Download Clinical Functional MRI: Presurgical Functional Neuroimaging by Christoph Stippich MD (auth.), C. Stippich MD (eds.) PDF

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" lower than physiological stipulations. inside of scientific neuroimaging, fMRI is establishing up a brand new diagnostic box by way of measuring and visualizing mind functionality. even if, 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 functions. by way of localizing and lateralizing particular mind capabilities in person sufferers, in addition to epileptogenic zones, fMRI enables the choice of a secure remedy and the making plans and function of function-preserving neurosurgery.

State of the paintings fMRI techniques are awarded, with precise attention of sensible facets, imaging and information processing, common and pathological findings, and diagnostic percentages and boundaries. The suitable info on mind body structure, useful neuroanatomy, imaging strategy, and technique is supplied by way of well-known specialists in those fields. extra chapters tackle the validity of presurgical sensible localization, multimodality practical neuroimaging, mind plasticity, and pitfalls, guidance, and methods. The booklet is designed to be of price to newbies, proficient clinicians and specialists alike.

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Extra resources for Clinical Functional MRI: Presurgical Functional Neuroimaging

Sample text

The term Xb indicates matrixvector multiplication. 16 shows a graphical representation of a GLM. Time courses of the signal, predictors and residuals have been arranged in column form with time running from top to bottom as in the system of equations. Given the data y and the design matrix X, the GLM fitting procedure has to fi nd a set of beta values explaining the data as good as possible. The time course values yˆ predicted by the model are obtained by the linear combination of the predictors: yˆ = Xb A good fit would be achieved with beta values leading to predicted values yˆ which are as close as possible to the measured values y.

1995). 05 is met. This approach does not require spatial smoothing and appears highly appropriate for fMRI data. The only disadvantage is that the method is quite computer intensive.

Using a pseudo-inverse or SVD approach, we would now obtain three beta values (plus the constant), one for the rest condition, one for main condition 1 and one for main condition 2. While the values of beta weights might not be interpretable, correct inferences of contrasts can be obtained if an additional restriction is met, typically that the sum of the contrast coefficients equals 0. To test whether main condition 1 differs significantly from the rest condition, the contrast c = [-1 +1 0] would now be used.

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