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

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

DOI:

https://doi.org/10.1609/aaai.v28i1.8925

Keywords:

Alzheimer's disease, kernels, cognition, neuropsychological testing, spatially sensitive, DTI, APOE

Abstract

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

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Published

2014-06-21

How to Cite

Ansari, M. H., Coen, M., Bendlin, B., Sager, M., & Johnson, S. (2014). A Spatially Sensitive Kernel to Predict Cognitive Performance from Short-Term Changes in Neural Structure. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8925

Issue

Section

Main Track: Machine Learning Applications