Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images
Abstract
There are growing numbers of studies using machine learning approaches to characterize patterns of anatomical difference discernible from neuroimaging data. The high-dimensionality of image data often raises a concern that feature selection is needed to obtain optimal accuracy. Among previous studies, mostly using fixed sample sizes, some show greater predictive accuracies with feature selection, whereas others do not. In this study, we compared four common feature selection methods. 1) Pre-selected region of interests (ROIs) that are based on prior knowledge. 2) Univariate t-test filtering. 3) Recursive feature elimination (RFE), and 4) t-test filtering constrained by ROIs. The predictive accuracies achieved from different sample sizes, with and without feature selection, were compared statistically. To demonstrate the effect, we used grey matter segmented from the T1-weighted anatomical scans collected by the Alzheimer's disease Neuroimaging Initiative (ADNI) as the input features to a linear support vector machine classifier. The objective was to characterize the patterns of difference between Alzheimer's disease (AD) patients and cognitively normal subjects, and also to characterize the difference between mild cognitive impairment (MCI) patients and normal subjects. In addition, we also compared the classification accuracies between MCI patients who converted to AD and MCI patients who did not convert within the period of 12 months. Predictive accuracies from two data-driven feature selection methods (t-test filtering and RFE) were no better than those achieved using whole brain data. We showed that we could achieve the most accurate characterizations by using prior knowledge of where to expect neurodegeneration (hippocampus and parahippocampal gyrus). Therefore, feature selection does improve the classification accuracies, but it depends on the method adopted. In general, larger sample sizes yielded higher accuracies with less advantage obtained by using knowledge from the existing literature.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The neural network method critically depends on relatively larger training and test datasets compared to image processing methods.
“Hence, the neural network method critically depends on relatively larger training and test datasets compared to image processing methods [52-54].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [52]
Neural networks require significant amounts of data to accurately perform a classification, especially with increasing number of classes.
“However, they require significant amounts of data to accurately perform a classification, especially with increasing number of classes [52-54].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [52]
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Self-citationThe Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855 · see citing works
Libsvm : A library for support vector machines
Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,124 · see citing works
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954 · see citing works
Pattern Recognition and Machine Learning
(2007) · Kybernetes · doi:10.1108/03684920710743466 · Cited by 8,540 · see citing works
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122 · see citing works
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 7,843 · see citing works
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507 · see citing works
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576 · see citing works
A review of feature selection techniques in bioinformatics
Saeys, Y., Inza, I., & Larrañaga, P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm344 · Cited by 5,252 · see citing works
The Nature of Statistical Learning Theory
Sain, S. R., & Vapnik, V. N. (1996) · Technometrics · doi:10.2307/1271324 · Cited by 4,097 · see citing works
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,358 · see citing works
Multimodal classification of Alzheimer's disease and mild cognitive impairment
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.008 · Cited by 1,312 · see citing works
Construction of a 3D probabilistic atlas of human cortical structures
Shattuck, D. W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K. L., Poldrack, R. A., Bilder, R. M., & Toga, A. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.031 · Cited by 1,208 · see citing works
Automatic classification of MR scans in Alzheimer’s disease.
Kloppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., Fox, N. C., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2008) · Brain · doi:10.1093/brain/awm319 · Cited by 1,170 · see citing works
Cuingnet, R., Gérardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M., Marie Chupin, A., Benali, H., & Olivier Colliot, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.013 · Cited by 962 · see citing works
Mackay, D. J. C. (1995) · Network Computation in Neural Systems · doi:10.1088/0954-898x/6/3/011 · Cited by 915 · see citing works
Mourão-Miranda, J., Bokde, A. L. W., Born, C., Hampel, H. J., & Stetter, M. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.06.070 · Cited by 700 · see citing works
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
Martino, F. D., Valente, G., Staeren, N., Ashburner, J., Goebel, R., & Formisano, E. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.06.037 · Cited by 501 · see citing works
Gérardin, E., Chételat, G., Marie Chupin, A., Cuingnet, R., Desgranges, B., Kim, H., Niethammer, M., Dubois, B., Lehéricy, S., Garnero, L., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.036 · Cited by 415 · see citing works
Disease state prediction from resting state functional connectivity
Craddock, R. C., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22159 · Cited by 411 · see citing works
Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment
Chételat, G., Desgranges, B., de la Sayette, V., Viader, F., Eustache, F., & Baron, J. (2002) · NeuroReport · doi:10.1097/00001756-200210280-00022 · Cited by 405 · see citing works
Detection of grey matter loss in mild Alzheimer's disease with voxel based morphometry
Frisoni, G. B. (2002) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.73.6.657 · Cited by 386 · see citing works
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns
Yamashita, O., Sato, M.-A., Yoshioka, T., Tong, F., & Kamitani, Y. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.050 · Cited by 319 · see citing works
Sparse logistic regression for whole-brain classification of fMRI data
Ryali, S., Supekar, K., Abrams, D. A., & Menon, V. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.02.040 · Cited by 263 · see citing works
Predicting clinical scores from magnetic resonance scans in Alzheimer's disease
Stonnington, C. M., Chu, C., Klöppel, S., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.051 · Cited by 256 · see citing works
Computing average shaped tissue probability templates
Ashburner, J., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.008 · Cited by 232 · see citing works
Prognostic and diagnostic potential of the structural neuroanatomy of depression
Costafreda, S. G., Chu, C., Ashburner, J., & Fu, C. H. Y. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006353 · Cited by 232 · see citing works
Self-citationA voxel based morphometry study on mild cognitive impairment
Pennanen, C. (2004) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2004.035600 · Cited by 219 · see citing works
fMRI Activity Patterns in Human LOC Carry Information about Object Exemplars within Category
Eger, E., Ashburner, J., Haynes, J., Dolan, R. J., & Rees, G. E. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2008.20019 · Cited by 184 · see citing works
Prediction and interpretation of distributed neural activity with sparse models
CARROLL, M., Cecchi, G., Rish, I., Garg, R. D., & RAO, A. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.08.020 · Cited by 164 · see citing works
Yong Fan, A., Shen, D. G., & Davatzikos, C. A. (2005) · Lecture notes in computer science · doi:10.1007/11566465_1 · Cited by 148 · see citing works
Multivariate examination of brain abnormality using both structural and functional MRI
Yong Fan, A., Rao, H., Hurt, H., Giannetta, J. M., Korczykowski, M., Shera, D. M., Avants, B., Gee, J. C., Wang, J., & Shen, D. G. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.009 · Cited by 135 · see citing works
Filipovych, R., & Davatzikos, C. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.066 · Cited by 130 · see citing works
Interpretable Classifiers for fMRI Improve Prediction of Purchases
Grosenick, L., Greer, S. M., & Knutson, B. D. (2008) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2008.926701 · Cited by 109 · see citing works
Marquand, A. F., Simoni, S. D., O’Daly, O., Williams, S., Mourão-Miranda, J., & Mehta, M. A. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2011.9 · Cited by 94 · see citing works
Automatic detection of preclinical neurodegeneration
Klöppel, S., Chu, C., Tan, G. C.-Y., Draganski, B., Johnson, H. J., Paulsen, J. S., Kienzle, W., Tabrizi, S. J., Ashburner, J., Frackowiak, R. S. J., et al. (2009) · Neurology · doi:10.1212/01.wnl.0000341768.28646.b6 · Cited by 92 · see citing works
Independent Component Analysis-Based Classification of Alzheimer's Disease MRI Data
Yang, W., Lui, R. L.-M., Gao, J., Chan, T. F.-C., Yau, S., Sperling, R. A., & Huang, X. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-101371 · Cited by 85 · see citing works
Precuneus amyloid burden is associated with reduced cholinergic activity in Alzheimer disease
Ikonomović, M. D., Klunk, W. E., Abrahamson, E. E., Wuu, J., Mathis, C. A., Scheff, S. W., Mufson, E. J., & DeKosky, S. T. (2011) · Neurology · doi:10.1212/wnl.0b013e3182231419 · Cited by 84 · see citing works
Kernel regression for fMRI pattern prediction
Chu, C., Ni, Y., Tan, G. C.-Y., Saunders, C. J., & Ashburner, J. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.058 · Cited by 81 · see citing works
Pathological Aging of the Brain
de Bastos-Leite, A. J., Scheltens, P., & Barkhof, F. (2004) · Topics in Magnetic Resonance Imaging · doi:10.1097/01.rmr.0000168070.90113.dc · Cited by 67 · see citing works
Predictive Accuracy as an Achievable Goal of Science
Forster, M. R. (2002) · Philosophy of Science · doi:10.1086/341840 · Cited by 57 · see citing works
Multivariate models of inter-subject anatomical variability
Ashburner, J., & Klöppel, S. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.059 · Cited by 45 · see citing works
Discrimination of AD and normal subjects from MRI: Anatomical versus statistical regions
Peláez‐Coca, M. D., Bossa, M. N., & Olmos, S. C. (2010) · Neuroscience Letters · doi:10.1016/j.neulet.2010.10.007 · Cited by 13 · see citing works
Chu, C., Bandettini, P. A., Ashburner, J., Marquand, A. F., & Klöppel, S. (2010) · doi:10.1109/wbd.2010.11 · Cited by 6 · see citing works
Flagged first, then by citations
1 of the 50 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Orrù, G., Pettersson-Yeo, W., Marquand, A. F., Sartori, G., & Mechelli, A. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.01.004 · Cited by 1,012 · see citing works
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912 · see citing works
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622 · see citing works
A Review of Feature Reduction Techniques in Neuroimaging
Mwangi, B., Tian, T. S., & Soares, J. C. (2014) · Neuroinformatics · doi:10.1007/s12021-013-9204-3 · Cited by 559 · see citing works
PRoNTo: Pattern Recognition for Neuroimaging Toolbox
Schrouff, J., Rosa, M. J., Rondina, J. M., Marquand, A. F., Chu, C., Ashburner, J., Phillips, C. L. M., Richiardi, J., & Mourão-Miranda, J. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9178-1 · Cited by 424 · see citing works
Landmark-based deep multi-instance learning for brain disease diagnosis
Liu, M., Zhang, J., Adeli, E., & Shen, D. G. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.10.005 · Cited by 424 · see citing works
Cui, Z., & Gong, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.001 · Cited by 372 · see citing works
Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.11.001 · Cited by 348 · see citing works
Wolfers, T., Buitelaar, J. K., Beckmann, C. F., Franke, B., & Marquand, A. F. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.08.001 · Cited by 290 · see citing works
Wang, S., Celebi, M. E., Zhang, Y., Yu, X., Lu, S., Yao, X., Zhou, Q., Miguel, M.-G., Tian, Y., Górriz, J. M., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.07.001 · Cited by 245 · see citing works
Falahati, F., Westman, E., & Simmons, A. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-131928 · Cited by 229 · see citing works
Zhou, T., Liu, M., Thung, K., & Shen, D. G. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2913158 · Cited by 226 · see citing works
Zhu, X., Suk, H., Lee, S., & Shen, D. G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2466616 · Cited by 218 · see citing works
Multiple instance learning for classification of dementia in brain MRI
Tong, T., Wolz, R., Gao, Q., Guerrero, R., Hajnal, J. V., & Rueckert, D. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.04.006 · Cited by 213 · see citing works
Radiological images and machine learning: Trends, perspectives, and prospects
Zhang, Z., & Sejdić, E. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.02.017 · Cited by 236 · see citing works
Lombardi, G., Crescioli, G., Cavedo, E., Lucenteforte, E., Casazza, G., Bellatorre, A.-G., Lista, C., Costantino, G., Frisoni, G., Virgili, G., et al. (2020) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd009628.pub2 · Cited by 214 · see citing works
Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network
Liu, J., Li, M., Lan, W., Wu, F., Pan, Y. F., & Wang, J. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2635144 · Cited by 212 · see citing works
Westman, E., Aguilar, C., Muehlboeck, J., & Simmons, A. (2012) · Brain Topography · doi:10.1007/s10548-012-0246-x · Cited by 206 · see citing works
Zhu, X., Suk, H.-I., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.078 · Cited by 202 · see citing works
Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs
Liu, S., Masurkar, A. V., Rusinek, H., Chen, J., Zhang, B., Zhu, W., Fernandez‐Granda, C., Razavian, N., & Initiative, F. T. A. D. N. (2022) · Scientific Reports · doi:10.1038/s41598-022-20674-x · Cited by 205 · see citing works
Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology
Forghani, R., Savadjiev, P., Chatterjee, A., Muthukrishnan, N., Reinhold, C., & Forghani, B. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.07.001 · Cited by 197 · see citing works
Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease
Janghel, R. R., & Rathore, Y. K. (2020) · IRBM · doi:10.1016/j.irbm.2020.06.006 · Cited by 197 · see citing works
Multivoxel Pattern Analysis for fMRI Data: A Review
Mahmoudi, A., Takerkart, S., Regragui, F., Boussaoud, D., & Brovelli, A. (2012) · Computational and Mathematical Methods in Medicine · doi:10.1155/2012/961257 · Cited by 199 · see citing works
Richhariya, B., Tanveer, M., & Rashid, A. H. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101903 · Cited by 190 · see citing works
Combining complex networks and data mining: Why and how
Zanin, M., Papo, D., Sousa, P. A., Menasalvas, E., Nicchi, A., Kubik, E., & Boccaletti, S. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.04.005 · Cited by 185 · see citing works
Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images
Zhang, J., Liu, M., An, L., Gao, Y., & Shen, D. G. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2704614 · Cited by 181 · see citing works
Quantifying performance of machine learning methods for neuroimaging data
Jollans, L., Boyle, R. T., Eric Artiges, A., Banaschewski, T., Desrivières, S., Grigis, A., Martinot, J., Paus, T., Smolka, M. N., Walter, H., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.082 · Cited by 165 · see citing works
Samper‐Gonzàlez, J., Burgos, N., Bottani, S., Fontanella, S., Lu, P., Marcoux, A., Routier, A., Guillon, J., Bacci, M., Wen, J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.042 · Cited by 171 · see citing works
Alzheimer's disease detection using depthwise separable convolutional neural networks
Liu, J., Li, M., Luo, Y., Yang, S., Li, W., & Bi, Y. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106032 · Cited by 178 · see citing works
Towards a brain‐based predictome of mental illness
Rashid, B., & Calhoun, V. D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25013 · Cited by 160 · see citing works
Clinical prediction from structural brain MRI scans: A large-scale empirical study
Initiative, F. T. A. D. N., Sabuncu, M. R., & Konukoglu, E. (2015) · Neuroinformatics · doi:10.1007/s12021-014-9238-1 · Cited by 161 · see citing works
Trends in Heart-Rate Variability Signal Analysis
Ishaque, S., Khan, N., & Krishnan, S. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.639444 · Cited by 175 · see citing works
Locally linear embedding (LLE) for MRI based Alzheimer's disease classification
Liu, X., Tosun, D., Weiner, M. W., & Schuff, N. W. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.033 · Cited by 166 · see citing works
Sun, H., Chen, Y., Huang, Q., Lui, S., Huang, X., Shi, Y., Xu, X., Sweeney, J. A., & Gong, Q. (2017) · Radiology · doi:10.1148/radiol.2017170226 · Cited by 159 · see citing works
Computer aided Alzheimer's disease diagnosis by an unsupervised deep learning technology
Bi, X., Li, S., Xiao, B., Li, Y., Wang, G., & Ma, X. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.11.111 · Cited by 152 · see citing works
How to control for confounds in decoding analyses of neuroimaging data
Snoek, L., Miletić, S., & Scholte, H. S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.074 · Cited by 143 · see citing works
Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis
Liu, M., Zhang, D., Shen, D. G., & Initiative, T. A. D. N. (2013) · Human Brain Mapping · doi:10.1002/hbm.22254 · Cited by 133 · see citing works
Feature Selection Based on the SVM Weight Vector for Classification of Dementia
Bron, E. E., Smits, M., Niessen, W. J., & Klein, S. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2432832 · Cited by 123 · see citing works
Individualized Prediction and Clinical Staging of Bipolar Disorders Using Neuroanatomical Biomarkers
Mwangi, B., Wu, M., Cao, B., Passos, I. C., Lavagnino, L., Keser, Z., Zunta‐Soares, G. B., Hasan, K. M., Kapczinski, F. P., & Soares, J. C. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.01.001 · Cited by 110 · see citing works
Machine Learning-Enabled Internet of Things (IoT): Data, Applications, and Industry Perspective
Bzai, J., Alam, F., Dhafer, A., Bojović, M., Altowaijri, S. M., Niazi, I. K., & Mehmood, R. (2022) · Electronics · doi:10.3390/electronics11172676 · Cited by 110 · see citing works
Sundermann, B., Beverborg, M. O. L., & Pfleiderer, B. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00692 · Cited by 109 · see citing works
Alzheimer's Disease Risk Assessment Using Large-Scale Machine Learning Methods
Casanova, R., Hsu, F., Sink, K. M., Rapp, S. R., Williamson, J. D., Resnick, S. M., Espeland, M. A., & Initiative, A. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077949 · Cited by 107 · see citing works
Decoding fMRI Signatures of Real-world Autobiographical Memory Retrieval
Rissman, J., Chow, T. E., Reggente, N., & Wagner, A. D. (2016) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00920 · Cited by 100 · see citing works
Brain connectivity and novel network measures for Alzheimer’s disease classification
Prasad, G., Joshi, S. H., Nir, T., Toga, A., & Thompson, P. M. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.04.037 · Cited by 106 · see citing works
Kim, Y., & Na, K. (2017) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.06.024 · Cited by 102 · see citing works
Larroza, A., Moratal, D., Paredes‐Sánchez, A., Soria‐Olivas, E., Chust, M. L., Arribas, L., & Arana, E. (2015) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24913 · Cited by 101 · see citing works
Short term soil moisture forecasts for potato crop farming: A machine learning approach
Dubois, A., Teytaud, F., & Vérel, S. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105902 · Cited by 101 · see citing works
Meta-matching as a simple framework to translate phenotypic predictive models from big to small data
He, T., An, L., Chen, P., Chen, J., Feng, J., Bzdok, D., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01059-9 · Cited by 96 · see citing works
Sinkala, M., Mulder, N., & Martin, D. P. (2020) · Scientific Reports · doi:10.1038/s41598-020-58290-2 · Cited by 100 · see citing works
How early can we predict Alzheimer's disease using computational anatomy?
Adaszewski, S., Dukart, J., Kherif, F., Frackowiak, R. S. J., & Draganski, B. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.06.015 · Cited by 99 · see citing works
A Survey on Neuromarketing Using EEG Signals
Khurana, V., Gahalawat, M., Kumar, P., Roy, P. P., Dogra, D. P., Scheme, E., & Soleymani, M. (2021) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2021.3065200 · Cited by 105 · see citing works
Popuri, K., Ma, D., Wang, L., & Beg, M. F. (2020) · Human Brain Mapping · doi:10.1002/hbm.25115 · Cited by 96 · see citing works
Khedher, L., Illán, I. A., Górriz, J. M., Ramı́rez, J., Brahim, A., & Meyer‐Baese, A. (2016) · International Journal of Neural Systems · doi:10.1142/s0129065716500507 · Cited by 93 · see citing works
Pulini, A. A., Kerr, W. T., Loo, S. K., & Lenartowicz, A. (2019) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.06.003 · Cited by 85 · see citing works
Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain
Chamberland, M., Raven, E. P., Genc, S., Duffy, K., Descoteaux, M., Parker, G. D., Tax, C. M. W., & Jones, D. K. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.020 · Cited by 88 · see citing works
Zhao, Y., Wood, E. P., Mirin, N., Vedanthan, R., Cook, S., & Chunara, R. (2020) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2021.04.016 · Cited by 89 · see citing works
Texture Analysis in Magnetic Resonance Imaging: Review and Considerations for Future Applications
Larroza, A., Bodı́, V., & Moratal, D. (2016) · InTech eBooks · doi:10.5772/64641 · Cited by 88 · see citing works
Kwon, H., Ali, Z. A., & Wong, B. M. (2022) · Environmental Science & Technology Letters · doi:10.1021/acs.estlett.2c00530 · Cited by 91 · see citing works
Hilbert, K., Lueken, U., Muehlhan, M., & Beesdo‐Baum, K. (2017) · Brain and Behavior · doi:10.1002/brb3.633 · Cited by 87 · see citing works
Noghabaei, M., Han, K. K., & Albert, A. (2021) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0002130 · Cited by 86 · see citing works
Changes in cortical thickness across the lifespan in major depressive disorder
Truong, W., Minuzzi, L., Soares, C. N., Frey, B. N., Evans, A. C., MacQUEEN, G. M., & Hall, G. B. (2013) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2013.09.003 · Cited by 83 · see citing works
Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia
Initiative, A. D. N., Tohka, J., Moradi, E., & Huttunen, H. J. (2016) · Neuroinformatics · doi:10.1007/s12021-015-9292-3 · Cited by 84 · see citing works
Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors
Bratić, B., Kurbalija, V., Ivanović, M. D., Oder, I., & Bosnić, Z. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1071-x · Cited by 83 · see citing works
Decision-making ability, psychopathology, and brain connectivity
Moutoussis, M., Garzón, B., Neufeld, S., Bach, D. R., Rigoli, F., Goodyer, I. M., Bullmore, E. T., Fonagy, P., Jones, P. B., Hauser, T. U., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.04.019 · Cited by 83 · see citing works
Zhang, T., Liao, Q., Zhang, D., Zhang, C., Yan, J. R., Ngetich, R. K., Zhang, J., Jin, Z., & Li, L. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.688926 · Cited by 83 · see citing works
Gould, I. C., Shepherd, A. M., Laurens, K. R., Cairns, M. J., Carr, V. J., & Green, M. J. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.09.009 · Cited by 83 · see citing works
Explainable AI: A review of applications to neuroimaging data
Farahani, F. V., Fiok, K., Lahijanian, B., Karwowski, W., & Douglas, P. K. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.906290 · Cited by 85 · see citing works
Predicting outcome in clinically isolated syndrome using machine learning
Wottschel, V., Alexander, D. C., Kwok, P., Chard, D., Stromillo, M. L., Stefano, N. D., Thompson, A. J., Miller, D. H., & Ciccarelli, O. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.11.021 · Cited by 81 · see citing works
Feature selection before EEG classification supports the diagnosis of Alzheimer’s disease
Trambaiolli, L. R., Spolaôr, N., Lorena, A. C., Anghinah, R., & Sato, J. R. (2017) · Clinical Neurophysiology · doi:10.1016/j.clinph.2017.06.251 · Cited by 81 · see citing works
Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases
Tülay, E. E., Metin, B., Tarhan, N., & Arıkan, M. K. (2018) · Clinical EEG and Neuroscience · doi:10.1177/1550059418782093 · Cited by 78 · see citing works
Farhan, S., Fahiem, M. A., & Tauseef, H. (2014) · Computational and Mathematical Methods in Medicine · doi:10.1155/2014/862307 · Cited by 79 · see citing works
Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives
Bansal, N. K., Mishra, S., Dixit, H., Porwal, S., Singh, P., & Singh, T. (2023) · Energy Technology · doi:10.1002/ente.202300735 · Cited by 82 · see citing works
Brain MR Image Classification for Alzheimer’s Disease Diagnosis Based on Multifeature Fusion
Xiao, Z., Ding, Y., Lan, T., Zhang, C., Luo, C., & Qin, Z. (2017) · Computational and Mathematical Methods in Medicine · doi:10.1155/2017/1952373 · Cited by 77 · see citing works
Voluntary Enhancement of Neural Signatures of Affiliative Emotion Using fMRI Neurofeedback
Moll, J., Weingartner, J., Bado, P., Basílio, R., Sato, J., Melo, B., Bramati, I., de Oliveira-Souza, R., & Zahn, R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097343 · Cited by 78 · see citing works
Machine learning classification of ADHD and HC by multimodal serotonergic data
Kautzky, A., Vanicek, T., Philippe, C., Kranz, G., Wadsak, W., Mitterhauser, M., Hartmann, A., Hahn, A., Hacker, M., Rujescu, D., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0781-2 · Cited by 68 · see citing works
A Complex Systems Perspective on Neuroimaging Studies of Behavior and Its Disorders
Turkheimer, F., Rosas, F. E., Dipasquale, O., Martins, D., Fagerholm, E. D., Expert, P., Váša, F., Lord, L.-D., & Leech, R. (2021) · The Neuroscientist · doi:10.1177/1073858421994784 · Cited by 70 · see citing works
Lei, B., Chen, S., Ni, D., & Wang, T. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00077 · Cited by 72 · see citing works
Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer’s Disease
Xin, B., Kawahara, Y., Wang, Y., & Gao, W. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8977 · Cited by 71 · see citing works
Presymptomatic cortical thinning in familial Alzheimer disease
Weston, P. S. J., Nicholas, J. M., Lehmann, M., Ryan, N. S., Liang, Y., Macpherson, K., Marc Modat, A., Rossor, M. N., Schott, J. M., Sebastien Ourselin, A., et al. (2016) · Neurology · doi:10.1212/wnl.0000000000003322 · Cited by 71 · see citing works
Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection
Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66 · see citing works
Genetic algorithm with logistic regression feature selection for Alzheimer’s disease classification
Divya, R., Kumari, R. S. S., & Initiative, T. A. D. N. (2021) · Neural Computing and Applications · doi:10.1007/s00521-020-05596-x · Cited by 69 · see citing works
Combining multiple anatomical MRI measures improves Alzheimer's disease classification
de Vos, F., Schouten, T. M., Hafkemeijer, A., Dopper, E. G. P., Swieten, J. C. V., de Rooij, M., van der Grond, J., & Rombouts, S. A. R. B. (2016) · Human Brain Mapping · doi:10.1002/hbm.23147 · Cited by 68 · see citing works
Association rule-based feature selection method for Alzheimer’s disease diagnosis
Chaves, R., Ramı́rez, J., Górriz, J. M., & Puntonet, C. G. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.04.075 · Cited by 67 · see citing works
Liu, J., Wang, J., Hu, B., Wu, F., & Pan, Y. F. (2017) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2017.2707139 · Cited by 66 · see citing works
Ota, K., Oishi, N., Ito, K., & Fukuyama, H. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.08.020 · Cited by 65 · see citing works
Multi-scale semi-supervised clustering of brain images: Deriving disease subtypes
Wen, J., Varol, E., Sotiras, A., Yang, Z., Chand, G. B., Erus, G., Shou, H., Abdulkadir, A., Hwang, G., Dwyer, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102304 · Cited by 65 · see citing works
Spulber, G., Simmons, A., Muehlboeck, J., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H., Spenger, C., Lovestone, S., et al. (2013) · Journal of Internal Medicine · doi:10.1111/joim.12028 · Cited by 63 · see citing works
Machine learning fMRI classifier delineates subgroups of schizophrenia patients
Bleich‐Cohen, M., Jamshy, S., Sharon, H., Weizman, R., Intrator, N., Poyurovsky, M., & Hendler, T. (2014) · Schizophrenia Research · doi:10.1016/j.schres.2014.10.033 · Cited by 62 · see citing works
A comparison of three brain atlases for MCI prediction
Ota, K., Oishi, N., Ito, K., & Fukuyama, H. (2013) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2013.10.003 · Cited by 58 · see citing works
Retico, A., Bosco, P., Cerello, P., Fiorina, E., Chincarini, A., & Fantacci, M. E. (2014) · Journal of Neuroimaging · doi:10.1111/jon.12163 · Cited by 57 · see citing works
Gori, I., Giuliano, A., Muratori, F., Saviozzi, I., Oliva, P., Tancredi, R., Cosenza, A., Tosetti, M., Calderoni, S., & Retico, A. (2015) · Journal of Neuroimaging · doi:10.1111/jon.12280 · Cited by 55 · see citing works
Lee, N., & Kwon, O. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.11.031 · Cited by 56 · see citing works
Forouzannezhad, P., Abbaspour, A., Li, C., Fang, C., Williams, U., Cabrerizo, M., Barreto, A. B., Andrian, J. H., Rishe, N. D., Curiel, R. E., et al. (2019) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2019.108544 · Cited by 52 · see citing works
Liu, J., Li, M., Pan, Y. F., Wu, F., Chen, X., & Wang, J. (2017) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2017.2751074 · Cited by 52 · see citing works
Li, R., Perneczky, R., Yakushev, I. Y., Förster, S., Kurz, A., Drzezga, A. E., Krämer, S., & Initiative, A. D. N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122731 · Cited by 52 · see citing works
Wade, B., Joshi, S. H., Gutman, B. A., & Thompson, P. M. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.09.034 · Cited by 50 · see citing works
Willette, A. A., Calhoun, V. D., Egan, J. M., & Kapogiannis, D. (2014) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2014.08.005 · Cited by 49 · see citing works
Sundermann, B., Bode, J., Lueken, U., Westphal, D., Gerlach, A. L., Straube, B., Wittchen, H.-U., Ströhle, A., Wittmann, A., Konrad, C., et al. (2017) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2017.00099 · Cited by 46 · see citing works
Joint detection and clinical score prediction in Parkinson's disease via multi-modal sparse learning
Lei, H., Huang, Z., Zhang, J., Zhang, Y., Tan, E.-L., Zhou, F., & Lei, B. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.03.038 · Cited by 48 · see citing works
Automated, High Accuracy Classification of Parkinsonian Disorders: A Pattern Recognition Approach
Marquand, A. F., Filippone, M., Ashburner, J., Girolami, M., Mourão-Miranda, J., Barker, G. J., Williams, S., Leigh, P. N., & Blain, C. R. V. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069237 · Cited by 44 · see citing works
- Authors
- Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C.
- Affiliations
- National Institutes of Health (US)
- National Yang Ming University (TW)
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-03
- Volume
- 60
- Issue
- 1
- Pages
- 59-70
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2019583087
- Cited by
- 319
- References
- 50
- Citations by year
- 2026: 3 · 2025: 14 · 2024: 14 · 2023: 24 · 2022: 22 · 2021: 38 · 2020: 33 · 2019: 25 · 2018: 19 · 2017: 32 · 2016: 25 · 2015: 20 · 2014: 25 · 2013: 16 · 2012: 9
- FWCI
- 11.01
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust
- National Science Council (100-2627-B-010-004-, NSC 100-2628-E-010-002-MY3)
- National Institutes of Health
- National Health Research Institutes (NHRI-EX100-9813EC)
- National Institute of Mental Health
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- feature selection, sample size, anatomical MRI, Alzheimer's disease, mild cognitive impairment, grey matter, region of interest selection, recursive feature elimination, hippocampus, parahippocampal gyrus, neurodegeneration, linear support vector machine, classification accuracy, ADNI
- MeSH terms
- Aged, Alzheimer Disease, Female, Humans, Magnetic Resonance Imaging, Male, Reproducibility of Results, Sample Size, Neuroimaging, Cognitive Dysfunction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
