Brain Imaging and Behavior

, Volume 9, Issue 2, pp 204–212 | Cite as

Who wants a free brain scan? Assessing and correcting for recruitment biases in a population-based sMRI pilot study

  • Mary GanguliEmail author
  • Ching-Wen Lee
  • Tiffany Hughes
  • Beth E. Snitz
  • Jennifer Jakubcak
  • Ranjan Duara
  • Chung-Chou H. Chang
Original Research


Neuroimaging research is usually conducted in volunteers who meet a priori selection criteria. Selection/volunteer bias is assumed but cannot be assessed. During an ongoing population-based cohort study of 1982 older adults, we asked 1702 active participants about their interest in undergoing a research brain scan. Compared with those not interested, the 915 potentially interested individuals were significantly younger, more likely to be male, better educated, generally healthier, and more likely to be cognitively intact and dementia-free. In 48 of the interested individuals, we conducted a previously reported pilot structural magnetic resonance imaging (sMRI) study modelling mild cognitive impairment (MCI) vs. normal cognition, and Clinical Dementia Rating (CDR) = 0.5 vs. CDR = 0, as a function of sMRI atrophy ratings. We now compare these 48 individuals (1) with all interested participants, to assess selection bias; (2) with all who had been asked about their interest, to assess volunteer bias; and (3) with the entire study cohort, to assess attrition bias from those who had dropped out before the question was asked. Using these data in propensity score models, we generated weights which we applied to logistic regression models reanalyzing the data from the pilot sMRI study. These weighted models adjusted, in turn, for selection bias, interest/volunteer bias, and attrition bias. They show fewer regions of interest to be associated with MCI/ CDR than were in the original unweighted models. When study participants are drawn from a well-characterized population, they can be compared with non-participants, and the information used to correct study results for potential bias and thus provide more generalizable estimates.


Epidemiology Standard error Survey sampling Propensity scores 



The work reported here was supported in part by grants # R01 AG023651, K07 AG044395, K23AG083479 from the National Institute on Aging, US Department of Health and Human Services.

Conflict of interest

Authors Ganguli, Lee, Hughes, Jakubcak, Snitz, Chang, and Duara declare no conflicts of interest. Authors Ganguli, Lee, Hughes, Jakubcak, and Chang are supported by grant # R01 AG023651 from the National Institute on Aging. Dr. Ganguli is also supported by grant # K07 AG044395, Dr. Snitz is supported by grant # K23 AG08347.


  1. An, A. B. (2002). Performing logistic regression on survey data with the new SURVEYLOGISTIC procedure. Proceedings of the Twenty-Seventh Annual SAS® Users Group International Conference. pp. 258–27. Cary, NC: SAS Institute Inc., Paper 258–27.Google Scholar
  2. Becker, J. T., Duara, R., Lee, C. W., Teverovsky, L., Snitz, B. E., Chang, C. C., & Ganguli, M. (2012). Cross-validation of brain structural biomarkers and cognitive aging in a community-based study. International Psychogeriatrics, 24, 1065–1075.CrossRefPubMedCentralPubMedGoogle Scholar
  3. Bryan, R. N., Cai, J., Burke, G., et al. (1999). Prevalence and anatomic characteristics of infarct-like lesions on MR images of middle-aged adults: the Atherosclerosis Risk in Communities Study. American Journal of Neuroradiology, 20, 1273–1280.PubMedGoogle Scholar
  4. De Groot, J. C., de Leeuw, F.-E., Oudkerk, M., Hofman, A., Jolles, J., & Breteler, M. M. B. (2000). Cerebral white matter lesions and depressive symptoms in elderly adults. Archives of General Psychiatry, 57, 1071–1076.CrossRefPubMedGoogle Scholar
  5. DeCarli, C., Massaro, J., Harvey, D., Hald, J., Tullberg, M., Au, R., et al. (2005). Measures of brain morphology and infarction in the Framingham Heart Study: establishing what is normal. Neurobiology of Aging, 26, 491–510.CrossRefPubMedGoogle Scholar
  6. Department of Health, Education, and Welfare, Belmont Report. (1979). The Belmont Report: Ethical principles and guidelines for the protection of human subjects of research. Scholar
  7. Duara, R., Loewenstein, D. A., Shen, Q., Barker, W., Varon, D., Grieg, M. T., et al. (2013). The utility of age-specific cut-offs for visual rating of medial temporal atrophy in classifying Alzheimer’s disease, MCI, and cognitively normal elderly subjects. Frontiers in Aging Neuroscience, 5, 47.CrossRefPubMedCentralPubMedGoogle Scholar
  8. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.CrossRefPubMedGoogle Scholar
  9. Freedman, D. A., & Berk, R. A. (2008). Weighting regressions by propensity scores. Evaluation Review, 32, 392–409.CrossRefPubMedGoogle Scholar
  10. Ganguli, M., Lytle, M. E., Reynolds, M. D., & Dodge, H. H. (1998). Random versus volunteer selection for a community-based study. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 53, M39–M46.CrossRefGoogle Scholar
  11. Ganguli, M., Snitz, B. E., Vander, B. J., & Chang, C.-C. H. (2009). How much do depressive symptoms affect cognition at the population level? The Monongahela-Youghiogheny Healthy Aging Team (MYHAT) study. International Journal of Geriatric Psychiatry, 24, 1277–1284.CrossRefPubMedCentralPubMedGoogle Scholar
  12. Ganguli, M., Chang, C.-C. H., Snitz, B. E., Saxton, J. A., Vander, B. J., & Lee, C. W. (2010). Prevalence of mild cognitive impairment by multiple classifications: the MYHAT Project. The American Journal of Geriatric Psychiatry, 8, 674–683.CrossRefGoogle Scholar
  13. Gorkiewicz, M. (2009). Using propensity score with receiver operating characteristics (ROC) and Bootstrap to evaluate effect size in observational studies. Biocybernetics and Biomedical Engineering, 29, 41–61.Google Scholar
  14. Havlik, R. J., Foley, D. J., Sayer, B., Masaki, K., White, L., & Launer, L. J. (2002). Variability in midlife systolic blood pressure is related to late-life brain white matter lesions: the Honolulu- Asia Aging Study. Stroke, 33, 26–30.CrossRefPubMedGoogle Scholar
  15. Jaramillo, S. A., Felton, D., Andrews, L., Desiderio, L., Hallarn, R. K., Jackson, S. D., et al. (2007). Women’s Health Initiative Memory Study Research Group. Enrollment in a brain magnetic resonance study: results from the Women’s Health Initiative Memory Study Magnetic Resonance Imaging Study (WHIMS-MRI). Academic Radiology, 14, 603–612.CrossRefPubMedCentralPubMedGoogle Scholar
  16. Kukull, W. A., & Ganguli, M. (2012). Generalizability: the trees, the forest, and the low-hanging fruit. Neurology, 78(23), 1886–1891.CrossRefPubMedCentralPubMedGoogle Scholar
  17. Longstreth, W. T., Manolio, T. A., Arnold, A., Burke, G. L., Bryan, N., Jungreis, C. A., et al. (1996). Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. Stroke, 27, 1274–1282.CrossRefPubMedGoogle Scholar
  18. Morris, J. C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43, 2412–2414.CrossRefPubMedGoogle Scholar
  19. Oswald, L. M., Wand, G. S., Zhu, S., & Selby, V. (2013). Volunteerism and self-selection bias in human positron emission tomography neuroimaging research. Brain Imaging and Behavior, 7, 163–176.CrossRefPubMedCentralPubMedGoogle Scholar
  20. Pfeffermann, D. (1996). The use of sampling weights for survey data analysis. Statistical Methods in Medical Research, 5, 239–261.CrossRefPubMedGoogle Scholar
  21. Stürmer, T., Schneeweiss, S., Avorn, J., & Glynn, R. J. (2005). Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. American Journal of Epidemiology, 162, 279–289.CrossRefPubMedCentralPubMedGoogle Scholar
  22. Urs, R., Potter, E., Barker, W., Appel, J., Loewenstein, D. A., Zhao, W., & Duara, R. (2009). Visual rating system for assessing magnetic resonance images: a tool in the diagnosis of mild cognitive impairment and Alzheimer disease. Journal of Computer Assisted Tomography, 33(1), 73–78.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Mary Ganguli
    • 1
    • 2
    • 3
    • 10
    Email author
  • Ching-Wen Lee
    • 1
  • Tiffany Hughes
    • 1
  • Beth E. Snitz
    • 2
  • Jennifer Jakubcak
    • 1
  • Ranjan Duara
    • 4
    • 5
    • 6
    • 7
  • Chung-Chou H. Chang
    • 8
    • 9
  1. 1.School of Medicine, Department of PsychiatryUniversity of PittsburghPittsburghUSA
  2. 2.School of Medicine, Department of NeurologyUniversity of PittsburghPittsburghUSA
  3. 3.Graduate School of Public Health, Department of EpidemiologyUniversity of PittsburghPittsburghUSA
  4. 4.Wien Center for Alzheimer’s Disease and Memory DisordersMount Sinai Medical CenterMiami BeachUSA
  5. 5.Departments of Medicine, Neurology and Psychiatry and Behavioral Sciences, Miller School of MedicineUniversity of MiamiMiamiUSA
  6. 6.Department of NeurologyFlorida International University College of MedicineMiamiUSA
  7. 7.Department of NeurologyUniversity of Florida College of MedicineGainesvilleUSA
  8. 8.School of Medicine, Department of General MedicineUniversity of PittsburghPittsburghUSA
  9. 9.Graduate School of Public Health, Department of BiostatisticsUniversity of PittsburghPittsburghUSA
  10. 10.Western Psychiatric Institute and ClinicPittsburghUSA

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