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A Standardized [18F]-FDG-PET Template for Spatial Normalization in Statistical Parametric Mapping of Dementia

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Abstract

[18F]-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) is a widely used diagnostic tool that can detect and quantify pathophysiology, as assessed through changes in cerebral glucose metabolism. [18F]-FDG PET scans can be analyzed using voxel-based statistical methods such as Statistical Parametric Mapping (SPM) that provide statistical maps of brain abnormalities in single patients. In order to perform SPM, a “spatial normalization” of an individual’s PET scan is required to match a reference PET template. The PET template currently used for SPM normalization is based on [15O]-H2O images and does not resemble either the specific metabolic features of [18F]-FDG brain scans or the specific morphological characteristics of individual brains affected by neurodegeneration. Thus, our aim was to create a new [18F]-FDG PET aging and dementia-specific template for spatial normalization, based on images derived from both age-matched controls and patients. We hypothesized that this template would increase spatial normalization accuracy and thereby preserve crucial information for research and diagnostic purposes. We investigated the statistical sensitivity and registration accuracy of normalization procedures based on the standard and new template—at the single-subject and group level—independently for subjects with Mild Cognitive Impairment (MCI), probable Alzheimer’s Disease (AD), Frontotemporal lobar degeneration (FTLD) and dementia with Lewy bodies (DLB). We found a significant statistical effect of the population-specific FDG template-based normalisation in key anatomical regions for each dementia subtype, suggesting that spatial normalization with the new template provides more accurate estimates of metabolic abnormalities for single-subject and group analysis, and therefore, a more effective diagnostic measure.

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References

  • Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., et al. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging–Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 270–279.

    Article  Google Scholar 

  • Anchisi, D., Borroni, B., Franceschi, M., Kerrouche, N., Kalbe, E., Beuthien-Beumann, B., Cappa, S., et al. (2005). Heterogeneity of brain glucose metaboism in mild cognitive impairment and clinical progression to Alzheimer disease. Archives of Neurology, 62(11), 1728–1733.

    Article  PubMed  Google Scholar 

  • Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. NeuroImage, 38(1), 95–113.

    Article  PubMed  Google Scholar 

  • Ashburner, J., & Friston, K. J. (1999). Nonlinear spatial normalization using basis functions. Human Brain Mapping, 7(4), 254–266.

    Article  PubMed  CAS  Google Scholar 

  • Bartenstein, P., Asenbaum, S., Catafau, A., Halldin, C., Pilowski, L., Pupi, A., et al. (2002). European Association of Nuclear Medicine procedure guidelines for brain imaging using [(18)F]FDG. European Journal of Nuclear Medicine and Molecular Imaging, 29(10), BP43–BP48.

    PubMed  CAS  Google Scholar 

  • Berti, V., Osorio, R. S., Mosconi, L., Li, Y., De Santi, S., & de Leon, M. J. (2010). Early detection of Alzheimer’s disease with PET imaging. Neurodegenerative Diseases, 7(1–3), 131–135.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Bornebroek, M., & Bretelera, M. B. (2004). Epidemiology of non-AD dementia. Clinical Neuroscience Reserach, 3, 349–361.

    Article  Google Scholar 

  • Buchert, R., Wilke, F., Chakrabarti, B., Martin, B., Brenner, W., Mester, J., & Clausen, M. (2005). Adjusted scaling of FDG positron emission tomography images for statistical evaluation in patients with suspected Alzheimer’s disease. Journal of Neuroimaging, 15, 348–355.

    Article  PubMed  Google Scholar 

  • Caroli, A., Prestia, A., Chen, K., Ayutyanont, N., Landau, S. M., Madison, C. M., et al. (2012). Summary metrics to assess Alzheimer disease-related hypometabolic pattern with 18F-FDG PET: head-to-head comparison. Journal of Nuclear Medicine, 53(4), 592–600.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  • Chételat, G., Desgranges, B., de la Sayette, V., Viader, F., Eustache, F., & Baron, J. C. (2003). Mild cognitive impairment: Can FDG-PET predict who is to rapidly convert to Alzheimer’s disease? Neurology, 60(8), 1374–1377.

    Article  PubMed  Google Scholar 

  • Crivello, F., Schormann, T., Tzourio-Mazoyer, N., Roland, P. E., Zilles, K., & Mazoyer, B. M. (2002). Comparison of spatial normalization procedures and their impact on functional maps. Human Brain Mapping, 16(4), 228–250.

    Article  PubMed  Google Scholar 

  • Dubois, B., Feldman, H. H., Jacova, C., Cummings, J. L., Dekosky, S. T., Barberger-Gateau, P., et al. (2010). Revising the definition of Alzheimer’s disease: a new lexicon. Lancet Neurology, 9(11), 1118–1127.

    Article  Google Scholar 

  • Eickhoff, S., Stephan, K. E., Mohlberg, H., Grefkes, C., Fink, G. R., Amunts, K., et al. (2005). A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage, 25(4), 1325–1335.

    Article  PubMed  Google Scholar 

  • Evans, A. C., Collins, D. L., Mills, S. R., Brown, E. D., Kelly, R. L., & Peters, T. M. (1993). 3D statistical neuroanatomical models from 305 MRI volumes. IEEE-Nuclear Science Symposium and Medical Imaging Conference Proceeding, 1813–1817.

  • Foster, N. L., Heidebrink, J. L., Clark, C. M., Jagust, W. J., Arnold, S. E., Barbas, N. R., et al. (2007). FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease. Brain, 130(Pt10), 2616–2635.

    Article  PubMed  Google Scholar 

  • Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. P., Frith, C. D., & Frackowiak, R. S. (1994). Statistical parametric maps in functional imaging: a general linear approach. Human Brain Mapping, 2(4), 189–210.

    Article  Google Scholar 

  • Gispert, J. D., Pascau, J., Reig, S., Martinez-Lazaro, R., Molina, V., Garcia-Barreno, P., et al. (2003). Influence of the normalization template on the outcome of statistical parametric mapping of PET scans. NeuroImage, 19(3), 601–612.

    Article  PubMed  CAS  Google Scholar 

  • Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J., & Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14(1 Pt1), 21–36.

    Article  PubMed  CAS  Google Scholar 

  • Herholz, K., Salmon, E., Perani, D., Baron, J.-C., Holthoff, V., Frölich, L., Schönknecht, P., et al. (2002). Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. NeuroImage, 17(1), 302–316.

    Article  PubMed  CAS  Google Scholar 

  • Ishii, K., Willoch, F., Minoshima, S., Drzezga, A., Ficaro, E. P., Cross, D. J., et al. (2001). Statistical brain mapping of 18F-FDG PET in Alzheimer’s disease: validation of anatomic standardization for atrophied brains. Journal of Nuclear Medicine, 42(4), 548–557.

    PubMed  CAS  Google Scholar 

  • Jack, C. R., Jr., Albert, M. S., Knopman, D. S., McKhann, G. M., Sperling, R. A., Carrillo, M. C., et al. (2011). Introduction to the recommendations from the National Institute on Aging–Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 257–262.

    Article  Google Scholar 

  • Kherif, F., Poline, J. P., Mériaux, S., Benali, H., Flandin, G., & Brett, M. (2003). Group analysis in functional neuroimaging: selecting subjects using similarity measures. NeuroImage, 20(4), 2197–2208.

    Article  PubMed  Google Scholar 

  • Krishnan, S., Slavin, M. J., Tran, T. T., Doraiswamy, P. M., & Petrella, J. R. (2006). Accuracy of spatial normalization of the hippocampus: implications for fMRI research in memory disorders. NeuroImage, 31(2), 560–571.

    Article  PubMed  Google Scholar 

  • Magistretti, P. J. (2000). Cellular bases of functional brain imaging: insights from neuron-glia metabolic coupling. Brain Research, 886(1–2), 108–112.

    Article  PubMed  CAS  Google Scholar 

  • Markiewicz, P. J., Matthews, J. C., Declerck, J., & Herholz, K. (2009). Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimer’s disease. NeuroImage, 46(2), 472–485.

    Article  PubMed  CAS  Google Scholar 

  • Martino, M. E., Guzman de Villoria, J., Lacalle-Aurioles, M., Olazaran, J., Cruz, I., Navarro, E., et al. (2013). Comparison of different methods of spatial normalization of FDG-PET brain images in the voxel-wise analysis of MCI patients and controls. Annals of Nuclear Medicine. doi:10.1007/s12149-013-0723-7.

    PubMed  Google Scholar 

  • McKeith, I. G., Dickson, D. W., Lowe, J., Emre, M., O’Brien, J. T., Feldman, H., et al. (2005). Diagnosis and management of dementia with Lewy bodies: third report of the DLB Consortium. Neurology, 65(12), 1863–1872.

    Article  PubMed  CAS  Google Scholar 

  • McKhann, G. M., Albert, M. S., Grossman, M., Miller, B., Dickson, D., & Trojanowski, J. Q. (2001). Clinical and pathological diagnosis of frontotemporal dementia: report of the Work Group on Frontotemporal Dementia and Pick’s Disease. Archives of Neurology, 58, 1803–1809.

    Article  PubMed  CAS  Google Scholar 

  • McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Jr., Kawas, C. H., Klunk, W. E., et al. (2011). The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging– Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 263–269.

    Article  Google Scholar 

  • Minoshima, S., Frey, K. A., Koeppe, R. A., Foster, N. L., & Kuhl, D. E. (1995). A diagnostic approach in Alzheimer’s disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET. Journal of Nuclear Medicine, 36(7), 1238–1248.

    PubMed  CAS  Google Scholar 

  • Minoshima, S., Giordani, B., Berent, S., Frey, K. A., Foster, N. L., & Kuhl, D. E. (1997). Metabolic reduction in the posterior cingulate cortex in very early Alzheimer’s disease. Annals of Neurology, 42(1), 85–94.

    Article  PubMed  CAS  Google Scholar 

  • Minoshima, S., Foster, N. L., Sima, A. A., Frey, K. A., Albin, R. L., & Kuhl, D. E. (2001). Alzheimer’s disease versus dementia with Lewy bodies: cerebral metabolic distinction with autopsy confirmation. Annals of Neurology, 50(3), 358–365.

    Article  PubMed  CAS  Google Scholar 

  • Morbelli, S., Drzezga, A., Perneczky, R., Frisoni, G. B., Caroli, A., van Berckel, B. N., et al. (2012). Resting metabolic connectivity in prodromal Alzheimer’s disease. A European Alzheimer Disease Consortium (EADC) project. Neurobiology of Aging, 33(11), 2533–2550.

    Article  PubMed  Google Scholar 

  • Mori, T., Ikeda, M., Fukuhara, R., Nestor, P. J., & Tanabe, H. (2006). Correlation of visual hallucinations with occipital rCBF changes by donepezil in DLB. Neurology, 66(6), 935–937.

    Article  PubMed  CAS  Google Scholar 

  • Mosconi, L., Tsui, W. H., Herholz, K., Pupi, A., Drzezga, A., Lucignani, G., et al. (2008). Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. Journal of Nuclear Medicine, 49(3), 390–398.

    Article  PubMed  PubMed Central  Google Scholar 

  • Neary, D., Snowden, J. S., Gustafson, L., Passant, U., Stuss, D., Black, S., et al. (1998). Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology, 51, 1546–1554.

    Article  PubMed  CAS  Google Scholar 

  • Patterson, J. C., Lilien, D. L., Takalkar, A., & Pinkston, J. B. (2011). Early detection of brain pathology suggestive of early AD using objective evaluation of FDG-PET scans. International Journal of Alzheimer’s Disease. doi:10.4061/2011/946590.

    PubMed Central  Google Scholar 

  • Perani, D. (2008). Functional neuroimaging of cognition. In: M. J. Aminoff, F. Boller, & D. F. Swaab (Eds.), Handbook of Clinical Neurology (pp.61–111). Elsevier.

  • Prince, M., Bryce, R., & Ferr, C. (2011). The benefits of early diagnosis and intervention. In: Alzheimer’s Disease International World Alzheimer Report 2011. Institute of Psychiatry. King’s College London. Alzheimer’s Disease International.

  • Rascovsky, K., Hodges, J. R., Knopman, D., Mendez, M. F., Kramer, J. H., Neuhaus, J., et al. (2011). Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain, 134(Pt9), 2456–2477.

    Article  PubMed  PubMed Central  Google Scholar 

  • Ridgway, G., Omar, R., Ourselin, S., Hill, D., Warren, J., & Fox, N. (2009). Issues with threshold masking in voxel-based morphometry of atrophied brains. NeuroImage, 44(1), 99–111.

    Article  PubMed  Google Scholar 

  • Rorden, C., Bonilha, L., Fridriksson, J., Bender, B., & Karnath, H. O. (2012). Age-specific CT and MRI templates for spatial normalization. NeuroImage, 61(4), 957–965.

    Article  PubMed  PubMed Central  Google Scholar 

  • Salmon, E., Garraux, G., Delbeuck, X., Collette, F., Kalbe, E., Zuendorf, G., et al. (2003). Predominant ventromedial frontopolar metabolic impairment in frontotemporal dementia. NeuroImage, 20(1), 435–440.

    Article  PubMed  Google Scholar 

  • Salmon, E., Kerrouche, N., Perani, D., Lekeu, F., Holthoff, V., Beuthien- Baumann, B., et al. (2009). On the multivariate nature of brain metabolic impairment in Alzheimer’s disease. Neurobiology of Aging, 30(2), 186–197.

    Article  PubMed  CAS  Google Scholar 

  • Sestini, S., Castagnoli, A., & Mansi, L. (2010). The new FDG brain revolution: the neurovascular unit and the default network. European Journal of Nuclear Medicine and Molecular Imaging, 37(5), 913–916.

    Article  PubMed  Google Scholar 

  • Signorini, M., Paulesu, E., Friston, K., Perani, D., Colleluori, A., Lucignani, G., et al. (1999). Rapid assessment of regional cerebral metabolic abnormalities in single subjects with quantitative and nonquantitative [18F]FDG PET: a clinical validation of statistical parametric mapping. NeuroImage, 9(1), 63–80.

    Article  PubMed  CAS  Google Scholar 

  • Spence, J. S., Carmack, P. S., Gunst, R. F., Schucany, W. R., Woodward, W. A., & Haley, R. W. (2006). Using a white matter reference to remove the dependency of global signal on experimental conditions in SPECT analyses. NeuroImage, 32(1), 49–53.

    Article  PubMed  Google Scholar 

  • Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., et al. (2011). Towards defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 280–292.

  • Teipel, S. J., Sabri, O., Grothe, M., Barthel, H., Prvulovic, D., Buerger, K., et al. (2013). Perspectives for multimodal neurochemical and imaging biomarkers in Alzheimer’s disease. Journal of Alzheimer’s Disease, 33(S1), S329–S347.

    PubMed  Google Scholar 

  • Teune, L. K., Bartels, A. L., de Jong, B. M., Willemsen, A. T., Eshuis, S. A., de Vries, J. J., et al. (2010). Typical cerebral metabolic patterns in neurodegenerative brain diseases. Movement Disorders, 25(14), 2395–2404.

    Article  PubMed  Google Scholar 

  • Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273–289.

    Article  PubMed  CAS  Google Scholar 

  • Wenzel, F., Young, S., Wilke, F., Apostolova, I., Arlt, S., Jahn, H., et al. (2010). B-spline-based stereotactical normalization of brain FDG PET scans in suspected neurodegenerative disease: impact on voxel-based statistical single-subject analysis. NeuroImage, 50(3), 994–1003.

    Article  PubMed  Google Scholar 

  • Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L. O., et al. (2004). Mild cognitive impairment-beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. Journal of Internal Medicine, 256(3), 240–246.

    Article  PubMed  CAS  Google Scholar 

  • Yakushev, I., Hammers, A., Fellgiebel, A., Schmidtmann, I., Scheurich, A., Buchholz, H. G., et al. (2009). SPM-based count normalization provides excellent discrimination of mild Alzheimer’s disease and amnesic mild cognitive impairment from healthy aging. NeuroImage, 44(1), 43–50.

    Article  PubMed  Google Scholar 

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Acknowledgments

We thank the EADC-PET Consortium (Alexander Drzezga and Robert Perneczky [Munich], Mira Didic and Eric Guedj [Marseilles], Bart N. Van Berckel and Rik Ossenkoppele [Amsterdam], Flavio Nobili and Silvia Morbelli [Genoa], Giovanni Frisoni, Anna Caroli [Brescia]) for kindly providing EADC-PET imaging data for the purposes of the current study. This study was supported in part by the 6th European Framework Program Network of Excellence “DIMI” LSHB-CT-2005-512146, and the 7th Framework Programme for Research and Technological Development “DECIDE” RI-261593 and in part by the Italian Ministry of Education in the framework of the Project of Strategic National Interest “Invecchiamento: innovazioni tecnologiche e molecolari per un miglioramento della salute dell’anziano” allocated to the National Research Council. Karl Friston and John Ashburner are based at The Wellcome Trust Centre for Neuroimaging, which is supported by core funding from the Wellcome Trust [091593/Z/10/Z].

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The authors declare that they have no conflict of interest.

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Correspondence to Pasquale Anthony Della Rosa or Daniela Perani.

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*Alexander Drzezga and Robert Perneczky [Munich], Mira Didic and Eric Guedj [Marseilles], Bart N. Van Berckel and Rik Ossenkoppele [Amsterdam], Flavio Nobili and Silvia Morbelli [Genoa]

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Della Rosa, P.A., Cerami, C., Gallivanone, F. et al. A Standardized [18F]-FDG-PET Template for Spatial Normalization in Statistical Parametric Mapping of Dementia. Neuroinform 12, 575–593 (2014). https://doi.org/10.1007/s12021-014-9235-4

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