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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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
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
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
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Neural networks for pattern recognition
Bishop, C. (1994) · Choice Reviews Online · doi:10.5860/choice.31-5500 · Cited by 18,537
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
Learning with Kernels
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934
PLS-regression: a basic tool of chemometrics
Wold, S., Sjöström, M., & Eriksson, L. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00155-1 · Cited by 10,084
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
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096

Show all 193 references

Research works printed but not linked: 9

Bishop, C. (2006). Pattern recongition and machine learning. New York: Springer.
Castro, E., Martinez-Raman, M., Pearlson, G., Sui, J., & Calhoun, V. D. (2011b). Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia. NeuroImage, 58, 526–536. · DOI
Cohen, J. (1998). Statistical power analysis for the behavioural sciences- 2nd edition. New Jersey: Lawrence Erbaum Associates.
Johansen-Berg, H., & Behrens, T. (2009). Diffusion MRI - from quantitative measurements to in-vivo neuroanatomy. London: UK, Academic Press.
Marquand, A., Howard, M., Brammer, M., Chu, C., Coen, S., & Mourao-Miranda, J. (2010b). Quantitative prediction of subjective pain intensity from whole-brain fMRI data using Gaussian processes. NeuroImage, 49, 2178–2189. · DOI
Rasmussen, C., and Williams, C. (2006). Gaussian Processes for Machine Learning MIT Press · DOI · Paper page
Theodoridis, S., & Koutroumbas, K. (2009). Pattern Recognition (4th ed.). California: Elseiver.
Tipping, M. (2001). Sparse Bayesian Learning and the relevance vector machine. J ournal of Machine Learning Research, 1(211), 244.
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011b). Multimodal classification of Alzheimer's disease and mild cognitive impairment. NeuroImage, 55, 856–867. · DOI

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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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