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Issue No.01 - Jan.-Feb. (2013 vol.10)
pp: 173-180
Xia Wu , State Key Lab. of Cognitive Neurosci. & Learning, Beijing Normal Univ., Beijing, China
Juan Li , State Key Lab. of Cognitive Neurosci. & Learning, Beijing Normal Univ., Beijing, China
Napatkamon Ayutyanont , Banner Good Samaritan PET Center, Banner Alzheimer's Inst. (BAI), Phoenix, AZ, USA
Hillary Protas , Banner Good Samaritan PET Center, Banner Alzheimer's Inst. (BAI), Phoenix, AZ, USA
William Jagust , Sch. of Public Health, Univ. of California Berkeley, Berkeley, CA, USA
Adam Fleisher , Banner Good Samaritan PET Center, Banner Alzheimer's Inst. (BAI), Phoenix, AZ, USA
Eric Reiman , Banner Good Samaritan PET Center, Banner Alzheimer's Inst. (BAI), Phoenix, AZ, USA
Li Yao , State Key Lab. of Cognitive Neurosci. & Learning, Beijing Normal Univ., Beijing, China
Kewei Chen , Banner Good Samaritan PET Center, Banner Alzheimer's Inst. (BAI), Phoenix, AZ, USA
Given a single index, the receiver operational characteristic (ROC) curve analysis is routinely utilized for characterizing performances in distinguishing two conditions/groups in terms of sensitivity and specificity. Given the availability of multiple data sources (referred to as multi-indices), such as multimodal neuroimaging data sets, cognitive tests, and clinical ratings and genomic data in Alzheimer's disease (AD) studies, the single-index-based ROC underutilizes all available information. For a longtime, a number of algorithmic/analytic approaches combining multiple indices have been widely used to simultaneously incorporate multiple sources. In this study, we propose an alternative for combining multiple indices using logical operations, such as “AND,” “OR,” and “at least n” (where n is an integer), to construct multivariate ROC (multiV-ROC) and characterize the sensitivity and specificity statistically associated with the use of multiple indices. With and without the “leave-one-out” cross-validation, we used two data sets from AD studies to showcase the potentially increased sensitivity/specificity of the multiV-ROC in comparison to the single-index ROC and linear discriminant analysis (an analytic way of combining multi-indices). We conclude that, for the data sets we investigated, the proposed multiV-ROC approach is capable of providing a natural and practical alternative with improved classification accuracy as compared to univariate ROC and linear discriminant analysis.
Indexes, Sensitivity and specificity, Alzheimer's disease, Human computer interaction, Neuroimaging, Accuracy, Biomarkers,receiver operational characteristic (ROC), Alzheimer's dementia (AD), multiple indices, multiV-ROC
Xia Wu, Juan Li, Napatkamon Ayutyanont, Hillary Protas, William Jagust, Adam Fleisher, Eric Reiman, Li Yao, Kewei Chen, "The Receiver Operational Characteristic for Binary Classification with Multiple Indices and Its Application to the Neuroimaging Study of Alzheimer's Disease", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.10, no. 1, pp. 173-180, Jan.-Feb. 2013, doi:10.1109/TCBB.2012.141
[1] W.G. Rosen, R.C. Mohs, and K.L. Davis, “A New Rating Scale for Alzheimer's Disease,” Am J. Psychiatry, vol. 141, pp. 1356-1364, 1984.
[2] A. Rey, “L'examen Psychologique Dans Les Cas D'encephalopathie Traumatique,” Archiv Psychologie, vol. 28, pp. 286-340, 1941.
[3] J.C. Morris, “The Clinical Dementia Rating (CDR): Current Version and Scoring Rules,” Neurology, vol. 43, pp. 2412-2414, 1993.
[4] M.F. Folstein, S.E. Folstein, and P.R. McHugh, “Mini-Mental State a Practical Method for Grading the Cognitive State of Patients for the Clinician,” J. Psychiatric Research, vol. 12, no. 3, pp. 189-198, 1975.
[5] D.S. Karow, L.K. McEvoy, C. Fennema-Notestine, D.J. Hagler, R.G. Jennings, J.B. Brewer, C.K. Hoh, and A.M. Dale, “Relative Capability of MR Imaging and FDG PET to Depict Changes Associated with Prodromal and Early Alzheimer Disease,” Radiology, vol. 256, pp. 932-942, 2010.
[6] J.L. Whitwell, M.M. Shiung, S.A. Przybelski, S.D. Weigand, D.S. Knopman, B.F. Boeve, R.C. Petersen, and C.R. Jack, “MRI Patterns of Atrophy Associated with Progression to AD in Amnestic Mild Cognitive Impairment,” Neurology, vol. 70, pp. 512-520, 2008.
[7] Greicius MD et al., “Default-Mode Network Activity Distinguishes Alzheimer's Disease from Healthy Aging: Evidence from Functional MRI,” Proc. Nat'l Academy of Sciences USA, vol. 101, pp. 4637-4642, 2004.
[8] X. Wu et al., “Altered Default Mode Network Connectivity in Alzheimer's Disease---A Resting Functional MRI and Bayesian Network Study,” Human Brain Mapping, vol. 32, pp. 1868-1881, 2011.
[9] K. Herholz et al., “Discrimination between Alzheimer Dementia and Controls by Automated Analysis of Multicenter FDG PET,” NeuroImage, vol. 17, pp. 302-316, 2002.
[10] D.R. Thal et al., “Phases of a Beta-Deposition in the Human Brain and Its Relevance for the Development of AD,” Neurology, vol. 58, pp. 1791-1800, 2002.
[11] A.S. Fleisher et al., “Using Positron Emission Tomography and Florbetapir F 18 to Image Cortical Amyloid in Patients with Mild Cognitive Impairment or Dementia Due to Alzheimer Disease,” Archives of Neurology, vol. 68, pp. 1404-1411, 2011.
[12] D. Goodenough, K. Rossman, and L. Lusted, “Radiographic Applications of Receiver Operating Characteristic (ROC) Curves,” Radiology, vol. 110, pp. 89-95, 1974.
[13] J. Swets, “ROC Curve Analysis Applied to the Evaluation of Medical Imaging Techniques,” Invest Radiology, vol. 14, pp. 109-121, 1979.
[14] C. Xiong et al., “Combining Correlated Diagnostic Tests: Application to Neuropathologic Diagnosis of Alzheimer's Disease,” Medical Decision Making, vol. 24, no. 6, 659-669, 2004.
[15] F. Gao et al., “Estimating Optimum Linear Combination of Multiple Correlated Diagnostic Tests at a Fixed Specificity with Receiver Operating Characteristic Curves,” J. Data Science, vol. 6, pp. 1-11, 2008.
[16] J. Ye et al., “Heterogeneous Data Fusion for Alzheimer's Disease Study,” Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), 2008.
[17] N. Balakrishnan, Handbook of the Logistic Distribution. Marcel Dekker, Inc., 1991.
[18] B. Krishnapuram et al., “Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds,” IEEE Trans Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 957-968, June 2005.
[19] R.A. Fisher, “The Use of Multiple Measurements in Taxonomic Problems,” Ann. of Eugenics, vol. 7, no. 2, pp. 179-188, 1936.
[20] R.O. Duda, P.E. Hart, and D.H. Stork, Pattern Classification, second ed. Wiley Interscience, 2000.
[21] R.A. Fisher, “The Statistical Utilization of Multiple Measurements,” Ann. Eugenics, vol. 8, pp. 376-386, 1938.
[22] V.N. Vapnik, The Nature of Statistical Learning Theory. Springer, 1995.
[23] S.M. Landau et al., “Associations between Cognitive, Functional, and FDG-PET Measures of Decline in AD and MCI,” Neurobiology of Aging, vol. 32, pp. 1207-1218, 2011.
[24] K. Chen et al., “Linking Functional and Structural Brain Images with Multivariate Network Analyses: A Novel Application of the Partial Least Square Method,” NeuroImage, vol. 47, pp. 602-610, 2009.
[25] G.E. Alexander et al., “Age-Related Regional Network of MRI Gray Matter in the Rhesus Macaque,” J. Neuroscience, vol. 28, no. 11, pp. 2710-2718, 2008.
[26] R. Li et al., “Large-Scale Directional Connections among Multi Resting-State Neural Networks in Human Brain: A Functional MRI and Bayesian Network Modeling Study,” NeuroImage, vol. 51, pp. 1035-1042, 2011.
[27] E.K. Shultz, “Multivariate Receiver-Operating Characteristic Curve Analysis: Prostate Cancer Screening as an Example,” Clinical Chemistry, vol. 41, pp. 1248-1255, 1995.
[28] K. Chen et al., “The Alzheimer's Disease Neuroimaging Initiative, Characterizing Alzheimer's Disease Using a Hypometabolic Convergence Index,” NeuroImage, vol. 56, pp. 52-60, 2011.
[29] R. Li et al., “Attention-Related Networks in Alzheimer's Disease: A Resting Functional MRI Study,” Human Brain Mapping, vol. 33, pp. 1076-88, 2011.
[30] R. Payam, T. Lei, and L. Huan, “Cross Validation,” Encyclopedia of Database Systems, M. Tamer Özsu and L. Liu, eds., Springer, 2009.
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