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3-dimensional velocity fields of neural dynamics reveal singularities and propagation pathways

Poster No:

1333

Submission Type:

Abstract Submission

Authors:

Miao Cao¹, Jiayi Liu², Wei Cui², Xiongfei Wang³, Simon Vogrin¹, Jia-hong Gao², David White⁴, Chris Plummer⁵, Will Woods¹

Institutions:

¹Swinburne University of Technology, Melbourne, Victoria, ²Peking University, Beijing, Beijing, ³Capital Medical University, Beijing, Beijing, ⁴Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University of T, Melbourne, Victoria, ⁵Swinburne University of Technology, Melbourne, Vicrtoria

First Author:

Miao Cao
Swinburne University of Technology
Melbourne, Victoria

Co-Author(s):

Jiayi Liu
Peking University
Beijing, Beijing

Wei Cui
Peking University
Beijing, Beijing

Xiongfei Wang
Capital Medical University
Beijing, Beijing

Simon Vogrin
Swinburne University of Technology
Melbourne, Victoria

Jia-hong Gao
Peking University
Beijing, Beijing

David White, A/Prof
Centre for Mental Health and Brain Sciences, School of Health Sciences, Swinburne University of T
Melbourne, Victoria

Chris Plummer
Swinburne University of Technology
Melbourne, Vicrtoria

Will Woods
Swinburne University of Technology
Melbourne, Victoria

Introduction:

Modelling neural dynamics, characterized by fast-evolving physiological and pathological activity, across multiple spatial-temporal scales is crucial to understand the underlying mechanisms that give rise to such phenomena. High dimensionality of neural recordings and complexity of fast-evolving spatio-temporal patterns remain as major challenges for computational models. Our previous attempts have enabled a unique combination of neuroimaging and neural mass models to temporospatially delineate the ictogenic brain regions that are responsible for seizure generation.

Methods:

Here, we extended the previously proposed framework (Cao et al., 2022) to three-dimensional velocity fields of the whole-brain MEG/EEG source space including cortical and subcortical structures.

Results:

We revealed the field singularities and dynamical patterns that pinpoint and track the underlying functional pathways of pathological and physiological dynamics (including visual and auditory activity).

Conclusions:

Such findings may lead to better understanding of brain's function-structure coupling as well as clinical applications, including identifying epileptogenic brain regions and preserving eloquent cortex for brain tumour and epilepsy surgery.

Modeling and Analysis Methods:

EEG/MEG Modeling and Analysis ¹

Methods Development ²

Keywords:

Computational Neuroscience

Data analysis

Electroencephaolography (EEG)

Epilepsy

MEG

Modeling

Neurological

^1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

Resting state

Task-activation

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Patients

Was this research conducted in the United States?

Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

EEG/ERP

MEG

Structural MRI

For human MRI, what field strength scanner do you use?

3.0T

Which processing packages did you use for your study?

Free Surfer

Provide references using APA citation style.

Cao, M., Galvis, D., Vogrin, S. J., Woods, W. P., Vogrin, S., Wang, F., … Cook, M. J. (2022). Virtual intracranial EEG signals reconstructed from MEG with potential for epilepsy surgery. Nature Communications, 13(1), 994. doi: 10.1038/s41467-022-28640-x

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