publication . Article . Other literature type . 2011

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

Casanova Ramon; Whitlow Christopher T.; Wagner Benjamin; Williamson Jeff; Shumaker Sally A.; Maldjian Joseph A.; Espeland Mark A.;
Open Access English
  • Published: 01 Oct 2011 Journal: Frontiers in Neuroinformatics (issn: 1662-5196, Copyright policy)
  • Publisher: Frontiers Media S.A.
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...
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, Artificial intelligence, business.industry, business, Normalization (statistics), Data mining, computer.software_genre, computer, Population, education.field_of_study, education, White matter, medicine.anatomical_structure, medicine, Neuroimaging, Cognition, Alzheimer's disease, medicine.disease, Elastic net regularization, Computer science
Related Organizations
Funded by
  • Funder: Wellcome Trust (WT)
NIH| Alzheimers Disease Neuroimaging Initiative
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
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