Machine learning classifiers and fMRI: A tutorial overview
Open accessAbstract
Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).
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Filter methods allow a more direct interpretation of results than other dimensionality reduction methods such as principal component analysis.
“Unlike other methods for dimensionality reduction, such as principal component analysis, this approach allows a more direct interpretation of the results (Huys et al., 2016; Pereira et al., 2009).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Pereira et al., 2009
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Hidden Stages of Cognition Revealed in Patterns of Brain Activation
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- Authors
- Pereira, F., Mitchell, T., & Botvinick, M.
- Affiliations
- Princeton University (US)
- Carnegie Mellon University (US)
- Corresponding author
- Pereira, Francisco
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2009-03
- Volume
- 45
- Issue
- 1
- Pages
- S199-S209
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2892746
- OpenAlex
- W2158485497
- Cited by
- 1,624
- References
- 49
- Citations by year
- 2026: 38 · 2025: 50 · 2024: 75 · 2023: 85 · 2022: 77 · 2021: 102 · 2020: 126 · 2019: 118 · 2018: 108 · 2017: 138 · 2016: 116 · 2015: 108 · 2014: 116 · 2013: 115 · 2012: 89
- FWCI
- 4.05
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS053366, NS053366)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- machine learning classifiers, functional MRI, pattern classification, pattern localization, statistical significance, brain decoding
- MeSH terms
- Algorithms, Artificial Intelligence, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Software
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