By Christoph Stippich MD (auth.), C. Stippich MD (eds.)
Functional magnetic resonance imaging (fMRI) has contributed considerably to development in neuroscience by means of allowing noninvasive imaging of the "human mind at paintings" below physiological stipulations. inside of medical neuroimaging, fMRI is starting up a brand new diagnostic box through measuring and visualizing mind functionality. besides the fact that, fMRI isn't really but a regular diagnostic imaging procedure.
This textbook is dedicated to preoperative fMRI in sufferers with mind tumors and epilepsies, that are the main well-established medical purposes. via localizing and lateralizing particular mind features in person sufferers, in addition to epileptogenic zones, fMRI allows the choice of a secure remedy and the making plans and function of function-preserving neurosurgery.
State of the paintings fMRI techniques are provided, with distinct attention of useful elements, imaging and knowledge processing, common and pathological findings, and diagnostic probabilities and barriers. The proper details on mind body structure, sensible neuroanatomy, imaging procedure, and method is equipped via famous specialists in those fields. additional chapters deal with the validity of presurgical sensible localization, multimodality useful neuroimaging, mind plasticity, and pitfalls, information, and methods. The e-book is designed to be of price to novices, informed clinicians and specialists alike.
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Additional resources for Clinical Functional MRI: Presurgical Functional Neuroimaging
B Thresholding result when using the false discovery rate approach (FDR). c Thresholding result when using the Bonferroni method. The p values shown on top of each panel have been used to threshold the map as provided by the respective method. The FDR method shows more voxels as signiﬁcant because it is less conservative than the Bonferroni method. maps. 01) could be used, which would indicate the probability of obtaining the observed effect, quantiﬁed by an R, t or F statistic, solely due to noise ﬂuctuations.
E. the value in one time course cannot be used to predict the corresponding value in the other time course. While the statistical principles are the same in correlation analysis as described for mean comparisons, the null hypothesis now corresponds to the statement that the population correlation coefﬁcient U equals zero (H0: U = 0). By including the number of data points N, the error probability can be computed assessing how likely it is that an observed correlation coefﬁcient would occur solely due to noise ﬂuctuations in the signal time course.
The most essential steps of these preprocessing operations include: (1) head motion detection and correction, (2) slice scan timing correction, (3) removal of linear and non-linear trends in voxel time courses and (4) spatial and temporal smoothing of the data. 1 Detection and Correction of Head Motion The quality of fMRI data is strongly hampered in the presence of substantial head movements. Most people reject data sets for further analysis if head motion of 5 mm or more is detected. Although head motion can be corrected in image space, displacements of the head in the scanner reduce the homogeneity of the magnetic ﬁeld, which is ﬁne-tuned (“shimmed”) prior to functional scans for a given head position.