Different Sources of Nitric Oxide Mediate Neurovascular Coupling in the Lateral Geniculate Nucleus of the Cat

Understanding the link between neuronal responses (NRs) and metabolic signals is fundamental to our knowledge of brain function and it is a milestone in our efforts to interpret data from modern non invasive optical techniques such as fMRI, which are based on the close coupling between metabolic demand of active neurons and local changes in blood flow. The challenge is to unravel the link. Here we show, using spectrophotometry to record oxyhaemoglobin and methemoglobin (surrogate markers of cerebral flow and nitric oxide levels respectively) together with extracellular neuronal recordings in vivo and applying a multiple polynomial regression model, that the markers are able to predict up about 80% of variability in NR. Furthermore, we show that the coupling between blood flow and neuronal activity is heavily influenced by nitric oxide (NO). While NRs show the typical saturating response, blood flow shows a linear behaviour during contrast-response curves, with nitric oxide from different sources acting differently for low and high intensity.


INTRODUCTION
The relationship between cerebral blood fl ow, and neural activity is complex, and by no means completely understood. Non-invasive optical techniques such as fMRI can relate neuronal activity to changes in haemodynamic signals (Kwong et al., 1992;Ogawa et al., 1993;Drake and Iadecola, 2007). Traditionally, fMRI measures a blood-oxygen-level dependent signal and assumes a linear relationship between neuronal and metabolic responses (Logothetis, 2002). However, this assumed linear relationship has now begun to be challenged (Lauritzen, 2001;Nielsen and Lauritzen, 2001;Sheth, 2004;Li and Freeman, 2007;Rasmussen et al., 2008). For example, haemodynamic coupling has been shown to fi t well to a power law function (Li and Freeman, 2007, utilising a similar preparation to the one described here). However, this was shown to be linearly coupled to stimulus intensity, as previously demonstrated in rat cerebral cortex (Lauritzen, 2001;Nielsen and Lauritzen, 2001). In the search for a all encompassing description of the relationship between blood fl ow, stimulus intensity and neural response, Rasmussen et al. (2008) have developed a general parametric mathematical model which characterises deviations from linearity using data derived from the cerebellum, showing that the neurovascular system exhibits linear behaviour in response to stimuli of low frequency and short duration. Here we investigate the relationship using a combination of new technologies, in a well characterised CNS system, the lateral geniculate nucleus (LGN) in the visual thalamus of the cat.
As noted above, the mechanisms underlying neurovascular coupling are still under debate but include several possibilities (which are not necessarily mutually exclusive), including direct control by neurons (Krimer et al., 1998;Paspalas and Papadopoulos, 1998;Yang et al., 2000;Cauli et al., 2004), changes in K + concentration as result of enhanced activity (Faraci and Heistad, 1998), Understanding the link between neuronal responses (NRs) and metabolic signals is fundamental to our knowledge of brain function and it is a milestone in our efforts to interpret data from modern non invasive optical techniques such as fMRI, which are based on the close coupling between metabolic demand of active neurons and local changes in blood fl ow. The challenge is to unravel the link. Here we show, using spectrophotometry to record oxyhaemoglobin and methemoglobin (surrogate markers of cerebral fl ow and nitric oxide levels respectively) together with extracellular neuronal recordings in vivo and applying a multiple polynomial regression model, that the markers are able to predict up about 80% of variability in NR. Furthermore, we show that the coupling between blood fl ow and neuronal activity is heavily infl uenced by nitric oxide (NO). While NRs show the typical saturating response, blood fl ow shows a linear behaviour during contrast-response curves, with nitric oxide from different sources acting differently for low and high intensity.
of the Spanish Physiology Society and the International Council for Laboratory Animal Science and the European Union, and were approved by the university Animal Care and Use Committee.
End-tidal CO 2 , ECG, EEG, temperature and blood pressure (BP, mmHg) were monitored continuously throughout the experiment. Once a stable state was reached, any variation in the monitored parameters commensurate with a change in the depth of anaesthesia was compensated for by alterations in the level of isofl urane. Wound margins were treated with lidocaine hydrochloride with adrenaline administered subcutaneously. Ear bars were coated with lidocaine gel. The eyes were treated with atropine methonitrate and phenylephrine hydrochloride, protected with zero power contact lenses and brought to focus on a semi-opaque tangent screen 57 cm distant, using ancillary lenses. Visual stimuli were viewed monocularly through 3 mm artifi cial pupils. To further reduce possible eye movement artefacts, posts attached to the stereotaxic frame were fi xed to the sclera. At the end of the experiment, all animals were painlessly killed by anaesthetic overdose.

RECORDINGS
Extracellular single units were recorded (Plexon Inc, Dallas, TX, USA) using tungsten microelectrodes. Spectroscopic measurements of OxyHb and MetHb (markers of cerebral fl ow and nitric oxide levels, respectively) were obtained through a pair of optical fi bres attached to the electrode thereby allowing us to record all signals from the location. Light (460-800 nm) was passed through one optical fi bre and scattered light was collected by the second and sent to a linear CCD detector device (Oceans optics, Eerbeek, Netherlands) via a compact built-in monochromator. OxyHb, expressed in absorbance arbitrary units, was calculated using the formula: [(576 nm) − 0.55 × (567 nm) − 0.45 × (587 nm)/15448] × 150 MetHb was calculated from the absorbance at 634 nm (González-Mora et al., 2002).

VISUAL STIMULATION
Computer-controlled visual stimuli (Lohmann Research Equipment, Germany) were presented on a computer monitor (refresh rate 128 Hz, see de Labra et al., 2007). Stimuli consisted of full fi eld (8° × 8°) sinusoidal gratings with spatial and temporal frequencies qualitatively selected to produce the best response. Stimulus contrast was defi ned as [(L max − L min )/(L max + L min )] × 100. Our basic experimental paradigm fi rstly established control responses for spontaneous cell fi ring and baseline spectroscopy signals (no visual stimulus present). Then the appropriate drifting sinusoidal grating stimulus was presented at a fi xed contrast between 0 and 100%, and the responses were compared with those obtained in basal conditions. The typical experimental paradigm included continuous visual stimulation for 2 min with spectral sample durations of 150 ms, which allowed a suffi cient number of trials to assess the robustness of the response. The inter stimulus period lasted 2 min.

PHARMACOLOGY
To test the hypothesis that NO from different sources acts at different levels of activity, we fi rst blocked NO synthesis and analysed the responses produced at high (100% contrast) and low (10% contrast) intensity stimulation. Two different antagonists of NOS were used: 7-nitroindazole (7-NI; specifi c for neuronal NOS, nNOS) and the non-specifi c blocker l-nitroarginine (l-NOArg). Blockers of NOS were injected IV (l-NOArg 5 mg/kg in saline and 7-NI 4 mg/kg dissolved in 0.1 ml DMSO, see Theobald Jr, 2003), and data were collected between 2 and 30 min after injection. Effects on neuronal responses (NRs) were used as a measure of the degree of effi cacy of the drug. In previous control experiments (data not shown) we compared the effect of l-NOArg applied by microiontophoresis with 7-NI IV administration as we have done in this study. The results obtained on cell fi ring were essentially the same, and from this we decided that 7-NI systemic administration was able to affect LGN cells fi ring much as shown previously using local application of different NOS inhibitors (see for instance Cudeiro et al., 1994Cudeiro et al., , 1996.

ANALYSIS
Results are shown either as raw values or as percentage of the baseline unless otherwise indicated. Statistical analyses were performed using an appropriate t-test; p-values <0.05 were considered statistically signifi cant.

Mathematical validity -modelling from the data
The relationship among MetHb, OxyHb, NR and stimulation intensity (ST) has been analysed using multiple polynomial regression models (see, for instance, Seber and Lee, 2003). 56 observations with the four variables were recorded. Two polynomial regression models were considered in order to explain each of the variables (MetHb, OxyHb and NR) in terms of the remaining covariables. Polynomial models of a suffi ciently high order have been fi tted to every dataset. The statistical signifi cance of the estimated principal coeffi cients of the model has been examined via their p-values. As a consequence, the degree of the polynomial is reduced if such estimated coeffi cients are not signifi cant. After selecting a fi nal model via this procedure, its corresponding determination coeffi cient (R 2 ) is reported.
For our data, the fi nal fi t was either linear or a second degree polynomial in one or two covariates. Least squares estimates of the model coeffi cients were obtained. The F-test was used as a signifi cance test to check if every explanatory variable could be removed from the model or if the polynomial degree could be reduced. Diagnostic tools, as residual plots, have been used to confi rm the validity of the model as well as to suggest increasing the polynomial degree.
We used the data obtained to check the robustness of the relationship between parameter measures: to predict each of the values of OxyHb, MetHb and NR from the others we have used two different levels of aggregation: the measurement level and the subject level. In the fi rst approach, for every fi xed value of ST, the experiment was repeated several times obtaining different values of OxyHb, MetHb and NR. The second approach tries to avoid the intra-subject variability by averaging the values of OxyHb, MetHb and NR along the different measurements performed for every fi xed value of ST in each animal. These analyses have been carried out for the whole data set.

Analysis of the coupling of blood fl ow to neural responses
To bring together all the fi ndings, and provide a simple demonstration of the effect of NO on the relationship between neural activation and blood fl ow, we quantifi ed the relationship between blood fl ow, neural response and NO activity, by calculating the percentage of change of blood fl ow (OxyHb levels) and neural response before and during the effect of 7-NI, while stimulating at either high (100%) or low (10%) contrast.

GENERAL FINDINGS
A total of 72 recordings were completed. A typical example of the spectrophotometric signal for OxyHb and MetHb during spontaneous and visual driven activity is shown in Figure 1A. The presentation of the visual stimulus (a full fi eld sinusoidal drifting grating -in this case, maximum contrast) evoked an increase in absorbance, as shown in the area within the box. The strength of the signal was relatively constant during the presentation of the visual stimulus. The inset box shows the visually elicited response from a single cell recorded simultaneously. Graphs in Figure 1B illustrate the average increase in OxyHb and MetHb during the stimulus presentation for all the recording sessions (both signifi cantly elevated, p < 0.05), suggesting blood fl ow and NO production were both signifi cantly increased during visual stimulation.

RELATIONSHIP TO STIMULUS INTENSITY
NR, as well as OxyHb and MetHb measurements, changed with stimulus intensity. Figure 2 shows the changes measured during the presentation of a visual stimulus which was varied in contrast, for all recordings (i.e. population data). 0% represents the control, unstimulated condition. For NR (top), there is measurable spontaneous activity, as shown on the Y-axis. For the OxyHb and MetHb, each value is given as the percentage change over the basal value. All parameters show a clear increment with contrast, but only the neuronal signal shows signs of saturation at higher contrast. In order to obtain a deeper understanding of the interactions between the measured variables, we applied multiple polynomial regression models. The number of observations where all four variables (NR, OxyHb, MetHb and contrast) were successfully collected was 56. One outlier was detected. The following regression analyses were performed with this value excluded from the sample. the NR variability (R 2 = 0.36001). The root relative mean squared error of the prediction for this fi t is 0.3704. This means that NR can be predicted with a relative error of about 37% using this model ( Figure 3A). This does not mean that NR is not infl uenced by the intensity of the stimulus, but stimulus intensity does not provide any additional value to the model.  Figure 3C). In our second approach, multiple polynomial regression models were applied again to the MEAN values of OxyHb, MetHb and NR obtained for the different stimulation intensities in each animal. This was done to get rid of the sampling variability of MetHb, OxyHb and NR within every subject. As a consequence, intra-subject variability is not explained by the following models. The number of observations where all four variables were fully collected was 19, and no outliers were detected. (iv) NR can be partially explained via a linear model with two covariates: MetHb and OxyHb. The p-value of ST in the full model is p = 0.5628. The linear two-covariate regression model to explain neuronal response is NR = 10.6949 + 0.559294· OxyHb + 52.3099· MetHb, which explains about 84% of the NR variability (R 2 = 0.8430). The root relative mean squared error of the prediction for this fi t is 0.1806. This means that NR can be predicted with a relative error of about 18% using this model ( Figure 4A). OxyHb (p = 0.7668) and NR (p = 0.3322) are not signifi cant when added to the model. The root relative mean squared error of the prediction for this fi t is 0.2713. This means that MetHb can be predicted with a relative error of about 27% using this model ( Figure 4C).

RELATIONSHIP TO NO SOURCE
The results above show a relationship between OxyHb and MetHb which opens a question about how NO production is controlled. In the visual thalamus NO has two major sources (i) endothelial NO, with neuronal and/or astrocyte mediated regulation and (ii) neuronal NO released from the afferent cholinergic-nitrergic fi bres arising in the parabrachial region of the brainstem which in cats are the only source of neuronal NO in the LGN (Bickford et al., 1993, but see Section "Discussion" and Bickford et al., 1999). A plausible explanation is that both sources of NO are brought into play at different levels of neural activity. We tested this hypothesis by comparing the effects of a specifi c nNOS blocker to that of a non-specifi c blocker. Injection of l-NOArg produced an increase in BP (mean ± SEM: 123 ± 9/86 ± 4 to 136 ± 8/103 ± 3 p ≤ 0.05, t-test). This was detected ∼14 min after injection. In agreement with published data (Schulz et al., 1995;Cholet et al., 1997;Koss and Yu, 2000;Jiang et al., 2002), 7-NI did not signifi cantly modify BP.
The results are shown in Figure 5. Figure 5A shows that both inhibitors signifi cantly reduce NRs at high (contrast = 100%) and low intensity stimulation (contrast = 10%), with the effect of l-NOArg being more pronounced in both cases, as expected from the non-specifi c inhibitor. Interestingly, while l-NOArg shown a similar effect on NO and OxyHb levels (Figures 5B,C, respectively) causing a reduction in both parameters at all stimulus intensities, 7-NI shows a stimulus specifi c effect, acting only at high intensity stimulation, not affecting at evoked NO and OxyHb during low contrast stimulation. The data support the view that nNOS reduces fi ring at high and low contrast, but differentially affects blood fl ow. We examined this phenomenon in another way in Figure 6. This fi gure compares the percentage of change of blood fl ow (OxyHb levels) and neural response before and during the effect of 7-NI, for low and high contrast stimulation. While both parameters are signifi cantly reduced at high contrast stimulation (cell fi ring 27%: 55 ± 16 to 40 ± 10; OxyHb 45%: 31 ± 10 to 14 ± 4.7), no signifi cant effect is seen on blood fl ow during the presentation of low contrast stimuli (8.7%, from 6.9 ± 1.8 to 7.5 ± 0.5), even when neuronal activity levels are signifi cantly reduced (64%, 19.5 ± 5 to 7 ± 1).This indicates a signifi cant decoupling of blood fl ow and neural responses at these two stimulus intensities.

DISCUSSION
Our results demonstrate a relationship between NO production, blood fl ow, and NRs, showing that NO is able to regulate blood fl ow in active brain areas, with different sources of nitric oxide acting specifi cally at different levels of response. The production of NO necessary to maintain higher neuronal activity and guarantee the availability of higher amounts of OxyHb appears to be derived from nNOS. In this scenario, at low intensity stimulation an initial increase in blood fl ow is obtained by a mechanism that requires small increments in basal NO concentrations. Our data can not explain the mechanism by which low levels of driven visual activity are able to produce small increments of NO, but strongly suggests a link to eNOS activation. Under basal conditions, tonic release of NO is a signifi cant regulator of resting cerebral blood fl ow. It has been demonstrated that eNOS is an important source of the NO that contributes to the basal tone of cerebral arteries (Tanaka et al., 1991;Wang et al., 1995;Atochin et al., 2003) yet it is know that dilation of cerebral vessels occurs following neural stimulation (Cox et al., 1993;Iadecola et al., 1997). In our case a simple explanation of our data would be to suggest that the initial changes in NO derives from the endothelial isoform of NOS present in astrocytes (Wiencken and Casagrande, 1999) acting in response the retinal glutamatergic input to LGN cells. It is known that glutamate can mobilise Ca 2+ stores in astrocytic endfeet and this leads to a dilatation of local cerebral arterioles (Zonta et al., 2003). Several mediators have been implicated in these vascular changes but signifi cantly includes NO produced by astrocytic eNOS activation (see Iadecola and Nedergaard, 2007 for a recent review).
On the other hand, stimulation at high intensity (in our case by using high contrast) would activate the brainstem system. Available evidence shows that, in the thalamus of the cat, nNOS is specifically colocalised within the cholinergic brainstem-thalamic axons (the major source of NO of neuronal origin in the cat's LGN, see Bickford et al., 1993, but see also Bickford et al., 1999, suggesting a local NOS presence in a small proportion of intrinsic inhibitory interneurons) and it is also known that NO production in the LGN as a result of activity in the brainstem activating system is directly relevant to visual processing (for a review, see Cudeiro and Rivadulla, 1999). With the data we present here, we can suggest that brainstem inputs seem to be capable of regulating neuronal activity by means of NO release that acts not only on neurons but also on blood vessels, modulating the oxygen stores necessary for neural responses. Although the variations observed in MetHb levels (the NO marker) were relatively small, it is important to underline that they were statistically signifi cant and clearly associated with the magnitude of visual responses. To date, there are few studies using spectrophotometry as a technique to evaluate variations of NO levels; however, our data fi t observations made by others quite well (Kelm et al., 1997;González-Mora et al., 2002). We must question to what extent our data could be related to "peripheral" changes, for example changes in blood pressure? It has been demonstrated that systemic application of 7-NI does not affect BP (Schulz et al., 1995;Cholet et al., 1997;Koss and Yu, 2000;Jiang et al., 2002). Therefore the simplest explanation for our data is that 7-NI is inhibiting NO synthesis and consequently reducing NO levels. On the other hand, l-NOArg has been shown to increase BP. Our results are in agreement with available data which shows that, in cats, mean BP increases signifi cantly after ∼20 min of l-NOArg application (IV), returning to control values ∼10 min later (Yabe et al., 1998). However there are several reasons to believe that this change is not responsible for our fi ndings: (i) The effects obtained in our experiments following l-NOArg administration were similar in nature to those obtained with 7-NI (although stronger since presumably both nNOS and eNOS were affected). (ii) In our experiments we started data collection 2 min after l-NOArg administration and we kept going for 20-30 min. Since we observed signifi cant changes in cell fi ring, OxyHb, and MetHb levels as early as 4 min after drugs application, it is unlikely that the observed effects were due to peripheral changes, (iii) in an attempt to reduce such side effect we selected doses of NOS blockers which were submaximal, thereby reducing but not abolishing NO production, and (iv) the effects observed on cell responses were similar to those observed with local application of NOS inhibitors (Cudeiro et al., 1994(Cudeiro et al., , 1996. Consequently we feel confi dent that the effects we report are a direct result of NOS inhibition, rather than secondary effects. Our data reinforces the view that there are at least two sources of NO extant in our preparation: nNOS from the parabrachial innervation (or locally in a sub-population of interneurons, see above) and locally derived eNOS, associated with the vasculature and related tissues. Their relative contribution to the neural fi ring and vascular responses seem to be related to the intensity of (visual) stimulation. Interestingly, recent evidence suggests that in the rat somatosensory cortex a certain amount of increased neuronal activity is required for cerebral blood fl ow to increase, and that there is a linear coupling between blood fl ow and neuronal activity within a limited range of stimulation intensities and frequencies (Nielsen and Lauritzen, 2001). This fi nding has been expanded further by means of a general mathematical model applied to rat cerebellum (Rasmussen et al., 2008). In our hands, and in the range of intensities (contrasts) used, the spectroscopy signal also changed linearly. However, it is important to bear in mind that in our experiments we did not systematically change parameters of the stimulus other than contrast, rather we always selected the "optimal set" of other parameters (spatial and temporal frequency, etc.,) for each sampled cell. Such a restriction in parameter space may well provide the reason why we did not detect deviations from linearity, suggesting that for optimal (or near optimal) stimulation LGN cells show a linear behaviour -we will return to this below. However, basically, our data shows that at low intensity of stimulation an initial increase in OxyHb levels is obtained by a mechanism that involves small increments in NO probably related to eNOS activation. As the intensity of stimulation increases, there is a parallel increment of NO production, but now derived from a shift from eNOS to nNOS, utilising a neuronal source. Thus in a sense one might regard the eNOS activation as part of the range of basal activation, or low-pass activity, with nNOS activation acting as a high-pass switch or augmentation, activated as "stimulation" increases (whether by increased contrast, as in our case, or other stimulus derived shifts, which might include attention etc). On the other hand, nonlinear behaviours in the fMRI signal have been previously reported (Yang et al., 2000;Logothetis et al., 2001;Sheth, 2004). Despite the physiological differences between models, awake humans in fMRI studies or anaesthetised animals, we propose that the dual NO mediated mechanism described here could underlie some nonlinearities in neurovascular coupling. We will return to these nonlinearities below.
We have attempted to analyse our methods and results to derive possible mathematical relationships between the measured parameters, in order to begin to derive a predictive space in which physiological measures of one parameter could be used to predict another, thereby rendering direct measurement unnecessary. Given the extent of the parameter space involved, detailed analysis of the results obtained with the models used in our study show that OxyHb (and MetHb) levels can only partially explain NRs with relative error between 37 and 18%, depending on the level of aggregation employed. We must accept that we chose to record only single neuronal activity and that OxyHb levels are likely to be infl uenced by the (large!) number of activated neurons. Indeed, as an example, it is likely that the population of active neurons will increase with stimulus intensity, as higher intensities "bring in" neurons operating outside their preferred stimulus range. This aside, our data can support the view taken by several workers (Logothetis et al., 2001;Freeman, 2007, 2008;Nir et al., 2008;Rasmussen et al., 2008) that a substantial part of the signal is dependent on other components of NR, different from spiking activity. For example, data related to increases in the cerebellar cortex blood fl ow found a strong correlation between the maximal amplitudes of fi eld potentials and blood fl ow (Mathiesen et al., 1998(Mathiesen et al., , 2000. Moreover, using a mathematical model, it has been shown the existence of temporal coupling between activity in cerebellar nerve cells and local increases in blood fl ow, but increased spike activity was not a condition for evoking a blood fl ow increment (Mathiesen et al., 1998(Mathiesen et al., , 2000. How accurate is the prediction of neuronal activity from our surrogate markers of blood fl ow and nitric oxide? Two different levels of combination have been considered in our study, in order to predict the behaviour of OxyHb, MetHb and NR: the measurement level, which attempts to fi nd a single model for the measurement variables at the level of the whole population of subjects; and the subject level, that tries to avoid the intra-subject variability by averaging the values. Generally speaking the fi rst approach seems less successful in accurately predicting the individual values of any of the three variables (OxyHb, MetHb or NR). However, the situation is different when using the second approach. The average value of NR (over several measurements) can accurately be predicted just by using the MetHb and OxyHb data, with a relative error of 18%. Similarly, average values of MetHb can be predicted reasonably well by ST. In this case, the relative prediction error is about 27%. These results, mainly those concerned with estimations of neuronal activity from changes in haemoglobin, might have important implications in the interpretation of techniques such as fMRI. The fi t for OxyHb is far from optimal. Only 28% of the average OxyHb variability (relative error 70%) can be explained via the regression model given above. This is, somehow, a paradoxical outcome, and probably refl ects a weakness in the model at this level of aggregation since neither NR or MetHb add relevant information and hence are not used for OxyHb prediction. This could explain why the results obtained for OxyHb prediction were so poor. More involved statistical techniques are available to analyse the relationship among OxyHb, MetHb, NR and ST, including mixed models and nonparametric or semiparametric regression estimation. However, due to the "curse of dimensionality" the sample size required to apply nonparametric methods is very large. For this reason the use of these statistical techniques is beyond the scope of this paper.
The fi nal issue to address is straight forward. Although confounding the source of NO, our data, as exemplifi ed in Figure 6, clearly suggests that there is no clear linear coupling between blood fl ow and neural response elicited by stimuli of low and high intensity, when control by NO production is used to dissect the relationship. We believe that the simplest explanation for this suggestion is the differential activation of the two iso-forms of NOS available to the nervous system, for the control of physiological situations at lower levels of intensity, versus the requirement for heightened neural activation, (and concomitant blood fl ow) induced by high intensity stimulation. We suggest that this is a model for thalamic function which should be investigated further, possibly through other sensory modalities, and higher levels of cognitive engagement.