Cited by 559Functional Brain Connectivity StudiesRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare

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A Review of Feature Reduction Techniques in Neuroimaging

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Mwangi, Benson, Tian, Tian Siva, Soares, Jair C.·2014·Neuroinformatics

DOI 10.1007/s12021-013-9204-3PMC PMC4040248

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Abstract

Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.

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Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder
Costafreda, S. G., Fu, C. H. Y., Picchioni, M., Toulopoulou, T., McDonald, C., Kravariti, E., Walshe, M., Prata, D. P., Murray, R. M., & McGuire, P. K. (2011) · BMC Psychiatry · doi:10.1186/1471-244x-11-18 · Cited by 196
A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia
Yang, H., Liu, J., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Pearlson, G. D., & Calhoun, V. D. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00192 · Cited by 193
Performance of blind source separation algorithms for fMRI analysis using a group ICA method
Correa, N. M., Adalı, T., & Calhoun, V. D. (2006) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2006.10.017 · Cited by 191
Control of Spatial and Feature-Based Attention in Frontoparietal Cortex
Greenberg, A. S., Esterman, M. S., Wilson, D., Serences, J. T., & Yantis, S. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.4248-09.2010 · Cited by 188
High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables
Wang, Y., Yong Fan, A., Bhatt, P., & Davatzikos, C. A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.092 · Cited by 184
Penalized least squares regression methods and applications to neuroimaging
Bunea, F., She, Y., Ombao, H., Gongvatana, A., Devlin, K., & Cohen, R. A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.028 · Cited by 184
Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer's disease
López, M., Ramı́rez, J., Górriz, J. M., Álvarez, I., Salas-Gonzalez, D., Segovia, F., Chaves, R., Padilla, P., & Gómez-Río, M. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.06.025 · Cited by 165
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Schölkopf, B., & Smola, A. J. (2002) · Cited by 165
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
Automatic sleep staging using fMRI functional connectivity data
Tagliazucchi, E., von Wegner, F., Morzelewski, A., Borisov, S. V., Jahnke, K., & Laufs, H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.036 · Cited by 154
Generalizable Patterns in Neuroimaging: How Many Principal Components?
Hansen, L. K., Larsen, J. O., Nielsen, F. Å., Strother, S. C., Rostrup, E., Savoy, R. L., LANGE, N. D., Sidtis, J. J., Svarer, C., & Paulson, O. B. (1999) · NeuroImage · doi:10.1006/nimg.1998.0425 · Cited by 154
Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease
Vounou, M., Janoušová, E., Wolz, R., Stein, J. L., Thompson, P. M., Rueckert, D., & Montana, G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.029 · Cited by 147
Block-Coordinate Gradient Descent Method for Linearly Constrained Nonsmooth Separable Optimization
Tseng, P., & Yun, S. (2008) · Journal of Optimization Theory and Applications · doi:10.1007/s10957-008-9458-3 · Cited by 144
Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning
Formisano, E., Martino, F. D., & Valente, G. (2008) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2008.01.052 · Cited by 141
SVM-based computer-aided diagnosis of the Alzheimer's disease using t-test NMSE feature selection with feature correlation weighting
Chaves, R., Ramı́rez, J., Górriz, J. M., López, M., Salas-Gonzalez, D., Álvarez, I., & Segovia, F. (2009) · Neuroscience Letters · doi:10.1016/j.neulet.2009.06.052 · Cited by 138
Anticipation causes increased blood flow to the anterior cingulate cortex
Murtha, S. J. E., Chertkow, H. M., Beauregard, M., Dixon, R., & Evans, A. C. (1996) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1996)4:2 · Cited by 137
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
The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data
Mourão-Miranda, J., Reynaud, E., McGlone, F. P., Calvert, G. A., & Brammer, M. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.08.016 · Cited by 131
Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI
Dukart, J., Mueller, K., Barthel, H., Villringer, A., Sabri, O., & Schroeter, M. L. (2012) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2012.04.007 · Cited by 129
Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief
Douglas, P. K., Harris, S., Yuille, A., & Cohen, M. S. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.002 · Cited by 123
Diffusion based abnormality markers of pathology: Toward learned diagnostic prediction of ASD
Ingalhalikar, M., Parker, D., Bloy, L., Roberts, T. P. L., & Verma, R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.023 · Cited by 121
Brain Reading Using Full Brain Support Vector Machines for Object Recognition: There Is No “Face” Identification Area
Hanson, S. J., & Halchenko, Y. O. (2007) · Neural Computation · doi:10.1162/neco.2007.09-06-340 · Cited by 117
Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation
Graña, M., Termenón, M., Savio, A. M., González-Pinto, A. M., Echeveste, J., Pérez, J. M., & Besga, A. (2011) · Neuroscience Letters · doi:10.1016/j.neulet.2011.07.049 · Cited by 114
Discovery and replication of gene influences on brain structure using LASSO regression
Omid Kohannim, A. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00115 · Cited by 114
Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses
Toussaint, P., Perlbarg, V., Bellec, P., Désarnaud, S., Lacomblez, L., Doyon, J., Habert, M., & Benali, H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.091 · Cited by 112
Female children with autism spectrum disorder: An insight from mass-univariate and pattern classification analyses
Calderoni, S., Retico, A., Biagi, L., Tancredi, R., Muratori, F., & Tosetti, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.070 · Cited by 108
Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity
Coutanche, M. N., Thompson‐Schill, S. L., & Schultz, R. T. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.04.016 · Cited by 107
Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: A machine learning approach
Mwangi, B., Hasan, K. M., & Soares, J. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.02.055 · Cited by 107
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Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington's disease using machine learning techniques
Rizk-Jackson, A., Stoffers, D., Sheldon, S., Kuperman, J., Dale, A., Goldstein, J., Corey-Bloom, J., Poldrack, R. A., & Aron, A. R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.273 · Cited by 107
The Quantitative Evaluation of Functional Neuroimaging Experiments: Mutual Information Learning Curves
Kjems, U., Hansen, L. K., Anderson, J., Frutiger, S. A., Muley, S., Sidtis, J. J., Rottenberg, D. A., & Strother, S. C. (2002) · NeuroImage · doi:10.1006/nimg.2001.1033 · Cited by 106
Individual Prediction of Cognitive Decline in Mild Cognitive Impairment Using Support Vector Machine-Based Analysis of Diffusion Tensor Imaging Data
Haller, S., Nguyen, D. D., Rodriguez, C. K., Emch, J., Gold, G., Bartsch, A., Lövblad, K., & Giannakopoulos, P. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-100840 · Cited by 104
Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia
Yoon, U., Lee, J., Im, K., Shin, Y., Cho, B. H., Kim, I. Y., Kwon, J. S., & Kim, S. I. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.11.021 · Cited by 101
Disorder-Specific Predictive Classification of Adolescents with Attention Deficit Hyperactivity Disorder (ADHD) Relative to Autism Using Structural Magnetic Resonance Imaging
Lim, L., Marquand, A., Cubillo, A. A., Smith, A. B., Chantiluke, K., Simmons, A., Mehta, M., & Rubia, K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0063660 · Cited by 101
Neuroanatomical Differences in Toddler Boys With Fragile X Syndrome and Idiopathic Autism
Hoeft, F., Walter, E., Lightbody, A. A., Hazlett, H. C., Chang, C., Piven, J., & Reiss, A. L. (2010) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2010.153 · Cited by 100
Model optimizing and feature selecting for support vector regression in time series forecasting
He, W., Wang, Z., & Jiang, H. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.11.010 · Cited by 98
Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques
Cheng, W., Ji, X., Zhang, J., & Feng, J. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00058 · Cited by 97
Pattern Classification of Working Memory Networks Reveals Differential Effects of Methylphenidate, Atomoxetine, and Placebo in Healthy Volunteers
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 96
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Butson, C. R., Cooper, S. E., Henderson, J. M., & McIntyre, C. C. (2006) · Cited by 94
High Dimensional Classification of Structural MRI Alzheimer?s Disease Data Based on Large Scale Regularization
Casanova, R., Whitlow, C. T., Wagner, B., Williamson, J. D., Shumaker, S. A., Maldjian, J. A., & Espeland, M. A. (2011) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2011.00022 · Cited by 93
Prediction of illness severity in patients with major depression using structural MR brain scans
Mwangi, B., Matthews, K., & Steele, J. D. (2011) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.22806 · Cited by 90
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Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD
Sidhu, G. S., Asgarian, N., Greiner, R., & Brown, M. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00074 · Cited by 87
Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements
Caprihan, A., Pearlson, G. D., & Calhoun, V. D. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.255 · Cited by 83
Cerebral Microhemorrhage and Iron Deposition in Mild Cognitive Impairment: Susceptibility-weighted MR Imaging Assessment
Haller, S., Bartsch, A., Nguyen, D. D., Rodriguez, C. K., Emch, J., Gold, G., Lövblad, K., & Giannakopoulos, P. (2010) · Radiology · doi:10.1148/radiol.10100612 · Cited by 81
Decoding representations of scenes in the medial temporal lobes
Bonnici, H. M., Kumaran, D., Chadwick, M. J., Weiskopf, N., Hassabis, D., & Maguire, E. A. (2011) · Hippocampus · doi:10.1002/hipo.20960 · Cited by 80
Independent Component Analysis
Stone, J. V. (2005) · Encyclopedia of Statistics in Behavioral Science · doi:10.1002/0470013192.bsa297 · Cited by 79
Discriminative Analysis of Brain Function at Resting-State for Attention-Deficit/Hyperactivity Disorder
Zhu, C., Zang, Y., Liang, M., Tian, L., Yong He, A., Li, X. B., Sui, M. Q., Wang, Y. F., & Jiang, T. (2005) · Lecture notes in computer science · doi:10.1007/11566489_58 · Cited by 76
Pattern Recognition and Functional Neuroimaging Help to Discriminate Healthy Adolescents at Risk for Mood Disorders from Low Risk Adolescents
Mourão-Miranda, J., Oliveira, L., Ladouceur, C. D., Marquand, A. F., Brammer, M. J., Birmaher, B. B., Axelson, D., & Phillips, M. L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029482 · Cited by 75
Characterization of groups using composite kernels and multi-source fMRI analysis data: Application to schizophrenia
Castro, E., Martínez‐Ramón, M., Pearlson, G. D., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., & Calhoun, V. D. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.044 · Cited by 75
Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction
Sato, J. R., Hoexter, M. Q., Fujita, A., & Rohde, L. A. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00068 · Cited by 74
Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations
Bray, S. (2009) · Frontiers in Human Neuroscience · doi:10.3389/neuro.09.032.2009 · Cited by 74
Recursive Cluster Elimination Based Support Vector Machine for Disease State Prediction Using Resting State Functional and Effective Brain Connectivity
Deshpande, G., Li, Z., Santhanam, P., Coles, C. D., Lynch, M. E., Hamann, S. B., & Hu, X. P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0014277 · Cited by 72
Dissociable effects of methylphenidate, atomoxetine and placebo on regional cerebral blood flow in healthy volunteers at rest: A multi-class pattern recognition approach
Marquand, A. F., O’Daly, O., Simoni, S. D., Alsop, D. C., Maguire, R. P., Williams, S., Zelaya, F. O., & Mehta, M. A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.058 · Cited by 70
Linking functional and structural brain images with multivariate network analyses: A novel application of the partial least square method
Chen, K., Reiman, E. M., Huan, Z., Caselli, R. J., Bandy, D. J., Ayutyanont, N., & Alexander, G. E. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.04.053 · Cited by 70
Visualization of nonlinear kernel models in neuroimaging by sensitivity maps
Rasmussen, P. M., Madsen, K. H., Lund, T. E., & Hansen, L. K. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.035 · Cited by 68
Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net
Shen, L., Kim, S., Qi, Y., Inlow, M. H., Swaminathan, S., Nho, K. T., Wan, J., Risacher, S. L., Shaw, L. M. J., Trojanowski, J. Q., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24446-9_4 · Cited by 66
Developmental changes in multivariate neuroanatomical patterns that predict risk for psychosis in 22q11.2 deletion syndrome
Gothelf, D., Hoeft, F., Ueno, T., Sugiura, L., Lee, A. D., Thompson, P. M., & Reiss, A. L. (2010) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2010.07.008 · Cited by 65
The role of neuroimaging in diagnosis and personalized medicine-current position and likely future directions
Brammer, M. J. (2009) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2009.11.4/mbrammer · Cited by 64
Detecting stable distributed patterns of brain activation using Gini contrast
Langs, G., Menze, B. H., Danial Lashkari, A., & Golland, P. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.074 · Cited by 62
Classification Based on Cortical Folding Patterns
Duchesnay, É., Cachia, A., Roche, A., Rivière, D., Cointepas, Y., Orfanos, D. P., Zilbovicius, M., Martinot, J., Régis, J. M., & Mangin, J.-F. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.892501 · Cited by 61
Feature selection and classification of imbalanced datasets
Duchesnay, E., Cachia, A., Boddaert, N., Chabane, N., Mangin, J.-F., Martinot, J.-L., Brunelle, F., & Zilbovicius, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.011 · Cited by 60
Partial least squares correlation of multivariate cognitive abilities and local brain structure in children and adolescents
Ziegler, G., Dahnke, R., Winkler, A. D., & Gaser, C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.088 · Cited by 59
Dynamical components analysis of fMRI data through kernel PCA
Thirion, B., & Faugeras, O. D. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00316-1 · Cited by 58
A new statistical method for testing hypotheses of neuropsychological/MRI relationships in schizophrenia: partial least squares analysis
Nestor, P. G., O’Donnell, B. F., McCarley, R. W., Niznikiewicz, M. A., Barnard, J., Shen, Z. J., Bookstein, F. L., & Shenton, M. E. (2002) · Schizophrenia Research · doi:10.1016/s0920-9964(00)00171-7 · Cited by 56
Multivariate Pattern Analysis of Functional Magnetic Resonance Imaging Data Reveals Deficits in Distributed Representations in Schizophrenia
Yoon, J. H., Tamir, D., Minzenberg, M., Ragland, J. D., Ursu, S., & Carter, C. S. (2008) · Biological Psychiatry · doi:10.1016/j.biopsych.2008.07.025 · Cited by 43
Task-driven ICA feature generation for accurate and interpretable prediction using fMRI
Duff, E. P., Trachtenberg, A. J., Mackay, C. E., Howard, M. A., Wilson, F. J., Smith, S. M., & Woolrich, M. W. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.053 · Cited by 40
Classification of Alzheimer's Disease from structural MRI using sparse logistic regression with optional spatial regularization
Rao, A. W., Lee, Y., Gass, A., & Monsch, A. U. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091115 · Cited by 37
Detecting disease-causing genes by LASSO-Patternsearch algorithm
Shi, W., Lee, K. E., & Wahba, G. (2007) · BMC Proceedings · doi:10.1186/1753-6561-1-s1-s60 · Cited by 35
Using non-negative matrix factorization for single-trial analysis of fMRI data
Lohmann, G., Volz, K. G., & Ullsperger, M. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.05.031 · Cited by 34
The relevance of relevance
Subramanian, D., Greiner, R., & Pearl, J. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00075-1 · Cited by 31
Combining Graph and Machine Learning Methods to Analyze Differences in Functional Connectivity Across Sex
Casanova, R., Whitlow, C. T., Wagner, B., Espeland, M. A., & Maldjian, J. A. (2012) · The Open Neuroimaging Journal · doi:10.2174/1874440001206010001 · Cited by 31
Hippocampal Surface Mapping of Genetic Risk Factors in AD via Sparse Learning Models
ADNI, Wan, J., Kim, S., Inlow, M. H., Nho, K. T., Swaminathan, S., Risacher, S. L., Fang, S., Weiner, M. W., Beg, M. F., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23629-7_46 · Cited by 31
Predicting temporal lobe volume on MRI from genotypes using L1-L2 regularized regression
Omid Kohannim, A., Hibar, D. P., Jahanshad, N., Stein, J. L., Hua, X., Toga, A. W., Jack, C. R., Weinen, M. W., & Thompson, P. M. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235766 · Cited by 30
Multivariate decoding of brain images using ordinal regression
Doyle, O., Ashburner, J., Zelaya, F. O., Williams, S., Mehta, M. A., & Marquand, A. F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.036 · Cited by 29
Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging.
Nho, K. T., Shen, L., Kim, S., Risacher, S. L., West, J. D., Foroud, T., Jack, C. R., Weiner, M. W., & Saykin, A. J. (2010) · PubMed Central · Cited by 27
Nonlinear denoising and analysis of neuroimages with kernel principal component analysis and pre-image estimation
Rasmussen, P. M., Abrahamsen, T. J., Madsen, K. H., & Hansen, L. K. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.096 · Cited by 27
Identification of Atrophy Patterns in Alzheimer’s Disease Based on SVM Feature Selection and Anatomical Parcellation
Mesrob, L., Magnin, B., Olivier Colliot, A., Sarazin, M., Hahn‐Barma, V., Dubois, B., Gallinari, P., Lehéricy, S., Kinkingnéhun, S., & Benali, H. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-79982-5_14 · Cited by 26
Predicting subject-driven actions and sensory experience in a virtual world with Relevance Vector Machine Regression of fMRI data
Valente, G., Martino, F. D., Esposito, F., Goebel, R., & Formisano, E. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.062 · Cited by 26
Predictive classification of individual magnetic resonance imaging scans from children and adolescents
Johnston, B. A., Mwangi, B., Matthews, K., Coghill, D., & Steele, J. D. (2012) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-012-0319-0 · Cited by 25
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Selection of spectro-temporal patterns in multichannel MEG with support vector machines for schizophrenia classification
Ince, N. F., Goksu, F., Pellizzer, G., Tewfik, A. H., & Stephane, M. (2008) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2008.4649973 · Cited by 24
Detection of structural differences between the brains of schizophrenic patients and controls
Kovalev, V. A., Petrou, M., & Suckling, J. (2003) · Psychiatry Research Neuroimaging · doi:10.1016/s0925-4927(03)00070-2 · Cited by 22
Joint estimation of multiple clinical variables of neurological diseases from imaging patterns
Fan, Y., Kaufer, D., & Shen, D. G. (2010) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2010.5490120 · Cited by 22
Quantitative analysis in clinical applications of brain MRI using independent component analysis coupled with support vector machine
Chai, J., Chen, C. C., Chiang, C., Ho, Y., Chen, H., Ouyang, Y., Yang, C.-W. W., Lee, S.-K., & Chang, C. (2010) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.22210 · Cited by 20
Brain decoding: Opportunities and challenges for pattern recognition
Ville, D. V. D., & Lee, S. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.06.001 · Cited by 16
Sparse Regression Models of Pain Perception
Rish, I., Cecchi, G., Baliki, M. N., & Apkarian, A. V. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15314-3_20 · Cited by 13
A principal component network analysis of prefrontal-limbic functional magnetic resonance imaging time series in schizophrenia patients and healthy controls
Rǎdulescu, A., & Mujica-Parodi, L. R. (2009) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2009.04.017 · Cited by 13
Population classification based on structural morphometry of cortical sulci
Duchesnay, É., Roche, A., Rivière, D., Orfanos, D. P., Cointepas, Y., & Mangin, J. (2005) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2004.1398776 · Cited by 13
Prediction of Successful Memory Encoding from fMRI Data.
Balci, S. K., Sabuncu, M. R., Yoo, J., Ghosh, S., Whitfield, S., Gabrieli, J. D. E., & Golland, P. (2008) · PubMed · doi:10.1901/jaba.2008.2008-97 · Cited by 10
Discriminative analysis of resting-state brain functional connectivity patterns of Attention-Deficit Hyperactivity Disorder using Kernel Principal Component Analysis
Wang, X., Jiao, Y., & Lu, Z. (2011) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2011.6019911 · Cited by 10
Multimodal Neuroimaging Predictors for Cognitive Performance Using Structured Sparse Learning
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From journal articles to computational models: a new automated tool
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Authors: Mwangi, B., Tian, T. S., & Soares, J. C.
Affiliations: The University of Texas Health Science Center at Houston (US)
University of Houston (US)
Corresponding author: Mwangi, Benson
ORCID: Mwangi, Benson
Soares, Jair C.
Venue: Neuroinformatics
Publisher: Springer Science and Business Media LLC
Published: 2013-09-08
Volume: 12
Issue: 2
Pages: 229-244
ISSN: 1539-2791 (ISSN-L)
1559-0089
DOI: 10.1007/s12021-013-9204-3
PMC: PMC4040248
OpenAlex: W2036439084
Cited by: 559
References: 193
Citations by year: 2026: 34 · 2025: 50 · 2024: 54 · 2023: 49 · 2022: 43 · 2021: 87 · 2020: 53 · 2019: 55 · 2018: 42 · 2017: 30 · 2016: 29 · 2015: 19 · 2014: 13 · 2013: 1
FWCI: 1.77
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Neuroinformatics (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: Pfizer
National Institute of Mental Health (R01MH085667, MH R01 085667)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: feature reduction, dimensionality reduction, feature selection, neuroimaging, voxel selection, curse of dimensionality, overfitting, machine learning, clinical prediction, patient classification
MeSH terms: Artificial Intelligence, Brain, Humans, Statistics as Topic, Neuroimaging

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A Review of Feature Reduction Techniques in Neuroimaging

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