Genetic, structural and functional imaging biomarkers for early detection of conversion from MCI to AD

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7510 LNCS, Issue , 2012, Pages 132-140

  Singh, Nikhil ^a   Wang, Angela Y ^a   Sankaranarayanan, Preethi ^a   Fletcher, P Thomas ^a   Joshi, Sarang ^a  

^a UNIVERSITY OF UTAH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMARKERS; MEDICAL COMPUTING; NEUROIMAGING;

FUNCTIONAL IMAGING; HIGH-DIMENSIONAL; IMAGING BIOMARKERS; IMAGING MODALITY; MILD COGNITIVE IMPAIRMENTS (MCI); MODELING STRATEGY; SUPERVISED FEATURE EXTRACTIONS; UNIFIED FRAMEWORK;

DIAGNOSIS;

AMYLOID BETA PROTEIN; AMYLOID BETA PROTEIN (1 42); AMYLOID BETA-PROTEIN (1-42); APOLIPOPROTEIN E; BIOLOGICAL MARKER; PEPTIDE FRAGMENT;

ALGORITHM; ALZHEIMER DISEASE; ARTICLE; BRAIN; DIAGNOSTIC IMAGING; DISEASE COURSE; FACTUAL DATABASE; GENOTYPE; HUMAN; METABOLISM; METHODOLOGY; MILD COGNITIVE IMPAIRMENT; NERVE CELL; PATHOLOGY; POSITRON EMISSION TOMOGRAPHY; REGRESSION ANALYSIS; RISK FACTOR; THEORETICAL MODEL;

ALGORITHMS; ALZHEIMER DISEASE; AMYLOID BETA-PEPTIDES; APOLIPOPROTEINS E; BIOLOGICAL MARKERS; BRAIN; DATABASES, FACTUAL; DIAGNOSTIC IMAGING; DISEASE PROGRESSION; GENOTYPE; HUMANS; LEAST-SQUARES ANALYSIS; MILD COGNITIVE IMPAIRMENT; MODELS, THEORETICAL; NEURONS; PEPTIDE FRAGMENTS; POSITRON-EMISSION TOMOGRAPHY; RISK FACTORS;

MLCS; MLOWN;

EID: 84872505906     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33415-3_17     Document Type: Conference Paper

References (10)

1. Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., Trojanowski, J.Q.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging 32(12), 2322.e19–2322.e27 (2011)
2. Lemoine, B., Rayburn, S., Benton, R.: Data Fusion and Feature Selection for Alzheimer’s Diagnosis. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS, vol. 6334, pp. 320–327. Springer, Heidelberg (2010)
3. Weiner, M.W., et al.: The Alzheimers Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimer’s and Dementia, S1–S68 (2012)
4. Kohannim, O., et al.: Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiology of Aging 31(8), 1429–1442 (2010)
5. Younes, L., Arrate, F., Miller, M.: Evolutions equations in computational anatomy. NeuroImage 45(1S1), 40–50 (2009)
6. Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, 151–160 (2004)
7. Vialard, F.X., et al.: Diffeomorphic 3D image registration via geodesic shooting using an efficient adjoint calculation. IJCV, 1–13 (2011)
8. Minoshima, S., Frey, K.A., Koeppe, R.A., Foster, N.L., Kuhl, D.E.: A diagnostic approach in Alzheimers disease using three-dimensional stereotactic surface projections of Fluorine-18-FDG PET. J. of Nuclear Medicine 36(7), 1238–1248 (1995)
9. Bookstein, F.L.: Partial Least Squares: A dose-response model for measurement in the behavioral and brain sciences. Psycoloquy 5(23) (1994) (revised)
10. Singh, N., Fletcher, P.T., Preston, J.S., Ha, L., King, R., Marron, J.S., Wiener, M., Joshi, S.: Multivariate Statistical Analysis of Deformation Momenta Relating Anatomical Shape to Neuropsychological Measures. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 529–537. Springer, Heidelberg (2010)