Neural Field Theory Based Modelling of Cortico-Hippocampal Dynamics
Poster No:
1550
Submission Type:
Abstract Submission
Authors:
Richa Phogat1, Anna Behler2, Saurabh Sonkusare3, James Pang4, Nikitas Koussis5, James Roberts6, Jordan DeKraker7, Alex Fornito4, Peter Robinson8, Michael Breakspear9
Institutions:
1The University of Newcastle,, Newcastle, NSW, Australia, 2The University of Newcastle, Newcastle, NSW, 3The University of Newcastle, Australia, Newcastle, Australia, 4Monash University, Clayton, Victoria, 5University of Newcastle, New Lambton Heights, NSW, 6QIMR Berghofer Medical Research Institute, Brisbane, QLD, 7McGill University, Montreal, QC, 8The University of Sydney, Sydney, Australia, 9The University of Newcastle, New Lambton Heights, NSW
First Author:
Co-Author(s):
Introduction:
Cortico-hippocampal interactions are fundamental to cognitive processes as well as neurological disorders, yet the mechanisms underlying their coordinated dynamics remain poorly understood. We address this knowledge gap by modelling the cortex and the hippocampus as interconnected neural sheets, incorporating geometrical and physiological principles in their dynamical and connectivity profiles. We do this by developing a computational model of cortico-thalamic-hippocampal dynamics using Neural Field Theory (NFT) framework.
Methods:
Our model considers the cortex and the hippocampus as interconnected 2D neural sheets, coupled via direct cortico-hippocampal interactions while each independently forms a feedback loop with the thalamus (Fig 1a). The cortical and hippocampal dynamics are each decomposed into eigenmodes representing spatial patterns of activity on their sheets. This eigenmode decomposition allows complex dynamics to be reduced to fundamental spatio-temporal oscillatory patterns. Reciprocal cortico-hippocampal coupling was implemented using an exponentially decaying proximity-based scheme which preserves local neighbourhood continuity, an important physical constraint. This was achieved by mapping cortical and hippocampal surfaces to a common Cartesian space using quasi-conformal mapping, ensuring a distance-preserving interaction matrix (Fig 1b-g).
Results:
Considered in isolation (with coupling set to zero), cortico-thalamic and hippocampal-thalamic systems, show canonical brain rhythms including alpha, beta, and theta rhythms. Introducing cortico-hippocampal coupling pushes the system as a whole toward a critical state, triggering state transitions that do not arise in the two isolated systems. The cortico-hippocampal coupling also leads to mode mixing, facilitating the transfer of frequencies between the cortex and the hippocampus. We tested these predictions against intracranial electroencephalographic data from human patients with epilepsy ( Fig 2). Empirical seizures show a frequency transitions during seizure progression, from high-frequency oscillations at seizure onset to low-frequency theta-band activity and slow waves at termination. Similar seizure onsets and frequency transitions occur in the in silico seizure simulated with corticohippocampal NFT.
Conclusions:
This corticohippocampal NFT provides a novel biophysical model to study cortical-hippocampal interactions in health and during seizures. Our work demonstrates that cortico-hippocampal coupling introduces state transitions and mode-mixing which are observed in intracranial EEG. The mode-mixing facilitated by the cortico-hippocampal coupling, along with gain amplification in the cortico-thalamic-hippocampal loop can destabilize the neural activity, providing an explanation for the frequent involvement of the hippocampus in seizure activity. Hence, this work offers fundamental insights into brain rhythms and lays the groundwork for understanding broader cortical-subcortical interactions in health and disease.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Methods Development 1
Other Methods
Keywords:
Computational Neuroscience
1|2Indicates the priority used for review
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Provide references using APA citation style.
Pang, J. C. (2023). Geometric constraints on human brain function. Nature, 618(7965), 566-574.
Rennie, C. J. (1999). Effects of local feedback on dispersion of electrical waves in the cerebral cortex. Physical Review E, 59(3), 3320.