A Spatially Sensitive Kernel to Predict Cognitive Performance from Short-Term Changes in Neural Structure

This paper introduces a novel framework for performing machine learning on longitudinal neuroimaging datasets. These datasets are characterized by their size, particularly their width (millions of features per data input). Specifically, we address the problem of detecting subtle, short-term changes in neural structure that are indicative of cognitive change and correlate with risk factors for Alzheimer’s disease. We introduce a new spatially-sensitive kernel that allows us to reason about individuals, as opposed to populations. In doing so, this paper presents the first evidence demonstrating that very small changes in white matter structure over a two year period can predict change in cognitive function in healthy adults.

Authors

• M. Hidayath Ansari University of Wisconsin-Madison
• Michael Coen University of Wisconsin-Madison
• Barbara Bendlin University of Wisconsin-Madison
• Mark Sager University of Wisconsin-Madison
• Sterling Johnson University of Wisconsin-Madison

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