Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome.

ABSTRACT: An emerging trend is to use regression-based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome-based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains an

SUBMITTER: Feng G 

PROVIDER: S-EPMC9294303 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature