Imaging regional changes in the spontaneous activity of the brain: an extension of the minimum-norm least-squares estimate.
This paper describes methods for inferring mathematically unique local distributions of primary cortical current that underly changes in the average pattern of power of the ongoing ("spontaneous") extracranial magnetic field of the brain. In previous work we demonstrated that mathematically unique solutions to the inverse problem are possible for current sources of the brain's field, without assuming a small set of current dipoles as a source model. In principle, it is possible to locate and delineate patterns of current of any configuration. In practice this approach applies to synchronized neuronal activity, e.g., activity which is known to underly average evoked or event-related brain responses. This paper extends that approach to local changes in incoherent activity, e.g., activity yielding fields or potentials that tend to be self-cancelling when averaged over time. This includes the spontaneous brain activity normally treated as background noise when it accompanies event-related responses. We demonstrate that local changes in this ongoing incoherent activity may also be uniquely delineated in space and time. The solution is a covariance matrix characterizing activity across an image surface. Its diagonal elements represent the spatial pattern of mean current power. Evidence is reviewed indicating that the distribution of the brain's magnetic field, due to both its synchronized and incoherent neural activity, is affected by early sensory-perceptual processes and by higher cognitive processes. Hence, in principle, the ability to delineate both kinds of sources in space and time makes it possible to form more comprehensive dynamic functional images of the human brain.
Wang
J Z
JZ
Department of Physics, New York University, NY 10003.
Kaufman
L
L
Wiliamson
S J
SJ
D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D006801:Humans; D016018:Least-Squares Analysis; D008280:Magnetics; D008959:Models, Neurological