A supervised clustering approach for fMRI-based inference of brain states

By V. Michel, Alexandre Gramfort, Gaël Varoquaux, Evelyn Eger, Christine Keribin, Bertrand Thirion. 2011

130 citations recorded by the source corpus.

We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal. Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches. Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Medical Imaging, Neuroscience, Signal Processing

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. Bertrand Thirion
3. Jean-Baptiste Poline

In Computer Vision and Pattern Recognition

1. Jean-Baptiste Poline
2. Elvis Dohmatob
3. Gaël Varoquaux
4. Bertrand Thirion

In Neuroscience

1. Jean-Baptiste Poline
2. Gaël Varoquaux
3. Bertrand Thirion

In Signal Processing

1. Elvis Dohmatob
2. Gaël Varoquaux
3. Bertrand Thirion

Citations in the corpus

• Which fMRI clustering gives good brain parcellations? (2014, methodology): “…Magnetic Resonance Images (rs-fMRIs) ( Yeo et al. , 2011; Blumensath et al. , 2012; Craddock et al. , 2012; Kahnt et al. , 2012; Wig et al. , 2013), from activation data ( Flandin et al. , 2002; Lashkari et al. , 2010, 2012; Michel et al. , 2012), or from meta-analyses ( Eickhoff et al. , 2011).”