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Neuroimage. 2019 Jun;193:35-45. doi: 10.1016/j.neuroimage.2019.02.057. Epub 2019 Mar 1.

Ten simple rules for predictive modeling of individual differences in neuroimaging.

Author information

1
Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Statistics and Data Science, Yale University, USA; Child Study Center, Yale School of Medicine, USA; Interdepartmental Neuroscience Program, Yale School of Medicine, USA. Electronic address: dustin.scheinost@yale.edu.
2
Interdepartmental Neuroscience Program, Yale School of Medicine, USA.
3
Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA.
4
Department of Electrical Engineering, Yale University, USA.
5
Department of Biomedical Engineering, Yale University, USA.
6
Department of Psychiatry, Yale School of Medicine, USA.
7
Child Study Center, Yale School of Medicine, USA; Department of Psychiatry, Yale School of Medicine, USA.
8
Department of Psychology, Yale University, USA.
9
Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Interdepartmental Neuroscience Program, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA.

Abstract

Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.

KEYWORDS:

Classification; Connectome; Cross-validation; Machine learning; Neural networks

PMID:
30831310
PMCID:
PMC6521850
[Available on 2020-06-01]
DOI:
10.1016/j.neuroimage.2019.02.057
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