Objective To develop a geometric model from computed tomographic (CT) images in non-

Synaptically released glutamate evokes slow IPSPs mediated by metabotropic glutamate receptors (mGluRs) in midbrain dopamine neurons. These mGluR IPSPs are caused by release of Ca(2+) from intracellular stores and subsequent activation of small-conductance Ca(2+)-activated K(+) channels (SK channels). To further investigate the intracellular mechanisms involved, the effect of photolyzing intracellular caged inositol 1,4,5-triphosphate (InsP(3)) on membrane conductance and intracellular Ca(2+) concentration ([Ca(2+)](i)) was examined in rat midbrain slices. Photolytic release of InsP(3) elicited a transient outward current and a sharp rise in [Ca(2+)](i) that lasted for approximately 5 sec. Apamin, a blocker of SK channels, abolished the InsP(3)-induced outward current without affecting the rise in [Ca(2+)](i). Depleting intracellular Ca(2+) stores with cyclopiazonic acid completely blocked both the outward current and the Ca(2+) transient elicited by InsP(3). InsP(3)-evoked Ca(2+) mobilization was not affected by blockade of ryanodine receptors with ruthenium red, whereas depleting ryanodine-sensitive Ca(2+) stores with ryanodine almost eliminated InsP(3)-induced Ca(2+) release. Increasing the size of intracellular Ca(2+) stores by means of prolonged depolarization added a late component to the outward current and a slow component to the rising phase of [Ca(2+)](i). These effects of depolarization were blocked by ruthenium red. These results show that InsP(3) activates SK channels by releasing Ca(2+) from InsP(3)-sensitive stores that also contain ryanodine receptors. Increasing intracellular Ca(2+) stores boosts InsP(3)-evoked responses by invoking Ca(2+)-induced Ca(2+) release through ryanodine receptors. This intracellular signaling pathway may play a significant role in regulating the excitability of midbrain dopamine neurons.

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Objectives We investigated the accuracy of high-field proton magnetic resonance spectroscopy (1H-MRS) and fluorine-18 2-fluoro-deoxyglucose positron emission tomography (18F-FDG-PET) for diagnosis of glioma progression following tumor resection, stereotactic radiation and chemotherapy. Methods Twelve post-therapy patients with histology proven gliomas (6 grade II and 6 grade III) presented with Magnetic Resonance Imaging (MRI) and clinical symptoms suggestive but not conclusive of progression were entered into the study. 1H-MRS data were acquired and 3D volumetric maps of choline (Cho) over creatine (Cr) were generated. Intensity of 18F-FDG uptake was evaluated on a semiquantitative scale. Results The accuracy of 1H-MRS and 18F-FDG-PET imaging for diagnosis of glioma progression was 75% and 83% respectively. Classifying the tumors by grade improved accuracy of 18F-FDG-PET to 100% in high-grade gliomas and accuracy of 1H-MRS to 80% in low-grade tumors. Spearman's analysis demonstrated a trend between 18F-FDG uptake and tumor grading (ρ = 0.612, p-value = 0.272). The results of 18F-FDG-PET and 1H-MRS were concordant in 75% (9/12) of cases. Conclusion The combination of 1H-MRS data and 18F-FDG-PET imaging can enhance detection of glioma progression. 1H-MRS imaging was more accurate in low-grade gliomas and 18F-FDG-PET provided better accuracy in high-grade gliomas.

Objectives: The ability to differentiate between brain tumor progression and radiation therapy induced necrosis is critical for appropriate patient management. In order to improve the differential diagnosis, we combined fluorine-18 2-fluoro-deoxyglucose positron emission tomography (18 F-FDG PET), proton magnetic resonance spectroscopy (1 H MRS) and histological data to develop a multi-parametric machine-learning model. Methods: We enrolled twelve post-therapy patients with grade 2 and 3 gliomas that were suspicious of tumor progression. All patients underwent 18 F-FDG PET and 1 H MRS. Maximal standardized uptake value (SUVmax) of the tumors and reference regions were obtained. Multiple 2D maps of choline (Cho), creatine (Cr), and N-acetylaspartate (NAA) of the tumors were generated. A support vector machine (SVM) learning model was established to take imaging biomarkers and histological data as input vectors. A combination of clinical follow-up and multiple sequential MRI studies served as the basis for assessing the clinical outcome. All vector combinations were evaluated for diagnostic accuracy and cross validation. The optimal cutoff value of individual parameters was calculated using Receiver operating characteristic (ROC) plots. Results: The SVM and ROC analyses both demonstrated that SUVmax of the lesion was the most significant single diagnostic parameter (75% accuracy) followed by Cho concentration (67% accuracy). SVM analysis of all paired parameters showed SUVmax and Cho concentration in combination could achieve 83% accuracy. SUVmax of the lesion paired with SUVmax of the white matter as well as the tumor Cho paired with the tumor Cr both showed 83% accuracy. These were the most significant paired diagnostic parameters of either modality. Combining all four parameters did not improve the results. However, addition of two more parameters, Cho and Cr of brain parenchyma contralateral to the tumor, increased the accuracy to 92%. Conclusion: This study suggests that SVM models may improve detection of glioma progression more accurately than single parametric imaging methods. Research support: National Cancer Institute, Cancer Center Support Grant Supplement Award, Imaging Response Assessment Teams.

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