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Monday, July 20 • 7:00pm - 8:00pm
P172: Spatiotemporal brain waves on resting-state MEG data

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James Pang, Paula Sanz-Leon, Jonathan Hadida, Leonardo Gollo, Mark Woolrich, James A Roberts

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Human brain function relies on the integration and coordination of neuronal activity on multiple scales. Several works have revealed that this is possible through spontaneous or evoked synchronization of activities of neural circuits in the brain, allowing spatially correlated patterns that propagate in time to emerge, known as brain waves [1]. These brain waves have been observed in empirical macroscopic and mesoscopic measurements [2,3] and computational brain network models [4], and have been shown to support various brain functions such as visual perception [5]. However, brain waves are rarely investigated in resting-state experimental settings (i.e., without performing an explicit task).

Here, we investigate large-scale spatiotemporal brain waves in resting-state human magnetoencephalography (MEG), which is becoming a popular imaging modality due to its high spatial and temporal resolution, enabling more accurate analysis of macroscopic brain waves. We use source reconstructed single-subject MEG data projected onto the cortical surface and then decompose the signal into various typical frequency bands from delta to gamma. We find that organized patterns of waves traveling in space and time exist in the resting-state data at the different frequency bands; an example of which is shown in the time snapshots of the alpha-filtered MEG signal in Fig. 1A and the corresponding phase maps in Fig. 1B. Using the methods in [4] for estimating instantaneous phase speeds, we find that, in general, waves with higher temporal frequencies tend to propagate more rapidly (Fig. 1C). In addition, the speeds match those in the literature using other modalities (e.g., electrocorticography in [2]), suggesting the reliability of our analyses. In summary, our work shows that macroscopic brain waves can be observed in resting-state MEG data even for a single subject, enabling the use of MEG alongside computational models in future investigations on how brain waves affect and relate to large-scale brain networks and the emergence of cognition and behavior.

[1] Muller et al. Cortical travelling waves: Mechanisms and computational principles. Nature Reviews Neuroscience19(5):255-268, 2018.
[2] Zhang et al. Theta and alpha oscillations are traveling waves in the human neocortex. Neuron 98:1269-1281, 2018.
[3] Rubino et al. Propagating waves mediate information transfer in the motor cortex. Nature Neuroscience 9:1549-1557, 2006.
[4] Roberts et al. Metastable brain waves. Nature Communications 10:1056, 2019.
[5] Zanos et al. A sensorimotor role for traveling waves in primate visual cortex 85:615-627, 2015.

avatar for James Pang

James Pang

QIMR Berghofer Medical Research Institute
Postdoc working on neuroimaging, networks, and computational models

Monday July 20, 2020 7:00pm - 8:00pm CEST
Slot 08