Joint Data Harmonization and Group Cardinality Constrained Classification

  • Yong ZhangEmail author
  • Sang Hyun Park
  • Kilian M. Pohl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9900)


To boost the power of classifiers, studies often increase the size of existing samples through the addition of independently collected data sets. Doing so requires harmonizing the data for demographic and acquisition differences based on a control cohort before performing disease specific classification. The initial harmonization often mitigates group differences negatively impacting classification accuracy. To preserve cohort separation, we propose the first model unifying linear regression for data harmonization with a logistic regression for disease classification. Learning to harmonize data is now an adaptive process taking both disease and control data into account. Solutions within that model are confined by group cardinality to reduce the risk of overfitting (via sparsity), to explicitly account for the impact of disease on the inter-dependency of regions (by grouping them), and to identify disease specific patterns (by enforcing sparsity via the \(l_0\)-‘norm’). We test those solutions in distinguishing HIV-Associated Neurocognitive Disorder from Mild Cognitive Impairment of two independently collected, neuroimage data sets; each contains controls and samples from one disease. Our classifier is impartial to acquisition difference between the data sets while being more accurate in diseases seperation than sequential learning of harmonization and classification parameters, and non-sparsity based logistic regressors.


Mild Cognitive Impairment Disease Accuracy Disease Cohort Group Sparsity Block Coordinate Descent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported in part by the NIH grants U01 AA017347, AA010723, K05-AA017168, K23-AG032872, and P30 AI027767. We thank Dr. Valcour for giving us access to the UHES data set. With respect to the ADNI data, collection and sharing for this project was funded by the NIH Grant U01 AG024904 and DOD Grant W81XWH-12-2-0012. Please see for further details.


  1. 1.
    Jovicich, J., et al.: Multisite longitudinal reliability of tract-based spatial statistics in diffusion tensor imaging of healthy elderly subjects. Neuroimage 101, 390–403 (2014)CrossRefGoogle Scholar
  2. 2.
    Moradi, E., et al.: Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data. bioRxiv (2016)Google Scholar
  3. 3.
    Sabuncu, M.R.: A universal and efficient method to compute maps from image-based prediction models. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 353–360. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-10443-0_45 Google Scholar
  4. 4.
    Zhang, Y., et al.: Computing group cardinality constraint solutions for logistic regression problems. Medical Image Analysis (2016, in press)Google Scholar
  5. 5.
    Sanmarti, M., et al.: HIV-associated neurocognitive disorders. J.M.P. 2(2) (2014)Google Scholar
  6. 6.
    Lu, Z., Zhang, Y.: Sparse approximation via penalty decomposition methods. SIAM J. Optim. 23(4), 2448–2478 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Nir, T.M., et al.: Mapping white matter integrity in elderly people with HIV. Hum. Brain Mapp. 35(3), 975–992 (2014)CrossRefGoogle Scholar
  8. 8.
    Pfefferbaum, A., et al.: Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85 years) measured with atlas-based parcellation of MRI. Neuroimage 65, 176–193 (2013)CrossRefGoogle Scholar
  9. 9.
    Fisher, R.: The logic of inductive inference. J. Roy. Stat. Soc. 1(98), 38–54 (1935)zbMATHGoogle Scholar
  10. 10.
    Chang, L., et al.: Impact of apolipoprotein E \(\epsilon \)4 and HIV on cognition and brain atrophy: antagonistic pleiotropy and premature brain aging. Neuroimage 4(58), 1017–1027 (2011)CrossRefGoogle Scholar
  11. 11.
    Thompson, P.M., et al.: 3D mapping of ventricular and corpus callosum abnormalities in HIV/AIDS. Neuroimage 31(1), 12–23 (2006)CrossRefGoogle Scholar
  12. 12.
    Nestor, S.M., et al.: Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s disease neuroimaging initiative database. Brain 131(9), 2443–2454 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Psychiatry and Behavioral SciencesStanford UniversityStanfordUSA
  2. 2.Center of Health SciencesSRI International Menlo ParkUSA

Personalised recommendations