Strategies for feature extraction from structural brain imaging in lesion-deficit modelling.

ABSTRACT: High-dimensional modelling of post-stroke deficits from structural brain imaging is highly relevant to basic cognitive neuroscience and bears the potential to be translationally used to guide individual rehabilitation measures. One strategy to optimise model performance is well-informed feature selection and representation. However, different feature representation strategies were so far used, and it is not known what strategy is best for modelling purposes. The present study compared the three common main strategies: voxel-wise representation, lesion-anatomical componential feature reduction and region-wise atlas-based feature representation. We used multivariate, machine-learning-based lesion-deficit models to predict post-stroke deficits based on structural lesion data. Support vector r

SUBMITTER: Kasties V 

PROVIDER: S-EPMC8519857 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature