Mean Diffusional Kurtosis in Patients with Glioma: Initial Results with a Fast Imaging Method in a Clinical Setting.

BACKGROUND AND PURPOSE: Diffusional kurtosis imaging is an MR imaging technique that provides microstructural information in biologic systems. Its application in clinical studies, however, is hampered by long acquisition and postprocessing times. We evaluated a new and fast (2 minutes 46 seconds) diffusional kurtosis imaging method with regard to glioma grading, compared it with conventional diffusional kurtosis imaging, and compared the diagnostic accuracy of fast mean kurtosis (MK') to that of the widely used mean diffusivity. MATERIALS AND METHODS: MK' and mean diffusivity were measured in the contrast-enhancing tumor core, the perifocal hyperintensity (indicated on T2 FLAIR images), and the contralateral normal-appearing white and gray matter of 34 patients (22 with high-grade and 12 with low-grade gliomas). MK' and mean diffusivity in the different tumor grades were compared by using a Wilcoxon rank sum test. Receiver operating characteristic curves and the areas under the curve were calculated to determine the diagnostic accuracy of MK' and mean diffusivity. RESULTS: MK' in the tumor core, but not mean diffusivity, differentiated high-grade from low-grade gliomas, and MK' differentiated glioblastomas from the remaining gliomas with high accuracy (area under the curveMK' = 0.842; PMK' < .001). MK' and mean diffusivity identified glioblastomas in the group of high-grade gliomas with similar significance and accuracy (area under the curveMK' = 0.886; area under the curvemean diffusivity = 0.876; PMK' = .003; Pmean diffusivity = .004). The mean MK' in all tissue types was comparable to that obtained by conventional diffusional kurtosis imaging. CONCLUSIONS: The diffusional kurtosis imaging approach used here is considerably faster than conventional diffusional kurtosis imaging methods but yields comparable results. It can be accommodated in clinical protocols and enables exploration of the role of MK' as a biomarker in determining glioma subtypes or response evaluation.

Modelling cardiac signal as a confound in EEG-fMRI and its application in focal epilepsy studies.

Cardiac noise has been shown to reduce the sensitivity of functional Magnetic Resonance Imaging (fMRI) to an experimental effect due to its confounding presence in the blood oxygenation level-dependent (BOLD) signal. Its effect is most severe in particular regions of the brain and a method is yet to take it into account in routine fMRI analysis. This paper reports the development of a general and robust technique to improve the reliability of EEG-fMRI studies to BOLD signal correlated with interictal epileptiform discharges (IEDs). In these studies, ECG is routinely recorded, enabling cardiac effects to be modelled, as effects of no interest. Our model is based on an over-complete basis set covering a linear relationship between cardiac-related MR signal and the phase of the cardiac cycle or time after pulse (TAP). This method showed that, on average, 24.6 +/- 10.9% of grey matter voxels contained significant cardiac effects and 22.3 +/- 24.1% of those voxels exhibiting significantly IED-correlated BOLD signal also contained significant cardiac effects. We quantified the improvement of the TAP model over the original model, without cardiac effects, by evaluating changes in efficiency, with respect to estimating the contrast of the effects of interest. Over voxels containing significant, cardiac-related signal, efficiency was improved by 18.5 +/- 4.8%. Over the remaining voxels, no improvement was demonstrated. This suggests that, while improving sensitivity in particular regions of the brain, there is no risk that the TAP model will reduce sensitivity elsewhere.

Mixed-effects and fMRI studies.

This note concerns mixed-effect (MFX) analyses in multisession functional magnetic resonance imaging (fMRI) studies. It clarifies the relationship between mixed-effect analyses and the two-stage "summary statistics" procedure (Holmes, A.P., Friston, K.J., 1998. Generalisability, random effects and population inference. NeuroImage 7, S754) that has been adopted widely for analyses of fMRI data at the group level. We describe a simple procedure, based on restricted maximum likelihood (ReML) estimates of covariance components, that enables full mixed-effects analyses in the context of statistical parametric mapping. Using this procedure, we compare the results of a full mixed-effects analysis with those obtained from the simpler two-stage procedure and comment on the situations when the two approaches may give different results.