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MULTI-TASK SPARSE SCREENING FOR PREDICTING FUTURE CLINICAL SCORES USING LONGITUDINAL CORTICAL THICKNESS MEASURES.

Jie Zhang1, Yanshuai Tu1, Qingyang Li1

  • 1School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ.

Proceedings. IEEE International Symposium on Biomedical Imaging
|July 20, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces Multi-task Sparse Screening (MSS), an unsupervised algorithm for Alzheimer's disease (AD) research. MSS improves prediction of clinical scores using brain cortical thickness data from longitudinal studies.

Keywords:
Alzheimer’s DiseaseCortical ThicknessDictionary LearningGroup LassoMulti-task

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Area of Science:

  • Neuroimaging
  • Biomedical Data Analysis

Background:

  • In-vivo cortical thickness estimation via MRI is crucial for detecting brain atrophy in preclinical Alzheimer's disease (AD).
  • High-dimensional cortical thickness data and small sample sizes pose challenges for feature selection in AD diagnosis and prognosis.
  • Predicting future clinical scores using longitudinal cortical thickness data remains difficult.

Purpose of the Study:

  • To develop an advanced method for feature selection in longitudinal cortical thickness data for AD.
  • To improve the accuracy of predicting clinical scores in individuals at risk for AD.
  • To introduce an unsupervised dictionary learning algorithm for enhanced AD analysis.

Main Methods:

  • Proposed an unsupervised dictionary learning algorithm named Multi-task Sparse Screening (MSS).
  • Formulated and solved a multi-task problem using top-p significant features.
  • Utilized longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

Main Results:

  • Demonstrated improved correlation coefficients compared to existing algorithms.
  • Showcased reduced root mean square errors in predictions.
  • Empirically validated the effectiveness of MSS on a large dataset (N = 2797).

Conclusions:

  • MSS offers a significant advancement in analyzing longitudinal cortical thickness data for AD.
  • The algorithm enhances the ability to predict clinical outcomes in AD research.
  • This method provides a more accurate approach to feature selection for AD diagnosis and prognosis.