Regularized logistic discrimination with basis expansions for the early detection of Alzheimer's disease based on three-dimensional MRI data

Advances in Data Analysis and Classification

Volumn 7, Issue 1, 2013, Pages 109-119

  Araki, Yuko ^a   Kawaguchi, Atsushi ^a   Yamashita, Fumio ^b  

^a KURUME UNIVERSITY   (Japan)

^b IWATE MEDICAL UNIVERSITY   (Japan)

Author keywords

Alzheimer's disease; Dimension reduction; Regularized logistic discrimination; Three dimensional MRI data; Tuning parameter selection

Indexed keywords

DIAGNOSIS; DIMENSIONALITY REDUCTION; MAGNETIC RESONANCE IMAGING; NEURODEGENERATIVE DISEASES;

ALZHEIMER'S DISEASE; DIMENSION REDUCTION; DIMENSION REDUCTION TECHNIQUES; DISCRIMINANT FUNCTIONS; REGULARIZED LOGISTIC DISCRIMINATION; SENSITIVITY AND SPECIFICITY; THREE-DIMENSIONAL DATA; TUNING PARAMETER;

CLASSIFICATION (OF INFORMATION);

EID: 84875375527     PISSN: 18625347     EISSN: 18625355     Source Type: Journal    
DOI: 10.1007/s11634-013-0127-5     Document Type: Article

References (18)

1. Ashburner J, Friston KJ (2000) Voxel-based morphometry-the methods. Neuroimage 11: 805-821.
2. Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26: 839-851.
3. Baron JC, Chetelat G, Desgranges B, Perchey G, Landeau B, de la Sayette V, Eustache F (2001) In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. Neuroimage 14: 298-309.
4. Casanova R, Maldjian JA, Espeland MA (2011a) Evaluating the Impact of Different Factors on Voxel-Based Classification Methods of ADNI Structural MRI Brain Images. Int J Biomed Data Min 1: B110102.
5. Casanova R, Whitlow CT, Wagner B, Williamson J, Shumaker SA, Maldjian JA, Espeland MA (2011b) High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization. Front Neuroinform 5: 22.
6. Craven P, Wahba G (1979) Smoothing noisy data with spline functions. Numerische Mathematik 31: 377-390.
7. Cuingnet R, Gerardin E, Tessieras J, Auzias G, Lehericy S, Habert MO, Chupin M, Benali H, Colliot O (2011) Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage 15: 766-781.
8. Green P, Silverman B (1994) Nonparametric regression and generalized linear models: A roughness penalty approach. Chapman and Hall, London.
9. Hirata Y, Matsuda H, Nemoto K, Ohnishi T, Hirao K, Yamashita F, Asada T, Iwabuchi S, Samejima H (2005) Voxel-based morphometry to discriminate early Alzheimer's disease from controls. Neurosci Lett 382: 269-274.
10. Karas GB, Burton EJ, Rombouts SA, van Schijndel RA, O'Brien JT, Scheltens P, McKeith IG, Williams D, Ballard C, Barkhof F (2003) A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimized voxel-based morphometry. Neuroimage 18: 895-907.
11. Kloppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, Fox NC, Jack CR Jr, Ashburner J, Frackowiak RS (2008) Automatic classification of MR scans in Alzheimer's disease. Brain 131: 681-689.
12. Morris JC (1993) The clinical dementia rating (CDR): current version and scoring rules. Neurology 43: 2412-2414.
13. Reiss PT, Ogden RT (2010) Functional generalized linear models with images as predictors. Biometrics 66: 61-69.
14. Saranli A, Baykal B (1998) Complexity reduction in radial basis function networks by using radial B-spline functions. Neurocomputing 18: 183-194.
15. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15: 273-289.
16. Vapnik V (1996) The Nature of Statistical Learning Theory. Springer-Verlag, New York.
17. Wood SN (2006) Generalized Additive Models: An Introduction with R. Taylor & Francis, Bristol.
18. Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semi parametric generalized linear models. J Roy Statist Soc Ser B 73: 3-36.