The measure of randomness by leave-one-out prediction error in the analysis of EEG after laser painful stimulation in healthy subjects and migraine patients.
OBJECTIVE
We aimed to perform a quantitative analysis of event-related modulation of EEG activity, resulting from a not-warned and a warned paradigm of painful laser stimulation, in migraine patients and controls, by the use of a novel analysis, based upon a parametric approach to measure predictability of short and noisy time series.


METHODS
Ten migraine patients were evaluated during the not-symptomatic phase and compared to seven age and sex matched controls. The dorsum of the right hand and the right supraorbital zone were stimulated by a painful CO(2) laser, in presence or in absence of a visual warning stimulus. An analysis time of 1s after the stimulus was submitted to a time-frequency analysis by a complex Morlet wavelet and to a cross-correlation analysis, in order to detect the development of EEG changes and the most activated cortical regions. A parametric approach to measure predictability of short and noisy time series was applied, where time series were modeled by leave-one-out (LOO) error.


RESULTS
The averaged laser-evoked potentials features were similar between the two groups in the alerted and not alerted condition. A strong reset of the beta rhythms after the painful stimuli was seen for three groups of electrodes along the midline in patients and controls: the predictability of the series induced by the laser stimulus changed very differently in controls and patients. The separation was more evident after the warning signal, leading to a separation with P-values of 0.0046 for both the hand and the face.


CONCLUSIONS
As painful stimulus causes organization of the local activity in cortex, EEG series become more predictable after stimulation. This phenomenon was less evident in migraine, as a sign of an inadequate cortical reactivity to pain.


CONCLUSIONS
The LOO method enabled to show in migraine subtle changes in the cortical response to pain.
de Tommaso
M
M
TIRES-Center of Innovative Technologies for Signal Detection and Processing, University of Bari, Italy. m.detommaso@neurol.uniba.it
Marinazzo
D
D
Stramaglia
S
S
D000328:Adult; D001611:Beta Rhythm; D005074:Evoked Potentials, Visual; D005260:Female; D006801:Humans; D007834:Lasers; D008297:Male; D020326:Migraine without Aura / Q000175:diagnosis / Q000503:physiopathology*; D008959:Models, Neurological; D010146:Pain / Q000175:diagnosis / Q000503:physiopathology*; D011237:Predictive Value of Tests; D011930:Reaction Time / Q000502:physiology