Biophysically Principled Computational Neural Network Modeling of Magneto-/Electro-Encephalography Measured Human Brain Oscillations
Brain rhythms are the most prominent signal measured noninvasively in humans with magneto-/electro-encephalography (MEG/EEG). MEG/EEG measured rhythms have been shown to be functionally relevant and signature changes are used as markers of disease states. Despite the importance of understanding the underlying neural mechanisms creating these rhythms, relatively little is known about their in vivo origin in humans. There are obvious challenges in linking the extracranially measured signals directly to neural activity with invasive studies in humans, and although animal models are well suited for such studies, the connection to human brain function under cognitively relevant tasks is often lacking. Biophysically principled computational neural modeling provides an attractive means to bridge this critical gap. Here, we describe a method for creating a computational neural model capturing the laminar structure of cortical columns and how this model can be used to make predictions on the cellular and circuit level mechanisms of brain oscillations measured with MEG/EEG. Specifically, we describe how the model can be used to simulate current dipole activity, the common macroscopic signal inferred from MEG/EEG data. We detail the development and application of the model to study the spontaneous somatosensory mu-rhythm, containing mu-alpha (7–14 Hz) and mu-beta (15–29 Hz) components. We describe a novel prediction on the neural origin on the mu-rhythm that accurately reproduces many characteristic features of MEG data and accounts for changes in the rhythm with attention, detection, and healthy aging. While the details of the model are specific to the somatosensory system, the model design and application are based on general principles of cortical circuitry and MEG/EEG physics, and are thus amenable to the study of rhythms in other frequency bands and sensory systems.
Key wordsComputational modeling MEG imaging Alpha rhythm Beta rhythm Current dipole
I thank my long-term close collaborator Dr. Christopher Moore whose insight was crucial to shaping the concepts for this work. Dr. Moore’s expertise in somatosensory neural dynamics was essential to model development, MEG experimental design, and interpretation of simulated and experimental data. I thank Drs. Matti Hamalainen and Steve Stufflebeam for expertise in MEG methods and analysis and theoretical developments to compare MEG and model results, Seppo Ahlfors for Figure 4. Mike Sikora for consultant work on model implementation and analysis in NEURON. Dorea Vierling-Claasen for thoughtful reading of manuscript and further model extension. Dominique Pritchett for implementation of MEG experimental paradigms and Michael Halassa for preliminary testing of model predictions with optogenetic techniques in rodents. This work was supported by National Institute of Mental Health Grant K25-MH-072941, and the Athinoula A. Martinos Center for Biomedical Imaging, Mass. General Hospital.
- 1.Berger H (1969) On the electroencephalogram of man. Electroencephalogr Clin Neurophysiol 28(Suppl):37Google Scholar
- 20.Vierling-Claassen D, Cardin J, Moore CI et al (2010) Computational modeling of distinct neocortical oscillations driven by cell-type selective optogenetic drive: separable resonant circuits controlled by low-threshold spiking and fast-spiking interneurons. Front Hum Neurosci 4:198PubMedCrossRefGoogle Scholar
- 59.Jones SR, Halassa M, Pritchett DL et al (2010) A neocortical beta origin hypothesis: computational modeling, electrophysiological, and optogentic studies. Program No. 683. 2010 Neuroscience Meeting Planner. Society for Neuroscience, San Diego, CAGoogle Scholar