ABSTRACT
Sparse sampling functional MRI (ssfMRI) enables stronger primary auditory cortex blood oxygen level-dependent (BOLD) signal by acquiring volumes interspersed with silence, reducing the physiological artifacts associated with scanner noise. Recent calculations of type I error rates associated with resting-state fMRI suggest that the techniques used to model the hemodynamic response function (HRF) might be resulting in higher false positives than is generally acceptable. In the present study, we analyze ssfMRI to determine type I error rates associated with whole brain and primary auditory cortex voxel-wise activation patterns. Study participants (n = 15, age 27.62 ± 3.21 years, range: 22-33 years; 6 females) underwent ssfMRI. An optimized paradigm was used to determine the HRF to auditory stimuli, which was then substituted for silent stimuli to ascertain false positives. We report that common techniques used for analyzing ssfMRI result in high type I error rates. The whole brain and primary auditory cortex voxel-wise analysis resulted in similar error distributions. The number of type I errors for P < 0.05, P < 0.01, and P < 0.001 for the whole brain was 7.88 ± 9.29, 2.37 ± 3.54, and 0.53 ± 0.96% and for the auditory cortex was 9.02 ± 1.79, 2.95 ± 0.91, and 0.58 ± 0.21%, respectively. When conducting a ssfMRI analysis, conservative α level should be employed (α < 0.001) to bolster the results in the face of false positive results.
ABSTRACT
AIM: The aim of this investigation was to develop a model-based dosing algorithm for busulfan and identify an optimal sampling scheme for use in routine clinical practice. METHODS: Clinical data from an ongoing study (n = 29) in stem cell transplantation patients were used for the purposes our analysis. A one compartment model was selected as basis for sampling optimization and subsequent evaluation of a suitable dosing algorithm. Internal and external model validation procedures were performed prior to the optimization steps using ED-optimality criteria. Using systemic exposure as parameter of interest, dosing algorithms were considered for individual patients with the scope of minimizing the deviation from target range as determined by AUC(0,6 h). RESULTS: Busulfan exposure after oral administration was best predicted after the inclusion of adjusted ideal body weight and alanine transferase as covariates on clearance. Population parameter estimates were 3.98 h(-1), 48.8 l and 12.3 l h(-1) for the absorption rate constant, volume of distribution and oral clearance, respectively. Inter-occasion variability was used to describe the differences between test dose and treatment. Based on simulation scenarios, a dosing algorithm was identified, which ensures target exposure values are attained after a test dose. Moreover, our findings show that a sparse sampling scheme with five samples per patient is sufficient to characterize the pharmacokinetics of busulfan in individual patients. CONCLUSION: The use of the proposed dosing algorithm in conjunction with a sparse sampling scheme may contribute to considerable improvement in the safety and efficacy profile of patients undergoing treatment for stem cell transplantation.