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Functional magnetic resonance imaging (fMRI) performed by echo-planar imaging (EPI) is often highly distorted, and it is therefore necessary to coregister the functional to undistorted anatomical images, especially for clinical applications. This pilot study provides an evaluation of human and automatic coregistration results in the human motor cortex of normal and pathological brains. Ten healthy right-handed subjects and ten right-handed patients performed simple right hand movements during fMRI. A reference point chosen at a characteristic anatomical location within the fMRI sensorimotor activations was transferred to the high resolution anatomical MRI images by three human fMRI experts and by three automatic coregistration programs. The 3D distance between the median localizations of experts and programs was calculated and compared between patients and healthy subjects. Results show that fMRI localization on anatomical images was better with the experts than software in 70% of the cases and that software performance was worse for patients than healthy subjects (unpaired t-test: P = 0.040). With 45.6 mm the maximum disagreement between experts and software was quite large. The inter-rater consistency was better for the fMRI experts compared to the coregistration programs (ANOVA: P = 0.003). We conclude that results of automatic coregistration should be evaluated carefully, especially in case of clinical application.

Purpose: To evaluate the impact of data quality on the localization of brain activation in functional magnetic resonance imaging (fMRI) and to explore whether the temporal contrast-to-noise-ratio (CNR) provides a quantitative parameter to estimate fMRI quality., Materials and Methods: We investigated two methods for defining the CNR by comparing them on a single-run, single session, as well as on a group-wise basis. The CNRs of healthy subjects and a group of patients with brain lesions were calculated using two different strategies: one based on a general linear model (GLM) analysis (CNR_SPM), and one that acts as an adaptive low-pass filter and assumes that the high-frequency components contain the temporal noise (CNR_SG). Runs with low CNR were identified as outliers using a common exclusion criterion (2 x standard deviation (SD))., Results: The results of the two CNR methods are highly correlated. Both between and within subjects and patients the CNR showed quite large variations, but the average CNR did not differ between a group of healthy subjects and a patient group. In total, seven of 213 runs (3.3% of all runs) had to be excluded when CNR_SG was used, and 14 of 213 (6.6%) runs had to be excluded when CNR_SPM was used., Conclusion: Calculating the CNR using an adaptive low-pass filter gives similar results to a GLM-based approach and could be advantageous for cases in which the hemodynamic response function (HRF) differs significantly from common assumptions. The CNR can be used to identify and exclude runs with suboptimal CNR, and to identify sessions with insufficient data quality. The CNR may serve as a quantitative and intuitive parameter to assess the performance and quality of clinical fMRI investigations, including information on both functional performance (contrast) and data quality (noise caused by the system and physiology)., ((c) 2007 Wiley-Liss, Inc.)

Objectives: The validity of 3 Tesla motor functional magnetic resonance imaging (fMRI) in patients with gliomas involving the primary motor cortex was investigated by intraoperative navigated motor cortex stimulation (MCS)., Methods: Twenty two patients (10 males, 12 females, mean age 39 years, range 10-65 years) underwent preoperative fMRI studies, performing motor tasks including hand, foot, and mouth movements. A recently developed high field clinical fMRI technique was used to generate pre-surgical maps of functional high risk areas defining a motor focus. Motor foci were tested for validity by intraoperative motor cortex stimulation (MCS) employing image fusion and neuronavigation. Clinical outcome was assessed using the Modified Rankin Scale., Results: FMRI motor foci were successfully detected in all patients preoperatively. In 17 of 22 patients (77.3%), a successful stimulation of the primary motor cortex was possible. All 17 correlated patients showed 100% agreement on MCS and fMRI motor focus within 10 mm. Technical problems during stimulation occurred in three patients (13.6%), no motor response was elicited in two (9.1%), and MCS induced seizures occurred in three (13.6%). Combined fMRI and MCS mapping results allowed large resections in 20 patients (91%) (gross total in nine (41%), subtotal in 11 (50%)) and biopsy in two patients (9%). Pathology revealed seven low grade and 15 high grade gliomas. Mild to moderate transient neurological deterioration occurred in six patients, and a severe hemiparesis in one. All patients recovered within 3 months (31.8% transient, 0% permanent morbidity)., Conclusions: The validation of clinically optimised high magnetic field motor fMRI confirms high reliability as a preoperative and intraoperative adjunct in glioma patients selected for surgery within or adjacent to the motor cortex.

Recent publications analyzing the influence of spatial smoothing on fMRI brain activation results demonstrated that smoothing may artificially combine activations from adjacent though functionally and anatomically distinct brain regions and that activation from large draining vessels may be smoothed into neighboring neuronal tissue. To investigate whether functional localizations may be artificially shifted by the smoothing procedure we performed replicability measurements. Localization centers of motor hand activations achieved during different conditions (isolated hand movements and simultaneous hand and chin movements) were compared with respect to smoothing effects. The voxel with the highest probability to represent a true positive activation was localized with a non-smoothed and a standard 4 x 4 x 6 mm smoothed correlational data analysis technique. Results show an increase of motor center aberrations between measurements by about 100% due to data smoothing indicating a statistically significant decrease in localization replicability.