Login Register Cart 0
Generic placeholder image

Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

One-year Follow-up Study of Hippocampal Subfield Atrophy in Alzheimer's Disease and Normal Aging

Author(s): Nuwan Madusanka, Heung-Kook Choi*, Jae-Hong So, Boo-Kyeong Choi and Hyeon Gyun Park

Volume 15, Issue 7, 2019

Page: [699 - 709] Pages: 11

DOI: 10.2174/1573405615666190327102052

Price: $65

Abstract

Background: In this study, we investigated the effect of hippocampal subfield atrophy on the development of Alzheimer’s disease (AD) by analyzing baseline magnetic resonance images (MRI) and images collected over a one-year follow-up period. Previous studies have suggested that morphological changes to the hippocampus are involved in both normal ageing and the development of AD. The volume of the hippocampus is an authentic imaging biomarker for AD. However, the diverse relationship of anatomical and complex functional connectivity between different subfields implies that neurodegenerative disease could lead to differences between the atrophy rates of subfields. Therefore, morphometric measurements at subfield-level could provide stronger biomarkers.

Methods: Hippocampal subfield atrophies are measured using MRI scans, taken at multiple time points, and shape-based normalization to a Montreal neurological institute (MNI) ICBM 152 nonlinear atlas. Ninety subjects were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and divided equally into Healthy Controls (HC), AD, and mild cognitive impairment (MCI) groups. These subjects underwent serial MRI studies at three time-points: baseline, 6 months and 12 months.

Results: We analyzed the subfield-level hippocampal morphometric effects of normal ageing and AD based on radial distance mapping and volume measurements. We identified a general trend and observed the largest hippocampal subfield atrophies in the AD group. Atrophy of the bilateral CA1, CA2- CA4 and subiculum subfields was higher in the case of AD than in MCI and HC. We observed the highest rate of reduction in the total volume of the hippocampus, especially in the CA1 and subiculum regions, in the case of MCI.

Conclusion: Our findings show that hippocampal subfield atrophy varies among the three study groups.

Keywords: Alzheimer's disease, hippocampal, subfield atrophy, biomarker, mild cognitive impairment, normal aging, neurodegenerative diseases, radial distance.

Graphical Abstract

[1]
Lee P, Ryoo H, Park J, Jeong Y. Morphological and microstructural changes of the hippocampus in early MCI: A study utilizing the alzheimer’s disease neuroimaging initiative database. J Clin Neurol 2017; 13(2): 144-54.
[http://dx.doi.org/10.3988/jcn.2017.13.2.144] [PMID: 28176504]
[2]
Chow N, Hwang KS, Hurtz S, et al. Comparing 3T and 1.5T MRI for mapping hippocampal atrophy in the Alzheimer’s disease neuroimaging Initiative. AJNR Am J Neuroradiol 2015; 36(4): 653-60.
[http://dx.doi.org/10.3174/ajnr.A4228] [PMID: 25614473]
[3]
Apostolova LG, Dinov ID, Dutton RA, et al. 3D comparison of hippocampal atrophy in amnestic mild cognitive impairment and Alzheimer’s disease. Brain 2006; 129(Pt 11): 2867-73.
[http://dx.doi.org/10.1093/brain/awl274] [PMID: 17018552]
[4]
Prestia A, Cavedo E, Boccardi M, et al. Hippocampal and amygdalar local structural differences in elderly patients with schizophrenia. Am J Geriatr Psychiatry 2015; 23(1): 47-58.
[http://dx.doi.org/10.1016/j.jagp.2014.01.006] [PMID: 24534522]
[5]
Blanken AE, Hurtz S, Zarow C, et al. Associations between hippocampal morphometry and neuropathologic markers of Alzheimer’s disease using 7 T MRI. Neuroimage Clin 2017; 15: 56-61.
[http://dx.doi.org/10.1016/j.nicl.2017.04.020] [PMID: 28491492]
[6]
Leh SE, Kälin AM, Schroeder C, et al. Volumetric and shape analysis of the thalamus and striatum in amnestic mild cognitive impairment. J Alzheimers Dis 2016; 49(1): 237-49.
[http://dx.doi.org/10.3233/JAD-150080] [PMID: 26444755]
[7]
Frisoni GB, Sabattoli F, Lee AD, Dutton RA, Toga AW, Thompson PM. In vivo neuropathology of the hippocampal formation in AD: a radial mapping MR-based study. Neuroimage 2006; 32(1): 104-10.
[http://dx.doi.org/10.1016/j.neuroimage.2006.03.015] [PMID: 16631382]
[8]
Imabayashi E, Matsuda H, Tabira T, et al. Comparison between brain CT and MRI for voxel-based morphometry of Alzheimer’s disease. Brain Behav 2013; 3(4): 487-93.
[http://dx.doi.org/10.1002/brb3.146] [PMID: 24381817]
[9]
Li B, Shi J, Gutman BA, et al. Influence of APOE genotype on hippocampal atrophy over time - An N=1925 surface-based ADNI study. PLoS One 2016; 11(4)e0152901
[http://dx.doi.org/10.1371/journal.pone.0152901] [PMID: 27065111]
[10]
Thompson PM, Hayashi KM, De Zubicaray GI, et al. Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage 2004; 22(4): 1754-66.
[http://dx.doi.org/10.1016/j.neuroimage.2004.03.040] [PMID: 15275931]
[11]
Lin M, Fwu PT, Buss C, et al. Developmental changes in hippocampal shape among preadolescent children. Int J Dev Neurosci 2013; 31(7): 473-81.
[http://dx.doi.org/10.1016/j.ijdevneu.2013.06.001] [PMID: 23773912]
[12]
Raji CA, Lopez OL, Kuller LH, Carmichael OT, Becker JT. Age, Alzheimer disease, and brain structure. Neurology 2009; 73(22): 1899-905.
[http://dx.doi.org/10.1212/WNL.0b013e3181c3f293] [PMID: 19846828]
[13]
Madsen SK, Ho AJ, Hua X, et al. 3D maps localize caudate nucleus atrophy in 400 Alzheimer’s disease, mild cognitive impairment, and healthy elderly subjects. Neurobiol Aging 2010; 31(8): 1312-25.
[http://dx.doi.org/10.1016/j.neurobiolaging.2010.05.002] [PMID: 20538376]
[14]
Morris JC. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993; 43(11): 2412-4.
[http://dx.doi.org/10.1212/WNL.43.11.2412-a] [PMID: 8232972]
[15]
Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL. A new clinical scale for the staging of dementia. Br J Psychiatry 1982; 140(6): 566-72.
[http://dx.doi.org/10.1192/bjp.140.6.566] [PMID: 7104545]
[16]
Teresi JA. Mini-Mental State Examination (MMSE): scaling the MMSE using item response theory (IRT). J Clin Epidemiol 2007; 60(3): 256-9.
[http://dx.doi.org/10.1016/j.jclinepi.2006.06.009] [PMID: 17292019]
[17]
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12(3): 189-98.
[http://dx.doi.org/10.1016/0022-3956(75)90026-6] [PMID: 1202204]
[18]
Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 2005; 53(4): 695-9.
[http://dx.doi.org/10.1111/j.1532-5415.2005.53221.x] [PMID: 15817019]
[19]
Pangman VC, Sloan J, Guse L. An examination of psychometric properties of the mini-mental state examination and the standardized mini-mental state examination: implications for clinical practice. Appl Nurs Res 2000; 13(4): 209-13.
[http://dx.doi.org/10.1053/apnr.2000.9231] [PMID: 11078787]
[20]
He Y, Liang B, Yang J, Li S, He J. An iterative closest points algorithm for registration of 3D laser scanner point clouds with geometric features. Sensors (Basel) 2017; 17(8): 1862.
[http://dx.doi.org/10.3390/s17081862] [PMID: 28800096]
[21]
Zhou Z, Tu J, Geng C, et al. Accurate and robust non-rigid point set registration using student’s-t mixture model with prior probability modeling. Sci Rep 2018; 8(1): 8742.
[http://dx.doi.org/10.1038/s41598-018-26288-6] [PMID: 29880859]
[22]
Pottmann H, Leopoldseder S, Hofer M. Registration without ICP. Comput Vis Image Underst 2004; 95(1): 54-71.
[http://dx.doi.org/10.1016/j.cviu.2004.04.002]
[23]
Sharp GC, Lee SW, Wehe DK. ICP registration using invariant features. IEEE Trans Pattern Anal Mach Intell 2002; 24(1): 90-102.
[http://dx.doi.org/10.1109/34.982886]
[24]
Besl PJ, McKay ND. A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 1992; 14(2): 239-56.
[http://dx.doi.org/10.1109/34.121791]
[25]
Gold S, Rangarajan A, Lu C-P, Pappu S, Mjolsness E. New algorithms for 2D and 3D point matching. Pattern Recognit 1998; 31(8): 1019-31.
[http://dx.doi.org/10.1016/S0031-3203(98)80010-1]
[26]
Amberg B, Romdhani S, Vetter T. Optimal step nonrigid icp algorithms for surface registration.In:IEEE Conference on Computer Vision and Pattern Recognition 2007; Minneapolis, MN, USA. 1-8.
[http://dx.doi.org/10.1109/CVPR.2007.383165]
[27]
Tagliasacchi A, Schröder M, Tkach A, Bouaziz S, Botsch M, Pauly M. Robust articulated-icp for real-time hand tracking. Comput Graph Forum 2015; 34(5): 101-14.
[http://dx.doi.org/10.1111/cgf.12700]
[28]
Lee TC, Kashyap RL, Chu CN. Building skeleton models via 3-d medial surface axis thinning algorithms. CVGIP Graph Models Image Process 1994; 56(6): 462-78.
[http://dx.doi.org/10.1006/cgip.1994.1042]
[29]
Bertrand G, Malandain G. A note on “building skeleton models via 3-d medial surface/axis thinning algorithms”. Graph Models Image Proc 1995; 57(6): 537-8.
[http://dx.doi.org/10.1006/gmip.1995.1045]
[30]
Yushkevich PA, Zhang H, Gee JC. Continuous medial representation for anatomical structures. IEEE Trans Med Imaging 2006; 25(12): 1547-64.
[http://dx.doi.org/10.1109/TMI.2006.884634] [PMID: 17167991]
[31]
Yushkevich PA. Continuous medial representation of brain structures using the biharmonic PDE. Neuroimage 2009; 45(1)(Suppl.): S99-S110.
[http://dx.doi.org/10.1016/j.neuroimage.2008.10.051] [PMID: 19059348]
[32]
Styner M, Gerig G, Lieberman J, Jones D, Weinberger D. Statistical shape analysis of neuroanatomical structures based on medial models. Med Image Anal 2003; 7(3): 207-20.
[http://dx.doi.org/10.1016/S1361-8415(02)00110-X] [PMID: 12946464]
[33]
Yushkevich PA, Detre JA, Mechanic-Hamilton D, et al. Hippocampus-specific fMRI group activation analysis using the continuous medial representation. Neuroimage 2007; 35(4): 1516-30.
[http://dx.doi.org/10.1016/j.neuroimage.2007.01.029] [PMID: 17383900]
[34]
Moretti DV, Prestia A, Fracassi C, et al. Volumetric differences in mapped hippocampal regions correlate with increase of high alpha rhythm in Alzheimer’s disease. Int J Alzheimers Dis 2011.2011208218
[http://dx.doi.org/10.4061/2011/208218] [PMID: 21760984]
[35]
Morra JH, Tu Z, Apostolova LG, et al. Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Hum Brain Mapp 2009; 30(9): 2766-88.
[http://dx.doi.org/10.1002/hbm.20708] [PMID: 19172649]
[36]
Shenton ME, Gerig G, McCarley RW, Székely G, Kikinis R. Amygdala-hippocampal shape differences in schizophrenia: the application of 3D shape models to volumetric MR data. Psychiatry Res 2002; 115(1-2): 15-35.
[http://dx.doi.org/10.1016/S0925-4927(02)00025-2] [PMID: 12165365]
[37]
Prestia A, Cavedo E, Boccardi M, et al. Hippocampal and amygdalar local structural differences in elderly patients with schizophrenia. Am J Geriatr Psychiatry 2015; 23(1): 47-58.
[http://dx.doi.org/10.1016/j.jagp.2014.01.006] [PMID: 24534522]
[38]
Thompson PM, Hayashi KM, De Zubicaray GI, et al. Mapping hippocampal and ventricular change in Alzheimer disease. Neuroimage 2004; 22(4): 1754-66.
[http://dx.doi.org/10.1016/j.neuroimage.2004.03.040] [PMID: 15275931]
[39]
Chakravarty MM, Rapoport JL, Giedd JN, et al. Striatal shape abnormalities as novel neurodevelopmental endophenotypes in schizophrenia: a longitudinal study. Hum Brain Mapp 2015; 36(4): 1458-69.
[http://dx.doi.org/10.1002/hbm.22715] [PMID: 25504933]
[40]
Marani R, Renò V, Nitti M, D’Orazio T, Stella E. A Modified Iterative Closest Point Algorithm for 3D Point Cloud Registration. Comput Civ Infrastruct Eng 2016; 31(7): 515-34.
[http://dx.doi.org/10.1111/mice.12184]
[41]
Madusanka N, Choi H-K, So J-H, Choi B-K. Alzheimer ’s disease Classification Based on Multi-feature Fusion. Curr Med Imaging Rev 2018; 14(2): 161-9.
[http://dx.doi.org/10.2174/1573405614666181012102626]
[42]
Frankó E, Joly O. Evaluating Alzheimer’s Disease Progression Using Rate of Regional Hippocampal Atrophy. PLoS One 2013; 8(8)e71354
[http://dx.doi.org/10.1371/journal.pone.0071354]
[43]
Apostolova LG, Mosconi L, Thompson PM, et al. Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal. Neurobiol Aging 2010; 31(7): 1077-88.
[http://dx.doi.org/10.1016/j.neurobiolaging.2008.08.008] [PMID: 18814937]
[44]
Perrotin A, de Flores R, Lamberton F, et al. Hippocampal subfield volumetry and 3D surface mapping in subjective cognitive decline. J Alzheimers Dis 2015; 48(S1): 141-50.

Rights & Permissions Print Cite
Article Metrics
39
6
1
© 2024 Bentham Science Publishers | Privacy Policy