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Color stimuli were calibrated using methods described elsewhere (Brainard, 1989
The Journal of Neuroscience, September 15, 2002, 22(18):8148-8157
Chromatic Light Adaptation Measured using Functional Magnetic Resonance Imaging
Alex R. Wade and Brian A. Wandell Department of Psychology, Stanford University, Stanford, California 94305
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
Sensitivity changes, beginning at the first stages of visual transduction, permit neurons with modest dynamic range to respond to contrast variations across an enormous range of mean illumination. We have used functional magnetic resonance imaging (fMRI) to investigate how these sensitivity changes are controlled within the visual pathways. We measured responses in human visual area V1 to a constant-amplitude, contrast-reversing probe presented on a range of mean backgrounds. We found that signals from probes initiated in the L and M cones were affected by backgrounds that changed the mean absorption rates in the L and M cones, but not by background changes seen only by the S cones. Similarly, signals from S cone-initiated probes were altered by background changes in the S cones, but not by background changes in the L and M cones. Performance in psychophysical tests under similar conditions closely mirrored the changes in V1 fMRI signals. We compare our data with simulations of the visual pathway from photon catch rates to cortical blood-oxygen level-dependent signals and show that the quantitative fMRI signals are consistent with a simple model of mean-field adaptation based on Naka-Rushton (Naka and Rushton, 1966
) adaptation mechanisms within cone photoreceptor classes.
Key words: fMRI; light adaptation; cones; simulation; V1; Naka-Rushton
INTRODUCTION
The sensitivity of the human photopic visual system declines with increasing ambient intensity. This sensitivity regulation is part of a process called light adaptation. Light adaptation is an important computational step in stabilizing object appearance across illumination conditions. It involves neural mechanisms that operate as early as the cone photoreceptors. Two different models for a light adaptation mechanism at this stage have been considered: it either "resides within individual photoreceptors or operates on signals from individual receptors" (He and Macleod, 1998
The behavioral color literature contains many measurements demonstrating that color appearance is regulated by changes in the gain (multiplicative scaling) of cone signals (Chichilnisky and Wandell, 1996
There have also been electrophysiological measurements of cone gain control. Valeton and van Norren (1983)
In this paper we assess light adaptation mechanisms under conditions in which a large, steady, uniform background influences the response to a spatially and temporally localized probe. We compare behavioral and physiological responses obtained using functional magnetic resonance imaging (fMRI) and psychophysical thresholds and describe a simulation that begins with the cone absorptions, includes a model of neuronal processing, and predicts both the fMRI signal and psychophysical performance.
MATERIALS AND METHODS
Color calibration procedures and display system
Stimuli were designed and presented using the psychophysics toolbox software (Brainard, 1997Spatial and temporal stimulus properties
The probe stimuli were circular, phase-reversing "dartboard" patterns composed of superimposed concentric circles and radial sectors of alternating positive and negative contrast (Fig. 1a). The stimulus diameter spanned 10° of visual angle and was presented in the center of a monitor that subtended 32°. The dartboard pattern had an angular spatial frequency of 12 cycles per 2radians and a radial spatial frequency of 0.25 cycles per degree. The contrast of the fMRI stimulus alternated at 2 Hz (square-wave modulation). Probe amplitudes were either 2% of the long (L) plus middle (M) or 9% of the small (S) cone class amplitude at the midpoint of the background range. For example, if the cone photoisomerization rates (p*) for the L, M, and S cones at the midpoint background were 2700 p*, 2200 p*, and 1100 p*, respectively, and the probe was defined along the S cone isolating direction, then the constant probe amplitude was set to ±99 S-cone p*. These midpoint contrasts were chosen so that they were roughly twice the psychophysically determined target detection threshold contrast on a mean gray background.
The constant amplitude probe stimuli were superimposed on a full-field, spatially homogeneous background whose mean level varied with time. Slight, additional modifications to the probe/background stimulus were made depending on the type of experiment (fMRI or psychophysical target discrimination). These are described later in this section. In practice, some deviation from the ideal case of cone independence must exist because of inaccuracies in the measurement of the monitor spectra and the limited precision of display RGB settings (10 bit). From repeated calibrations and calculations, and because of the good separation between S cone and L and M cone absorption curves, the cross-talk between different cone class excitations is minimal. Assuming standard macular pigment densities and ignoring L and M cone polymorphisms, the worst case is the cross-talk between the S-cone modulating probe and the M cones. In this case, the S-cone probe causes a modulation of the M-cone isomerization rate that is ~4% of that experienced by the S cones. For example, when the S-cone modulation is 9%, the worst case M-cone modulation is 0.36%. This cross-talk is unlikely to alter our results significantly.
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Figure 1. Stimulus description. a, In the fMRI experiment, the probe stimuli were presented in a block design of 18 sec probe stimulation followed by 18 sec of mean field. The mean field slowly changed value, but the amplitude of the probe stimulus was fixed. Ten blocks were presented in a single scan. The probe contrast was 1% as measured on a neutral gray background. Throughout, the subject attended to a small fixation mark that occasionally flickered. The subject's task was to indicate when the fixation flickered. b, The psychophysical experiment consisted of a sequence of trials. Each trial lasted 1900 msec, and a single session comprised 240 trials. The subjects indicated the quadrant of a missing sector of the probe. The mean field slowly changed value, but the amplitude of the probe stimulus was fixed. See Materials and Methods for details.
FMRI methods
MR instruments. The fMRI experimental apparatus has been described elsewhere (Wandell et al., 1999(L+M) probe experiments. An additional subject (W.A.P.) participated in the L+M cone probe experiments only. At least 12 repetitions of each probe/background combination were obtained from each subject. Visual area identification. Retinotopic visual areas were identified for all subjects using phase-encoded retinotopic mapping procedures described previously by this and other labs (Engel et al., 1997
The ROIs were further restricted within each session to those voxels activated by a reference scan presented at the start of the scan session. This reference scan consisted of a high-contrast checkerboard pattern with the same spatial and temporal characteristics as the probe described above. The reference probe was presented against a constant gray-level background in an 18 sec on/18 sec off block design. The color contrast of the reference scan dartboard was the same as that of the probes in the subsequent adaptation scans except that the contrast was constant and high enough to produce a robust response in V1 on each presentation. The purpose of the reference scan was to restrict the analysis to those voxels that could potentially be activated by the probe in the adaptation scans that followed. The reference scans also provided an estimate of the hemodynamic delay so that this could be accounted for in the calculation of the response amplitudes (see MR signal processing). Results are presented for V1 only. Signals from other areas were found to have lower signal-to-noise ratios and were not analyzed fully in this study.
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Figure 2. The locations of visual areas V1, V2, and V3 are shown in an expanded view of subject B.W.'s left hemisphere. The locations of these visual areas were identified for each subject in separate phase-encoded retinotopic mapping experiments. The data from those experiments were represented on a flattened representation of the cortical gray matter (not shown here), and the area boundaries were identified from reversals of the angular field representation. The retinotopic mapping procedures identified several visual areas. In this paper we present data from area V1 only.
Psychophysical methods
Psychophysical stimuli. Psychophysical test stimuli were conceptually similar to those used in the fMRI experiment. The probe was a constant-amplitude contrast pattern superimposed on a background that changed slowly along either the same or a different color direction. In the behavioral experiments, one sector of the dartboard probe (angle
Using the simulated cortical activity, we predict both the BOLD signal and the psychophysical performance. To predict the amplitude and time course of the BOLD signal, the cortical activity is convolved with a hemodynamic response function that blurs the time-varying activity (Fig. 3d). Because we are now operating in arbitrary scale units, the simulated cortical signal is multiplicatively scaled to bring the simulated BOLD signal into register with the measured signal. Scaling by this common factor does not change the predicted ratios of the responses. The change in psychophysical performance is estimated from the cortical signal using signal detection theory as described in detail in the Appendix. Note that the scaling factors that we estimate from the measured BOLD data are also used to scale the simulated cortical signals when we calculate the ideal observer psychophysical curves using different probe amplitudes.
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Figure 3. An overview of the simulation method. The graphs illustrate an S cone probe presented on a background change seen by the S cones. a, The photoisomerization rates (p*) of the three cone classes (dashed line, L cone; dotted line, M cone; solid line, S cone). b, The output of the opponent-colors response after removal of the mean. c, The rectified time series. d, The simulated BOLD signal after smoothing the time series in c. The gray-shaded region denotes one cycle of the stimulus alternation. See Materials and Methods for details.
RESULTS
FMRI
Figure 4 shows the time series of the fMRI BOLD signal measured in two different conditions. In one condition (Fig. 4a), the probe and background variations were both visible to the L and M cones but not the S cones. In the other condition (Fig. 4b), the probe was visible to the L and M cones, but the background change was visible only to the S cones. These data illustrate the main experimental effect. When the probe and background changes are seen by the same cone classes, the changing background significantly influences the signal amplitude. When the probe and background changes are seen by different cone classes, the time series amplitude remains constant whereas the background changes.
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Figure 4. The time series of the fMRI signal. The response to an S cone-initiated probe is shown. a, The background change was visible to the L and M cones, but not the S cones. b, The background change was visible to the S cones, but not the L and M cones.
Figure 5 shows the change in probe amplitude at different background levels. The probe amplitude signal change is expressed as a percentage change of the mean BOLD signal level (see Materials and Methods). Background levels are expressed in units of background cone isomerization rates for the cone class that was varied. For example, in the condition where the background changed along the S cone color axis, the test backgrounds generated S-cone isomerization rates of between ~600 and 2000 S-cone isomerization events per second.
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Figure 5. The amplitude of the BOLD signal is shown as a function of the mean background isomerization rate. a shows the amplitude measurements for background changes seen only by the L and M cones. The test probe was either an S cone-initiated stimulus (dashed line) or an L+M cone-initiated stimulus (solid line). b shows the amplitude measurement for background changes seen only by the S cones. The test probe was either an L+M cone-initiated (solid line) or an S cone-initiated (dashed line) stimulus. The data are average amplitudes from two observers, each with at least 12 samples per observer. See Materials and Methods for details.
Figure 5a show the effects of changing the background (L+M) cone absorptions on a signal generated by S and L+M cone probes. The response amplitude to the S cone probe (Fig. 5a, dashed line) is invariant across changes in the mean L+M cone excitation, whereas the response of the L+M cone probe (solid line) changes from ~0.9 to 0.5% BOLD contrast. Similarly, Figure 5b show the effect of changing the background along the S cone color direction for an L+M or S cone isolating probe. There is no significant change in the probe response amplitude for the L+M cone probe (Fig. 5b, solid line), but the signal caused by the S cone probe (dashed line) drops from ~1.7 to 1.1% BOLD contrast.
The theoretical significance of these values will be considered later when we develop the simulation predictions. It is interesting to note that at the highest background levels, the probe was close to the target discrimination threshold as measured in the psychophysical task described below. Even so, an easily measurable BOLD response was present.
Psychophysics
Figure 6 shows the results of the psychophysical experiment. These graphs plot percentage of correct sector identification as a function of mean background cone isomerization rates. As in the fMRI experiments, the probes were constant amplitude. The general principles observed in the fMRI measurements are repeated in the psychophysical graphs for all four subjects. In the cases where the background and probe varied along the same color axis, increasing the background level reduced target discrimination performance. When the change was along a different color direction, the background variation had no significant effect on performance.
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Figure 6. The psychophysical performance in the sector-discrimination task. a, Performance is shown for the condition when the background change is seen by the L and M cones. b, Performance is shown when the background change was seen by the S cones. In both figures, the dashed line shows the constant performance to an S cone-initiated probe. The solid line shows the performance when the probe was defined by excursions along the L+M axis.
For these experiments the probe amplitudes were set to be near detection threshold on a neutral gray background. The subjects felt that performance deteriorated because the background changes reduced the probe visibility. Naturally, when the probes are below threshold, sector discrimination must fall to chance (25% correct). Although we did not explicitly measure contrast detection under these conditions, it is safe to conclude that when probe and background colors differed there was very little change in target detection accuracy.
Simulation
Figure 7 shows the amplitude of the simulated BOLD response in the four main experimental conditions. The panel on the left shows the change in amplitude changes for a background change seen by the L+M cones; the test probe is either a S cone probe (dashed line) or an L+M probe (solid line). The panel on the right shows the simulated amplitude change for a background change seen by the S cones, again, for an S cone and L+M probe. These BOLD amplitude simulations are quite similar to the measurements shown in Figure 5. The simulation quantitatively predicts the drop in sensitivity caused by increasing the background isomerization rate. This suggests that the change in sensitivity of the cones themselves, along with the removal of the instantaneous background, as measured by Valeton and van Norren (1983)
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Figure 7. The simulated BOLD amplitude responses. a, Simulations for a changing L+M cone background. b, Simulations for a changing S cone background. The dashed and solid lines show amplitudes for S cone- and L+M cone-initiated probes, respectively. These simulations should be compared with the data in Figure 4. See Materials and Methods for details.
To predict the psychophysical performance, we must relate the simulated cortical signal to the observer's discrimination performance. We have applied an ideal observer analysis to the simulated cortical input signals. Using assumptions about the cortical noise distribution and signal thresholding described in the Appendix, we use the simulated cortical signal to predict the probability of correct sector discrimination. Figure 8 shows four such curves derived from simulated cortical signals. These signals were calculated on the basis of the mean probe amplitudes used in the psychophysical experiments (L+M cone probe modulation of 0.8% mean gray background level; S cone probe modulation of 2.2% mean gray background level).
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Figure 8. Simulations of psychophysical detection performance based on an ideal observer model. Two free parameters (the width of the noise distribution s and a signal threshold t) were estimated using a least-squares fit between the simulation and the data shown in Figure 6. The same parameters are used in all plots. a shows the simulated performance when the background change is defined by a change in L+M cone catch. b shows the simulated performance when the background change is seen by the S cones only. Dashed lines show data for simulated S cone probes. Solid lines are plots for L+M cone probes.
In the simulated cortical signal (Fig. 7), the amplitude of the response to the test in the orthogonal color direction decreases slightly. This slight decrease also causes a prediction of slightly reduced psychophysical performance (Fig. 8). The reason for these increases can be traced to imperfections in the simulated cone isolation and in the method used to remove the instantaneous mean from the retinal signal (subtraction of a temporally low-pass-filtered version of the signal; see Appendix).
DISCUSSION
Models of light adaptation
At a general level, the question we asked in these experiments was this: how do spatially homogeneous mean fields affect the visual response to superimposed probes?
It is widely accepted that cone adaptation in the general case can be modeled using the linear equation (Delahunt and Brainard, 2000
where Lout, Mout, and Sout are the zero offset outputs from the L, M, and S cone systems, and Lin, Min, and Sin are the inputs to those systems and G1... 3 are functions of the LMS background levels Lbg, Mbg, and Sbg.
(1) In our experiments, with spatially and temporally simple adapting fields, we tested whether the adaptation that we saw was consistent with the simpler model:
where the functions G1,2 are now each dependent on only two cone classes and G3 is dependent on only a single, third cone class. If this model holds, then we expect to see little effect of changing the photon catch in one cone class (for example, the S cones) on fMRI BOLD signals that originate in another cone class (for example, the L cones). We would conclude that, under these conditions, S cone adaptation is regulated at the level of the individual cones.
(2) This is what we observed in both the fMRI and behavioral measurements (the logic of the behavioral experiments is identical to that described above except that we also include an implicit assumption that target discrimination performance is monotonically related to cortical signal levels).
For these probe patterns and background changes, light adaptation gain mechanisms in the S cone-initiated pathway are regulated by signals that share the spectral properties of the cone itself. We did not observe significant effects of the L and M cones on S cone signals, nor are there strong effects of the S cones on L and M cone signals. This segregation of adaptation is generally consistent with the segregation between S cone and L and M cone signals in the outer retina (e.g., H1, H2 cells). In anatomical studies, H1 cells have been shown to contact primarily L and M cones, whereas H2 cell connections are strongly biased in favor of S cones (Ahnelt and Kolb, 1994
On the basis of results from our simulation of the visual pathway from photoisomerization rates to BOLD signal, we find that the light levels that induce these gain changes are slightly lower than the levels of light adaptation measured for dissociated outer segments (Schneeweis and Schnapf, 1999
The validity of this model could be tested by varying the L and M cone catches independently, just as we did with the S cone catches.
Related literature
The existence of a cone-level gain control mechanism demonstrated by in vitro recordings (Schneeweis and Schnapf, 1999
There have also been a substantial number of behavioral measurements of light adaptation that suggest interactions between cone types in the regulation of cone gain. Some of these experiments require viewing conditions that are outside the range that we can generate using a CRT (Mollon and Polden, 1976
The differences between our results and those of Delahunt and Brainard (2000)
Spatial structure in the adapting background may increase the role of post-receptoral mechanisms. Our probe/background combinations are designed to minimize the role of spatial processing and would not change the gain in post-receptoral mechanisms that respond mainly to contrast. It is also possible that even if there are psychophysically detectable interactions between cone classes at high cone contrasts, our use of near-threshold probe stimuli made the detection of such interaction unlikely.
One of the more puzzling aspects of the fMRI measurements is the persistence of a significant signal at contrast levels that are very close to detection threshold. Several authors have reported BOLD signal modulations at or below detection threshold in experiments on attention (Kastner et al., 1999
Stimulus range limitations
Although the background illumination in our experiments was restricted by the gamut of the display devices, the range was adequate to measure substantial adaptation and also to differentiate two models of light adaptation dynamics that are current in the literature (Valeton and van Norren, 1983
Conclusions
Light adaptation mechanisms of retinal origin can be detected and quantitatively accounted for in fMRI signals obtained from visual area V1. The size of the gain regulation is consistent with estimates from psychophysical measurements under quite similar conditions. The patterns of psychophysical and fMRI experiments are both consistent with a process controlled mainly by receptor gain control, and for these experimental conditions we cannot reject the hypothesis that the gain is regulated within the receptor itself.
We find that the output of our current simulation of the visual pathway is in excellent agreement with the signals measured using fMRI and psychophysics. We expect to extend this simulation to more complicated spatiotemporal stimuli and to model more sophisticated aspects of post-receptoral retinal and cortical processing and fMRI physics. We will distribute the Matlab source code for this simulator on the Internet and encourage comments and modifications from other members of the scientific community. Using this quantitative model, we should be able to develop sensitive tests of the computations performed by post-receptoral neurons and perhaps even cortical mechanisms that act on stimulus properties at a much larger spatial scale.
FOOTNOTES Received March 11, 2002; revised June 14, 2002; accepted June 20, 2002.
This work was supported by National Institutes of Health Grant RO1 EY03614. We thank Robert Dougherty.
Correspondence should be addressed to A. R. Wade, 420 Jordan Hall, Stanford University, Stanford, CA 94305 MC2130. E-mail: wade{at}white.stanford.edu.
APPENDIX: SIMULATION OF BOLD SIGNAL CHANGE AND PSYCHOPHYSICS
In this Appendix we describe the simulator calculations used to predict the BOLD signal and psychophysical performance. The simulation consists of four computational stages that are roughly analogous to the well known processing steps in the visual pathways. After these stages, there is a fifth step that translates the underlying neural signal into either a BOLD response or a psychophysical performance level: (1) cone isomerization rates; (2) cone voltage outputs; (3) contrast and color-opponency: retinal ganglion cell signal; (4) rectified amplitude: cortical signal; (5) translation to specific dependent measure: (5a) BOLD signal: temporal blurring (hemodynamic response function); (5b) psychophysics: noise, thresholding, and ideal observer calculation based on the signal in stage 4.
The computations are described in more detail below. The Matlab code for the simulation will be provided on request.
Cone isomerization
The display spectral radiance functions were measured at 4 nm intervals (watts per nanometers per steradian per meters squared per seconds) using a Photoresearch P650 photospectrometer. Rodieck (1998)
Note that the only deviation from Rodieck's values is our estimate of S cone efficiency. Rodieck gives a value 0.07 for a foveal S cone where the macular pigment density is highest. Because our stimuli had a spatial extent of 10°, we calculated a higher value for the mean S cone optical efficiency based on a Gaussian macular pigment distribution with a full width at half maximum of 1° (Hammond et al., 1997
For the simulation, the mean pupil size at this illumination level was chosen on the basis of data in Wyszecki and Stiles (1982)
Cone output voltages
Cone output voltages were computed using a general model that has been confirmed by several groups using various techniques (Boynton and Whitten, 1970
The specific quantitative values that we have used are from Valeton and van Norren (1983)
(3) is the semisaturation constant. The exponent n is found experimentally to be near 0.75. The parameters Vm and
This conversion step is important because the semisaturation constant of the cones must depend on the photoisomerization rate of the cone and not the photopic luminance of the light source (Trolands). For example, short-wavelength light can cause a high isomerization rate in the S cones, but it has a negligible luminance or Troland value. We suggest that stimuli for future experiments of this type include a specific radiometric unit to permit the unambiguous conversion to isomerization rates as well as a conversion to photometric units if desired.
We also compared these results with a simulation based on cone output photovoltage data from Schneeweis and Schnapf (1999)
Figure 9 shows a comparison of simulated response sensitivity curves generated using the Valeton and van Norren (1983)
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Figure 9. Predicted response sensitivity curves based on models from Valeton and van Norren (1983)
Contrast estimation and opponent colors (retinal ganglion cell)
At this point in the calculation, we have a prediction of the time-varying cone voltage as the stimulus changes throughout the experiment. As the background isomerizations for a particular cone class increase, the cone output voltage increases, the gain is reduced, and the temporal contrast of the signal decreases. At this stage of the calculation, we remove the local mean signal and compute the local temporal contrast. Convolving the time-varying cone voltage with an exponential function (T1/2 of 200 msec) produces a local temporal average cone voltage; we subtract this average from the complete time series to generate the local temporal contrast. Finally, signals from the three cone types are combined into three channels, one luminance and two opponent, creating L+M, L
V1 Cortical signal
The signal in V1 caused by the combined input from the opponent stage is modeled rectification and summation of the opponent inputs channels. Recent research suggests that the BOLD signal may be dependent on the inputs to a visual area rather than the outputs (Logothetis et al., 2001
BOLD prediction
To predict the BOLD signal, the local signal amplitude is convolved with a hemodynamic response function described by Worsley (http://www.bic.mni.mcgill.ca/users/keith/). This resulting time course matches the general properties of the fMRI signal. The absolute size of the MR signal is rather arbitrary at this stage in the simulation, because it depends on the relationship between absolute signal levels at the input to the cortex, their influence on blood oxygenation, and the physics of the detection of the BOLD signal by a hypothetical scanner. All that we hope of these stages is that they each perform approximately linear mappings from input to output so that the ratio of the final simulated signal can be compared with the ratio of the corresponding measured BOLD response (Boynton et al., 1996
The final step of the calculation, then, is to scale the fMRI signals originating from the L+M and S probes. The scaling factors are calculated independently for S and L+M probes and operate on the cortical signal before the convolution by the hemodynamic response function. Scaling factors that minimized the mean sum of squared error between the predicted and observed fMRI signals were used. The reason for applying the scaling factors to the cortical signal is that the same scaling factors were used when computing the ideal observer psychophysical response functions. These functions were calculated on the basis of the amplitude of the cortical signal (a measure of neuron firing rate) and not the derived BOLD signal. Note that multiplicative scaling of this type makes no difference to the predicted sensitivity changes. Hence, the predicted and observed amplitudes should be compared using changes in amplitudes (slope) of the simulated and actual signal amplitude values.
Psychophysical prediction
To predict psychophysical performance, we must calculate how the observer uses the simulated cortical signal to judge which quadrant contains the missing sector. Following the literature, we assume that the cortical signal is thresholded (Carandini and Ferster, 2000
The simulation provides us with a measure of the cortical signal. For a cortical signal with amplitude S, a cortical threshold value,
, we calculate the effective signal available to the observer as:
Given a noise standard deviation of
The term
is the cumulative distribution of a normal random variable with mean 0 and SD
p (x|A) is the probability density of a signal with mean value A. The probability correct represents the chance that the value x arises from the signal distribution, A, and that all of the three noise quadrants have a value less than x. We assume that the SD of the noise and signal + noise distributions are identical. The free parameters
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