Skip to main content

Advertisement

SpringerLink
Log in

Early prediction of Alzheimer’s disease using longitudinal volumetric MRI data from ADNI

  • Published:
Health Services and Outcomes Research Methodology Aims and scope Submit manuscript

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disease and the most common form of dementia, affecting many millions around the world. Accurate prediction of AD is crucial for effective intervention. We develop a longitudinal data prediction framework based on functional data analysis to identify when an early prediction can reasonably be made. As the regional brain atrophy is related to AD progression, we fit our model to the longitudinal volumetric changes of five regions of interest (ROIs) quantified with MRIs: hippocampus (H), entorhinal cortex (EC), middle temporal cortex (MTC), fusiform gyrus (FG) and whole brain (WB). To evaluate the AD prediction based on each ROI and the combinations of some of them, we compare different choices by their accuracy, sensitivity, specificity and area under the curve (AUC) through training and testing procedures. The results show that these ROI volumes have prediction power as early as 3 years in advance. Among all the models, the overall sensitivity is around \(80\%\), specificity is above \(70\%\), accuracy is around \(75\%\) and AUC above \(80\%\). Among all the ROIs, EC is the best predictor (with the AUCs above 0.83 for 1-year and 2-year advanced prediction), followed by MTC and hippocampus. We also find that the combination of H + EC + MTC is the best combination (with AUCs of 0.86 for 1-year, 0.85 for 2-year, and 0.82 for 3-year advanced prediction). The key finding is that the AUC of 1-year prediction is not much different from that of 3-year prediction. In other words, we can use 3-year advanced prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Adaszewski, S., Dukart, J., Kherif, F., Frackowiak, R., Draganski, B., Alzheimer’s Disease Neuroimaging Initiative, et al.: How early can we predict Alzheimer’s disease using computational anatomy? Neurobiol. Aging 34(12), 2815–2826 (2013)

  • Aksu, Y., Miller, D.J., Kesidis, G., Bigler, D.C., Yang, Q.X.: An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients. PLoS ONE 6(10), e25074 (2011)

    Article  CAS  Google Scholar 

  • Arco, J.E., Ramírez, J., Górriz, J.M., Puntonet, C.G., Ruz, M.: Short-term prediction of MCI to AD conversion based on longitudinal MRI analysis and neuropsychological tests. In: Innovation in Medicine and Healthcare 2015, Springer, pp. 385–394 (2016)

  • Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  • Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O., Alzheimer’s Disease Neuroimaging Initiative, et al.: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56(2), 766–781 (2011)

    Article  Google Scholar 

  • Eskildsen, S.F., Coupé, P., García-Lorenzo, D., Fonov, V., Pruessner, J.C., Collins, D.L., Alzheimer’s Disease Neuroimaging Initiative, et al.: Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage 65, 511–521 (2013)

    Article  Google Scholar 

  • Fennema-Notestine, C., Hagler, D.J., McEvoy, L.K., Fleisher, A.S., Wu, E.H., Karow, D.S., Dale, A.M.: Structural MRI biomarkers for preclinical and mild Alzheimer’s disease. Human Brain Mapp. 30(10), 3238–3253 (2009)

    Article  Google Scholar 

  • Hall, P., Maiti, T.: Choosing trajectory and data type when classifying functional data. Biometrika 99, 799–811 (2012)

    Article  Google Scholar 

  • Hartig, M., Truran-Sacrey, D., Raptentsetsang, S., Simonson, A., Mezher, A., Schuff, N., Weiner, M.: UCSF Freesurfer Methods. ADNI: Alzheimer’s Disease Neuroimaging Initiative, San Francisco (2014)

  • Jack, C.R., Knopman, D.S., Jagust, W.J., Shaw, L.M., Aisen, P.S., Weiner, M.W., Petersen, R.C., Trojanowski, J.Q.: Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9(1), 119–128 (2010)

    Article  CAS  Google Scholar 

  • Karow, D.S., McEvoy, L.K., Fennema-Notestine, C., Hagler Jr., D.J., Jennings, R.G., Brewer, J.B., Hoh, C.K., Dale, A.M.: Relative capability of MR imaging and FDG PET to depict changes associated with prodromal and early Alzheimer disease. Radiology 256(3), 932–942 (2010)

    Article  Google Scholar 

  • Lee, S.H., Bachman, A.H., Yu, D., Lim, J., Ardekani, B.A., Alzheimer’s Disease Neuroimaging Initiative, et al.: Predicting progression from mild cognitive impairment to Alzheimer’s disease using longitudinal callosal atrophy. Alzheimer’s & Dement. Diagn. Assess. Dis. Monit. 2, 68–74 (2016)

  • Leung, K.K., Barnes, J., Ridgway, G.R., Bartlett, J.W., Clarkson, M.J., Macdonald, K., Schuff, N., Fox, N.C., Ourselin, S., Alzheimer’s Disease Neuroimaging Initiative, et al.: Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer’s disease. Neuroimage 51(4), 1345–1359 (2010)

    Article  Google Scholar 

  • Leung, K.K., Bartlett, J.W., Barnes, J., Manning, E.N., Ourselin, S., Fox, N.C., Alzheimer’s Disease Neuroimaging Initiative, et al.: Cerebral atrophy in mild cognitive impairment and Alzheimer disease rates and acceleration. Neurology 80(7), 648–654 (2013)

    Article  CAS  Google Scholar 

  • Li, Y., Wang, Y., Wu, G., Shi, F., Zhou, L., Lin, W., Shen, D., Alzheimer’s Disease Neuroimaging Initiative, et al.: Discriminant analysis of longitudinal cortical thickness changes in Alzheimer’s disease using dynamic and network features. Neurobiol. Aging 33(2), 427–e15 (2012)

  • McDonald, C., McEvoy, L., Gharapetian, L., Fennema-Notestine, C., Hagler, D., Holland, D., Koyama, A., Brewer, J., Dale, A., Alzheimer’s Disease Neuroimaging Initiative, et al.: Regional rates of neocortical atrophy from normal aging to early Alzheimer disease. Neurology 73(6), 457–465 (2009)

    Article  CAS  Google Scholar 

  • Misra, C., Fan, Y., Davatzikos, C.: 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(4), 1415–1422 (2009)

    Article  Google Scholar 

  • Müller, H.G.: Functional modelling and classification of longitudinal data. Scand. J. Stat. 32(2), 223–240 (2005)

    Article  Google Scholar 

  • Weiner, M.W., Veitch, D.P., Aisen, P.S., Beckett, L.A., Cairns, N.J., Green, R.C., Harvey, D., Jack, C.R., Jagust, W., Liu, E., et al.: The Alzheimer’s disease neuroimaging initiative: a review of papers published since its inception. Alzheimer’s Dement. 9(5), e111–e194 (2013)

    Article  Google Scholar 

  • Yao, F., Müller, H.G., Wang, J.L.: Functional data analysis for sparse longitudinal data. J. Am. Stat. Assoc. 100(470), 577–590 (2005)

    Article  CAS  Google Scholar 

  • Zhang, D., Shen, D., Alzheimer’s Disease Neuroimaging Initiative, et al.: Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PloS ONE 7(3), e33182 (2012)

    Article  CAS  Google Scholar 

  • Zhang, L., Lim, C.Y., Maiti, T., Li, Y., Choi, J., Bozoki, A., Zhu, D.C., Key Laboratory of Applied Statistics of the Ministry of Education (KLAS), for the Alzheimer’s Disease Neuroimaging Initiative: Analysis of conversion of Alzheimer’s disease using a multi-state Markov model. Statistical methods in medical research p 0962280218786525 (2018)

Download references

Author information

Authors and Affiliations

  1. Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824, USA

    Yingjie Li & Taps Maiti

  2. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA

    Liangliang Zhang

  3. Neuroscience Program and Department of Neurology, Michigan State University, East Lansing, MI, 48824, USA

    Andrea Bozoki

  4. Departments of Radiology and Psychology, Michigan State University, East Lansing, MI, 48824, USA

    David C. Zhu

  5. School of Mechanical Engineering, Yonsei University, Seoul, Republic of Korea

    Jongeun Choi

Authors
  1. Yingjie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liangliang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Bozoki
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David C. Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jongeun Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Taps Maiti
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

for the Alzheimer’s Disease Neuroimaging Initiative

Corresponding author

Correspondence to Liangliang Zhang.

Ethics declarations

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database(adni.loni.usc.edu).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Data used in 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 analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Appendix

Appendix

The prediction performances for MRI plus model are listed in this section. While adding more relevant variables, MRI plus model performs better than the MRI base model, especially for 2 years and 3 years prediction. See Tables 11, 12, 13 and 14. For the statistical inference on the coefficients of logistic regression with inclusion and exclusion of gender, please see Tables 15 and 16.

Table 11 Prediction performance using single ROI (MRI plus model)
Full size table
Table 12 Prediction performance using combination of two ROIs (MRI plus model)
Full size table
Table 13 Prediction performance using combinations of three ROIs (MRI plus model)
Full size table
Table 14 Prediction performance using combination of four or more ROIs (MRI plus model)
Full size table
Table 15 Parameter estimation with gender (MRI plus model of hippocampus for 1y prediction)
Full size table
Table 16 Parameter estimation without gender (MRI plus model of hippocampus for 1y prediction)
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Zhang, L., Bozoki, A. et al. Early prediction of Alzheimer’s disease using longitudinal volumetric MRI data from ADNI. Health Serv Outcomes Res Method 20, 13–39 (2020). https://doi.org/10.1007/s10742-019-00206-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10742-019-00206-3

Keywords

Search

Navigation