Multivariate pattern analysis of fMRI: The early beginnings

By James V. Haxby. NeuroImage, 2012

375 citations recorded by the source corpus.

In 2001, we published a paper on the representation of faces and objects in ventral temporal cortex that introduced a new method for fMRI analysis, which subsequently came to be called multivariate pattern analysis (MVPA). MVPA now refers to a diverse set of methods that analyze neural responses as patterns of activity that reflect the varying brain states that a cortical field or system can produce. This paper recounts the circumstances and events that led to the original study and later developments and innovations that have greatly expanded this approach to fMRI data analysis, leading to its widespread application.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Geometry and Topology

View Paper: https://www.semanticscholar.org/paper/c513d17dcb86b02ae4f87c7c217df1e1e4d26637

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Gaël Varoquaux
2. Russell A. Poldrack
3. Tal Yarkoni
4. Thomas E. Nichols
5. Todd S. Braver
6. Krzysztof J. Gorgolewski
7. Joset A. Etzel
8. Andrew C. Connolly

In Computer Vision and Pattern Recognition

1. Tal Yarkoni
2. Todd S. Braver
3. Joset A. Etzel
4. Jeffrey M. Zacks
5. Gaël Varoquaux
6. Russell A. Poldrack
7. Andrew C. Connolly
8. J. Swaroop Guntupalli

In Geometry and Topology

1. Russell A. Poldrack
2. Tal Yarkoni
3. James V. Haxby
4. Thomas E. Nichols
5. Jeffrey M. Zacks
6. Todd S. Braver
7. Gaël Varoquaux
8. Andrew C. Connolly

Citations in the corpus

• Decoding neural representational spaces using multivariate pattern analysis. (2014, background): “A region’s function was identified by determining which task activated it most strongly.”
• NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain (2014, background): “, 2004)​ ), weight maps (coming from multivariate pattern analysis methods; ​ (Haxby, 2012)​ ), and group­level lesion maps (from clinical studies).”
• Behavior, Sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation (2016, neutral): “Moreover, it may be discussed whether indeed something like “the” true location of an effect exists, given recent accounts of distributed coding and representation (Bzdok et al., 2015; Haxby, 2012; Kriegeskorte, 2009; Rissman and Wagner, 2012).”
• Searchlight analysis: Promise, pitfalls, and potential (2013, background): “Multivariate pattern analysis (MVPA) of functional MRI (fMRI) data has grown steadily since its beginnings in 2001(Haxby, 2012).”