Effect of Attentive Fixation in Macaque Thalamus and Cortex

D. B. Bender and M. Youakim

Department of Physiology and Biophysics, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, New York 14214


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Bender, D. B. and M. Youakim. Effect of Attentive Fixation in Macaque Thalamus and Cortex. J. Neurophysiol. 85: 219-234, 2001. Attentional modulation of neuronal responsiveness is common in many areas of visual cortex. We examined whether attentional modulation in the visual thalamus was quantitatively similar to that in cortex. Identical procedures and apparatus were used to compare attentional modulation of single neurons in seven different areas of the visual system: the lateral geniculate, three visual subdivisions of the pulvinar [inferior, lateral, dorsomedial part of lateral pulvinar (Pdm)], and three areas of extrastriate cortex representing early, intermediate, and late stages of cortical processing (V2, V4/PM, area 7a). A simple fixation task controlled transitions among three attentive states. The animal waited for a fixation point to appear (ready state), fixated the point until it dimmed (fixation state), and then waited idly to begin the next trial (idle state). Attentional modulation was estimated by flashing an identical, irrelevant stimulus in a neuron's receptive field during each of the three states; the three responses defined a "response vector" whose deviation from the line of equal response in all three states (the main diagonal) indicated the character and magnitude of attentional modulation. Attentional modulation was present in all visual areas except the lateral geniculate, indicating that modulation was of central origin. Prevalence of modulation was modest (26%) in pulvinar, and increased from 21% in V2 to 43% in 7a. Modulation had a push-pull character (as many cells facilitated as suppressed) with respect to the fixation state in all areas except Pdm where all cells were suppressed during fixation. The absolute magnitude of attentional modulation, measured by the angle between response vector and main diagonal expressed as a percent of the maximum possible angle, differed among brain areas. Magnitude of modulation was modest in the pulvinar (19-26%), and increased from 22% in V2 to 41% in 7a. However, average trial-to-trial variability of response, measured by the coefficient of variation, also increased across brain areas so that its difference among areas accounted for more than 90% of the difference in modulation magnitude among areas. We also measured attentional modulation by the ratio of cell discharge due to attention divided by discharge variability. The resulting signal-to-noise ratio of attention was small and constant, 1.3 ± 10%, across all areas of pulvinar and cortex. We conclude that the pulvinar, but not the lateral geniculate, is as strongly affected by attentional state as any area of visual cortex we studied and that attentional modulation amplitude is closely tied to intrinsic variability of response.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

It is now clear that attention can affect the responsiveness of neurons throughout visual cortex. Visually responsive cortex includes a number of distinct areas beyond striate cortex, or V1. Beginning with V2, these extrastriate areas are organized into two partially segregated, roughly hierarchical systems (reviews in Felleman and Van Essen 1991; Maunsell and Newsome 1987; Ungerleider and Mishkin 1982; Van Essen 1985). One includes dorsally located areas such as V3A, MT, and MST and leads into area 7a in the inferior parietal lobule. The other includes more ventrally located areas such as V4 and TEO and leads into area TE in the temporal lobe. Recordings from single neurons in many of these areas show that neuronal excitability depends on the animal's attentive state (reviews in Colby 1991; Desimone and Duncan 1995; Lock and Bender 1999; Maunsell 1995; Motter 1998). Typically the effect of attention is modest: a small increase or decrease in magnitude of response to a visual stimulus relative to a control condition. Such modulation can be found at virtually every level of the cortical hierarchy, including V1. A variety of behavioral paradigms have been used to manipulate attention, and these show that the prevalence and magnitude of attentional modulation can depend substantially on both the behavioral paradigm and the cortical area in which its effects are measured. Furthermore factors such as task difficulty, the extent to which a task engages the functions of an area, and whether multiple stimuli compete for attention all can affect the modulation (Luck et al. 1997; Motter 1993; Richmond and Sato 1987).

To what extent does the thalamus contribute to, or participate in, the attentional modulation that is so widespread throughout visual cortex? Three thalamic nuclei are closely interrelated with visual cortex: the lateral geniculate nucleus, the pulvinar, and the reticular nucleus of the thalamus. All have been thought to be involved in one form of attention or another (e.g., Guillery et al. 1998; Koch and Ullman 1985; Olshausen et al. 1993). The lateral geniculate projects almost exclusively to V1 with little or no output to extrastriate cortex. Layer 6 of both extrastriate and striate cortex project back to the geniculate, potentially modulating transmission through it. The pulvinar has at least three distinct visual subdivisions. The inferior (PI) and lateral pulvinar (PL) contain two separate visuotopic maps (Bender 1981). PI is driven by input from V1 (Bender 1983) but also receives input from extrastriate cortex and the superior colliculus. It projects mainly to V2, V3, V3A, and MT. PL likewise receives input from V1 and extrastriate cortex, but may have a particular affinity for V2: both areas have the distinctive visuotopic organization that is characterized by a second-order transformation of the hemifield (Allman and Kaas 1974; Bender 1981). PL projects extensively to more ventral areas of extrastriate cortex, including V4, TEO, and TE. Both PI and PL could thus influence, or be influenced by, attentional modulation in the early and intermediate stages of the cortical hierarchy. A third area, the dorsomedial portion of the lateral pulvinar, is visually responsive but its visuotopic organization has not yet been determined (Bender 1981). Referred to as "Pdm" (Petersen et al. 1985), it has connections with area 7a (Asanuma et al. 1985; Baleydier and Morel 1992) and is thus more closely related to the highest level of the dorsal pathway. The reticular nucleus of the thalamus is driven both by descending input from striate and extrastriate cortex and by branches of ascending thalamocortical axons from geniculate and pulvinar. It projects not to cortex but rather back on pulvinar and geniculate, providing an indirect route by which cortex potentially could modulate excitability in these nuclei (reviews in Guillery et al. 1998; Mitrofanis and Guillery 1993).

There is thus ample opportunity for thalamic involvement in the attentional modulation found in cortex. However, evidence of that involvement has been ambiguous. For the pulvinar, glucose utilization in human subjects and reversible inactivation in monkeys both suggest a role in attention (Desimone et al. 1990; LaBerge and Buchsbaum 1990), but lesions of the pulvinar generally do not. Pulvinar lesions do not impair visual search, and those deficits that have been found with attention-dependent tasks (e.g., Chalupa et al. 1976; Ogren et al. 1984; Ungerleider and Christensen 1979) either have not been replicated or may have resulted from damage to neighboring structures such as the corticotectal tract (Bender and Baizer 1984, 1990; Bender and Butter 1987; Nagel-Leiby et al. 1984). Attentional modulation of single neurons has been found in Pdm (Petersen et al. 1985), but there has been no quantitative comparison with neuronal modulation in cortex. For the lateral geniculate, there has been little attempt to find attentional modulation like that in cortex at the single neuron level.

Our main goal in this study was thus to see whether attentional modulation like that in cortex was detectable in single neurons of the lateral geniculate and pulvinar and, if so, to compare its prevalence and strength with modulation in cortex. Because attentional modulation can differ among cortical areas, it was important to compare across different levels of the cortical hierarchy as well as across all three visual subdivisions of the pulvinar.

Looking at an object and fixating it is an elemental act of attention. To manipulate attention, we used a simple fixation task that required an animal to wait for a spot of light to appear, look at the spot until it dimmed, and then wait idly for a chance to begin the next trial. We estimated attentional modulation by comparing responses to another stimulus, irrelevant to the animal, flashed in a neuron's receptive field during each of those three states. Although simple, this widely used fixation task has the advantage that all animals were likely to find it equally easy and perform it in similar fashion. Further, the stimulus used to probe cell excitability was not itself an object of attention, thereby avoiding the potential confounding of receptive-field selectivity and attentional specificity.

Such a fundamental behavior as fixation can generate strong attentional modulation, and with a character that differs, it has been argued, among cortical areas. In area 7a, for example, responses are typically threefold larger during fixation than when waiting for the fixation point to appear or performing no task at all (Mountcastle et al. 1981). In area V4, by contrast, responses during fixation are the same as when waiting for the fixation point (Mountcastle et al. 1987), and in area TE, responses are suppressed during fixation (Richmond et al. 1983). It was thus also of interest to see which cortical pattern of modulation thalamic modulation might resemble. For comparison we chose cortical areas V2, V4, and 7a. As a group, they span the cortical hierarchy and are easily accessible from a single recording cylinder. The choice also permitted an independent evaluation of previous findings in V4 and 7a (Mountcastle et al. 1981, 1987).

In this study, we found that almost all brain areas had a push-pull form of modulation in which some cells were facilitated and others were suppressed during fixation. Allowing for that, and the difference among brain areas in response variability, we found the strength of modulation was about the same in all areas of both pulvinar and cortex.


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Animal preparation and procedures

Eight juvenile male macaques weighing between 3.5 and 7.3 kg were used. Naive animals were first trained on the fixation tasks described in the following text. Standard aseptic technique was used to implant the skull with hardware for recording and painless fixation of the head. A subscleral, magnetic-field search-coil was also implanted in one eye (Judge et al. 1980) for monitoring eye position. For surgery, animals were anesthetized with halothane in 70% N2O-O2, and rectal temperature, electrocardiogram (EKG), and respiration were continually monitored. Penicillin G was given as prophylaxis against infection. All surgical procedures and animal use protocols were reviewed and approved by the Institutional Animal Care and Use Committee. Animals continued training for 3-8 mo with head fixed until performance was consistently accurate despite repeated and irregular stoppages of the task.

Apparatus

Stimuli were rear-projected on a Polacoat tangent screen, 57 cm in front of the animal. The screen extended >30° in all directions from straight ahead; beyond that, a black matte surface filled the visible field. A lever and small "ready-light" (see following text) were mounted in front of the animal at waist level. A 0.2° diam He-Ne laser beam provided the fixation point, and a tungsten-illuminated rectangle of adjustable height, width, orientation, and color served to probe neuronal excitability. Fixation point and probe luminance were controlled by liquid-crystal film shutters with rise and fall times <1 ms and <10 ms, respectively. Maximum probe luminance was 30 cd/m2, 15 dB above background luminance. Independent, temperature-stabilized, servo-controlled, mirror galvanometers positioned the stimuli. Galvanometer and eye position signals were modified by second-order polynomials in screen coordinates so that fixation point, probe, and eye position all tracked to better than ±0.2° over the central ±15° of the screen. This was verified physiologically by electronically locking a 0.4° square spot relative to the fixation point so that the spot fell on the receptive field of a lateral geniculate nucleus (LGN) neuron and showing that the spot always evoked a response from the cell as the animal tracked the fixation point over the central ±15°.

Behavioral tasks

All animals performed a simple fixation task for which we operationally defined three different behavioral states. The animal had to wait for a small fixation point to appear (the ready state), fixate the spot and wait for it to dim (the fixation state), and then respond quickly to the dimming to receive reward. There was also a period of idleness (the idle state) during which responding was discouraged with a 4 s time-out. Neuron excitability was probed by flashing a visual stimulus at the same retinal location, regardless of eye position, during each state. The probe stimulus was irrelevant to the animal in that its appearance was uncorrelated with reward.

We used three minor variants of this task, described in the following text, to address potentially troublesome methodological issues in previous studies (Mountcastle et al. 1981, 1987). Those studies had measured excitability for the idle state in a time period different from that for the other two states, potentially introducing an order effect, and there had been no behavioral indication of readiness. Thus in task 1, we randomly interleaved stimulus presentations for all three states on a trial-to-trial basis and required an explicit ready response to define the ready state. Task 2 was virtually identical to that used by Mountcastle. Task 3 eliminated uncertainty in fixation point location, a factor common to both tasks 1 and 2 and the Mountcastle task. In the end, task variants had no effect on the main findings of this study.

TASK 1. This task (Fig. 1, top) was used for testing the majority (61%) of cells. A small, dim ready-light turned on to indicate that the animal could start a trial by depressing a lever. Doing so turned off the ready-light and, after a delay (the ready state), turned on the fixation point at a randomly chosen position within ±15° of straight ahead. After another delay (the fixation state), the fixation point dimmed and the animal had to release the lever within 0.7 s to receive reward. There followed a 3-9 s idle period (the idle state) after which the ready-light turned on again. On each trial, the probe stimulus was flashed with equal probability during one of the three states.



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Fig. 1. Sequence of events during 3 variants of fixation task. Dashed lines indicate variable length intervals. Stimulus event lines: upward and downward deflections indicate onset and offset, respectively; half-steps indicate dimming. rdy L, ready light; fp, fixation point; probe, visual stimulus flashed in receptive field (vertical displacements of the probe event lines indicate probe appears only once during trial).

TASK 2. This task (Fig. 1, middle) was used for 31% of the cells, and closely matched the paradigm used by Mountcastle et al. (1987). There was no ready-light, and the idle state was not interleaved with fixation trials. Without warning, the fixation point appeared, and the animal had to depress the lever within 0.7 s to start the trial. After a delay (the fixation state), the fixation point dimmed and the animal had to release the lever within 0.7 s for reward. After another delay (the ready state), the fixation point reappeared at a different location for the next trial. The probe was flashed twice on every trial, once during the fixation state and once during the ready state. Each block of 8-16 such trials was followed by a 1- to 2-min idle period during which the probe was flashed every 5-8 s. The entire sequence, fixation trials followed by idleness, was repeated two to five times to minimize potential order effects.

TASK 3. For 8% of the cells, the position of the fixation point was fixed at the center of the tangent screen (Fig. 1, bottom). As in task 1, the ready-light turned on to signal that a trial could be started. Depressing the lever turned it off and started one of two types of trials, each equally likely. Either the fixation point turned on (the fixation state) and then dimmed or there was no fixation point and the animal was simply assumed to be in the ready state. In either case, the probe was flashed within 0.5-1.6 s. There followed a 3- to 9-s idle period during which the probe was flashed with probability 0.5.

For all task variants, we imposed constraints to minimize potentially confounding effects of eye and arm movement and to ensure that probe (and eye) positions were comparable across states. The probe was presented only if gaze direction was within ±15° of straight ahead, eye position had been stationary for >= 170-270 ms, and no trial event (e.g., lever movement, fixation point change) had occurred in the previous 400 ms. Rapid foveation and accurate fixation were encouraged by aborting trials if gaze failed to acquire the fixation point within 350 ms or left it by more than ±1° at any time. During the ready and idle states, any saccade during probe presentation immediately extinguished the probe and data were discarded; after training, animals showed no inclination to look in the probe's direction.

Recording methods and procedure

Epoxy-coated tungsten and glass-coated Pt-Ir microelectrodes were used to record from thalamus and cortex, respectively. To ensure recording from single isolated neurons, waveforms were digitized at 17 kHz and matched against a stored template.

We studied every reliably isolated cell. The receptive field was plotted if possible, and an optimal probe selected, while the animal fixated. Next the state-dependent modulation of excitability was estimated as the animal ran a block of 30-150 trials on one of the three tasks described in the preceding text. The same probe was used for the entire block. For LGN cells, the probe was typically 0.8° square so as to always cover the receptive field despite slight galvo positioning errors. Moving stimuli were often used, and these were swept so that receptive-field stimulation was the same despite small positioning errors. These precautions eliminated variations in receptive-field stimulation as a source of response variance. Probe duration was typically 250 ms although durations as short as 150 ms and as long as 300 ms were occasionally used. Gaze direction was used to electronically lock the probe to the retina, thereby providing nearly identical retinal stimulation on every presentation in each behavioral state.

Data analysis

Each cell yielded three histograms of response evoked by the probe, one for each behavioral state. During the ready and idle states, there were frequent eye movements (though rarely toward the probe). When discharge related to those saccades appeared to contaminate a response evoked by the probe, data from that probe presentation were discarded, as were any data for which the eye moved during probe presentation. This often resulted in different numbers of probe presentations per histogram. Only cells with >= 15 presentations were used; 75% of the cells had 30 presentations. Response magnitude was computed as spikes/second for counts within a time window. For each cell, the window had a fixed width, just wide enough to encompass the largest of the three responses, but <500 ms. The window was positioned for each histogram to maximize counts, thereby eliminating the effect of small differences in response timing that could result from moving stimuli with small positioning errors. Window widths averaged 240 ms in the pulvinar and V2, and slightly longer (290 ms) in the LGN, V4/PM, and area 7a, reflecting the larger proportion of sustained responses in those areas. A measure of "spontaneous," i.e., ongoing, discharge was defined similarly, except the window always ended just prior to stimulus onset. We did not subtract ongoing from stimulus-evoked discharge (see DISCUSSION).

Only cells for which the response exceeded ongoing discharge in at least one behavioral state (t-test, P < 0.01) were used. To determine whether behavioral state affected responses, a one-way repeated-measures ANOVA with three levels of state was done on the response measures (P < 0.02). We tested all cells for inhomogeneity of variance among states (Bartlett's test) because an inhomogeneity can lead to false positives. Few cells showed it (8%, P < 0.02), and when such inhomogeneity was found it clearly could not have led to false positives. Inspection of the individual histograms for such cells showed that the largest effects of attention naturally led to greater inhomogeneity: at least one attentional state necessarily had a small response, and thus small variance, producing inhomogeneity.

For neurons significantly affected by behavioral state, we explored that effect using an analysis that gave equal weight to all three states. Each cell with a significant ANOVA was represented by a single vector, r, in a three-dimensional space whose axes represented response strength (in spikes/s) in the ready, fixation, and idle state. The deviation of that vector from the main diagonal of the space (i.e., the locus of equal response, and thus of no attentional modulation) preserved all information about the differential effect of the three states on excitability. This approach, which differs from that of Mountcastle et al. (1981, 1987) who compared responses in only two states at a time, was needed because no single pair of states was sufficient to characterize state-dependent excitability for all brain areas.

Proportions were compared using chi 2 or Fisher's exact probability test. Unless otherwise noted, magnitudes were compared using the Mann-Whitney U test or Kruskal-Wallis test. In the latter case, z scores based on within-group ranks were used to interpret inter-group differences.

Histological procedures

At the end of recording, marking penetrations were made to aid in reconstruction of recording locations. Frozen sections were cut in the coronal plane at 50 µ and stained with cresyl-echt. The cut surface of the brain was photographed every 500 µ as an aid to reconstruction of the cortical surface.

Cortical recording sites were located on a reconstruction of the cortical surface made from the section photographs (Fig. 2A). Sites were assigned to area 7a, the dorsal part of the exposed prelunate gyrus, or area V2, based on the stained sections. Sites on the prelunate gyrus extended from a point several mm medial to the medial terminus of the lateral sulcus to a point just lateral to the medial end of the superior temporal sulcus; this area is termed PM by Maguire and Baizer (1984), is included as part of V4 by other authors (Gattass et al. 1988), and is essentially the same region in which the "V4" cells reported in Mountcastle et al. (1987) were located. We refer to this area as "V4/PM." Some sites could have been located in the adjacent area "DP" (Andersen et al. 1990a) since criteria for distinguishing among the three areas have yet to be clearly defined. Ninety-five percent of the cells were recorded within 1.7 mm of the cortical surface, except in V2 where 80% were buried in the posterior bank of the lunate sulcus.



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Fig. 2. A: standardized drawing of left hemisphere showing regions where penetrations entered to record from cortical area V2 (horizontal hatching), area V4/PM (vertical hatching), and area 7a (left hatching). Scale: 1 cm. B: standardized coronal sections through left thalamus indicating regions in PI (left hatching), PL (horizontal hatching), and Pdm (vertical hatching) where recorded cells were estimated to lie. cs, central sulcus; ec, external calcarine s.; ios, inferior occipital s.; ip, intraparietal s.; lat, lateral s.; ls, lunate s.; sts, superior temporal s.; LGN, lateral geniculate nucleus; MG, medial geniculate n.; PI, inferior pulvinar n.; PL, lateral pulvinar n.; PM, medial pulvinar n.; SC, superior colliculus.

Thalamic recording sites (Fig. 2B) were assigned to the geniculate, and inferior (PI), lateral (PL), or dorsomedial part of the lateral pulvinar (Pdm) on the basis of receptive-field properties (Bender 1982), visuotopic organization (Bender 1981), and location of the penetration relative to the posterior tip of the geniculate. PI and PL were easily identifiable by their distinctive mirror-image visuotopic maps. Pdm is a visually responsive zone, just dorsomedial and adjacent to the periphery of the lower field representation of PL (labeled "visual" in Fig. 11, C-E, Bender 1981); although penetrations through this area can yield systematic shifts in receptive-field position, its visuotopic organization has not been reported, and it may contain more than one visual area. Almost all lateral geniculate cells were estimated to lie within layers 5-6, based on depth, ocular dominance and whether responses were sustained or transient.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

We found that both prevalence and magnitude of attentional modulation differed substantially across brain areas. Because the character of the modulation, i.e., the relative response magnitudes among the three attentional states, also differed among brain areas, we had to devise a measure of the magnitude that was independent of that character. Using that measure, we found that modulation was absent in the geniculate, modest in the pulvinar, and increased from modest in area V2 to reach, in area 7a, the highest level seen in any of the brain areas studied. However, we also found systematic differences among brain areas in trial-to-trial variability of the response evoked by a visual stimulus. When we scaled the magnitude of attentional modulation in terms of this variability, using a kind of "signal-to-noise" ratio, we found that modulation was about the same in all brain areas beyond the lateral geniculate nucleus.

In the following sections, we compare first the prevalence, then the character, and then the absolute magnitude of attentional modulation among brain areas. We next describe how brain areas differ in response variability, and show that the signal-to-noise ratio of attention is roughly constant at ~1.3. Because task variant had no significant effect on modulation (with minor exceptions described at the end of RESULTS), we pooled data across all cells, regardless of variant.

Prevalence of attentional modulation

We recorded from 699 neurons in the thalamus and cortex of eight monkeys (Table 1). For each cell, we compared the responses evoked by the receptive-field stimulus flashed in the three behavioral states, ready, fixation, and idle, using a repeated measures, one-way ANOVA with three levels of state. A cell was considered to be significantly affected by attentional state if the F ratio from the ANOVA was significant at P < 0.02. 


                              
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Table 1. Cells tested for attentional modulation

THALAMUS. The prevalence of attentional modulation differed markedly between LGN and pulvinar. There was no evidence of modulation in the LGN. Only 2% of the cells (2/98) had a significant F ratio (P < 0.02), no more than expected by chance.

By contrast, about a quarter of the pulvinar cells (26%, 61/237) were affected by attention (see Fig. 3). Prevalence did not differ significantly among pulvinar subdivisions, but in the inferior and lateral subdivisions attentional modulation was slightly more common in central vision. A third of the cells (33%, 32/94) with receptive-field eccentricities <10° were affected by behavioral state, whereas only 20% (16/81) of the cells with more eccentric receptive-fields were affected (chi 2-test, P < 0.03, excluding 6 cells with incomplete receptive-field data).



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Fig. 3. Proportions of cells significantly affected by behavioral state in different areas of thalamus and cortex. Bars, based on ANOVA with P < 0.02; dots, based on ANOVA with P < 0.05 (see text); error bars, 1 SE based on binomial variance; N, number of neurons recorded in each area.

CORTEX. As shown in Fig. 3, the prevalence of attentional modulation increased at higher levels of the cortical hierarchy (chi 2-test, df = 2, P < 0.001). Compared with the pulvinar, prevalence in area V2 (21%, 19/91) and in area V4/PM (31%, 28/91) was about the same, whereas prevalence in area 7a (43%, 79/182) was significantly higher (P < 0.0001). As in the pulvinar, prevalence in area 7a was slightly higher (57%, 17/30) among cells with more centrally located receptive fields (probe eccentricity <0.10°) than for cells with more peripheral fields (41%, 62/152, probe eccentricity >10°), but the difference did not reach significance.

In estimating prevalence, we used an alpha  = 0.02 so that for subsequent analyses there would be no more than one to two false positives in the sample of significantly affected cells. Had we used a more conventional alpha  = 0.05 (see DISCUSSION), the corresponding estimates of prevalence would have been 36% for the pulvinar and 26-56% for cortex (Fig. 3, dots).

Character of attentional modulation

The ANOVA results identified cells affected by behavioral state but not the nature of the effect. To examine that, we discarded all unaffected cells and then looked at the patterns of relative response among the three states. Mountcastle et al. (1981) had found that almost all cells in area 7a gave their largest responses during fixation. However, we found virtually all possible patterns of relative excitability in both thalamus and cortex with no marked preponderance of high responsiveness during fixation in any area. For any one cell, the stimulus-evoked response in one state might be either smaller or larger than that evoked in any other state.

Figure 4 shows some typical examples of the different patterns of excitability seen in pulvinar neurons. In Fig. 4A, a cell in PL gave its strongest response during fixation with responses during both the ready and idle states only about half as strong. In Fig. 4B, a cell in Pdm shows the opposite pattern, giving no response during fixation and strong responses during the ready and idle states. Other cells gave their strongest response in the ready state (Fig. 4C) and still others in the idle state (Fig. 4D).



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Fig. 4. Responses from 4 different pulvinar neurons to identical stimuli flashed in the ready (Rdy), fixation (Fix), and idle (Idl) states. A: PL neuron, stimulus speed 25°/s. B and D: dorsal portion of the lateral pulvinar (Pdm) neurons. C: PI neuron. Top: individual trials; bottom: averaged histogram (smoothed with 40 ms, 0 phase shift, triangular impulse-response filter). Responses for each state plotted in order of occurrence, but those on the same line did not necessarily occur on the same trial. Horizontal bar indicates stimulus duration; time marker, 100 ms; Sp/s, spikes/second.

In cortex, we saw the same diversity in patterns of excitability, though differences between states often seemed more extreme in area 7a. Figure 5A shows an area 7a cell giving a characteristically strong response during fixation. Other 7a cells gave similarly strong responses during the idle state (Fig. 5B) or ready state. Figure 5C shows a V2 cell with a pattern of excitability similar to that in Fig. 5A, though less pronounced.



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Fig. 5. Responses from 3 different cortical neurons. A and B: area 7a neurons. C: V2 neuron. Conventions as in Fig. 4.

Despite the variety among individual cells in patterns of relative excitability, when populations of cells in different brain regions were compared, clear though subtle differences among regions were apparent. To compare cells, each was represented by a single vector in three-dimensional space, the cell's response vector, r; the three components of r were the responses, in spikes/second, evoked in the fixation, ready, and idle states. Figure 6A illustrates the response vector for the cell in Fig. 4A; it points above the main diagonal (representing equal responses in all 3 states) because the response during fixation is about twice that in the other two states. Because response magnitudes, and thus response vector lengths, differed substantially among cells, we used a unit vector in the direction of r to represent the relative excitability of the cell in the three states. Each cell, regardless of overall response strength, could then have its relative excitability represented by a single point on a unit sphere. Figure 6B shows the unit response vector representation of relative excitability for the four cells illustrated in Fig. 4. A cell that showed no attentional modulation (equal responses in all three states) would be represented by the × in Fig. 6B, where the main diagonal intersects the unit sphere.



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Fig. 6. A: response vector, r, for neuron whose responses are shown in Fig. 4A; dashed line, main diagonal; dotted lines, components of r for the fixation, ready, and idle states. B: unit response vectors shown as points (large dots) on the surface of a unit sphere for the 4 correspondingly labeled neurons in Fig. 4, A-D. For each cell, the relative excitability in each state can be read directly from the nearest grid lines since both grid lines and point are drawn on the sphere's surface. Viewer's perspective line for all coordinate systems of unit spheres is down main diagonal; ×, unit vector along main diagonal; dashed lines, constant relative-response contours for fixation state; solid lines, constant relative-response contours for ready and idle states.

Relative excitabilities were plotted as unit vectors for all cells significantly affected by behavioral state. Figure 7 shows unit vector plots for cells in the three pulvinar subdivisions, Fig. 8 shows vector plots for cells in the three cortical areas. The plots suggested not only a general similarity between thalamus and cortex but also subtle differences among brain areas.



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Fig. 7. Unit response vectors for all pulvinar neurons significantly affected by behavioral state. Heavy dashed lines: optimal discriminant plane that includes main diagonal; heavy dotted lines: ideal discriminant plane based on fixation as a sole factor. A: neurons in PI. B: neurons in PL. C: neurons in Pdm. Other conventions as in Fig. 6B.



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Fig. 8. Unit response vectors for all cortical neurons significantly affected by behavioral state. A: neurons in V2. B: neurons in V4/PM. C: neurons in area 7a. Conventions as in Fig. 7.

The most obvious similarity between thalamus and cortex was that cells in all brain regions segregated into one of two groups: those that gave stronger responses during fixation (which we call "F+" cells), and those that gave weaker responses during fixation ("F-" cells). To show this, we computed for the cells in each brain region that plane which both included the main diagonal of the unit sphere and maximized the root-mean-square distance of all the unit response vectors from the plane. It is thus the decision plane that maximally divides the cells of a given brain region into two groups from the point of no attentional modulation.1 The intersection of the plane for each brain area with the unit sphere is shown as a heavy dashed line in Figs. 7 and 8. For every brain area, the plane was tilted <15° off normal to the plane that included both the fixation-state axis and main diagonal (Figs. 7 and 8, dotted lines). Thus for all brain areas, the fixation state was just about the best single-axis discriminant of the effect of behavioral state on excitability.

Another similarity between thalamus and cortex was that (with the sole exception of Pdm) there were roughly similar proportions of F+ and F- cells in both pulvinar and cortex. In the inferior and lateral pulvinar, 60% of the neurons were F+ cells (PI, 16/28; PL, 14/22). Likewise in cortex, 52% of the neurons were F+ cells (V2, 11/19; V4/PM, 12/28; 7a, 42/79). Thus in all regions but Pdm, the act of attentive fixation exerted a "push-pull" effect on neuronal excitability, with some cells gaining, and others losing, responsiveness. Pdm was unique in having only F- cells (see Fig. 7C). In effect, these pulvinar neurons simply shut down when the animal fixated. This made Pdm a frustrating area to record from and may in part be responsible for our low estimate of prevalence there: some cells may have been passed over as unresponsive when in fact they were unresponsive only during fixation.

There were also subtle differences among brain areas. First, excitability in the ready and idle states could be highly correlated or not. Thus the ready and idle components of the unit response vectors were strongly correlated in PL (Spearman rank-order correlation rho  = 0.59) and in area V2 (rho  = 0.59), but essentially uncorrelated in PI (rho  = 0.05) and in areas V4/PM (rho  = -0.08) and 7a (rho  = 0.15). Second, and reminiscent of the findings of Mountcastle et al. (1981), area 7a was unique in having many cells responding almost exclusively during fixation; 37% of the significantly modulated area 7a cells had a fixation-state component of 0.8 or more (above latitude 0.8 in Fig. 8C). By contrast, only 10% of the cells in V2, and 11% in V4/PM, had such a high relative responsiveness during fixation, with even fewer (5%) in the pulvinar.

In showing that cells divided into two groups with respect to the point of zero modulation, we had constrained the discriminant plane to include the main diagonal. To avoid any potential bias from that constraint, we also did a principal components analysis based on the covariance matrix for the normalized discharge rates in the fixation, ready, and idle states, analyzing each brain area separately. The results confirmed the dominance of fixation in determining excitability. For every brain area, the first principal component (i.e., eigenvector with the largest eigenvalue) accounted for 65-88% of total variance, and its direction was the same as the discriminant plane's normal within 3-9° (except for Pdm). Furthermore, the second (orthogonal) principal component accounted for only 12-14% of variance for PL and V2 cells but for more than twice that in other brain areas, thus confirming the relatively high correlation of ready and idle state excitability in PL and V2.

In summary, attentional modulation had a push-pull character with respect to the fixation state in all brain areas but one, Pdm, where there was exclusively depression of excitability during fixation. Relative excitabilities in the ready and idle states could be strongly correlated as in PL and area V2, or not, as in PI and cortical areas V4/PM and 7a.

Magnitude of attentional modulation

Because the character of attentional modulation could vary substantially among cells and brain areas, we needed a measure of the magnitude of attentional modulation that, for each cell, would be unaffected by that cell's particular pattern of state-dependent excitability. As a dimensionless measure of modulation amplitude, we used the angle between the response vector and the main diagonal, expressed as a percent of the largest possible angle (54.7°); it is completely independent of the character of modulation. We call this measure of attentional modulation the "percent angular modulation." A cell that responded equally in all three states would have 0% angular modulation, while a cell that responded in one state but not the other two would have 100% modulation; the intermediate case, equal responses in two states with none in the third, corresponds to 64% modulation.

On this measure, we found clear differences among brain regions. Figure 9 shows for each brain area the average percent angular modulation for all cells that were significantly affected by attention.



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Fig. 9. Average amplitude of attentional modulation expressed as percent angular modulation for all neurons significantly affected by behavioral state. Error bars, 1 SE; N, number of neurons significantly affected by state in the indicated brain area.

In the pulvinar, angular modulation was modest, averaging just 21% of the maximum possible. This roughly corresponds to a 45% increase (or decrease) in response during fixation relative to that, for example, in the idle state. Average modulation was slightly larger in Pdm (26%) than in PI (19%) or PL (20%), but the difference among subdivisions did not reach significance.

In cortex, angular modulation increased with level in the cortical hierarchy (P < 0.0001, Kruskal-Wallis, df = 2). Average modulation was just 22% in V2, no different from that in the pulvinar. Modulation was larger in area V4/PM (32%) and larger still in area 7a (41%), where it reached the largest value of any brain area studied (representing roughly a 2.5-fold difference in response between fixation and idle states). Compared to the pulvinar, modulation in area V4/PM was significantly larger than in either PI or PL though not different from Pdm (P < 0.001, Kruskal-Wallis, df = 3), while modulation in area 7a was significantly larger than for all of the pulvinar divisions (P < 0.0001, Kruskal-Wallis, df = 3).

Response variability in different brain areas

We had noticed that trial-to-trial variability in responsiveness seemed high in area 7a, where modulation was largest, and thus wondered whether modulation strength might be linked to the variability of an area for other brain areas as well. We looked at the variability of all responsive cells, not just those significantly affected by state, because we wanted to estimate variability of the whole brain area, not just of a particular subset of cells within it. Because we had to have a dimensionless measure of variability or "noise" to compare with the dimensionless measure of modulation strength, we used the percent error in cell response strength in the usual form of a coefficient of variation, µ = SDerr/gm, where SDerr is the standard deviation based on the error sum of squares, and gm the grand mean, from the cell's ANOVA. Thus µ represents the percent noise left after removing all effects of behavioral state. (The percentage, rather than absolute, error was essential because response magnitude varied systematically across brain areas, see following text.)

In the thalamus, percent variability was least in the LGN (average µ = 0.25) where attentional modulation was undetectable (see Fig. 10A). For the pulvinar, percent variability was half again as large in PI and PL, where attentional modulation was modest, and largest of all in Pdm, the pulvinar subdivision with largest attentional modulation (average µ = 0.45; P < 0.001, Kruskal-Wallis, df = 2).



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Fig. 10. A: average trial-to-trial response variability for all neurons recorded (N) in each brain area. B: comparison of average attentional modulation (significantly affected neurons only) per brain area and average response variability (all neurons) per area. - - -, linear regression of the 6 points. C: average response vector magnitude for all neurons in the indicated brain area. All error bars represent 1 SE.

In cortex, percent variability increased with level in the cortical hierarchy (P < 0.0001, Kruskal-Wallis, df = 2). In area V2, percent variability (average µ = 0.41) was comparable to that in the pulvinar. It was larger in area V4/PM (average µ = 0.54) and still larger in area 7a where it reached the highest value (average µ = 0.60) of any brain region studied. Variability in area V4/PM was significantly larger than in either PI or PL (P < 0.0001, Kruskal-Wallis, df = 3) although not different from Pdm, while in area 7a percent variability was significantly larger than in any pulvinar division (P < 0.0001, Kruskal-Wallis, df = 3).

Comparing across all brain regions (except the geniculate), to a first approximation, there was a roughly constant relation between the average magnitude of attentional modulation and the average "noise" in an area. Figure 10B plots, for each area, the percent angular modulation averaged over all significantly affected cells in the area against the average µ for all cells in the area. Viewed in this way, both thalamic and cortical areas were essentially the same in their magnitude of attentional modulation. Linear regression of modulation on variability (Fig. 10B, - - -) showed that modulation was about three-fourths of the noise (regression slope = 78.8, intercept not significantly different from 0). Furthermore, the average µ of an area accounted for virtually all (adjusted R2 = 94%) of the variance in angular modulation across areas.

It should be noted that response strength, in spikes/second, also varied among brain regions but in the opposite sense: discharge went down as attention and noise went up. We used the length of a cell's response vector, |r|, to represent response strength. Figure 10C shows the average response strength for all cells in an area whether or not a cell was significantly affected by attention. Responses were strongest in the LGN and decreased by about half in the pulvinar. In cortex, responses were slightly stronger in V2 than in the pulvinar, but decreased in area V4/PM and again in area 7a to be the smallest, on average, of any brain area.

Signal-to-noise ratio of attention

The fact that the average magnitude of attentional modulation was a roughly constant fraction of a brain area's variability suggested that a signal-to noise ratio might be a more appropriate measure of attentional modulation than the absolute measure, percent angular modulation, that we had first computed. Thus for each cell that was significantly affected by attention, we defined a signal-to-noise measure of the attentional effect, S/N, as follows. The "signal" was defined by resolving a cell's raw response vector into two orthogonal components (see Fig. 11A). One (at, the attention vector) was perpendicular to the main diagonal and thus represented the entire effect of attentional state, in spikes/second. The other component, in the direction of the main diagonal, represented the amount of discharge, in spikes/second, unaffected by attention and played no role in this analysis. The "noise," in spikes/second, was simply taken to be the error term in the cell's ANOVA (SDerr as defined in the preceding text). S/N was then computed as the dimensionless ratio of the magnitude of the attention vector, |at|, to the noise: S/N = |at|/SDerr.



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Fig. 11. A: resolution of response vector, r, in Fig. 6A into attention vector, at, and an orthogonal vector along main diagonal (- - -). B: average signal-to-noise ratio of attention for all neurons significantly affected by behavioral state in the indicated brain area. - - -, average of all significantly affected neurons, regardless of brain area; error bars, 1 SE.

Figure 11B shows the average signal-to-noise ratios for each brain area. On this measure of attentional modulation, brain areas hardly differed at all. The signal-to-noise ratio was roughly constant, to within ±10%, for all areas, in both thalamus and cortex. Furthermore, attention had a rather modest effect, just 30% greater than noise; the average S/N for all affected cells, in all areas, was 1.30. Interestingly, the uniqueness of area 7a in having many cells with a high relative responsiveness during fixation (>0.8) disappeared: the average S/N of those cells (1.40) was no different from that for the rest of the significantly affected area 7a cells (1.41).

Ongoing discharge and behavioral task variants

Attentional state affected the ongoing or "spontaneous" discharge for a relatively small proportion of cells. Prevalence (repeated-measures, 1-way ANOVA, P < 0.02) was about the same among pulvinar (18%, 43/238) and cortical neurons (17%, 61/356) and did not differ among areas. Prevalence was still smaller among lateral geniculate neurons (12%, 12/98). The most common effect was suppression of ongoing discharge during fixation. In all brain areas, modulation of ongoing and stimulus-evoked discharge were statistically independent (chi 2-test) so that very few cells showed modulation of both ongoing and stimulus-evoked discharge.

We had used three minor variants of the fixation task (see Fig. 1), the major differences being whether an explicit "ready response" was required (task 1), whether the idle state appeared in a separate block of time (task 2), and whether the fixation point appeared in a predictable location (task 3) or not. In general, task variant did not significantly affect any quantitative measure related to attentional modulation, so that the major aspects of attentional modulation appear not to depend on details of the task. Prevalence, percent angular modulation, response variability, response magnitude, and the S/N of attention were all unaffected by task variant in both pulvinar and cortex as a whole. The character of attentional modulation, as reflected in the proportion of F+ cells, was likewise unaffected by task variant. Furthermore the trends of increasing percent angular modulation and percent response variability with level in the cortical hierarchy were present whether task 1 or 2 had been used (too few cells had been tested with task 3 to tell). However, we did find two minor effects of task variant. First, cells in the inferior pulvinar had a smaller average S/N of attention in task 2 (0.82) compared with the other two tasks (1.4, P < 0.005), resulting from a combined, but marginal, increase in noise and decrease in percent angular modulation. Second, cells in area 7a showed greater response variability (µ) in task 2 (average µ = 0.64) compared with task 1 (average µ = 0.56, P < 0.05). Thus minor differences in task nature may affect some aspects of modulation in some brain areas.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

We defined three successive periods during performance of a simple fixation task, the ready, fixation, and idle states. We found that the transitions from one state to the next were accompanied by marked changes in neural responsiveness when that was assessed with visual stimuli irrelevant to the animal. Those changes occurred at all levels of the visual system beyond the lateral geniculate nucleus and were as common in the pulvinar as in the lower levels of the cortical hierarchy. Furthermore, the state of attentive fixation imposed a push-pull modulation of responsiveness in both pulvinar and cortex; during fixation, as many cells gained as lost excitability. Finally, the magnitude of the excitability change, when scaled to a cell's variability of response, was the same in all areas of pulvinar and cortex.

In the following, we discuss each of these findings before concluding that the pulvinar must be as much involved as cortex in the modulation of excitability that accompanies the act of fixation.

Parts of the visual system affected by attentive fixation

About a third of the cells in each pulvinar subdivision were clearly affected by changes in behavioral state. (Although we classified significantly affected cells using P < 0.02 in RESULTS, in this discussion, prevalence percents are based on P < 0.05 so as to be comparable with other studies.) Previous pulvinar studies have looked at how attention directed at a stimulus affects the response to that stimulus. These have reported either no effect of attention (e.g., Salzmann 1995), or effects in some pulvinar subdivisions but not others depending on the task and the motor response, particularly if eye movements (which themselves affect excitability in the inferior and lateral pulvinar) are involved (Acuna et al. 1983; Petersen et al. 1985; Robinson et al. 1986, 1990, 1991). The present results show that attention during a fixation task changes excitability for about the same fraction of cells in each pulvinar subdivision. This relatively uniform outcome may have resulted in part from the fact that we eliminated eye-movement effects, probed excitability with a stimulus that was not an object for attention, and kept experimental conditions constant (e.g., task, apparatus, stimulus conditions) for all brain areas studied. Indeed, that seemingly minor differences in behavioral state, for example between ready and idle, could so profoundly alter excitability for some cells underlines the importance of controlling behavioral context.

Unlike the pulvinar, the lateral geniculate showed no evidence of attentional modulation. This is the more remarkable since detection of modulation in the geniculate should have been favored by the small coefficients of variation found there---the smallest of any brain area we examined. Its absence suggests that the effects of attention in pulvinar and cortex must be of central origin and could not have resulted from some peripheral factor correlated with behavioral state such as pupil diameter, average stimulus luminance, or stimulus positioning within the receptive field.

The absence of modulation in the geniculate also deserves comment because some models of attention assign the lateral geniculate a central role (Crick 1984; Guillery et al. 1998; Koch and Ullman 1985; Sillito et al. 1994). Certainly the excess of nonretinal over retinal inputs to the geniculate seems consistent with the idea. The striking difference in synaptic relations formed by ascending retinal afferents and descending corticogeniculate afferents has suggested that corticogeniculate input may modulate, if not fundamentally change, traffic through the geniculate (reviews in Guillery 1995; Sherman and Guillery 1996). At least for the act of fixation, however, our results provide no support for such a role. That surprised us since state-dependent modulation is present in V1, comparable in prevalence and magnitude to that in other areas (Bender, unpublished observations), and layer 6 in V1 projects back to the geniculate (Fitzpatrick et al. 1994; Hendrickson et al. 1978; Lund et al. 1975). It may be that only more global and powerful changes in state, such as those occurring in general arousal, or during transitions from sleep to wakefulness, significantly affect geniculate excitability.

In comparing pulvinar with cortex, it is important to recognize the clear trend of increasing prevalence in cortex, from 25% in V2 to 40% in V4/PM to 55% in area 7a. This might suggest that the changes in state accompanying fixation are increasingly important to the functioning of higher cortical levels, but not across pulvinar levels. The high prevalence in area 7a is consistent with previous studies using similar procedures (Mountcastle et al. 1981; Steinmetz et al. 1994). However, selective attention paradigms have not revealed consistent trends, with some studies showing increases (Robinson et al. 1978, 1980; Wurtz and Mohler 1976), some no change (Motter 1993; Treue and Maunsell 1996), and others decreases in prevalence across levels in the cortical hierarchy (Ferrera et al. 1994; Moran and Desimone 1985).

We also found that behavioral state affected ongoing discharge rate, although independently of stimulus-evoked discharge, as did Mountcastle et al. (1981) for area 7a. Many selective-attention paradigms have likewise found attentional modulation of the ongoing discharge (e.g., Connor et al. 1997; Ferrera et al. 1994; Haenny et al. 1988; Luck et al. 1997). We did not subtract spontaneous from evoked discharge in computing response measures because there was no way to know whether discharge or its deviation from the ongoing rate was the more "meaningful" measure. Had we subtracted spontaneous, as many studies do, the prevalence of attentional modulation could have been as much as 10-15 percentage points higher.

We conclude that attentive fixation affects about equal numbers of cells in all parts of the pulvinar and about as many as in the lower levels of the cortical hierarchy. Modulation in the pulvinar, however, is only about half as common as at the highest level of the hierarchy we studied, area 7a.

Comparison with previous studies of attentive fixation

At first glance, our findings appear to differ in two respects from those of previous studies (Constantinidis and Steinmetz 1996; Mountcastle et al. 1981, 1987; Steinmetz et al. 1994). In area 7a, we found about as many cells facilitated as suppressed during fixation, whereas Mountcastle et al. (1981) reported almost all "light-sensitive" neurons were facilitated during fixation. In addition, we found the character of modulation in areas 7a and V4/PM was similar, whereas Mountcastle et al. (1987) concluded that it differed. However, both discrepancies probably result mainly from slight differences in experimental procedure and data analysis rather than from major substantive differences. For example, we studied every cell encountered whether it responded during fixation or not. The Mountcastle studies examined only those that were visually responsive during fixation and not those that responded best in some other condition such as arm projection (Motter and Mountcastle 1981). That procedural difference could tend to exclude many cells that we classified as F-, and certainly those F- cells that gave little or no response during fixation (despite good responses in, for example, the idle state---see Fig. 5B). Furthermore we made a single three-way comparison among states for each cell, whereas the Mountcastle studies made pair-wise comparisons. Our comparison generated some F- cells (Fig. 8C) whose responses during fixation clearly were bigger than during the ready (or during the idle) state; those F- cells would have appeared facilitated during fixation in one, though not both, of the two possible pair-wise comparisons. Likewise, the apparent difference in modulation character between 7a and V4/PM was based on equal responsiveness in a pair-wise comparison between the fixation and ready states (Mountcastle et al. 1987). We too found equal responsiveness for many cells in both those states, but would have treated many as simply unaffected by attention because their excitability was the same in all three states.

There also remains the possibility that differences in recording location within area 7a, and perhaps individual differences among animals, factors which we were unable to separate, contributed to the difference in character of modulation between studies. We found, for example, that F+ cells tended to be located more laterally in 7a than F- cells. Of course some systematic difference in visuomotor task or performance between the studies might account for the differing results, although that seems unlikely because the different task variants we used, including one virtually identical to that used in the Mountcastle et al. experiments, did not affect our findings.

In magnitude of attentional modulation, our results for areas 7a and V4/PM appear virtually identical to those of Mountcastle et al. (1981, 1987), provided allowance is made for the differences in method and data treatment. Thus when we computed the ratio of fixation to idle state responses for what should have been a comparable sample of cells (those significantly facilitated during fixation relative to the idle state), we found almost identical results: average facilitation during fixation was 3.3:1 for area 7a and 3.5:1 for area V4/PM compared with a ratio of 3.4:1 for both areas in the Mountcastle studies.

Factors affecting excitability during attentive fixation

The fixation task we used involved aspects of both directed spatial attention and attention in the sense of mental effort or vigilance. Attention was directed at the fixation point during the fixation state and not directed in the other two states. We presume mental effort differed across all three states, potentially affecting responsiveness (Spitzer and Richmond 1991; Spitzer et al. 1988). Although the stimulus used to probe cell excitability was irrelevant to the animal, who appeared to ignore it and did not look in its direction, we have no way of knowing whether the animal attended to it or not in one state or another. Our experiment was not designed to distinguish between different forms of attention but rather to see whether changes in excitability accompanied performance of a fixation task and how big the changes were in different brain areas. Nevertheless, because those excitability changes had different characters in some areas, it seems appropriate to speculate on what factors in addition to fixation might, or might not, have affected excitability.

It seems unlikely that general arousal level was a significant factor because it seemed roughly constant across states: rapid eye movements were common and brisk during ready and idle periods, and responses to ready-light onset (when required) and fixation point dimming had comparably short latencies. Furthermore global changes in arousal should have affected both geniculate and pulvinar, yet modulation was present only in the latter. Nor was adaptation to the behavioral task, in contrast to some studies (Fischer and Boch 1985), likely to have been important owing to the very extensive overtraining prior to recording. Some form of sensory interaction between probe and either fixation point or other visual contour could not account for differences between the ready and idle states because retinal stimulation was equivalent in both; such an interaction is not significant for area 7a cells (Mountcastle et al. 1981) but could be in other areas we recorded from. Finally, the direction of gaze per se probably was not a factor in the state-dependent changes in excitability that we describe. Angle of gaze affects excitability in areas 7a, V3a, and even in the lateral geniculate and V1 of the cat (Andersen and Mountcastle 1983; Andersen et al. 1990b; Barash et al. 1991; Galletti and Battaglini 1989; Lal and Friedlander 1990; Weyand and Malpeli 1993), but any such effect should have averaged out in our data because gaze direction was about equally distributed, within the central ±15°, across trials in all three states.

The single most important factor, accounting for the greatest change in excitability in every brain area, was the act of fixation itself (or something correlated with it). For each brain area, the best discriminant plane separating unit response vectors was nearly perpendicular to a pure fixation factor. However, fixation was a sufficient factor only for area V2 and the lateral pulvinar. In those areas, unit response vectors lay nearly in a single plane, owing to a high correlation between ready and idle state excitability, so that a single factor could explain their dispersion from the main diagonal. For all other brain areas, there was a much wider dispersion of response vectors, which would require at least a second factor. That is, "foveal work" alone could not account for the variety of excitability changes in these areas if only because ready and idle state excitabilities were often very different. There are several such candidate factors, and their importance may have differed with brain area. For example, ocular convergence might have differed systematically between states thereby generating, for disparity-sensitive neurons, an apparent difference in excitability. Disparity could have been significant for V2 and the lateral pulvinar, but was not for area 7a. We tested a number of 7a cells under both monocular and binocular viewing conditions and found state-dependent modulation unaffected by the difference, a result found also in area TE (Richmond et al. 1983). On the other hand, whether an animal's eye or arm was instrumental in a given state may have been critical for area 7a cells, as shown by Snyder et al. (1997), but there is no reason to think it so for V2 or for the inferior and lateral pulvinar. The direction of attention relative to gaze may have been a factor for area V4 (Connor et al. 1997), and thus for areas directly connected with V4 such as the lateral pulvinar. It is also possible that for any one cell, different factors could have been important at different times; beyond the requirement to press a ready key to start a trial, and fixate, there was little control to keep the animal's behavior constant. Still, such a drift in behavior should not have biased our results because the different attentional states were interleaved across trials or blocks of trials.

In all brain areas except Pdm, fixation increased excitability for about half the cells and decreased it for the rest, suggesting that the act of fixation produces in the aggregate a push-pull form of modulation. That result is a common finding in selective attention studies, although push and pull are not always so nearly equal (Bushnell et al. 1981; Colby et al. 1993; Goldberg and Wurtz 1972; Moran and Desimone 1985; Motter 1993). Why fixation produced only suppression in Pdm remains a mystery; it may be related to that area's connection to parietal cortex.

Magnitude of excitability changes during attentive fixation

One central question is whether attentional modulation in the pulvinar is as large as in cortex. That raises the issue of how to scale the magnitude of modulation in making the comparison, whether as a percent of the maximum modulation possible or in terms of the more "natural" unit of trial-to-trial variability. Although old (Fechner 1860), the issue is important because the areas compared differ in other major ways, unrelated to attention. Not surprisingly, different approaches give different answers.

When we scaled attentional modulation as a percent of the maximum possible modulation, the pulvinar appeared similar to V2; angular modulation averaged 20-25% in the pulvinar and ~20% in V2. However, we also found that angular modulation increased with cortical level, averaging ~30% in V4/PM and ~40% in area 7a. Attentional modulation in the pulvinar was thus like that at the lowest, but only half that at the highest, level of the cortical hierarchy. Among studies of selective attention, some have found increasing modulation at higher levels, for example comparing MT, MST, and area 7a (Ferrera et al. 1994), but others have not, for example comparing areas V4 and TE (Moran and Desimone 1985). On balance, it seems reasonable to conclude that modulation, as a percent of the maximum possible, is certainly smaller in the pulvinar than in area 7a, and probably about as large as in area V2. From this point of view, attention might appear a less potent determinant of excitability in the pulvinar than in at least the higher levels of cortex.

From a different point of view, however, attentional modulation was the same in pulvinar and cortex. Scaling modulation in terms of trial-to-trial variability of response, whether for brain area or individual cell, produced a measure of attention that was invariant across all areas of pulvinar and cortex.

Considering first average values across brain areas, although the absolute measure of state-dependent modulation (percent angular modulation) varied across areas, so did the intrinsic variability (as measured by the coefficient of variation of discharge rate) of all neurons within an area. As a result, for cells affected by behavioral state, modulation was the same constant fraction (~75%) of an area's variability, regardless of brain area. This raises the question of what could account for the difference among areas in variability. Differences cannot be related to an effect of behavioral state itself because any such effect was removed from our measure of variability by the ANOVA. Further, variance did not change with attentional state, a result also found in a study of selective attention (McAdams and Maunsell 1999), for 90% of all cells. The difference among areas in response window width (50 ms, see METHODS) also could not be responsible because window width and variability were only weakly related: a ±25-ms difference in width led to only a ±1.5% change in response SD. In part, differences in variability are probably related to differences among areas in discharge rate per se. We found a fourfold drop in average discharge rate between the lateral geniculate nucleus and area 7a. In areas V1, MT, and V4 of awake monkeys, spike rate and variance are almost linearly related (Britten et al. 1993; McAdams and Maunsell 1999; Snowden et al. 1992; Vogels et al. 1989), and the same is true in the lateral geniculate nucleus and V1 of anesthetized cats (Hartveit and Heggelund 1994; Tolhurst et al. 1983). If that relation also holds for all the areas we studied, then larger coefficients of variation would have to occur at higher levels of the cortical hierarchy simply because discharge rates are lower there and the coefficient of variation has, by definition, an inverse square-root dependence on discharge rate.2 This may not be the whole story, however, because the coefficients of variation in our data were even larger at more central levels than predicted by an inverse square-root extrapolation from the geniculate. Perhaps more cognitive factors, uncontrolled by the experimental paradigm, contribute additional variability at the higher levels.

The approximate constancy of attentional modulation, when scaled by variability, extended to the single cell as well. When the effect of attention was expressed as a signal-to-noise ratio for individual cells, i.e., discharge rate attributable to attention divided by discharge rate attributable to error, that ratio was constant for all cells significantly affected by state, regardless of brain location. What was surprising was how small that constant effect was. It averaged just 30% larger than noise. Even a state-dependent change in discharge of four- or fivefold turned out to be minor compared with the variability in discharge that was unrelated to state. Similarly modest values were found for area 7a neurons by Mountcastle et al. (1987), who reported an average d' of 1.2 when comparing fixation and ready-like states. That d' would have slightly underestimated our signal-to-noise ratio, because the sigma  estimate on which it was based was obtained from fixation state responses only (making it too large) rather than pure error variance (as was our signal-to-noise ratio). Whether such modest effects of attention, compared with noise, are typical of other studies in other brain areas is difficult to determine since most studies do not scale attentional modulation in terms of variability. Ferrera et al. (1994) report data indicating average d' values3 of 0.3-0.6 for effects of selective attention in areas MT, MST, 7a, and V4, suggesting even smaller effects. However, those averages included cells unaffected by attention, which naturally would have reduced d' estimates for affected cells.

Because we describe signal-to-noise ratios only for cells significantly affected by state, there is a built-in bias against small ratios; the effect of state had to have been greater than error variance to meet the critical F value at P < 0.02 in the ANOVA. There certainly was no upper bound on signal-to-noise ratios, however, so the small and near-constant signal-to-noise ratios across all areas are all the more noteworthy. In many studies of selective attention, absolute measures of modulation magnitude (i.e., not scaled to variability) have been modest in value (review in Maunsell 1995). It has been argued that if only the right task was used in the right brain area, attentional modulation could be much larger (Maunsell 1995; Richmond and Sato 1987; Treue and Maunsell 1996). The present results suggest that may not be so when modulation is scaled to variability, and that modulation may be substantially more constant across task and brain area when measured this way.

We conclude that attentive fixation has just as large an effect on excitability in all parts of the pulvinar as in any area of cortex, at least when measured relative to the noise. From that point of view there is no reason to think the pulvinar subordinate to cortex. Furthermore variability appears to be an intrinsic and central aspect of the visual system, and the impact of attention seems tightly regulated to just barely stay abreast of it. Although we have argued that the effect of attention is roughly the same in all brain areas, it was slightly, though not significantly, larger in area 7a. Area 7a angular modulation was ~15% above the regression line for the other areas, as was average signal-to-noise ratio. Thus more refined measures may show area 7a to have a more distinctive role in attentional modulation than our data suggest.

Neural systems affecting excitability during attentive fixation

There are extensive reciprocal connections between the parts of the pulvinar and cortex that we recorded from. The cortical areas also interact through a network of corticocortical connections, and they could potentially influence the pulvinar indirectly through the superior colliculus. It is certainly conceivable that within this extensive network the pulvinar could control cortical excitability, or vice-versa, in generating the attentional modulation we saw. However, the commonality in prevalence and signal-to-noise ratio of attention across all brain areas suggests a more complex genesis, involving cooperative reciprocal interactions between pulvinar and cortex, with neither thalamus nor cortex playing the dominant role. Perhaps a degree of compartmentalization or channeling within those interactions might account for the close similarity of modulation seen for such corticothalamic pairs as V2-PL and V4/PM-PI. In any case, reciprocal connections between pulvinar and cortex are almost certainly not the only source of attentional modulation we saw. Preliminary findings indicate that large kainic acid lesions of the pulvinar eliminate, in area 7a cells, the increased excitability during fixation, but not the decreased excitability.

Other systems that might contribute to the attentional modulation we saw include the noradrenergic and cholinergic "neuromodulatory" systems arising from brain stem and basal forebrain. Both innervate the thalamic and cortical areas we studied, and both can modulate cell excitability through a variety of different mechanisms that could easily account for the effects of state we saw (McCormick 1989, 1992; McCormick et al. 1993; Sato et al. 1989; Xiang et al. 1998). Locus coeruleus discharge itself might seem an unlikely determinant of modulation because locus coeruleus cells are activated by low-probability, unexpected, out-of-context events (Aston-Jones et al. 1994, 1997; but see Usher et al. 1999), whereas our receptive-field stimulus, fixation point, and ready light were all high probability, routine, and expected events. However, a regulatory role in keeping the signal-to-noise ratio of attention constant is certainly conceivable. Nucleus basalis cells, in contrast, might seem better suited to produce the phasic, state-dependent changes in excitability we saw because they can discharge strongly and differentially in different phases of even a well-practiced task (Richardson and DeLong 1986; Wilson and Rolls 1990).

In summary, the simple act of fixation alters excitability of a third to a half the cells in every stage of the visual system beyond the lateral geniculate nucleus, although not in the geniculate itself. About half those cells gain, the others losing, excitability with the exception of the dorsomedial part of the lateral pulvinar where all cells were suppressed during fixation. How such a push-pull modulation would be reflected, if at all, in averaging measures such as PET and fMRI is unclear, and interpretation of data from those techniques in this task would appear difficult. Perhaps most remarkable, the change in excitability is small, only a third larger than the intrinsic variability of evoked response, and fixed in all areas of pulvinar and cortex. That small and constant signal-to-noise ratio of attention naturally prompts the speculation that disorders of attention might reflect abnormally high or low values of a signal-to-noise ratio; for example, attention deficits might result from values too low, perseveration from values too high. From that perspective, it may be as interesting to know what regulates the constancy of the signal-to-noise ratio as to know what generates the modulation itself.


    ACKNOWLEDGMENTS

We thank K. Zernhelt-Wolf, B. Zeeb, and A. Townsend for excellent technical assistance in various phases of this study.

This work was supported by National Institutes of Health Grants EY-02254 and NS-32936, the McDonnell Foundation, and State University of New York at Buffalo Research Development Funds.


    FOOTNOTES

1 If p is a unit vector normal to the discriminant plane, and &dcirc; is a unit vector on the main diagonal, and &rcirc;i is the unit response vector of the ith cell in a brain area, then we chose the p for that area to maximize <FENCE><LIM><OP>&Sgr;</OP><LL><IT>i</IT></LL></LIM>(<B><A><AC>p</AC><AC>ˆ</AC></A></B> · <B><A><AC>r</AC><AC>ˆ</AC></A><SUB>i</SUB></B>)<SUP>2</SUP></FENCE><SUP>1&cjs0823;  2</SUP>, subject to the constraint p · &dcirc; = 0.

2 Because the coefficient of variation, µ, is defined as µ = sigma /mean, if sigma 2 ~ mean, then µ ~ 1/mean1/2.

3 Ferrera et al. (1994) report nonparametric measures of performance as areas under the Receiver Operating Characteristic (ROC) curve, which we then transformed to d' values for an ideal observer of equivalent performance.

Address for reprint requests: D. B. Bender, Dept. of Physiology and Biophysics, 329 Cary Hall, SUNY/AB Medical School, Buffalo, NY 14214.

Received 29 December 1999; accepted in final form 21 September 2000.


    REFERENCES
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ABSTRACT
INTRODUCTION
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DISCUSSION
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0022-3077/01 $5.00 Copyright © 2001 The American Physiological Society