B Q Rosen1, G P Krishnan2, P Sanda3, M Komarov4, T Sejnowski5, N Rulkov6, I Ulbert7, L Eross8, J Madsen9, O Devinsky10, W Doyle11, D Fabo12, S Cash13, M Bazhenov14, E Halgren15. 1. Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States. Electronic address: bqrosen@ucsd.edu. 2. Department of Medicine, University of California, San Diego, La Jolla, CA, United States. Electronic address: gkrishnan@ucsd.edu. 3. Department of Medicine, University of California, San Diego, La Jolla, CA, United States; Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic. Electronic address: psanda@ucsd.edu. 4. Department of Medicine, University of California, San Diego, La Jolla, CA, United States. Electronic address: maxim.a.komarov@gmail.com. 5. Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; The Salk Institute, La Jolla, CA, United States. Electronic address: terry@salk.edu. 6. BioCiruits Institute, University of California, San Diego, La Jolla, CA, United States. Electronic address: nrulkov@ucsd.edu. 7. Institute of Cognitive Neuroscience and Psychology, Hungarian Academy of Science, Budapest, Hungary; Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, Budapest, Hungary. Electronic address: ulbert.istvan@ttk.mta.hu. 8. Faculty of Information Technology and Bionics, Peter Pazmany Catholic University, Budapest, Hungary; Department of Functional Neurosurgery, National Institute of Clinical Neurosciences, Budapest, Hungary. Electronic address: eross@oiti.hu. 9. Departments of Neurosurgery, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States. Electronic address: joseph.madsen@childrens.harvard.edu. 10. Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, United States. Electronic address: od4@nyu.edu. 11. Comprehensive Epilepsy Center, New York University School of Medicine, New York, NY, United States. Electronic address: wkd1@nyu.edu. 12. Epilepsy Centrum, National Institute of Clinical Neurosciences, Budapest, Hungary. Electronic address: dfabo@partners.org. 13. Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; Department of Medicine, University of California, San Diego, La Jolla, CA, United States; Departments of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. Electronic address: scash@mgh.harvard.edu. 14. Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; Department of Medicine, University of California, San Diego, La Jolla, CA, United States. Electronic address: bazhenov@salk.edu. 15. Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, United States; Department of Radiology, University of California, San Diego, La Jolla, CA, United States; Department of Neurosciences, University of California, San Diego, La Jolla, CA, United States. Electronic address: ehalgren@ucsd.edu.
Abstract
BACKGROUND: Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain. METHOD: We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors. RESULTS: The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted. COMPARISON WITH EXISTING METHODS: Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales. CONCLUSIONS: This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.
BACKGROUND: Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain. METHOD: We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors. RESULTS: The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted. COMPARISON WITH EXISTING METHODS: Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales. CONCLUSIONS: This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.