A generative framework for estimating brain energy landscapes from continuous neuroimaging observations.
6 affiliations
- 1Department of Statistics, Hankuk University of Foreign Studies, Yong-In, Republic of Korea.
- 2Graduate School of Medical Science, Brain Korea 21 Project, Department of Nuclear Medicine, Department of Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea.
- 3Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea.
- 4Department of Scientific Computing, Pukyong National University, Busan, Republic of Korea.
- 5Department of Cognitive Science, Yonsei University, Seoul, Republic of Korea.
- 6Brain Research Institute, Epilepsy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
Abstract
Pairwise maximum entropy models (pMEMs) are gaining increasing attention as a principled way to study nonlinear brain-state transitions. By representing each brain region as a binary active/inactive state and estimating the least-biased probability distribution over all such network configurations, pMEMs reconstruct an energy landscape in which local minima correspond to stable or metastable brain states and transitions reflect movement across attractor basins. However, neuroimaging recordings are continuous and spatially mixed, forcing conventional pMEM analyses to demean and hard-threshold each channel. This ad hoc binarization discards amplitude information and can conflate spatially correlated noise or source mixing with genuine neural coupling. We introduce a hierarchical generative pMEM that links latent binary network states to continuous observations. Node-specific baseline and gain parameters model amplitude modulation, compound-symmetry noise captures shared residual covariance, and, for electroencephalography/magnetoencephalography (EEG/MEG), the electromagnetic leadfield is included as a generative forward model rather than a preprocessing inverse. Model parameters and latent states are jointly estimated using exact posterior enumeration for the small networks analyzed here and a mean-field extension for larger networks. Controlled simulations demonstrated two complementary advantages of the proposed framework. Residual-covariance modeling improved recovery of pMEM fields, couplings, and latent states by accounting for shared fluctuations, with the clearest gains when residual spatial correlation was sufficiently strong. The generative leadfield model further improved sensor-space inference by avoiding inverse-first source reconstruction, including settings in which covariance modeling alone offered limited gains. Resting-state EEG validation showed higher residual correlation during eyes-closed than eyes-open rest, consistent with elevated and spatially coherent alpha activity. Applied further to propofol sedation EEG (alpha-band amplitude envelopes, 8-13 Hz), the fitted 6-node default mode network (DMN) model showed pipeline-dependent condition effects in residual spatial correlation and basin entropy, not apparent under the conventional demean-based pMEM.
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BibTeX
@article{jeong2026generative,
title = {{A generative framework for estimating brain energy landscapes from continuous neuroimaging observations.}},
author = {Seok-Oh Jeong and Euisun Kim and Jiyoung Kang and Jinseok Eo and Dongmyeong Lee and Hae-Jeong Park},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = oct,
doi = {10.1162/imag.a.1415},
url = {https://europepmc.org/article/MED/42953883},
note = {CC BY},
}
RIS
TY - JOUR
TI - A generative framework for estimating brain energy landscapes from continuous neuroimaging observations.
AU - Seok-Oh Jeong
AU - Euisun Kim
AU - Jiyoung Kang
AU - Jinseok Eo
AU - Dongmyeong Lee
AU - Hae-Jeong Park
PY - 2026
DA - 2026/10/27
JO - Imaging neuroscience (Cambridge, Mass.)
DO - 10.1162/imag.a.1415
UR - https://europepmc.org/article/MED/42953883
AB - Pairwise maximum entropy models (pMEMs) are gaining increasing attention as a principled way to study nonlinear brain-state transitions. By representing each brain region as a binary active/inactive state and estimating the least-biased probability distribution over all such network configurations, pMEMs reconstruct an energy landscape in which local minima correspond to stable or metastable brain states and transitions reflect movement across attractor basins. However, neuroimaging recordings are continuous and spatially mixed, forcing conventional pMEM analyses to demean and hard-threshold each channel. This ad hoc binarization discards amplitude information and can conflate spatially correlated noise or source mixing with genuine neural coupling. We introduce a hierarchical generative pMEM that links latent binary network states to continuous observations. Node-specific baseline and gain parameters model amplitude modulation, compound-symmetry noise captures shared residual covariance, and, for electroencephalography/magnetoencephalography (EEG/MEG), the electromagnetic leadfield is included as a generative forward model rather than a preprocessing inverse. Model parameters and latent states are jointly estimated using exact posterior enumeration for the small networks analyzed here and a mean-field extension for larger networks. Controlled simulations demonstrated two complementary advantages of the proposed framework. Residual-covariance modeling improved recovery of pMEM fields, couplings, and latent states by accounting for shared fluctuations, with the clearest gains when residual spatial correlation was sufficiently strong. The generative leadfield model further improved sensor-space inference by avoiding inverse-first source reconstruction, including settings in which covariance modeling alone offered limited gains. Resting-state EEG validation showed higher residual correlation during eyes-closed than eyes-open rest, consistent with elevated and spatially coherent alpha activity. Applied further to propofol sedation EEG (alpha-band amplitude envelopes, 8-13 Hz), the fitted 6-node default mode network (DMN) model showed pipeline-dependent condition effects in residual spatial correlation and basin entropy, not apparent under the conventional demean-based pMEM.
SN - 2837-6056
ER -