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.