Logan Grosenick: Center for Mind, Brain, and Computation & Department of
Bioengineering, Stanford University.
TITLE: Fast classification, regression, and multivariate methods for
sparse but structured data with applications to whole-brain fMRI and
volumetric calcium imaging
ABSTRACT: Modern neuroimaging methods allow the rapid collection of
large (> 100,000 voxel) volumetric time-series. Consequently there has
been a growing interest in applying supervised (classification,
regression) and unsupervised (factor analytic) machine learning
methods to uncover interesting patterns in these rich data.
However, as classically formulated, such approaches are difficult to
interpret when ﬁt to correlated, multivariate data in the presence of
noise. In such cases, these models may suffer from coefficient
instability and sensitivity to outliers, and typically return dense
rather than parsimonious solutions. Furthermore, on large data they
can take an unreasonably long time to compute.
I will discuss ongoing research in the area of sparse but structured
methods for classification, regression, and factor analysis that aim
to produce interpretable solutions and to incorporate realistic
physical priors in the face of large, spatially and temporally
correlated data. Two examples--whole-brain classification of
spatiotemporal fMRI data and nonnegative sparse PCA applied to 3D
calcium imaging--will be presented.