publication . Article . Other literature type . 2011

High dimensional classification of structural MRI Alzheimer's disease data based on large scale regularization.

Joseph A. Maldjian;
Open Access
  • Published: 01 Oct 2011
Abstract
In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avo...
Subjects
free text keywords: machine learning, Logistic regression, ADNI, curse of dimensionality, elastic net, GLMNET, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neuroscience, Original Research, high dimensional, large scale regularization, Biomedical Engineering, Neuroscience (miscellaneous), Computer Science Applications
Funded by
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
,
WT
Project
  • Funder: Wellcome Trust (WT)
Communities
Neuroinformatics
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