Investigating neural–hemodynamic coupling and the hemodynamic response function in the awake rat

www.elsevier.com/locate/ynimg NeuroImage 32 (2006) 33 – 48

Investigating neural–hemodynamic coupling and the hemodynamic response function in the awake rat Chris Martin,* John Martindale, Jason Berwick, and John Mayhew SPiNSN, Department of Psychology, The University of Sheffield, Western Bank, Sheffield S10 2TN, UK Received 19 May 2005; revised 16 December 2005; accepted 20 February 2006 Available online 24 May 2006 An understanding of the relationship between changes in neural activity and the accompanying hemodynamic response is crucial for accurate interpretation of functional brain imaging data and in particular the blood oxygen level-dependent (BOLD) fMRI signal. Much physiological research investigating this topic uses anesthetized animal preparations, and yet, the effects of anesthesia upon the neural and hemodynamic responses measured in such studies are not well understood. In this study, we electrically stimulated the whisker pad of both awake and urethane anesthetized rats at frequencies of 1 – 40 Hz. Evoked field potential responses were recorded using electrodes implanted into the contralateral barrel cortex. Changes in hemoglobin oxygenation and concentration were measured using optical imaging spectroscopy, and cerebral blood flow changes were measured using laser Doppler flowmetry. A linear neural – hemodynamic coupling relationship was found in the awake but not the anesthetized animal preparation. Over the range of stimulation conditions studied, hemodynamic response magnitude increased monotonically with summed neural activity in awake, but not in anesthetized, animals. Additionally, the temporal structure of the hemodynamic response function was different in awake compared to anesthetized animals. The responses in each case were well approximated by gamma variates, but these were different in terms of mean latency (approximately 2 s awake; 4 s anesthetized) and width (approximately 0.6 s awake; 2.5 s anesthetized). These findings have important implications for research into the intrinsic signals that underpin BOLD fMRI and for biophysical models of cortical hemodynamics and neural – hemodynamic coupling. D 2006 Elsevier Inc. All rights reserved.

Introduction The predominant functional brain imaging technique of blood oxygen level-dependent (BOLD) fMRI exploits the hemodynamic response of brain tissue that follows changes in neural activity (Logothetis et al., 2001). A detailed understanding of the

* Corresponding author. Fax: +44 114 2766515. E-mail address: [email protected] (C. Martin). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter D 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2006.02.021

relationship between changes in neural activity and the hemodynamic response is therefore important in interpreting the signals acquired in BOLD fMRI. A number of recent studies have addressed this issue directly, reporting measures of both neural and hemodynamic responses obtained in identical stimulation conditions (e.g., Ances et al., 2000; Devor et al., 2003; HewsonStoate et al., 2005; Jones et al., 2004; Lindauer et al., 1993; Mathiesen et al., 1998; Nakao et al., 2001; Nemoto et al., 2004; Ngai et al., 1999; Nielsen and Lauritzen, 2001; Sheth et al., 2003; Thompson et al., 2003). A potential limitation of much of this research is that it uses anesthetized animals, since there is evidence to suggest that cerebral hemodynamics, vascular reactivity, and/or cerebral metabolism are different under different anesthetic regimes (Austin et al., 2005; Bonvento et al., 1994; Jones and Diamond, 1995; Kaisti et al., 2003; Lindauer et al., 1993; Linde et al., 1999; Oz et al., 2004). These issues are further complicated by the possibility that anesthesia disrupts the putative mechanisms that relate neural activity changes to the hemodynamic response function. For example, Nakao et al. (2001) discusses how differing findings regarding the role of neuronal nitric oxide in neurovascular coupling may be attributable to the use of anesthesia. Taken together, these findings have important implications for attempts to construct mathematical models relating neural and hemodynamic responses to the fMRI BOLD signal. The aim of research in this area is to refine the interpretation of functional imaging signals recorded from awake humans in terms of underlying neural processes. Unanesthetized animal preparations are therefore a critical counterpart to the anesthetized animal research models which are already used extensively. As such, we have previously reported an awake animal preparation for studying the physiological basis of functional imaging signals (Martin et al., 2002). A number of studies have demonstrated that hemodynamic responses to physiological stimulation may differ between anesthetized and unanesthetized animal preparations. For example, fMRI studies in rat (Lahti et al., 1998, 1999; Peeters et al., 2001; Sicard et al., 2003) and optical imaging spectroscopy studies in monkey (Shtoyerman et al., 2000) or rat (Berwick et al., 2002) report significantly larger responses in unanesthetized animals. Using data from optical imaging spectroscopy studies in awake and

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C. Martin et al. / NeuroImage 32 (2006) 33 – 48

anesthetized rats, Berwick et al. (2002) estimated fMRI BOLD signal and oxidative metabolism changes in somatosensory cortex following whisker stimulation and found significant differences between the estimates for awake and anesthetized rats. However, the issue of how neural – hemodynamic coupling is affected by anesthetic state remains unaddressed. The effects of anesthetic state upon other features of the hemodynamic response function are also unknown. This includes the relationship between changes in cerebral blood flow (CBF) and cerebral blood volume (CBV), commonly approximated by the expression 1 + DCBV = (1 + DCBF)a , where a is a scaling constant (Grubb et al., 1974). Any differences in the temporal structure of the hemodynamic response function attributable to anesthetic state may have important implications for research in this area. The rodent whisker-to-barrel system has a well-defined topography and in the cortex a highly concomitant blood supply. As such, it has become a popular model for investigation of neurovascular coupling in animals albeit using a range of anesthetic regimes (e.g., Devor et al., 2003 [urethane]; Gerrits et al., 2000 [urethane]; Hewson-Stoate et al., 2005 [urethane]; Martindale et al., 2003 [urethane]; Nakao et al., 2001 [alphachloralose]; Nielsen and Lauritzen, 2001 [alpha-chloralose]; Sheth et al., 2003 [enflurane]; Ureshi et al., 2004 [alpha-chloralose]). The research reported here explores the relationship between experimentally obtained measures of cortical neural and hemodynamic responses to whisker stimulation in a fully conscious rat preparation and compares this to data obtained using a urethane anesthetized rat preparation similar to that used in previous studies. hemodynamic responses were recorded using laser Doppler flowmetry (cerebral blood flow changes) and optical imaging spectroscopy (cerebral blood volume and oxygenation changes). The combination of such techniques provides for complete measurement of the hemodynamic response (Dunn et al., 2003; Jones et al., 2001). Recent research indicates that evoked field potentials (EFPs) are most highly correlated with hemodynamic changes (Caesar et al., 2003; Jones et al., 2004; Logothetis et al., 2001), and in this study, these were recorded using electrodes implanted in somatosensory barrel cortex.

Materials and methods

publications from this laboratory and shall therefore only be briefly described here (e.g., Hewson-Stoate et al., 2005; Jones et al., 2001; Martindale et al., 2003). Animals were anesthetized with urethane (1.25 g/kg i.p.), the left femoral artery and vein were catheterized and a tracheostomy was performed to allow monitoring and control of physiological variables. Blood gases and other physiological parameters were within the normal range (mean arterial blood pressure = 100 – 110 mHg; mean PO2 = 92 T 3.4; mean PCO2 = 33.7 T 3.7; mean O2 saturation = 96.6 T 0.4%). Animals were placed in a stereotaxic frame (Kopf Instruments), and a region of skull overlying the right somatosensory cortex was thinned to translucency using a dental drill. A thermostatically controlled heating blanket was used to maintain body temperature at ¨37-C. To deliver electrical stimulation to the anesthetized animals, tungsten needle electrodes were inserted into the contralateral whisker pad to a depth of ¨3 mm. To locate barrel cortex, single wavelength imaging was performed (Zheng et al., 2001). The visible cortex was illuminated with a monochromatic light source (¨590 nm) and remitted images passed into a 12-bit CCD camera (SMD 1M60). At this wavelength, changes in reflectance principally represent changes in total hemoglobin concentration. Data were collected for 30 trials, with an interstimulus interval (ISI) of 25 s and stimulus onset at 8 s. Stimuli consisted of a 1-s train of 1.2 mA electrical pulses delivered at 5 Hz. Images were analyzed using a modified signal source separation algorithm (Molgedey and Schuster, 1994) as previously described (Zheng et al., 2001). This procedure yields spatially discrete activation maps of barrel cortex that show excellent concordance with cytochrome oxidase histology (Jones et al., 2001, 2002; Zheng et al., 2001). The activation maps were registered with images of the cortical surface to guide placement of the laser Doppler probe or spectrograph (see Fig. 1). Optical imaging spectroscopy and laser Doppler flowmetry were performed concurrently to measure the hemodynamic responses to whisker stimulation. After localization of the barrel cortex, a spectrograph was mounted on the camera and positioned such that the slit (100 Am wide) was sited appropriately over the center of the active region. Following placement of the spectrograph, an LDF probe (Probe 403, Perimed, Stockholm, fiber separation 0.25 mm) was also placed over this region (¨1 mm from skull surface) avoiding large blood vessels.

Experimental overview Experimental Group 2—neural responses in anesthetized animals Neural and hemodynamic responses were recorded from separate groups of awake and anesthetized animals, such that the data reported in this paper are taken from experiments conducted in 4 groups of animals (n = 6 in each case). Animals to be used for awake recording were handled extensively and trained to accept comfortable restraint prior to surgical preparation. Details of the training procedures are described in Martin et al. (2002). In all cases, animals were female hooded Lister rats weighing between 200 and 300 g, kept in a 12-h dark/light cycle environment at a temperature of 22-C with food and water ad libitum. Experimental Group 1—hemodynamic responses in anesthetized animals Full details of the surgical and experimental procedures for recording hemodynamic and neural (see below) responses in acute, anesthetized preparation are provided in a number of recent

Under urethane anesthesia, animals were prepared with vascular catheters, a tracheostomy and a thinned translucent skull as described for Group 1 above. Single wavelength imaging was performed to locate barrel cortex. Field potentials were recorded using a 16-channel electrode probe (100 Am recording site spacing, 177 Am2 recording site area, 33 – 200 Am diameter (tip-shank); impedance: 1.5 – 2.7 MV; NeuroNexus Technologies, Ann Arbor, NI). This enabled us to select, in offline analysis, the optimal cortical depth from which the field potentials recordings should be taken. A small burr hole was then made in the thin cranial window over the barrel cortex and using a microdrive, the electrode was inserted perpendicular into the cortex to a maximum depth of 1500Am. Recordings were referenced to an electrode inserted into dorsal neck muscle. Electrical stimulation of the whisker pad was provided by tungsten needle electrodes, as described in the above section.

C. Martin et al. / NeuroImage 32 (2006) 33 – 48

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Fig. 1. Intrinsic signal optical imaging of rat somatosensory cortex using remitted 590 nm light from a representative animal. Localized increases in blood volume and oxygenation following whisker pad stimulation cause decreased signal intensity. (A) Somatosensory cortical surface as viewed through thinned skull. (B) The temporal changes in signal intensity at each pixel are used to generate the activation image. (C) Time series corresponding to signal changes shown in panel B. Electrical stimulation (1 s).

Experimental Group 3—hemodynamic responses in awake animals Many of the details of the awake animal training, preparation and data collection procedures are reported in Martin et al. (2002). After training, animals were anesthetized with an intraperitoneal injection of ketamine and xylazine (1.1 ml/kg). Under surgical anesthesia, the skull was exposed, and a section overlying somatosensory cortex was thinned to translucency as described above. A coating of clear cyanoacrylate was applied to the thin cranial window to provide increased strength. Two skull screws were then secured into burr holes drilled in the contralateral skull, one anterior and another posterior to the thinned skull region. A chronically implanted chamber was then placed over the thinned region of skull and secured in dental cement which also engulfed the skull screws. To deliver electrical stimulation to the awake animals, Teflon-coated tungsten microwires (diameter 0.2 mm, Advent Research Materials Ltd., Oxford, UK) were chronically implanted into the contralateral whisker pad during surgery. The wires were fed subcutaneously to a connector set in dental cement adjacent to the imaging chamber, which was coupled to the stimulator device during experimental sessions. The procedure for doing this was very similar to that described by Devilbiss and Waterhouse (2002). All wounds were closed, and the animals were treated with an analgesic (Rimadyl, 0.05 ml). Accurate location of barrel cortex for subsequent imaging sessions was guided by single wavelength imaging experiments which were performed after the completion of surgical procedures but before recovery from anesthesia. The data collection and stimulation procedures for single wavelength imaging were as described for the anesthetized preparation. Animals were then left to recover for 3 – 5 days. Due to technical limitations, it was not possible to perform OIS and LDF concurrently in awake preparations and so separate OIS and LDF experimental sessions were conducted alternately. For each optical imaging spectroscopy experiment, animals were placed into a harness and an endoscope Fdocking unit_ was screwed into the implanted chamber. The harness was then suspended on a frame and to reduce head movements, a pneumatically operated clamp secured the implanted chamber. The experimental apparatus is illustrated in Fig. 2. The imaging chamber was filled with saline (warmed to 37-C) and a 3 mm medical endoscope (Endoscan Ltd.) screwed into position in the chamber. The endoscope, which provides both illumination of the

cortex and transmission of the remitted images to a 12-bit SMD (1M60) digital camera, permits fast configuration of the imaging apparatus. This is necessary to keep animal restraint time to a minimum and avoids the need to finely adjust the position of either the animal or the camera for optimal image transmission. A spectrograph was mounted on the camera with the slit (250 Am wide) positioned over the area of maximum activation as identified in the single wavelength experiments. It was necessary to use a wider slit width in the awake preparation imaging experiments to compensate for a reduction in light caused by use of the endoscope. For laser Doppler flowmetry experiments, the animal was placed in the harness, and the imaging chamber was clamped as described for OIS experiments. An LDF probe (a Probe 403, Perimed, Stockholm, fiber separation 0.25 mm) was placed over the identified active region (¨1 mm from skull surface) avoiding large blood vessels. Experimental Group 4—neural responses in awake animals Animals were trained and surgically prepared with a thin cranial window and subcutaneous stimulating microwires as described above for Group 3. A small burr hole was then made in the thinned cranial window over the barrel cortex for electrode implantation. Location of barrel cortex was guided by both stereotaxic coordinates and by the highly regular pattern of vasculature overlying the cortex in this region (Cox et al., 1993; Moskalenko et al., 1996). The electrode probe (identical to that used in the anesthetized animals—Group 2) was inserted as described for the anesthetized preparation. This electrode was then firmly secured in place by filling the implanted chamber with dental cement. All wounds were closed, and the animals were treated with an analgesic (Rimadyl, 0.05 ml) before being left to recover for 3 – 5 days. For each electrophysiology experiment, animals were placed into a harness and to reduce head movements a pneumatically operated clamp secured the implanted chamber. Recordings were referenced to an electrode connected to the implanted metal chamber. The experimental apparatus is illustrated in Fig. 2. Stimulation All stimulus presentation was controlled through a 1401 plus (CED Ltd., UK) running custom-written code with stimulus onset

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Fig. 2. Experimental apparatus. The animal is placed in a harness and suspended from a secure frame. The head is restrained by means of a pneumatic clamp, which holds the cranial implant firmly. Optical or electrophysiological recordings may then be made from the cortex.

time-locked to the CCD camera or electrophysiology data acquisition. Electrical stimuli were delivered to the whisker pad contralateral to the cortical hemisphere from which recordings were made. For each experimental session, 2-s stimuli of 1, 2, 5, 10, 20, and 40 Hz were presented in randomly interleaved batches of 60 trials, providing 10 trials at each frequency. The stimulus pulse width was 0.3 ms. Pilot studies revealed that stimulation amplitudes above 0.5 mA in the awake animal often elicited painrelated responses rendering stimuli in this range inappropriate. However, stimuli below 0.5 mA in the anesthetized animal model produced responses that were small (i.e., <2%) and frequently noisy, even after much trial averaging. From the outset, therefore, we chose to use a stimulation intensity that produced reliable, robust hemodynamic responses in each anesthetic state. Across the different stimulation frequencies, the intensities chosen produced a range of clearly detectable hemodynamic responses, the magnitudes of these ranges overlapping considerably between the anesthetic states. We do not believe it will be possible to use identical stimulation intensities and elicit robust hemodynamic and/or neural responses of comparable magnitude in both anesthetic states. The amplitude of the stimulus was always 1.2 mA for experiments using anesthetized animals. It was found that in most cases a stimulus of ¨0.3 mA provided robust responses in the awake animal with no indication that it was noxious. The interstimulus interval (ISI) was 25 s for all OIS or LDF experimental sessions and 5 s for all electrophysiology experimental sessions. Data acquisition and analysis The data reported here for awake animals are taken from 11 OIS, 12 LDF and 20 electrophysiological recording sessions. The data reported here for anesthetized animals are taken from 12 OIS, 17 LDF and 10 electrophysiological recording sessions. Errors shown represent the standard error of the mean across sessions.

Spectral images were acquired by the CCD camera at 15 frames per second, and the data were written directly to hard disk. The LDF signal was digitized continuously using a 1401 plus (CED Ltd., UK). The time constant of the LDF recordings was 0.2 s, and the bandwidth was 12 kHz. For electrophysiological data acquisition, a head stage was attached to the implanted electrode connector pins, coupling the probe to Medusa Bioamps (Tucker Davis Technologies, Alachua, FL, USA). Electrophysiological data were sampled at 6 kHz to give field potential recordings from each of the 16 linearly arranged recording sites on the electrode probe. All data were analyzed off-line. Data were first sorted according to stimulation frequency and then averaged over trials for each experimental session. Spectral data were then submitted to pathlength scaling analysis (Mayhew et al., 1998) to resolve the changes in oxy- and deoxy-hemoglobin concentrations. The current study used wavelengths in the range 505 – 620 nm and assumed a baseline hemoglobin concentration of 50 AMol and a baseline oxygen saturation of 0.5. Changes in hemoglobin concentration and cerebral blood flow are reported in fractional terms. Adopting an assumption of constant hematocrit (Kleinfeld et al., 1998) enables fractional changes in total hemoglobin concentration to be equated with fractional changes in cerebral blood volume. This is useful for comparing the relationship between CBF and CBV changes across anesthetic states (see below). Field potential data were filtered to remove 50 Hz mains noise. The major motivation for using the multichannel electrodes in the current study was to ensure an optimal depth could be chosen for recording the evoked potentials in the barrel cortex. This is instead of the more common process of advancing the electrode gradually and recording at each depth before selecting the optimal depth and securing the electrode accordingly (e.g., Nielsen and Lauritzen, 2001). For each recording session, the electrode channel demonstrating the largest evoked changes in response to stimuli was selected to produce a single time series for each stimulus trial. This location typically corresponded to a depth of ¨600 Am, within the layer IV boundaries of approximately 450 – 800 Am below the

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surface (Armstrong-James et al., 1992). Due to the chronic nature of the awake preparations, we cannot rule out the possibility of slight movement of the electrode over time and between recording sessions. Given that Mitzdorf (1987) estimates that field potential signals originate from within ¨0.5 – 3 mm of the electrode tip, we do not believe that variability in recording depth is significant in terms of the field potential measures made here. The volumes of sensitivity of the various measurement techniques employed are discussed further below.

Results Neural responses Electrical stimulation of the whisker pad evoked a brief, negative-going field potential response to each stimulus pulse, measured in contralateral barrel cortex. In the anesthetized animals, this response was principally monophasic, while in the awake animals, the response contained a subsequent positive component (although this was not visible at higher stimulation frequencies). The amplitude of the field potential responses was approximately an order of magnitude smaller in the awake animals than in the anesthetized animals (Fig. 3). The shape of the negative-going field potential responses for each condition is shown in more detail in Fig. 3B. There are clear latency differences between the anesthetic conditions. To enable com-

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parison of the responses between anesthetic and stimulation frequency conditions, only the amplitude of this initial field potential decrease is considered in subsequent analysis. As expected, within each anesthetic condition, there was no significant difference between the absolute magnitude of the response to the first stimulus pulse across stimulation frequency conditions (not shown). Following the first pulse response, however, stimulation at frequencies of 1 – 40 Hz (2-s trains) was characterized by a reduction in the amplitude of the field potential decrease to successive stimulation pulses. After the first pulse response, the average subsequent pulse response was reduced in size at all stimulation frequencies above 1 Hz in anesthetized animals or 2 Hz in awake animals (Fig. 4). This reduction was significantly more pronounced in the anesthetized condition (2-factor mixed design ANOVA; F = 4.14, df = 6,26, P < 0.005). To compare the effect of stimulation frequency upon total neural activity between anesthetic conditions and to reduce interanimal variability, the data sets were normalized. This also enabled the relative effects of stimulation frequency to be compared between the anesthetic states independently of the differing response magnitudes. For each condition of each experimental session, the mean time series was scaled such that the peak magnitude of the response to the first pulse in each stimulus train was set to unity. Data sets were than averaged across experimental sessions and animals. This was done separately for the awake and anesthetized condition data sets. The magnitude of

Fig. 3. (A) Evoked field potential recordings made from barrel cortex following contralateral electrical whisker stimulation (left whisker pad) at frequencies from 1 – 40 Hz. Awake animals were stimulated at ¨ 0.3 mA. Anesthetized animals were stimulated at 1.2 mA. Stimulation began at 0 s and lasted for 2 s. (B) Expanded view of the normalized response to first stimulus pulse in each train (first 25 ms of response shown; grey = awake, black = anesthetized).

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Fig. 4. The magnitude of field potential responses, averaged over the range 2nd to last stimulus pulses, shown as a fraction of the magnitude of the first stimulus pulse. Error bars indicate standard error of the mean.

the initial negative deflection in the field potential following each stimulus pulse was then extracted and summed to provide a measure of total neural activity in each experimental condition (Fig. 5). The summed normalized neural activity in awake animals exceeded that of the anesthetized animals as the frequencydependent response reduction increased. Hemodynamics For all stimulation frequencies and for both anesthetic conditions, the expected hemodynamic response profile was observed following electrical stimulation of the whisker pad. This consisted of increased cerebral blood flow, volume (Hbt), and HbO2 concentration but decreased Hbr concentration as freshly oxygenated blood perfused the imaged tissue volume (Fig. 6). Changes are with respect to an 8-s prestimulus baseline period (only the preceding 2 s shown). As observed in previous OIS studies using anesthetized (e.g., Jones et al., 2001, using urethane in rat; Malonek and Grinvald, 1996, using sodium pentothal in cat) and awake (Berwick et al., 2002; Martin et al., 2002; Shtoyerman et al., 2000) animal preparations, an early increase in deoxyhemoglobin concentration was present in both the anesthetized and awake conditions (Fig. 6A and B, insets). This increase in the mean time series was significant (P < 0.01, one sample z test) with respect to an 8-s prestimulus baseline at all stimulation frequencies in the anesthetized preparation (0.4 – 1.1%) and at the 5- and 40-Hz stimulation frequencies awake condition (0.58 and 0.68% respectively). See Table 1. The magnitudes of the significant Hbr increases (peak value) are reported in Table 1 (no values are reported for the conditions where no increase was detectable). In the anesthetized animals, the magnitudes of these increases were similar to those reported previously by Jones et al. (2001) under similar experimental conditions (Table 1, Fig. 6A inset). The peak percentage changes in the hemodynamic responses of both anesthetic conditions were within a similar range for the 1- to 10-Hz stimulation frequencies (Table 1). Above 10 Hz, however, response magnitudes in the awake animals increased considerably above those of anesthetized animals. To compare the effect of stimulation frequency between anesthetic conditions irrespective of actual response magnitudes, the hemodynamic response peaks (for

CBF, Hbt, HbO2, and Hbr) were normalized such that the peak response magnitude in the 1-Hz stimulation conditions was set to unity. Under this normalization scheme, it is clear that peak response magnitudes increased over the stimulation frequency range 1 – 5 Hz in both anesthetic conditions, but that above 5-Hz stimulation, response magnitudes continued to increase with increasing stimulation frequency in the awake condition but not in the anesthetized condition (Fig. 7). Mixed design ANOVAs revealed significant effects of stimulation frequency upon hemodynamic response magnitude (P < 0.001 for all response measures) and significant interaction effects of stimulation frequency and anesthetic condition upon hemodynamic response magnitude (P < 0.001 for all response measures). Hemodynamic responses in the anesthetized condition evolved more slowly than those in the awake condition and peaked ¨0.5 – 1.0 s later (Table 2). Taking the time to response peak as a latency measure, mixed design ANOVAs revealed significant effects of stimulation frequency upon hemodynamic response latency (P < 0.05 for all response measures). There was a significant interaction effect of stimulation frequency and anesthetic condition upon oxyhemoglobin response latency (P < 0.005) but not for the other hemodynamic response measures. Also, there was a significant effect of anesthetic condition upon hemodynamic response latency (P < 0.05 for all response measures). Importantly, within either anesthetic condition, the shape of the hemodynamic response function did not appear to be significantly altered by stimulation frequency, although both quantitative and qualitative differences in responses were evident between anesthetic conditions (Figs. 3 and 4, Table 2). Inspection of Table 2 indicates that the time to peak increased with stimulation frequency in awake animals but did not appear to increase in the anesthetized animals, except up to 5 Hz. In terms of stimulation frequency condition, response latency appears closely linked to response magnitude (Table 1). It is possible that latency differences between stimulation frequency conditions could have resulted from differences in the time taken to reach differing response magnitudes (with rate of increase remaining constant). However, the latencies of the CBF responses to 20-Hz and 40-Hz stimulation were still notably larger (2.44 T 0.28 s vs. 3.30 T 0.10 s and 2.72 T 0.10 s vs. 3.48 T 0.18 s, respectively) in the anesthetized condition than in the awake

Fig. 5. Summed neural activity, plotted against stimulation frequency. Error bars indicate standard error of the mean.

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Fig. 6. (A) hemodynamic responses, recorded in barrel cortex of anesthetized and (B) awake animals following contralateral electrical whisker stimulation at frequencies from 1 – 40 Hz. anesthetized animals were stimulated at 1.2 mA, awake animals were stimulated at ¨0.3 mA. The stimulus lasted for 2 s, commencing at 0 s. The stimulus pulse width was 0.3 ms. Insets show close-up images of Hbr, HbO2, and Hbt changes at 0 – 2 s after stimulation onset.

condition, despite larger (in percentage terms) responses in the awake condition at these stimulation frequencies (Table 1). Cerebral blood flow and cerebral blood volume The relationship between changes in cerebral blood flow and blood volume is commonly expressed in terms of the equation 1 + #CBV = (1 + DCBF)a (Grubb et al., 1974). To compare the relationship between peak flow and volume changes across experimental conditions in terms of a, the logged peak flow and volume changes can be plotted together (Fig. 8). A bivariate (type II) regression was applied to derive linear fits to the plotted data. The relationship between peak flow and peak volume changes was different for the different anesthetic conditions. Considering the anesthetic conditions separately, the accuracy of a linear fit to the data in Fig. 8 indicates that a simple power law to relate CBV and CBF changes is appropriate. The a estimates applicable to the above equation relating CBF and CBV changes, derived from the slopes of the straight line fits (1/slope), were 0.25 T 0.04 and 0.36 T 0.03 for the awake and anesthetized conditions respectively. Values for a were calculated separately for each anesthetic and stimulation frequency condition (using peak CBF and Hbt response values) and were entered into the equation defined above to estimate Hbt (CBV) changes from measured CBF changes (Fig. 9). The resultant time series indicate that the method provides a good estimate of Hbt changes in most cases, although with some error as the responses return towards baseline in the awake animals. The values for a used to obtain these predictions (shown in Fig. 9) are both lower and more variable in the awake conditions than in the anesthetized conditions. Neural – hemodynamic coupling Plotting normalized peak hemodynamic response magnitudes (CBF or Hbt data shown, although the results are similar for HbO2 and Hbr) against summed normalized neural activity reveals qualitatively different neural – hemodynamic coupling relationships

for the awake and anesthetized preparations (Fig. 10). Exponential functions provided a good fit to the normalized data in the awake condition (R 2 = 0.97 for both hemodynamic measures). General second order polynomials provided an adequate fit to the data in the anesthetized condition (R 2 = 0.85, Hbt; R 2 = 0.87, CBF). The axes for each plot indicate that the dynamic range over which response magnitudes were distributed was much larger in the awake condition than in the anesthetized condition. However, these plots report normalized units, and as shown in Fig. 3, the absolute neural response magnitudes are much smaller in the awake condition. This may be because the stimulus intensity used was necessarily much lower in the awake condition. Plotting the absolute (nonnormalized) data for both anesthetic conditions together reveals a more complex relationship (Fig. 11). At the lower levels of summed neural activity (<0.01 V), the peak Hbt concentration increases with increasing levels of neural activity. This is also the case in the plot of CBF against summed neural activity, although this is less clear. However, above summed neural activity values of 0.01 V, where only anesthetized response data are found, this relationship no longer holds.

Discussion In this study, stimulation frequency was manipulated in order to elicit a range of neural and hemodynamic response magnitudes in rodent barrel cortex. Data were collected from both awake and urethane anesthetized preparations, and stimulation intensity was fixed at optimum levels for each anesthetic state. The magnitude ranges of the elicited cortical hemodynamic responses exhibited considerable overlap between the two anesthetic states, allowing comparison of the hemodynamic temporal response. There is evidence that the hemodynamic response function is different in awake, compared to anesthetized preparations. Neural (field potential) responses were also recorded and in the awake animals, a linear neural – hemodynamic coupling relationship was found, while in anesthetized animals, this relationship was nonlinear,

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Table 1 Haemodynamic response magnitude—percentage change %

Stimulation frequency (Hz) 1

2

5

Awake HbO2 TSEM Hbr TSEM Hbt TSEM CBF TSEM Hbr Inc. TSEM

3.11 0.68 2.11 0.38 0.74 0.18 5.06 0.99 – –

3.27 1.08 1.98 0.62 0.71 0.29 8.40 1.19 – –

6.57 1.22 3.62 0.53 1.82 0.45 12.35 1.47 0.58 0.28

Anesthetized HbO2 TSEM Hbr TSEM Hbt TSEM CBF TSEM Hbr Inc. TSEM

5.43 0.67 1.92 0.55 2.37 0.28 5.06 0.63 0.42 0.12

9.06 1.11 3.12 0.79 3.83 0.49 8.40 1.10 0.68 0.17

11.95 1.80 4.53 1.26 4.68 0.62 12.35 1.94 1.03 0.17

10

20

40

7.62 1.50 4.26 0.70 2.03 0.50 9.52 1.17 – –

12.47 2.17 7.03 1.02 3.32 0.69 6.54 2.09 – –

18.19 2.72 9.57 1.22 5.06 0.87 8.12 3.50 0.68 0.28

9.90 1.90 3.59 1.17 3.90 0.46 9.52 1.80 0.93 0.24

7.29 1.52 2.41 0.86 3.06 0.45 6.54 1.06 0.58 0.25

8.29 2.05 2.64 1.10 3.62 0.53 8.12 1.63 0.67 0.26

Mixed design ANOVAs revealed that (i) there were significant effects of stimulation frequency on all response measures (HbO2, F = 8.39; P < 0.001; Hbr, F = 8.14 P < 0.001; Hbt, F = 8.89, P < 0.001; CBF, F = 13.03; P < 0.001). (ii) There was no significant effect of anesthetic condition. (iii) There were significant interaction effects of stimulation frequency and anesthetic condition on all response measures (HbO2, F = 18.67; P < 0.001; Hbr, F = 12.83 P < 0.001; Hbt, F = 19.56, P < 0.001; CBF, F = 10.58; P < 0.001). The magnitude of early (0 – 2 s) increases in the mean Hbr concentration time series (where these were detectable with respect to baseline variability) are included (Hbr Inc.).

over the range of responses recorded. However, the magnitude range of the summed neural activity measures differed between anesthetic states and as such, neural – hemodynamic coupling relationships cannot be compared directly between the two anesthetic conditions. Neural responses The field potential (FP) responses recorded in anesthetized animals were larger than those of awake animals. It is likely that the different stimulation intensities used in each anesthetic condition contributed to this. The difference in response magnitude may also have been due to differences in the thalamocortical transmission of the sensory inputs. For example, Castro-Alamancos and Oldford (2002) reported that sensory evoked responses were suppressed under conditions of cortical arousal, attributing this to activity-dependent depression of thalamocortical synapses. Interpretation of the effect of stimulation frequency on neural response data may draw on recent electrophysiological studies which show that thalamocortical relay of somatosensory information is reduced at stimulation frequencies above ¨2 Hz in the anesthetized rat (Ahissar et al., 2000; Castro-Alamancos, 2002). This reduction may be less in the aroused animal (Fanselow and Nicolelis, 1999; Poggio and Mountcastle, 1963) such that in awake or Factivated_ states, stimulation frequencies up to 40 Hz may be

faithfully relayed through the thalamus to the cortex (CastroAlamancos, 2002). Chapin et al. (1981) reported an average response cut-off frequency for somatosensory cortical neurons at ¨7 Hz in the (halothane) anesthetized rat and ¨14 Hz in the awake rat. One may expect that increasingly faithful relay of thalamic activation to the barrel column would produce an increasingly linear relationship between summed neural activity and stimulation frequency. This hypothesis is supported by the present results, where cortical responses in the awake animal are less Fsuppressed_ at higher stimulation frequencies than those of the anesthetized animal. Anesthesia affects the hemodynamic response function Within an anesthetic condition, the temporal profiles of the hemodynamic response functions for the different stimulation frequencies were very similar (Fig. 6). However, there appeared to be consistent temporal differences in the hemodynamic response functions between awake and anesthetized conditions, with awake animal responses increasing faster, peaking earlier, and returning to baseline more quickly than anesthetized animal responses (Table 2 and Results section). Magnitude differences have received detailed consideration in several recent BOLD fMRI studies looking at hemodynamic responses in the awake and anesthetized animal (Lahti et al., 1998, 1999; Peeters et al., 2001; Sachdev et al., 2003; Wyrwicz et al., 2000), but latency differences have received little attention. The fact that no latency differences between awake or anesthetized animal responses were reported in these other studies may be attributed to the fact that the temporal resolution of the usual (fMRI) techniques is too low Table 2 Haemodynamic responses—time to peak (s) Time (s)

Stimulation frequency (Hz) 1

2

5

10

20

40

Awake HbO2 TSEM Hbr TSEM Hbt TSEM CBF TSEM

2.15 0.27 2.58 0.54 2.29 0.49 1.94 0.30

2.34 0.16 2.38 1.11 2.25 0.32 1.99 0.24

2.68 0.10 3.05 0.23 2.38 0.08 2.44 0.28

2.35 0.23 2.63 0.77 2.36 0.33 2.11 0.08

2.76 0.07 3.21 0.24 2.33 0.11 2.49 0.13

2.87 0.05 3.24 0.12 2.58 0.04 2.72 0.10

Anesthetized HbO2 TSEM Hbr TSEM Hbt TSEM CBF TSEM

2.80 0.15 3.51 0.34 2.38 0.07 3.11 0.15

3.12 0.16 4.10 0.17 2.77 0.11 3.07 0.23

3.40 0.18 4.14 0.15 2.87 0.10 3.30 0.10

3.16 0.22 3.74 0.37 2.89 0.16 3.07 0.22

3.08 0.21 3.62 0.50 2.72 0.18 3.11 0.22

3.18 0.19 3.29 0.56 2.88 0.15 3.48 0.18

Mixed design ANOVAs revealed that (i) there were significant effects of stimulation frequency on all response measures (HbO2, F = 5.15; P < 0.01; Hbr, F = 2.90 P < 0.05; Hbt, F = 3.49, P < 0.05; CBF, F = 3.51; P < 0.05). (ii) There were significant effects of anesthetic condition on all response measures (HbO2, F = 12.27; P < 0.01; Hbr, F = 9.40, P < 0.01; Hbt, F = 7.13, P < 0.05; CBF, F = 31.29; P < 0.001). (iii) There was a significant interaction effect of stimulation frequency and anesthetic condition on HbO2 latency ( F = 6.34; P < 0.005).

C. Martin et al. / NeuroImage 32 (2006) 33 – 48

41

Fig. 7. Mean peak hemodynamic responses, measured for HbO2, Hbr, Hbt and CBF changes, plotted against stimulation frequency. Error bars indicate standard error of the mean.

to detect the kind of differences found here. The temporal differences can be more clearly demonstrated by plotting the normalized response curves together (Fig. 12A). The Hbr data were not included in this analysis due to the complexity introduced by the initial increase in this chromophore, and the 1-Hz and 2-Hz frequency stimulation conditions were also excluded due to the relatively lower signal to noise ratios. For most of the data however, it can be shown that the response shapes are in general well approximated by gamma variates (R 2 > 0.9, Fig. 12 and Table 3). This is in agreement with previous studies (Bandettini and Cox, 2000; Cohen, 1973; Martindale et al., 2003). The fit for the awake CBF response data is less satisfactory (R 2 = 0.73), although this is likely to be due to the late (>3 s) tendency for the CBF responses to decrease below baseline, as the fit is excellent for the first 3.5 s poststimulus (R 2 = 0.96). The gamma variates are very similar for each hemodynamic response measure within an anesthetic condition (Fig. 12 and see Table 3 for the parameters), but the latency (mean) and width of the variates were both notably larger for the response measures in the anesthetized condition. This suggests underlying differences in the hemodynamic response functions. These differences may have important implications for the design, analysis and interpretation of functional imaging studies, where a detailed temporal model of hemodynamic changes is essential. The finding here that the magnitude of hemodynamic responses to somatosensory stimulation asymptotes at ¨5 Hz in anesthetized preparations is in line with previous reports using both electrical and mechanical stimulation under a variety of anesthetic regimes (Matsuura et al., 1999; Moskalenko et al., 1996; Nielsen and Lauritzen, 2001; Sheth et al., 2003; Vogel and Kuschinsky, 1996; Woolsey et al., 1996). Gerrits et al. (1998) reports linear increases in peak CBF with frequencies up to 10.5 Hz in a urethane

anesthetized preparation but did not stimulate at higher frequencies. Initial increases in deoxy-hemoglobin concentration are observed in many of the experimental conditions. This corresponds to the Fdeoxy-dip_ observed in human BOLD fMRI studies (Ernst and Hennig, 1994; Hu et al., 1997; Janz et al., 2000; Menon et al., 1995; Yacoub et al., 2001). It is believed that this phenomenon reflects a rapid rise in the cerebral metabolic rate of oxygen consumption by activated tissue (CMRO2) that precedes the subsequent large increase in CBF. Evidence for the presence of the Fdip_ in the awake rat preparation was previously reported in Berwick et al. (2002). However, this result was complicated by the

Fig. 8. The relationship between changes in cerebral blood flow and total hemoglobin (åcerebral blood volume). The width and height of the crosses represents the standard error of the mean for the Hbt and CBF measurements respectively. The parameters of the regression lines were as follows: [Awake] Slope = 3.94 T 0.57, Intercept = 0.09 T 0.92, Weighted Correlation Coefficient = 0.98; [anesthetized] Slope = 2.74 T 0.25, Intercept = 1.67 T 0.78, Weighted Correlation Coefficient = 0.99.

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C. Martin et al. / NeuroImage 32 (2006) 33 – 48

Fig. 9. The prediction of total hemoglobin (åCBV) changes from cerebral blood flow changes using the exponential relationship 1 + DCBV = (1 + DCBF)a . The a values used for each experimental condition are shown. Dashed lines represent predicted CBV.

presence of a transient (¨0.5 s) blood volume increase attributed to a startle response elicited by the air-puff stimuli. In the present study, the dip is clearly present in each of the anesthetized conditions, and although less prominent in the awake animal data, the deoxy-hemoglobin increase is significant in the 5-Hz and 40-

Hz conditions. As has been reported previously (e.g., Jones et al., 2001; Malonek et al., 1997), these increases in Hbr were not accompanied by decreases in oxy-hemoglobin concentration. This is thought to result from a transient increase in fractional oxygen extraction (due to increased oxidative metabolism) in the presence

Fig. 10. The relationship between summed neural activity and peak hemodynamic response in awake and anesthetized rats. Data are in normalized units. For illustrative purposes, exponential functions were fitted to the awake condition data (R 2 > 0.96), while a general second order polynomial was fitted to the data in the anesthetized condition (R 2 > 0.84). The width and height of the crosses represent the standard error of the mean for the summed neural activity and peak hemodynamic responses respectively.

C. Martin et al. / NeuroImage 32 (2006) 33 – 48

43

Fig. 11. The relationship between summed neural activity and peak hemodynamic response in awake and anesthetized rats. The width and height of the crosses represents the standard error of the mean for the summed neural activity and peak hemodynamic responses respectively. Functions such as those fitted in Fig. 10 are also plotted.

of initial increases in arterial blood volume (see Buxton, 2001, and the insets to the Hbt plots in Fig. 6). Cerebral blood flow and volume The Fpower law_ proposed by Grubb et al. (1974) is commonly exploited to produce estimates of volume changes from measured flow changes obtained during functional imaging experiments, for example, to permit CMRO2 estimation (Feng et al., 2003; Hoge et al., 1999a,b; Kim et al., 1999; Smith et al., 2002). Furthermore, it is commonly used in biophysical modelling of the hemodynamic response to activation (Buxton and Frank, 1997; Hyder et al., 1998; Zheng et al., 2002). More recently, the relationship between cerebral blood flow and volume changes has been investigated directly in awake humans (e.g., Hoge et al., 2005; Ito et al., 2003; Rostrup et al., 2005). Although originally proposed on the basis of steady-state hypercapnia data, the power law has been shown to be valid for brief or extended stimulation and transient hypercapnia (Jones et al., 2001, 2002; Mandeville et al., 1999). The present study reports hemodynamic response data from different anesthetic and stimulation conditions, evoking a broad range of different response magnitudes. It therefore represents an appropriate space

in which to further examine the validity or otherwise of FGrubb’s Law_. The results suggest that a power law is broadly applicable (Fig. 8) and provides for adequate prediction of CBV changes from measured CBF changes (Fig. 9). However, there are a number of caveats to this. Firstly, the values determined for the exponent (a), whether obtained from the slope of the linear fit in Fig. 8 or from the values calculated separately for each stimulation condition, were consistently lower in the awake than in the anesthetized preparation. This suggests that anesthesia may affect the relationship between cerebral blood flow and cerebral blood volume changes. Secondly, there is considerable variability in a values between the anesthetic states and also within the different stimulation conditions of the awake preparation. The power law relationship was proposed on the basis of data from Fsteady-state_ conditions (Grubb et al., 1974). The stimuli used here are of only 2-s duration, and the blood flow and volume changes elicited are wholly dynamic. Despite this, both here and elsewhere (Jones et al., 2001), there is reasonable consistency in a values calculated across a range of brief stimulation conditions in anesthetized preparations. The considerable variability of a values in the awake condition, together with the shorter latency of the hemodynamic responses, may highlight the limitations of applying this steady-

Fig. 12. (A) Comparison of the hemodynamic response function. The mean hemodynamic response measures (CBF, Hbt and HbO2) for the 5- to 40-Hz stimulation conditions were normalized between 0 and 1 (grey lines) and the mean time series (grand mean) was calculated in each case (black lines). Gamma variates were fitted to the grand means using a nonlinear least squares algorithm (dashed lines—awake; dotted lines—anesthetized). The parameters of the gamma variates are reported in Table 3. (B) For easier comparison, the CBF-fitted gamma variates from panel A are enlarged and shown separately.

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C. Martin et al. / NeuroImage 32 (2006) 33 – 48

Table 3 Parameters of fitted g-variates Mean

Width

Amplitude

R2

Awake CBF Hbt HbO2

2.26 2.45 2.77

0.41 0.56 0.65

1.47 1.78 1.95

0.73 0.99 0.97

Anesthetized CBF Hbt HbO2

4.00 3.72 4.28

2.95 3.67 4.12

3.71 4.24 4.63

0.94 0.90 0.95

A non-linear least squares algorithm (Levenberg-Marquardt algorithm) implemented in MatLab (function: lsqnonlin) was used to optimize the parameters in each case. The g-variates are displayed in Fig. 12. The coefficient of determination (R 2) is included to indicate the quality of the fit of each g-variate to the respective mean time series.

state model to an even more dynamic situation. Recent work in this laboratory has led to the development of a fully dynamic model to related CBF and CBV changes, and this is currently being applied to data from both awake and anesthetized preparations (Kong et al., 2004). Wu et al. (2002) demonstrated the general validity of the power law in biophysical modelling of the BOLD response. Importantly, however, they found values of the exponent a to be regionally specific and cautioned that estimating CMRO2 from fMRI data requires site-specific determination of a. Similarly, Rostrup et al. (2005) calculated values for a following CBV and CBF measurements made using positron emission tomography imaging in awake humans and found values ranging from 0.46 – 0.73 depending upon cortical region. Variability in a may thus be attributed to a number of factors including the species, imaging modality, anesthetic, or site of activation. It should be noted that in the present study, error may have been introduced by the consecutive, rather than concurrent measurement of CBF and Hbt (CBV) in the awake preparation. While attempts were made to locate the same region of cortex for both OIS and laser Doppler flowmetry in each experimental session for a particular animal, the regions may have differed very slightly. In short, a power law appears broadly applicable in relating CBF and CBV changes across a range of stimuli and physiological perturbations (i.e., anesthesia, hypercapnia), while determination of a single common value for a may be unachievable; especially under brief stimulation conditions. Coupling of neural activity to hemodynamics and effect of anesthesia In discussing the relationships between neural and hemodynamic response data, it is important to consider the volumes of sensitivity of each of the respective measures used in this study. Two questions are important here: do the various measures sample the same volume; are the sampled volumes expected to change as a result of animal and/or anesthetic? Mitzdorf (1987) estimates local FPs to have sensitivity in the range 0.5 – 3 mm from the electrode tip. Signals from OIS were recently estimated to have high sensitivity at cortical depths of 0 – 350 Am (Kennerley et al., 2005). Monte Carlo simulations of light transport though tissue conducted in this laboratory suggest that although the maximal sensitivity is in the range ¨0 – 0.3 mm cortical depth (LDF/OIS), >70% of the

LDF and >90% of the OIS signal is distributed through the first 1 mm of cortical depth (unpublished data, available on request). Given Mitzdorf’s minimum estimate of 0.5 mm for the FP radius of sensitivity and that the approximate depth from which the FP data in this paper are taken was 0.5 – 0.6 mm, there is likely to be considerable overlap in the regions from which the respective measures are made. It is also relevant here that the blood supply to and from the barrel column is routed via the cortical surface, and hence, there is very little in terms of blood oxygenation, volume, or flow changes that will not be captured by the optical measures. Thus, electrical and optical measures are considered to have a large degree of overlap in volumes of sensitivity, and both types of measure are considered to be representative of all activity within the barrel structure. The sensitivity profiles of each of the measures will be dictated largely by physical properties of the tissue (such as scattering coefficients or impedances). Although precise values for these parameters are not known, it seems unlikely that these will vary significantly between anesthetic state or subject and so will not affect the conclusions drawn here. Summed EFPs have been shown to be the aspect of neural activity most closely related to the magnitude of hemodynamic changes giving rise to the BOLD signal (see review by Logothetis, 2003). Previous studies in anesthetized preparations have reported linear relationships between the peak hemodynamic response and summed neural activity (Martindale et al., 2003; Mathiesen et al., 1998; Ngai et al., 1999; Sheth et al., 2003). Recent work in this laboratory has explored this relationship under manipulations of both stimulation frequency and intensity (Hewson-Stoate et al., 2005). This study reported that the relationship was linear for the stimulation frequency range explored (1 – 7 Hz) and within midrange stimulation intensities (0.6 – 1.4 mA) but nonlinear for higher stimulation intensities. The present study stands in broad agreement with these previous findings for lower stimulation frequencies but reports that when taking into account a broader range of stimulation frequencies, the coupling relationship in anesthetized animals is highly nonlinear. One explanation for the nonlinearities is that the larger summed neural activity exceeds the capabilities of the neural – hemodynamic coupling mechanisms themselves. The breakdown of linear coupling at high stimulation intensities reported by Hewson-Stoate et al. (2005) and Jones et al. (2004) may lend support to this argument, as although the stimulation intensity used in the present study was lower than the Fhigh-intensity_ stimuli used in their work, the use of higher stimulation frequencies may have somehow emulated this highintensity effect. There are disparate results throughout the literature, however, for example, Nielsen and Lauritzen (2001), using alpha-chloralose anesthetized rats and infra-orbital nerve stimulation, also demonstrate a sublinear coupling relationship, much like that reported in this urethane anesthetized preparation. Using the same anesthetic, Sheth et al. (2004) report a linear neural – hemodynamic coupling relationship in their alpha-chloralose anesthetized rat preparation (hindpaw stimulation). A potentially important difference between the results of the Sheth et al. (2004) paper and those reported here in anesthetized animals is the magnitude range of the summed neural activity measures (Sheth et al: 1 – 5 mV, our data: 5 – 25 mV). It is possible that the neural – hemodynamic coupling relationship found in our urethane anesthetized animals is not evident in their data because the range of neural response magnitudes is so much lower (i.e., at the lower end (1 – 15 mV), our summed neural activity may correlate linearly with hemodynamics too, but in our preparation, we were

C. Martin et al. / NeuroImage 32 (2006) 33 – 48

able to elicit responses of larger magnitude, and it was here that the nonlinearities were revealed). An alternative possible explanation for the asymptotic coupling relationship identified in the anesthetized preparation may be that a hemodynamic response Fceiling_ is reached. However, to maintain a linear relationship, hemodynamic response peaks would need to have been approximately 18%, 22%, and 23% (CBF) in the 10-, 20-, and 40-Hz conditions respectively. This is below the mean response maximum recorded in the awake preparation at 40 Hz (24.9% CBF) and well within the physiologically responsive range reported previously in similar anesthetized preparations (Gerrits et al., 2000; Jones et al., 2001; Lindauer et al., 1993; Nielsen and Lauritzen, 2001). It appears unlikely therefore that the apparent uncoupling observed here is due to a saturation of the hemodynamic response mechanism at 5 Hz in the anesthetized preparation. However, this may still be case if baseline hemodynamic parameters (e.g., CBF) were higher in the anesthetized preparation such that the response Fceiling_ was reached with a relatively much smaller percentage increase. However, the evidence suggests that if anything, the reverse is true (e.g., Hyder et al., 2002; Kaisti et al., 2003; Osborne, 1997). In the awake preparation, robust neural and hemodynamic responses could be elicited using a lower (nonnoxious) stimulation intensity. The neural – hemodynamic coupling relationship was very different over the full frequency range to that reported here for the anesthetized preparation, with hemodynamic response magnitude increasing approximately linearly with summed neural activity. Similar neural – hemodynamic coupling relationships have been observed in a number of previous studies using anesthetized rodent preparations both from this laboratory (for mid-range stimuli—Jones et al., 2004; Hewson-Stoate et al., 2005) and elsewhere using a variety of anesthetics (Devor et al., 2003 [urethane]; Nemoto et al., 2004 [enflurane]; Sheth et al., 2004 [alpha-chloralose]). The present results from the unanesthetized preparation are in good agreement with this previous work in anesthetized animals. Due to the necessarily different stimulation intensities used for the awake and anesthetized preparations, and the different neural response magnitudes, it is difficult to compare the neural – hemodynamic coupling relationships between anesthetic states or speculate on their cause. The differences in neural – hemodynamic coupling may be attributable to either the different stimulation intensities or some effect of anesthetic state, or possibly an interaction of the two. This issue is further complicated by the fact that the larger neural responses in the anesthetized preparation (to the first pulse in the stimulus train at least) may be attributable to lower thalamocortical suppression that has been reported in anesthetized preparations (Castro-Alamancos and Oldford, 2002) and not just the higher stimulation intensity. An important question is why do the large neural responses and greater magnitude of summed neural activity in the awake animal not lead to hemodynamic responses that are at least as large as those reported in the awake preparation for lower levels of summed neural activity? Furthermore, why do the hemodynamic responses to stimuli at 40 Hz in the anesthetized animals not even exceed those at 5 Hz, despite an increase in summed neural activity of ¨60%? It could be that in the anesthetized state, the relatively greater attenuation of neural responses following the first stimulus in a train means that at higher stimulation frequencies, evoked neural activity (subsequent to the first pulse) is less able to Ftrigger_ the mechanisms mediating neurovascular coupling: they are

45

Fsubthreshold_. An alternative explanation is that anesthesia is interfering with normal neurovascular coupling. This possibility has been raised repeatedly by numerous investigators (Bonvento et al., 1994; Buxton, 2001; Nakao et al., 2001; Sicard et al., 2003; Vanzetta and Grinvald, 2001). Further work in both awake and anesthetized preparations is needed to resolve these uncertainties. The fact that different investigators report differences in the neural – hemodynamic coupling relationship must be attributable in part to the many permutations of stimulus duration, intensity, and frequency that are used, in addition to different sites of stimulation (e.g., hindpaw, forepaw, whisker, infraorbital nerve). Differences in anesthetic regime may also be significant, both in terms of possible effects upon neural – hemodynamic coupling and in terms of the effects upon neuronal response patterns. As research seeking to identify the relationship between neural and hemodynamic responses advances, it will become increasingly important to consider in detail how parameters of the neural Finput_ (e.g., rate, amplitude) may affect the function of the putative neural – hemodynamic coupling mechanism(s). Furthermore, it is important to consider the physiological (and behavioral) validity of the range of stimuli being used in studies such as this. Under anesthesia, certain stimulation protocols (i.e., using a wide range of intensities) may be experimentally desirable (in that they elicit a broad range of robust responses) but may not be meaningful for the awake state. Generalizing research findings between animal studies in which different anesthetic agents (and regimes) are used is a problematic and highlights the need for unanesthetized preparations such as that used here. Urethane anesthesia was used in this study, and while it is true that other anesthetics such as alpha-chloralose are commonly used in functional imaging studies, urethane has been used in many studies investigating the neural – hemodynamic coupling relationship (e.g., Devor et al., 2003; Gerrits et al., 2000; Hewson-Stoate et al., 2005; Jones et al., 2004). As such, it is an appropriate choice of agent in the context of this research. Furthermore, for comparable stimuli the hemodynamic response function in the urethane anesthetized animal reported here and, in previous papers from this laboratory, is very similar in form to that observed by others under other anesthetic agents (e.g., alphachloralose: see Ureshi et al., 2004 or Sheth et al., 2004). Finally, in a recent paper, Devor et al. (2005) reported that the spatiotemporal pattern of the hemodynamic response in barrel cortex was unaffected by choice of anesthetic agent (alpha-chloralose vs. urethane).

Summary and conclusions This paper reports a linear relationship between summed neural activity and peak hemodynamic responses in an animal model free from the potential complexities of anesthesia. This finding provides support to a number of previous studies carried out using anesthetized animal models, where similar neural – hemodynamic coupling relationships have been found. Importantly however, the relationship identified was different to that observed in some other studies using anesthetized animal models, including the urethane anesthetized preparation also reported here. Further research in unanesthetized animals will be necessary to determine to what extent these differences are attributable to the effects of anesthesia upon neural – hemodynamic coupling mechanisms. The key question is whether the hemodynamic signals measured in BOLD fMRI

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C. Martin et al. / NeuroImage 32 (2006) 33 – 48

studies may be accurately interpreted in terms of neural activity changes. The implicit assumption of many investigators, that in awake subjects, increases in hemodynamic Factivity_ (i.e., the BOLD response) reflect approximately linear increases in neural activity does seem to be supported by this research in awake animals. This paper also provides evidence that the temporal structure of the hemodynamic response is affected by anesthesia. This suggests that in identifying specific temporal models of the hemodynamic response function, data from anesthetized animal preparations should be used with some caution. It is clear that in refining biophysical models of the fMRI BOLD signal, and in researching neurovascular coupling, data from awake animal preparations will be important.

Acknowledgments This work was supported by MRC project grants G0100538 and G0400606 and NIH grant 1R01NS044567-011. The authors would like to thank Dr. Nicola Hewson-Stoate, Dr. Myles Jones, and Dr. David Johnston for their input, and the technical staff of the laboratory (Marion Simkins, Natalie Walton, Malcolm Benn) for their assistance with this work. We gratefully acknowledge the Centre for Neural Communication Technology (University of Michigan, now NeuroNexus Technologies, Ann Arbor, NI) and the NIH grant that supported them (NIH NIBIB grant P41-RR09754) for the supply of, and assistance with, multi-channel electrophysiology probes.

References Ahissar, E., Sosnik, R., Haidarliu, S., 2000. Transformation from temporal to rate coding in a somatosensory thalamocortical pathway. Nature 406 (6793), 302 – 306. Ances, B.M., Zarahn, E., Greenberg, J.H., Detre, J.A., 2000. Coupling of neural activation to blood flow in the somatosensory cortex of rats is time-intensity separable, but not linear. J. Cereb. Blood Flow Metab. 20 (6), 921 – 930. Armstrong-James, M., Fox, K., Das-Gupta, A., 1992. Flow of excitation within rat barrel cortex on striking a single vibrissa. J. Neurophysiol. 68 (4), 1345 – 1358. Austin, V.C., Blamire, A.M., Allers, K.A., Sharp, T., Styles, P., Matthews, P.M., Sibson, N.R., 2005. Confounding effects of anesthesia on functional activation in rodent brain: a study of halothane and alphachloralose anesthesia. NeuroImage 24 (1), 92 – 100. Bandettini, P.A., Cox, R.W., 2000. Event-related fMRI contrast when using constant interstimulus interval: theory and experiment. Magn. Reson. Med. 43 (4), 540 – 548. Berwick, J., Martin, C., Martindale, J., Jones, M., Johnston, D., Zheng, Y., Redgrave, P., Mayhew, J., 2002. Hemodynamic response in the unanesthetized rat: intrinsic optical imaging and spectroscopy of the barrel cortex. J. Cereb. Blood Flow Metab. 22 (6), 670 – 679. Bonvento, G., Charbonne, R., Correze, J.L., Borredon, J., Seylaz, J., Lacombe, P., 1994. Is alpha-chloralose plus halothane induction a suitable anesthetic regimen for cerebrovascular research? Brain Res. 665 (2), 213 – 221. Buxton, R.B., 2001. The elusive initial dip. NeuroImage 13 (6 Pt. 1), 953 – 958. Buxton, R.B., Frank, L.R., 1997. A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J. Cereb. Blood Flow Metab. 17 (1), 64 – 72. Caesar, K., Gold, L., Lauritzen, M., 2003. Context sensitivity of activity-

dependent increases in cerebral blood flow. Proc. Natl. Acad. Sci. U. S. A. 100 (7), 4239 – 4244. Castro-Alamancos, M.A., 2002. Different temporal processing of sensory inputs in the rat thalamus during quiescent and information processing states in vivo. J. Physiol. 539 (Pt. 2), 567 – 578. Castro-Alamancos, M.A., Oldford, E., 2002. Cortical sensory suppression during arousal is due to the activity-dependent depression of thalamocortical synapses. J. Physiol. 541 (Pt. 1), 319 – 331. Chapin, J.K., Waterhouse, B.D., Woodward, D.J., 1981. Differences in cutaneous sensory response properties of single somatosensory cortical neurons in awake and halothane anesthetized rats. Brain Res. Bull. 6 (1), 63 – 70. Cohen, L.B., 1973. Changes in neuron structure during action potential propagation and synaptic transmission. Physiol. Rev. 53 (2), 373 – 418. Cox, S.B., Woolsey, T.A., Rovainen, C.M., 1993. Localized dynamic changes in cortical blood flow with whisker stimulation corresponds to matched vascular and neuronal architecture of rat barrels. J. Cereb. Blood Flow Metab. 13 (6), 899 – 913. Devilbiss, D.M., Waterhouse, B.D., 2002. Determination and quantification of pharmacological, physiological, or behavioral manipulations on ensembles of simultaneously recorded neurons in functionally related neural circuits. J. Neurosci. Methods 121 (2), 181 – 198. Devor, A., Dunn, A.K., Andermann, M.L., Ulbert, I., Boas, D.A., Dale, A.M., 2003. Coupling of total hemoglobin concentration, oxygenation, and neural activity in rat somatosensory cortex. Neuron 39 (2), 353 – 359. Devor, A., Ulbert, I., Dunn, A.K., Narayanan, S.N., Jones, S.R., Andermann, M.L., Boas, D.A., Dale, A.M., 2005. Coupling of the cortical hemodynamic response to cortical and thalamic neuronal activity. Proc. Natl. Acad. Sci. U. S. A. 102 (10), 3822 – 3827. Dunn, A.K., Devor, A., Bolay, H., Andermann, M.L., Moskowitz, M.A., Dale, A.M., Boas, D.A., 2003. Simultaneous imaging of total cerebral hemoglobin concentration, oxygenation, and blood flow during functional activation. Opt. Lett. 28 (1), 28 – 30. Ernst, T., Hennig, J., 1994. Observation of a fast response in functional MR. Magn. Reson. Med. 32 (1), 146 – 149. Fanselow, E.E., Nicolelis, M.A., 1999. Behavioral modulation of tactile responses in the rat somatosensory system. J. Neurosci. 19 (17), 7603 – 7616. Feng, C.M., Liu, H.L., Fox, P.T., Gao, J.H., 2003. Dynamic changes in the cerebral metabolic rate of O2 and oxygen extraction ratio in eventrelated functional MRI. NeuroImage 18 (2), 257 – 262. Gerrits, R.J., Raczynski, C., Greene, A.S., Stein, E.A., 2000. Regional cerebral blood flow responses to variable frequency whisker stimulation: an autoradiographic analysis. Brain Res. 864 (2), 205 – 212. Gerrits, R.J., Stein, E.A., Greene, A.S., 1998. Blood flow increases linearly in rat somatosensory cortex with increased whisker movement frequency. Brain Res. 783 (1), 151 – 157. Grubb, R.L. Jr., Raichle, M.E., Eichling, J.O., Ter-Pogossian, M.M., 1974. The effects of changes in PaCO2 on cerebral blood volume, blood flow, and vascular mean transit time. Stroke 5 (5), 630 – 639. Hewson-Stoate, N., Jones, M., Martindale, J., Berwick, J., Mayhew, J., 2005. Further nonlinearities in neurovascular coupling in rodent barrel cortex. NeuroImage 24 (2), 565 – 574. Hoge, R.D., Atkinson, J., Gill, B., Crelier, G.R., Marrett, S., Pike, G.B., 1999. Investigation of BOLD signal dependence on cerebral blood flow and oxygen consumption: the deoxyhemoglobin dilution model. Magn. Reson. Med. 42 (5), 849 – 863. Hoge, R.D., Atkinson, J., Gill, B., Crelier, G.R., Marrett, S., Pike, G.B., 1999. Linear coupling between cerebral blood flow and oxygen consumption in activated human cortex. Proc. Natl. Acad. Sci. U. S. A. 96 (16), 9403 – 9408. Hoge, R.D., Franceschini, M.A., Covolan, R.J., Huppert, T., Mandeville, J.B., Boas, D.A., 2005. Simultaneous recording of task-induced changes in blood oxygenation, volume, and flow using diffuse optical imaging and arterial spin-labeling MRI. NeuroImage 25 (3), 701 – 707. Hu, X., Le, T.H., Ugurbil, K., 1997. Evaluation of the early response in

C. Martin et al. / NeuroImage 32 (2006) 33 – 48 fMRI in individual subjects using short stimulus duration. Magn. Reson. Med. 37 (6), 877 – 884. Hyder, F., Rothman, D.L., Shulman, R.G., 2002. Total neuroenergetics support localized brain activity: implications for the interpretation of fMRI. Proc. Natl. Acad. Sci. U. S. A. 99 (16), 10771 – 10776. Hyder, F., Shulman, R.G., Rothman, D.L., 1998. A model for the regulation of cerebral oxygen delivery. J. Appl. Physiol. 85 (2), 554 – 564. Ito, H., Kanno, I., Ibaraki, M., Hatazawa, J., Miura, S., 2003. Changes in human cerebral blood flow and cerebral blood volume during hypercapnia and hypocapnia measured by positron emission tomography. J. Cereb. Blood Flow Metab. 23 (6), 665 – 670. Janz, C., Schmitt, C., Speck, O., Hennig, J., 2000. Comparison of the hemodynamic response to different visual stimuli in single-event and block stimulation fMRI experiments. J. Magn. Reson. Imaging 12 (5), 708 – 714. Jones, E.G., Diamond, I.T., 1995. Cerebral Cortex. The Barrel Cortex of Rodents. Plenum Press, New York. Jones, M., Berwick, J., Johnston, D., Mayhew, J., 2001. Concurrent optical imaging spectroscopy and laser-Doppler flowmetry: the relationship between blood flow, oxygenation, and volume in rodent barrel cortex. NeuroImage 13 (6 Pt. 1), 1002 – 1015. Jones, M., Berwick, J., Mayhew, J., 2002. Changes in blood flow, oxygenation, and volume following extended stimulation of rodent barrel cortex. NeuroImage 15 (3), 474 – 487. Jones, M., Hewson-Stoate, N., Martindale, J., Redgrave, P., Mayhew, J., 2004. Nonlinear coupling of neural activity and CBF in rodent barrel cortex. NeuroImage 22 (2), 956 – 965. Kaisti, K.K., Langsjo, J.W., Aalto, S., Oikonen, V., Sipila, H., Teras, M., Hinkka, S., Metsahonkala, L., Scheinin, H., 2003. Effects of sevoflurane, propofol, and adjunct nitrous oxide on regional cerebral blood flow, oxygen consumption, and blood volume in humans. Anesthesiology 99 (3), 603 – 613. Kennerley, A.J., Berwick, J., Martindale, J., Johnston, D., Papadakis, N., Mayhew, J.E., 2005. Concurrent fMRI and optical measures for the investigation of the hemodynamic response function. Magn. Reson. Med. 54 (2), 354 – 365. Kim, S.G., Rostrup, E., Larsson, H.B., Ogawa, S., Paulson, O.B., 1999. Determination of relative CMRO2 from CBF and BOLD changes: significant increase of oxygen consumption rate during visual stimulation. Magn. Reson. Med. 41 (6), 1152 – 1161. Kleinfeld, D., Mitra, P.P., Helmchen, F., Denk, W., 1998. Fluctuations and stimulus-induced changes in blood flow observed in individual capillaries in layers 2 through 4 of rat neocortex. Proc. Natl. Acad. Sci. U. S. A. 95 (26), 15741 – 15746. Kong, Y., Zheng, Y., Johnston, D., Martindale, J., Jones, M., Billings, S., Mayhew, J., 2004. A model of the dynamic relationship between blood flow and volume changes during brain activation. J. Cereb. Blood Flow Metab. 24 (12), 1382 – 1392. Lahti, K.M., Ferris, C.F., Li, F., Sotak, C.H., King, J.A., 1998. Imaging brain activity in conscious animals using functional MRI. J. Neurosci. Methods 82 (1), 75 – 83. Lahti, K.M., Ferris, C.F., Li, F., Sotak, C.H., King, J.A., 1999. Comparison of evoked cortical activity in conscious and propofol-anesthetized rats using functional MRI. Magn. Reson. Med. 41 (2), 412 – 416. Lindauer, U., Villringer, A., Dirnagl, U., 1993. Characterization of CBF response to somatosensory stimulation: model and influence of anesthetics. Am. J. Physiol. 264 (4 Pt. 2), H1223 – H1228. Linde, R., Schmalbruch, I.K., Paulson, O.B., Madsen, P.L., 1999. The KetySchmidt technique for repeated measurements of global cerebral blood flow and metabolism in the conscious rat. Acta Physiol. Scand. 165 (4), 395 – 401. Logothetis, N.K., 2003. The underpinnings of the BOLD functional magnetic resonance imaging signal. J. Neurosci. 23 (10), 3963 – 3971. Logothetis, N.K., Pauls, J., Augath, M., Trinath, T., Oeltermann, A., 2001. Neurophysiological investigation of the basis of the fMRI signal. Nature 412 (6843), 150 – 157. Malonek, D., Grinvald, A., 1996. Interactions between electrical activity

47

and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science 272 (5261), 551 – 554. Malonek, D., Dirnagl, U., Lindauer, U., Yamada, K., Kanno, I., Grinvald, A., 1997. Vascular imprints of neuronal activity: relationships between the dynamics of cortical blood flow, oxygenation, and volume changes following sensory stimulation. Proc. Natl. Acad. U.S.A. 94 (26), 14826 – 14831. Mandeville, J.B., Marota, J.J., Ayata, C., Zaharchuk, G., Moskowitz, M.A., Rosen, B.R., Weisskoff, R.M., 1999. Evidence of a cerebrovascular postarteriole windkessel with delayed compliance. J. Cereb. Blood Flow Metab. 19 (6), 679 – 689. Martin, C., Berwick, J., Johnston, D., Zheng, Y., Martindale, J., Port, M., Redgrave, P., Mayhew, J., 2002. Optical imaging spectroscopy in the unanaesthetised rat. J. Neurosci. Methods 120 (1), 25 – 34. Martindale, J., Mayhew, J., Berwick, J., Jones, M., Martin, C., Johnston, D., Redgrave, P., Zheng, Y., 2003. The hemodynamic impulse response to a single neural event. J. Cereb Blood Flow Metab. 23 (5), 546 – 555. Mathiesen, C., Caesar, K., Akgoren, N., Lauritzen, M., 1998. Modification of activity-dependent increases of cerebral blood flow by excitatory synaptic activity and spikes in rat cerebellar cortex. J. Physiol. 512 (Pt. 2), 555 – 566. Matsuura, T., Fujita, H., Seki, C., Kashikura, K., Yamada, K., Kanno, I., 1999. CBF change evoked by somatosensory activation measured by laser-Doppler flowmetry: independent evaluation of RBC velocity and RBC concentration. Jpn. J. Physiol. 49 (3), 289 – 296. Mayhew, J., Zhao, L., Hou, Y., Berwick, J., Askew, S., Zheng, Y., Coffey, P., 1998. Spectroscopic investigation of reflectance changes in the barrel cortex following whisker stimulation. Adv. Exp. Med. Biol. 454, 139 – 148. Menon, R.S., Ogawa, S., Hu, X., Strupp, J.P., Anderson, P., Ugurbil, K., 1995. BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals. Magn. Reson. Med. 33 (3), 453 – 459. Mitzdorf, U., 1987. Properties of the evoked potential generators: current source-density analysis of visually evoked potentials in the cat cortex. Int. J. Neurosci. 33 (1 – 2), 33 – 59. Molgedey, L., Schuster, H.G., 1994. Separation of a mixture of independent signals using time delayed correlations. Phys. Rev. Lett. 72, 3634 – 3637. Moskalenko, Y.E., Dowling, J.L., Liu, D., Rovainen, C.M., Semernia, V.N., Woolsey, T.A., 1996. LCBF changes in rat somatosensory cortex during whisker stimulation monitored by dynamic H2 clearance. Int. J. Psychophysiol. 21 (1), 45 – 59. Nakao, Y., Itoh, Y., Kuang, T.Y., Cook, M., Jehle, J., Sokoloff, L., 2001. Effects of anesthesia on functional activation of cerebral blood flow and metabolism. Proc. Natl. Acad. Sci. U. S. A. 98 (13), 7593 – 7598. Nemoto, M., Sheth, S., Guiou, M., Pouratian, N., Chen, J.W., Toga, A.W., 2004. Functional signal- and paradigm-dependent linear relationships between synaptic activity and hemodynamic responses in rat somatosensory cortex. J. Neurosci. 24 (15), 3850 – 3861. Ngai, A.C., Jolley, M.A., D’Ambrosio, R., Meno, J.R., Winn, H.R., 1999. Frequency-dependent changes in cerebral blood flow and evoked potentials during somatosensory stimulation in the rat. Brain Res. 837 (1 – 2), 221 – 228. Nielsen, A.N., Lauritzen, M., 2001. Coupling and uncoupling of activitydependent increases of neuronal activity and blood flow in rat somatosensory cortex. J. Physiol. 533 (Pt. 3), 773 – 785. Osborne, P.G., 1997. Hippocampal and striatal blood flow during behavior in rats: chronic laser Doppler flowmetry study. Physiol. Behav. 61 (4), 485 – 492. Oz, G., Berkich, D.A., Henry, P.G., Xu, Y., LaNoue, K., Hutson, S.M., Gruetter, R., 2004. Neuroglial metabolism in the awake rat brain: CO2 fixation increases with brain activity. J. Neurosci. 24 (50), 11273 – 11279.

48

C. Martin et al. / NeuroImage 32 (2006) 33 – 48

Peeters, R.R., Tindemans, I., De Schutter, E., Van der Linden, A., 2001. Comparing BOLD fMRI signal changes in the awake and anesthetized rat during electrical forepaw stimulation. Magn. Reson. Imaging 19 (6), 821 – 826. Poggio, G.F., Mountcastle, V.B., 1963. The functional properties of ventrobasal thalamic neurons studied in unanesthetized monkeys. J. Neurophysiol. 26, 775 – 806. Rostrup, E., Knudsen, G.M., Law, I., Holm, S., Larsson, H.B., Paulson, O.B., 2005. The relationship between cerebral blood flow and volume in humans. NeuroImage 24 (1), 1 – 11. Sachdev, R.N., Champney, G.C., Lee, H., Price, R.R., Pickens, D.R. III, Morgan, V.L., Stefansic, J.D., Melzer, P., Ebner, F.F., 2003. Experimental model for functional magnetic resonance imaging of somatic sensory cortex in the unanesthetized rat. NeuroImage 19 (3), 742 – 750. Sheth, S., Nemoto, M., Guiou, M., Walker, M., Pouratian, N., Toga, A.W., 2003. Evaluation of coupling between optical intrinsic signals and neuronal activity in rat somatosensory cortex. NeuroImage 19 (3), 884 – 894. Sheth, S.A., Nemoto, M., Guiou, M., Walker, M., Pouratian, N., Toga, A.W., 2004. Linear and nonlinear relationships between neuronal activity, oxygen metabolism, and hemodynamic responses. Neuron 42 (2), 347 – 355. Shtoyerman, E., Arieli, A., Slovin, H., Vanzetta, I., Grinvald, A., 2000. Long-term optical imaging and spectroscopy reveal mechanisms underlying the intrinsic signal and stability of cortical maps in V1 of behaving monkeys. J. Neurosci. 20 (21), 8111 – 8121. Sicard, K., Shen, Q., Brevard, M.E., Sullivan, R., Ferris, C.F., King, J.A., Duong, T.Q., 2003. Regional cerebral blood flow and BOLD responses in conscious and anesthetized rats under basal and hypercapnic conditions: implications for functional MRI studies. J. Cereb. Blood Flow Metab. 23 (4), 472 – 481. Smith, A.J., Blumenfeld, H., Behar, K.L., Rothman, D.L., Shulman, R.G., Hyder, F., 2002. Cerebral energetics and spiking frequency: the

neurophysiological basis of fMRI. Proc. Natl. Acad. Sci. U. S. A. 99 (16), 10765 – 10770. Thompson, J.K., Peterson, M.R., Freeman, R.D., 2003. Single-neuron activity and tissue oxygenation in the cerebral cortex. Science 299 (5609), 1070 – 1072. Ureshi, M., Matsuura, T., Kanno, I., 2004. Stimulus frequency dependence of the linear relationship between local cerebral blood flow and field potential evoked by activation of rat somatosensory cortex. Neurosci. Res. 48 (2), 147 – 153. Vanzetta, I., Grinvald, A., 2001. Evidence and lack of evidence for the initial dip in the anesthetized rat: implications for human functional brain imaging. NeuroImage 13 (6 Pt. 1), 959 – 967. Vogel, J., Kuschinsky, W., 1996. Decreased heterogeneity of capillary plasma flow in the rat whisker-barrel cortex during functional hyperemia. J. Cereb. Blood Flow Metab. 16 (6), 1300 – 1306. Woolsey, T.A., Rovainen, C.M., Cox, S.B., Henegar, M.H., Liang, G.E., Liu, D., Moskalenko, Y.E., Sui, J., Wei, L., 1996. Neuronal units linked to microvascular modules in cerebral cortex: response elements for imaging the brain. Cereb. Cortex 6 (5), 647 – 660. Wu, G., Luo, F., Li, Z., Zhao, X., Li, S.J., 2002. Transient relationships among BOLD, CBV, and CBF changes in rat brain as detected by functional MRI. Magn. Reson. Med. 48 (6), 987 – 993. Wyrwicz, A.M., Chen, N., Li, L., Weiss, C., Disterhoft, J.F., 2000. fMRI of visual system activation in the conscious rabbit. Magn. Reson. Med. 44 (3), 474 – 478. Yacoub, E., Shmuel, A., Pfeuffer, J., Van De Moortele, P.F., Adriany, G., Ugurbil, K., Hu, X., 2001. Investigation of the initial dip in fMRI at 7 Tesla. NMR Biomed. 14 (7 – 8), 408 – 412. Zheng, Y., Johnston, D., Berwick, J., Mayhew, J., 2001. Signal source separation in the analysis of neural activity in brain. NeuroImage 13 (3), 447 – 458. Zheng, Y., Martindale, J., Johnston, D., Jones, M., Berwick, J., Mayhew, J., 2002. A model of the hemodynamic response and oxygen delivery to brain. NeuroImage 16 (3 Pt. 1), 617 – 637.