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Automated MR morphometry to predict Alzheimer’s disease in mild cognitive impairment

  • Klaus H. Fritzsche
  • Bram Stieltjes
  • Sarah Schlindwein
  • Thomas van Bruggen
  • Marco Essig
  • Hans-Peter Meinzer
Orignal Article

Abstract

Purpose

Prediction of progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is challenging but essential for early treatment. This study aims to investigate the use of hippocampal atrophy markers for the automatic detection of MCI converters and to compare the predictive value to manually obtained hippocampal volume and temporal horn width.

Methods

A study was performed with 15 patients with Alzheimer and 18 patients with MCI (ten converted, eight remained stable in a 3-year follow-up) as well as 15 healthy subjects. MRI scans were obtained at baseline and evaluated with an automated system for scoring of hippocampal atrophy. The predictive value of the automated system was compared with manual measurements of hippocampal volume and temporal horn width in the same subjects.

Results

The conversion to AD was correctly predicted in 77.8% of the cases (sensitivity 70%, specificity 87.5%) in the MCI group using automated morphometry and a plain linear classifier that was trained on the AD and healthy groups. Classification was improved by limiting analysis to the left cerebral hemisphere (accuracy 83.3%, sensitivity 70%, specificity 100%). The manual linear and volumetric approaches reached rates of 66.7% (40/100%) and 72.2% (60/87.5%), respectively.

Conclusion

The automatic approach fulfills many important preconditions for clinical application. Contrary to the manual approaches, it is not observer-dependent and reduces human resource requirements. Automated assessment may be useful for individual patient assessment and for predicting progression to dementia.

Keywords

Brain atrophy Classification Early detection Converter Baseline Imaging biomarker 

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References

  1. 1.
    Petersen RC et al (2001) Practice parameter: early detection of dementia: mild cognitive impairment (an evidence-based review). Report of the quality standards subcommittee of the American Academy of Neurology. Neurology 56(9): 1133–1142PubMedGoogle Scholar
  2. 2.
    Mitchell AJ, Shiri-Feshki M (2009) Rate of progression of mild cognitive impairment to dementia—meta-analysis of 41 robust inception cohort studies. Acta Psychiatr Scand 119(4): 252–265CrossRefPubMedGoogle Scholar
  3. 3.
    Duchesne S et al (2008) MRI-based automated computer classification of probable AD versus normal controls. IEEE Trans Med Imaging 27(4): 509–520CrossRefPubMedGoogle Scholar
  4. 4.
    Ramani A, Jensen JH, Helpern JA (2006) Quantitative MR imaging in Alzheimer disease. Radiology 241(1): 26–44CrossRefPubMedGoogle Scholar
  5. 5.
    Batmanghelich N, Taskar B, Davatzikos C (2009) A general and unifying framework for feature construction, in image-based pattern classification. Inf Process Med Imaging 21: 423–434CrossRefPubMedGoogle Scholar
  6. 6.
    McEvoy LK et al (2009) Alzheimer disease: quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment. Radiology 251(1): 195–205CrossRefPubMedGoogle Scholar
  7. 7.
    Lehericy S et al (2007) Magnetic resonance imaging of Alzheimer’s disease. Eur Radiol 17(2): 347–362CrossRefPubMedGoogle Scholar
  8. 8.
    Albert M et al (2004) The use of MRI and PET for clinical diagnosis of dementia and investigation of cognitive impairment: a consensus report, in Alzheimer’s Association Neuroimaging WorkgroupGoogle Scholar
  9. 9.
    Desikan RS et al (2009) Temporoparietal MR imaging measures of atrophy in subjects with mild cognitive impairment that predict subsequent diagnosis of Alzheimer disease. AJNR Am J Neuroradiol 30(3): 532–538CrossRefPubMedGoogle Scholar
  10. 10.
    Nestor SM et al (2008) Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s disease neuroimaging initiative database. Brain 131(Pt 9): 2443–2454CrossRefPubMedGoogle Scholar
  11. 11.
    Risacher SL et al (2009) Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res 6(4): 347–361CrossRefPubMedGoogle Scholar
  12. 12.
    Julkunen V et al (2009) Cortical thickness analysis to detect progressive mild cognitive impairment: a reference to Alzheimer’s disease. Dement Geriatr Cogn Disord 28(5): 404–412CrossRefPubMedGoogle Scholar
  13. 13.
    Kovacevic S, Rafii MS, Brewer JB (2009) High-throughput, fully automated volumetry for prediction of MMSE and CDR decline in mild cognitive impairment. Alzheimer Dis Assoc Disord 23(2): 139–145CrossRefPubMedGoogle Scholar
  14. 14.
    Eckerstrom C et al (2008) Small baseline volume of left hippocampus is associated with subsequent conversion of MCI into dementia: the Goteborg MCI study. J Neurol Sci 272(1–2): 48–59CrossRefPubMedGoogle Scholar
  15. 15.
    Convit A et al (2000) Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimer’s disease. Neurobiol Aging 21(1): 19–26CrossRefPubMedGoogle Scholar
  16. 16.
    Giesel FL et al (2006) Temporal horn index and volume of medial temporal lobe atrophy using a new semiautomated method for rapid and precise assessment. AJNR Am J Neuroradiol 27(7): 1454–1458PubMedGoogle Scholar
  17. 17.
    Apostolova LG et al (2006) Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch Neurol 63(5): 693–699CrossRefPubMedGoogle Scholar
  18. 18.
    Ferrarini L et al (2009) Morphological hippocampal markers for automated detection of Alzheimer’s disease and mild cognitive impairment converters in magnetic resonance images. J Alzheimers Dis 17(3): 643–659PubMedGoogle Scholar
  19. 19.
    Fan Y et al (2008) Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39(4): 1731–1743CrossRefPubMedGoogle Scholar
  20. 20.
    Teipel SJ et al (2007) Multivariate deformation-based analysis of brain atrophy to predict Alzheimer’s disease in mild cognitive impairment. Neuroimage 38(1): 13–24CrossRefPubMedGoogle Scholar
  21. 21.
    Duchesne S et al (2008) Amnestic MCI future clinical status prediction using baseline MRI features. Neurobiol AgingGoogle Scholar
  22. 22.
    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(4): 1415–1422CrossRefPubMedGoogle Scholar
  23. 23.
    Fritzsche K et al (2008) Quantifizierung neurodegenerativer Veränderungen bei der Alzheimer Krankheit—Evaluierung eines automatischen Verfahrens. In: Tolxdorff T et al (eds) Bildverarbeitung für die Medizin. Springer, Heidelberg, pp 363–367Google Scholar
  24. 24.
    Fritzsche KH et al (2008) A computational method for the estimation of atrophic changes in Alzheimer’s disease and mild cognitive impairment. Comput Med Imaging Graph 32(4): 294–303CrossRefPubMedGoogle Scholar
  25. 25.
    Golebiowski M, Barcikowska M, Pfeffer A (1999) Magnetic resonance imaging-based hippocampal volumetry in patients with dementia of the Alzheimer type. Dement Geriatr Cogn Disord 10(4): 284–288CrossRefPubMedGoogle Scholar
  26. 26.
    Juottonen K et al (1999) Comparative MR analysis of the entorhinal cortex and hippocampus in diagnosing Alzheimer disease. AJNR Am J Neuroradiol 20(1): 139–144PubMedGoogle Scholar
  27. 27.
    Laakso MP et al (1996) Hippocampal volumes in Alzheimer’s disease, Parkinson’s disease with and without dementia, and in vascular dementia: an MRI study. Neurology 46(3): 678–681PubMedGoogle Scholar
  28. 28.
    Teipel SJ et al (2006) Comprehensive dissection of the medial temporal lobe in AD: measurement of hippocampus, amygdala, entorhinal, perirhinal and parahippocampal cortices using MRI. J Neurol 253(6): 794–800CrossRefPubMedGoogle Scholar
  29. 29.
    Erkinjuntti T et al (1993) Temporal lobe atrophy on magnetic resonance imaging in the diagnosis of early Alzheimer’s disease. Arch Neurol 50(3): 305–310PubMedGoogle Scholar
  30. 30.
    Frisoni GB et al (1996) Linear measures of atrophy in mild Alzheimer disease. AJNR Am J Neuroradiol 17(5): 913–923PubMedGoogle Scholar
  31. 31.
    Pennanen C et al (2004) Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging 25(3): 303–310CrossRefPubMedGoogle Scholar
  32. 32.
    Juottonen K et al (1998) Major decrease in the volume of the entorhinal cortex in patients with Alzheimer’s disease carrying the apolipoprotein E epsilon4 allele. J Neurol Neurosurg Psychiatry 65(3): 322–327CrossRefPubMedGoogle Scholar
  33. 33.
    Zhang Y et al (2008) Usefulness of computed tomography linear measurements in diagnosing Alzheimer’s disease. Acta Radiol 49(1): 91–97CrossRefPubMedGoogle Scholar
  34. 34.
    Morris JC et al (1989) The consortium to establish a registry for Alzheimer’s disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology 39(9): 1159–1165PubMedGoogle Scholar
  35. 35.
    Levy R (1994) Aging-associated cognitive decline. Working party of the International Psychogeriatric Association in collaboration with the World Health Organization. Int Psychogeriatr 6(1): 63– 68CrossRefPubMedGoogle Scholar
  36. 36.
    McKhann G et al (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(7): 939–944PubMedGoogle Scholar
  37. 37.
    Gao FQ et al (2003) A reliable MR measurement of medial temporal lobe width from the Sunnybrook dementia study. Neurobiol Aging 24(1): 49–56CrossRefPubMedGoogle Scholar
  38. 38.
    Maleike D et al (2009) Interactive segmentation framework of the medical imaging interaction toolkit. Comput Methods Programs Biomed 96(1): 72–83CrossRefPubMedGoogle Scholar
  39. 39.
    Ashburner J, Friston KJ (1999) Nonlinear spatial normalization using basis functions. Hum Brain Mapp 7(4): 254–266CrossRefPubMedGoogle Scholar
  40. 40.
    Ashburner J, Friston K (1997) Multimodal image coregistration and partitioning—a unified framework. Neuroimage 6(3): 209–217CrossRefPubMedGoogle Scholar
  41. 41.
    Barnes J et al (2005) Does Alzheimer’s disease affect hippocampal asymmetry? Evidence from a cross-sectional and longitudinal volumetric MRI study. Dement Geriatr Cogn Disord 19(5–6): 338–344CrossRefPubMedGoogle Scholar
  42. 42.
    Zahn R et al (2004) Hemispheric asymmetries of hypometabolism associated with semantic memory impairment in Alzheimer’s disease: a study using positron emission tomography with fluorodeoxyglucose-F18. Psychiatry Res 132(2): 159–172CrossRefPubMedGoogle Scholar
  43. 43.
    Bigler ED et al (2002) Dementia, asymmetry of temporal lobe structures, and apolipoprotein E genotype: relationships to cerebral atrophy and neuropsychological impairment. J Int Neuropsychol Soc 8(7): 925–933CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2010

Authors and Affiliations

  • Klaus H. Fritzsche
    • 1
  • Bram Stieltjes
    • 2
  • Sarah Schlindwein
    • 1
  • Thomas van Bruggen
    • 1
  • Marco Essig
    • 2
  • Hans-Peter Meinzer
    • 1
  1. 1.Division of Medical and Biological InformaticsGerman Cancer Research CenterHeidelbergGermany
  2. 2.Division of RadiologyGerman Cancer Research CenterHeidelbergGermany

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