A Review of Feature Reduction Techniques in Neuroimaging
Open accessAbstract
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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When feature vectors from multiple modalities are concatenated, as in early and joint fusion, high-dimensional vectors are generated that can be difficult for machine learning models to learn without overfitting unless a large number of input samples are available (the curse of dimensionality).
“This is chiefly because when feature vectors from multiple modalities are concatenated, such as in early and joint fusion, high-dimensional vectors are generated which can be difficult for machine learning models to learn without overfitting, unless a large number of input samples are available. This is the so-called “curse of dimensionality” in machine learning52,53.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [53]
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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
- Venue
- Neuroinformatics
- Published
- 2013-09-08
- Volume
- 12
- Issue
- 2
- Pages
- 229-244
- ISSN
- 1539-2791 (ISSN-L)
- 1559-0089
- 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
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 ›
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