Whole-brain dynamical modelling for classification of Parkinson's disease.

ABSTRACT: Simulated whole-brain connectomes demonstrate enhanced inter-individual variability depending on the data processing and modelling approach. By considering the human brain connectome as an individualized attribute, we investigate how empirical and simulated whole-brain connectome-derived features can be utilized to classify patients with Parkinson's disease against healthy controls in light of varying data processing and model validation. To this end, we applied simulated blood oxygenation level-dependent signals derived by a whole-brain dynamical model simulating electrical signals of neuronal populations to reveal differences between patients and controls. In addition to the widely used model validation via fitting the dynamical model to empirical neuroimaging data, we invented a model v

SUBMITTER: Jung K 

PROVIDER: S-EPMC9798283 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature