Abstract
Improving the ability to assess potential stroke deficit may aid the selection of patients most likely to benefit from acute stroke therapies. Methods based only on ‘at risk’ volumes or initial neurological condition do predict eventual outcome but not perfectly. Given the close relationship between anatomy and function in the brain, we propose the use of a modified version of partial least squares (PLS) regression to examine how well stroke outcome covary with infarct location. The modified version of PLS incorporates penalized regression and can handle either binary or ordinal data. This version is known as partial least squares with penalized logistic regression (PLS-PLR) and has been adapted from its original use for high-dimensional microarray data. We have adapted this algorithm for use in imaging data and demonstrate the use of this algorithm in a set of patients with aphasia (high level language disorder) following stroke.
Highlights
Correlations between brain lesions and clinical symptoms have yielded valuable insights into brain function in the past
Improving the ability to assess potential stroke deficit may aid the selection of patients most likely to benefit from acute stroke therapies
Given the close relationship between anatomy and function in the brain, we propose the use of a modified version of partial least squares (PLS) regression to examine how well stroke outcome covary with infarct location
Summary
Correlations between brain lesions and clinical symptoms have yielded valuable insights into brain function in the past. We have recently demonstrated that the relationship between tissue damage assessed at the voxel level and neurological disability can be predicted using a new method: Ridge Penalized Logistic Partial Least Squares (RPL-PLS). This method allows both stroke extent and location to be incorporated into the predictive model for neurological deficit. WPLS penalizes or regularizes PLS model by giving samples different weights (based on their relevance to the study) This additional weight determines how much each observation in the data set influences the final parameter estimates and the ‘dispersion matrix’, from logistical regression, can be severed as weights for the WPLS (detailed in methods section).
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