Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data

2015 ◽  
Vol 103 (9) ◽  
pp. 1507-1530 ◽  
Author(s):  
Sven Dahne ◽  
Felix Bieszmann ◽  
Wojciech Samek ◽  
Stefan Haufe ◽  
Dominique Goltz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Neuroimaging ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Neuroimaging Data

scholarly journals Quantifying performance of machine learning methods for neuroimaging data

NeuroImage ◽  
2019 ◽  
Vol 199 ◽  
pp. 351-365 ◽  
Author(s):  
Lee Jollans ◽  
Rory Boyle ◽  
Eric Artiges ◽  
Tobias Banaschewski ◽  
Sylvane Desrivières ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Neuroimaging Data

scholarly journals Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019

2020 ◽  
Vol 11 ◽  
Author(s):  
Pedro F. Da Costa ◽  
Jessica Dafflon ◽  
Walter H. L. Pinaya
Keyword(s):  
Machine Learning ◽  
Brain Aging ◽  
Predictive Analytics ◽  
Neurological Diseases ◽  
Absolute Error ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Neuroimaging Data ◽  
Brain Age

As we age, our brain structure changes and our cognitive capabilities decline. Although brain aging is universal, rates of brain aging differ markedly, which can be associated with pathological mechanism of psychiatric and neurological diseases. Predictive models have been applied to neuroimaging data to learn patterns associated with this variability and develop a neuroimaging biomarker of the brain condition. Aiming to stimulate the development of more accurate brain-age predictors, the Predictive Analytics Competition (PAC) 2019 provided a challenge that included a dataset of 2,640 participants. Here, we present our approach which placed between the top 10 of the challenge. We developed an ensemble of shallow machine learning methods (e.g., Support Vector Regression and Decision Tree-based regressors) that combined voxel-based and surface-based morphometric data. We used normalized brain volume maps (i.e., gray matter, white matter, or both) and features of cortical regions and anatomical structures, like cortical thickness, volume, and mean curvature. In order to fine-tune the hyperparameters of the machine learning methods, we combined the use of genetic algorithms and grid search. Our ensemble had a mean absolute error of 3.7597 years on the competition, showing the potential that shallow methods still have in predicting brain-age.

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