Encyclopedia of Clinical Neuropsychology

2018 Edition
| Editors: Jeffrey S. Kreutzer, John DeLuca, Bruce Caplan

Machine Learning

  • Derin CobiaEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-57111-9_9058

Synonyms

Activation data; Computer algorithms; Computer-assisted instrumentation; Diffusion imaging; Functional magnetic resonance imaging, fMRI; Functional neural systems; Image interpretation; Large data sets; Magnetic resonance imaging, MRI; Multiple database analyses; Neuroimaging; Pattern classification; Pattern recognition; Quantitative measurement; Support vector machine, SVM

Definition

Machine learning is the process of using computer algorithms to develop models by iteratively learning from data independent of human intervention. The advantage is that the learning algorithms make use of all available data, with the models adapting or “learning” as new data are introduced. Common examples include popular social networking and online shopping sites that employ machine learning to identify preferences and provide recommendations to its users. Regarding neuroimaging, machine learning is being utilized to understand underlying dimensions of important functional neural systems...

This is a preview of subscription content, log in to check access.

References and Readings

  1. Abdulkadir, A., Mortamet, B., Vemuri, P., Jack Jr., C. R., Krueger, G., & Kloppel, S. (2011). Effects of hardware heterogeneity on the performance of SVM Alzheimer’s disease classifier. NeuroImage, 58(3), 785–792.  https://doi.org/10.1016/j.neuroimage.2011.06.029.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Gorlin, S., Meng, M., Sharma, J., Sugihara, H., Sur, M., & Sinha, P. (2012). Imaging prior information in the brain. Proceedings of the National Academy of Sciences of the United States of America, 109(20), 7935–7940.  https://doi.org/10.1073/pnas.1111224109.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Hinrichs, C., Singh, V., Xu, G., & Johnson, S. C. (2011). Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population. NeuroImage, 55(2), 574–589.  https://doi.org/10.1016/j.neuroimage.2010.10.081.CrossRefPubMedGoogle Scholar
  4. Koutsouleris, N., Gaser, C., Bottlender, R., Davatzikos, C., Decker, P., Jager, M., …, & Meisenzahl, E. M. (2010). Use of neuroanatomical pattern regression to predict the structural brain dynamics of vulnerability and transition to psychosis. Schizophrenia Research, 123(2–3), 175–187.  https://doi.org/10.1016/j.schres.2010.08.032.CrossRefGoogle Scholar
  5. Wang, Y., Fan, Y., Bhatt, P., & Davatzikos, C. (2010). High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables. NeuroImage, 50(4), 1519–1535.  https://doi.org/10.1016/j.neuroimage.2009.12.092.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Wang, L., Alpert, K. I., Calhoun, V. D., Cobia, D. J., Keator, D. B., King, M. D., …, & Ambite, J. L. (2016). SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration. NeuroImage, 124(Pt B), 1155–1167.  https://doi.org/10.1016/j.neuroimage.2015.06.065.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Psychology and Neuroscience CenterBrigham Young UniversityProvoUSA