title: Sparse weightings for collapsing inverse solutions to cortical parcellations optimize M/EEG source reconstruction accuracy.
abstract: BACKGROUND
  Source-reconstructed magneto- and electroencephalography (M/EEG) are promising tools for investigating the human functional connectome. To reduce data, decrease noise, and obtain results directly comparable to magnetic resonance imaging (MRI), M/EEG source data can be collapsed into a cortical parcellation. For most collapsing approaches, however, it remains unclear if collapsed parcel time series accurately represent the coherent source dynamics within each parcel.
  
  
  METHODS
  We introduce a collapse-weighting-operator optimization approach that maximizes parcel fidelity, i.e., the phase correlation between original source dynamics and collapsed parcel time series, and thereby the accuracy with which the source dynamics are retained in forward and inverse modeling.
  
  
  RESULTS
  The sparse, optimized weighting operator increased parcel fidelity 57-73% and true positive rate of interaction mapping from 0.33 to 0.84 in comparison to a non-sparse weighting approach. These improvements were robust for variable source topographies and parcellation resolutions. Critically, in real inverse-modeled MEG data, the optimized operator yielded close-to-perfect intra-parcel coherence.
  
  
  METHODS
  Previous suggestions for obtaining parcel time series include averaging all source time series within each anatomical parcel or using exclusively the time series of the voxel with maximum power. These methods are sensitive to signal heterogeneity and outlier sources. The approach advanced here avoids these problems.
  
  
  CONCLUSIONS
  The optimized operator is suitable for collapsing real source-reconstructed M/EEG data into any cortical parcellation. The enhanced time series reconstruction fidelity yields improved accuracy of subsequent analyses of both local dynamics and large-scale interaction mapping.
authors: Neuroscience Center, University of Helsinki, P.O. Box 56, FIN-00014, Finland. Electronic address: onerva.korhonen@aalto.fi.
authors: Onerva
authors: O
authors: Korhonen
authors: Neuroscience Center, University of Helsinki, P.O. Box 56, FIN-00014, Finland.
authors: Satu
authors: S
authors: Palva
authors: Neuroscience Center, University of Helsinki, P.O. Box 56, FIN-00014, Finland.
authors: J Matias
authors: JM
authors: Palva
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D016014:Linear Models; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted
keywords: Collapsed inverse solution; Cortical parcellation; Interaction mapping; Linear mixing; M/EEG