Structural degree predicts functional network connectivity: a multimodal resting-state fMRI and MEG study.
Communication between neuronal populations in the human brain is characterized by complex functional interactions across time and space. Recent studies have demonstrated that these functional interactions depend on the underlying structural connections at an aggregate level. Multiple imaging modalities can be used to investigate the relation between the structural connections between brain regions and their functional interactions at multiple timescales. We investigated if consistent modality-independent functional interactions take place between brain regions, and whether these can be accounted for by underlying structural properties. We used functional MRI (fMRI) and magnetoencephalography (MEG) recordings from a population of healthy adults together with a previously described structural network. A high overlap in resting-state functional networks was found in fMRI and especially alpha band MEG recordings. This overlap was characterized by a strongly interconnected functional core network in temporo-posterior brain regions. Anatomically realistically coupled neural mass models revealed that this strongly interconnected functional network emerges near the threshold for global synchronization. Most importantly, this functional core network could be explained by a trade-off between the product of the degrees of structurally-connected regions and the Euclidean distance between them. For both fMRI and MEG, the product of the degrees of connected regions was the most important predictor for functional network connectivity. Therefore, irrespective of the modality, these results indicate that a functional core network in the human brain is especially shaped by communication between high degree nodes of the structural network.
Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands. Electronic address: p.tewarie@vumc.nl.
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P
Tewarie
Department of Clinical Neurophysiology and Magnetoencephalography Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
A
A
Hillebrand
Department of Clinical Neurophysiology and Magnetoencephalography Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, University Medical Center, Amsterdam, The Netherlands; Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands.
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E
van Dellen
Department of Anatomy & Neurosciences, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
M M
MM
Schoonheim
Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
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F
Barkhof
Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
C H
CH
Polman
Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada.
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C
Beaulieu
National Key Laboratory of Cognitive Neuroscience and Learning, School of Brain and Cognitive Sciences, Beijing Normal University, Beijing, China.
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G
Gong
Department of Clinical Neurophysiology and Magnetoencephalography Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Department of Physics and Medical Technology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
B W
BW
van Dijk
Department of Clinical Neurophysiology and Magnetoencephalography Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
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CJ
Stam
D000328:Adult; D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology; D009434:Neural Pathways / Q000033:anatomy & histology / Q000502:physiology
Degree product; Euclidean distance; Functional connectivity; Functional networks; MEG; Neural mass; Structural networks; fMRI