Visual evoked potentials and multiple sclerosis

Abstract

Conventional use of the visual evoked potential (VEP) in the diagnosis of multiple sclerosis (MS) has relied almost entirely on one measure, the latency of the major surface-positive component (P100) wave. This measure, however, reflects a combination of excitatory and inhibitory cortical processes, and does not provide a simple estimate of transmission delay. In the present study, a detailed method of analysis was developed to explore additional response parameters and to compare response measures of MS patients with age-similar controls.;VEP responses were elicited by square-wave contrast-reversal of a checkerboard pattern from ten patients with definite MS and 12 controls. Discrete Fourier analysis was applied to filter transient VEPs in the frequency domain. An inverse Fourier transform reconstructed the time domain waveform, characterized by a series of amplitude and latency measures (P60, N80, and P100).;Frequency spectra for amplitude and phase measures were plotted for all controls and MS patients. Phase functions were split into low (2-12 Hz), middle (14-26 Hz), and high (32-40 Hz) frequency bands, with separate linear regression analyses applied to provide estimates of "apparent delay." In addition, power of the response {dollar}\rm(\mu V\sp2){dollar} was calculated for these frequency bands.;The filtered VEP waveforms yielded a measurable P100 for all controls and for 90% of MS patients. The P60 and N80 peak measurements which traditionally have not been analyzed, were measured reliably for all controls and 80% of MS patients. Monocular latencies and interocular latency differences of the P60, N80, and P100 peak measurements were significantly prolonged in the MS group.;Phase spectra of the transient VEPs exhibited distinct linear segments in the low, middle, and high frequency bands. Middle-frequency delay measures (14-26 Hz) were found to differ significantly between the two groups. Multivariate statistical analyses were used to evaluate the aggregate of measures. The data generated linear composite measures which may prove to enhance diagnostic capabilities.;It is suggested that this detailed form of analysis be applied in future research, as these novel approaches may prove to be valuable for clinical diagnosis and monitoring of patients with MS.