DOI QR코드

DOI QR Code

Measurement of Precuneal and Hippocampal Volumes Using Magnetic Resonance Volumetry in Alzheimer's Disease

  • Ryu, Seon-Young (Department of Neurology, Daejeon St. Mary's Hospital, The Catholic University of Korea College of Medicine) ;
  • Kwon, Min-Jeong (Department of Radiology, Daejeon St. Mary's Hospital, The Catholic University of Korea College of Medicine) ;
  • Lee, Sang-Bong (Department of Neurology, Daejeon St. Mary's Hospital, The Catholic University of Korea College of Medicine) ;
  • Yang, Dong-Won (Department of Neurology, Seoul St. Mary's Hospital, The Catholic University of Korea College of Medicine) ;
  • Kim, Tae-Woo (Department of Neurology, Daejeon St. Mary's Hospital, The Catholic University of Korea College of Medicine) ;
  • Song, In-Uk (Department of Neurology, Incheon St. Mary's Hospital, The Catholic University of Korea College of Medicine) ;
  • Yang, Po-Song (Department of Radiology, Daejeon St. Mary's Hospital, The Catholic University of Korea College of Medicine) ;
  • Kim, Hyun-Jeong (Department of Radiology, Daejeon St. Mary's Hospital, The Catholic University of Korea College of Medicine) ;
  • Lee, Ae-Young (Department of Neurology, Chungnam National University Hospital)
  • Received : 2010.03.22
  • Accepted : 2010.08.18
  • Published : 2010.12.31

Abstract

Background and Purpose: Alzheimer's disease (AD) is associated with structural alterations in the medial temporal lobe (MTL) and functional alterations in the posterior cortical region, especially in the early stages. However, it is unclear what mechanisms underlie these regional discrepancies or whether the posterior cortical hypometabolism reflects disconnection from the MTL lesion or is the result of local pathology. The precuneus, an area of the posteromedial cortex that is involved in the early stages of AD, has recently received a great deal of attention in functional neuroimaging studies. To assess the relationship between the precuneus and hippocampus in AD, we investigated the volumes of these two areas using a magnetic resonance volumetric method. Methods: Twenty-three subjects with AD and 14 healthy age-matched controls underwent T1-we-ighted three-dimensional volumetric brain magnetic resonance imaging. Volumetric measurements were performed in the precuneus and hippocampus. Results: Compared to controls, AD patients exhibited a significant reduction in total precuneal volume, which was more prominent on the right side, and significant bilateral reductions in hippocampal volume. No correlation was found between the total volumes of the precuneus and hippocampus in the AD group. Conclusions: These results suggest that volumetric measurements of both the precuneus and hippocampus are useful radiological indices for the diagnosis of AD. Furthermore, the lack of correlation is attributable to local pathology rather than being a secondary consequence of MTL pathology.

Keywords

References

  1. Lehericy S, Marjanska M, Mesrob L, Sarazin M, Kinkingnehun S. Magnetic resonance imaging of Alzheimer's disease. Eur Radiol 2007;17:347-362. https://doi.org/10.1007/s00330-006-0341-z
  2. Glodzik-Sobanska L, Rusinek H, Mosconi L, Li Y, Zhan J, de Santi S, et al. The role of quantitative structural imaging in the early diagnosis of Alzheimer's disease. Neuroimaging Clin N Am 2005;15:803-826. https://doi.org/10.1016/j.nic.2005.09.004
  3. Herholz K, Carter SF, Jones M. Positron emission tomography imaging in dementia. Br J Radiol 2007;80:S160-S167. https://doi.org/10.1259/bjr/97295129
  4. Du AT, Schuff N, Amend D, Laakso MP, Hsu YY, Jagust WJ, et al. Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer's disease. J Neurol Neurosurg Psychiatry 2001;71:441-447. https://doi.org/10.1136/jnnp.71.4.441
  5. Killiany RJ, Gomez-Isla T, Moss M, Kikinis R, Sandor T, Jolesz F, et al. Use of structural magnetic resonance imaging to predict who will get Alzheimer's disease. Ann Neurol 2000;47:430-439. https://doi.org/10.1002/1531-8249(200004)47:4<430::AID-ANA5>3.0.CO;2-I
  6. Geuze E, Vermetten E, Bremner JD. MR-based in vivo hippocampal volumetrics: 2. Findings in neuropsychiatric disorders. Mol Psychiatry 2005;10:160-184. https://doi.org/10.1038/sj.mp.4001579
  7. Duvernoy HM. The human brain: surface, three-dimensional sectional anatomy with MRI, and blood supply. 2nd ed. New York: Springer Wien New York, 1999.
  8. Salamon G, Salamon-Murayama N, Mongkolwat P, Russell EJ. Magnetic resonance imaging study of the parietal lobe: anatomic and radiologic correlations. Adv Neurol 2003;93:23-42.
  9. Cavanna AE, Trimble MR. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 2006;129:564-583. https://doi.org/10.1093/brain/awl004
  10. Mintun MA, Larossa GN, Sheline YI, Dence CS, Lee SY, Mach RH, et al. [11C]PIB in a nondemented population: potential antecedent marker of Alzheimer disease. Neurology 2006;67:446-452. https://doi.org/10.1212/01.wnl.0000228230.26044.a4
  11. Baron JC, ChEtelat G, Desgranges B, Perchey G, Landeau B, de la Sayette V, et al. In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. Neuroimage 2001;14:298-309. https://doi.org/10.1006/nimg.2001.0848
  12. Ramani A, Jensen JH, Helpern JA. Quantitative MR imaging in Alzheimer's disease. Radiology 2006;241:26-44. https://doi.org/10.1148/radiol.2411050628
  13. Ashburner J, Friston KJ. Why voxel-based morphometry should be used. Neuroimage 2001;14:1238-1243. https://doi.org/10.1006/nimg.2001.0961
  14. Ashburner J, Friston KJ. Voxel-based morphometry--the methods. Neuroimage 2000;11:805-821. https://doi.org/10.1006/nimg.2000.0582
  15. Kang Y, Na DL, Hahn S. A validity study on the Korean Mini-Mental State Examination (K-MMSE) in dementia patients. J Korean Neurol Assoc 1997;15:300-308.
  16. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's disease. Neurology 1984;34:939-944. https://doi.org/10.1212/WNL.34.7.939
  17. Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993;43:2412-2414.
  18. Kang YW, Na DL, Hahn SH. Seoul neuropsychological screening battery. Incheon: Human Brain Research & Consulting Co., 2003.
  19. Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain. New York: Thieme Medical Publishers, 1988.
  20. Gath I, Geva AB. Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 1989;11:773-781. https://doi.org/10.1109/34.192473
  21. Kwon MJ, Han YJ, Shin IH, Park HW. Hierarchical fuzzy segmentation of brain MR images. Int J Imaging Syst Technol 2003;13:115-125. https://doi.org/10.1002/ima.10035
  22. Eritaia J, Wood SJ, Stuart GW, Bridle N, Dudgeon P, Maruff P, et al. An optimized method for estimating intracranial volume from magnetic resonance images. Magn Reson Med 2000;44:973-977. https://doi.org/10.1002/1522-2594(200012)44:6<973::AID-MRM21>3.0.CO;2-H
  23. Zhou SY, Suzuki M, Takahashi T, Hagino H, Kawasaki Y, Matsui M, et al. Parietal lobe volume deficits in schizophrenia spectrum disorders. Schizophr Res 2007;89:35-48. https://doi.org/10.1016/j.schres.2006.08.032
  24. Duvernoy HM. The human hippocampus, functional anatomy, vascularisation and serial sections with MRI. 3rd ed. New York: Springer-Verlag Berlin Heidelberg, 2005.
  25. Watson C, Andermann F, Gloor P, Jones-Gotman M, Peters T, Evans A, et al. Anatomic basis of amygdaloid and hippocampal volume measurement by magnetic resonance imaging. Neurology 1992;42:1743-1750. https://doi.org/10.1212/WNL.42.9.1743
  26. Pantel J, O'Leary DS, Cretsinger K, Bockholt HJ, Keefe H, Magnotta VA, et al. A new method for the in vivo volumetric measurement of the human hippocampus with high neuroanatomical accuracy. Hippocampus 2000;10:752-758. https://doi.org/10.1002/1098-1063(2000)10:6<752::AID-HIPO1012>3.0.CO;2-Y
  27. Ciumas C, Montavont A, Ryvlin P. Magnetic resonance imaging in clinical trials. Curr Opin Neurol 2008;21:431-436.
  28. Geuze E, Vermetten E, Bremner JD. MR-based in vivo hippocampal volumetrics: 1. Review of methodologies currently employed. Mol Psychiatry 2005;10:147-159. https://doi.org/10.1038/sj.mp.4001580
  29. Frisoni GB, Testa C, Zorzan A, Sabattoli F, Beltramello A, Soininen H, et al. Detection of grey matter loss in mild Alzheimer's disease with voxel based morphometry. J Neurol Neurosurg Psychiatry 2002;73:657-664. https://doi.org/10.1136/jnnp.73.6.657
  30. Chetelat G, Desgranges B, De La Sayette V, Viader F, Eustache F, Baron JC. Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment. Neuroreport 2002;13:1939-1943. https://doi.org/10.1097/00001756-200210280-00022
  31. Foundas AL, Leonard CM, Mahoney SM, Agee OF, Heilman KM. Atrophy of the hippocampus, parietal cortex, and insula in Alzheimer's disease: a volumetric magnetic resonance imaging study. Neuropsychiatry Neuropsychol Behav Neurol 1997;10:81-89.
  32. Barnes J, Scahill RI, Schott JM, Frost C, Rossor MN, Fox NC. Does Alzheimer's disease affect hippocampal asymmetry? Evidence from a cross-sectional and longitudinal volumetric MRI study. Dement Geriatr Cogn Disord 2005;19:338-344. https://doi.org/10.1159/000084560
  33. Wolf H, Grunwald M, Kruggel F, Riedel-Heller SG, Angerhofer S, Hojjatoleslami A, et al. Hippocampal volume discriminates between normal cognition; questionable and mild dementia in the elderly. Neurobiol Aging 2001;22:177-186. https://doi.org/10.1016/S0197-4580(00)00238-4
  34. Rusinek H, Endo Y, De Santi S, Frid D, Tsui WH, Segal S, et al. Atrophy rate in medial temporal lobe during progression of Alzheimer disease. Neurology 2004;63:2354-2359. https://doi.org/10.1212/01.WNL.0000148602.30175.AC
  35. Golebiowski M, Barcikowska M, Pfeffer A. Magnetic resonance imaging-based hippocampal volumetry in patients with dementia of the Alzheimer type. Dement Geriatr Cogn Disord 1999;10:284-288. https://doi.org/10.1159/000017133
  36. Ishii K, Kawachi T, Sasaki H, Kono AK, Fukuda T, Kojima Y, et al. Voxel-based morphometric comparison between early-and late-onset mild Alzheimer's disease and assessment of diagnostic performance of z score images. AJNR Am J Neuroradiol 2005;26:333-340.
  37. Kinkingnehun S, Sarazin M, Lehericy S, Guichart-Gomerz E, Hergueta T, Dubois B. VBM anticipates the rate of progression of Alzheimer disease: a 3-year longitudinal study. Neurology 2008;70:2201-2211. https://doi.org/10.1212/01.wnl.0000303960.01039.43
  38. Karas G, Scheltens P, Rombouts S, van Schijndel R, Klein M, Jones B, et al. Precuneus atrophy in early-onset Alzheimer's disease: a morphometric structural MRI study. Neuroradiology 2007;49:967-976. https://doi.org/10.1007/s00234-007-0269-2
  39. Whitwell JL, Jack CR Jr. Neuroimaging in dementia. Neurol Clin 2007; 25:843-857. https://doi.org/10.1016/j.ncl.2007.03.003
  40. Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Ann Neurol 1997;42:85-94. https://doi.org/10.1002/ana.410420114
  41. Mistur R, Mosconi L, Santi SD, Guzman M, Li Y, Tsui W, et al. Current Challenges for the Early Detection of Alzheimer's Disease: Brain Imaging and CSF studies. J Clin Neurol 2009;5:153-166. https://doi.org/10.3988/jcn.2009.5.4.153
  42. Chetelat G, Desgranges B, Landeau B, Mezenge F, Poline JB, de la Sayette V, et al. Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer's disease. Brain 2008;131:60-71.
  43. Zhang Y, Schuff N, Jahng GH, Bayne W, Mori S, Schad L, et al. Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease. Neurology 2007;68:13-19. https://doi.org/10.1212/01.wnl.0000250326.77323.01
  44. Greicius MD, Supekar K, Menon V, Dougherty RF. Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb Cortex 2009;19:72-78. https://doi.org/10.1093/cercor/bhn059
  45. Greicius MD, Srivastava G, Reiss AL, Menon V. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A 2004;101:4637-4642. https://doi.org/10.1073/pnas.0308627101
  46. Villain N, Desgranges B, Viader F, de la Sayette V, Mezenge F, Landeau B, et al. Relationships between hippocampal atrophy, white matter disruption, and gray matter hypometabolism in Alzheimer's disease. J Neurosci 2008;28:6174-6181. https://doi.org/10.1523/JNEUROSCI.1392-08.2008
  47. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 2005;25:7709-7717. https://doi.org/10.1523/JNEUROSCI.2177-05.2005
  48. Nelson PT, Abner EL, Scheff SW, Schmitt FA, Kryscio RJ, Jicha GA, et al. Alzheimer's-type neuropathology in the precuneus is not increased relative to other areas of neocortex across a range of cognitive impairment. Neurosci Lett 2009;450:336-339. https://doi.org/10.1016/j.neulet.2008.11.006
  49. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A 2001;98:676-682. https://doi.org/10.1073/pnas.98.2.676
  50. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 2008;1124:1-38. https://doi.org/10.1196/annals.1440.011
  51. Shannon BJ, Buckner RL. Functional-anatomic correlates of memory retrieval that suggest nontraditional processing roles for multiple distinct regions within posterior parietal cortex. J Neurosci 2004;24:10084-10092. https://doi.org/10.1523/JNEUROSCI.2625-04.2004
  52. Ries ML, Carlsson CM, Rowley HA, Sager MA, Gleason CE, Asthana S, et al. Magnetic resonance imaging characterization of brain structure and function in mild cognitive impairment: a review. J Am Geriatr Soc 2008;56:920-934. https://doi.org/10.1111/j.1532-5415.2008.01684.x
  53. Vogt BA, Finch DM, Olson CR. Functional heterogeneity in cingulate cortex: the anterior executive and posterior evaluative regions. Cereb Cortex 1992;2:435-443.
  54. Gusnard DA, Raichle ME, Raichle ME. Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci 2001;2:685-694. https://doi.org/10.1038/35094500

Cited by

  1. Sun Ginseng Protects Endothelial Progenitor Cells From Senescence Associated Apoptosis vol.36, pp.1, 2012, https://doi.org/10.5142/jgr.2012.36.1.78
  2. Hippocampal volume and memory in narcoleptics with cataplexy vol.13, pp.4, 2012, https://doi.org/10.1016/j.sleep.2011.09.017
  3. Adverse Effects of 24 Hours of Sleep Deprivation on Cognition and Stress Hormones vol.8, pp.2, 2010, https://doi.org/10.3988/jcn.2012.8.2.146
  4. Brain-derived neurotrophic factor gene polymorphisms, neurotransmitter levels, and depressive symptoms in an elderly population vol.34, pp.6, 2010, https://doi.org/10.1007/s11357-011-9313-6
  5. EEG upper/low alpha frequency power ratio relates to temporo-parietal brain atrophy and memory performances in mild cognitive impairment vol.5, pp.None, 2010, https://doi.org/10.3389/fnagi.2013.00063
  6. Usefulness of medial temporal lobe atrophy visual rating scale in detecting Alzheimer's disease: Preliminary study vol.16, pp.3, 2010, https://doi.org/10.4103/0972-2327.116951
  7. Electroencephalography reveals lower regional blood perfusion and atrophy of the temporoparietal network associated with memory deficits and hippocampal volume reduction in mild cognitive impairment d vol.11, pp.None, 2015, https://doi.org/10.2147/ndt.s78830
  8. Association of EEG, MRI, and regional blood flow biomarkers is predictive of prodromal Alzheimer’s disease vol.11, pp.None, 2010, https://doi.org/10.2147/ndt.s93253
  9. Theta and alpha EEG frequency interplay in subjects with mild cognitive impairment: evidence from EEG, MRI, and SPECT brain modifications vol.7, pp.None, 2010, https://doi.org/10.3389/fnagi.2015.00031
  10. Mild Cognitive Impairment: Structural, Metabolical, and Neurophysiological Evidence of a Novel EEG Biomarker vol.6, pp.None, 2010, https://doi.org/10.3389/fneur.2015.00152
  11. Understanding early dementia: EEG, MRI, SPECT and memory evaluation vol.6, pp.1, 2010, https://doi.org/10.1515/tnsci-2015-0005
  12. Electroencephalography-driven approach to prodromal Alzheimer’s disease diagnosis: from biomarker integration to network-level comprehension vol.11, pp.None, 2010, https://doi.org/10.2147/cia.s103313
  13. Cerebral Perfusion Changes after Acetyl-L-Carnitine Treatment in Early Alzheimer's Disease Using Single Photon Emission Computed Tomography vol.16, pp.1, 2010, https://doi.org/10.12779/dnd.2017.16.1.26
  14. Interactions with the integrative memory model vol.42, pp.None, 2010, https://doi.org/10.1017/s0140525x19002024
  15. The Application of Unsupervised Clustering Methods to Alzheimer’s Disease vol.13, pp.None, 2010, https://doi.org/10.3389/fncom.2019.00031
  16. Sex Differences in the Complexity of Healthy Older Adults’ Magnetoencephalograms vol.21, pp.8, 2010, https://doi.org/10.3390/e21080798
  17. MRI Segmentation and Classification of Human Brain Using Deep Learning for Diagnosis of Alzheimer’s Disease: A Survey vol.20, pp.11, 2010, https://doi.org/10.3390/s20113243
  18. Empathy and Theory of Mind in Alzheimer’s Disease: A Meta-analysis vol.26, pp.10, 2020, https://doi.org/10.1017/s1355617720000478
  19. The effects of cerebral amyloidopathy on regional glucose metabolism in older adults with depression and mild cognitive impairment while performing memory tasks vol.54, pp.7, 2010, https://doi.org/10.1111/ejn.15461