Brain Imaging and Behavior

, Volume 12, Issue 6, pp 1583–1595 | Cite as

Volumetric comparison of hippocampal subfields extracted from 4-minute accelerated vs. 8-minute high-resolution T2-weighted 3T MRI scans

  • Shan Cong
  • Shannon L. Risacher
  • John D. West
  • Yu-Chien Wu
  • Liana G. Apostolova
  • Eileen Tallman
  • Maher Rizkalla
  • Paul Salama
  • Andrew J. SaykinEmail author
  • Li ShenEmail author


The hippocampus has been widely studied using neuroimaging, as it plays an important role in memory and learning. However, hippocampal subfield information is difficult to capture by standard magnetic resonance imaging (MRI) techniques. To facilitate morphometric study of hippocampal subfields, ADNI introduced a high resolution (0.4 mm in plane) T2-weighted turbo spin-echo sequence that requires 8 min. With acceleration, the protocol can be acquired in 4 min. We performed a comparative study of hippocampal subfield volumes using standard and accelerated protocols on a Siemens Prisma 3T MRI in an independent sample of older adults that included 10 cognitively normal controls, 9 individuals with subjective cognitive decline, 10 with mild cognitive impairment, and 6 with a clinical diagnosis of Alzheimer’s disease (AD). The Automatic Segmentation of Hippocampal Subfields (ASHS) software was used to segment 9 primary labeled regions including hippocampal subfields and neighboring cortical regions. Intraclass correlation coefficients were computed for reliability tests between 4 and 8 min scans within and across the four groups. Pairwise group analyses were performed, covaried for age, sex and total intracranial volume, to determine whether the patterns of group differences were similar using 4 vs. 8 min scans. The 4 and 8 min protocols, analyzed by ASHS segmentation, yielded similar volumetric estimates for hippocampal subfields as well as comparable patterns of differences between study groups. The accelerated protocol can provide reliable imaging data for investigation of hippocampal subfields in AD-related MRI studies and the decreased scan time may result in less vulnerability to motion.


Hippocampal subfields Magnetic resonance imaging Segmentation Volumetric analysis Alzheimer’s disease 



Data analysis was supported in part by the following grants from the National Institutes of Health: R01 EB022574 and R01 LM011360 to LS, P30 AG10133, R01 AG19771 and U01 AG024904 (IU Subcontract) to AJS, R01 AG040770 to LA, and K01 AG049050 to SLR.


  1. Adler, D. H., Pluta, J., Kadivar, S., Craige, C., Gee, J. C., Avants, B. B., & Yushkevich, P. A. (2014). Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI. Neuroimage, 84, 505–523.CrossRefGoogle Scholar
  2. Apostolova, L. G., Dutton, R. A., Dinov, I. D., Hayashi, K. M., Toga, A. W., Cummings, J. L., & Thompson, P. M. (2006). Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch Neurol, 63, 693–699.CrossRefGoogle Scholar
  3. Apostolova, L. G., Mosconi, L., Thompson, P. M., Green, A. E., Hwang, K. S., Ramirez, A., Mistur, R., Tsui, W. H., & de Leon, M. J. (2010a). Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal. Neurobiol Aging, 31, 1077–1088.CrossRefGoogle Scholar
  4. Apostolova, L. G., Thompson, P. M., Green, A. E., Hwang, K. S., Zoumalan, C., Jack, C. R. Jr., Harvey, D. J., Petersen, R. C., Thal, L. J., Aisen, P. S., Toga, A. W., Cummings, J. L., & Decarli, C. S. (2010b). 3D comparison of low, intermediate, and advanced hippocampal atrophy in MCI. Hum Brain Mapp, 31, 786–797.CrossRefGoogle Scholar
  5. Bonnici, H. M., Chadwick, M. J., Kumaran, D., Hassabis, D., Weiskopf, N., & Maguire, E. A. (2012). Multi-voxel pattern analysis in human hippocampal subfields. Front Hum Neurosci, 6, 290.CrossRefGoogle Scholar
  6. Braak, H., & Braak, E. (1995). Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiology of Aging, 16, 271–278.CrossRefGoogle Scholar
  7. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (pp. 20–26). Hillsdale: Lawrence Earlbaum Associates.Google Scholar
  8. de Flores, R., La Joie, R., & Chetelat, G. (2015). Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience, 309, 29–50.CrossRefGoogle Scholar
  9. Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004). Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 101, 4637–4642.Google Scholar
  10. Hindy, N. C., Ng, F. Y., & Turk-Browne, N. B. (2016). Linking pattern completion in the hippocampus to predictive coding in visual cortex. Nat Neurosci, 19, 665–667.CrossRefGoogle Scholar
  11. Horgusluoglu, E., Nudelman, K., Nho, K., & Saykin, A. J. (2017). Adult neurogenesis and neurodegenerative diseases: A systems biology perspective. Am J Med Genet B Neuropsychiatr Genet, 174, 93–112.CrossRefGoogle Scholar
  12. Huang, Y., Coupland, N. J., Lebel, R. M., Carter, R., Seres, P., Wilman, A. H., & Malykhin, N. V. (2013). Structural changes in hippocampal subfields in major depressive disorder: a high-field magnetic resonance imaging study. Biological Psychiatry, 74, 62–68.CrossRefGoogle Scholar
  13. Hunsaker, M. R., & Amaral, D. G. (2014). A semi-automated pipeline for the segmentation of rhesus macaque hippocampus: validation across a wide age range. PLoS One, 9, e89456.Google Scholar
  14. Kirov, I. I., Hardy, C. J., Matsuda, K., Messinger, J., Cankurtaran, C. Z., Warren, M., Wiggins, G. C., Perry, N. N., Babb, J. S., & Goetz, R. R. (2013). In vivo 7T imaging of the dentate granule cell layer in schizophrenia. Schizophrenia Research, 147, 362–367.Google Scholar
  15. La Joie, R., Perrotin, A., de La Sayette, V., Egret, S., Doeuvre, L., Belliard, S., Eustache, F., Desgranges, B., & Chetelat, G. (2013). Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer’s disease and semantic dementia. Neuroimage Clin, 3, 155–162.CrossRefGoogle Scholar
  16. Libby, L. A., Ekstrom, A. D., Ragland, J. D., & Ranganath, C. (2012). Differential connectivity of perirhinal and parahippocampal cortices within human hippocampal subregions revealed by high-resolution functional imaging. The Journal of Neuroscience, 32, 6550–6560.CrossRefGoogle Scholar
  17. Malykhin, N., Lebel, R. M., Coupland, N., Wilman, A. H., & Carter, R. (2010). In vivo quantification of hippocampal subfields using 4.7T fast spin echo imaging. Neuroimage, 49, 1224–1230.Google Scholar
  18. Manning, E. N., Leung, K. K., Nicholas, J. M., Malone, I. B., Cardoso, M. J., Schott, J. M., Fox, N. C., Barnes, J., & for the ADNI. (2017). A comparison of accelerated and non-accelerated MRI scans for brain volume and boundary shift integral measures of volume change: evidence from the ADNI dataset. Neuroinformatics, 15(2), 215–226.Google Scholar
  19. McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1, 30.CrossRefGoogle Scholar
  20. Merkel, B., Steward, C., Vivash, L., Malpas, C. B., Phal, P., Moffat, B. A., Cox, K. L., Ellis, K. A., Ames, D. J., & Cyarto, E. V. (2015). Semi-automated hippocampal segmentation in people with cognitive impairment using an age appropriate template for registration. Journal of Magnetic Resonance Imaging, 42, 1631–1638.CrossRefGoogle Scholar
  21. Mueller, S., Stables, L., Du, A., Schuff, N., Truran, D., Cashdollar, N., & Weiner, M. (2007). Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T. Neurobiol Aging, 28, 719–726.CrossRefGoogle Scholar
  22. Mueller, S. G., Schuff, N., Yaffe, K., Madison, C., Miller, B., & Weiner, M. W. (2010). Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer’s disease. Hum Brain Mapp, 31, 1339–1347.CrossRefGoogle Scholar
  23. Mueller, S. G., & Weiner, M. W. (2009). Selective effect of age, Apo e4, and Alzheimer’s disease on hippocampal subfields. Hippocampus, 19, 558–564.CrossRefGoogle Scholar
  24. Olsen, R. K., Palombo, D. J., Rabin, J. S., Levine, B., Ryan, J. D., & Rosenbaum, R. S. (2013). Volumetric analysis of medial temporal lobe subregions in developmental amnesia using high-resolution magnetic resonance imaging. Hippocampus, 23, 855–860.CrossRefGoogle Scholar
  25. Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage, 56, 907–922.CrossRefGoogle Scholar
  26. Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999). Mild cognitive impairment: clinical characterization and outcome. Arch Neurol, 56, 303–308.CrossRefGoogle Scholar
  27. Pluta, J., Yushkevich, P., Das, S., & Wolk, D. (2012). In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI. Journal of Alzheimer’s Disease, 31, 85–99.CrossRefGoogle Scholar
  28. Risacher, S. L., Kim, S., Nho, K., Foroud, T., Shen, L., Petersen, R. C., Jack, C. R., Beckett, L. A., Aisen, P. S., & Koeppe, R. A. (2015). APOE effect on Alzheimer’s disease biomarkers in older adults with significant memory concern. Alzheimer’s & Dementia, 11, 1417–1429.CrossRefGoogle Scholar
  29. Sawilowsky, S. S. (2009). New effect size rules of thumb. Journal of Modern Applied Statistical Methods, 8(2), 597–599.Google Scholar
  30. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 86, 420.CrossRefGoogle Scholar
  31. Van Leemput, K., Bakkour, A., Benner, T., Wiggins, G., Wald, L. L., Augustinack, J., Dickerson, B. C., Golland, P., & Fischl, B. (2009). Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus, 19, 549–557.CrossRefGoogle Scholar
  32. Wang, H., Das, S. R., Suh, J. W., Altinay, M., Pluta, J., Craige, C., Avants, B., Yushkevich, P. A., & for the ADNI. (2011). A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation. Neuroimage, 55, 968–985.Google Scholar
  33. Wang, H., Suh, J. W., Das, S. R., Pluta, J. B., Craige, C., & Yushkevich, P. A. (2013). Multi-atlas segmentation with joint label fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 611–623.Google Scholar
  34. Winterburn, J. L., Pruessner, J. C., Chavez, S., Schira, M. M., Lobaugh, N. J., Voineskos, A. N., & Chakravarty, M. M. (2013). A novel in vivo atlas of human hippocampal subfields using high-resolution 3T magnetic resonance imaging. Neuroimage, 74, 254–265.Google Scholar
  35. Wisse, L., Gerritsen, L., Zwanenburg, J. J., Kuijf, H. J., Luijten, P. R., Biessels, G. J., & Geerlings, M. I. (2012). Subfields of the hippocampal formation at 7T MRI: In vivo volumetric assessment. Neuroimage, 61, 1043–1049.CrossRefGoogle Scholar
  36. Wisse, L. E., Kuijf, H. J., Honingh, A. M., Wang, H., Pluta, J. B., Das, S. R., Wolk, D. A., Zwanenburg, J. J., Yushkevich, P. A., & Geerlings, M. I. (2016). Automated hippocampal subfield segmentation at 7T MRI. American Journal of Neuroradiology, 37, 1050–1057.CrossRefGoogle Scholar
  37. Yassa, M. A., & Stark, C. E. (2011). Pattern separation in the hippocampus. Trends Neurosci, 34, 515–525.CrossRefGoogle Scholar
  38. Yushkevich, P. A., Wang, H., Pluta, J., Das, S. R., Craige, C., Avants, B. B., Weiner, M. W., & Mueller, S. (2010). Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI. Neuroimage, 53, 1208–1224.CrossRefGoogle Scholar
  39. Yushkevich, P. A., Amaral, R. S. C., Augustinack, J. C., Bender, A. R., Bernstein, J. D., Boccardi, M., Bocchetta, M., Burggren, A. C., Carr, V. A., Chakravarty, M. M., et al. (2015a). Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol. Neuroimage, 111, 526–541.Google Scholar
  40. Yushkevich, P. A., Pluta, J. B., Wang, H., Xie, L., Ding, S. L., Gertje, E. C., Mancuso, L., Kliot, D., Das, S. R., & Wolk, D. A. (2015b). Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Human Brain Mapping, 36, 258–287.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Neuroimaging, Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA
  2. 2.Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisUSA
  3. 3.Department of Electrical and Computer EngineeringPurdue UniversityIndianapolisUSA
  4. 4.Department of NeurologyIndiana University School of MedicineIndianapolisUSA
  5. 5.Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisUSA

Personalised recommendations