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Department of Neurology and Neuroscience, Weill Medical College and Graduate School of Medical Sciences of Cornell University,New York, New York 10021
Submitted 7 January 2003; accepted in final form 10 July 2003
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ABSTRACT |
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INTRODUCTION |
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; Green 1957). These signals appear at the end of the saccade and are differentially modulated by the direction of the eye movement (Skrandies and Laschke 1997). Tasks requiring attention, such as reading, increase the amplitude of these signals and shorten their latency (Barlow 1971; Fourment et al. 1976).
In animals, saccades are correlated with transient local field potentials (LFPs) in the lateral geniculate nucleus (LGN) of the monkey and cat (Brooks and Gershon 1971; Feldman and Cohen 1968; Jeannerod and Sakai 1970), and in the temporal (Sobotka et al. 2002), parietal (Pesaran et al. 2002), and frontal lobes (Klostermann et al. 1994; Seidemann et al. 2002) of the cerebral cortex and mesolimbic structures (septum and hippocampal formation: Sobotka and Ringo 1997) of the monkey. As with the human scalp-EMPs, the LFP-EMPs recorded in mesolimbic structures of the alert monkey are differentially modulated by the direction of the saccade and by the visual environment (Sobotka and Ringo 1997). In a lighted environment, these LFP-EMPs are generally faster (shorter latency for time-to-peak) and more biphasic (larger peak-to-trough excursions) than in the dark.
A strong extraretinal contribution to the EMPs is emphasized by the following observations: 1) complete retinal loss through optic nerve section or photocoagulation does not entirely eliminate EMPs in the cat LGN (Brooks and Gershon 1971; Jeannerod and Saki 1970); 2) EMPs in the cat cortex and LGN appear both with caloric stimulation of the labyrinths under flaxedil paralysis sufficient to prevent saccades and with electrical stimulation of the pontine reticular formation with concomitant saccades when the paralytic is removed (Brooks and Gershon 1971); 3) EMPs are not produced in the monkey LGN by rapid mechanical displacement of the eye (Feldman and Cohen 1968).
Taken together, both the human and animal studies suggest that EMPs represent a signal in thalamic and cortical populations that is produced, in part, by the networks responsible for generating and controlling saccadic eye movements. The appearance of EMPs in the LGN, ventral visual pathway (including the temporal lobe), and mesolimbic structures—all regions of the brain not typically included in the oculomotor network (Wurtz 1996)—may therefore reflect the arrival of corollary motor activity (McCloskey 1981; Sommer and Wurtz 2002). Such corollary discharge signals may originate in the brain stem, superior colliculus (SC), lateral intraparietal area (LIP), and/or the frontal eye fields (FEF). The role of this corollary activity in the occipitotemporal (OT) pathway has not been fully resolved.
Here we examine EMPs in alert monkeys performing a pattern-recognition task. The timing and frequency content of EMPs, their regional differences in the OT pathway, their interactions with visual transients, and the influence of behavior (direction of saccadic eye movement) on their dynamics are quantified. Our goal is to determine, in more detail, the extent to which EMPs reflect corollary motor signals, to suggest how they are generated, and what their role may be in active vision.
The relationship between EMP signals recorded in the OT pathway and corollary oculomotor activity can be clarified by the temporal relationship between the time of saccade onset and the change in power in local population activity. For example, activity that precedes and is coincident with saccade onset may be associated with remapping of receptive fields (Duhamel et al. 1992; Umeno and Goldberg 1997; reviewed in Ross et al. 2001), "interrupts" from attentional circuit breakers (Corbetta and Shulman 2002) that lead to the generation of orienting saccades, and executive commands originating in the frontal eye fields that are associated with the selection of targets and implementation of scan paths (Bruce et al. 1985). Because of their timing, these EMPs cannot be generated by the visual reafference, the activity generated in the retina by the movement of the eye. On the other hand, activity that follows saccade onset and offset may represent either visual reafference or the exafference activity, that is, the activity produced by an interaction between the visual reafference and the corollary discharge (McCloskey 1981).
In addition to the timing of these signals with respect to saccade onset, other characteristics may also be useful for illuminating the origin of EMPs and their relationship to active vision. For example, regional differences in the timing and frequency content of EMPs would suggest that signals related to saccadic eye movements enter and influence the different stages of visual processing in the OT pathway through a variety of channels. Directional EMPs would indicate that the oculomotor network provides a significant component to the saccade-related activity in the OT pathway. Finally, interactions between visual transients and EMPs can be evaluated in terms of the gating of visually evoked activity by the saccade and its corollary activity.
We recorded LFPs using high-impedance microelectrodes at locations in the occipital and temporal lobes of monkeys during a task that required sustained attention and the scanning of a series of high-contrast dynamic images, conditions that maximize the amplitude and shorten the latency of EMPs (lambda waves). We examine the LFPs in a 500-ms interval centered on the onset of the horizontal component of saccades made during the trials. Here we seek to produce a time–frequency representation of the average LFP around the time of the saccade as one might do when processing acoustic signals that have a high signal-to-noise ratio. The EMP is not subtracted from each saccade-triggered LFP in the time-domain before spectral analysis. Instead, a spectrogram is computed for a number of different estimates of the EMP to establish confidence intervals for the EMP spectrogram. Time–frequency spectrograms are calculated to capture a portrait of the presaccadic, transsaccadic, and postsaccadic activation generated in the local cortical population. The spectrograms are evaluated to determine the influence of saccade direction, cortical location, and timing of visual transients on the time and frequency content of the LFPs during the perisaccadic interval.
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METHODS |
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LFPs were recorded from sites within the occipital and temporal lobes of 2 adult male macaque monkeys (Macaca mulatta), monkeys M and S, performing a pattern-recognition task. All animal care and both surgical and experimental procedures conformed to guidelines established by the National Institutes of Health and the Institutional Animal Care and Use Committee of the Weill Medical College of Cornell University.
RECORDING LOCATIONS. Using sterile surgical technique, recording chambers (Crist Instrument) and a head holder were attached under isoflurane general anesthesia to the skulls of the monkeys at positions selected to provide access to regions of the extrastriate visual cortex in both the occipital and temporal lobes. For monkey M, one chamber was placed over the occipital lobe (P12, L20) and a second chamber was centered over the temporal lobe (A10, L21). Both of these chambers were placed over the right hemisphere. For monkey S, 3 chambers were used: occipital lobe chambers for the left and right hemispheres (P12, L20) and a right hemisphere temporal lobe chamber (A10, L21). The recording chamber placements were made after MR imaging (T1-weighted images made in a 1.5-T field, juvenile head coil) with the animal held in an MR-compatible stereotaxic frame that was then used in the subsequent surgeries. With monkey S a second set of images was taken during the course of the studies with tungsten microelectrodes left at productive recording sites. A custom-designed head coil was used for this second imaging session and the axial, sagittal, and coronal images were combined into a full 3-dimensional volume to better trace the path of the microelectrodes through the brain. For monkey M, some of the elements used for building the cranial implant were made of steel, so it was not possible to obtain an MRI after surgery. Instead, a fluoroscope was used to produce images of the microelectrodes and guide tubes in vivo.
Based on the imaging data, and with reference to a recently published Macaca fascicularis atlas (Martin and Bowden 2000), detailed microelectrode track reconstructions relating depth to receptive field size and eccentricity (Gattass et al. 1988), and from our own single unit recordings, we assigned our occipital lobe LFP recording sites as follows: V1 for recording depths in the range of 0–1.6 mm; V2–V3, 2–8 mm; ventral V1–V2, 8–14 mm. Ventral V4 was at the same depth as ventral (upper and parafoveal visual field) V1–V2 but accessed from more anterior positions in the chamber. The temporal lobe recordings were subdivided into 3 groups. The first, shallower group of visual units and LFP responses encountered in the temporal lobe tracks (at depths of 18–20 mm in monkey M, and starting at 16 mm in monkey S) were assigned to the inferior bank of the superior temporal sulcus (STS). The deeper recordings, encountered after passing through a region devoid of single units (white matter), were assigned to the inferotemporal cortex (area TE at depths beginning at 29 mm in monkey M). More medial penetrations at this depth entered the parahippocampal gyrus (PHG).
LOCAL FIELD POTENTIAL RECORDING. Extracellular recordings were made with epoxy-insulated tungsten microelectrodes (FHC, Bowdonham, ME), with a nominal impedance of 1–4 M
. All the data reported here were obtained by recording simultaneously with 2 microelectrodes. In one series of recording sessions, one electrode was placed in the occipital lobe with the other electrode placed at temporal lobe sites (monkeys M and S). In the other series of experiments, one electrode was placed in the left occipital lobe and the second microelectrode was situated in the right occipital lobe (monkey S). The microelectrodes were advanced with hydraulic microdrives (Narashige) through guide tubes inserted through the dura. For the temporal lobe recordings the guide tubes were lowered 13 mm into the brain to protect the microelectrode tips and to straighten the trajectory of the microelectrode travel. A grid system (Crist Instrument) was used in the chambers to organize guide tube placements.
The signals from each electrode were separated into spike and LFP channels (Tucker-Davis Technologies, Gainesville, FL). After appropriate band-pass filtering the LFPs were sampled at 200 Hz and the 1- to 10-kHz channels were sorted on-line for spike shapes with either matched template filters or neural network classifiers (Chandra and Optican 1997).
All the recordings were monopolar with the ground tied to either skull screws (monkey S) or a scalp EEG lead (monkey M). The same ground was used for both microelectrodes in the dual recordings. Monopolar LFP recordings (like those reported here) are not as localized as single-unit or bipolar LFP recordings, nor as delocalized as scalp EEGs and visually evoked potentials (VEPs). An important question is: how localized or delocalized are these LFP recordings? In a separate series of experiments we demonstrate that EMPs extracted from monopolar LFP recordings vary significantly across distances as little as 400 µm along vertically oriented electrode tracks. These data and the relevant methods of analysis are explored in SUPPLEMENT 3 (the Supplemental Material is available at the Journal of Neurophysiology web site).1
ASSESSMENT OF VISUAL ACTIVITY. All of the LFP recordings were obtained from positions in the recording track associated with single-unit and multiunit activity. Visually driven units were located by detecting the presence of modulated responses to a checkerboard pattern that could be varied in contrast, check size, check aspect ratio, and frequency of stimulation period (as appearance–disappearance or contrast–reversal). After isolation of single units, activating regions (Snodderly and Gur 1995) of some of the units were outlined by hand with a mouse controlling the positions of bars and squares, and the rate and type of contrast modulation (black–white, red–green). In many recording sessions, a standard checkerboard m-sequence (50 Hz), sized to cover one of the hand-mapped receptive fields, was run with the animal performing a concurrent fixation task.
EYE MOVEMENT RECORDING AND DETECTION OF SACCADES. The monkeys' heads were fixed to the primate chair through the implanted head holder during all of the recording sessions. Eye position measurements were recorded using the horizontal and vertical analog outputs from the E5000 infrared video eye-tracking system (ASL, Bedford, MA). The animal's gaze position was calibrated each day before experiments began, and then whenever necessary to ensure the accuracy of the calibration. Horizontal and vertical signals were processed to determine the occurrence of a saccade, its amplitude, velocity, direction, and positions of fixation.
Saccade onset is detected when the eye speed surpasses a threshold equal to the smallest reliable difference (about 1.3°) between 2 consecutive positions divided by 10 ms (2 x temporal sampling resolution of the eye tracker signals), corresponding to 130°/s. Saccade end is detected when the velocity falls back below the threshold. Saccades that are preceded by signals with unusually low variance, such as a flat line attributed to the loss of signal (as can be produced by a blink), are not included in the analysis. The average position recorded during periods that have signal but no detectable velocity components are taken as the centers of positions of fixation. Saccades that are separated by a fixation period 
80 ms are fused through interpolation if they are both in the same cardinal direction (left, right, up, down) and discarded if they are not.
EXPERIMENTAL CONTROL AND BEHAVIORAL TASK. The monkeys performed a pattern-recognition task that required them to report the appearance of a target during the presentation of a sequence of similar images. The rate of image change (2, 3, or 4 Hz) was kept in a range that produced better than chance performance on the tasks. Shown in Fig. 1C is the target image with superimposed numerals indicating the temporal modulation sequence (Fig. 1A) assigned to each hexagonal region ("cube") in the image. The modulation moves the white diamond from the top (-1, Fig. 1B) to the bottom (+1, Fig. 1B) of the hexagonal region; for the "Y"-junction this produces what appears to be a cube viewed and lighted from above (-1) or a cube viewed and lighted from below (+1). Eight of the 10 regions are modulated, whereas the center 2 regions (9, 10) are static. The modulation sequence is a 6th-order m-sequence (63 elements in length) (Sutter 1992). The 8 modulated cube regions are assigned to different shifts of the m-sequence. In this study, the neural activity was not correlated against the m-sequence to produce estimates of the responses to the appearances of either of the elements (-1 or +1) in Fig. 1B at each of the eight positions (1–8) in Fig. 1C (this is reported in Purpura et al. 1999). Here the modulation sequence is used to produce a set of images whose subregions (cube elements) are nearly uncorrelated in space and in time when considered pairwise, and also have nearly zero higher-order correlations at each position for up to 6 transitions in the past. At least one cube element is modulated on each transition and, on average, 4 of the elements are flipped.
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The use of an m-sequence to specify the transitions in the subregions and the relationship between the transitions in different subregions reduces the degree of correlation between subsequent samples of the stimulus. This will be true whether the visual system samples a sequence in the stimulus through extended periods of fixation or by selecting different regions of the stimulus by making attentional shifts and saccades across the display. Thus the saccade-related neural activity in the perisaccadic interval will be less influenced by correlations in the visual display. Presaccadic spatial adaptation has been shown to influence the responses to visual stimuli presented during a saccade (Judge et al. 1980) and after the saccade (Gawne and Woods 2003). Here we have balanced these potential effects by presenting the same cube configuration (-1 or +1, followed by -1 or +1, respectively; see Fig. 1B) as often as the opposite configuration (-1 or +1 followed by +1 or -1, respectively) in subsequent frames in many regions of the stimulus. Having only one instance of pattern appearance at the beginning of each trial, followed by a period of pattern modulation, also provides a more balanced sequence of visual inputs across the trials: the overall values of luminance in each of the hexagonal subregions are the same for both configurations -1 or +1 (Fig. 1B), and regions of contrast modulation are fragmented in time and space across the sequence of images and trials.
Subsequences of the m-sequence were presented on each trial. The sequences overlapped on subsequent trials. A new trial always began one slot behind where it ended at the termination of the last trial, before the last presentation of the target. In Fig. 1A staggered bands (labeled "Trial #1," "Trial #2," and "Trial #3") beneath the m-sequences indicate which configurations of the multi-input m-sequence were used in each of the 3 trials shown. A vertical bar within each band shows where the target replaced the next configuration in the sequence. The length of each trial was selected at random from a choice of 0 (a catch trial where only the target is shown), 2, 4, 5, or 6 s before appearance of the target. In the examples shown here, the first 3 trials would run for 5, 4, and 5 s. Recognition of the target is signaled by a bar release between 150 to 600 ms following the target. The target is followed by the next configuration in the multi-input m-sequence to provide a backward mask of the target before the screen is returned to a featureless mean gray background. The size of the liquid reward (juice or water) is scaled to the length of the trial.
The visual stimuli were presented on a Mitsubishi XC-2930C CRT (27° H x 21° V screen) placed 114 cm in front of the monkey. Images were displayed with a frame rate of 95.5 Hz. The mean luminance of the featureless gray background was 50 cd/m2 with the white, light gray, and dark gray regions of the hexagonal elements (Fig. 1B) at 100, 80, and 15 cd/m2, respectively. Each hexagonal region, or cube, was approximately 3.5° H x 4° V, with the overall configuration of 10 conjoined cubes subtending about 14° H x 10° V on the screen (Fig. 1C). The surrounding gray background was left on the screen between trials. Graphics were produced with a VSG 2/3 (Cambridge Research, Cambridge, UK) video board running on a Pentium PC. Real-time control of the graphics computer, monitoring and recording of gaze position, recording of neural activity, monitoring behavioral responses, and reward delivery were performed with a second Pentium PC running TEMPO (Reflective Computing, St. Louis, MO). Coordination of the 2 PCs was administered through a proprietary data transfer protocol (Reflective Computing).
GROUPINGS OF SACCADES FOR ANALYSIS. Saccades are generated in a number of different contexts during the trials. In this study, we consider groupings based only on the presence of a significant horizontal component in the eye movement signals. We classify the saccades into different analysis groups based on several factors: 1) The direction of the saccade with respect to the laterality of the recording site. Saccades are thus either contraversive (directed away from the hemisphere of the brain containing the recording site) or ipsiversive (directed toward the recording site). 2) The direction of the saccade in body-centered coordinates; that is, leftward saccades are to the left side of the body. 3) The time of a stimulus sequence interchange (visual transient) with respect to the time of the saccade in the 500-ms analysis window centered on the saccade. Groups are formed for: i) saccades that are preceded (within 250 ms) by a visual transient; ii) saccades that are followed (within 250 ms) by a visual transient; iii) saccades produced between 2 visual transients occurring within the same 500-ms window (3- and 4-Hz stimulation). 4) Consecutive contraversive (or leftward) saccades are placed in 2 separate groups as are consecutive ipsiversive (rightward) saccades. This separation is made to reduce the confluence of the postsaccadic phase of one leftward saccade, for example, with the presaccadic phase of the next, or previous, leftward saccade. Consecutive saccades that have the same direction but that are separated by <250 ms are dropped from the analysis.
All of the analysis groups for the EMPs were established before any statistical testing. The goal here was to first organize the field potential data by behavioral criteria such as the direction of saccade and then establish whether intrinsic components of the EMP waveforms could be segregated based on statistical tests.
Figure 2 illustrates how a sequence of saccades made over 2 trials is divided into analysis groups L1, L2, R1, and R2. Successive saccades in the same direction are alternately assigned to groups 1 and 2 (L1 and L2 or R1 and R2) regardless of the presence of intervening saccades of the opposite direction. For example, beginning with the first leftward saccade in Trial 1, every other leftward saccade is assigned to group L1 even if a rightward saccade intervenes or a new trial commences. The other leftward saccades are assigned to group L2. The rightward saccades are similarly assigned to groups R1 and R2. In this example, by the end of Trial 2, there will have been 3 L1 saccades, 2 L2 saccades, and 3 each of R1 and R2.
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Data analysis
SPECTROGRAMS. Among the features one would like to collect from saccade-triggered averages of LFPs are: 1) the time-to-peak power in the signals; 2) the shape of the waveforms [are they dominated by one sinusoidal component or do they exhibit frequency modulation (FM)?]; 3) the extent to which they are influenced by the timing of stimulus transitions in the perisaccadic interval, saccade direction, and recording location.
Dominating this type of analysis, as with the analysis of all evoked field-potential data, are questions concerning what components of the LFP activity are robust and how much confidence can one place on the estimates of such extracted features. LFP averages are often about one-fourth to one-tenth the amplitude of the range exhibited on individual trials. Thus the issue becomes, in the face of a relatively low signal-to-noise ratio, what is the most efficient way of analyzing such data. The answer is to make estimates in a restricted range in the spectrotemporal domain (Percival and Walden 1993) using an approach that allows multiple independent estimates to be made from the same data set. Multitaper spectral analysis (Thomson 1982) provides a means to produce such estimates; the availability of these multiple estimates improves the reliability of conclusions that can be made about features extracted from the physiological signals of interest. Multitaper spectral analysis also establishes a rigorous set of guidelines for obtaining an optimal tiling of the spatiotemporal domain that avoids violating theoretical limits imposed on time and frequency by the uncertainty relation (Jarvis and Mitra 2001; Mitra and Pesaran 1999; Percival and Walden 1993).
The difficulty with spectral estimation is that the statistical properties of samples of neural activity change with time, violating the stationarity requirements of all spectral analysis methods. It is sometimes possible to produce cyclo-stationarity by taking samples collected during repetitive stimulation or repeated behavioral actions. During the temporal intervals defined by these sampling procedures, however, the neural activity is often still nonstationary. To produce an estimate of how the spectrum of the LFP changes with time around the saccade we calculate Thomson's high-resolution multitaper spectrogram (Thomson 2002) using the saccade onset time to define the temporal interval for drawing samples from the trials. Spectrograms minimize the violation of the stationarity requirement by calculating spectral quantities over a smaller temporal window in the interval of interest, in our case the perisaccadic interval, and shifting the window over that interval. A brief description of Thomson's version of the spectrogram technique is provided in SUPPLEMENTS 1 and 2 (the Supplemental Material is available at the Journal of Neurophysiology web site).2
ESTIMATE OF THE STANDARD ERROR IN THE SPECTROGRAM. The goal here is to extract features from the spectrograms of the average LFPs to characterize how saccades modulate the neural activity in local cortical populations. Spectrograms are 2-dimensional (2-D). As such, a salient feature or region within a spectrogram can be defined by its area, the orientation and eccentricity of a best-fitting ellipse, and the 2-D coordinates of its centroid. Thus one problem with carrying out such a region-of-interest (ROI) analysis is determining what is a salient and robust feature in the spectrogram. Here, we establish a threshold level of power spectral density in each spectrogram (see APPENDIX A). This value is then compared point by point in the spectrogram with a 95% confidence interval for the mean computed at each point using all the samples in a subset (e.g., those associated with rightward saccades only) of the LFPs. If a point in the spectrogram has a confidence interval that does not bracket the threshold (noise) level for the spectrogram, then this point is included in a region of interest. If the threshold value does fall within the point's confidence interval, then that point in the spectrogram is set to zero power spectral density.
Each LFP sample referenced to a saccade contributes additively and independently to our estimate of the spectrogram of the saccade-triggered average LFP. As a consequence, the jackknife standard error calculation can be used to produce a confidence interval for the spectrogram of the mean LFP (Efron and Tribshirani 1998; Thomson and Chave 1991). This is shown in APPENDIX A.
ROI (REGION-OF-INTEREST) ANALYSIS. To extract statistically significant regions from the spectrograms we need to define the lower bound and the maximum value for the distribution of values in the spectrogram (Savg in APPENDIX A). The estimates of the lower bound and maximum are used as inputs to the MATLAB function (MATLAB, v6.1) ROICOLOR. This function determines the coordinates in a 2-D array where the power ranges between 2 levels. We have chosen a noise level (seeAPPENDIX A) and an estimate of maximum power (see APPENDIX B) in the spectrogram as the 2 levels. The extracted regions are then passed through a continuity filter (BWLABEL function, MATLAB) to determine which of the values in the ROI are connected to other values in the spectrogram by more than the corners of a time–frequency pixel. The assumption here is that continuity in the time–frequency plane reflects waveform integrity in the LFPs. Because the spectrograms have been tested against a threshold (noise level) before application of ROICOLOR and BWLABEL, the ROIs are maps of areas in the spectrograms that have significant power as determined by the jackknife procedure.
Using the MATLAB function REGIONPPROPS, we also extract the values of several parameters from each ROI. These parameters include the area, the coordinates of the centroid (centers of mass), and the orientation and eccentricity of a best-fitting ellipse. The areas of the ROIs are used to identify the largest ROI in a spectrogram. The centroid of an ROI is its weighted center. The weights are formed from the distributions of power in the frequency and time domains. This estimate uses all of the data describing a region, so focusing on the largest ROIs strengthens the estimate of its centroid.
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RESULTS |
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), we will treat these different types of runs as only repeats of the same pattern-recognition task for the analyses presented here. Studying active vision
An illustration of the events constituting a typical trial is shown in Fig. 3. Once the monkey's gaze enters the screen and dwells there for 
250 ms, the first configuration slotted for the current trial appears (Fig. 3A, image frame 49). In this trial, 10 configurations are presented (interchange rate of 2 Hz) before the target is displayed (Fig. 3B, top row, image frame 127). As can be seen from the eye movement traces in Fig. 3, the monkey was free to shift its gaze within a region bounded by the screen during the course of the trial. Eight saccades with detectable rightward components and 10 saccades with leftward components can be extracted from the eye movement records for this trial. The eye movement traces and LFPs recorded simultaneously in the occipital and temporal lobes for this trial are shown in Fig. 4. A typical recording session provided behavioral and neural data from 250 similarly organized trials.
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As seen in Fig. 4, LFPs recorded in both occipital (V2) and temporal lobe (TE) sites contain contributions from external visual transients, and an additional set of transients more closely correlated with the times of saccades (asterisks in V2 LFP). Our goal here is to characterize the spectrotemporal content of the neural activity in an interval surrounding the saccades so that the activation time of the EMP can be reliably determined and associated with a particular saccade and recording location. The emphasis here is on the average LFP activity triggered by the saccade (the EMP). The average LFPs obtained from all the trials in one recording session, using saccade onset time for the averaging reference, are shown in Fig. 5, A and B. Transient components in the V2 and TE LFPs are seen in the perisaccadic intervals centered on the saccades. The saccades with significant leftward (contraversive) and rightward (ipsiversive) horizontal components are associated with different average LFP waveforms. For the V2 and TE recordings, the rightward saccades produce larger transient average LFPs in the postsaccadic interval. We will see later that the bulk of the occipital lobe EMPs in our recordings show, on average, less directional modulation (see Figs. 10 and 11) than the example presented here in Figs. 5, 6, 7, 8. We highlight this less-typical example because it allows us to demonstrate how our methods of analysis can reveal differences in the timing and waveforms of EMPs associated with leftward and rightward saccades even when the LFPs are recorded from the same cortical location.
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Characterizing the signals in detail during active vision where saccades are often made in rapid succession requires addressing a number of concerns. An averaging epoch of 1 s on either side of the saccade onset time can introduce multiple eye movement events into the pre- and postsaccadic phases of each estimate. As can be seen in Fig. 4, the rate of saccade generation is approximately 3/s. Restricting the temporal averaging window to 500 ms centered on the saccade time increases the independence of the samples. A 250-ms postsaccadic phase of neural activity is less likely to contain the presaccadic activity appearing 250 ms before the next eye movement. A 500-ms window also reduces the chance that the activity associated with undetected saccades (i.e., saccades that did not pass the automated velocity criterion comparison) will contribute to either the pre- or postsaccadic components of the LFPs. Closer inspection of Fig. 4 reveals that there was 1 leftward (at about 900 ms) and 2 rightward (at about 3,800 and about 4,900 ms) small-amplitude saccades that were missed by our conservative saccadic detection algorithm in this trial alone. These 3 small-amplitude saccades probably represent fixational eye movements. They are not produced at an abnormally high rate and, as a consequence, are preceded and followed by periods of 250 ms that do not contain large-amplitude saccades. Thus these small-amplitude saccades would not have contaminated the perisaccadic intervals of the saccades obtained from this trial. As discussed in METHODS Data analysis, an attempt was made a priori to maximize the independence of the LFP samples by forming separate analysis categories for saccades of different directions, for consecutive saccades of the same direction, and for saccades preceded or followed by stimulus transitions.
Averages formed with 500-ms time windows are shown in Fig. 6, A and B. Here, for the purpose of demonstration, the averages for ipsiversive (rightward) and contraversive (leftward) saccades are formed from all of the saccades with no segregation based on m-sequence transition times (stimulus transients) or saccade ordering. The LFP activity associated with each saccade is shown in Fig. 6, C and D.
In the saccade-by-saccade V2 and TE Left plots (Fig. 6, C and D), 31 of 119 leftward saccades are followed by an m-sequence transition and a rightward saccade, with the rightward saccades having an average latency of 200 ms with respect to the stimulus transients. Fifty-three of the remaining leftward saccades are not paired with any rightward saccades. Thirty-five additional leftward saccades are paired with rightward saccades but their temporal relationship with respect to the former is more variable.
Two prominent features appear in the TE Left plot (Fig. 6D, top panel). The blue swatch in the presaccadic phase appears to track the stimulus transitions. However, this feature leads the m-sequence transitions by 50 ms. Thus the most likely source for a segment of this blue region is the LFP activity associated with rightward saccades that were generated in response to some of the stimulus transients in the postsaccadic phase of the leftward saccades. The second prominent feature in the TE left plot (Fig. 6D, top panel) is the region of positive LFP voltage values (yellow-reds) that appears to straddle the saccade onset time (0 ms) over the sample set. Because the stimulus transitions move through this region rather than delimit its onset side, it is unlikely that the positive peak in the EMP (see Fig. 6B, left) is a visually evoked potential. There may be interactions between the stimuli and saccade-related activity but these are difficult to extract from the postsaccadic components of the averages of the signals that we focus on in this study (see following text, Interactions between visual transients and saccades).
The extent to which the averages calculated for one particular saccade grouping parameter are distorted by the activity of oppositely directed surrounding saccades is reduced by several factors. First, putting sequential saccades with the same horizontal component (leftward, for example) into 2 separate analysis groups disrupts the "wraparound" effect seen in the saccade-by-saccade plots in the TE Left plot (Fig. 6D). This procedure allows for time to develop between the postsaccadic phase of one leftward saccade and the presaccadic phase of the next leftward saccade to be included in the analysis group. As a consequence, the timing of an interaction between a stimulus transition and a saccade appearing late in the postsaccadic phase of an oppositely directed saccade will tend not to reinforce a similar interaction 2 saccades away. There will be even less reinforcement of these types of interactions when the saccade groups are formed across trials. Second, the emphasis here is on estimating the EMP, and the quality of this estimate formed by using the saccade onset time as the averaging trigger. The averaging procedure reinforces the signals timed to this trigger and leads to partial cancellation of LFP components produced by oppositely directed saccades appearing in the perisaccadic interval.
Spectrograms
Time–frequency spectrograms for the average LFPs presented in Fig. 6 are shown in Fig. 7. For these spectrograms, the inputs to the spectral analysis are the averages calculated from 119 leftward saccade-referenced LFPs (Fig. 7, A and C) and 109 rightward saccade-referenced LFPs (Fig. 7, B and D). Traditionally, calculation of a spectrogram involves the subtraction of the average signal from each signal sample before computing the Fourier transform. Here we produce a time–frequency representation of the average LFP, not of the residual variance remaining after subtraction of the average. The nonzero power spectral density values in the spectrograms were established by setting a threshold based on an estimate of the standard error of the mean spectrogram (see METHODS, Data analysis, and APPENDIX A).
The centroids of the largest ROIs in the spectrograms in Fig. 8 are indicated by the white stars; the white circles indicate the maxima. The orientation and eccentricity of the major axis of the best-fitting ellipse to an ROI summarize the temporal waveform of the transient response in the perisaccadic interval. For example, an orientation of 90° from the vertical and an eccentricity near 1 are associated with a temporal waveform that does not sweep through frequency bands over the course of the response. The temporal waveform thus resembles a mixture of a few damped sinusoids restricted to a narrow band of frequencies (Figs. 6B, leftward saccades, and 8C). An orientation of 0° or 180° and an eccentricity near 1, on the other hand, indicates that the temporal waveform is more short-lived but with a much richer frequency content as is often produced by a transient response. Finally, a more oblique orientation is produced by temporal waveforms that display FM. In Fig. 6A (rightward saccades), the average LFPs exhibit a period of rapid modulation, after the saccade, that is then followed by a slower component. Note that the spectrogram for this waveform shows a frequency sweep with the largest ROI starting at higher frequencies and progressing to lower frequencies (Figs. 7B, 8B). The major axes are shown for the largest, or solitary ROIs in each part of Fig. 8. The major axes provide a useful descriptor of the temporal waveforms of the LFPs produced in the perisaccadic interval.
ROI centroids and spectrogram maxima in the time–frequency plane
The numerous estimates of EMP characteristics obtained from multiple recording locations, and from saccades produced in a variety of different contexts during active vision, can be condensed into a smaller set of numbers that describe these waveforms. Figure 9 illustrates that the EMPs can populate different portions of the time–frequency plane. Here the centroids and maxima split largely over recording site. Figure 9A maps the time–frequency positions of the centroids of the largest ROIs extracted from the spectrograms obtained from the left and right occipital lobes during one recording session. In Fig. 9B the maxima of the spectrograms are mapped. For both Fig. 9A and Fig. 9B, the black disks are values extracted from left occipital cortex spectrograms, whereas the white disks are obtained from the right occipital cortex. Each disk is from a spectrogram calculated from a unique set of LFPs defined by one of the analysis groups (see METHODS, Grouping of saccades for analysis, and Fig. 2). For the sake of clarity, the disks are not labeled with analysis group designators. In Fig. 9, C and D similar maps are shown for the data obtained from a right occipital lobe (V2 location in black) and right temporal lobe (TE location in white) pair recording. Examples of LFPs, EMPs, spectrograms, and ROIs drawn and composed from this data set appear in Figs. 4, 5, 6, 7, 8. Figure 9C is a map of centroids of the largest ROIs in the spectrograms and Fig. 9D maps the spectrogram maxima.
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Other spatial relationships exist in these data and those extracted from the other recordings. An analysis of clustering in the time–frequency plane appears below in ROI centroids and maxima in the time–frequency plane: measures of clustering. We will show (see Fig. 9) that if the 2 channels in the data sets are combined, distinct clusters of centroids and maxima in the time–frequency plane can be found that are formed around the direction of the saccade. The more dramatic separation of centroids (Fig. 9C, and to a lesser extent Fig. 9A) and maxima (Fig. 9B) based on recording location, apparent from visual inspection of the positions of the black and white disks, is also reinforced by the quantitative analysis of the spatial relationships in the time–frequency plane. We will also show (Figs. 10 and 11) that if we base our analyses on the time or frequency positions of the centroids and maxima alone, a separation based on saccade direction will not be apparent, on average, for the occipital lobe EMPs, but will be more evident for those recorded in the temporal lobe.
Response timing and frequency content of EMPs
In Figs. 10 and 11 and in Table 1 we show that occipital and temporal lobe EMPs can be differentiated based on their timing and frequency content.
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Figure 10 summarizes the positions of the centroids of the largest ROIs in the spectrograms, in time, with respect to saccade onset, and in frequency, for all the analysis groups, runs, and recording sessions obtained for this study. Saccade directions are referenced to whether they are contraversive or ipsiversive to the recording site. Reorganizing the saccade directions by body-centered coordinates (leftward or rightward) made no significant changes to the distributions for the occipital lobe. For the temporal lobe results, no recordings were made in the left hemisphere; consequently, contraversive is synonymous with leftward and all ipsiversives are rightward saccades. The total number of centroid estimates used to produce the distributions is given in the title of each relative-frequency time histogram.
For both contraversive and ipsiversive saccades, signal power of the EMPs is centered in the postsaccadic phase of the perisaccadic interval. The range between 75 and 150 ms after saccade onset contains about 67% of the centroids associated with contraversive saccades in the occipital lobe (Fig. 10A). That same range includes about 61% of ipsiversive occipital lobe EMP centroids (Fig. 10B). The mode of both distributions is 100 ms with similar means, 73 ms for contraversive, and 78 ms for ipsiversive saccades.
For temporal lobe EMPs, the distributions of the centroid latencies (with respect to saccade onset) of the largest ROIs are more dissimilar when comparing across direction of saccade. In Fig. 10C, only 30% of the centroids are clustered between 75 and 150 ms. This percentage inflates to 41% if the interval expands to 175 ms. On the other hand, for ipsiversive saccades in the temporal lobe, 53% of the centroids lie in the interval including 75 and 150 ms, and 65% are in the 75- to 175-ms postsaccadic interval. Correspondingly, the modes and means of the 2 distributions are dissimilar: modes of 0 and 150 ms for contraversive and ipsiversive saccades, respectively; means of 41 and 85 ms for contraversive and ipsiversive saccades, respectively.
The centroid distributions in time and frequency for the occipital lobe EMPs are significantly different from those compiled for the temporal lobe. Visual inspection of the temporal frequency histograms in Fig. 10 (bottom boxes in A–D) suggests that the distributions are skewed toward higher frequencies in the occipital lobe than they are in the temporal lobe. This result agrees with the picture that emerges from Figs. 7 and 8 that shows elongation of spectrogram ROIs into higher frequencies for occipital lobe EMPs. This elongation raises the centroids of the occipital lobe ROIs in the frequency dimension above those found in many temporal lobe spectrograms. Note that even though ipsiversive saccades produce EMPs in the temporal lobe that are more localized in time (Fig. 10D), their frequency content is about the same as that for the more temporally delocalized contraversive temporal lobe EMPs.
To more objectively evaluate the uniqueness of the centroid distributions presented in Fig. 10, the 2-sample Kolmogorov–Smirnov (KS2) test was performed (KSTEST2 in MATLAB v6.1) on each pair of distributions in the figure. The time and frequency coordinates of the centroids were evaluated separately. The hypothesis that any 2 of the centroid ensembles, such as the time values for contraversive occipital lobe centroids and their ipsiversive counterparts, were drawn from the same distribution was rejected if the P value obtained from the test was <0.025 (2-tailed, 95% confidence level). The results of these tests are summarized in Fig. 11.
Table 1 presents some of the values useful for characterizing the distributions shown in Fig. 10 (see Table 1 caption for details), and Fig. 11 presents the results of the KS2 tests. The "Tdist" values summarize how the distributions are balanced about saccade onset time (0 ms). "Tdist" is defined as Tdist = [n(pst) - n(pre)]/[n(pst) + n(pre)], where n(pst) and n(pre) are the number of spectrograms that had centroids in the presaccadic and postsaccadic phase, respectively, of the perisaccadic interval. Thus if the number is 1, all of the centroids in the distribution are piled up at 250 ms in the postsaccadic phase; for -1 the concentration is at -250 ms in the presaccadic phase; and for small fractions, the centroids are then more evenly dispersed around saccade onset time. In the columns arranged below the header "Maxima" in Table 1 are values extracted from the distributions of the maxima of the spectrograms. These distributions are not shown.
The conclusions to be drawn from Table 1 and Fig. 11 are that: 1) the direction of the saccade does not have as much influence on the activation time of the EMPs (as measured by the position of the centroids in time) in the occipital lobe recording sites as it does in the temporal lobe sites; 2) most of the activation elicited by the saccade in the local neural population is postsaccadic in the occipital lobe, but can be either presaccadic or postsaccadic in the temporal lobe if the saccade is contraversive, and is mostly postsaccadic for ipsiversive saccades; 3) the EMP waveforms are centered at 100 ms after saccade onset in the occipital lobe for both directions of saccade; 4) temporal lobe EMPs are centered at 150 ms for ipsiversive saccades and at saccade onset (0 ms) for contraversive saccades; 5) there are more EMPs with higher temporal frequency content in the occipital lobe than are found in the temporal lobe.
Similar conclusions can be drawn from an examination of the spectrogram maxima. Overall, the pattern of distributions that are distinguishable from one another is the same as that found with the centroids. However, there is a general shift of the distributions of the maxima to longer latencies and lower frequencies than what is seen with the centroids (Table 1). Thus the trend is that the peak power of the EMPs comes later in the postsaccadic interval and is associated with a slower component in the waveform. A notable exception is evident for the OC spectrograms (Table 1). The maximum power in these EMPs appears 50 ms after saccade onset but the LFP activity in the EMP is centered at 100 ms after saccade onset.
Interactions between visual transients and saccades
As illustrated in Figs. 3 and 4, saccades occur during the ongoing presentation of a sequence of images. Thus the perisaccadic interval used to analyze the EMPs frequently contains a stimulus transition that appears either before or after the saccade. For 2 of the modulation rates (3 and 4 Hz), stimulus transitions can occur in both the presaccadic and postsaccadic phases of the 500-ms perisaccadic interval. Table 2 lists the modes and means extracted from distributions of ROI centroids and spectrogram maxima for EMPs with a visual transient in the presaccadic phase of the perisaccadic interval ("O1" and "T1"), in the postsaccadic phase ("O2" and "T2"), or in both presaccadic and postsaccadic phases ("O1&2" and "T1&2"). Tdist values for the centroid and maxima time distributions are also listed.
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As determined by KS2 tests (test results are not plotted), the distributions of the times of the occipital and temporal lobe ROI centroids are not differentiated by the appearance of a single stimulus transient in either the presaccadic or postsaccadic interval alone. The distributions of spectrogram maxima follow a pattern similar to that of the centroids: the maximum power in the occipital lobe EMPs is shifted to the postsaccadic phase of the perisaccadic interval regardless of whether a visual transient appears before or after the saccade. This suggests that the visual transient does not interact very strongly with the saccade in the generation of the EMP. As seen in Fig. 6C (bottom panel, rightward saccades), the stimulus transitions move through the saccade-triggered LFPs with little impact on the time course of the voltage fluctuations in the postsaccadic phase.
When 2 stimulus transitions appear within the perisaccadic interval, however, the activation times of the occipital lobe EMPs are distributed more evenly between the pre- and postsaccadic intervals (in Table 2, Tdist = 0.59 for "O1&2" compared with values of 0.79 and 0.69 for "O1" and "O2," respectively). The KS2 tests indicate that the "O1&2" distributions are only significantly different from the "O1" distributions of centroid times (P < 0.01) and frequencies (P < 0.0001). When 2 visual transients appear in a 500-ms perisaccadic interval (3- or 4-Hz m-sequence modulation rates), their appearance within the interval is restricted to a smaller range of times than is possible with the single transients (see SUPPLEMENT 2, Fig. S1). The more regular timing of the double transients may thus allow for reinforcement of the visually evoked activity in the presaccadic phase that then shifts the distribution of the activation times, and the frequency content of the EMPs toward the presaccadic phase and to higher frequencies, respectively. The visual transients appearing in the postsaccadic phase are masked by the postsaccadic concentration of power of the occipital lobe EMPs (see Fig. 10). As a consequence, the "O2" and "O1&2" distributions are not significantly different. In the temporal lobe, the distributions of the centroids and maxima are not significantly influenced by the timing of visual transients: activation times of the EMPs remain more evenly distributed about saccade onset time than those recorded in the occipital lobe EMPs regardless of the timing of the visual transients (Table 2).
Analysis of the orientation and eccentricity of the spectrogram ROIs
In Figs. 12, 13, 14 and in Table 3 we show that there are significantly more occipital lobe EMPs that exhibit FM than are seen in the temporal lobe.
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The orientation and shape of the largest ROIs in the spectrograms provide a characterization of the time–frequency content of the EMPs. The orientation indicates the extent of frequency sweep in the time evolution of the LFP, whereas the eccentricity of the best-fitting ellipse suggests the extent to which the signals are equally localized or delocalized in the time and frequency dimensions. In Fig. 12, A and C the orientation of the ROIs as a function of the timing of their centroids is shown for the same recording pairs and analysis groups as shown in Fig. 9, A and C. The eccentricities of the best-fitting ellipses to the ROIs are shown in Fig. 12, B and D. Eccentricity can range in value between 2 extremes: 0 for a circle and 1 for a line.
The majority of the spectrogram ROIs extracted from the occipital lobe EMPs (white and black disks in Fig. 12A, black disks only in Fig. 12C) show a marked tendency to cluster away from the center of the range of values (90°). Contrary to this pattern, the temporal lobe orientation values in Fig. 12C (white disks) are more restricted to a zone around 90°. As described above, an orientation of 90° indicates that the ROI of the spectrogram shows little FM over the course of its appearance: the EMP is more like a damped sinusoid centered at a single frequency than a "chirp." As shown in these 2 panels, however, the occipital lobe ROIs have orientations placed more near 0° and 180°, indicating that their EMPs are more like chirps than damped sinusoids. The eccentricity values shown in Fig. 12, B and D are, for the most part, above 0.5. This result suggests that the majority of the ROIs of the spectrograms are more elongated than circular in the time–frequency plane across many of the conditions under which the analysis groups are formed, such as direction of saccade or timing of a stimulus transient. There is a tendency for the temporal lobe spectrogram ROIs to become more elongated in the time–frequency plane because their centroids move to longer latencies after the saccades. Because this is matched by the appearance of orientations away from 90°, the elongation is more a reflection of an increase in their frequency content than in their duration.
Figure 13 provides a set of distributions that collect the values of orientation and eccentricity extracted from all of the spectrograms. The distributions are organized around the occipital (Fig. 13, A and B) and temporal (Fig. 13, C and D) lobe recording sites, and around the direction of the associated saccades, contraversive (A and C) and ipsiversive (B and D). Figure 14 encapsulates the results of the KS2 test applied to every pair of distributions presented in Fig. 13, separately for orientation and eccentricity. The distributions of the orientations of the temporal lobe ROIs are dominated by a mode of 90° (Table 3). This is true for both contraversive and ipsiversive saccades. The occipital lobe ROI orientations are distributed more toward values near 20°, signifying the greater prevalence of FM in these EMPs. The emergence of more weight in the distribution at 90° for the contraversive occipital lobe ROIs and at 20° for the ipsiversive temporal lobe ROIs causes these distributions to look nearly the same in the KS2 test.
ROI centroids and maxima in the time–frequency plane: measures of clustering
To further characterize the EMPs, the "spatial" relationships of the ROI centroids and spectrogram maxima in the time–frequency plane were evaluated. The analyses presented above emphasize the distribution of centroids and maxima with respect to either time or frequency. As such, the procedures constitute a form of univariate analysis. Here we consider the influence of parameters such as recording location, direction of saccade, and timing of stimulus transients on the timing and frequency content of the EMPs as joint-dependent variables.
The "spatial" organization of the centroids and maxima in Fig. 9 in the time–frequency plane can be evaluated using measures of distance or separation. For example, the average Euclidean distance between all of the occipital lobe centroids in the time–frequency plane in Fig. 9C can be compared against the average distance between the temporal lobe centroids. Combining the centroids from both channels of recording and ordering the distances within the set provides a means for forming clusters in the time–frequency plane in the perisaccadic interval.
A hierarchical cluster tree is formed by making successive subdivisions of the sets of centroids or maxima based on establishing the distance between the centers of mass of the 2 subdivisions. Because the algorithm follows successive binary subdivisions of the set, it is possible to define clusters involving a small fraction of the original set of values of time and frequency. The question at each stage of the algorithm is: how can the remaining unorganized data be divided into 2 clusters that maximize the distance between their centers of mass? Here the clusters are evaluated for their inclusion of groups of centroids or maxima that are delineated by the direction of the saccade, the region of the cortex (occipital or temporal lobes, left occipital or right occipital), and the timing of a stimulus transient. The number of members of each group that fall into both clusters is tabulated. The likelihood of such an apportionment of the time–frequency plane appearing by chance is judged by a procedure outlined in APPENDIX C.
Clusters for saccade direction in the sets of centroids and maxima presented in Fig. 9 are indicated by the rectangles. For Fig. 9A, the cluster centered in higher temporal frequencies (enclosed by the closed-line rectangle) is for contraversive saccades, whereas that for ipsiversive saccades (dashed-line rectangle) is centered at lower frequencies. Note that the lower temporal frequency cluster involves centroids extracted from both recording locations. This is also the case in Fig. 9D. In Fig. 9C, the clusters do not mix occipital and temporal lobe EMPs, but although significant, the clusters represent a relatively small subset of the total range of time–frequency values exhibited by the EMPs generated during the scan paths for this particular recording. The maxima in Fig. 9B could not be segregated by saccade direction.
Table 4 presents the number of pair recordings that show evidence for clustering. From Table 4 we see that different cortical sites have dissimilar EMP waveforms in nearly half of the pair recordings. About 24–30% of pair recordings (centroids–maxima) have directional EMPs, with different time–frequency content for ipsiversive and contraversive saccades. Finally, the timing of the stimulus transient with respect to saccade onset produces significant clustering in time–frequency in only 5 out of the 34 pair recordings.
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In Table 4 and Fig. 9, if we had required that clusters for saccade direction be formed from either one channel or the other, there would be much less evidence for directional EMPs. If we consider only the distribution of time values separately from those of the frequency coordinates in the time–frequency plane, as we do in the univariate analysis in Figs. 10 and 11, the evidence for directional EMPs is significantly reduced for the occipital lobe. Nonetheless, there are striking individual examples of directional EMPs in our occipital lobe recordings (Figs. 5, 6, 7, 8, S1–S2). Directional corollary oculomotor inputs may be less uniformly distributed to the occipital lobe than they are in the temporal lobe, and therefore directional EMPs may be more difficult to encounter in the occipital lobe without targeting specialized regions of the cortex.
Summary
EMPs in the occipital lobe are largely postsaccadic events. The modes of the distributions of activation times (as measured by the centroids) are 100 ms after saccade onset regardless of saccade direction. In the temporal lobe, EMPs are both presaccadic and postsaccadic events if the saccades are contraversive. If the saccades are ipsiversive, the temporal lobe EMPs are almost entirely postsaccadic events and are concentrated at a later time in the postsaccadic interval than those generated in the occipital lobe. The frequencies incorporated into the temporal lobe EMPs are also in a lower band than those in the occipital lobe, regardless of the direction of the saccade.
Both contraversive and ipsiversive saccades produce time–frequency "chirps" in occipital lobe EMPs. Ipsiversive and contraversive saccades produce somewhat different degrees of FM in the temporal lobe EMPs. Contraversive saccades are associated with temporal lobe EMPs that are more like damped sinusoids centered at one frequency, whereas ipsiversive saccades produce some temporal lobe EMPs that undergo a sweep through several frequencies.
Saccades with 2 stimulus transitions (at 3 and 4 Hz) in their perisaccadic intervals are associated with occipital lobe EMPs that shift somewhat toward the presaccadic phase of the intervals. The majority of the occipital EMPs, however, are centered in the postsaccadic phase regardless of the timing of external visual transients.
Centroids and maxima can be segregated in the time–frequency plane by recording location in about half of the recording pairs. Saccade direction and the timing of stimulus transitions in the perisaccadic interval also produce distinct clusters for the OT pathway as a whole, although these clusters were not as robustly represented in our data sets as those based on cortical region.
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DISCUSSION |
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For primate vision to work, the internal representation of the visual world must be assembled across numerous shifts of gaze. It has long been conjectured that fast eye movements (saccades) are associated with a corollary discharge that reports to visual processing centers information about the eye movement, including the duration (amplitude) and direction of the saccade (reviewed in Sommer and Wurtz 2002). This corollary discharge may appear around the time of each eye movement to shift and reinterpret the activity in the multiple retinotopic maps that are distributed throughout the OT pathway (reviewed in Andersen et al. 1993). It may also play a role in reducing the blur signal produced by the movement of the retina across the visual scene during the saccade (reviewed in Krekelberg et al. 2003 and McCloskey 1981). Although protecting the observer from experiencing frequent bouts of visual smear, this corollary activity may also contribute to a loss of sensitivity during the saccade (reviewed in Krekelberg et al. 2003; Niemeier et al. 2003; Ross et al. 2001; Thiele et al. 2002) and to impairments in judgment of target position (Morrone et al. 1997), spatial relationships (Cai et al. 1997), and timing (Yarrow et al. 2001) in the perisaccadic interval. A retinal contribution to the suppression of activity during saccades has been recently identified (Ölveczky et al. 2003; Roska and Werblin 2003).
Active vision, therefore places the neural operations of visual processing in an oculomotor context. If the time between saccades is long enough, the responses of single visual neurons to a flashed stimulus are nearly identical to those seen during periods of prolonged fixation (DiCarlo and Maunsell 2000). However, when a rapid sequence of saccades is used in a visual task, such as the pattern-recognition task studied here, then the neural activity produced in the ventral stream must reflect both the analysis of the spatiotemporal patterns of contrast appearing on the retina and the oculomotor behavior framing the capture of visual information (Gallant et al. 1998; Gawne and Woods 2003; Richmond et al. 1999).
The question we have addressed here is, can the EMPs recorded in the OT pathway be considered as products of the activity generated in the distributed oculomotor network in the primate brain, or alternatively, do they only reflect visual activity in the retinogeniculate pathway?
Occipital lobe EMPs
Because the occipital lobe EMPs are largely postsaccadic events that are not commonly influenced by saccade direction, it is difficult to associate them with the corollary oculomotor activity that may be responsible for saccade preparation and receptive field remapping. The EMPs could represent the visual reafference that arrives at the cortex after the saccade. However, there are potential sources for postsaccadic activation from within the oculomotor network. Corticocortical projections originating in the parietal and frontal lobes are known to terminate in extrastriate visual cortex (Leichnetz and Goldberg 1988). Parietal and frontal regions demonstrate robust postsaccadic single-unit activity that is tuned for saccade amplitude and direction [inferior parietal lobule 7a: Barash et al. 1991a; FEF: Bruce and Goldberg 1985; prefrontal cortex (PFC): Funahashi et al. 1989; supplementary motor area (SMA): Schall 1991]. Suppression after refixation is also observed (FEF: Bruce 1990), and a withdrawal of excitatory inputs could contribute to the LFP activity seen in the occipital lobe. "Pause-rebound" units in SMA (Schall 1991) neurons that are transiently silenced during a saccade produce a burst of activity at the start of the next fixation. Thus the rebound, postsaccadic phase of the SMA responses could also play a role in the generation of the postsaccadic phase of the occipital lobe EMPs.
Although top-down corticocortical projections to the occipital lobe from oculomotor centers in the cortex may play a role in shaping the occipital lobe EMPs, the perisaccadic activity in the thalamus may also be crucial, and perhaps of more importance, given the less dramatic influence of saccade direction on LGN perisaccadic activity. As reviewed above, studies of LFP and both single-unit and intracellular recordings in the LGN of monkeys and cats over the last 30 yr have demonstrated the presence of robust eye movement–related activity in the thalamus (see also Hu et al. 1989; Ramcharan et al. 2001), even in the absence of retinal stimulation (Lee and Malpeli 1998). Recently, a detailed analysis of single-unit responses of alert monkey LGN neurons has confirmed the presence of an extraretinal signal in the thalamus (Reppas et al. 2002). This extraretinal signal interacts with retinal activity to produce an envelope of neural discharge that evolves from an initial phase of presaccadic-to-transsaccadic suppression to a period of excitation peaking in the postsaccadic phase at about 75–100 ms after onset of the saccade. This eye movement–related pause-rebound single-unit activity can be found in both the magnocellular and parvocellular layers of the LGN, but different visual environments are required to enhance the postsaccadic excitation in the different populations.
The overall envelope of saccade-related activity in the monkey LGN overlaps with the time course of the EMPs we observe in the occipital lobe. It is possible therefore that the EMP activity observed in the occipital lobe represents the impact of saccade-related thalamic activation on the visual cortex. Although the modes and means of the centroids of the spectrogram ROIs are shifted to longer latencies than the thalamic activity (by 10–30 ms), intracortical processing likely adds additional delays to the wave of excitation emerging from the thalamus.
The participation of the thalamus in generating EMPs in the cortex may extend beyond the LGN. Pause-rebound activation associated with saccades, but that is neither tuned for the direction or amplitude of the eye movement, has been described in the central lateral and paracentral nucleus of the intralaminar nuclei (ILN) (Schlag and Schlag-Rey 1984; Schlag-Rey and Schlag 1984) and in the inferior and medial pulvinar of the monkey (Robinson et al. 1986). The time course and range of changes in activity levels of these single-unit responses resemble those seen in the monkey LGN. The pulvinar in the monkey is well situated to broadcast its postsaccadic rebound discharge simultaneously to multiple targets in both striate and early extrastriate visual cortex. Reciprocal projections are present between the inferior pulvinar and V4 (Adams et al. 2000; Bender 1981; Shipp 2001; Ungerleider et al. 1983) and V2 and V3 (Adams et al. 2000; Shipp 2001). Striate cortex has connections with the more lateral divisions of the inferior pulvinar (Benevento and Rezak 1976; Gutierrez and Cusick 1997; Ungerleider et al. 1983).
The burst phase of the pause-rebound (pause-burst) activation in the LGN, pulvinar, and ILN could be a crucial component of the sequence of events leading to synchronized activation of more than one cortical laminae (Swadlow et al. 2002). The anatomical range of the cortical projections of the thalamic nuclei may distribute this synchronization across a number of cortical areas at the time of saccade onset. The bursts originating in a number of thalamic nuclei could lead to the production of the postsaccadic EMPs because rebound excitation in the thalamus plays a crucial role in triggering postinhibitory rebound excitation in the cortex (Grenier et al. 1998).
The results we present here and the studies we have reviewed suggest that the EMPs in the occipital lobe reflect the action of corollary oculomotor signals, where the site of extraretinal influence is in the thalamus. As such, the EMPs in the occipital lobe may be produced by cortical responses to retinogeniculate and thalamocortical activity that is modulated at the time of the saccade. Thus the occipital lobe EMPs may be generated by activity that is more akin to an exafference signal, a signal that combines visual reafference generated during the saccade, the retinal activity generated at the start of the new period of fixation, and an oculomotor corollary discharge (McCloskey 1981). Even if this exafference signal is removed by synaptic depression at the input layers of the striate (Carandini et al. 2002; Freeman et al. 2002) and extrastriate cortex, the transient depression may itself produce a brief synchronization in small populations of cortical neurons and thereby contribute to the generation of a field potential at the time of a saccade.
Temporal lobe EMPs
We report here that the temporal lobe sites in the right hemisphere have EMPs that are distributed more equally across the perisaccadic interval for contraversive saccades than for ipsiversive saccades. Thus unlike in the occipital lobe there is significant directional tuning of the EMPs in the temporal lobe. Sobotka and coworkers earlier demonstrated evidence for tuning for the direction of saccades in LFP (Sobotka and Ringo 1997) and single-unit recordings (Sobotka et al. 1997) in the medial temporal lobe. Two of their 16 LFP recordings had postsaccadic EMPs with significantly different waveforms associated with different saccade directions, and 17% of their single units displayed a similar sensitivity.
The presaccadic signals we see in the temporal lobe could represent neural activity involved in the preparation to make a saccade. In dorsal oculomotor areas of the cortex, the presaccadic activity appears to be involved in a number of functions: 1) target selection (seen in LIP: Gottlieb 2002; Gottlieb et al. 1998; and FEF: Schall and Hanes 1993; Schall and Thompson 1999); 2) tuning movement fields for direction and amplitude of intended saccades (seen in LIP: Barash et al. 1991a,b; FEF: Bruce and Goldberg 1985); 3) predictive remapping of retinotopic coordinates (seen in LIP: Colby et al. 1995; Duhamel et al. 1992; FEF: Umeno and Goldberg 1997); 4) coregistration of retinotopic coordinates with extraretinal coordinates (seen in LIP: Andersen 1997); 5) establishing a delay-period activity during mnemonic tasks (seen in LIP: Gnadt and Andersen 1988; Pesaran et al. 2002; FEF: Bruce and Goldberg 1985; PFC: Funahashi et al. 1989). Because LIP and FEF are either mono- or disynaptically connected with the posterior inferotemporal cortex (Webster et al. 1994), it is possible that the presaccadic activity appearing in these parietal and frontal regions could play an important role in generating the local population activity we see in the temporal lobe.
However, why should more presaccadic activity be apparent in the temporal lobe for contraversive saccades than for ipsiversive saccades? That is, why should the temporal lobe be under the control of corollary oculomotor activity for only one saccade direction?
Some of the saccades in the scan paths exhibited by the monkeys (Figs. 3 and 4) can be associated with specific visual events in the trials. This was demonstrated by calculating the reverse correlation between the times of saccade onset and the m-sequence controlling the transitions in the visual display (Kalik et al. 1999): a subset of the saccades was significantly correlated with transitions at a number of locations, in particular, leftward saccades with position 2 in the array of hexagons (see Fig. 1C) for monkey M, at a latency just greater than one transition time (250, 333, or 500 ms). Thus for monkey M, the positions of fixation at the end of the saccades tended to place most of the image largely in the right or left visual hemifield. As a consequence, parafoveal receptive fields in striate and extrastriate areas of opposite hemispheres may detect transitions on different sides of the visual stimulus. The presaccadic activity we see in the temporal lobe of the right hemisphere before leftward saccades may be a reflection, in part, of an orienting response to the detection of a stimulus transition in the left visual field that is not at the current focus of processing.
Two attentional systems are available to visual information processing in the primate brain (reviewed in Corbetta and Shulman 2002). The first system involves bilaterally distributed cortical areas in the dorsoparietal frontal eye field and dorsolateral prefrontal cortex. This network organizes top-down control, selecting targets for focal processing as part of goal-directed behaviors. The second system, which in humans is lateralized to the right hemisphere, includes cortical areas at the junction between the parietal and temporal lobes as well as in the ventral frontal cortex. This network is activated by salient or unexpected events not at the current focus of processing, in any sensory modality. The network acts as a "circuit breaker" (Corbetta and Shulman 2002) to the dorsal system, allowing for a shift to a new focus of processing. This shift often includes a saccade.
In our studies, EMPs recorded in the right hemisphere of the temporal lobe associated with leftward saccades may be more presaccadic because this activity reflects the generation of executive commands issued from the FEF to make contraversive saccades as part of a scan path strategy, in addition to the operation of the attentional circuit breaker. Although in humans it is possible that similar presaccadic activity should be present in the temporal lobe for ipsiversive (rightward) saccades, their diminished number in our recordings may be a consequence of the lack of hemispheric specialization in the monkey. That is, rightward saccades may be preceded by more presaccadic EMPs if the recordings were made in the left temporal lobe. The late, postsaccadic EMP activity seen in our temporal lobe recordings associated with ipsiversive saccades may reflect the EMPs passed up from occipital lobe sites to the temporal lobe through corticocortical pathways.
Frequency sweeps in EMPs
As shown in Figs. 7, 8, 12, and 13 and Table 3, the occipital lobe EMPs exhibit a greater degree of frequency sweep (transitions between frequencies in the spectrograms) during their time course than those recorded in the temporal lobe. A greater degree of frequency sweep in the spectrogram translates to a waveform that either speeds up or slows down its modulation during its time course (Fig. 6A, rightward saccades, Figs. 7B and 8B).
In the visual cortex (Shao and Burkhalter 1999), electrical stimulation of feedforward, interlaminar, and horizontal connections produces membrane potential waveforms that undergo FM in supragranular layer pyramidal cells. Activation of corticocortical feedback, on the other hand, produces enough polysynaptic excitatory postsynaptic potentials (EPSPs) to mask the inhibitory postsynaptic potentials (IPSPs) (Burkhalter 2002). Unlike feedforward activation, intracellular waveforms produced by feedback activation will exhibit less FM and reflect only the residual depolarization produced by the synaptic barrage. Thus a local field potential recording made in the cortex should be mostly monophasic (less rich in frequency content) after synaptic activation by corticocortical feedback and more frequency modulated when driven by feedforward pathways.
Our interpretation of the LFPs in terms of the dynamics of synaptic and intracellular events is derived in part from studies of the LGN where intracellular recordings were made simultaneously with extracellular field potentials (Hu et al. 1989). Monophasic negative-going extracellular field potentials, with a smooth return toward the baseline, are coincident with transient (about 150 ms in duration) depolarization of thalamic relay neurons. When a positive-going deflection briefly interrupts the negative-going field potential, an IPSP is seen in the simultaneously recorded intracellular potential. A second, slower positive-going deflection often appears after the negative field potential recovers and this phase is coincident with a more slowly developing IPSP in the intracellular recordings.
If an interpretation about synaptic and intracellular events can be extrapolated from LFPs, then the work of Burkhalter suggests that ipsiversive saccades are followed by activation of interareal feedforward and local connections in the cortex. This conclusion is based on the predominance of frequency-modulated field potentials in the postsaccadic phase of the perisaccadic interval in both occipital and temporal lobe recordings. The greater prevalence in the temporal lobe of EMPs with simpler temporal waveforms (inferred from the small degree of frequency sweep in their spectrograms) suggests that the temporal lobe receives more feedback input around the time of a contraversive saccade than does the occipital lobe. Ipsiversive saccades generate temporal lobe EMPs that take on more of the dynamics associated with the activation of feedforward pathways in the occipital lobe.
The analysis of the frequency sweeps in the ROIs of the spectrograms reinforces our interpretation of the EMPs discussed above. The occipital lobe EMPs, which we suggest are dominated by saccade-modulated activity in retinogeniculate and thalamocortical pathways, have similar dynamics to the activity in local cortical populations produced by feedforward pathways. The temporal lobe EMPs associated with contraversive saccades appear to have the right timing to be related to corollary oculomotor activity. These EMPs have similar dynamics to the activity generated by feedback pathways.
In this report, to conclude, we describe a methodology for characterizing eye movement potentials (EMPs) recorded from pairs of locations in the occipitotemporal (OT) pathway of alert monkeys performing a pattern-recognition task. Using this methodology, we establish regional differences in the activation time, frequency content, and waveforms of the EMPs. We also find that saccade direction influences cortical EMPs differently in the occipital and temporal lobes of the monkey. Our results suggest that the EMPs in the OT pathway are signatures of the corollary signals that are generated in the distributed oculomotor network at the time of each saccade. In the occipital lobe, these corollary signals are injected subcortically into the retinogeniculate and thalamocortical pathways and as such become combined with sensory afferent activity. In the temporal lobe, the corollary signals are combined with mechanisms of attention and executive control and appear first at the level of the cortex. The corollary signals create an oculomotor context for visual information processing in the OT pathway. An oculomotor context may play a role in building representations from multiple fixations (Wallenstein et al. 1998) and in the formation or dissolution of visual working memory traces in the temporal lobe (Sobotka et al. 2002) and prefrontal cortex (Gutkin et al. 2000). Analysis of the coherence between the population activities in the occipital and temporal lobes and a detailed examination of single-unit responses in the perisaccadic interval will further elucidate how such corollary oculomotor signals impact local cortical circuits, corticocortical communication, and their role in active vision.
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APPENDIX A |
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1 = {X2, X3,..., XM}, 2 = {X1, X3,..., XM}, M = {X1, X2,..., XM-1}, and so forth. From these "drop-one" sets we then calculate a series of spectrograms S1(n, f), S2(n, f),..., SM(n, f), each calculated from a different subset of the original data trials. S(n, f) is the value of the spectrogram at the time point, n in the frequency band centered on f (see supplement 1). The jackknife estimate of the standard error of a random variable also requires that the variable be normally distributed. If we consider the spectrogram as our random variable then we must transform its values so that they form a normally distributed ensemble. One way to do this is to take the log transform of the drop-one spectrograms (Thomson and Chave 1991), with
 | (AI) |
(Efron and Tibshirani 1998). We restrict our consideration to the values in the Slog_avg(n, f) spectrogram that are in the upper quartile of a normal distribution fit to the log-transformed power spectral densities. The lower bound of this upper quartile is unique for each Slog_avg(n, f) spectrogram. If the lower bound falls above the lower range of the confidence interval constructed from the jackknife procedure at some point (n, f), then Savg is set to zero at this point. Otherwise, the value of Savg at (n, f) is retained. Savg is obtained by taking exp(Slog_avg). This procedure ensures that when Savg at (n, f) is retained, it is at least within the upper quartile of values in Savg with an error rate of 5% set by the trial-to-trial variability in the spectrograms. Thresholded spectrograms are shown in Figs. 7 and 8. The lower bound of the upper quartile of the values in Slog_avg was used for the threshold (noise level) because the values in the upper quartile were typically further out in the tail of the empirical cumulative distribution than predicted by a normal distribution. Thus the values in the lower quartiles are thought to better represent the "noise" in the spectrograms. |
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APPENDIX B |
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). These adjustments depend on the position of the numerical maximum in the function, and on the rate of change around the maximum. The number of samples used for the fits is typically small. Thus estimates of the maxima have easily interpretable significance but less certainty. However, the maxima in the dimensions of time and frequency are extracted from multiple independent functions (for both time or frequency). This reduces the error in the estimates of the maxima. |
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APPENDIX C |
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 | (C1) |
The decrease in disorder produced by the clustering is calculated as
 | (C2) |
The probability of a group member winding up in either of the 2 clusters is set to p(C1) = (a11 + a12)/atotal, and p(C2) = (a21 + a22)/atotal, the marginal probabilities for the clusters. The marginal probabilities for the groups must reflect the numbers from the groups in the particular subset of centroids or maxima that are being clustered. Thus p(G1) = (a11 + a21)/atotal and p(G2) = (a12 + a22)/atotal. If any of the entries aij = 0, then the corresponding term (i, j) in the sum (Eq. C2) is set to zero.
Equation C2 is calculated for each branch point in a cluster tree for the groups defined by saccade direction, recording location, and stimulus timing. To determine whether the 2 clusters produced at the branch represent a clustering in time–frequency for any of the groups, the value produced by Eq. C2 is located in a cumulative distribution function that is constructed by calculating Eq. C2 for 1,000 sets of values a11, a12, a21, and a22. The number of members of group 1 in cluster 1 is used as the upper limit for the partitioning of group 1 between 2 clusters by a binominal random number generator with the probability of success or failure for inclusion in a cluster set to 0.5. For example, if the number of centroids in a particular time–frequency cluster associated with contraversive saccades is 5, then a11 can be set to a value ranging from 0 to 5 (chosen by the binomial random number generator with P = 0.5), and a21 is set to 5 - a11. Similarly, if the number of ipsiversive saccades in the other cluster is 15, then a22 can be set to a value ranging from 0 to 15 (chosen by the binomial random number generator with P = 0.5), and a12 is set to 15 - a22.
The goal here is to determine the likelihood of the mapping of saccade direction onto 2 clusters isolated on the basis of a distance metric in the time–frequency plane. That is, how often can one get the value for Eq. C2 that is based on measurements from a distribution of the groups in the time–frequency plane by chance alone? An empirical cumulative distribution function can be produced from the sample of values assembled by calculating Eq. C2 1,000 times with input variables obtained from running the binomial random number generator 1,000 times to produce both the a11 (and a21) values and the a22 (and a12) values. The value of Eq. C2 that is >95% of all the simulated values can be read off from this empirical cumulative probability distribution. If the value of Eq. C2 produced from the clustering of the centroids is equal to or greater than this 95% value, then the partitioning of the groups into the time–frequency clusters is held to be significant.
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DISCLOSURES |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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1 The Supplemental Material for this article is available online at http://jn.physiology.org/cgi/full/00011.2003/DC1. 
2 The Supplemental Material for this article is available online at http://jn.physiology.org/cgi/full/00011.2003/DC1.
Address for reprint requests and other correspondence: K. Purpura, Department of Neurology and Neuroscience, Suite LC-805, Weill Medical College and Graduate School of Medical Sciences of Cornell University, 1300 York Ave, New York, NY 10021 (E-mail: kpurpura{at}med.cornell.edu).
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 C. Rajkai, P. Lakatos, C.-M. Chen, Z. Pincze, G. Karmos, and C. E. Schroeder Transient Cortical Excitation at the Onset of Visual Fixation Cereb Cortex, January 1, 2008; 18(1): 200 - 209. [Abstract] [Full Text] [PDF]
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 G Foffani, A. M Bianchi, G Baselli, and A Priori Movement-related frequency modulation of beta oscillatory activity in the human subthalamic nucleus J. Physiol., October 15, 2005; 568(2): 699 - 711. [Abstract] [Full Text] [PDF]
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