Oscillatory response function: towards a parametric model of rhythmic brain activity.

Rhythmic brain activity, measured by magnetoencephalography (MEG), is modulated during stimulation and task performance. Here, we introduce an oscillatory response function (ORF) to predict the dynamic suppression-rebound modulation of brain rhythms during a stimulus sequence. We derived a class of parametric models for the ORF in a generalized convolution framework. The model parameters were estimated from MEG data acquired from 10 subjects during bilateral tactile stimulation of fingers (stimulus rates of 4 Hz and 10 Hz in blocks of 0.5, 1, 2, and 4 s). The envelopes of the 17-23 Hz rhythmic activity, computed for sensors above the rolandic region, correlated 25%-43% better with the envelopes predicted by the models than by the stimulus time course (boxcar). A linear model with separate convolution kernels for onset and offset responses gave the best prediction. We studied the generalizability of this model with data from 5 different subjects during a separate bilateral tactile sequence by first identifying neural sources of the 17-23 Hz activity using cortically constrained minimum norm estimates. Both the model and the boxcar predicted strongest modulation in the primary motor cortex. For short-duration stimulus blocks, the model predicted the envelope of the cortical currents 20% better than the boxcar did. These results suggest that ORFs could concisely describe brain rhythms during different stimuli, tasks, and pathologies.