Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data

By Jeremy R. Manning, Rajesh Ranganath, Kenneth A. Norman, David M. Blei. 2014

43 citations recorded by the source corpus.

The neural patterns recorded during a neuroscientific experiment reflect complex interactions between many brain regions, each comprising millions of neurons. However, the measurements themselves are typically abstracted from that underlying structure. For example, functional magnetic resonance imaging (fMRI) datasets comprise a time series of three-dimensional images, where each voxel in an image (roughly) reflects the activity of the brain structure(s)–located at the corresponding point in space–at the time the image was collected. FMRI data often exhibit strong spatial correlations, whereby nearby voxels behave similarly over time as the underlying brain structure modulates its activity. Here we develop topographic factor analysis (TFA), a technique that exploits spatial correlations in fMRI data to recover the underlying structure that the images reflect. Specifically, TFA casts each brain image as a weighted sum of spatial functions. The parameters of those spatial functions, which may be learned by applying TFA to an fMRI dataset, reveal the locations and sizes of the brain structures activated while the data were collected, as well as the interactions between those structures.

Subfields: Biophysics, Computational Theory and Mathematics, Geometry and Topology

How researchers see this paper

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

In Computational Theory and Mathematics

1. David M. Blei
2. Alp Kucukelbir
3. Jon D. McAuliffe

In Geometry and Topology

1. James V. Haxby
2. Jon D. McAuliffe

Citations in the corpus

• Variational Inference: A Review for Statisticians (2016, background): “…(Wool-rich et al. 2004), spatial models (Sato et al. 2004; Zumer et al. 2007; Kiebel et al. 2008; Wipf and Nagarajan 2009; Lashkari et al. 2012; Nathoo et al. 2014), brain-computer interfaces (Sykacek, Roberts, and Stokes 2004), and factor models (Manning et al. 2014; Gershman et al. 2014).”
• A Reduced-Dimension fMRI Shared Response Model (2015, methodology): “Topographic Factor Analysis (TFA) [16] is a factor model using a topographic basis composed of spherical Gaussians with different centers and widths.”
• Decomposing Spatiotemporal Brain Patterns into Topographic Latent Sources (2014, unknown): “TLSA decomposes fMRI data into latent sources defined over 3D image volumes, but does not model the timecourse of eventrelated brain activity (but see Manning et al., 2014).”