Journal Article FZJ-2022-06382

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Whole-brain dynamical modelling for classification of Parkinson’s disease

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2023
Guarantors of Brain [Großbritannien]

Brain communications 5(1), fcac331 () [10.1093/braincomms/fcac331]

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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 validation against behavioural data, such as subject classes, which we refer to as behavioural model fitting and show that it can be beneficial for Parkinsonian patient classification. Furthermore, the results of machine learning reported in this study also demonstrated that the performance of the patient classification can be improved when the empirical data are complemented by the simulation results. We also showed that the temporal filtering of blood oxygenation level-dependent signals influences the prediction results, where filtering in the low-frequency band is advisable for Parkinsonian patient classification. In addition, composing the feature space of empirical and simulated data from multiple brain parcellation schemes provided complementary features that improved prediction performance. Based on our findings, we suggest that combining the simulation results with empirical data is effective for inter-individual research and its clinical application.

Classification:

Contributing Institute(s):
  1. Gehirn & Verhalten (INM-7)
Research Program(s):
  1. 5232 - Computational Principles (POF4-523) (POF4-523)

Appears in the scientific report 2023
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Medline ; Creative Commons Attribution CC BY 4.0 ; DOAJ ; OpenAccess ; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Web of Science Core Collection
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Open Access

 Datensatz erzeugt am 2022-12-29, letzte Änderung am 2023-09-29


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