Voxel- and ROI-based statistical analyses of PET parameters for guidance in the surgical treatment of intractable mesial temporal lobe epilepsy

Asymmetry index in anatomically symmetrized FDG-PET for improved epileptogenic focus detection in pharmacoresistant epilepsy

OBJECTIVE

Positron emission tomography (PET) imaging has assumed an essential role in the presurgical evaluation of epileptogenic foci in drug-resistant epilepsy by identifying the hypometabolic cerebral cortex. The authors herein designed a pilot study to test a novel technique of PET asymmetry after anatomical symmetrization coregistered to MRI (PASCOM), utilizing interhemispheric metabolic asymmetry on interictal fluorine 18-labeled fluorodeoxyglucose (FDG)-PET to better localize the epileptogenic zone.

METHODS

The authors analyzed interictal FDG-PET scans from 23 patients with drug-resistant epilepsy, mean (± SD) age 20.9 ± 13.1 years old, who had an Engel class I postsurgical outcome while followed up for > 12 months. T1-weighted and FLAIR MRI were used to create a patient-specific, structurally symmetrical template. The asymmetry index (AI) image was computed to detect the cerebral region of hypometabolism using different z-score threshold criteria to optimize sensitivity and specificity. The detected regions were compared with the resection cavity on postoperative MRI using predefined anatomical labels. PASCOM was compared with the visual analysis of FDG-PET by a nuclear medicine consultant blinded to other clinical data (VIS) and visual analysis during multidisciplinary team discussion (MDT). The efficacy of each technique was compared based on a performance score (S), sensitivity, specificity, and correct lateralization of epileptogenicity.

RESULTS

The mean S was maximum (1.30 ± 1.23) for AI images when thresholded at z > 4 and retaining the cluster of more than 100 voxels containing the peak AI value (Z4C) with 73.03% sensitivity and 96.43% specificity. The mean S was minimum for VIS (0.27 ± 0.31). The mean sensitivity was maximum for MDT (85.04%) and minimum for Z5C (AI images thresholded at z > 5 and clustered; 59.47%), whereas the mean specificity was maximum for Z5C (97.77%) and minimum for VIS (64.60%). Z3C (AI images thresholded at z > 3 and clustered) and Z4C were able to correctly identify the side of epileptogenicity in all the patients.

CONCLUSIONS

The PASCOM technique with a Z4C threshold had a maximum performance score with good sensitivity and specificity in localizing and lateralizing the epileptogenic zone. The described technique outperformed the conventional visual analysis of FDG-PET and hence warrants further prospective verification.

Machine Learning-Derived Multimodal Neuroimaging of Presurgical Target Area to Predict Individual's Seizure Outcomes After Epilepsy Surgery

Objectives: Half of the patients who have tailored resection of the suspected epileptogenic zone for drug-resistant epilepsy have recurrent postoperative seizures. Although neuroimaging has become an indispensable part of delineating the epileptogenic zone, no validated method uses neuroimaging of presurgical target area to predict an individual's post-surgery seizure outcome. We aimed to develop and validate a machine learning-powered approach incorporating multimodal neuroimaging of a presurgical target area to predict an individual's post-surgery seizure outcome in patients with drug-resistant focal epilepsy. Materials and Methods: One hundred and forty-one patients with drug-resistant focal epilepsy were classified either as having seizure-free (Engel class I) or seizure-recurrence (Engel class II through IV) at least 1 year after surgery. The presurgical magnetic resonance imaging, positron emission tomography, computed tomography, and postsurgical magnetic resonance imaging were co-registered for surgical target volume of interest (VOI) segmentation; all VOIs were decomposed into nine fixed views, then were inputted into the deep residual network (DRN) that was pretrained on Tiny-ImageNet dataset to extract and transfer deep features. A multi-kernel support vector machine (MKSVM) was used to integrate multiple views of feature sets and to predict seizure outcomes of the targeted VOIs. Leave-one-out validation was applied to develop a model for verifying the prediction. In the end, performance using this approach was assessed by calculating accuracy, sensitivity, and specificity. Receiver operating characteristic curves were generated, and the optimal area under the receiver operating characteristic curve (AUC) was calculated as a metric for classifying outcomes. Results: Application of DRN-MKSVM model based on presurgical target area neuroimaging demonstrated good performance in predicting seizure outcomes. The AUC ranged from 0.799 to 0.952. Importantly, the classification performance DRN-MKSVM model using data from multiple neuroimaging showed an accuracy of 91.5%, a sensitivity of 96.2%, a specificity of 85.5%, and AUCs of 0.95, which were significantly better than any other single-modal neuroimaging (all p ˂ 0.05). Conclusion: DRN-MKSVM, using multimodal compared with unimodal neuroimaging from the surgical target area, accurately predicted postsurgical outcomes. The preoperative individualized prediction of seizure outcomes in patients who have been judged eligible for epilepsy surgery could be conveniently facilitated. This may aid epileptologists in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.

Voxel-Based Analysis of [18F]-FDG Brain PET in Rats Using Data-Driven Normalization

Introduction: [18F]-FDG PET is a widely used imaging modality that visualizes cellular glucose uptake and provides functional information on the metabolic state of different tissues in vivo. Various quantification methods can be used to evaluate glucose metabolism in the brain, including the cerebral metabolic rate of glucose (CMR_glc) and standard uptake values (SUVs). Especially in the brain, these (semi-)quantitative measures can be affected by several physiological factors, such as blood glucose level, age, gender, and stress. Next to this inter- and intra-subject variability, the use of different PET acquisition protocols across studies has created a need for the standardization and harmonization of brain PET evaluation. In this study we present a framework for statistical voxel-based analysis of glucose uptake in the rat brain using histogram-based intensity normalization.

Methods: [18F]-FDG PET images of 28 normal rat brains were coregistered and voxel-wisely averaged. Ratio images were generated by voxel-wisely dividing each of these images with the group average. The most prevalent value in the ratio image was used as normalization factor. The normalized PET images were voxel-wisely averaged to generate a normal rat brain atlas. The variability of voxel intensities across the normalized PET images was compared to images that were either normalized by whole brain normalization, or not normalized.

To illustrate the added value of this normal rat brain atlas, 9 animals with a striatal hemorrhagic lesion and 9 control animals were intravenously injected with [18F]-FDG and the PET images of these animals were voxel-wisely compared to the normal atlas by group- and individual analyses.

Results: The average coefficient of variation of the voxel intensities in the brain across normal [18F]-FDG PET images was 6.7% for the histogram-based normalized images, 11.6% for whole brain normalized images, and 31.2% when no normalization was applied. Statistical voxel-based analysis, using the normal template, indicated regions of significantly decreased glucose uptake at the site of the ICH lesion in the ICH animals, but not in control animals.

Conclusion: In summary, histogram-based intensity normalization of [18F]-FDG uptake in the brain is a suitable data-driven approach for standardized voxel-based comparison of brain PET images.

A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy

PURPOSE

Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET) imaging has been used for the evaluation of pediatric epilepsy. However, the epileptic focus is very difficult to be identified by visual assessment since it may present either hypo- or hyper-metabolic abnormality with unclear boundary. This study aimed to develop a novel symmetricity-driven deep learning framework of PET imaging for the identification of epileptic foci in pediatric patients with temporal lobe epilepsy (TLE).

METHODS

We retrospectively included 201 pediatric patients with TLE and 24 age-matched controls who underwent ¹⁸F-FDG PET-CT studies. ¹⁸F-FDG PET images were quantitatively investigated using 386 symmetricity features, and a pair-of-cube (PoC)-based Siamese convolutional neural network (CNN) was proposed for precise localization of epileptic focus, and then metabolic abnormality level of the predicted focus was calculated automatically by asymmetric index (AI). Performances of the proposed framework were compared with visual assessment, statistical parametric mapping (SPM) software, and Jensen-Shannon divergence-based logistic regression (JS-LR) analysis.

RESULTS

The proposed deep learning framework could detect the epileptic foci accurately with the dice coefficient of 0.51, which was significantly higher than that of SPM (0.24, P < 0.01) and significantly (or marginally) higher than that of visual assessment (0.31-0.44, P = 0.005-0.27). The area under the curve (AUC) of the PoC classification was higher than that of the JS-LR (0.93 vs. 0.72). The metabolic level detection accuracy of the proposed method was significantly higher than that of visual assessment blinded or unblinded to clinical information (90% vs. 56% or 68%, P < 0.01).

CONCLUSION

The proposed deep learning framework for ¹⁸F-FDG PET imaging could identify epileptic foci accurately and efficiently, which might be applied as a computer-assisted approach for the future diagnosis of epilepsy patients.

TRIAL REGISTRATION

NCT04169581. Registered November 13, 2019 Public site: https://clinicaltrials.gov/ct2/show/NCT04169581.

Intensity normalization methods in brain FDG-PET quantification

BACKGROUND

The lack of standardization of intensity normalization methods and its unknown effect on the quantification output is recognized as a major drawback for the harmonization of brain FDG-PET quantification protocols. The aim of this work is the ground truth-based evaluation of different intensity normalization methods on brain FDG-PET quantification output.

METHODS

Realistic FDG-PET images were generated using Monte Carlo simulation from activity and attenuation maps directly derived from 25 healthy subjects (adding theoretical relative hypometabolisms on 6 regions of interest and for 5 hypometabolism levels). Single-subject statistical parametric mapping (SPM) was applied to compare each simulated FDG-PET image with a healthy database after intensity normalization based on reference regions methods such as the brain stem (RRBS), cerebellum (RRC) and the temporal lobe contralateral to the lesion (RRTL), and data-driven methods, such as proportional scaling (PS), histogram-based method (HN) and iterative versions of both methods (iPS and iHN). The performance of these methods was evaluated in terms of the recovery of the introduced theoretical hypometabolic pattern and the appearance of unspecific hypometabolic and hypermetabolic findings.

RESULTS

Detected hypometabolic patterns had significantly lower volumes than the introduced hypometabolisms for all intensity normalization methods particularly for slighter reductions in metabolism . Among the intensity normalization methods, RRC and HN provided the largest recovered hypometabolic volumes, while the RRBS showed the smallest recovery. In general, data-driven methods overcame reference regions and among them, the iterative methods overcame the non-iterative ones. Unspecific hypermetabolic volumes were similar for all methods, with the exception of PS, where it became a major limitation (up to 250 cm³) for extended and intense hypometabolism. On the other hand, unspecific hypometabolism was similar far all methods, and usually solved with appropriate clustering.

CONCLUSIONS

Our findings showed that the inappropriate use of intensity normalization methods can provide remarkable bias in the detected hypometabolism and it represents a serious concern in terms of false positives. Based on our findings, we recommend the use of histogram-based intensity normalization methods. Reference region methods performance was equivalent to data-driven methods only when the selected reference region is large and stable.

Evaluation of brain FDG PET images in temporal lobe epilepsy for lateralization of epileptogenic focus using data mining methods

Background/aim

In temporal lobe epilepsy (TLE), brain positron emission tomography (PET) performed with F-18 fluorodeoxyglucose (FDG) is commonly used for lateralization of the epileptogenic temporal lobe. In this study, we aimed to evaluate the success of quantitative analysis of brain FDG PET images using data mining methods in the lateralization of the epileptogenic temporal lobe.

Materials and methods

Presurgical interictal brain FDG PET images of 49 adult mesial TLE patients with a minimum of 2 years of postsurgical follow-up and Engel I outcomes were retrospectively analyzed. Asymmetry indices were calculated from PET images from the mesial temporal lobe and its contiguous structures. The J48 and the logistic model tree (LMT) data mining algorithms were used to find classification rules for the lateralization of the epileptogenic temporal lobe. The classification results obtained by these rules were compared with the physicians’ visual readings and the findings of single-patient statistical parametric mapping (SPM) analyses in a test set of 18 patients. An additional 5-fold cross-validation was applied to the data to overcome the limitation of a relatively small sample size.

Results

In the lateralization of 18 patients in the test set, J48 and LMT methods were successful in 16 (89%) and 17 (94%) patients, respectively. The visual consensus readings were correct in all patients and SPM results were correct in 16 patients. The 5-fold cross- validation method resulted in a mean correct lateralization ratio of 96% (47/49) for the LMT algorithm. This ratio was 88% (43 / 49) for the J48 algorithm.

Conclusion

Lateralization of the epileptogenic temporal lobe with data mining methods using regional metabolic asymmetry values obtained from interictal brain FDG PET images in mesial TLE patients is highly accurate. The application of data mining can contribute to the reader in the process of visual evaluation of FDG PET images of the brain.

PET Imaging of 18F-FDG, 11C-methionine, 11C-flumazenil, and 11C-4DST in Progressive Multifocal Leukoencephalopathy

The use of positron emission tomography (PET) imaging in progressive multifocal leukoencephalopathy (PML) has rarely been reported. We herein report a set of PET images in a 63-year-old patient with PML. In PML lesions, the uptake of ¹⁸F-fluorodeoxyglucose, ¹¹C-methionine, ¹¹C-flumazenil, and [methyl-¹¹C]4'-thiothymidine was decreased, increased, decreased, and unchanged, respectively. These results suggest that glucose metabolism decreased, protein synthesis increased, neuronal integrity decreased, and the DNA synthesis and cellular proliferation of host cells were not activated in PML lesions. These results may reflect very little infiltration by inflammatory cells and active infection with JC virus in this case.

Comparative Study of Voxel-Based Epileptic Foci Localization Accuracy between Statistical Parametric Mapping and Three-dimensional Stereotactic Surface Projection

Introduction

Fluorine-18-fluorodeoxyglucose positron-emission tomography (¹⁸F-FDG-PET) is widely used to help localize the hypometabolic epileptogenic focus for presurgical evaluation of drug-refractory epilepsy patients. Two voxel-based brain mapping methods to interpret ¹⁸F-FDG-PET, statistical parametric mapping (SPM) and three-dimensional stereotactic surface projection (3D-SSP), improve the detection rate of seizure foci. This study aimed to compare the consistency of epileptic focus detection between SPM and 3D-SSP for ¹⁸F-FDG-PET brain mapping analysis.

Methods

We retrospectively reviewed the clinical, electroecephalographic, and brain imaging results of 35 patients with refractory epilepsy. ¹⁸F-FDG-PET studies were revaluated by SPM, 3D-SSP, and visual assessment, and the results were compared to the magnetic resonance imaging (MRI) lesion location and to the presumed epileptogenic zone (PEZ) defined by video-electroencephalogram and other clinical data. A second consistency study compared PET analyses to histopathology and surgical outcomes in the 19 patients who underwent lesion resection surgery.

Results

Of the 35 patients, consistency with the PEZ was 29/35 for SPM, 25/35 for 3D-SSP, 14/35 for visual assessment, and 10/35 for MRI. Concordance rates with the PEZ were significantly higher for SPM and 3D-SSP than for MRI (P < 0.05) and visual assessment (P < 0.05). Differences between SPM and 3D-SSP and between visual assessment and MRI were not significant. In the 19 surgical patients, concordance with histopathology/clinical outcome was 14/19 for SPM, 15/19 for 3D-SSP, 14/19 for visual assessment, and 9/19 for MRI (P > 0.05). A favorable Engel outcome (class I/II) was found in 16 of 19 cases (84%), and failure of seizure control was found in 3 of 19 patients (class III/IV).

Conclusion

Voxel-based ¹⁸F-FDG-PET brain mapping analysis using SPM or 3D-SSP can improve the detection rate of the epileptic focus compared to visual assessment and MRI. Consistency with PEZ was similar between SPM and 3D-SSP; according to their own characteristics, 3D-SSP is recommended for primary evaluation due to greater efficiency and operability of the software, while SPM is recommended for high-accuracy localization of complex lesions. Therefore, joint application of both software packages may be the best solution for FDG-PET analysis of epileptic focus localization.

Neurofunctional Signature of Hyperfamiliarity for Unknown Faces

Hyperfamiliarity for unknown faces is a rare selective disorder that consists of the disturbing and abnormal feeling of familiarity for unknown faces, while recognition of known faces is normal. In one such patient we investigated with a multimodal neuroimaging design the hitherto undescribed neural signature associated with hyperfamiliarity feelings. Behaviorally, signal detection methods revealed that the patient's discrimination sensitivity between familiar and unfamiliar faces was significantly lower than that of matched controls, and her response criterion for familiarity decisions was significantly more liberal. At the neural level, while morphometric analysis and single-photon emission CT (SPECT) showed the atrophy and hypofunctioning of the left temporal regions, functional magnetic resonance imaging (fMRI) revealed that hyperfamiliarity feelings were selectively associated to enhanced activity in the right medial and inferior temporal cortices. We therefore characterize the neurofunctional signature of hyperfamiliarity for unknown faces as related to the loss of coordinated activity between the complementary face processing functions of the left and right temporal lobes.

Application of dynamic susceptibility contrast-enhanced perfusion in temporal lobe epilepsy

BACKGROUND

Accurately locating the epileptogenic focus in temporal lobe epilepsy (TLE) is important in clinical practice. Single-photon emission computed tomography (SPECT) and positron-emission tomography (PET) have been widely used in the lateralization of TLE, but both have limitations. Magnetic resonance perfusion imaging can accurately and reliably reflect differences in cerebral blood flow and volume.

PURPOSE

To investigate the diagnostic value of dynamic susceptibility contrast-enhanced (DSC) perfusion magnetic resonance imaging (MRI) in the lateralization of the epileptogenic focus in TLE.

MATERIAL AND METHODS

Conventional MRI and DSC-MRI scanning was performed in 20 interictal cases of TLE and 20 healthy volunteers. The relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) of the bilateral mesial temporal lobes of the TLE cases and healthy control groups were calculated. The differences in the perfusion asymmetry indices (AIs), derived from the rCBV and rCBF of the bilateral mesial temporal lobes, were compared between the two groups.

RESULTS

In the control group, there were no statistically significant differences between the left and right sides in terms of rCBV (left 1.55 ± 0.32, right 1.57 ± 0.28) or rCBF (left 99.00 ± 24.61, right 100.38 ± 23.46) of the bilateral mesial temporal lobes. However, in the case group the ipsilateral rCBV and rCBF values (1.75 ± 0.64 and 96.35 ± 22.63, respectively) were markedly lower than those of the contralateral side (2.01 ± 0.79 and 108.56 ± 26.92; P < 0.05). Both the AI of the rCBV (AI(rCBV); 13.03 ± 10.33) and the AI of the rCBF (AI(rCBF); 11.24 ± 8.70) of the case group were significantly higher than that of the control group (AI(rCBV) 5.55 ± 3.74, AI(rCBF) 5.12 ± 3.48; P < 0.05). The epileptogenic foci of nine patients were correctly lateralized using the 95th percentile of the AI(rCBV) and AI(rCBF) of the control group as the normal upper limits.

CONCLUSION

In patients with TLE interictal, both rCBV and rCBF of the ipsilateral mesial temporal lobe were markedly lower than that of healthy control subjects. DSC-MRI can provide lateralization for TLE.

Optimizing statistical parametric mapping analysis of 18F-FDG PET in children

BACKGROUND

Statistical parametric mapping (SPM) procedure is an objective tool to analyze 18F-fluoro-2-deoxy-d-glucose-positron-emission tomography (FDG-PET) images and a useful complement to visual analysis. However, SPM requires a comparison to control data set that cannot be obtained in healthy children for ethical reasons. Using adults as controls showed some limitations. The purpose of the present study was to generate and validate a group of pseudo-normal children as a control group for FDG-PET studies in pediatrics.

METHODS

FDG-PET images of 47 children (mean ± SD age 10.2 ± 3.1 years) with refractory symptomatic (MRI-positive, n = 20) and cryptogenic (MRI-negative, n = 27) focal epilepsy planned for surgery were analyzed using visual and SPM analysis. Performances of SPM analysis were compared using two different control groups: (1) an adult control group consisting of healthy young adults (n = 25, 30.5 ± 5.8 years, adult PET template) and (2) a pediatric pseudo-control group consisting of patients (n = 24, 10.6 ± 3.1 years, children PET template) with refractory focal epilepsy but with negative MRI and with PET considered normal not only on visual analysis but also on SPM.

RESULTS

Among the 47 children, visual analysis succeeded detecting at least one hypometabolic area in 87% of the cases (interobserver kappa = 0.81). Regarding SPM analysis, the best compromise between sensitivity and specificity was obtained with a threshold of p less than 0.001 as an extent of more than 40 voxels. There was a significant concordance to detect hypometabolic areas between both SPM analyses [kappa (K) = 0.59; p < 0.005] and between both SPM and visual analyses (K = 0.45; p < 0.005), in symptomatic (K = 0.74; p < 0.005) as in cryptogenic patients (K = 0.26; p < 0.01). The pediatric pseudo-control group dramatically improved specificity (97% vs. 89%; p < 0.0001) by increasing the positive predictive value (86% vs. 65%). Sensitivity remained acceptable although it was not better (79% vs. 87%, p = 0.039). The main impact was to reduce by 41% the number of hypometabolic cortical artifacts detected by SPM, especially in the younger epileptic patients, which is a key point in clinical practice.

CONCLUSIONS

This age-matched pseudo-control group is a way to optimize SPM analysis of FDG-PET in children with epilepsy. It might also be considered for other brain pathologies in pediatrics in the future.

PET of serotonin 1A receptors and cerebral glucose metabolism for temporal lobectomy

The objective of this study was to compare 5-hydroxytryptamine receptor 1A (5-HT(1A)) PET with cerebral metabolic rate of glucose (CMRglc) PET for temporal lobectomy planning.

METHODS

We estimated 5-HT(1A) receptor binding preoperatively with (18)F-trans-4-fluoro-N-2-[4-(2-methoxyphenyl) piperazin-1-yl]ethyl-N-(2-pyridyl) cyclohexane carboxamide ((18)F-FCWAY) PET and CMRglc measurement with (18)F-FDG in regions drawn on coregistered MRI after partial-volume correction in 41 patients who had anterior temporal lobectomy with at least a 1-y follow-up. Surgery was tailored to individual preresection evaluations and intraoperative electrocorticography. Mean regional asymmetry values and the number of regions with asymmetry exceeding 2 SDs in 16 healthy volunteers were compared between seizure-free and non-seizure-free patients. (18)F-FCWAY but not (18)F-FDG and MRI data were masked for surgical decisions and outcome assessment.

RESULTS

Twenty-six of 41 (63%) patients seizure-free since surgery had significantly different mesial temporal asymmetries, compared with 15 non-seizure-free patients for both (18)F-FCWAY (F(1,39) = 5.87; P = 0.02) and (18)F-FDG PET (F(1,38) = 5.79; P = 0.021). The probability of being seizure-free was explained by both (18)F-FDG and (18)F-FCWAY PET, but not MRI, with a significant additional (18)F-FCWAY effect (chi(2)(2) = 9.8796; P = 0.0072) after the probability of being seizure-free was explained by (18)F-FDG. Although MRI alone was not predictive, any combination of 2 lateralizing imaging studies was highly predictive of seizure freedom.

CONCLUSION

Our study provides class III evidence that both 5-HT(1A) receptor PET and CMRglc PET can contribute to temporal lobectomy planning. Additional studies should explore the potential for temporal lobectomy based on interictal electroencephalography and minimally invasive imaging studies.

Usefulness of extent analysis for statistical parametric mapping with asymmetry index using inter-ictal FGD-PET in mesial temporal lobe epilepsy

OBJECTIVE

Inter-ictal (18)F-2-fluoro-deoxy-D: -glucose-positron emission tomography (FDG-PET) is widely used for preoperative evaluation to identify epileptogenic zones in patients with temporal lobe epilepsy. In this study, we combined statistical parametric mapping (SPM) with the asymmetry index and volume-of-interest (VOI) based extent analysis employing preoperative FDG-PET in unilateral mesial temporal lobe epilepsy (MTLE) patients. We also evaluated the detection utility of these techniques for automated identification of abnormalities in the unilateral hippocampal area later confirmed to be epileptogenic zones by surgical treatment and subsequent good seizure control.

METHODS

FDG-PET scans of 17 patients (9 males, mean age 35 years, age range 16-60 years) were retrospectively analyzed. All patients had been preoperatively diagnosed with unilateral MTLE. The surgical outcomes of all patients were Engel class 1A or 1B with postoperative follow-up of 2 years. FDG-PET images were spatially normalized and smoothed. After two voxel-value adjustments, one employing the asymmetry index and the other global normalization, had been applied to the images separately, voxel-based statistical comparisons were performed with 20 controls. Peak analysis and extent analysis in the VOI in the parahippocampal gyrus were conducted for SPM. For the extent analysis, a receiver operating characteristic (ROC) curve was devised to calculate the area under the curve and to determine the optimal threshold of extent.

RESULTS

The accuracy of the method employing the asymmetry index was better than that of the global normalization method for both the peak and the extent analysis. The ROC analysis results, for the extent analysis, yielded an area under the curve of 0.971, such that the accuracy and optimal extent threshold of judgment were 92 and 32.9%, respectively.

CONCLUSION

Statistical z-score mapping with the asymmetry index was more sensitive for detecting regional glucose hypometabolism and more accurate for identifying the side harboring the epileptogenic zone using inter-ictal FDG-PET in unilateral MTLE than z-score mapping with global normalization. Moreover, the automated determination of the side with the epileptogenic zone in unilateral MTLE showed improved accuracy when the combination of SPM with the asymmetry index and extent analysis was applied based on the VOI in the parahippocampal gyrus.

Development of PET radiopharmaceuticals and their clinical applications at the Positron Medical Center

The Positron Medical Center has developed a large number of radiopharmaceuticals and 36 radiopharmaceuticals have been approved for clinical use for studying aging and geriatric diseases, especially brain functions. Positron emission tomography (PET) has been used to provide a highly advanced PET-based diagnosis. The current status of the development of radiopharmaceuticals, and representative clinical and methodological results are reviewed.

Influence of prefrontal target region on the efficacy of repetitive transcranial magnetic stimulation in patients with medication-resistant depression: a [(18)F]-fluorodeoxyglucose PET and MRI study

It is currently unknown whether the antidepressant effect of repetitive transcranial magnetic stimulation (rTMS) depends on specific characteristics of the stimulated frontal area, such as metabolic changes. We investigated the effect of high-frequency rTMS, administered over the most hypometabolic prefrontal area in depressed patients in a two-site, double-blind, randomized placebo-controlled add-on study. Forty-eight patients with medication-resistant major depression underwent magnetic resonance imaging and [(18)F]-fluorodeoxyglucose positron emission tomography (PET) in order to determine a target area for rTMS. After randomization to PET-guided (n = 16), standard (n = 18), or sham rTMS (n = 14) conditions, the patients received 10 sessions of 10-Hz rTMS (1600 pulses/session) at 90% motor threshold. Change from baseline in Montgomery-Asberg Depression Rating Scale (MADRS) scores did not differ between PET-guided, standard and sham groups at 2-wk end-point. Exploratory comparison of left PET-guided (n = 9), right PET-guided, standard, and sham rTMS revealed significant effects. The highest improvement in MADRS scores was observed with left PET-guided (60 + or - 31%), significantly superior to sham (30 + or - 37%, p = 0.01) and right-guided (31 + or - 33%, p = 0.02) stimulation. Comparison between left PET-guided and standard rTMS (49 + or - 28%) was not significant (p = 0.12). Comparison between stimulation over dorsolateral prefrontal cortex (BA 9-46), stimulation of other areas, and sham rTMS was statistically significant. Stimulation over BA 9-46 region (n = 15) was superior to sham rTMS (p = 0.02). The results do not support the general hypothesis of increased antidepressant effects of high-frequency rTMS with prefrontal hypometabolism-related PET guidance. Nonetheless, whether metabolism and anatomy characteristics of left frontal area underneath the coil might account for an increase or speeding up of rTMS effects needs further investigation.