Temporally constraining source imaging estimates in an underdetermined neural system with eigenmodes of cortical geometry
Abstract
Geometric eigenmodes provide a compact and biologically grounded representation of large-scale neural activity. Previous work demonstrated that they can mitigate the underdetermined nature of electroencephalographic (EEG) and magnetoencephalographic (MEG) source localisation, an ill-posed inverse problem in which neural activity is reconstructed from non-invasive recordings. Beyond their spatial structure, neural field theory predicts the temporal evolution of eigenmodes through analytically derived transfer functions. Motivated by this framework, the present work investigates whether these transfer functions can be used to introduce temporal constraints into EEG source imaging. The approach is evaluated using simulated seizure dynamics generated by coupled Epileptor neural mass models. Transfer functions derived directly from neural field theory were found to be generally ineffective as temporal constraints for source localisation, primarily because they neglect cross-eigenmode coupling. Incorporating empirically estimated coupling terms substantially improves localisation performance, particularly in noisy conditions. Although estimating these eigenmode coupling interactions from experimental data remains challenging, the findings motivate dynamical source imaging approaches that combine spatial eigenmode structure with empirically informed cross-modal dynamics.
Related
- EigenBrain maps cortical molecular architecture from functional, geometric, and structural eigenmodes
- Certified Scalable Enclosures for Uncertain Underdetermined Systems
- Neural dynamics of reconstructing knowledge structure from temporally disordered inputs
- Population structure and genetic relatedness of temporally diverse Mycoplasma bovis strains circulating in Europe
- Spatio-temporally regulated maternal RNA decay factor essential for zebrafish development
BibTeX
@misc{siu2026temporally,
title = {{Temporally constraining source imaging estimates in an underdetermined neural system with eigenmodes of cortical geometry}},
author = {Pok Him Siu and Philippa J. Karoly and Artemio Soto-Breceda and Mark J. Cook and David B. Grayden},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.00809},
eprint = {2609.00809},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.00809},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Temporally constraining source imaging estimates in an underdetermined neural system with eigenmodes of cortical geometry
AU - Pok Him Siu
AU - Philippa J. Karoly
AU - Artemio Soto-Breceda
AU - Mark J. Cook
AU - David B. Grayden
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.00809
UR - https://arxiv.org/abs/2609.00809
AB - Geometric eigenmodes provide a compact and biologically grounded representation of large-scale neural activity. Previous work demonstrated that they can mitigate the underdetermined nature of electroencephalographic (EEG) and magnetoencephalographic (MEG) source localisation, an ill-posed inverse problem in which neural activity is reconstructed from non-invasive recordings. Beyond their spatial structure, neural field theory predicts the temporal evolution of eigenmodes through analytically derived transfer functions. Motivated by this framework, the present work investigates whether these transfer functions can be used to introduce temporal constraints into EEG source imaging. The approach is evaluated using simulated seizure dynamics generated by coupled Epileptor neural mass models. Transfer functions derived directly from neural field theory were found to be generally ineffective as temporal constraints for source localisation, primarily because they neglect cross-eigenmode coupling. Incorporating empirically estimated coupling terms substantially improves localisation performance, particularly in noisy conditions. Although estimating these eigenmode coupling interactions from experimental data remains challenging, the findings motivate dynamical source imaging approaches that combine spatial eigenmode structure with empirically informed cross-modal dynamics.
ER -