Locally Linear Embedding and fMRI Feature Selection in Psychiatric Classification

@article{Sidhu2019LocallyLE,
  title={Locally Linear Embedding and fMRI Feature Selection in Psychiatric Classification},
  author={G. Sidhu},
  journal={IEEE Journal of Translational Engineering in Health and Medicine},
  year={2019},
  volume={7},
  pages={1-11}
}
  • G. Sidhu
  • Published 2019
  • Computer Science, Medicine
  • IEEE Journal of Translational Engineering in Health and Medicine
Background: Functional magnetic resonance imaging (fMRI) provides non-invasive measures of neuronal activity using an endogenous Blood Oxygenation-Level Dependent (BOLD) contrast. This article introduces a nonlinear dimensionality reduction (Locally Linear Embedding) to extract informative measures of the underlying neuronal activity from BOLD time-series. The method is validated using the Leave-One-Out-Cross-Validation (LOOCV) accuracy of classifying psychiatric diagnoses using resting-state… Expand
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