Travelling waves in the brain: Where do they come from, where do they go?
Poster No:
1578
Submission Type:
Abstract Submission
Authors:
Jace Cruddas1, James Pang2, Alex Fornito2
Institutions:
1Monash University, Melbourne, Victoria, 2Monash University, Clayton, Victoria
First Author:
Co-Author(s):
Introduction:
Since Hans Berger's seminal work [1] on detecting alpha waves in the human occipital lobe, waves have been synonymous with brain activity. These waves have been characterized extensively in the temporal domain, but waves of excitation also propagate through space in a way that is less understood. Spatiotemporal wave dynamics are now being revealed by invasive wide-field imaging techniques, but there is a dearth of appropriate methods for their characterization with non-invasive techniques like functional magnetic resonance imaging (fMRI).
Here, we draw upon methods in climate science [2] and data mining [3] to clearly delineate spatiotemporal waves in fMRI data that exist on a single subject basis and answer some fundamental questions, such as where do they come from, where do they go and what paths do they take?
Here, we draw upon methods in climate science [2] and data mining [3] to clearly delineate spatiotemporal waves in fMRI data that exist on a single subject basis and answer some fundamental questions, such as where do they come from, where do they go and what paths do they take?
Methods:
Figure 1 shows the methodology used in this work. We initially draw upon a method developed for climate science [2], fast multidimensional ensemble empirical mode decomposition, to separate out the pre-processed cortical data into a set of complete empirical modes, each ordered by their temporal frequency, covering a different section of the frequency spectrum. This allows us to empirically separate the noisy, untrackable signals contained in the first high frequency mode from the remaining trackable signals contained in the lower order modes.
We then convert each empirical mode into a binary matrix, containing only non-zero values for the local temporal maxima of each voxel. Using this sparse matrix, we can then track how those maxima move through space and time by connecting local maxima that are spatiotemporally neighbouring each other. We then create a graph containing the information about the observed spatiotemporal dynamics. From this graph we can use established data mining methods for dynamic graphs [3].
We then convert each empirical mode into a binary matrix, containing only non-zero values for the local temporal maxima of each voxel. Using this sparse matrix, we can then track how those maxima move through space and time by connecting local maxima that are spatiotemporally neighbouring each other. We then create a graph containing the information about the observed spatiotemporal dynamics. From this graph we can use established data mining methods for dynamic graphs [3].
·Fig 1 Illustration of the wave tracking algorithim.
Results:
We applied our approach to the Human Connectome Project (HCP) 1200 resting-state fMRI data set. We found that spontaneous activity is dominated by global and local waves that propagate across the cortical surface. We found clear repeatable and directionally constrained pathways, See Fig. 2a, along with a set of common regions representing wave sources and sinks. We also found non-local wave propagation pathways that consisted of waves that simultaneously propagate over consistent paths without being spatially connected, See Fig 2b.
·Fig 2 a) Average time delay map for commonly occurring waves, illustrating the propagation pathways of waves. b-c) Time averaged connectivity, illustrating the effect of waves and their connectivity.
Conclusions:
We introduce a new approach to shed light on the role waves play in cortical processing. The approach offers a richer characterization of both the temporal and spatial spread of brain activity than traditional methods, offering insights into how the spatiotemporal evolution of brain activity is shaped by its region and network organization.
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Methods Development 1
Task-Independent and Resting-State Analysis
Keywords:
Cortex
Data analysis
FUNCTIONAL MRI
Modeling
Source Localization
Statistical Methods
Systems
Workflows
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
2. Wu Z., Feng J., Qiao F. and Tan Z.-M. (2016) Fast multidimensional ensemble empirical mode decomposition for the analysis of big spatiotemporal datasets, Phil. Trans. R. Soc. 374, 20150197.
3. Fournier-Viger P., He G., Cheng C., et al. (2020) A survey of pattern mining in dynamic graphs. WIREs Data Mining Knowl Discov. 10.