1. A system for classifying neurophysiologic activity of a first subject, the system comprising: a data input configured to receive brain activity data corresponding to an idle state of the brain of the first subject, the brain activity data representing a time series of neurophysiologic activity acquired by a sensor system arranged to detect spatial and temporal neural signaling in the subject utilizing magnetic fields produced in a multiplicity of brain regions; and a processor communicatively coupled to the data input, and programmed to: process each set of brain activity data to produce a corresponding dynamic model of neural activity representing time-dependent coupling between neural populations of the brain of the first subject, including: processing the brain activity data to produce a prewhitened time series; computing partial cross correlations of the prewhitened time series to produce estimates of strength and sign of signaling between groups of the multiplicity of brain regions representing interactions of neural populations, based on an analysis of covariance of at least one type of partial cross correlations selected from the group consisting of: (a) positive partial cross correlations of the partial cross correlations of the prewhitened time series, and (b) negative partial cross correlations of the partial cross correlations of the prewhitened time series; performing a classification of the partial cross correlations to produce a measure of correlation of the brain activity data to validated reference data corresponding to a plurality of different neurophysiologic conditions.