Hyperedge bundling: A practical solution to spurious interactions in MEG/EEG source connectivity analyses

Autor: Muriel Lobier, Felix Siebenhühner, Satu Palva, Tuomas Puoliväli, J. Matias Palva, Sheng H. Wang
Přispěvatelé: Neuroscience Center, Helsinki Institute of Life Science HiLIFE, Clinicum, BioMag Laboratory, Department of Diagnostics and Therapeutics, Matias Palva / Principal Investigator
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Spurious correlation
Theoretical computer science
Computer science
ACCURACY
Artificial correlation
Electroencephalography
Signal
3124 Neurology and psychiatry
0302 clinical medicine
EEG
Point spread
0303 health sciences
Brain Mapping
MEG
CHALLENGES
medicine.diagnostic_test
Noise (signal processing)
Functional connectivity
05 social sciences
Brain
Magnetoencephalography
LOCALIZATION
Signal Processing
Computer-Assisted
FUNCTIONAL CONNECTIVITY
Neurology
NEUROSCIENCE
Graph (abstract data type)
High temporal resolution
Signal leakage
DISORDERS
Cognitive Neuroscience
Models
Neurological
050105 experimental psychology
03 medical and health sciences
medicine
Humans
0501 psychology and cognitive sciences
Computer Simulation
Spurious relationship
030304 developmental biology
business.industry
3112 Neurosciences
Signal mixing
Pattern recognition
3126 Surgery
anesthesiology
intensive care
radiology
SYNCHRONY
Graph theory
BRAIN NETWORKS
Electrophysiology
Volume conduction
Artificial intelligence
Nerve Net
business
030217 neurology & neurosurgery
ISSN: 1053-8119
DOI: 10.1101/219311
Popis: Inter-areal functional connectivity (FC), neuronal synchronization in particular, is thought to constitute a key systems-level mechanism for coordination of neuronal processing and communication between brain regions. Evidence to support this hypothesis has been gained largely using invasive electrophysiological approaches. In humans, neuronal activity can be non-invasively recorded only with magneto- and electroencephalography (MEG/EEG), which have been used to assess FC networks with high temporal resolution and whole-scalp coverage. However, even in source-reconstructed MEG/EEG data, signal mixing, or “source leakage”, is a significant confounder for FC analyses and network localization.Signal mixing leads to two distinct kinds of false-positive observations: artificial interactions (AI) caused directly by mixing and spurious interactions (SI) arising indirectly from the spread of signals from true interacting sources to nearby false loci. To date, several interaction metrics have been developed to solve the AI problem, but the SI problem has remained largely intractable in MEG/EEG all-to-all source connectivity studies. Here, we advance a novel approach for correcting SIs in FC analyses using source-reconstructed MEG/EEG data.Our approach is to bundle observed FC connections into hyperedges by their adjacency in signal mixing. Using realistic simulations, we show here that bundling yields hyperedges with good separability of true positives and little loss in the true positive rate. Hyperedge bundling thus significantly decreases graph noise by minimizing the false-positive to true-positive ratio. Finally, we demonstrate the advantage of edge bundling in the visualization of large-scale cortical networks with real MEG data. We propose that hypergraphs yielded by bundling represent well the set of true cortical interactions that are detectable and dissociable in MEG/EEG connectivity analysis.HighlightsA true interaction often is “ghosted” into a multitude of spurious edges (SI)Effective in controlling and illustrating SIHyperedges have much improved TPR and graph qualityAdvantages in visualizing connectivity
Databáze: OpenAIRE
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