio. Motion artefacts were correlated with the degree of paresis and with the functional task. Tasks involving more proximal muscles led to significantly more motion artefacts when compared with tasks that primarily involved distal muscles. Mean MR signal change during task performance was 2.5%.
CONCLUSIONS—Most of the artefacts of functional MRI can be reliably detected and at least in part be reduced or eliminated with the help of mathematical algorithms, appropriate pulse sequences and tasks, and—probably most important—by evaluating the fMRI raw data—that is, the MR signal time courses.



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