Estimation of neurophysiological parameters from the waking EEG using a biophysical model of brain dynamics

• Published in: Journal of theoretical biology Vol. 231; no. 3; pp. 413 - 433
• Main Authors: Rowe, Donald. L., Robinson, Peter. A., Rennie, Christopher. J.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 07-12-2004
• Subjects: Awareness; Biophysical Phenomena; Biophysics; Brain - physiology; Cortex; Electroencephalography; Humans; Models, Neurological; Network; Physiology; Thalamocortical; Physiology; Thalamocortical; Biophysics; Cortex; Network
• ISSN: 0022-5193; 1095-8541
• DOI: 10.1016/j.jtbi.2004.07.004

This paper presents the results from using electroencephalographic (EEG) data to estimate the values of key neurophysiological parameters using a detailed biophysical model of brain activity. The model incorporates spatial and temporal aspects of cortical function including axonal transmission delays, synapto-dendritic rates, range-dependent connectivities, excitatory and inhibitory neural populations, and intrathalamic, intracortical, corticocortical and corticothalamic pathways. Parameter estimates were obtained by fitting the model's theoretical spectrum to EEG spectra from each of 100 healthy human subjects. Statistical analysis was used to infer significant parameter variations occurring between eyes-closed and eyes-open states, and a correlation matrix was used to investigate links between the parameter variations and traditional measures of quantitative EEG (qEEG). Accurate fits to all experimental spectra were observed, and both inter-subject and between-state variability were accounted for by the variance in the fitted biophysical parameters, which were in turn consistent with known independent experimental and theoretical estimates. These values thus provide physiological information regarding the state. transitions (eyes-closed vs. eyes-open) and phenomena including cortical idling and alpha desynchronization. The parameters are also consistent with traditional qEEG, but are more informative, since they provide links to underlying physiological processes. To our knowledge, this is the first study where a detailed biophysical model of the brain is used to estimate neurophysiological parameters underlying the transitions in a broad range (0.25–50Hz) of EEG spectra obtained from a large set of human data.