Scientific article

Improved Characterization of Visual Evoked Potentials in Multiple Sclerosis by Topographic Analysis

Published inBrain topography, vol. 27, no. 2, p. 318-327
Publication date2014

In multiple sclerosis (MS), the combination of visual, somatosensory and motor evoked potentials (EP) has been shown to be highly correlated with the Expanded Disability Severity Scale (EDSS) and to predict the disease course. In the present study, we explored whether the significance of the visual EP (VEP) can be improved with multichannel recordings (204 electrodes) and topographic analysis (tVEP). VEPs were analyzed in 83 MS patients (median EDSS 2.0; 52 % with history of optic neuritis; hON) and 47 healthy controls (HC). TVEP components were automatically defined on the basis of spatial similarity between the scalp potential fields (topographic maps) of single subjects' VEPs and reference maps generated from HC. Non-ambiguous measures of latency, amplitude and configuration were derived from the maps reflecting the P100 component. TVEP was compared to conventional analysis (cVEP) with respect to reliability in HC, validity using descriptors of logistic regression models, and sensitivity derived from receiver operating characteristics curves. In tVEP, reliability tended to be higher for measurement of amplitude (p = 0.06). Regression models on diagnosis (MS vs. HC) and hON were more favorable using tVEP- versus cVEP-predictors. Sensitivity was increased in tVEP versus cVEP: 72 % versus 60 % for diagnosis, and 88 % versus 77 % for hON. The advantage of tVEP was most pronounced in pathological VEPs, in which cVEPs were often ambiguous. TVEP is a reliable, valid, and sensitive method of objectively quantifying pathological VEP in particular. In combination with other EP modalities, tVEP may improve the monitoring of disease course in MS.

  • Swiss National Science Foundation - 132952
Citation (ISO format)
HARDMEIER, Martin et al. Improved Characterization of Visual Evoked Potentials in Multiple Sclerosis by Topographic Analysis. In: Brain topography, 2014, vol. 27, n° 2, p. 318–327. doi: 10.1007/s10548-013-0318-6
Main files (1)
Article (Published version)
ISSN of the journal0896-0267

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