Corrigendum: Multi-Kernel Learning with Dartel Improves Combined MRI-PET Classification of Alzheimer's Disease in AIBL Data: Group and Individual Analyses
- 1Computational Neuroscience Research Team, Intelligent Systems Research Centre, School of Computing and Intelligent Systems, Faculty of Computing and Engineering, Ulster University, Londonderry, United Kingdom
- 2Division of Neurology, Cincinnati Childrens Hospital Medical Center, Cincinnati, OH, United States
- 3Institute of Clinical Science B, Centre for Public Health, Queens University Belfast, Belfast, United Kingdom
by Youssofzadeh, V., McGuinness, B., Maguire, L. P., and Wong-Lin, K. (2017). Front. Hum. Neurosci. 11:380. doi: 10.3389/fnhum.2017.00380
There are incorrect details in the Acknowledgments Section. The first sentence should read:
This work was performed under the Northern Ireland International Health Analytics Centre (IHAC) collaborative network project funded by Invest NI through Northern Ireland Science Park (Catalyst Inc.).
Wherein “New York, NY, USA” has been removed from the sentence.
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: Alzheimer's disease, classification, machine learning, multi-kernel learning, prediction, Australian imaging, biomarkers, lifestyle AIBL
Citation: Youssofzadeh V, McGuinness B, Maguire LP and Wong-Lin K (2017) Corrigendum: Multi-Kernel Learning with Dartel Improves Combined MRI-PET Classification of Alzheimer's Disease in AIBL Data: Group and Individual Analyses. Front. Hum. Neurosci. 11:457. doi: 10.3389/fnhum.2017.00457
Received: 23 August 2017; Accepted: 29 August 2017;
Published: 08 September 2017.
Edited and reviewed by: Farshid Sepehrband, Laboratory of Neuro Imaging - University of Southern California, United States
Copyright © 2017 Youssofzadeh, McGuinness, Maguire and Wong-Lin. 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.