Voxelwise dynamic Functional Connectivity using the DySCo approach
Poster No:
1255
Submission Type:
Abstract Submission
Authors:
Giuseppe de Alteriis1, Oliver Sherwood1, Alessandro Ciaramella2, Robert Leech3, Joana Cabral4, Federico Turkheimer25, Paul Expert6
Institutions:
1King's College London, London, London, 2University of Pisa, Pisa, Italy, 3Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, Greater London, 4ICVS - University of Minho, Braga, Braga, 5Department of Neuroimaging, Institute of Psychiatry,Psychology & Neuroscience, King’s College London, London, United Kingdom, 6University College London, London, UK
First Author:
Co-Author(s):
Robert Leech
Institute of Psychiatry, Psychology & Neuroscience, King’s College London
London, Greater London
Institute of Psychiatry, Psychology & Neuroscience, King’s College London
London, Greater London
Federico Turkheimer2
Department of Neuroimaging, Institute of Psychiatry,Psychology & Neuroscience, King’s College London
London, United Kingdom
Department of Neuroimaging, Institute of Psychiatry,Psychology & Neuroscience, King’s College London
London, United Kingdom
Introduction:
A key property of brain-wide networks is the dynamic nature of the interactions between their nodes. This is what the field of dynamic Functional Connectivity (dFC) investigates (Hutchison et al., 2013). dFC is the analysis of a matrix that evolves with time dFC(t). Commonly used dFC(t) matrices are the sliding window correlation/covariances (Allen et al., 2014), coactivations (Esfahlani et al. 2020), or instantaneous Phase Locking (Cabral et al., 2017). However, the main dFC approaches have been developed and applied mostly empirically, lacking a unifying theoretical framework, a general interpretation, a common EVD, and a common set of measures to quantify the dFC matrices properties. Moreover, the dFC field has been lacking ad-hoc algorithms to compute and process the matrices efficiently. This has prevented the field to show its full potential with high-dimensional datasets and/or real time applications.
Methods:
We introduce the Dynamic Symmetric Connectivity Matrix analysis framework (DySCo), with its associated repository. DySCo unifies in a single theoretical framework the dFC matrices above (and more), which share a common mathematical structure. Doing so it allows:
1) To use dFC as a tool to capture the spatiotemporal interaction patterns of data in a form that is easily translatable across different imaging modalities.
2) To define the DySCo measures, which quantify the properties and evolution of dFC in time: the amount of connectivity (norm), the similarity between matrices (distance), their informational complexity (entropy). By using and combining the DySCo measures it is possible to perform a full dFC analysis.
3) To leverage the Temporal Covariance EVD algorithm (TCEVD) to compute the measures in the eigenvector space instead of the matrix space. This is orders of magnitude faster and more memory efficient than algorithms working in the matrix space, without loss of information. This paves the way to voxelwise dFC.
1) To use dFC as a tool to capture the spatiotemporal interaction patterns of data in a form that is easily translatable across different imaging modalities.
2) To define the DySCo measures, which quantify the properties and evolution of dFC in time: the amount of connectivity (norm), the similarity between matrices (distance), their informational complexity (entropy). By using and combining the DySCo measures it is possible to perform a full dFC analysis.
3) To leverage the Temporal Covariance EVD algorithm (TCEVD) to compute the measures in the eigenvector space instead of the matrix space. This is orders of magnitude faster and more memory efficient than algorithms working in the matrix space, without loss of information. This paves the way to voxelwise dFC.
·the DySCo framework
Results:
Figure 1A summarizes the framework. The MATLAB + Python package is available at https://github.com/Mimbero/DySCo.
Fig. 1B showcases the computational speedup in an example typical dFC application: computing the Euclidean distance between matrices.
Figure 2 shows the application of sliding window correlation analysis to non-parcellated, voxelwise task (n-back) HCP data. The DySCo measures are sensitive to the evolution in time of whole-brain spatiotemporal patterns: the reconfiguration speed (speed of the evolution of correlation matrices) shows peaks at the switch between rest and task. The entropy timecourse (a measure of informational complexity of the dynamic matrices) is correlated to the task timecourse (p<0.001, R= 0.76). The FCD matrix (similarity in time of dynamic correlation matrices) reveals the temporal structure of the task.
Fig. 1B showcases the computational speedup in an example typical dFC application: computing the Euclidean distance between matrices.
Figure 2 shows the application of sliding window correlation analysis to non-parcellated, voxelwise task (n-back) HCP data. The DySCo measures are sensitive to the evolution in time of whole-brain spatiotemporal patterns: the reconfiguration speed (speed of the evolution of correlation matrices) shows peaks at the switch between rest and task. The entropy timecourse (a measure of informational complexity of the dynamic matrices) is correlated to the task timecourse (p<0.001, R= 0.76). The FCD matrix (similarity in time of dynamic correlation matrices) reveals the temporal structure of the task.
·Application to voxelwise fMRI
Conclusions:
We have proposed a generalized framework for dFC analyses.
This introduces a unique set of measures that are applicable to multiple dFC matrices. Our measures are validated on task fMRI data and provide tools to understand the evolution in time of spatiotemporal brain patterns.
The gain in computational speed is such that otherwise uncomputable matrices (more than 1 billion elements in our case) can be represented losslessly, and all the quantities of interest (norm, distance, entropy) recovered efficiently. This paves the way for parcellation-free analyses and real-time algorithms.
This introduces a unique set of measures that are applicable to multiple dFC matrices. Our measures are validated on task fMRI data and provide tools to understand the evolution in time of spatiotemporal brain patterns.
The gain in computational speed is such that otherwise uncomputable matrices (more than 1 billion elements in our case) can be represented losslessly, and all the quantities of interest (norm, distance, entropy) recovered efficiently. This paves the way for parcellation-free analyses and real-time algorithms.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling 2
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
Open-Source Code
Other - Functional Connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
Cabral, J., Vidaurre, D., Marques, P., Magalhães, R., Silva Moreira, P., Miguel Soares, J., Deco, G., Sousa, N., & Kringelbach, M. L. (2017). Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Scientific Reports, 7(1), Articolo 1. https://doi.org/10.1038/s41598-017-05425-7
Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., Handwerker, D. A., Keilholz, S., Kiviniemi, V., Leopold, D. A., de Pasquale, F., Sporns, O., Walter, M., & Chang, C. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage, 80, 360–378. https://doi.org/10.1016/j.neuroimage.2013.05.079
Zamani Esfahlani, F., Jo, Y., Faskowitz, J., Byrge, L., Kennedy, D. P., Sporns, O., & Betzel, R. F. (2020). High-amplitude cofluctuations in cortical activity drive functional connectivity. Proceedings of the National Academy of Sciences, 117(45), 28393-28401.