MEG-based imaging of focal neuronal current sources

• Published in: IEEE transactions on medical imaging Vol. 16; no. 3; pp. 338 - 348
• Main Authors: Phillips, J.W., Leahy, R.M., Mosher, J.C.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.1997; Institute of Electrical and Electronics Engineers
• Subjects: Bayes Theorem; Bayesian methods; Biological and medical sciences; Brain - physiology; Brain modeling; Current measurement; Electrodiagnosis. Electric activity recording; Humans; Image processing; Image Processing, Computer-Assisted; Inverse problems; Investigative techniques, diagnostic techniques (general aspects); Magnetic field measurement; Magnetoencephalography; Medical sciences; Nervous system; Phantoms, Imaging; Signal processing; SQUIDs; Superconducting device noise; Superconducting magnets; Bayes estimation; Test objet; Magnetoencephalography; Central nervous system; Signal processing; Brain (vertebrata); Neurophysiology; Inverse problem
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/42.585768

The authors describe a new approach to imaging neural current sources from measurements of the magnetoencephalogram (MEG) associated with sensory, motor, or cognitive brain activation. Many previous approaches to this problem have concentrated on the use of weighted minimum norm (WMN) inverse methods. While these methods ensure a unique solution, they do not introduce information specific to the MEG inverse problem, often producing overly smoothed solutions and exhibiting severe sensitivity to noise. The authors describe a Bayesian formulation of the inverse problem in which a Gibbs prior is constructed to reflect the sparse focal nature of neural current sources associated with evoked response data. The authors demonstrate the method with simulated and experimental phantom data, comparing its performance with several WMN methods.