Corrigendum: Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images
- 1Department of Biomedical Engineering, South China University of Technology, Guangzhou, China
- 2Guangdong General Hospital, Guangzhou, China
by Guo, S., Lai, C., Wu, C., Cen, G., and The Alzheimer's Disease Neuroimaging Initiative (2017). Front. Aging Neurosci. 9:146. doi: 10.3389/fnagi.2017.00146
Due to an oversight, the link used to list authors and grant authorship to Alzheimer's Disease Neuroimaging Initiative (ADNI) members was incorrect. The correct link is appearing below:
Authorship of ADNI members has been corrected.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way.
The original article has been updated.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Keywords: mild cognitive impairment, conversion, cortical feature, sparse-constrained regression, feature reduction, classification
Citation: Guo S, Lai C, Wu C, Cen G and The Alzheimer's Disease Neuroimaging Initiative (2017) Corrigendum: Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images. Front. Aging Neurosci. 9:293. doi: 10.3389/fnagi.2017.00293
Received: 24 June 2017; Accepted: 28 August 2017;
Published: 05 September 2017.
Edited and reviewed by: P. Hemachandra Reddy, Texas Tech University Health Sciences Center, United States
Copyright © 2017 Guo, Lai, Wu, Cen and The Alzheimer's Disease Neuroimaging Initiative. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Shengwen Guo, firstname.lastname@example.org
†The Alzheimer's Disease Neuroimaging Initiative (ADNI) unites researchers with study data as they work to define the progression of Alzheimer's disease. ADNI researchers collect, validate and utilize data such as MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers as predictors for the disease. For more details, please visit http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf