摘要
The spherical head model has been widely used in magnetoencephalography (MEG) as a simple forward model for calculating the external magnetic field producing by neural currents in a human brain. But this model may lead to an inaccurate result, even if the computation speed is fast. For more precise computation, realistic brain-shaped head model is used with the boundary element method (BME), but at greatly increased computational cost. When solving MEG inverse problem by using optimization methods, the forward problem must often be solved for thousands of possible source configurations. So if the brain-shaped head model is used in all iterative steps of optimization, it may be computationally infeasible for practical application. In this paper, we present a method about using compound head model in MEG inverse solution. In this method, first spherical head model is used for a rough estimation, then brain-shaped head model is adopted for more precise solution. Numerical simulation indicates that under the condition of same accuracy, the computation speed for the present method is about three times faster than a method using the brain-shaped head model at all iterations.
The spherical head model has been widely used in magnetoencephalography (MEG) as a simple forward model for calculating the external magnetic field producing by neural currents in a human brain. But this model may lead to an inaccurate result, even if the computation speed is fast. For more precise computation, realistic brain-shaped head model is used with the boundary element method (BME), but at greatly increased computational cost. When solving MEG inverse problem by using optimization methods, the forward problem must often be solved for thousands of possible source configurations. So if the brain-shaped head model is used in all iterative steps of optimization, it may be computationally infeasible for practical application. In this paper, we present a method about using compound head model in MEG inverse solution. In this method, first spherical head model is used for a rough estimation, then brain-shaped head model is adopted for more precise solution. Numerical simulation indicates that under the condition of same accuracy, the computation speed for the present method is about three times faster than a method using the brain-shaped head model at all iterations.
基金
This work is supported by the National Natural Science Foundation of China(No.30 0 0 0 0 34 )