[talks] M Carroll preFPO

[Melissa M. Lawson]

Melissa Carroll will present her preFPO at 2PM on Monday October 12
in Room 402.  The members of her committee are:  Rob Schapire, 
advisor; Ken Norman (Psychology) and Irina Rish (IBM), readers; 
David Blei and Peter Ramadge (ELE), nonreaders.  Everyone is invited 
to attend her talk.  Her abstract follows below.
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Capturing Spatial Structure of Response in Predictive fMRI Models

Functional MRI (fMRI) is commonly used to map between brain regions and 
mental functions.  Traditional analyses have focused on testing putative 
associations between large, known brain regions and broad mental tasks; 
however, machine learning techniques applied globally at the scale of 3D 
voxels have been used to produce fine-scaled predictive associations. 
Extracting meaningful insight into brain structure-function 
relationships from these techniques remains a challenge, though, 
partially because voxels are arbitrary discretizations of the underlying 
activation signal, leading to spatial auto-correlation and interfering 
with model interpretation.  I discuss methods that address this spatial 
structure with the goal of achieving well-predicting models that are 
also reliable and easily interpreted.

I first demonstrate that sparse regression methods produce more accurate 
mental state predictions than non-sparse methods.  Furthermore, I show 
that the Elastic Net hybrid regularized regression penalization produces 
models that select more reliable sets of voxels across experimental runs 
than the standard LASSO, without compromising prediction performance. 
This increased reliability comes in part from greater inclusion from 
within spatially localized clusters of activity, and the most predictive 
models found are those exhibiting distributed patterns of localized 
activity.

Given the local clustering properties of the fMRI signal, a natural way 
to improve reliability further might be to directly find these clusters. 
  Spatial filters have traditionally been used to smooth fMRI data and 
reduce noise; however, the optimal filter varies by task, region, and 
subject, and training models to find the most predictive filters can be 
computationally prohibitive.  I describe a parallel implementation of 
the LARS-EN algorithm that can exploit massively parallel architectures, 
and demonstrate the feasibility of using it to build models from 
hundreds or thousands of spatial filters, e.g. Gaussian or wavelet.  I 
show how this framework can be used as a test-bed for evaluating filter 
sets, with the potential to produce more reliable models without 
compromising prediction performance or parsimony.  I also show how these 
models can be interpreted to elucidate spatial properties of the neural 
response.

Finally, I introduce an alternative method that can potentially model 
more precise Gaussian spatial filters within a flexible framework.  This 
approach directly determines filter parameters by treating the fMRI 
activity as a distribution in space, characterized as a Mixture of 
Gaussians.  Unlike recent similar models, I show that by assuming a 
Bayesian hierarchical model for the data similar to that of Latent 
Dirichlet Allocation, one can exploit developments in variational 
inference algorithms for supervised LDA to tractably infer such 
probabilistic models within the desired predictive modeling framework.