Exploring neural oscillatory mechanisms underlying flexible whole-brain communication
Poster No:
1314
Submission Type:
Abstract Submission
Authors:
Varun Madan Mohan1, Thomas Varley2, Robin Cash1, Caio Seguin3, Andrew Zalesky4
Institutions:
1University of Melbourne, Melbourne, VIC, 2University of Vermont, Burlington, VT, 3University of Melbourne, Melbourne, Victoria, 4The University of Melbourne and Melbourne Health, Melbourne, VIC
First Author:
Co-Author(s):
Introduction:
The brain relies on quick, accurate, and flexible communication between regions to support healthy function. The brain's routing mechanism is highly complex, with several processes dynamically modulating communication, including attention, task/cognitive demands, and neural oscillations (Griffa et al., 2017). Knowledge of this mechanism is key to understanding brain network dynamics, and is central to the design of improved clinical interventions.
Neural oscillations have long been considered a likely basis of flexible communication, and multiple routing theories based on oscillatory power, coherence, and resonance have been proposed over the years (Bonnefond et al., 2017; Fries, 2005; Hahn et al., 2014; Jensen & Mazaheri, 2010). There is, however, a lack of consensus on what principles underlie flexible communication, particularly at the whole-brain scale.
Here, we aimed to explore the neural-oscillatory principles governing communication between brain regions. To this end, we applied a recently established measure called Event-marked Windowed Communication (EWC) (Madan Mohan et al., 2024) to infer putative communication events from MEG data, and tested whether the spatiotemporal propagation of these events can be explained by neural oscillatory mechanisms – specifically the power and coherence. We anticipated a non-uniform relationship between the oscillatory features and activity propagation across regions, possibly stemming from heterogeneities in regional oscillatory profiles.
Neural oscillations have long been considered a likely basis of flexible communication, and multiple routing theories based on oscillatory power, coherence, and resonance have been proposed over the years (Bonnefond et al., 2017; Fries, 2005; Hahn et al., 2014; Jensen & Mazaheri, 2010). There is, however, a lack of consensus on what principles underlie flexible communication, particularly at the whole-brain scale.
Here, we aimed to explore the neural-oscillatory principles governing communication between brain regions. To this end, we applied a recently established measure called Event-marked Windowed Communication (EWC) (Madan Mohan et al., 2024) to infer putative communication events from MEG data, and tested whether the spatiotemporal propagation of these events can be explained by neural oscillatory mechanisms – specifically the power and coherence. We anticipated a non-uniform relationship between the oscillatory features and activity propagation across regions, possibly stemming from heterogeneities in regional oscillatory profiles.
Methods:
Dataset
Resting-state MEG scans for 30 healthy subjects (13 males, age 22-35), were obtained from the Human Connectome Project (HCP) (Larson-Prior et al., 2013; Van Essen et al., 2013), source localised using the dynamical-SPM method in Brainstorm, and parcellated using the Schaefer 7-Network 100 region atlas (Niso et al., 2019). We used a group average probabilistic tractography SC of 1000 HCP subjects, retaining only the top 15% edges, to define anatomically connected regions (Fig.1A).
EWC
The communication between a source and all its anatomically connected neighbours ("targets") was inferred using EWC. Briefly, EWC defines communication windows near salient "events" in neural recordings, and estimates source-target FC within these windows. In this way, EWC captures the statistical analogue of activity propagation between neural elements (Fig.1A).
Neural-oscillatory measures
Within the same communication windows as above, the target power in the theta, alpha, beta, and gamma bands was estimated (Fig.2A). The Intersite Phase Coherence (ISPC) in the same bands was also computed from respective band-pass filtered phase time series (Fig.2B).
Resting-state MEG scans for 30 healthy subjects (13 males, age 22-35), were obtained from the Human Connectome Project (HCP) (Larson-Prior et al., 2013; Van Essen et al., 2013), source localised using the dynamical-SPM method in Brainstorm, and parcellated using the Schaefer 7-Network 100 region atlas (Niso et al., 2019). We used a group average probabilistic tractography SC of 1000 HCP subjects, retaining only the top 15% edges, to define anatomically connected regions (Fig.1A).
EWC
The communication between a source and all its anatomically connected neighbours ("targets") was inferred using EWC. Briefly, EWC defines communication windows near salient "events" in neural recordings, and estimates source-target FC within these windows. In this way, EWC captures the statistical analogue of activity propagation between neural elements (Fig.1A).
Neural-oscillatory measures
Within the same communication windows as above, the target power in the theta, alpha, beta, and gamma bands was estimated (Fig.2A). The Intersite Phase Coherence (ISPC) in the same bands was also computed from respective band-pass filtered phase time series (Fig.2B).
Results:
We observed spatial heterogeneity in EWC's dependence with target power in all frequency bands, suggesting a location-specific routing mechanism (Fig.2A). For instance, communication from posterior sources was found to be maximally dependent on target alpha power, and inversely proportional to gamma power, whereas anterior sources displayed little dependence on either measure.
Similar heterogeneities were observed when we explored interareal communication's dependence on ISPC (Fig.2B). Interestingly, in most brain regions, we noticed that coherence in the alpha, beta, and high gamma bands seemed to influence communication more strongly than the theta and low gamma bands. Additionally, the cortical distributions of EWC-ISPC correlations in the alpha and beta bands seemed to overlap with their typical power spectral distributions. This suggests that local power spectra may play a role in determining which frequency bands might be most viable for coherence-based communication.
Similar heterogeneities were observed when we explored interareal communication's dependence on ISPC (Fig.2B). Interestingly, in most brain regions, we noticed that coherence in the alpha, beta, and high gamma bands seemed to influence communication more strongly than the theta and low gamma bands. Additionally, the cortical distributions of EWC-ISPC correlations in the alpha and beta bands seemed to overlap with their typical power spectral distributions. This suggests that local power spectra may play a role in determining which frequency bands might be most viable for coherence-based communication.
Conclusions:
In this study, we explored the validity of oscillation-based communication theories at the whole-brain scale. We find that a single oscillatory feature fails to fully capture activity propagation of all regions, suggesting that the principles of flexible communication in the brain are regionally specific.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Task-Independent and Resting-State Analysis 2
Other Methods
Keywords:
Computational Neuroscience
Data analysis
MEG
Modeling
Other - Communication
1|2Indicates the priority used for review
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Provide references using APA citation style.
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Madan Mohan, V. (2024). Event-marked Windowed Communication: Inferring activity propagation from neural time series. - Preprint
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