Annals of Nuclear Medicine

, Volume 26, Issue 4, pp 319–326 | Cite as

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

  • Tsutomu Soma
  • Toshimitsu Momose
  • Miwako Takahashi
  • Keitraro Koyama
  • Kensuke Kawai
  • Kenya Murase
  • Kuni Ohtomo
Original article



Inter-ictal 18F-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.


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.


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.


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.


Asymmetry index Statistical parametric mapping FDG-PET Mesial temporal lobe epilepsy 


  1. 1.
    Wiebe S, Blume WT, Girvin JP, Eliasziw M. A randomized, controlled trial of surgery for temporal-lobe epilepsy. N Engl J Med. 2001;345:311–88.PubMedCrossRefGoogle Scholar
  2. 2.
    McIntosh AM, Kalnins RM, Mitchell LA, Fabinyi GC, Briellmann RS, Berkovic SF. Temporal lobectomy: long-term seizure outcome, late recurrence and risks for seizure recurrence. Brain. 2004;127:2018–30.PubMedCrossRefGoogle Scholar
  3. 3.
    Lee DS, Lee JS, Kang KW, Jang MJ, Lee SK, Chung JK, et al. Disparity of perfusion and glucose metabolism of epileptogenic zones in temporal lobe epilepsy demonstrated by SPM/SPAM analysis on 15O water PET, [18F]FDG-PET, and [99mTc]-HMPAO SPECT. Epilepsia. 2001;42:1515–22.PubMedCrossRefGoogle Scholar
  4. 4.
    Kim MA, Heo K, Choo MK, Cho JH, Park SC, Lee JD, et al. Relationship between bilateral temporal hypometabolism and EEG findings for mesial temporal lobe epilepsy: analysis of 18F-FDG PET using SPM. Seizure. 2006;15:56–63.PubMedCrossRefGoogle Scholar
  5. 5.
    Kumar A, Juhasz C, Asano E, Sood S, Muzik O, Chugani HT. Objective detection of epileptic foci by 18F-FDG PET in children undergoing epilepsy surgery. J Nucl Med. 2010;51:1901–7.PubMedCrossRefGoogle Scholar
  6. 6.
    Wong CH, Bleasel A, Wen L, Eberl S, Byth K, Fulham M, et al. The topography and significance of extratemporal hypometabolism in refractory mesial temporal lobe epilepsy examined by FDG-PET. Epilepsia. 2010;51:1365–73.PubMedCrossRefGoogle Scholar
  7. 7.
    Ohta Y, Nariai T, Ishii K, Ishiwata K, Mishina M, Senda M, et al. Voxel- and ROI-based statistical analyses of PET parameters for guidance in the surgical treatment of intractable mesial temporal lobe epilepsy. Ann Nucl Med. 2008;22:495–503.PubMedCrossRefGoogle Scholar
  8. 8.
    Kim YK, Lee DS, Lee SK, Kim SK, Chung CK, Chang KH, et al. Differential features of metabolic abnormalities between medial and lateral temporal lobe epilepsy: quantitative analysis of (18)F-FDG PET using SPM. J Nucl Med. 2003;44:1006–12.PubMedGoogle Scholar
  9. 9.
    Lee JJ, Kang WJ, Lee DS, Lee JS, Hwang H, Kim KJ, et al. Diagnostic performance of 18F-FDG PET and ictal 99mTc-HMPAO SPET in pediatric temporal lobe epilepsy: quantitative analysis by statistical parametric mapping, statistical probabilistic anatomical map, and subtraction ictal SPET. Seizure. 2005;14:213–20.PubMedCrossRefGoogle Scholar
  10. 10.
    Van Bogaert P, Massager N, Tugendhaft P, Wikler D, Damhaut P, Levivier M, et al. Statistical parametric mapping of regional glucose metabolism in mesial temporal lobe epilepsy. Neuroimage. 2000;12:129–38.PubMedCrossRefGoogle Scholar
  11. 11.
    Didelot A, Mauguiere F, Redoute J, Bouvard S, Lothe A, Reilhac A, et al. Voxel-based analysis of asymmetry index maps increases the specificity of 18F-MPPF PET abnormalities for localizing the epileptogenic zone in temporal lobe epilepsies. J Nucl Med. 2010;51:1732–9.PubMedCrossRefGoogle Scholar
  12. 12.
    Engel J Jr, Van Ness PC, Rasmussen TB, Ojemann LM. Outcome with respect to epileptic seizure. In: Engel Jr J, editor. Surgical treatment of the epilepsies. 2nd ed. New York: Raven Press; 1993. p. 609–21.Google Scholar
  13. 13.
    Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18:192–205.PubMedCrossRefGoogle Scholar
  14. 14.
    Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain. New York: Thieme; 1988.Google Scholar
  15. 15.
    Sakai Y, Kumano H, Nishikawa M, Sakano Y, Kaiya H, Imabayashi E, et al. Cerebral glucose metabolism associated with a fear network in panic disorder. Neuroreport. 2005;16:927–31.PubMedCrossRefGoogle Scholar
  16. 16.
    Fox PT, Mintun MA, Reiman EM, Raichle ME. Enhanced detection of focal brain responses using intersubject averaging and change-distribution analysis of subtracted PET images. J Cereb Blood Flow Metab. 1988;8:642–53.PubMedCrossRefGoogle Scholar
  17. 17.
    Lancaster JL, Rainey LH, Summerlin JL, Freitas CS, Fox PT, Evans AC, et al. Automated labeling of the human brain: a preliminary report on the development and evaluation of a forward-transform method. Hum Brain Mapp. 1997;5:238–42.PubMedCrossRefGoogle Scholar
  18. 18.
    Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, et al. Automated Talairach Atlas labels for functional brain mapping. Hum Brain Mapp. 2000;10:120–31.PubMedCrossRefGoogle Scholar
  19. 19.
    Brett M, Johnsrude IS, Owen AM. The problem of functional localization in the human brain. Nat Rev Neurosci. 2002;3:243–9.PubMedCrossRefGoogle Scholar
  20. 20.
    Metz CE, Herman BA, Roe CA. Statistical comparison of two ROC-curve estimates obtained from partially-paired datasets. Med Decis Mak. 1998;18:110–21.CrossRefGoogle Scholar
  21. 21.
    Kanetaka H, Matsuda H, Asada T, Ohnishi T, Yamashita F, Imabayashi E, et al. Effects of partial volume correction on discrimination between very early Alzheimer’s dementia and controls using brain perfusion SPECT. Eur J Nucl Med Mol Imaging. 2004;31:975–80.PubMedCrossRefGoogle Scholar
  22. 22.
    Minoshima S, Frey KA, Koeppe RA, Foster NL, Kuhl DE. A diagnostic approach in Alzheimer’s disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET. J Nucl Med. 1995;36:1238–48.PubMedGoogle Scholar
  23. 23.
    Mizumura S, Kumita S, Cho K, Ishihara M, Nakajo H, Toba M, et al. Development of quantitative analysis method for stereotactic brain image: assessment of reduced accumulation in extent and severity using anatomical segmentation. Ann Nucl Med. 2003;17:289–95.PubMedCrossRefGoogle Scholar
  24. 24.
    Matsuda H, Mizumura S, Nagao T, Ota T, Iizuka T, Nemoto K, et al. Automated discrimination between very early Alzheimer disease and controls using an easy Z-score imaging system for multicenter brain perfusion single-photon emission tomography. Am J Neuroradiol. 2007;28:731–6.PubMedGoogle Scholar
  25. 25.
    Yanase D, Matsunari I, Yajima K, Chen W, Fujikawa A, Nishimura S, et al. Brain FDG PET study of normal aging in Japanese: effect of atrophy correction. Eur J Nucl Med Mol Imaging. 2005;32:794–805.PubMedCrossRefGoogle Scholar
  26. 26.
    Kawachi T, Ishii K, Sakamoto S, Matsui M, Mori T, Sasaki M. Gender differences in cerebral glucose metabolism: a PET study. J Neurol Sci. 2002;199:79–83.PubMedCrossRefGoogle Scholar

Copyright information

© The Japanese Society of Nuclear Medicine 2012

Authors and Affiliations

  • Tsutomu Soma
    • 1
    • 3
    • 4
  • Toshimitsu Momose
    • 1
  • Miwako Takahashi
    • 1
  • Keitraro Koyama
    • 1
  • Kensuke Kawai
    • 2
  • Kenya Murase
    • 3
  • Kuni Ohtomo
    • 1
  1. 1.Department of Radiology, Graduate School of MedicineUniversity of TokyoTokyoJapan
  2. 2.Department of Neurosurgery, Graduate School of MedicineUniversity of TokyoTokyoJapan
  3. 3.Course of Health Science, Division of Medical Technology and Science, Department of Medical Physics and Engineering, Graduate School of MedicineOsaka UniversityOsakaJapan
  4. 4.Software Development Group, Product Management and Marketing DepartmentFUJIFILM RI Pharma Co., LtdTokyoJapan

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