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Beyond habituation: long-term repetition effects on visual event-related potentials in epileptic patients
ELSEVIER
Electroencephalography and clinical Neurophysiology103 (1997) 450-456
Beyond habituation: long-term repetition effects on visual event-related potentials in epileptic patients B. Kotchoubey a'*, D. Schneider b, C. Uhlmann c, H. Schleichert a, N. B i r b a u m e r a,d alnstitute of Medical Psychology and Behavioral Neurobiology, University of Tiibingen, Gartenstr. 29, 72074 Tiibingen, Germany bEpilepsy Center Kork, Kehl-Kork, Germany CFederal Center for Psychiatry, Ravensburg, Germany dUniversita degli Studi, Padova, Italy
Accepted for publication: 11 March 1997
Abstract Sixteen patients with partial epilepsy learned to produce positive or negative slow cortical potential shifts in a biofeedback condition during 20 consecutive training sessions. Visual ERPs to the presentation of the feedback and the discriminative stimulus were recorded at vertex. Regardless of the subjects' task (positivity versus negativity), amplitudes of the P2 (mean peak latency about 225 ms) and P3a (322 ms) components decreased across sessions, resulting in appearance and subsequent enhancement of a negative wave N2 (298 ms) between P2 and P3a. As N2 grew the P2 latency decreased and the P3a latency increased. Additionally, the P3b (472 ms) decreased with repetition, however, it did so slower than P2 and P3a. A comparison between the present data, on the one hand, and those obtained in the ERP habituation paradigm within one session, on the other hand, indicates that some repetition effects cannot be explained by habituation. © 1997 Elsevier Science Ireland Ltd. Keywords: Event-related potentials; Epilepsy; Habituation; Repetition; N2 wave; P3 wave
1. Introduction The most fundamental form of the modification of behavior is the change of a response with repeated presentation of an event. Usually, the magnitude of physiological responses decreases with repetition, which is referred to as habituation. Further, Thompson and Spencer (1966) developed a set of criteria to distinguish between the true habituation and similar states, such as adaptation or fatigue, where response decrements are also observed In humans, habituation of event-related potentials (ERPs) has been extensively studied within the last decades as a direct index of information processing in the intact brain (e.g. Frustorfer, 1971; Megela and Teyler, 1979; Sokolov, 1990). Two different processes have been described. In the
* Corresponding author. Fax: +49 7071 295956; e-mail: [email protected]
typical paradigm, trains of stimuli are presented with short interstimulus intervals and considerably longer inter-train intervals. Change of ERP component latencies within trains are referred to as short-term habituation, whereas changes between trains are referred to as long-term habituation. In both cases, decrease of the N1 (N100, N140) component has been found as a hallmark of the ERP habituation (e.g. Frustorfer, 1971; Lutzenberger et al., 1979; Ohman and Lader, 1972; Rust, 1977; Megela and Teyler, 1979; Kenemans et al., 1989). Another habituating component is P3 (P300) (Courchesne, 1978; Megela and Teyler, 1979; Ivey and Schmidt, 1993; Geisler and Polich, 1994), although its short-term habituation has been questioned by some authors (Polich and Mclsaac, 1994). Yet more long-term changes which do not happen between consecutive trains, but between sessions or days, have been much less investigated (e.g. R6sler and Manzey, 1986; Jodo and Inoue, 1990). However, such data might be of special interest, since most important human learning processes do not take place within one session. Rather,
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modification of human behavior occurs in a day-to-day manner. Funding is difficult to find for carrying out long trains of ERP experiments with normal subjects. In the present work, we studied ERP changes in epileptic patients who learned to regulate their slow cortical waves with the help of a biofeedback technique.
2. M e t h o d s
Sixteen epileptic patien~ts (7 males, aged 21-44, mean seizure history of 22.8 years), took part in the training program. The prevailing type of seizures was complex partial seizures, with the mean seizure frequency varying from 3 to 415 per month. Eight patients had temporal foci (5, right side), and two other patients had frontal foci (both, right side). In the remaining 6 patients, the focus was not localized exactly. All patient were characterized by good cognitive abilities (mean IQ 107.4, no sign of disturbance of frontal lobe functions, assessed by means of the Wisconsin Card Sort Test). Patients with any kind of non-epileptic seizures, or progressive neurological or psychiatric processes, were excluded. A]~I patients were medicated with one or two anti-convulsant substances. The medication regime was stable during the entire period of study, as well as at least 5 months prior to its beginning. With each patient, 20 biofeedback training sessions were carried out over 2 weeks. Each session consisted of 145 trials. In most trials, subjects learned to produce a slow potential shift while con~tinuous feedback was provided (feedback trials). In other trials, they had to produce these shifts without feedback (transfer trials). The percentage of transfer trials varied from 30% to 58%, according to individual performance. A feedback trial began with the presentation of a letter 'A' or ' B ' on a screen of a computer monitor together with a stylized rocket, whose position on the screen reflected the actual EEG potential amplitude referred to a baseline of 2 s prior to the appearance of the stimuli. Depending upon the letter ( ' A ' or 'B'), subjects were asked to modulate their potentials in either a negative or a positive direction. In both cases, the correct SCP shift was signaled in a forward rocket movement, whereas the inadequate change of the potential yielded a backward rocket movement. After 8 s, the rocket and the letter disappeared, marking the end of the trial. In transfer trials, only the letter 'A' or 'B' was presented, without the rocket. The EEG was recorded from Cz versus linked mastoid reference using a Neurofax (Nihon Kohden) amplifier with a high-frequency cut-off filter set at 30 Hz and the time constant at 10 s. Ag/AgC1 electrodes were affixed by means of Elefix (Nihon Kohden) electrode paste. EOG electrodes were placed 1 cm above ,'rod below the left eye. Electrode resistance was kept below 5 kfl. Data were digitized with a sampling rate of 100 Hz. ERPs elicited by letter/rocket presentation were averaged within each session according to the two tasks (required
negativity versus required positivity), separately for feedback trials and transfer trials. Prior to averaging, blink artifacts were corrected using the regression procedure of Gratton et al. Trials containing artifacts other than blinks were discarded. An inspection of ERP waveforms revealed considerable morphological differences between sessions (see Fig. 1). Two positive peaks, with latencies of about 250 and 350 ms, were stable, while other components were rather variable. For this reason, component amplitudes were assessed using area measures (mean amplitudes within certain time intervals), rather than peak amplitude measures. The intervals chosen corresponded to peaks or troughs which were observed in at least 60% of all ERP: N1 (70-160 ms), P2 (200-280 ms), N2 (290-350 ms), P3a (360-450 ms), N3 (460-500 ms) and P3b (510-600 ms). The same areas were measured in all cases regardless of whether a prominent peak was observed in the corresponding window or not. The mean amplitude during 2 s preceding stimulus presentation served as the baseline. Further, peak latencies were measured for the four most frequently occurring components, namely P2, N2, P3a and P3b (see latency windows above). There were several ERP, however, in which the N2 peak, or the P3b peak, or both, were not distinctly seen. In these cases, the latency of the largest negativity or positivity within the corresponding interval was measured. For N2, it was the latency of the largest trough between P2 and P3. Slow waves recorded later in the 8-s interval were analyzed as area measures over 2-s time windows beginning with the third second. These data are reported elsewhere (Kotchoubey et al., 1997, in press). Statistical analysis was conducted by means of a 3-way repeated-measures ANOVA with condition (positivity versus negativity), feedback presentation (feedback versus transfer), and session (20 levels). When appropriate, GreenFEEDBACK trials
TRANSFER trials
Fig. 1. Grandmean ERP waveformsin sessions (topto bottom) 1, 5, 10, 15
and 20. ERP were averagedacrosstask conditions separatelyfor feedback and transfer trials. Arrows indicate stimulus onset.
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3. Results
Table 1 Linear slopes of ERP parameters as a function of session
P2 amplitude N2 amplitude P3a amplitude P3b amplitude P2 latency P3a latency
3.1. ERP amplitudes
Negativity task
Positivity task
Feedback
Transfer
Feedback
Transfer
-0.37 -0.77 -0.53 -0.09 -1.76 1.09
-0.20 -0.49 -0.33 -0.07 -1.02 0.98
-0.39 -0.76 -0.51 -0.13 -1.85 1.26
-0.26 -0.53 --0.36 -0.14 -1.06 0.97
haus-Geisser epsilons were used for non-sphericity correction. Epsilon-corrected degrees of freedom are reported below. A trend analysis was used to evaluate ERP changes across sessions. Partial correlations (sessions partialed out) between ERP parameters were computed for each subject, separately for feedback trials and transfer trials. However, despite the fact that ERP changes differed between feedback and transfer trials (see below), the two resulting correlation matrices were practically identical. Thus in the next step, the correlations were computed across both types of trials. These individual correlations were then averaged using Fisher's z-transformation. Additionally, a 4-way mixed design ANOVA with focus as a between-subject factor (2 levels: 8 patients with temporal epilepsy versus 8 remaining patients) was carried out. The results of this analysis will not be reported because neither the main effect of focus nor its interactions approached the 10% significance level. 14
All areas measured in the present data became more negative with sessions. The main effect of the session was significant for N1 (F7,99 = 2.45, P = 0.026), P2 (F6,84 = 10.19, P < 0.001), N2 (F5,71 = 16.92, P < 0.001) and P3a (F4.65 = 12.81, P < 0.001), non-significant for N3 and marginally significant for P3b (F5,70 = 2.28, P = 0.06). The change of P2, N2, P3a and P3b was characterized by highly-significant linear and quadratic trends (linear: F2,18 -- 50.81, 76.97, 55.49 and 27.04, all P < 0.001, for P2, N2, P3a and P3b, respectively; quadratic: F3,17= 65.81, 87.1, 52.24 and 13.34, all P < 0.001, for P2, N2, P3a and P3b, respectively). Slopes of linear regression for amplitude and latency measures are presented in Table 1. Amplitudes of P2, P3a and P3b were larger (i.e. more positive) in transfer trials than in feedback trials (main effect of feedback: F1,15 = 25.15 (P < 0.001), 14.58 (P = 0.002) and 8.1 (P = 0.012), for P2, P3a and P3b, respectively). Accordingly, the N2 and N3 amplitudes were smaller (i.e. again more positive) in transfer trials than in feedback trials (El,15 - 21.67 (P < 0.001) and 10.68 (P = 0.005) for N2 and N3, respectively). Session-dependent amplitude changes of P2, N2 and P3a were steeper in transfer trials than in feedback trials (feedback by session interaction: F7A09 = 2.45 (P = 0.021), F7,112 = 3 . 0 0 ( P = 0 . 0 0 5 ) and F8,116 = 2.62 (P = 0.012), for P2, N2 and P3b, respectively).
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Fig. 2. Changes of P2 and N2 with repetition. The top row represents mean amplitudes within a designated window, and the bottom row represents peak latencies. X-axis, sessions; TFP, transfer trials, positivity task (white squares, approximated by a thin line); TFN, transfer trials, negativity task (black squares, thick line); FBP, feedback trials, positivity task (diamonds, approximated by a dashed line); FBN, feedback trials, negativity task (triangles, dotted line).
B. Kotchoubey et al. / Electroencephalography and clinical Neurophysiology 103 (1997) 450-456
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Fig. 3. Changes of P3a and P3b with repetition. For abbreviations see legend for Fig. 2.
In an analysis which includes 20 sessions, the main effect of feedback may have reflected feedback-related changes in later sessions and therefore be considered as a by-product of session by feedback interactions (which is evident in Figs. 2 and 3). Thus a separate 2-way ANOVA was performed for ERP recorded in the first session only. In these data, the feedback factor was significant for the amplitudes of N2 (F1,15 = 10.33, P = 0.006) and P3a (F1.15= 7.98, P = 0.013) and marginally significant for the P3b amplitude (F1,15 = 3.16, P = 0.096). All these amplitudes in the first session were significantly more positive (i.e. N2 was smaller and P3a and P3b were larger) in transfer trials than in feedback trials. 3.2. Latencies The peak latency of P2 decreased with sessions (F6,83 = 5.75, P < 0.001), while the peak latencies of P3a and P3b tended to increase with sessions (F6,92 = 2.91 (P = 0.015) and F6.85 = 2.15 (P = 0.053), for P3a and P3b, respectively). The N2 peak latency did not change with repetition (P = 0.46). Chan~ges of P2 and P3a were characterized by highly significant linear trends (F2.18 = 16.93 (P < 0.001) and 10.55 (P = 0.004), for P2 and P3a peak latencies, respectively), whereas only a quadratic trend reached significance for the P3b latency (F3,17= 8.72, P = 0.002). In the first session, the N2 peak latency was about 20 ms longer in transfer trials thma in feedback trials (FL15 = 6.20, P = 0.025). The P3a latency was significantly shorter with the negativity task (feedback trials only) than with the posi-
tivity task (both kinds of trials), leading to a significant task by feedback interaction (F1,]5 = 6.69, P = 0.021). No significant interactions between the session factor, on the one hand, and task and feedback, on the other hand, were found for any latency measure. 3.3. Relationships between ERP parameters All area measures were strongly correlated with each other (see Table 2). Correlation between P2, N2 and P3a amplitudes were close to 1.0. Further, all amplitudes correlated directly and significantly with the P2 peak latency, indicating that the variation of the latter may be a by-product of amplitude changes. The P3a peak latency tends to be inversely related with amplitudes. That is, the larger the positive components P2 and P3a, and the smaller the N2 valley between them, the longer the P2 latency, and the shorter the P3a latency. A factorization of the matrix presented in Table 2 reveals two factors explaining about 3/4 of the variance. The first factor was loaded by amplitudes of N2, P2, P3a and P3b, as well as the P2 peak latency. The second factor was built by the latencies of N2, P3a and P3b, wherein the first one was positively loaded, and the remaining two were negatively loaded.
4. Discussion
With repetition of daily training sessions, the amplitudes of P2 and P3a decreased dramatically, the amplitude of P3b
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B. Kotchoubey et al. /Electroencephalography and clinical Neurophysiology 103 (1997) 450-456
Table 2 Mean correlations between ERP measures,collapsed over tasks and types of trials Amplitudes
N1 amplitude P2 amplitude N2 amplitude P3a amplitude N3 amplitude P3b amplitude P2 latency N2 latency P3a latency
Latencies
N1
P2
N2
P3a
N3
P3b
P2
N2
P3a
P3b
1.0
0.602 1.0
0.550 0.868 1.0
0.542 0.844 0.833 1.0
0.351 0.619 0.574 0.622 1.0
0.396 0.705 0.642 0.659 0.789 1.0
0.432 0.632 0.675 0.644 0.373 0.470 1.0
-0.041 0.018 -0.032 -0.076 -0.277 -0.084 0.074 1.0
-0.238 -0.464 -0.436 -0.475 -0.061 -0.305 -0.596 -0.312 1.0
0.043 0.050 0.001 -0.065 -0.288 0.013 0.200 0.250 -0.343
decreased with a less steep slope, the amplitude of N2 increased, the P2 peak latency became shorter and the P3a peak latency grew. These changes were closely interrelated. Particularly, amplitudes of P2, N2 and P3a, as well as the latency of P2, were strongly correlated across sessions. Thus it may be suggested that there was only one factor, for example the decrement of P2, or P3b, or the increment of N2, which would explain the common dynamics of several ERP components. Which factor? As regards P2, some authors suggested that this component is non-habituating (e.g. Megela and Teyler, 1979; Kenemans et al., 1989). On the other hand, P3a was often described as one of the most clearly habituated ERP components in habituation experiments within one session (Megela and Teyler, 1979; Geisler and Polich, 1994). Further, some authors (Courchesne, 1978; Ivey and Schmidt, 1993) pointed to the existence of two separate P3 subeomponents, the earlier of them demonstrating a faster and more profound habituation than the later. The earlier P3 has often been suggested to be a component of the orienting response (Courehesne et al., 1975, 1984; Squires et al., 1975; Halgren et al., 1995). In the present data, the slope of the P3a amplitude with repetition was steeper, as compared with that of P3b, though the latter component also habituated. Thus a P3a interpretation (possibly related to the orienting response concept) would fit the habituation data better than a P2 interpretation. Nevertheless, both interpretations have obvious difficulties in explaining the observed latency changes. In both eases it remains unclear why the latency of one positive peak increased with repetition, and the latency of another one decreased with repetition. The hypothesis which would explain the largest part of the observed changes is the emergence and gradual increase of the N2 wave. I n its development' this wave pushes the neighboring positive waves apart, leading thereby not only to the decrease of their amplitudes, but also to latency decrement of the preceding peak (i.e. P2), and to latency increase of the following peak (i.e. P3a). Since the peak latency of N2 itself remains constant across sessions, there is no need to assume an additional process affecting latencies, in addition to that which affects amplitndes. Further, the present
hypothesis is in line with the findings that the N2 amplitude had the strongest correlations with other ERP parameters (Table 2), and that the slope of the N2 increase with sessions was steeper than the slopes with which the positive component amplitudes decreased (Table 1). This does not imply that nothing happened in ERPs with repetition besides N2 increment, but rather, that this increment had the largest impact on the data. It cannot be ruled out that, apart from this, a decrement of the P3 complex took place, as indicated by the fact that changes of the P3a latency and P3b amplitude were not parallel to that of the N2 amplitude. If we assume that the most prominent process happening with repetition of biofeedback training sessions was the development and increase of N2, the question arises as to what this process may have manifested. It is implausible that the nature of the subjects' activity, i.e. learning to produce directed slow potential shifts, might have large effects on the ERP evoked by the appearance of discriminative stimuli. ERP amplitudes and latencies did not correlate with the ampfitudes of subsequent slow waves. Further, while different strategies were used to produce positive versus negative SCP shifts, ERP changes in both conditions were vh-tually the same (neither main effects of task nor task by session interactions were significant for any ERP parameter). Two findings may have been due to epilepsy and the antiepileptic medication. First, peak latencies recorded in our patients may have been longer than they would have been in a similar situation in healthy subjects. This was particularly true for the P2 latency (Brinciotti, 1994). Therefore, P2 and N2 lay very close to each other, which may explain why the N2 increase had a very large effect on the P2 increase. Second, we did not lind any N1 decrement with repetition, while this decrement was observed in practically all ERP-habituation studies. The lack of change of N1 may result from the complexity of visual stimuli employed in the present study, but Kenemans et al. (1989) reported distinct N1 habituation to very complex visual stimuli. It seems more probable that the overall decrease and the non-reactivity of N1 was related to anti-eonvulsant medication (Rockstroh et al., 1991; Tuunsinen et al., 1995).
B. Kotchoubey et al. /Electroencephaiographyand clinical Neurophysiology 103 (1997)450-456
Nevertheless, we do not tend to believe that epilepsy and/ or antiepileptic drugs might have caused or greatly affected the main findings of the present study, namely the increase of N2 and the decrease of the P3 complex. At least in the extant literature about ERF' and epilepsy (e.g. Fukai et al., 1990; Smith et al., 1990; Paller et al., 1992; Halgren et al., 1995; Tuunainen et al., 19o5) we were not able to find any particularity of patients' late ERP components which would give a tip for the explanaticm of the N2 increase. We can only conjecture that although the nature of the subjects' task had seemingly no effect on the ERP dynamics, the very presence of a difficult task, regardless of its content, gave rise to processes differing from those observed in more passive habituation conditions. Similarly, Pauli et al. (1996) came to the conclusion that the betweensession P300 decrement is not related to specific task content, but rather to general learning or adaptation processes. In that study, subjects performed mental arithmetic tasks in the course of four sessions. The introduction of novel tasks in the fourth session resulted in an increase of the reaction time, but not of the P300 amplitude. Jodo and Inoue (1990) observed a decrease of the P3 latency to NoGo-stimuli in subjects performing a choice reaction time task during six daily sessions, with no latency change being obtained for Go-stimuli. R6sler and Manzey (1986) found a decrement of P3 amplitude (400-570 ms post-stimulus) across eight :~essions conducted during 2 consecutive days. (In addition, these authors found a decrease of the late positive slow wave, which had no correspondence in the present data.) The P3 decrement was regarded as reflecting a decrease of energy demanded by controlled perceptual processes, while perceptual operations became automated. When comparing these data with those obtained in the present study, some important differences should be mentioned. First, much more sessions were conducted in the present study than in all studies mentioned above. Second, both Jodo and Inoue (19901) and R6sler and Manzey (1986) strongly smoothed out their data before averaging, thereby suppressing high-frequency components. This low-pass filtering could hide N2 effects. An inspection of Fig. 1 indicates that the N2 component in our data would be strongly suppressed if all frequencies above 5 Hz were filtered out. Third, during biofeedback training, the cognitive operations immediately relating to the visual stimuli (i.e. perception of the letter and the rocket ship) were very simple, whereas the subsequent ta~,;k which was triggered by these stimuli was, on the contrary, very difficult. Although most patients were able to prodnce the required SCP shift at the end of training, statistically more often than not, the task remained extremely difficult for all of them. It may be speculated that the rise of the N2 with repeated training could have reflected re-distribution of cognitive resources elicited by the presentation of a stimulus, which was albeit very familiar, but signaled a still non-automated task. Further experiments are needed to determine whether the
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increase of N2, accompanied by the decrease of the neighboring positive components, is related to task difficulty or represents a rather general long-term repetition effect.
Acknowledgements This study was supported by the German Research society (DFG). We are indebted to V. Blankenhorn, W. Fr6scher and U. Strehl for their help in working with epileptic patients, and to an anonymous reviewer for his/her valuable criticism regarding ERP waveform analysis.
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Electroencephalography and clinical Neurophysiology103 (1997) 450-456
Beyond habituation: long-term repetition effects on visual event-related potentials in epileptic patients B. Kotchoubey a'*, D. Schneider b, C. Uhlmann c, H. Schleichert a, N. B i r b a u m e r a,d alnstitute of Medical Psychology and Behavioral Neurobiology, University of Tiibingen, Gartenstr. 29, 72074 Tiibingen, Germany bEpilepsy Center Kork, Kehl-Kork, Germany CFederal Center for Psychiatry, Ravensburg, Germany dUniversita degli Studi, Padova, Italy
Accepted for publication: 11 March 1997
Abstract Sixteen patients with partial epilepsy learned to produce positive or negative slow cortical potential shifts in a biofeedback condition during 20 consecutive training sessions. Visual ERPs to the presentation of the feedback and the discriminative stimulus were recorded at vertex. Regardless of the subjects' task (positivity versus negativity), amplitudes of the P2 (mean peak latency about 225 ms) and P3a (322 ms) components decreased across sessions, resulting in appearance and subsequent enhancement of a negative wave N2 (298 ms) between P2 and P3a. As N2 grew the P2 latency decreased and the P3a latency increased. Additionally, the P3b (472 ms) decreased with repetition, however, it did so slower than P2 and P3a. A comparison between the present data, on the one hand, and those obtained in the ERP habituation paradigm within one session, on the other hand, indicates that some repetition effects cannot be explained by habituation. © 1997 Elsevier Science Ireland Ltd. Keywords: Event-related potentials; Epilepsy; Habituation; Repetition; N2 wave; P3 wave
1. Introduction The most fundamental form of the modification of behavior is the change of a response with repeated presentation of an event. Usually, the magnitude of physiological responses decreases with repetition, which is referred to as habituation. Further, Thompson and Spencer (1966) developed a set of criteria to distinguish between the true habituation and similar states, such as adaptation or fatigue, where response decrements are also observed In humans, habituation of event-related potentials (ERPs) has been extensively studied within the last decades as a direct index of information processing in the intact brain (e.g. Frustorfer, 1971; Megela and Teyler, 1979; Sokolov, 1990). Two different processes have been described. In the
* Corresponding author. Fax: +49 7071 295956; e-mail: [email protected]
typical paradigm, trains of stimuli are presented with short interstimulus intervals and considerably longer inter-train intervals. Change of ERP component latencies within trains are referred to as short-term habituation, whereas changes between trains are referred to as long-term habituation. In both cases, decrease of the N1 (N100, N140) component has been found as a hallmark of the ERP habituation (e.g. Frustorfer, 1971; Lutzenberger et al., 1979; Ohman and Lader, 1972; Rust, 1977; Megela and Teyler, 1979; Kenemans et al., 1989). Another habituating component is P3 (P300) (Courchesne, 1978; Megela and Teyler, 1979; Ivey and Schmidt, 1993; Geisler and Polich, 1994), although its short-term habituation has been questioned by some authors (Polich and Mclsaac, 1994). Yet more long-term changes which do not happen between consecutive trains, but between sessions or days, have been much less investigated (e.g. R6sler and Manzey, 1986; Jodo and Inoue, 1990). However, such data might be of special interest, since most important human learning processes do not take place within one session. Rather,
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modification of human behavior occurs in a day-to-day manner. Funding is difficult to find for carrying out long trains of ERP experiments with normal subjects. In the present work, we studied ERP changes in epileptic patients who learned to regulate their slow cortical waves with the help of a biofeedback technique.
2. M e t h o d s
Sixteen epileptic patien~ts (7 males, aged 21-44, mean seizure history of 22.8 years), took part in the training program. The prevailing type of seizures was complex partial seizures, with the mean seizure frequency varying from 3 to 415 per month. Eight patients had temporal foci (5, right side), and two other patients had frontal foci (both, right side). In the remaining 6 patients, the focus was not localized exactly. All patient were characterized by good cognitive abilities (mean IQ 107.4, no sign of disturbance of frontal lobe functions, assessed by means of the Wisconsin Card Sort Test). Patients with any kind of non-epileptic seizures, or progressive neurological or psychiatric processes, were excluded. A]~I patients were medicated with one or two anti-convulsant substances. The medication regime was stable during the entire period of study, as well as at least 5 months prior to its beginning. With each patient, 20 biofeedback training sessions were carried out over 2 weeks. Each session consisted of 145 trials. In most trials, subjects learned to produce a slow potential shift while con~tinuous feedback was provided (feedback trials). In other trials, they had to produce these shifts without feedback (transfer trials). The percentage of transfer trials varied from 30% to 58%, according to individual performance. A feedback trial began with the presentation of a letter 'A' or ' B ' on a screen of a computer monitor together with a stylized rocket, whose position on the screen reflected the actual EEG potential amplitude referred to a baseline of 2 s prior to the appearance of the stimuli. Depending upon the letter ( ' A ' or 'B'), subjects were asked to modulate their potentials in either a negative or a positive direction. In both cases, the correct SCP shift was signaled in a forward rocket movement, whereas the inadequate change of the potential yielded a backward rocket movement. After 8 s, the rocket and the letter disappeared, marking the end of the trial. In transfer trials, only the letter 'A' or 'B' was presented, without the rocket. The EEG was recorded from Cz versus linked mastoid reference using a Neurofax (Nihon Kohden) amplifier with a high-frequency cut-off filter set at 30 Hz and the time constant at 10 s. Ag/AgC1 electrodes were affixed by means of Elefix (Nihon Kohden) electrode paste. EOG electrodes were placed 1 cm above ,'rod below the left eye. Electrode resistance was kept below 5 kfl. Data were digitized with a sampling rate of 100 Hz. ERPs elicited by letter/rocket presentation were averaged within each session according to the two tasks (required
negativity versus required positivity), separately for feedback trials and transfer trials. Prior to averaging, blink artifacts were corrected using the regression procedure of Gratton et al. Trials containing artifacts other than blinks were discarded. An inspection of ERP waveforms revealed considerable morphological differences between sessions (see Fig. 1). Two positive peaks, with latencies of about 250 and 350 ms, were stable, while other components were rather variable. For this reason, component amplitudes were assessed using area measures (mean amplitudes within certain time intervals), rather than peak amplitude measures. The intervals chosen corresponded to peaks or troughs which were observed in at least 60% of all ERP: N1 (70-160 ms), P2 (200-280 ms), N2 (290-350 ms), P3a (360-450 ms), N3 (460-500 ms) and P3b (510-600 ms). The same areas were measured in all cases regardless of whether a prominent peak was observed in the corresponding window or not. The mean amplitude during 2 s preceding stimulus presentation served as the baseline. Further, peak latencies were measured for the four most frequently occurring components, namely P2, N2, P3a and P3b (see latency windows above). There were several ERP, however, in which the N2 peak, or the P3b peak, or both, were not distinctly seen. In these cases, the latency of the largest negativity or positivity within the corresponding interval was measured. For N2, it was the latency of the largest trough between P2 and P3. Slow waves recorded later in the 8-s interval were analyzed as area measures over 2-s time windows beginning with the third second. These data are reported elsewhere (Kotchoubey et al., 1997, in press). Statistical analysis was conducted by means of a 3-way repeated-measures ANOVA with condition (positivity versus negativity), feedback presentation (feedback versus transfer), and session (20 levels). When appropriate, GreenFEEDBACK trials
TRANSFER trials
Fig. 1. Grandmean ERP waveformsin sessions (topto bottom) 1, 5, 10, 15
and 20. ERP were averagedacrosstask conditions separatelyfor feedback and transfer trials. Arrows indicate stimulus onset.
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3. Results
Table 1 Linear slopes of ERP parameters as a function of session
P2 amplitude N2 amplitude P3a amplitude P3b amplitude P2 latency P3a latency
3.1. ERP amplitudes
Negativity task
Positivity task
Feedback
Transfer
Feedback
Transfer
-0.37 -0.77 -0.53 -0.09 -1.76 1.09
-0.20 -0.49 -0.33 -0.07 -1.02 0.98
-0.39 -0.76 -0.51 -0.13 -1.85 1.26
-0.26 -0.53 --0.36 -0.14 -1.06 0.97
haus-Geisser epsilons were used for non-sphericity correction. Epsilon-corrected degrees of freedom are reported below. A trend analysis was used to evaluate ERP changes across sessions. Partial correlations (sessions partialed out) between ERP parameters were computed for each subject, separately for feedback trials and transfer trials. However, despite the fact that ERP changes differed between feedback and transfer trials (see below), the two resulting correlation matrices were practically identical. Thus in the next step, the correlations were computed across both types of trials. These individual correlations were then averaged using Fisher's z-transformation. Additionally, a 4-way mixed design ANOVA with focus as a between-subject factor (2 levels: 8 patients with temporal epilepsy versus 8 remaining patients) was carried out. The results of this analysis will not be reported because neither the main effect of focus nor its interactions approached the 10% significance level. 14
All areas measured in the present data became more negative with sessions. The main effect of the session was significant for N1 (F7,99 = 2.45, P = 0.026), P2 (F6,84 = 10.19, P < 0.001), N2 (F5,71 = 16.92, P < 0.001) and P3a (F4.65 = 12.81, P < 0.001), non-significant for N3 and marginally significant for P3b (F5,70 = 2.28, P = 0.06). The change of P2, N2, P3a and P3b was characterized by highly-significant linear and quadratic trends (linear: F2,18 -- 50.81, 76.97, 55.49 and 27.04, all P < 0.001, for P2, N2, P3a and P3b, respectively; quadratic: F3,17= 65.81, 87.1, 52.24 and 13.34, all P < 0.001, for P2, N2, P3a and P3b, respectively). Slopes of linear regression for amplitude and latency measures are presented in Table 1. Amplitudes of P2, P3a and P3b were larger (i.e. more positive) in transfer trials than in feedback trials (main effect of feedback: F1,15 = 25.15 (P < 0.001), 14.58 (P = 0.002) and 8.1 (P = 0.012), for P2, P3a and P3b, respectively). Accordingly, the N2 and N3 amplitudes were smaller (i.e. again more positive) in transfer trials than in feedback trials (El,15 - 21.67 (P < 0.001) and 10.68 (P = 0.005) for N2 and N3, respectively). Session-dependent amplitude changes of P2, N2 and P3a were steeper in transfer trials than in feedback trials (feedback by session interaction: F7A09 = 2.45 (P = 0.021), F7,112 = 3 . 0 0 ( P = 0 . 0 0 5 ) and F8,116 = 2.62 (P = 0.012), for P2, N2 and P3b, respectively).
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Fig. 2. Changes of P2 and N2 with repetition. The top row represents mean amplitudes within a designated window, and the bottom row represents peak latencies. X-axis, sessions; TFP, transfer trials, positivity task (white squares, approximated by a thin line); TFN, transfer trials, negativity task (black squares, thick line); FBP, feedback trials, positivity task (diamonds, approximated by a dashed line); FBN, feedback trials, negativity task (triangles, dotted line).
B. Kotchoubey et al. / Electroencephalography and clinical Neurophysiology 103 (1997) 450-456
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Fig. 3. Changes of P3a and P3b with repetition. For abbreviations see legend for Fig. 2.
In an analysis which includes 20 sessions, the main effect of feedback may have reflected feedback-related changes in later sessions and therefore be considered as a by-product of session by feedback interactions (which is evident in Figs. 2 and 3). Thus a separate 2-way ANOVA was performed for ERP recorded in the first session only. In these data, the feedback factor was significant for the amplitudes of N2 (F1,15 = 10.33, P = 0.006) and P3a (F1.15= 7.98, P = 0.013) and marginally significant for the P3b amplitude (F1,15 = 3.16, P = 0.096). All these amplitudes in the first session were significantly more positive (i.e. N2 was smaller and P3a and P3b were larger) in transfer trials than in feedback trials. 3.2. Latencies The peak latency of P2 decreased with sessions (F6,83 = 5.75, P < 0.001), while the peak latencies of P3a and P3b tended to increase with sessions (F6,92 = 2.91 (P = 0.015) and F6.85 = 2.15 (P = 0.053), for P3a and P3b, respectively). The N2 peak latency did not change with repetition (P = 0.46). Chan~ges of P2 and P3a were characterized by highly significant linear trends (F2.18 = 16.93 (P < 0.001) and 10.55 (P = 0.004), for P2 and P3a peak latencies, respectively), whereas only a quadratic trend reached significance for the P3b latency (F3,17= 8.72, P = 0.002). In the first session, the N2 peak latency was about 20 ms longer in transfer trials thma in feedback trials (FL15 = 6.20, P = 0.025). The P3a latency was significantly shorter with the negativity task (feedback trials only) than with the posi-
tivity task (both kinds of trials), leading to a significant task by feedback interaction (F1,]5 = 6.69, P = 0.021). No significant interactions between the session factor, on the one hand, and task and feedback, on the other hand, were found for any latency measure. 3.3. Relationships between ERP parameters All area measures were strongly correlated with each other (see Table 2). Correlation between P2, N2 and P3a amplitudes were close to 1.0. Further, all amplitudes correlated directly and significantly with the P2 peak latency, indicating that the variation of the latter may be a by-product of amplitude changes. The P3a peak latency tends to be inversely related with amplitudes. That is, the larger the positive components P2 and P3a, and the smaller the N2 valley between them, the longer the P2 latency, and the shorter the P3a latency. A factorization of the matrix presented in Table 2 reveals two factors explaining about 3/4 of the variance. The first factor was loaded by amplitudes of N2, P2, P3a and P3b, as well as the P2 peak latency. The second factor was built by the latencies of N2, P3a and P3b, wherein the first one was positively loaded, and the remaining two were negatively loaded.
4. Discussion
With repetition of daily training sessions, the amplitudes of P2 and P3a decreased dramatically, the amplitude of P3b
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Table 2 Mean correlations between ERP measures,collapsed over tasks and types of trials Amplitudes
N1 amplitude P2 amplitude N2 amplitude P3a amplitude N3 amplitude P3b amplitude P2 latency N2 latency P3a latency
Latencies
N1
P2
N2
P3a
N3
P3b
P2
N2
P3a
P3b
1.0
0.602 1.0
0.550 0.868 1.0
0.542 0.844 0.833 1.0
0.351 0.619 0.574 0.622 1.0
0.396 0.705 0.642 0.659 0.789 1.0
0.432 0.632 0.675 0.644 0.373 0.470 1.0
-0.041 0.018 -0.032 -0.076 -0.277 -0.084 0.074 1.0
-0.238 -0.464 -0.436 -0.475 -0.061 -0.305 -0.596 -0.312 1.0
0.043 0.050 0.001 -0.065 -0.288 0.013 0.200 0.250 -0.343
decreased with a less steep slope, the amplitude of N2 increased, the P2 peak latency became shorter and the P3a peak latency grew. These changes were closely interrelated. Particularly, amplitudes of P2, N2 and P3a, as well as the latency of P2, were strongly correlated across sessions. Thus it may be suggested that there was only one factor, for example the decrement of P2, or P3b, or the increment of N2, which would explain the common dynamics of several ERP components. Which factor? As regards P2, some authors suggested that this component is non-habituating (e.g. Megela and Teyler, 1979; Kenemans et al., 1989). On the other hand, P3a was often described as one of the most clearly habituated ERP components in habituation experiments within one session (Megela and Teyler, 1979; Geisler and Polich, 1994). Further, some authors (Courchesne, 1978; Ivey and Schmidt, 1993) pointed to the existence of two separate P3 subeomponents, the earlier of them demonstrating a faster and more profound habituation than the later. The earlier P3 has often been suggested to be a component of the orienting response (Courehesne et al., 1975, 1984; Squires et al., 1975; Halgren et al., 1995). In the present data, the slope of the P3a amplitude with repetition was steeper, as compared with that of P3b, though the latter component also habituated. Thus a P3a interpretation (possibly related to the orienting response concept) would fit the habituation data better than a P2 interpretation. Nevertheless, both interpretations have obvious difficulties in explaining the observed latency changes. In both eases it remains unclear why the latency of one positive peak increased with repetition, and the latency of another one decreased with repetition. The hypothesis which would explain the largest part of the observed changes is the emergence and gradual increase of the N2 wave. I n its development' this wave pushes the neighboring positive waves apart, leading thereby not only to the decrease of their amplitudes, but also to latency decrement of the preceding peak (i.e. P2), and to latency increase of the following peak (i.e. P3a). Since the peak latency of N2 itself remains constant across sessions, there is no need to assume an additional process affecting latencies, in addition to that which affects amplitndes. Further, the present
hypothesis is in line with the findings that the N2 amplitude had the strongest correlations with other ERP parameters (Table 2), and that the slope of the N2 increase with sessions was steeper than the slopes with which the positive component amplitudes decreased (Table 1). This does not imply that nothing happened in ERPs with repetition besides N2 increment, but rather, that this increment had the largest impact on the data. It cannot be ruled out that, apart from this, a decrement of the P3 complex took place, as indicated by the fact that changes of the P3a latency and P3b amplitude were not parallel to that of the N2 amplitude. If we assume that the most prominent process happening with repetition of biofeedback training sessions was the development and increase of N2, the question arises as to what this process may have manifested. It is implausible that the nature of the subjects' activity, i.e. learning to produce directed slow potential shifts, might have large effects on the ERP evoked by the appearance of discriminative stimuli. ERP amplitudes and latencies did not correlate with the ampfitudes of subsequent slow waves. Further, while different strategies were used to produce positive versus negative SCP shifts, ERP changes in both conditions were vh-tually the same (neither main effects of task nor task by session interactions were significant for any ERP parameter). Two findings may have been due to epilepsy and the antiepileptic medication. First, peak latencies recorded in our patients may have been longer than they would have been in a similar situation in healthy subjects. This was particularly true for the P2 latency (Brinciotti, 1994). Therefore, P2 and N2 lay very close to each other, which may explain why the N2 increase had a very large effect on the P2 increase. Second, we did not lind any N1 decrement with repetition, while this decrement was observed in practically all ERP-habituation studies. The lack of change of N1 may result from the complexity of visual stimuli employed in the present study, but Kenemans et al. (1989) reported distinct N1 habituation to very complex visual stimuli. It seems more probable that the overall decrease and the non-reactivity of N1 was related to anti-eonvulsant medication (Rockstroh et al., 1991; Tuunsinen et al., 1995).
B. Kotchoubey et al. /Electroencephaiographyand clinical Neurophysiology 103 (1997)450-456
Nevertheless, we do not tend to believe that epilepsy and/ or antiepileptic drugs might have caused or greatly affected the main findings of the present study, namely the increase of N2 and the decrease of the P3 complex. At least in the extant literature about ERF' and epilepsy (e.g. Fukai et al., 1990; Smith et al., 1990; Paller et al., 1992; Halgren et al., 1995; Tuunainen et al., 19o5) we were not able to find any particularity of patients' late ERP components which would give a tip for the explanaticm of the N2 increase. We can only conjecture that although the nature of the subjects' task had seemingly no effect on the ERP dynamics, the very presence of a difficult task, regardless of its content, gave rise to processes differing from those observed in more passive habituation conditions. Similarly, Pauli et al. (1996) came to the conclusion that the betweensession P300 decrement is not related to specific task content, but rather to general learning or adaptation processes. In that study, subjects performed mental arithmetic tasks in the course of four sessions. The introduction of novel tasks in the fourth session resulted in an increase of the reaction time, but not of the P300 amplitude. Jodo and Inoue (1990) observed a decrease of the P3 latency to NoGo-stimuli in subjects performing a choice reaction time task during six daily sessions, with no latency change being obtained for Go-stimuli. R6sler and Manzey (1986) found a decrement of P3 amplitude (400-570 ms post-stimulus) across eight :~essions conducted during 2 consecutive days. (In addition, these authors found a decrease of the late positive slow wave, which had no correspondence in the present data.) The P3 decrement was regarded as reflecting a decrease of energy demanded by controlled perceptual processes, while perceptual operations became automated. When comparing these data with those obtained in the present study, some important differences should be mentioned. First, much more sessions were conducted in the present study than in all studies mentioned above. Second, both Jodo and Inoue (19901) and R6sler and Manzey (1986) strongly smoothed out their data before averaging, thereby suppressing high-frequency components. This low-pass filtering could hide N2 effects. An inspection of Fig. 1 indicates that the N2 component in our data would be strongly suppressed if all frequencies above 5 Hz were filtered out. Third, during biofeedback training, the cognitive operations immediately relating to the visual stimuli (i.e. perception of the letter and the rocket ship) were very simple, whereas the subsequent ta~,;k which was triggered by these stimuli was, on the contrary, very difficult. Although most patients were able to prodnce the required SCP shift at the end of training, statistically more often than not, the task remained extremely difficult for all of them. It may be speculated that the rise of the N2 with repeated training could have reflected re-distribution of cognitive resources elicited by the presentation of a stimulus, which was albeit very familiar, but signaled a still non-automated task. Further experiments are needed to determine whether the
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increase of N2, accompanied by the decrease of the neighboring positive components, is related to task difficulty or represents a rather general long-term repetition effect.
Acknowledgements This study was supported by the German Research society (DFG). We are indebted to V. Blankenhorn, W. Fr6scher and U. Strehl for their help in working with epileptic patients, and to an anonymous reviewer for his/her valuable criticism regarding ERP waveform analysis.
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