Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity.
Christian RubbertChristian MathysChristiane JockwitzChristian J HartmannSimon B EickhoffFelix HoffstaedterSvenja CaspersClaudia R EickhoffBenjamin SiglNikolas A TeichertMartin SüdmeyerBernd TurowskiAlfons SchnitzlerJulian CaspersPublished in: The British journal of radiology (2019)
Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson's disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.
Keyphrases
- resting state
- functional connectivity
- end stage renal disease
- machine learning
- ejection fraction
- chronic kidney disease
- newly diagnosed
- magnetic resonance imaging
- peritoneal dialysis
- gene expression
- patient reported outcomes
- multiple sclerosis
- white matter
- brain injury
- magnetic resonance
- big data
- dna methylation
- diffusion weighted imaging