March 2012
Volume 53, Issue 3
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Visual Neuroscience  |   March 2012
Normalization of Visual Evoked Potentials Using Underlying Electroencephalogram Levels Improves Amplitude Reproducibility in Rats
Author Affiliations & Notes
  • Yuyi You
    From the Department of Ophthalmology, Australian School of Advanced Medicine, Macquarie University, Sydney, New South Wales, Australia; and
  • Johnson Thie
    From the Department of Ophthalmology, Australian School of Advanced Medicine, Macquarie University, Sydney, New South Wales, Australia; and
  • Alexander Klistorner
    From the Department of Ophthalmology, Australian School of Advanced Medicine, Macquarie University, Sydney, New South Wales, Australia; and
    the Save Sight Institute, Sydney University, Sydney, New South Wales, Australia.
  • Vivek K. Gupta
    From the Department of Ophthalmology, Australian School of Advanced Medicine, Macquarie University, Sydney, New South Wales, Australia; and
  • Stuart L. Graham
    From the Department of Ophthalmology, Australian School of Advanced Medicine, Macquarie University, Sydney, New South Wales, Australia; and
    the Save Sight Institute, Sydney University, Sydney, New South Wales, Australia.
  • Corresponding author: Yuyi You, Australian School of Advanced Medicine, Macquarie University, F10A, Level 1, 2 Technology Pl, North Ryde, Sydney, NSW 2109, Australia; yuyi.you@gmail.com
Investigative Ophthalmology & Visual Science March 2012, Vol.53, 1473-1478. doi:10.1167/iovs.11-8797
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      Yuyi You, Johnson Thie, Alexander Klistorner, Vivek K. Gupta, Stuart L. Graham; Normalization of Visual Evoked Potentials Using Underlying Electroencephalogram Levels Improves Amplitude Reproducibility in Rats. Invest. Ophthalmol. Vis. Sci. 2012;53(3):1473-1478. doi: 10.1167/iovs.11-8797.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: The visual evoked potential (VEP) is a frequently used noninvasive measurement of visual function. However, high-amplitude variability has limited its potential for evaluating axonal damage in both laboratory and clinical research. This study was conducted to improve the reliability of VEP amplitude measurement in rats by using electroencephalogram (EEG)-based signal correction.

Methods.: VEPs of Sprague-Dawley rats were recorded on three separate days within 2 weeks. The original VEP traces were normalized by EEG power spectrum, which was evaluated by Fourier transform. A comparison of intersession reproducibility and intersubject variability was made between the original and corrected signals.

Results.: Corrected VEPs showed lower amplitude intersession within-subject SD (Sw), coefficient of variation (CoV), and repeatability (R95) than the original signals (P < 0.001). The intraclass correlation coefficient (ICC) of the corrected traces (0.90) was also better than the original potentials (0.82). For intersubject variability, the EEG-based normalization improved the CoV from 44.64% to 30.26%. A linear correlation was observed between the EEG level and the VEP amplitude (r = 0.71, P < 0.0001).

Conclusions.: Underlying EEG signals should be considered in measuring the VEP amplitude. In this study, a useful technique was developed for VEP data processing that could also be used for other cortical evoked potential recordings and for clinical VEP interpretation in humans.

The visual evoked potential (VEP) is a measurement in visual electrophysiology that is frequently used as an objective means of evaluating the function of the visual pathway. It is understood to be generated at the level of the striate cortex by combined activity of postsynaptic potentials. 1 The VEP is an analogous phenomenon of auditory evoked potential (AEP) and somatosensory evoked potential (SEP), and all are electroencephalogram (EEG)-based signals that form the family of cortical evoked potentials (EPs). 2 The ongoing cortical activity, which has a major influence on sensory processing, should not be regarded as merely random background noise. 3 5 In a neurophysiologically based mathematical model, Jansen et al. 6 demonstrated that the spontaneous EEG and the flash VEP are generated by some of the same neural structures. 
It was suggested that the amplitude of the VEPs reflects the number of functional optic nerve fibers. Our previous work revealed a positive correlation between retinal nerve fiber layer thickness and VEP amplitude in optic neuritis patients. 7,8 We recently also demonstrated a strong linear relationship between the axonal loss and amplitude decrease in experimental optic nerve demyelination. 9 However, the relationship between axonal damage and amplitude was weaker than that between demyelination and latency delay, which may be due to the higher variability of the VEP amplitude. 9 The VEP has been extensively used in pharmacologic experiments and in assessing the neuroprotective effects of compounds, as it provides a means of monitoring neural activity and sensory processing in vivo. 10 The reproducibility of VEP amplitude is critical if the parameter is used to evaluate nerve functional change or neuronal apoptosis over time in some optic nerve disease models. 11  
Rats are the most common species used in laboratory VEP studies because of their accessibility and short growth cycle. 12 We have recently improved the reproducibility of VEP measurements in rats by using a modified electrode configuration, a novel mini-Ganzfeld visual stimulator, and early-peak analysis. 13 However, the variability of the amplitude was still relatively high, which may limit the use of the VEP in assessing axonal damage. The variability of VEP amplitude has been attributed to many factors in both animals and humans, such as cortical anatomy, 14 the conductivity of the electrodes and underlying tissues, 13 strain and age, 15 stimulus design, 13,16 and general level of cortical activity. 17 19 Some factors (e.g., electrode configuration and stimulus intensity) are easy to rule out by using a standardized recording protocol. However, the ongoing cortical activity is very difficult to control during different recording sessions. As mentioned, the VEP is an EEG-based signal, and we have reported the effect of EEG on multifocal VEP in humans. 20  
In this study, we scaled the original VEP traces using the ongoing EEG power spectrum level to minimize the effect of brain activity and to improve the reliability of VEP amplitude measurement, as just described. We repeatedly performed the VEP recording in rats by using a well-established protocol 9,13 and analyzed the intersession reproducibility and intersubject variability of the original and corrected VEPs, respectively. The purpose of this study was to explore a reliable data-processing technique to improve VEP amplitude reproducibility for laboratory research in animal models. The technique could also be considered for clinical VEP interpretation in humans. 
Methods
Animals
Twenty-five male Sprague-Dawley rats with a body weight of 300 to 350 g (10–12 weeks; Animal Research Centre, Perth, Western Australia, Australia) were used. All animals were maintained in an air-conditioned room with controlled temperature (21 ± 2 °C) and fixed daily 12-hour light/dark cycles. All procedures involving animals were conducted in accordance with the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes and the guidelines of the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. The animals were anesthetized with an intraperitoneal injection of ketamine (75 mg/kg) and medetomidine (0.5 mg/kg) for both surgery and electrophysiology recording. 
VEP Recording
Stainless steel skull screws (Micro Fasteners, Melbourne, Australia) were implanted through the skull into the visual cortex (7 mm behind the bregma and 3 mm lateral of the midline) as the positive electrodes. A reference screw electrode was placed on the midline, 3 mm rostral to the bregma. Dental cement (Rapid Repair; DeguDent GmbH, Hanau, Germany) was used to fix the screws. The skin of the head was closed, and at least 1 week was allowed for the animals to recover from the surgery. 
VEP recording was performed on the same eye (randomly selected) on three separate days within 2 weeks. The animals were anesthetized, placed in a dark room, and allowed to adapt to darkness for 5 minutes (we have recently confirmed the adequacy of such a short adaptation time using our methodology). 9,13 The body temperature was maintained at 37°C by a homoeothermic blanket system with a rectal thermometer probe (Harvard Apparatus; Holliston, MA). The pupils were dilated with 1.0% tropicamide eyedrops (Alcon Laboratories, Fort Worth, TX). The skin over the skull was opened. The positive screw over the contralateral visual cortex of the stimulated eye and the reference screw were connected to the amplifier using hook clips. A needle electrode (F-E3M-72; Grass, Warwick, RI) was inserted into the tail as the ground. The electrode impedance was measured (F-EZM5 Impedance Meter; Grass) and maintained below 5 kΩ. Visual stimuli were generated by a mini-Ganzfeld stimulator (3 cd · s/m2), and the photic stimulation was delivered 100 times at a frequency of 1 Hz. The time of measurement was 200 ms, and the sampling rate was 5000 samples/s. Responses were amplified 20,000 times and filtered by band-pass filter, with low and high cutoff frequencies of 1 and 100 Hz, respectively (BMA-400 Bioamplifier, CWE, Inc., Ardmore, PA). After the recording, the wound was closed and antibiotics administered. 
EEG-Based Correction
Fourier transform was used to quantify the amplitude of the underlying EEG during each recording session. The Fourier coefficients of the EEG sequence, x[n], were evaluated on the entire 100-second sequence. Let X[m] denote the Fourier coefficient, defined as   The Fourier transform was performed by MatLab Function FFT with M = 213 (The MathWorks, Inc., Natick MA). The choice of M allows the use of the Fast Fourier transform algorithm and ensures that the frequency interval is at most 1 Hz/point. (The sampling frequency was 5000 samples per second.) 
The amplitude of EEG was evaluated according to 30-Hz band power   where a and b are the indexes that correspond to 1 and 30 Hz, respectively (Fig. 1). The VEP traces were then corrected by the following formula:   where VEPC and VEPO represent corrected VEP and original VEP traces, respectively; EEGAVER is the average of EEGs from all the subjects; and EEGIND is the EEG recorded from an individual rat in a particular recording session. 
Figure 1.
 
(A) Raw EEG sequence (100 seconds). (B) Corresponding EEG power spectrum (Fast Fourier transform, 1–30 Hz).
Figure 1.
 
(A) Raw EEG sequence (100 seconds). (B) Corresponding EEG power spectrum (Fast Fourier transform, 1–30 Hz).
Data Analysis
The largest peak-to-trough amplitudes 20 for each VEP wave within the initial 100-ms interval were determined and used for amplitude analysis. Intersession reproducibility (recordings on three separate days) was determined by calculating the intersession within-subject SD (Sw), coefficient of variation (CoV), repeatability (R95), 21,22 and intraclass correlation coefficient (ICC). 23 The day 1 data were also used to evaluate intersubject variability, which was determined by between-subject SD (Sb) and intersubject CoV. The comparison of intersession reproducibility between original and corrected VEP signals was made by paired t-test. The F-test was used for the between-subject standard deviations. The correlation between EEG and VEP amplitudes was also examined by linear regression analysis. Statistical significance was defined as P < 0.05 (Prism, ver. 5.0; GraphPad, San Diego, CA). 
Results
Intersession Reproducibility
Figure 2 presents the original and corrected VEP traces from an individual rat and the corresponding EEG signals on 3 days. The original VEP amplitude on day 2 was smaller than those on days 1 and 3 (Fig. 2B), and the corresponding EEG amplitude on day 2 was also the smallest (Fig. 2A). The intersession reproducibility parameters are summarized in Table 1. The rectified VEPs showed significant lower amplitude Sw, CoV, and R95 than the original VEPs. The CoV value improved from 24.09% (original) to 16.49% (corrected). The intersession ICCs were 0.82 for the original and 0.90 for the corrected signals, respectively. 
Figure 2.
 
(A) Underlying EEG signals from an individual rats on three separate days. (B) Intersession original VEP traces. (C) Corrected VEP traces based on the corresponding EEG signals.
Figure 2.
 
(A) Underlying EEG signals from an individual rats on three separate days. (B) Intersession original VEP traces. (C) Corrected VEP traces based on the corresponding EEG signals.
Table 1.
 
Comparison of Sw, CoV, R95, and ICC between Original and EEG-Corrected VEP Amplitudes
Table 1.
 
Comparison of Sw, CoV, R95, and ICC between Original and EEG-Corrected VEP Amplitudes
Sw (μV) CoV (%) R95 (μV) ICC
Original 10.28 (6.25–14.30) 24.09 (14.65–33.53) 28.46 (17.31–39.62) 0.82 (0.65–0.92)
Corrected 7.31 (4.44–10.17) 16.49 (10.02–22.95) 20.24 (12.30–28.17) 0.90 (0.80–0.95)
P 0.001* <0.001* 0.001*
Intersubject Variability
Figure 3 shows the representative VEP traces from two individual rats before and after amplitude correction. The final VEP amplitudes of these two rats became similar after EEG-based normalization (Fig. 3C). The amplitudes of original and corrected VEPs from all the rats are plotted in Figure 4. The amplitude Sb was 21.21 μV for the raw traces and only 14.12 μV for the rectified VEPs (P = 0.02). Similarly, the intersubject CoV decreased from 44.64% to 30.26% after EEG-based correction. 
Figure 3.
 
(A) Underlying EEGs from two individual rats. (B) Original VEP traces from these two rats. (C) VEP traces after EEG-based correction.
Figure 3.
 
(A) Underlying EEGs from two individual rats. (B) Original VEP traces from these two rats. (C) VEP traces after EEG-based correction.
Figure 4.
 
Scatterplot of original and corrected VEP amplitudes. The amplitudes from original VEPs are more variable. Error bars, SEM.
Figure 4.
 
Scatterplot of original and corrected VEP amplitudes. The amplitudes from original VEPs are more variable. Error bars, SEM.
Correlation between EEG Level and VEP Amplitude
The linear regression analysis between VEP amplitude and the ongoing EEG power spectrum level is shown in Figure 5. A fairly high r value (r = 0.71, P < 0.0001) was observed between the amplitudes of these two cortical potentials. 
Figure 5.
 
Linear regression analysis between original VEP amplitudes and corresponding EEG levels (Fourier transform power spectrum; r = 0.71, P < 0.0001).
Figure 5.
 
Linear regression analysis between original VEP amplitudes and corresponding EEG levels (Fourier transform power spectrum; r = 0.71, P < 0.0001).
Discussion
The VEP has been used to assess visual performance in humans and in a wide variety of animals. 12 VEP recording in rats has been well described in studying the visual plasticity and in evaluating the visual system integrity. 11,24 26 Recently, we have optimized the VEP recording protocol and designed a mini-Ganzfeld flash stimulator 13 that provided superior eye isolation to assess the optic nerve conduction in an optic neuritis model. 9 In the present study, we developed an EEG-based signal processing technique, seeking to improve the reproducibility of VEP amplitude measurement in rats. It was suggested that the amplitude of VEP reflects the axonal damage in the visual pathway. Therefore, the intersession reproducibility is very important in VEP experiments, because chronological signals need to be compared to detect changes in optic nerve fibers. 
The generation of VEPs is related to both visual stimulation and intrinsic activity in the neuronal networks of the brain. The intrinsic activity has a larger influence on cortical activity patterns than visual stimuli, even in the primary visual cortex. 4 Therefore, the amplitude of VEP is rather small compared with that of the ongoing EEG. The external stimulation has to be repeated, usually 100 times, to minimize the noise from EEG, and it is estimated that after 100 repetitions, the signal-to-noise ratio is increased 10-fold. 2 The effect of sleep–awake states on rat VEP waveforms has been established by Meeren et al. 19 The normalization of the original VEP signal in relation to the underlying EEG can compensate for the effect of higher cortical activity on VEP amplitude. In animal research, different types of anesthetics and the level of anesthesia can affect the measurement of VEP parameters. 27,28 This effect is also potentially minimized using the EEG-based correction, as the background brain activity is standardized. 
The conductivity of the electrodes and the underlying tissue also has significantly impacts on the VEP amplitude. In rat recording with implanted screws, a major factor is the connection between hook clips and skull screw electrodes, which can be affected by adjacent blood clots and surrounding connective tissue edema. The impedance between the positive and negative electrodes should be routinely measured and controlled below 5 kΩ (usually, 2–3 kΩ). 29 However, when the resistance of the interface between screws and hook clips increases, there is less current flow (I) through the amplifier, and thereby the input voltage to the amplifier is reduced (Fig. 6). Therefore, a poor electrode connection leads to smaller VEP amplitudes. In theory, using the underlying EEG-based correction eliminates this electrode conductivity factor. 
Figure 6.
 
Schematic circuit diagram representing the effect of electrode conductivity on evoked potential amplitude. R B, the internal resistance of the rat's brain between positive and negative screws; R CS, the resistance of skull screws plus the resistance of the joint between screws and hook clips (ideally 0); R C, the resistance of hook clips and wires; and R CC, the external resistance of the rat's skull between hook clips. R C is very small and is approximately 0, because the hook clips are made of copper. The total impedance is Ω ≈ (R B + R CS1 + R CS2) × R CC/(R B + R CS1 + R CS2 + R CC) < 5 kΩ. According to Ohm's law, the measured EP amplitude VEP = V × R CC/(R B + R CS1 + R CS2 + R CC), where V is the real potential in the brain between positive and negative electrodes. Under an ideal recording condition, R B, R CS1, R CS2R CC, ∴ VEP ≈ V.
Figure 6.
 
Schematic circuit diagram representing the effect of electrode conductivity on evoked potential amplitude. R B, the internal resistance of the rat's brain between positive and negative screws; R CS, the resistance of skull screws plus the resistance of the joint between screws and hook clips (ideally 0); R C, the resistance of hook clips and wires; and R CC, the external resistance of the rat's skull between hook clips. R C is very small and is approximately 0, because the hook clips are made of copper. The total impedance is Ω ≈ (R B + R CS1 + R CS2) × R CC/(R B + R CS1 + R CS2 + R CC) < 5 kΩ. According to Ohm's law, the measured EP amplitude VEP = V × R CC/(R B + R CS1 + R CS2 + R CC), where V is the real potential in the brain between positive and negative electrodes. Under an ideal recording condition, R B, R CS1, R CS2R CC, ∴ VEP ≈ V.
Indeed, in the present study, we observed a significant improvement in the intersession reproducibility of the VEP amplitude after normalizing the raw signals (Fig. 2; Table 1). The CoV dropped from approximately 25% to 15%. This VEP analysis technique would be very useful in visual electrophysiological experiments, as it improves the reliability of data interpretation. However, the CoV of the amplitude after EEG-based correction was still higher than that of the latency (CoV ≈ 5%) from a previous study, 13 which indicates that some other cortical activity aspects (e.g., different rhythms of the EEG) may also play a role in affecting the amplitude measurement. 
The higher intersubject variability of VEP amplitude has limited its use in electrophysiological research, as it may be necessary to include a large sample to achieve statistical significance (e.g., P < 0.05). Our current results showed that after the EEG-based correction the intersubject variability also significantly improved (Figs. 3, 4), and the intersubject CoV decreased from 44.64% to 30.26%. The intersubject variability in VEP may also be due to the various levels of general brain activity and the electrode conductivity as described above, which can be standardized using the EEG-based correction. However, some other factors, such as age 15,30,31 and individual cortical anatomy, 12,14 that also contribute to intersubject variability cannot be eliminated. The positive linear correlation (Fig. 5) between the EEG and VEP amplitudes observed in this study was in accordance with the results from some previous studies of evoked potentials in both animals and humans, 4,18,20 which supported the assumption that similar influences account for amplitude variability of these two cortical potentials. 20  
In conclusion, we developed a practical technique for VEP data processing and interpretation. We observed a strong correlation between the VEP and the ongoing EEG amplitudes and significantly improved the intersession reproducibility and intersubject variability of the VEP amplitude measurement. We highly recommend that underlying EEG signals be considered in measuring the VEP amplitude. EEG-based VEP amplitude correction would be very useful, not only for laboratory research, but also for clinical VEP interpretation. Furthermore, as the VEP is a cortical evoked potential analogous to AEP and SEP, we hypothesize that this technique has potential for use in improving the reliability of other EP recordings. 
Footnotes
 Supported by the Ophthalmic Research Institute of Australia (ORIA) and the National Multiple Sclerosis Society (NMSS). AK was supported by the Sydney Foundation for Medical Research.
Footnotes
 Disclosure: Y. You, None; J. Thie, None; A. Klistorner, None; V.K. Gupta, None; S.L. Graham, None
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Figure 1.
 
(A) Raw EEG sequence (100 seconds). (B) Corresponding EEG power spectrum (Fast Fourier transform, 1–30 Hz).
Figure 1.
 
(A) Raw EEG sequence (100 seconds). (B) Corresponding EEG power spectrum (Fast Fourier transform, 1–30 Hz).
Figure 2.
 
(A) Underlying EEG signals from an individual rats on three separate days. (B) Intersession original VEP traces. (C) Corrected VEP traces based on the corresponding EEG signals.
Figure 2.
 
(A) Underlying EEG signals from an individual rats on three separate days. (B) Intersession original VEP traces. (C) Corrected VEP traces based on the corresponding EEG signals.
Figure 3.
 
(A) Underlying EEGs from two individual rats. (B) Original VEP traces from these two rats. (C) VEP traces after EEG-based correction.
Figure 3.
 
(A) Underlying EEGs from two individual rats. (B) Original VEP traces from these two rats. (C) VEP traces after EEG-based correction.
Figure 4.
 
Scatterplot of original and corrected VEP amplitudes. The amplitudes from original VEPs are more variable. Error bars, SEM.
Figure 4.
 
Scatterplot of original and corrected VEP amplitudes. The amplitudes from original VEPs are more variable. Error bars, SEM.
Figure 5.
 
Linear regression analysis between original VEP amplitudes and corresponding EEG levels (Fourier transform power spectrum; r = 0.71, P < 0.0001).
Figure 5.
 
Linear regression analysis between original VEP amplitudes and corresponding EEG levels (Fourier transform power spectrum; r = 0.71, P < 0.0001).
Figure 6.
 
Schematic circuit diagram representing the effect of electrode conductivity on evoked potential amplitude. R B, the internal resistance of the rat's brain between positive and negative screws; R CS, the resistance of skull screws plus the resistance of the joint between screws and hook clips (ideally 0); R C, the resistance of hook clips and wires; and R CC, the external resistance of the rat's skull between hook clips. R C is very small and is approximately 0, because the hook clips are made of copper. The total impedance is Ω ≈ (R B + R CS1 + R CS2) × R CC/(R B + R CS1 + R CS2 + R CC) < 5 kΩ. According to Ohm's law, the measured EP amplitude VEP = V × R CC/(R B + R CS1 + R CS2 + R CC), where V is the real potential in the brain between positive and negative electrodes. Under an ideal recording condition, R B, R CS1, R CS2R CC, ∴ VEP ≈ V.
Figure 6.
 
Schematic circuit diagram representing the effect of electrode conductivity on evoked potential amplitude. R B, the internal resistance of the rat's brain between positive and negative screws; R CS, the resistance of skull screws plus the resistance of the joint between screws and hook clips (ideally 0); R C, the resistance of hook clips and wires; and R CC, the external resistance of the rat's skull between hook clips. R C is very small and is approximately 0, because the hook clips are made of copper. The total impedance is Ω ≈ (R B + R CS1 + R CS2) × R CC/(R B + R CS1 + R CS2 + R CC) < 5 kΩ. According to Ohm's law, the measured EP amplitude VEP = V × R CC/(R B + R CS1 + R CS2 + R CC), where V is the real potential in the brain between positive and negative electrodes. Under an ideal recording condition, R B, R CS1, R CS2R CC, ∴ VEP ≈ V.
Table 1.
 
Comparison of Sw, CoV, R95, and ICC between Original and EEG-Corrected VEP Amplitudes
Table 1.
 
Comparison of Sw, CoV, R95, and ICC between Original and EEG-Corrected VEP Amplitudes
Sw (μV) CoV (%) R95 (μV) ICC
Original 10.28 (6.25–14.30) 24.09 (14.65–33.53) 28.46 (17.31–39.62) 0.82 (0.65–0.92)
Corrected 7.31 (4.44–10.17) 16.49 (10.02–22.95) 20.24 (12.30–28.17) 0.90 (0.80–0.95)
P 0.001* <0.001* 0.001*
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