Dynamic network properties from mesoscale iEEG and macroscale BOLD signals link heart rate changes
Poster No:
1801
Submission Type:
Abstract Submission
Authors:
Saurabh Sonkusare1, Kartik Iyer2, Johan van der Meer2, Vin Thai Nguyen2, Christine Guo3, Sasha Dionisio4, Michael Breakspear5
Institutions:
1University of Cambridge, Cambridge, United Kingdom, 2QIMR Berghofer Medical Research Medical Research Institute, Brisbane, Australia, 3QIMR Berghofer Medical Research Institute, Brisbane, Australia, 4Advanced Epilepsy Unit, Mater Centre for Neurosciences, Mater Hospitals, Brisbane, Australia, 5University of Newcastle, Newcastle, N/A
First Author:
Co-Author(s):
Sasha Dionisio
Advanced Epilepsy Unit, Mater Centre for Neurosciences, Mater Hospitals
Brisbane, Australia
Advanced Epilepsy Unit, Mater Centre for Neurosciences, Mater Hospitals
Brisbane, Australia
Introduction:
The brain's ability to rapidly adapt derives from the flexible reconfiguration of its functional networks (Sporns, Chialvo, Kaiser, & Hilgetag, 2004). The global trade-off between functional integration versus segregation reflects changes in physiological processes including arousal, which in turn depend on ascending neuromodulatory effects acting over multiple (short and long) time scales. However, the association of these dynamic network properties to fluctuations in bodily physiology such as changes in the heart rate (HR) has not been well quantified. Heart rate is modulated by the adrenergic-cholinergic balance has also been proposed to be linked to the neurotransmitter related brain states of integration and segregation (Shine, 2019) and thus provides a good physiological measure to link dynamic network properties.
Methods:
Here, we acquired data from two modalities 1) high-fidelity intracranial-EEG recordings from 12 patients undergoing clinical epilepsy evaluation, and 2) functional magnetic resonance imaging (fMRI) data from 18 healthy subjects. Schema of the study approach shown in Figure 1. The participants in both these datasets watched a short unedited emotional movie "The Butterfly Circus" while their respective data was acquired. For iEEG data, we first segment ECG data corresponding to the movie viewing data into 5 second epochs to account for variability in heart rate changes across patients. Subsequently, iEEG high frequency broad band activity (60-140 Hz), a metric of local neuronal firing and which has been found to be correlated wit the BOLD signal, of all the grey matter channels was computed and which was segmented into 5 second epochs. Adjacency matrices were constructed for each 5 second epochs, network properties of integration (global efficiency) and segregation (modularity) properties were obtained for each epoch. For fMRI data, standard pre-processing was employed (van der Meer et al., 2020) and then we extracted BOLD signals using AAL atlas. Subsequently, we used sliding window (~30 seconds) method to construct adjacency matrices and thresholding the matrices to obtain network properties of integration and segregation. Heart rate data corresponding to each TR was obtained using the TAPAS toolbox similar sliding windows used to obtain dynamic HR signal. For testing associations between HR and network properties, we calculated the group mean of the Pearson correlation between HR and integration and HR and segregation. To generate the null distribution, permutation testing with randomly circular shifting the data was used.
·Figure 1. Schema for analytical approach and hypothesis a) data from iEEG and fMRI (using AAL parcellation) b) High frequency activity for iEEG data used to construct networks c)hypothesis
Results:
We find dynamic integration states were negatively correlated with HR (r = .14, p<.0001), and segregation states were positively correlated, with HR (r = -.09, p<.0001) (Figure 2 top). In other words, integration or widespread communication was associated with higher heart rate and segregation states associated with lower heart rate. Notably, in fMRI data acquired from the participants viewing the same movie, we find the opposite pattern of results i.e. integration states associated with high heart rate (r = -.17, p<.0001) and segregation states associated with low hear rate (r = .15, p<.0001) (Figure 2 bottom).
·Figure 2. Association of HR with integration and segregation. Top - iEEG, bottom - fMRI
Conclusions:
We established with direct neuronal activity that integrations states were linked with higher heart rate and segregation states linked with lower heart rate. Thus our results also indirectly link brain network states to activity in neuromodulatory systems. However we found opposing pattern of results with with fMRI. These opposing findings from iEEG and fMRI highlight the complex relationship between the cortical activity, BOLD signals and cardiac homeostasis.
Emotion, Motivation and Social Neuroscience:
Emotional Perception
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
Imaging Methods Other 2
Keywords:
ELECTROPHYSIOLOGY
FUNCTIONAL MRI
Other - local field potentials, iEEG
1|2Indicates the priority used for review
Provide references using author date format
2) Shine, J. M. (2019). Neuromodulatory influences on integration and segregation in the brain. Trends in cognitive sciences, 23(7), 572-583
3) van der Meer, J. N., Breakspear, M., Chang, L. J., Sonkusare, S., & Cocchi, L. (2020). Movie viewing elicits rich and reliable brain state dynamics. Nature communications, 11(1), 1-14