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Topographic factor analysis: a Bayesian model for inferring brain networks from neural data.

Manning JR, Ranganath R, Norman KA, Blei DM - PLoS ONE (2014)

Bottom Line: 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.Specifically, TFA casts each brain image as a weighted sum of spatial functions.

View Article: PubMed Central - PubMed

Affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America; Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America.

ABSTRACT
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.

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Factors.A. Sample image. One coronal slice of a single brain image; high activations are shown in red and low activations are shown in blue. Examples of factors obtained using (B) PCA, (C) ICA, and (D) TFA are shown in the panels. The color scale for all panels is the same as for Figure 1.
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pone-0094914-g008: Factors.A. Sample image. One coronal slice of a single brain image; high activations are shown in red and low activations are shown in blue. Examples of factors obtained using (B) PCA, (C) ICA, and (D) TFA are shown in the panels. The color scale for all panels is the same as for Figure 1.

Mentions: By decomposing fMRI data into weighted combinations of spatial functions (Figure 2), TFA reveals some aspects of the structure underlying a dataset. Standard techniques, such as Principal Component Analysis (PCA; [3]) and Independent Component Analysis (ICA; [4], [5]) are closely related to TFA. For example, PCA may be used to obtain a set of factors that best explain the covariance structure of a set of observations, and ICA may be used to determine a set of distinct features that underly those observations (Figures 8B, C). Each observation in the original dataset may then be approximated by a weighted sum of a subset of those factors.


Topographic factor analysis: a Bayesian model for inferring brain networks from neural data.

Manning JR, Ranganath R, Norman KA, Blei DM - PLoS ONE (2014)

Factors.A. Sample image. One coronal slice of a single brain image; high activations are shown in red and low activations are shown in blue. Examples of factors obtained using (B) PCA, (C) ICA, and (D) TFA are shown in the panels. The color scale for all panels is the same as for Figure 1.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4012983&req=5

pone-0094914-g008: Factors.A. Sample image. One coronal slice of a single brain image; high activations are shown in red and low activations are shown in blue. Examples of factors obtained using (B) PCA, (C) ICA, and (D) TFA are shown in the panels. The color scale for all panels is the same as for Figure 1.
Mentions: By decomposing fMRI data into weighted combinations of spatial functions (Figure 2), TFA reveals some aspects of the structure underlying a dataset. Standard techniques, such as Principal Component Analysis (PCA; [3]) and Independent Component Analysis (ICA; [4], [5]) are closely related to TFA. For example, PCA may be used to obtain a set of factors that best explain the covariance structure of a set of observations, and ICA may be used to determine a set of distinct features that underly those observations (Figures 8B, C). Each observation in the original dataset may then be approximated by a weighted sum of a subset of those factors.

Bottom Line: 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.Specifically, TFA casts each brain image as a weighted sum of spatial functions.

View Article: PubMed Central - PubMed

Affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America; Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America.

ABSTRACT
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.

Show MeSH