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Linking brain dynamics to neuromodulatory systems using multiscale data and computational modelling

Poster No:

1224

Submission Type:

Abstract Submission

Authors:

Saurabh Sonkusare¹, Kartik Iyer², Richa Phogat³, Johan van der Meer², Luke Hearne⁴, Sasha Dionisio⁵, Michael Breakspear³

Institutions:

¹The University of Newcastle, Australia, Newcastle, Australia, ²QIMR Berghofer, Brisbane, Australia, ³The University of Newcastle, New Lambton Heights, NSW, ⁴QIMR Berghofer Medical Research Institute, Herston, Queensland, ⁵Department of Neurosciences, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia, Riyadh, Saudi Arabia

First Author:

Saurabh Sonkusare
The University of Newcastle, Australia
Newcastle, Australia

Co-Author(s):

Kartik Iyer
QIMR Berghofer
Brisbane, Australia

Richa Phogat, PhD
The University of Newcastle
New Lambton Heights, NSW

Johan van der Meer
QIMR Berghofer
Brisbane, Australia

Luke Hearne
QIMR Berghofer Medical Research Institute
Herston, Queensland

Sasha Dionisio
Department of Neurosciences, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia
Riyadh, Saudi Arabia

Michael Breakspear, PhD
The University of Newcastle
New Lambton Heights, NSW

Introduction:

The brain's dynamics continuously fluctuate between states of functional integration and segregation, reflecting processes operating over multiple time scales. Two key neuromodulatory brainstem nuclei-the locus coeruleus (LC) (adrenergic) and the nucleus basalis of Meynert (NBM) (cholinergic)-are hypothesized to regulate these integration and segregation states through their widespread and sparse projections across cortical and subcortical systems, respectively. However, direct empirical evidence supporting this hypothesis is lacking. Here, we leverage heart rate-modulated by the adrenergic-cholinergic balance-as a physiological proxy to connect neuromodulatory activity with brain integration and segregation states. We employ a movie-watching paradigm to evoke naturalistic brain responses with separate intracranial EEG (iEEG) and fMRI data acquisition experiments. To bridge these modalities, we use computational modeling to transform iEEG local field potentials (LFPs) into BOLD responses via the Balloon-Windkessel model, enabling validation of the fMRI-derived findings.

·Figure1. A) Neuromodulatory nuclei linked to integration-segregation states B) Data modalities used in this study C) Analytical approach D) Hypothesis

Methods:

We acquired iEEG data from 12 patients with epilepsy and fMRI data from 18 healthy participants while they watched a 20-minute unedited emotional movie, The Butterfly Circus.Study schema shown in Fig 1.
iEEG Analysis:Data was segmented into 5s epochs ensuring each segment had 3-4 heart beats and heart rate computed for each segment. We computed connectivity matrices high-frequency broadband activity (60–140 Hz, measure of local neuronal firing) of gray matter channels. 5 sec epochs were used. Network properties related to integration (global efficiency) and segregation (modularity) from connectivity matrices of each epoch were then obtained.
fMRI Analysis:Standard preprocessing pipelines (van der Meer et al., 2020) were applied. BOLD signals were extracted using an atlas comprising 216 cortical and subcortical regions. Sliding window analysis (~30 seconds) was used to compute dynamic connectivity matrices. Network properties of integration (global efficiency) and segregation (modularity) from connectivity matrices of each epoch were obtained. Heart rate (HR) data corresponding to each TR was derived using the TAPAS toolbox, and dynamic HR signals were similarly segmented with sliding windows.

Group mean correlations were computed to find associations between the integration and segregation time series and their corresponding heart rate. fMRI results were validated on an open-sourcemovie-watching fMRI dataset. Additionally, iEEG signals were transformed into BOLD responses using the Balloon-Windkessel model. This allowed us to replicate the fMRI-based analyses and assess the impact of the inherently slow BOLD signal on its relationship with heart rate.

Results:

Dynamic integration states were positively correlated with HR, and segregation states were negatively correlated with HR (Figure 2A). 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 low heart rate and segregation states associated with high heart rate (Figure 2B). This was validated with an open access fMRI movie watching dataset (Figure 2C). Work on computational modelling is currently in progress.

·Figure1. A) iEEG integration states linked with high heart rate, segregation states linked with low heart rate B) fMRI results showing integration linked with low heart rate and vice versa

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 of LC and NBM. However, we found opposing pattern of results with fMRI which was validated with another fMRI dataset. These opposing findings from iEEG and fMRI highlight the complex relationship between the cortical activity, BOLD signals and cardiac homeostasis. Results from computational modelling may reveal if the opposing pattern of results we found with fMRI are inherent due to the slow nature of haemodynamic BOLD responses.

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural) ¹

fMRI Connectivity and Network Modeling ²

Novel Imaging Acquisition Methods:

BOLD fMRI

EEG

Imaging Methods Other

Keywords:

Acetylcholine

Adrenaline

Computational Neuroscience

ELECTROCORTICOGRAPHY

FUNCTIONAL MRI

Modeling

^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.

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:

Functional MRI

Computational modeling

Other, Please specify - intracranial EEG

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

3.0T

Provide references using APA citation style.

Shine, J. M. (2019). Neuromodulatory influences on integration and segregation in the brain. Trends in cognitive sciences, 23(7), 572-583.

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