title: Increased connectivity between sensorimotor and attentional areas in Parkinson's disease.
abstract: BACKGROUND
  Our study is using Independent Component Analysis (ICA) to evaluate functional connectivity changes in Parkinson's disease (PD) in an unbiased manner.
  
  
  METHODS
  Resting-state functional magnetic resonance imaging (rs-fMRI) data was collected for 27 PD patients and 16 healthy subjects. Differences for intra- and inter-network connectivity between healthy subjects and patients were investigated using FMRIB Software Library (FSL) tools (Melodic ICA, dual regression, FSLNets).
  
  
  RESULTS
  Twenty-three ICA maps were identified as components of neuronal origin. For intra-network connectivity changes, eight components showed a significant connectivity increase in patients (p < 0.05); these were correlated with clinical scores and were largest for (sensori)motor networks. For inter-network connectivity changes, we found higher connectivity between the sensorimotor network and the spatial attention network (p = 0.0098) and lower connectivity between anterior and posterior default mode networks (DMN) (p =  0.024), anterior DMN and visual recognition networks (p = 0.026), as well as between visual attention and main dorsal attention networks (p = 0.03), for patients as compared to healthy subjects. The area under the Receiver Operating Characteristics (ROC) curve for the best predictor (partial correlation between sensorimotor and spatial attention networks) was 0.772. These functional alterations were not associated with any gray or white matter structural changes.
  
  
  CONCLUSIONS
  Our results show higher connectivity between sensorimotor and spatial attention areas in patients that may be related to the reduced movement automaticity in PD.
authors.affiliation: Medical Imaging Department, Clinical Hospital "Prof. Dr. Th. Burghele", 20, Panduri Street, Bucharest, 050659, Romania. onu.mihaela@gmail.com.|Carol Davila University of Medicine and Pharmacy, Biophysics, Bucharest, Romania. onu.mihaela@gmail.com.
authors.forename: Mihaela
authors.initials: M
authors.lastname: Onu
authors.affiliation: National Institute for Research and Development in Informatics, Artificial Intelligence and Bioinformatics Group, Bucharest, Romania.
authors.forename: Liviu
authors.initials: L
authors.lastname: Badea
authors.affiliation: University of Bucharest Emergency Hospital, Neurology Department, Bucharest, Romania.
authors.forename: Adina
authors.initials: A
authors.lastname: Roceanu
authors.affiliation: University of Rochester Medical Center, Department of Imaging Sciences and Rochester Center for Brain Imaging, Rochester, NY, USA.
authors.forename: Madalina
authors.initials: M
authors.lastname: Tivarus
authors.affiliation: University of Bucharest Emergency Hospital, Neurology Department, Bucharest, Romania.
authors.forename: Ovidiu
authors.initials: O
authors.lastname: Bajenaru
mesh_terms: D000368:Aged; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009434:Neural Pathways / Q000473:pathology*; D010300:Parkinson Disease / Q000473:pathology*; D066191:Sensorimotor Cortex / Q000473:pathology*
keywords: Disease classifier; Independent component analysis; Parkinson’s disease; Resting state functional magnetic resonance imaging