Supplemental Material Recording technique Multi-unit activity (MUA) was recorded from electrodes that were chronically implanted (Teflon-coated platinum-iridium wires) in the primary visual cortex representing the lower visual field, and positioned 1-2 mm below the cortical surface (see Roelfsema et al., 2004). Supplemental Figure 4a illustrates the method used to record multi-unit activity. Signals from the chronically implanted electrodes were first amplified and then filtered between 750 (18 db/octave) and 5000 Hz (12 db/octave) (Filt1 in Fig. 4a,b). In a number of cases it was possible to record single unit activity (SUA) from the chronically implanted electrodes by using a Schmidt trigger. Generally, however, we recorded multiunit activity. MUA was recorded by a full-wave rectification of the Filt1-signal (taking the absolute value of the voltage) followed by a low-pass filtering step at 500 Hz. MUA reflects the envelope of the Filt1 signal, and it thereby provides an instantaneous measure of the number and size of action potentials of neurons in the vicinity of the electrode tip (Legatt et al., 1980; Brosch et al., 1995) (Fig. 4b). Note that this signal does not include local field potentials (<500 Hz), since these are filtered out in the first step (Filt1). We recently compared MUA to single-unit activity in area V1 during attentiondemanding tasks, and found that MUA provides a reliable estimate of the average singleunit responses (Supèr and Roelfsema, 2005). This is expected, since MUA pools the responses of a number of single neurons in the vicinity of the recording electrode and population responses obtained with this method are therefore the same as population responses obtained with single-unit recordings. Pooling improves the signal-to-noise ratio, which is advantageous if the aim is to measure the average timing of attentional effects. Recordings with a sufficient signal-to-noise ratio were obtained from about 50% of the wires. Before the experiments, the receptive field (RF) borders were measured by determining the onset and offset of the visual response to a slowly moving light bar, for each of 8 movement directions (the method is described in detail in Supèr and Roelfsema, 2005). Median RF size was 0.98º (range 0.45º-1.9º), and average eccentricity was 3.7º
Khayat et al. 2 (range 1.1º-6.9º). Median orientation selectivity ratio (OSR, response to bar of optimal orientation and direction by least effective) was 1.9 (mean 2.1, range 1.2 to 7). We designed the curve stimuli in such a way that the segment in the RF matched as closely as possible the neuron s preferred orientation. In our sample of recording sites, 75% of neurons were therefore stimulated optimally, with a curve segment that was within 0-30 deg of their preferred orientation, whereas 25% were stimulated suboptimally with a curve segment that was within 30-60 deg. To examine whether neuronal response modulation depends on orientation selectivity, we computed the population responses for each category. Figure 5 shows the responses during normal trials of sites that were stimulated with a curve segment oriented optimally (N=44, left panel) and suboptimally (N=15, right panel). In both cases, the response to the target curve was stronger than the response to the distractor curve (p<0.001, paired t-test), and the MI did not differ significantly between the two groups (U-test, P>0.05). Thus, the attentional response modulation also occurs for neurons that are stimulated with a suboptimal orientation. Eye position control A gold ring was implanted under the conjunctiva of one eye to record the eye position with the double magnetic induction method (Bour et al., 1984), which has a resolution better than 0.l deg. The eye position signal was digitized at a rate of more than 500 Hz. During stimulus presentation, the monkeys had to maintain steady fixation within a 1 x1 fixation window centered on the fixation point. Because RFs are relatively small in area V1, small differences in the position of gaze around the fixation point between the normal and switch condition may therefore influence the strength and latency of the response enhancement and suppression. To exclude this possibility, we first removed all trials with microsaccades during stimulus presentation. Then we applied a stratification procedure (Roelfsema et al., 1998), which is illustrated in Fig. 6. Figure 6a shows the distribution of eye positions across trials with stimulus T of the normal condition (Left) and stimulus T D of the switch condition (Center). The stratification procedure removes trials from these two conditions until the eye position distributions are identical (within bins of 0.2º x 0.2º). The stratified eye position distribution is shown in Fig. 6a (Right). Note that the number of remaining trials in the stratified distribution is smaller than the
Khayat et al. 3 number of trials with either stimulus. This is due to the removal of trials with stimulus T in some of the bins and removal of trials with stimulus T D in other bins. A similar analysis of the eye position distributions across trials with stimulus D and stimulus D T is shown in Fig. 6b. Figure 6c shows the population data (N=59) of the same recording sites that are shown in Fig. 3a, but now after the stratification procedure. The response enhancement (light grey area) yielded a modulation index of 0.38 and occurred at a latency of 138 ms after the stimulus switch (lower panel), whereas the response suppression (dark grey area) yielded a modulation index of 0.18 and occurred later, at a latency of 182 ms. We conclude that the magnitude and latency of the response enhancement and suppression are not caused by a systematic difference in gaze position between the normal and switch conditions. Analysis of the latency of the response enhancement Various methods exist to measure the latency of neuronal responses or the modulation thereof. A commonly used procedure is to take the first of a number of time bins that satisfy a significance criterion (e.g. Lennie, 1981; Chelazzi et al., 2001; Maunsell and Gibson, 1992). However, if the difference in neuronal responses between conditions builds up gradually, this procedure yields biased estimates since it is sensitive to the amount of data that was collected. Shorter latencies are obtained if there are more trials. We therefore used an alternative latency estimate that is independent of the number of trials (see below), and is derived by fitting a function f(t) to the difference between the responses evoked by the target and distractor curve (see also Thompson et al., 1996; Roelfsema, et al., 2003). The shape of f(t) was derived from the following two assumptions: [1] the onset of response modulation has a gaussian distribution across trials, and [2] a fraction of the modulation dissipates exponentially. These assumptions yield the following two differential equations: $ m t) / $ t = #" m ( t) + g( t, µ, ) (1) 1 ( 1! for the dissipating modulation, and
Khayat et al. 4 " m ( t) / " t = g( t, µ, ) (2) 2! for the non-dissipating modulation. Here, g(t,µ,σ) is a gaussian density with mean µ and standard deviation σ, and α 1 is the time constant of dissipation. Thus, the total modulation equals f ( t) = k m ( t) + k m ( t) (3) 1 1 2 2 The solution to these equations was fitted to the response difference: f t Ad d Exp t G t 2 2 2 ( ) = /( + 1) # ( µ! + 0.5 "! $! )# (, µ + "!," ) + A/( d + 1) # G( t, µ," ) (4) where the constants k 1 and k 2 of Eq (3) are defined as follows: k1 = Ad /( d + 1) (5) k2 = A/( d + 1) (6) Thus, f(t) depends on 5 parameters: µ, σ, α, A, and d; G(t,µ,σ) is a cumulative gaussian, A is the amplitude of the function, and d determines the fraction of the modulation that dissipates. The latency of the response modulation was defined as the time at which the fitted function reached 33% of its maximum value. This value is arbitrary, but qualitatively similar results were obtained with other criteria (e.g. 25%, 50%, and 75%). With a criterion of 33%, the latency of enhancement and suppression at the population level equaled 144 ms and 210 ms, respectively (see Fig.3a). With criteria of 25%, 50% and 75%, the latency of the response enhancement (suppression) equaled 130 ms (202 ms), 162 ms (221 ms) and 190 ms (238 ms), respectively. We also analyzed the latency at the individual recording sites for which the response enhancement and suppression were both
Khayat et al. 5 significant (p<0.05, N=21; Fig. 3c). The median latency of the response enhancement (suppression) equaled 130 ms (187 ms), 134ms (201ms), 158 ms (223 ms) and 188 ms (250 ms) at criteria of 25%, 33%, 50% and 75%, respectively. A two-sided sign-test revealed that the latency of enhancement was significantly shorter than that of suppression, at each criterion (p<0.01). We investigated how our method depends on the amount of available data. Supplementary Figure 7 compares the latency of enhancement (or suppression) that is computed by fitting a curve onto the difference in responses averaged across 100% of the trials and across 50% of the trials. The analyses include all sites where the response enhancement (i.e. difference in responses between D and D T trials) and the response suppression (i.e. difference in responses between T and T D trials) were both significant (p<0.05, N=21). The latency measure for the enhancement (in red) and suppression (in black) at most recording sites is similar irrespective of the number of trials included in the analyses (p>0.4, two-sided sign-test). The median latency of enhancement equaled 134 and 138 ms as measured on 100% and 50% of trials, respectively. The median latency of suppression equaled 201 and 195 ms as measured on 100% and 50% of trials, respectively. These results show that fitting a function to the response difference indeed provides a reliable estimate of the latency that does not depend strongly on the amount of data. Computation of the confidence interval for the difference in latency To compute a 95% confidence interval for differences in the latency of attentional modulation between enhancement and suppression, a Monte-Carlo procedure was used (Press et al., 1986). This Monte-Carlo (bootstrapping) procedure simulated 10.000 data sets, by randomly selecting 59 recording sites with replacement from the population of all recording sites (N=59). In each data set, the latency of the enhancement and suppression were determined by using the curve-fitting method. This analysis resulted in a distribution of 10.000 latency differences and this distribution was used to compute the 95% confidence interval, which was equal to [39.8ms, 79.7ms].
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