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Differentiating Between Healthy Control Participants and Those with Mild Cognitive Impairment Using Volumetric MRI Data

Published online by Cambridge University Press:  27 May 2019

Renée DeVivo Open the ORCID record for Renée DeVivo [Opens in a new window] ,
Lauren Zajac ,
Asim Mian ,
Anna Cervantes-Arslanian ,
Eric Steinberg ,
Michael L. Alosco ,
Jesse Mez ,
Robert Stern ,
Ronald Killany  and
for the Alzheimer’s Disease Neuroimaging Initiative
Renée DeVivo
Affiliation:
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA Center for Biomedical Imaging, Boston University School of Medicine, Boston, Massachusetts, USA
Lauren Zajac
Affiliation:
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA Center for Biomedical Imaging, Boston University School of Medicine, Boston, Massachusetts, USA
Asim Mian
Affiliation:
Department of Radiology, Boston Medical Center, Boston, Massachusetts, USA
Anna Cervantes-Arslanian
Affiliation:
Department of Neurosurgery, Boston University School of Medicine, Boston, Massachusetts, USA
Eric Steinberg
Affiliation:
Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA
Michael L. Alosco
Affiliation:
Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
Jesse Mez
Affiliation:
Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
Robert Stern
Affiliation:
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA Department of Neurosurgery, Boston University School of Medicine, Boston, Massachusetts, USA Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
Ronald Killany*
Affiliation:
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA Center for Biomedical Imaging, Boston University School of Medicine, Boston, Massachusetts, USA Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA Boston University School of Public Health, Boston, Massachusetts, USA
for the Alzheimer’s Disease Neuroimaging Initiative
Affiliation:
Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA Center for Biomedical Imaging, Boston University School of Medicine, Boston, Massachusetts, USA Department of Radiology, Boston Medical Center, Boston, Massachusetts, USA Department of Neurosurgery, Boston University School of Medicine, Boston, Massachusetts, USA Boston University Alzheimer’s Disease Center, Boston, Massachusetts, USA Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA Boston University School of Public Health, Boston, Massachusetts, USA
*
Correspondence and reprint requests to: Ronald Killiany, Boston University School of Medicine, Center for Biomedical Imaging, 700 Albany Street, W701, Boston, MA 02118, USA. E-mail: killiany@bu.edu
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Abstract

Objective: To determine whether volumetric measures of the hippocampus, entorhinal cortex, and other cortical measures can differentiate between cognitively normal individuals and subjects with mild cognitive impairment (MCI). Method: Magnetic resonance imaging (MRI) data from 46 cognitively normal subjects and 50 subjects with MCI as part of the Boston University Alzheimer’s Disease Center research registry and the Alzheimer’s Disease Neuroimaging Initiative were used in this cross-sectional study. Cortical, subcortical, and hippocampal subfield volumes were generated from each subject’s MRI data using FreeSurfer v6.0. Nominal logistic regression models containing these variables were used to identify subjects as control or MCI. Results: A model containing regions of interest (superior temporal cortex, caudal anterior cingulate, pars opercularis, subiculum, precentral cortex, caudal middle frontal cortex, rostral middle frontal cortex, pars orbitalis, middle temporal cortex, insula, banks of the superior temporal sulcus, parasubiculum, paracentral lobule) fit the data best (R2 = .7310, whole model test chi-square = 97.16, p < .0001). Conclusions: MRI data correctly classified most subjects using measures of selected medial temporal lobe structures in combination with those from other cortical areas, yielding an overall classification accuracy of 93.75%. These findings support the notion that, while volumes of medial temporal lobe regions differ between cognitively normal and MCI subjects, differences that can be used to distinguish between these two populations are present elsewhere in the brain.

Keywords

NeuroimagingHealthy agingHippocampusEntorhinal cortexAtrophyLogistic models
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 25 , Issue 8 , September 2019 , pp. 800 - 810
Copyright
Copyright © INS. Published by Cambridge University Press, 2019. 

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Footnotes

*

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

References

REFERENCES

Aisen, P.S., Cummings, J., Jack, C.R., Morris, J.C., Sperling, R., Frölich, L., Jones, R.W., Dowsett, S.A., Matthews, B.R., Raskin, J., & Scheltens, P. (2017). On the path to 2025: understanding the Alzheimer’s disease continuum. Alzheimer’s Research & Therapy, 9, 60.CrossRefGoogle ScholarPubMed
Ashendorf, L., Alosco, M.L., Bing-Canar, H., Chapman, K.R., Martin, B., Chaisson, C.E., Dixon, D., Steinberg, E.G., Tripodis, Y., Kowall, N.W., & Stern, R.A., (2017). Clinical utility of select neuropsychological assessment battery tests in predicting functional abilities in dementia. Archives Clinical Neuropsychology, 33, 530–540. doi: 10.1093/arclin/acx100.CrossRefGoogle Scholar
Colliot, O., Chételat, G., Chupin, M., Desgranges, B., Magnin, B., Benali, H., Dubois, B., Garnero, L., Eustache, F., & Lehéricy, S. (2008). Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the Hippocampus. Radiology, 248, 194–201.CrossRefGoogle ScholarPubMed
Convit, A., de Asis, J., de Leon, M., Tarshish, C., de Santi, S., & Rusinek, H. (2000). Atrophy of the medial occipitotemporal, inferior, and middle temporal Gyri in non-demented elderly predict decline to Alzheimer’s disease. Neurobiology of Aging, 21, 19–26.CrossRefGoogle ScholarPubMed
Convit, A., de Leon, M., Tarshish, C., de Santi, S., Tsui, W., Rusinek, H., & George, H. (1997). Specific hippocampal volume reductions in individuals at risk for Alzheimer’s disease. Neurobiology of Aging, 18(2), 131–138.CrossRefGoogle ScholarPubMed
de Flores, R., La Joie, R., & Chételat, G. (2015). Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience, 309, 29–50.CrossRefGoogle ScholarPubMed
Desikan, R.S., Cabral, H.J., Fischl, B., Guttmann, C.R., Blacker, D., Hyman, B.T., Albert, M.S., & Killiany, R.J. (2009). Temporoparietal MR imaging measures of atrophy in subjects with mild cognitive impairment that predict subsequent diagnosis of Alzheimer disease. American Journal of Neuroradiology, 30(3), 532–538.CrossRefGoogle ScholarPubMed
Desikan, R.S., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., & Albert, M.S. (2006). An automated labeling system for Subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980.CrossRefGoogle ScholarPubMed
Du, A.T., Schuff, N., Amend, D., Laakso, M.P., Hsu, Y.Y., Jagust, W.J., Yaffe, K., Kramer, J.H., Reed, B., Norman, D., & Chui, H.C. (2001). Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. Journal of Neurology, Neurosurgery, and Psychiatry, 71(4), 441–447.CrossRefGoogle ScholarPubMed
Fan, Y., Batmanghelich, N., Clark, C., & Davatzikos, C. (2008). Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. NeuroImage, 39, 1731–1743.CrossRefGoogle ScholarPubMed
Galetta, K.M., Chapman, K.R., Essis, M.D., Alosco, M.L., Gillard, D., Steinberg, E., Dixon, D., Martin, B., Chaisson, C., Kowall, N.W., & Tripodis, Y. (2017). Screening utility of the King–Devick test in mild cognitive impairment and Alzheimer’s disease dementia. Alzheimer Disease Associated Disorders, 31(2), 152–158.CrossRefGoogle Scholar
Gómez-Isla, T., Price, J., McKeel, D. Jr. Morris, J. Growdon, J., & Hyman, B. (1996). Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer’s disease. Journal of Neuroscience, 16(14), 4491–4500.CrossRefGoogle ScholarPubMed
Hängii, J., Streffer, J., Jäncke, L., & Hock, C. (2011). Volumes of lateral temporal and parietal structures distinguish between healthy aging, mild cognitive impairment, and Alzheimer’s disease. Journal of Alzheimer’s Disease, 26, 719–734.CrossRefGoogle Scholar
Hanseeuw, B., Van Leemput, K., Kavec, M., Grandin, C., Seron, X., & Ivanoiu, A. (2011). Mild cognitive impairment: differential atrophy in the hippocampal subfields. American Journal of Neuroradiology, 32(9), 1658–1661.CrossRefGoogle ScholarPubMed
Iglesias, J.E., Augustinack, J.C., Nguyen, K., Player, C.M., Player, A., Wright, M., Roy, N., Frosch, M.P., McKee, A.C., Wald, L.L., & Fischl, B. (2015). A computational atlas of the Hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. NeuroImage, 115, 117–137.CrossRefGoogle ScholarPubMed
Jack, C.R. Jr Bernstein, M.A. Fox, N.C. Thompson, P. Alexander, G. Harvey, D. Borowski, B. Britson, P.J. Whitwell, L.J. Ward, C., & Dale, A.M. (2008). The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685–691.CrossRefGoogle Scholar
Karas, G., Scheltens, P., Rombouts, S., Visser, P., van Schijndel, R., Fox, N., & Barkhof, F. (2004). Global and local gray matter loss in mild cognitive impairment and Alzheimer’s disease. NeuroImage, 23, 708–716.CrossRefGoogle ScholarPubMed
Khan, W., Westman, E., Jones, N., Wahlund, L.O., Mecocci, P., Vellas, B., Tsolaki, M., Kłoszewska, I., Soininen, H., Spenger, C., & Lovestone, S., (2015). Automated hippocampal subfield measures as predictors of conversion from mild cognitive impairment to Alzheimer’s disease in two independent cohorts. Brain Topography, 28(5), 746–759.CrossRefGoogle ScholarPubMed
Killiany, R.J., Gomez-Isla, T., Moss, M., Kikinis, R., Sandor, T., Jolesz, F., Tanzi, R., Jones, K., Hyman, B.T., & Albert, M.S., (2000). Use of structural magnetic resonance imaging to predict who will get Alzheimer’s disease. Annals of Neurology, 47(4), 430–439.3.0.CO;2-I>CrossRefGoogle ScholarPubMed
La Joie, R., Perrotin, A., De La Sayette, V., Egret, S., Doeuvre, L., Belliard, S., Eustache, F., Desgranges, B., & Chételat, G. (2013). Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer’s disease and semantic dementia. NeuroImage: Clinical, 3, 155–162.CrossRefGoogle ScholarPubMed
Misra, C., Fan, Y., & Davatzikos, C. (2009). Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI. NeuroImage, 44, 1415–1422.CrossRefGoogle ScholarPubMed
Morris, J. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43, 2412–2414.CrossRefGoogle ScholarPubMed
Mueller, S., Schuff, N., Yaffe, K., Madison, C., Miller, B., & Weiner, M. (2010). Hippocampal atrophy patterns in mild cognitive impairment and Alzheimer’s Disease. Human Brain Mapping, 31(9), 1339–1347.CrossRefGoogle ScholarPubMed
Pennanen, C., Kivipelto, M., Tuomainen, S., Hartikainen, P., Hänninen, T., Laakso, M.P., Hallikainen, M., Vanhanen, M., Nissinen, A., Helkala, E.L., & Vainio, P. (2004). Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiology of Aging, 25(3), 303–310.CrossRefGoogle ScholarPubMed
Petersen, R., Caracciolo, B., Brayne, C., Gauthier, S., Jelic, V., & Fratiglioni, L. (2014). Mild cognitive impairment: a concept in evolution. Journal of Internal Medicine, 275, 214–228.CrossRefGoogle Scholar
Petersen, R.C., Aisen, P., Boeve, B.F., Geda, Y.E., Ivnik, R.J., Knopman, D.S., Mielke, M., Pankratz, V.S., Roberts, R., Rocca, W.A., & Weigand, S. (2013). Criteria for mild cognitive impairment due to Alzheimer’s disease in the community. Annals of Neurology, 74(2), 199–208.Google Scholar
Pini, L., Pievani, M., Bocchetta, M., Altomare, D., Bosco, P., Cavedo, E., Galluzzi, S., Marizzoni, M., & Frisoni, G. (2016). Brain atrophy in Alzheimer’s disease and aging. Ageing Research Reviews, 30, 25–48.CrossRefGoogle ScholarPubMed
Plant, C., Teipel, S.J., Oswald, A., Böhm, C., Meindl, T., Mourao-Miranda, J., Bokde, A.W., Hampel, H., & Ewers, M., (2010). Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. NeuroImage, 50(1), 162–174.CrossRefGoogle ScholarPubMed
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(1), 85–99.CrossRefGoogle ScholarPubMed
Spires-Jones, T. & Hyman, B. (2014). The intersection of amyloid beta and tau at synapses in Alzheimer’s disease. Neuron, 82(4), 756–771.CrossRefGoogle ScholarPubMed
Weiner, M.W., Veitch, D.P., Aisen, P.S., Beckett, L.A., Cairns, N.J., Cedarbaum, J., Green, R.C., Harvey, D., Jack, C.R., Jagust, W., & Luthman, J. (2015). Update of the Alzheimer’s disease neuroimaging initiative: A review of papers published since its inception. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 11(6), e1–e120.CrossRefGoogle Scholar
Westman, E., Simmons, A., Zhang, Y., Muehlboeck, J.S., Tunnard, C., Liu, Y., Collins, L., Evans, A., Mecocci, P., Vellas, B., & Tsolaki, M., (2011). Multivariate analysis of MRI data for Alzheimer’s disease, mild cognitive impairment and healthy controls. NeuroImage, 54(2), 1178–1187.CrossRefGoogle ScholarPubMed
Xu, Y., Jack, C.R., O’brien, P.C., Kokmen, E., Smith, G.E., Ivnik, R.J., Boeve, B.F., Tangalos, R.G., & Petersen, R.C. (2000). Usefulness of MRI measures of entorhinal cortex versus hippocampus in AD. Neurology, 54(9), 1760–1767.CrossRefGoogle ScholarPubMed
Zhou, M., Zhang, F., Zhao, L., Qian, J., & Dong, C. (2016). Entorhinal cortex: A good biomarker of mild cognitive impairment and mild Alzheimer’s disease. Reviews in the Neurosciences, 27(2), 185–195.CrossRefGoogle ScholarPubMed