title: Oscillatory response function: towards a parametric model of rhythmic brain activity.
abstract: 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.
authors: Brain Research Unit, Low Temperature Laboratory, Helsinki University of Technology, 02015 TKK, Finland. pavan@neuro.hut.fi
authors: Pavan
authors: P
authors: Ramkumar
authors: Lauri
authors: L
authors: Parkkonen
authors: Riitta
authors: R
authors: Hari
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D005260:Female; D006801:Humans; D016014:Linear Models; D015225:Magnetoencephalography / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D010507:Periodicity; D010812:Physical Stimulation; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D055698:Touch Perception / Q000502:physiology; D055815:Young Adult