CORRECTION article

Front. Hum. Neurosci., 08 September 2017

Sec. Cognitive Neuroscience

Volume 11 - 2017 | https://doi.org/10.3389/fnhum.2017.00457

Corrigendum: Multi-Kernel Learning with Dartel Improves Combined MRI-PET Classification of Alzheimer's Disease in AIBL Data: Group and Individual Analyses

  • 1. Computational Neuroscience Research Team, Intelligent Systems Research Centre, School of Computing and Intelligent Systems, Faculty of Computing and Engineering, Ulster University Londonderry, United Kingdom

  • 2. Division of Neurology, Cincinnati Childrens Hospital Medical Center Cincinnati, OH, United States

  • 3. Institute of Clinical Science B, Centre for Public Health, Queens University Belfast Belfast, United Kingdom

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.

Statements

Conflict of interest

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.

Summary

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

Volume

11 - 2017

Edited and reviewed by

Farshid Sepehrband, Laboratory of Neuro Imaging - University of Southern California, United States

Updates

Copyright

*Correspondence: Vahab Youssofzadeh

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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