Skip to main content

Advertisement

SpringerLink
Log in

Monte Carlo feature selection and rule-based models to predict Alzheimer’s disease in mild cognitive impairment

  • Dementias - Original Article
  • Published:
Journal of Neural Transmission Aims and scope Submit manuscript

Abstract

The objective of the present study was to evaluate a Monte Carlo feature selection (MCFS) and rough set Rosetta pipeline for generating rule-based models as a tool for comprehensive risk estimates for future Alzheimer’s disease (AD) in individual patients with mild cognitive impairment (MCI). Risk estimates were generated on the basis of age, gender, Mini-Mental State Examination scores, apolipoprotein E (APOE) genotype and the cerebrospinal fluid (CSF) biomarkers total tau (T-tau), phospho-tau181 (P-tau) and the 42 amino acid form of amyloid β (Aβ42) in two sets of longitudinally followed MCI patients (n = 217 in total). The predictive model was created in Rosetta, evaluated with the standard tenfold cross-validation approach and tested on an external set. Features were ranked and selected by the MCFS algorithm. Using the combined pipeline of MCFS and Rosetta, it was possible to predict AD among patients with MCI with an area under the receiver operating characteristics curve of 0.92. Risk estimates were produced for the individual patients and showed good correlation with actual diagnosis in cross validation, and on an external dataset from a new study. Analysis of the importance of attributes showed that the biochemical CSF markers contributed the most to the predictions, and that added value was gained by combining several biochemical markers. Despite a correlation with the biochemical markers, the genetic marker APOE ε4 did not contribute to the predictive power of the model.

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

Similar content being viewed by others

References

  • American Psychiatric Association (1987) Diagnostic and statistical manual of mental disorders, 3rd edn, revised. American Psychiatric Association, Arlington

    Google Scholar 

  • Arnaiz E, Jelic V, Almkvist O, Wahlund LO, Winblad B, Valind S, Nordberg A (2001) Impaired cerebral glucose metabolism and cognitive functioning predict deterioration in mild cognitive impairment. Neuroreport 12:851–855

    Article  PubMed  CAS  Google Scholar 

  • Blennow K, Zetterberg H (2006) Pinpointing plaques with PIB. Nat Med 12:753–754

    Article  PubMed  CAS  Google Scholar 

  • Blennow K, de Leon MJ, Zetterberg H (2006) Alzheimer’s disease. Lancet 368:387–403

    Article  PubMed  CAS  Google Scholar 

  • Borroni B, Anchisi D, Paghera B, Vicini B, Kerrouche N, Garibotto V, Terzi A, Vignolo LA, Di Luca M, Giubbini R, Padovani A, Perani D (2006) Combined 99mTc-ECD SPECT and neuropsychological studies in MCI for the assessment of conversion to AD. Neurobiol Aging 27:24–31

    Article  PubMed  CAS  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Brys M, Pirraglia E, Rich K, Rolstad S, Mosconi L, Switalski R, Glodzik-Sobanska L, De Santi S, Zinkowski R, Mehta P, Pratico D, Saint Louis LA, Wallin A, Blennow K, de Leon MJ (2009) Prediction and longitudinal study of CSF biomarkers in mild cognitive impairment. Neurobiol Aging 30:682–690

    Article  PubMed  CAS  Google Scholar 

  • Chetelat G, Eustache F, Viader F, De La Sayette V, Pélerin A, Mézenge F, Hannequin D, Dupuy B, Baron JC, Desgranges B (2005) FDG-PET measurement is more accurate than neuropsychological assessments to predict global cognitive deterioration in patients with mild cognitive impairment. Neurocase 11:14–25

    Article  PubMed  Google Scholar 

  • Draminski M, Rada-Iglesias A, Enroth S, Wadelius C, Koronacki J, Komorowski J (2009) Monte Carlo feature selection for supervised classification. Bioinformatics 25:1165–1172

    Article  Google Scholar 

  • Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, Belleville S, Brodaty H, Bennett D, Chertkow H, Cummings JL, de Leon M, Feldman H, Ganguli M, Hampel H, Scheltens P, Tierney MC, Whitehouse P, Winblad B (2006) Mild cognitive impairment. Lancet 367:1262–1270

    Article  PubMed  Google Scholar 

  • Hampel H, Teipel SJ, Fuchsberger T, Andreasen N, Wiltfang J, Otto M, Shen Y, Dodel R, Du Y, Farlow M, Möller HJ, Blennow K, Buerger K (2004) Value of CSF beta-amyloid1-42 and tau as predictors of Alzheimer’s disease in patients with mild cognitive impairment. Mol Psychiatry 9:705–710

    PubMed  CAS  Google Scholar 

  • Hansson O, Zetterberg H, Buchhave P, Londos E, Blennow K, Minthon L (2006) Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol 5:228–234

    Article  PubMed  CAS  Google Scholar 

  • Hansson O, Buchhave P, Zetterberg H, Blennow K, Minthon L, Warkentin S (2007) Combined rCBF and CSF biomarkers predict progression from mild cognitive impairment to Alzheimer’s disease. Neurobiol Aging 30:165–173

    Article  PubMed  Google Scholar 

  • Hertze J, Minthon L, Zetterberg H, Vanmechelen E, Blennow K, Hansson O (2010) Evaluation of CSF biomarkers as predictors of Alzheimer’s disease: a clinical follow-up study of 4.7 years. J Alzheimers Dis 21:1119–1128

    PubMed  CAS  Google Scholar 

  • Herukka SK, Hallikainen M, Soininen H, Pirttilä T (2005) CSF Abeta42 and tau or phosphorylated tau and prediction of progressive mild cognitive impairment. Neurology 6:1294–1297

    Article  Google Scholar 

  • Hvidsten TR, Komorowski J (2007) Rough sets in bioinformatics. In: Marek VW, Orlowska E, Slowinski R, Ziarko W (eds) Transactions on rough sets VII. LNCS, vol 4400. Springer, Heidelberg, pp 225–243

  • John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the eleventh conference on uncertainty in artificial intelligence, San Mateo, pp 338–345

  • Kierczak MDM, Koronacki J, Komorowski J (2010) Computational analysis of molecular interaction networks underlying change of HIV-1 resistance to selected reverse transcriptase inhibitors. Bioinform Biol Insights 4:137–146

    PubMed  CAS  Google Scholar 

  • Le Cessie S, van Houwelingen JC (1992) Ridge estimators in logistic regression. Appl Stat 41:191–201

    Article  Google Scholar 

  • Lewczuk P, Beck G, Ganslandt O, Esselmann H, Deisenhammer F, Regeniter A, Petereit HF, Tumani H, Gerritzen A, Oschmann P, Schröder J, Schönknecht P, Zimmermann K, Hampel H, Bürger K, Otto M, Haustein S, Herzog K, Dannenberg R, Wurster U, Bibl M, Maler JM, Reubach U, Kornhuber J, Wiltfang J (2006) International quality control survey of neurochemical dementia diagnostics. Neurosci Lett 409:1–4

    Article  PubMed  CAS  Google Scholar 

  • Mattsson N, Zetterberg H, Hansson O, Andreasen N, Parnetti L, Jonsson M, Herukka SK, van der Flier WM, Blankenstein MA, Ewers M, Rich K, Kaiser E, Verbeek M, Tsolaki M, Mulugeta E, Rosén E, Aarsland D, Visser PJ, Schröder J, Marcusson J, de Leon M, Hampel H, Scheltens P, Pirttilä T, Wallin A, Jönhagen ME, Minthon L, Winblad B, Blennow K (2009) CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment. JAMA 302:385–393

    Article  PubMed  CAS  Google Scholar 

  • Mattsson N, Andreasson U, Persson S, Arai H, Batish SD, Bernardini S, Bocchio-Chiavetto L, Blankenstein MA, Carrillo MC, Chalbot S, Coart E, Chiasserini D, Cutler N, Dahlfors G, Duller S, Fagan AM, Forlenza O, Frisoni GB, Galasko D, Galimberti D, Hampel H, Handberg A, Heneka MT, Herskovits AZ, Herukka SK, Holtzman DM, Humpel C, Hyman BT, Iqbal K, Jucker M, Kaeser SA, Kaiser E, Kapaki E, Kidd D, Klivenyi P, Knudsen CS, Kummer MP, Lui J, Lladó A, Lewczuk P, Li QX, Martins R, Masters C, McAuliffe J, Mercken M, Moghekar A, Molinuevo JL, Montine TJ, Nowatzke W, O’Brien R, Otto M, Paraskevas GP, Parnetti L, Petersen RC, Prvulovic D, de Reus HP, Rissman RA, Scarpini E, Stefani A, Soininen H, Schröder J, Shaw LM, Skinningsrud A, Skrogstad B, Spreer A, Talib L, Teunissen C, Trojanowski JQ, Tumani H, Umek RM, Van Broeck B, Vanderstichele H, Vecsei L, Verbeek MM, Windisch M, Zhang J, Zetterberg H, Blennow K (2011) The Alzheimer’s Association external quality control program for cerebrospinal fluid biomarkers. Alzheimers Dement 7:386–395

    Article  PubMed  CAS  Google Scholar 

  • McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34:939–944

    Article  PubMed  CAS  Google Scholar 

  • Mosconi L, Tsui WH, De Santi S, Li J, Rusinek H, Convit A, Li Y, Boppana M, de Leon MJ (2005) Reduced hippocampal metabolism in MCI and AD: automated FDG-PET image analysis. Neurology 64:1860–1867

    Article  PubMed  CAS  Google Scholar 

  • Nguyen HS (2006) Approximate Boolean reasoning: foundations and applications in data mining. LNCS 4100:334–506

    Google Scholar 

  • Nordlund A, Rolstad S, Hellstrom P, Sjogren M, Hansen S, Wallin A (2005) The Goteborg MCI study: mild cognitive impairment is a heterogeneous condition. J Neurol Neurosurg Psychiatry 76:1485–1490

    Article  PubMed  CAS  Google Scholar 

  • Pawlak Z (1982) Rough sets. Int J Parallel Program 11:341–356

    Google Scholar 

  • Petersen RC (2004) Mild cognitive impairment as a diagnostic entity. J Intern Med 256:183–194

    Article  PubMed  CAS  Google Scholar 

  • Riemenschneider M, Lautenschlager N, Wagenpfeil S, Diehl J, Drzezga A, Kurz A (2002) Cerebrospinal fluid tau and beta-amyloid 42 proteins identify Alzheimer disease in subjects with mild cognitive impairment. Arch Neurol 59:1729–1734

    Article  PubMed  CAS  Google Scholar 

  • Shaw LM, Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, Blennow K, Soares H, Simon A, Lewczuk P, Dean R, Siemers E, Potter W, Lee VM, Trojanowski JQ (2009) Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 65:403–413

    Article  PubMed  CAS  Google Scholar 

  • Verwey NA, van der Flier WM, Blennow K, Clark C, Sokolow S, De Deyn PP, Galasko D, Hampel H, Hartmann T, Kapaki E, Lannfelt L, Mehta PD, Parnetti L, Petzold A, Pirttila T, Saleh L, Skinningsrud A, Swieten JC, Verbeek MM, Wiltfang J, Younkin S, Scheltens P, Blankenstein MA (2009) A worldwide multicentre comparison of assays for cerebrospinal fluid biomarkers in Alzheimer’s disease. Ann Clin Biochem 46:235–240

    Article  PubMed  CAS  Google Scholar 

  • Visser PJ, Verhey F, Knol DL, Scheltens P, Wahlund LO, Freund-Levi Y, Tsolaki M, Minthon L, Wallin AK, Hampel H, Bürger K, Pirttila T, Soininen H, Rikkert MO, Verbeek MM, Spiru L, Blennow K (2009) Prevalence and prognostic value of CSF markers of Alzheimer’s disease pathology in patients with subjective cognitive impairment or mild cognitive impairment in the DESCRIPA study: a prospective cohort study. Lancet Neurol 8:619–627

    Article  PubMed  Google Scholar 

  • Zetterberg H, Wahlund LO, Blennow K (2003) Cerebrospinal fluid markers for prediction of Alzheimer’s disease. Neurosci Lett 352:67–69

    Article  PubMed  CAS  Google Scholar 

  • Zetterberg H, Pedersen M, Lind K, Svensson M, Rolstad S, Eckerström C, Syversen S, Mattsson UB, Ysander C, Mattsson N, Nordlund A, Vanderstichele H, Vanmechelen E, Jonsson M, Edman A, Blennow K, Wallin A (2007) Intra-individual stability of CSF biomarkers for Alzheimer’s disease over two years. J Alzheimers Dis 12:255–260

    PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported by grants from the Foundation of Polish Science, International PhD Projects (MPD) program to MK, the Ministry of Science and Higher Education (grant N301 239536) to JK, the Knut and Alice Wallenberg Foundation to JK and MGA, Swedish Foundation for Strategic Research to JK, the Swedish Research Council and the Swedish Brain Power project to HZ, OH, LM, AW and KB.

Conflict of interest

None.

Author information

Authors and Affiliations

  1. Postgraduate School for Molecular Medicine, Żwirki i Wigury 61 Street, 02-091, Warsaw, Poland

    Marcin Kruczyk

  2. Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics, Uppsala University, 751 24, Uppsala, Sweden

    Marcin Kruczyk & Jan Komorowski

  3. Interdisciplinary Centre for Mathematical and Computational Modelling, Warsaw University, 02-106, Warsaw, Poland

    Marcin Kruczyk & Jan Komorowski

  4. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden

    Henrik Zetterberg, Sindre Rolstad, Anders Wallin & Kaj Blennow

  5. UCL Institute of Neurology, London, WC1N 3BG, UK

    Henrik Zetterberg

  6. Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden

    Oskar Hansson & Lennart Minthon

  7. The Neuropsychiatric Clinic, Malmö University Hospital, Malmö, Sweden

    Oskar Hansson & Lennart Minthon

  8. Department of Chemistry, Environment and Feed Hygiene, National Veterinary Institute (SVA), Uppsala, Sweden

    Mats Gunnar Andersson

Authors
  1. Marcin Kruczyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Henrik Zetterberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oskar Hansson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sindre Rolstad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lennart Minthon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anders Wallin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kaj Blennow
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jan Komorowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mats Gunnar Andersson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Henrik Zetterberg, Jan Komorowski or Mats Gunnar Andersson.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kruczyk, M., Zetterberg, H., Hansson, O. et al. Monte Carlo feature selection and rule-based models to predict Alzheimer’s disease in mild cognitive impairment. J Neural Transm 119, 821–831 (2012). https://doi.org/10.1007/s00702-012-0812-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00702-012-0812-0

Keywords

Search

Navigation