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Further nonlinearities in neurovascular coupling in rodent barrel cortex
www.elsevier.com/locate/ynimg NeuroImage 24 (2005) 565 – 574
Further nonlinearities in neurovascular coupling in rodent barrel cortex Nicola Hewson-Stoate,* Myles Jones, John Martindale, Jason Berwick, and John Mayhew Neural Imaging Research Unit, Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, UK Received 24 June 2004; revised 30 July 2004; accepted 30 August 2004 Available online 23 November 2004 An essential prerequisite for the accurate interpretation of noninvasive functional brain imaging techniques, such as blood oxygen level dependent (BOLD) fMRI, is a thorough understanding of the coupling relationship between neural activity and the haemodynamic response. The current study investigates this relationship using rat barrel cortex as a model. Neural input was measured by applying current source density (CSD) analysis to multi-laminar field potentials to remove ambiguities regarding the origin of the signal inherent in single electrode recordings. Changes in cerebral blood flow (CBF) were recorded with a laser Doppler flowmetry probe. The magnitude of neural and CBF responses were modulated over a large range by altering both the intensity and frequency of electrical whisker pad stimulation. Consistent with previous findings [Devor, A., et al., 2003. Neuron 39, 353–359; Sheth, S.A., et al., 2004. Neuron 42, 347–355] a power law function well described the relationship between neural activity and haemodynamics. Despite the nonlinearity of the coupling over the whole data set, the relationship was very well approximated by a linear function over mid-range stimuli. Altering the frequency of stimulation at 1.2 mA shifted the neural activity and corresponding haemodynamic response along this linear region, reconciling recent reports of a nonlinear relationship [Devor, A., et al., 2003. Neuron 39, 353–359; Jones, M., et al., 2004. NeuroImage 22, 956–965; Sheth, S.A., et al., 2004. Neuron 42, 347–355] with previous work that found a linear coupling relationship when altering stimulation frequency [Martindale, J., et al., 2003. J. Cereb. Blood Flow Metab. 23, 546– 555; Ngai, A.C., et al., 1999. Brain Res. 837, 221–228; Sheth, S., et al., 2003. NeuroImage 19, 884–894]. Using stimuli within this linear range in imaging studies would simplify the interpretation of findings. D 2004 Elsevier Inc. All rights reserved. Keywords: Nonlinearities; Neurovascular coupling; Barrel cortex; Rat
Introduction Many noninvasive functional brain imaging techniques, such as BOLD fMRI (Kwong et al., 1992; Ogawa et al., 1992), infer neural * Corresponding author. Fax: +44 114 276 6515. E-mail address: [email protected] (N. Hewson-Stoate). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2004.08.040
activity from changes in haemodynamics. It is therefore essential to have a thorough understanding of neurovascular coupling for the optimal design and subsequent interpretation of studies using such imaging methodologies. The BOLD signal results from changes in the concentration of deoxy-haemoglobin, and is based on the balance between the local changes in blood flow (CBF), blood volume (CBV), and oxygen consumption (CMRO2), which accompany neural activity. The relationship between changes in CBF and CBV has been described (Grubb et al., 1974; Jones et al., 2001, 2002, Mandeville et al., 1999a), as has the relationship between CBF and CMRO2 (Hoge et al., 1999; Jones et al., 2001; Mayhew et al., 2001). It is now necessary to quantitatively characterise the coupling between neural activity and CBF, to advance biophysical models of the BOLD signal (Friston et al., 2000; Zheng et al., 2002). In addition, the interpretation of perfusion-based fMRI, which is able to provide information at the spatial resolution of cortical columns (Duong et al., 2001), relies upon an understanding of the coupling between neural activity and CBF. If changes in CBF are not directly proportional to changes in neural activity, inaccurate inferences may be made on the basis of stimulus-induced haemodynamic changes (Schwartz et al., 2003). There is discrepancy within recent work investigating the neuro-haemodynamic coupling relationship, both within and between research groups. When manipulating stimulation frequency, a linear relationship has been reported (Martindale et al., 2003; Ngai et al., 1999; Sheth et al., 2003). However, studies in which stimulus intensity is also modulated report a nonlinear neurovascular coupling relationship (Devor et al., 2003; Jones et al., 2004; Sheth et al., 2004). Nemoto et al. (2004) suggest that the linearity of the coupling relationship is dependent on the stimulation parameter being manipulated, and report significant differences in the coupling relationship when intensity of stimulation is manipulated compared to when the number of stimuli is manipulated. In an attempt to reconcile this discrepancy, we proposed that within a certain stimulus range, the relationship can be well approximated by a linear function, and that altering stimulation frequency serves to modulate the amount of integrated neural activity along this linear portion of the neurohaemodynamic relationship. This hypothesis was explicitly tested
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in the current study, which extends our previous work (Jones et al., 2004; Martindale et al., 2003) by systematically altering both stimulation frequency and stimulation intensity, therefore encompassing a more complete range of physiological stimuli within the rat barrel system. A 1.2-mA stimulus in the anaesthetised rat is believed to elicit responses typical of those in the unanaesthetised rat following stimulation at 0.2 mA, which produces whisker movement that appears to be just above the threshold of behavioral relevance (C. Martin, PhD Thesis). The range of stimuli used in the current study is therefore believed to be relevant to the awake behaving rat. When investigating the coupling relationship between neural activity and haemodynamics, it is first necessary to discern the most appropriate metric of neural activity to take. Previous studies have examined the relationship with both local field potential (LFP) recordings (Sheth et al., 2003, 2004) and spiking activity (Heeger et al., 2000; Smith et al., 2002). Despite the dfortuitous correlationT (Logothetis, 2003) between spiking output and haemodynamics, both CBF and BOLD responses may correlate with LFPs even in the absence of spiking output (Caesar et al., 2003; Logothetis and Wandell, 2004). Furthermore, we suggested that the greater spatial concordance of synaptic input with CBF compared to spiking output implies that CBF more accurately reflects metabolic demand resulting from neural input to the cortex, as opposed to the spiking output of the region (Jones et al., 2004). LFPs, which are commonly taken as a measure of neural input to a region (Nielsen and Lauritzen, 2001; Sheth et al., 2004), are generated by current sinks (active process of ions flowing into a cell to generate excitatory post-synaptic potentials), and current sources (the passive outflow of ions), which summate over the region of interest, rendering the interpretation of data from a single electrode inherently ambiguous. The measurement of LFPs at many depths throughout the cortex enables the application of CSD analysis, which disambiguates the neural signal by revealing the location and magnitude of the underlying current sinks and sources (Mitzdorf, 1985; Nakagawa and Matsumoto, 2000; Nicholson and Freeman, 1975). We find that in barrel cortex the results of CSD analysis are dominated by a prominent active current sink (~450 Am below the cortical surface), which corresponds to the site of thalamic input, and therefore believe that the magnitude of this sink is the optimal metric to take for the investigation of haemodynamic coupling. In the current study, the rat barrel cortex was used as a model to investigate the neural–CBF relationship following electrical stimulation of the whisker pad at several intensities (0–1.6 mA, in 0.2 mA steps) and frequencies (1, 3, 5, 7 Hz). This animal model is well suited to such an investigation because somatosensory stimulation produces spatially discrete neural and haemodynamic responses due to the highly ordered spatial topography (Chapin and Lin, 1984) and well-defined microvasculature (Cox et al., 1993; Patel, 1983). The magnitude of the measured neural input was compared with CBF data from a laser Doppler flowmetry probe positioned adjacent to the electrode. Consistent with previous work (Devor et al., 2003; Sheth et al., 2004), a nonlinear relationship was found between neural activity and the haemodynamic response, which is well described by a power law function ( y = ax c). As hypothesised (Jones et al., 2004), there is a portion of this relationship that can be well explained with a linear function, and at a mid-range stimulus intensity (1.2 mA), altering stimulus frequency shifts data points along this linear portion of the neural
activity–CBF relationship, reconciling this work with previous studies which demonstrate a linear neurovascular coupling (Martindale et al., 2003; Ngai et al., 1999; Sheth et al., 2003).
Materials and methods Experimental overview In anaesthetised rats, the skull overlying the barrel cortex was thinned to translucency, and the barrels were located using single wavelength optical imaging following electrical stimulation of the whisker pad. A small hole was made in the skull and dura within the active barrel region to allow the insertion of a 16-channel multielectrode orthogonal to the cortical surface. A laser Doppler flowmetry probe was positioned adjacent to the electrode. Electrophysiology and CBF data were collected while the whisker pad was electrically stimulated at different frequencies (3, 5, and 7 Hz) and intensities (0–1.6 mA in 0.2-mA steps). The 1-Hz data is taken from Jones et al. (2004). Animal preparation Female hooded Lister rats (total n = 15, n = z6 for each stimulus condition) weighing between 250 and 400 g were kept in a 12-h dark/light cycle environment with food and water ad libitum. Animals were anaesthetised with urethane (1.25 g/kg i.p.), and depth of anaesthesia was monitored periodically by assessing the toe pinch withdrawal reflex. Rectal temperature was maintained at 378C throughout surgical and experimental procedures using a homeothermic blanket (Harvard). A tracheotomy was performed to allow artificial ventilation and the monitoring of end-tidal CO2. Ventilation parameters were adjusted for individual animals to maintain blood-gas measurements and end-tidal CO2 within physiological limits. The femoral artery and vein were cannulated to allow the measurement of mean arterial blood pressure and infusion of drugs, respectively. Phenylephrine (0.13–0.26 mg/h) was infused to maintain blood pressure within physiological limits (MABP, 100–110 mm Hg) (Golanov et al., 1994; Nakai and Maeda, 1999). Animals were placed in a stereotaxic frame (Kopf Instruments), and the skull overlying the right somatosensory cortex thinned to translucency using a dental drill. Saline was used to cool the area throughout. A plastic dwellT was secured around the thinned skull with dental cement, and filled with saline (378C) to reduce specularities from the skull surface. All procedures were carried out in accordance with Home Office regulations. Localisation of barrel cortex using single wavelength optical imaging Somatosensory cortex was monochromatically illuminated (590 nm, narrow bandwidth interference filter, F5 nm), and images were recorded using a 12-bit CCD camera (SMD 1M60). Barrel cortex was activated by electrically stimulating the whisker pad (1.2 mA, 5 Hz, 1 s). In each trial, data were recorded for 23 s. Stimulus onset was at 8 s, and there was a 25-s inter-stimulus interval. Thirty trials were collected at a sampling frequency of 15 Hz. Images were analysed using a modified version of a signal source separation algorithm (Molgedey and Schuster, 1994) as previously described (Zheng et al., 2001). This procedure has been
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shown on numerous occasions to produce spatially discrete activations 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 electrophysiology and LDF probes (Fig. 1). Electrophysiology To measure neural responses throughout the depth of the cortex, 16 electrodes linearly mounted on a single probe (100-Am spacing, area of each site 177 Am2, impedance 1.5–2.7 MV, probe width: 33-Am tip, 123 Am at uppermost electrode; CNCT, University of Michigan) were inserted normal to the cortical surface. To allow the insertion of this probe, a small hole was made in the skull and dura overlying the active region as located by single wavelength optical imaging. The probe was inserted under micromanipulator control to a depth of 1550 Am (uppermost electrode approximately 50 Am from cortical surface). The electrodes were coupled to a data acquisition device (Medusa Bioamp, TDT, FL) with a custom written Matlabk (the Mathworks Inc, USA) interface. Recordings were averaged for each experimental session (20 trials in each condition), with stimulus onset djitteredT within a 20-ms window to minimise any effects of 50 Hz mains noise. The resultant evoked field potentials were sampled at 6 kHz with 12-bit resolution. The data were subjected to current source density analysis (CSD) as described previously (Jones et al., 2004; Martindale et al., 2003, see also Mitzdorf, 1985; Rappelsberger et al., 1981 for more general discussions). Assuming homogeneity and anisotropy of the rat barrel cortex, a 1-D CSD equation can be applied to depth-resolved field potentials in order to estimate underlying current sinks and sources. The data were subsequently interpo-
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lated and filtered to give an apparent spatial resolution of 50 Am (Fig. 2). The mean of the time series through the current sink was obtained for each data set, and normalised by dividing the time series by the amplitude of the response to the first dpulseT in the stimulus train for the 1.2-mA condition. This removes the influence of inter-animal variability. The absolute minima of the normalised time series following each pulse in the stimulus train was taken, and averaged across same condition trials in each animal, before the data for all the animals were collated, and averaged for each experimental condition. Laser Doppler flowmetry (LDF) To enable concurrent measurement of cerebral blood flow (CBF), an LDF probe (Peri-Flux 5010, Perimed, Stockholm, 780 nm illumination, fiber separation 0.25 mm) was positioned over the active area normal to the cortical surface, adjacent to the electrode. The maximum distance between the electrode and the LDF probe was 100 Am. Visual inspection of the cortical surface allowed large vessels to be avoiding during LDF probe placement. The signal from the LDF probe was analysed by an LDF spectrometer, which includes a proprietary linearisation algorithm (Nilsson, 1984) to reduce errors caused by changes in blood volume. The LDF signal was digitised continuously using a 1401plus (CED Ltd, UK). The time constant of the LDF recordings was 0.2 s, and the bandwidth was 12 kHz. Data were trial averaged within animals for each stimulus condition recorded (z40 trials per stimulus condition). All CBF data were then normalised by the peak CBF response elicited by an additional stimulus condition (1.2 mA, 1 Hz, 3 s) to both compensate for inter-animal variability and facilitate comparison between the present study and our previous work (Jones et al., 2004). The
Fig. 1. Single-wavelength imaging of barrel cortex. (A) Thin cranial window under 590-nm illumination. (B) Region of activation following electrical stimulation of the contralateral whisker pad (1.2 mA, 5 Hz, 1 s). These maps were used to guide placement of the 16-channel silicon electrode (C), and the laser Doppler flowmetry probe (not shown).
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Fig. 2. Multilaminar analysis of local field potentials. (A) Local field potential recordings in rodent barrel cortex from a 16-channel multielectrode from a representative animal following electrical stimulation of the whisker pad at 1.2 mA (first pulse in the stimulus train). (B) The spatial distribution of the current sinks and sources relative to cortical depth following current source density (CSD) analysis on the same data set.
normalised CBF responses were then averaged across animals, with the peak response from each experimental condition taken for comparison with the neural data. Stimulus presentation and paradigms All stimulus presentation was controlled through a 1401plus (CED Ltd) running custom-written code with stimulus onset time locked to the electrophysiology recording unit. Electrical stimulation of the entire whisker pad was delivered via stainless-steel electrodes (Plastics One Inc) inserted subcutaneously in a rostral– caudal direction. With the exception of the previously reported 1-Hz data (Jones et al., 2004), stimulus duration was 2 s. The intensity of stimulation varied between 0 and 1.6 mA in 0.2-mA steps. Increasing the intensity of the stimulation resulted in a larger rostral–caudal deflection of the whiskers. No changes in MABP, heart rate, or PCO2 were observed at any of the stimulus intensities, suggesting that CBF values were not contaminated by systemic physiological changes. Each experimental run consisted of 20 trials at each intensity, randomly interleaved, with a 15-s inter-stimulus interval to allow the haemodynamic response to return to baseline between successive trials. The stimulus paradigm was presented at either 3, 5, or 7 Hz and in the majority of cases z40 trials of data were collected at each intensity at two different frequencies for an animal.
Results Neural responses and the resulting changes in cerebral blood flow were measured in rat barrel cortex following electrical
stimulation of the whisker pad. The amount of elicited neural activity was modulated by systematically varying both the intensity (0–1.6 mA in 0.2-mA steps) and frequency (1, 3, 5, 7 Hz) of whisker stimulation. A probe consisting of 16 linearly mounted electrodes inserted normal to the cortical surface was used to measure local field potentials throughout the cortical depth (Fig. 1). Current source density analysis of the local field potentials recorded throughout the cortical lamina revealed the location of current sinks and sources (Fig. 2). A prominent current sink was consistently found at ~450 Am below the cortical surface and most likely reflects the input from thalamus. An example of the time series through this dprimaryT current sink is shown in Figs. 3A–D. Changes in CBF were measured by a laser Doppler flowmetry probe positioned b1 mm from the cortical surface (to which the probe was orthogonal) adjacent to the electrode. The neural response occurs on a millisecond time scale, resulting in an individual response to each pulse in the stimulus train. Due to the intrinsic difference in temporal resolution, responses to individual pulses were not resolvable in the CBF data, where a single response is observed for the entire stimulus train (Fig. 3G). To investigate the relationship between these two processes, a single metric was taken for neural activity and CBF. For the neural activity, the absolute value of the minimum of the normalized time series through the current sink following each pulse was calculated for each stimulus condition. Both the mean and sum of the neural activity were determined from this measure. The peak of the normalised CBF response was taken as a measure of the haemodynamic response to compare with the neural activity. The 1-Hz data is taken from Jones et al. (2004), and is shown again here for comparison with the higher frequency data. In all data plots, error bars denote standard errors.
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Fig. 3. Neuronal and haemodynamic responses to stimuli of varying frequency. (A–D) Normalised neural responses to 1.2 mA stimuli from a representative animal. With increasing stimulation frequency there is increased attenuation of the response following the initial pulse. (E, F) Mean and summed neural activity, respectively, averaged across all subjects. Mean neural activity decreases with increasing stimulation frequency, whereas summed neural activity increases up to 5 Hz, and subsequently decreases. (G, H) CBF responses to stimulation of varying frequency averaged across all subjects. Response size increases up to 5 Hz, and decreases at 7 Hz.
Effect of manipulating stimulus parameters on neural activity and CBF To demonstrate the effect of increasing stimulus frequency, the normalised time series through the primary current sink (1, 3, 5, 7 Hz at 1.2 mA) is shown from a representative animal (Figs. 3A–D). At 1 Hz, the pulses in the stimulus train are equivalent in size. As stimulation frequency increases to 3 Hz and above, attenuation of the second and subsequent pulses is observed. Previous work has shown that stimuli separated by less than 600 ms results in such attenuation of responses (Martindale et al., 2003; Ogawa et al., 2000), likely to be due to inhibitory interactions (Simons, 1985).
At 3 Hz and above, stimuli are separated by less than 600 ms; hence the observed attenuation that results in a decrease of mean neural activity with increasing stimulus frequency (Fig. 3E). At 1.2 mA, summed neural activity increases up to 5 Hz, and subsequently decreases at 7 Hz, as does the resulting CBF response (Figs. 3F–H), consistent with previous findings (Martindale et al., 2003; Ngai et al., 1999; Sheth et al., 2004). At a stimulation frequency of 5 Hz, increasing stimulation intensity from 0 to 1.6 mA results in a monotonic increase in both mean and summed neural activity (Figs. 4A,B) and in the magnitudes of the CBF responses (Figs. 4C,D). This monotonic increase with regard to stimulus intensity is also apparent at the
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Fig. 4. Neuronal and haemodynamic response to stimuli of varying intensity. (A, B) Mean and summed neural activity averaged over all animals to a 5-Hz stimulus of varying intensity (0–1.6 mA in 0.2-mA steps). Both mean and summed neural responses increase monotonically with increasing stimulus intensity. (C) Mean time series of the CBF response to the 5-Hz stimulus averaged over all the animals for each intensity. (D) Peak CBF response similarly increases monotonically with increasing stimulus intensity.
other frequencies considered (1, 3, 7 Hz), although at 1 Hz the neural and CBF responses begin to plateau at the top of the intensity range, a trend which is not observed in the higher frequency data (Figs. 5A–C). The observation that increasing stimulus frequency from 1 to 7 Hz results in a decrease in mean neural activity (Fig. 3E) is evident at all stimulus intensities (Fig. 5A). However, the trend for summed neural activity to increase up to 5 Hz, and decrease at 7 Hz (Fig. 3F) is only apparent above a stimulus intensity of 0.8 mA. Below this stimulation intensity, frequency has little effect of summed neural activity. The CBF responses follow a very similar pattern to that of summed neural activity (Figs. 5B,C). Relationship between neural activity and CBF The peak of the normalised CBF response was plotted against the summed normalised neural activity (Fig. 6). As summed neural activity increases, so does the CBF response. The ability of a linear and two nonlinear models to describe the data was evaluated. The linear model was constrained to pass through zero ( y = ax, dlinearT). The first nonlinear model did not have this constraint, therefore allowing the indication of a threshold effect ( y = ax + b, dthresholdT, e.g., Nielsen and Lauritzen, 2001). A positive x-
intercept (and equivalently, a negative y-intercept) would indicate the presence of a threshold of neural activity, which must be exceeded before a haemodynamic response is elicited. The final model evaluated was a power law ( y = ax c , dpower lawT). This relationship was found by Devor et al. (2003), and also well described the data collected by Sheth et al. (2004). The three models were optimised using a nonlinear least-squares algorithm (Levenberg Marquardt algorithm, MATLABk function dlsqnonlinT) and compared to the original data (Fig. 7). A chi-square dgoodness of fitT test was used to assess the quality of fit for each model, using the summed neural activity as the independent variable. As a rule of thumb, the greater the difference between the data and the model predictions, the larger the chisquare value. From Table 1, it can be seen that the linear and threshold models have the largest chi-square values (48.8, df 30; and 46.1, df 29, respectively), and the power law model has the smallest chi-square (25.8, df 29). Thus, the power law provides the best fit to the data. The critical chi-square values corresponding to the P = 0.05 level for the appropriate degrees of freedom are shown in Table 1. It can be seen that the chi-square values for the linear and threshold models are greater than these critical values, hence significantly different from the data. In contrast, the chi-square value for the power law model is much smaller than the critical
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Fig. 5. Haemodynamic and neural responses to stimuli of differing intensity (0–1.6 mA) and frequency (1–7 Hz). (A) Mean neural activity increases monotonically with increasing stimulus intensity, and decreases with increasing stimulation frequency. (B) Summed neural activity increases with increasing stimulus intensity, and above 0.8 mA, increases with increasing stimulation frequency up until 5 Hz. At 7 Hz, there is a decrease in summed neural activity. (C) CBF response increases monotonically with stimulation intensity and increases with stimulation frequency up until 5 Hz, after which the CBF response decreases.
value with a P value of 0.6. Clearly, this model provides the most acceptable fit to the data, consistent with the findings of Sheth et al. (2004).
and CBF both increase, and at 7 Hz both neural activity and CBF are shifted back down, due to the increased inhibition of pulses at the higher frequency being akin to decreasing stimulus intensity.
Reconciling the nonlinear relationship with previous research Over the large range of physiological stimuli presented in the current study, the relationship between neural activity and the haemodynamic response was best fit with a power law function. However, inspection of the data indicates the presence of a linear relationship within the mid-range of stimuli presented (0.6–1.4 mA, 3, 5 Hz, chi-square 0.91, df = 9, P = 0.99). Further analysis revealed that at an intensity of 1.2 mA, increasing the stimulation frequency shifts the neural activity along this linear portion of the neural–CBF relationship. From 1 to 5 Hz, the neural activity
Fig. 6. Coupling between neural activity and the haemodynamic response. Normalised CBF responses are plotted against normalised R neural activity for the mean across animals for each stimulation condition. CBF increases monotonically with increasing R neural activity.
Discussion Through concurrent electrophysiology and recording of changes in CBF in rat barrel cortex following somatosensory stimulation, the present study confirms and extends previous findings of a nonlinear power law relationship between neural activity and the haemodynamic response (Devor et al., 2003; Sheth et al., 2004). The stimulation parameters used are comparable to those used in fMRI experiments (Mandeville et al., 1999b; Marota et al., 1999; Ogawa et al., 2000; Silva and Koretsky, 2002; Silva et al., 2000), thereby rendering the findings relevant to models of BOLD fMRI. Although the findings of a power law coupling relationship in the current study do not directly inform models of metabolic-flow coupling (Buxton et al., 1998; Mandeville et al., 1999a), they may be used in the future to evaluate the validity of such models. The use of current source density analysis of LFPs removes the ambiguities inherent in single electrode recordings. As a result, it is possible to confirm that the nonlinear relationship is between CBF and neural input (as measured by activity within the dprimaryT current sink). The nature of the nonlinearities in the relationship between neural activity and haemodynamics differ from those formerly reported by our laboratory. We previously altered the intensity of stimulation (1 Hz, 3 s, 0–1.6 mA in steps of 0.2 mA), and measured neural activity and CBF simultaneously using methods identical to those presented here. With this range of stimuli, we found an inverse sigmoid relationship to well describe the neurovascular coupling relationship (Jones et al., 2004). This discrepancy highlights the need for many stimulation conditions to elicit a large range of neural and haemodynamic responses to ensure that the most accurate representation of the relationship is determined. By looking at the effect of altering stimulus frequency at a midrange intensity (1.2 mA), we have been able to reconcile the observed nonlinear relationship with findings of a linear relation-
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Fig. 7. Neurovascular coupling models. Comparison of the model fits to the data; y = ax (linear, A) y = ax + b (linear with no constraint, B), and y = ax c (power law, C).
ship in previous work in which stimulation frequency was altered in order to modulate neural activity and the corresponding haemodynamic response (Martindale et al., 2003; Nemoto et al., 2004; Ngai et al., 1999; Sheth et al., 2003). We hypothesised that there is a linear portion of the neural–CBF relationship, and that at a mid-range stimulus intensity, frequency-induced changes in neural activity would shift the neural data along this linear region of the neural–CBF relationship (Jones et al., 2004). Observation of the data indicates the presence of such a linear portion of the relationship when taking the data from the 0.6–1.4 mA, at 3–5-Hz stimulation conditions. Evidence was found to suggest that manipulating frequency at 1.2 mA moves the neural activity and haemodynamic response within this linear region of the coupling relationship. It is only when intensity and frequency are modulated outside this range that the neural activity and haemodynamic response are moved into the nonlinear extremes. The presence of nonlinearities in neurovascular coupling has implications for both the design and interpretation of imaging studies. If the relationship between neural activity and the haemodynamic response was found to be linear, accurate interpretation of imaging signals with respect to neural activity would be possible. In this study, the linear model provided the least good fit to the data (indicated by the largest chi-square value). The threshold model, which does not constrain the model to pass through the origin, provided a slightly better fit (a slightly smaller chi-square value). The intercept for the threshold model
Table 1 Evaluating the neurovascular coupling models
a b c v2 df * Critical v 2** P value
Linear constrained through 0
Linear with intercept
Power law
y = ax
y = ax + b
y = ax c
0.47 – – 48.79 30 43.77 0.017 (b0.05)
0.49 0.0718 – 46.05 29 42.56 0.023 (b0.05)
0.28 – 1.37 25.80 29 42.56 0.636 (N0.05)
The best-fit parameters are shown, along with the chi-square goodness of fit statistic for each of the models. * df = no. of data points no. of parameters in model. ** At the P = 0.05 level for the corresponding df.
was negative with respect to the y-axis, suggesting that a threshold level of neural activity must be exceeded before a haemodynamic response is reliably elicited, consistent with previous findings (Nielsen and Lauritzen, 2001; Sheth et al., 2004). The implication of this is that the absence of a haemodynamic response does not necessarily preclude the existence of neural activity. We note that the threshold found in the present study is small, however, and may represent a difference in the sensitivity of the methods employed to measure the haemodynamic response compared to that used to measure neural activity. The power law model predictions provided the best fit to the data, and it is concluded that this model is the most acceptable of those tested to describe the relationship between neural activity and the haemodynamic response, consistent with others (Devor et al., 2003; Sheth et al., 2004). The nature of this nonlinear relationship means that at the upper end of the scale, small changes in neural activity elicit disproportionately large increases in CBF, and at the lower end of the relationship, large changes in neural activity elicit only small changes in the CBF. Therefore, if researchers assume a linear coupling relationship, there is a danger of over estimating the amount of neural activity from haemodynamic signals if strong stimuli are employed, and a danger of under estimating the amount of neural activity from haemodynamic signals if weak stimuli are employed. Consequently, when designing imaging studies, it is advisable to choose stimuli that will elicit responses in the mid range of the relationship, where neurovascular coupling is approximately linear, as this will make subsequent interpretation of the data more simple. The present work uses the rat barrel cortex as a model. Recent work in humans has reported differences in the haemodynamic response across auditory, visual, and motor cortices (Birn et al., 2001; Soltysik et al., 2004). This highlights the need for regionspecific research into the neurohaemodynamic coupling relationship before noninvasive brain imaging techniques can fulfil their potential in allowing the accurate inference of neural activity from haemodynamic changes. The principal difference between brain regions with regard to haemodynamics is the baseline blood perfusion and volume (Rapoport et al., 1979). Whether neurovascular coupling is similar at different baseline perfusion rates is under investigation in our laboratory. In addition, the further complexities in neurovascular coupling for long duration stimuli (Ances et al., 2000) are currently being explored with multielectrode recording techniques (Martindale et al, in preparation).
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The important issue of the possible effect of anaesthesia on the neurovascular coupling relationship will be investigated in due course in the awake-rat preparation (Berwick et al., 2002; Martin et al., 2002).
Conclusion The magnitude of neural and haemodynamic responses within rat barrel cortex were modulated over a large range by varying the intensity and frequency of electrical whisker pad stimulation. Across this broad range, the relationship between neural activity and CBF is well described by a power law, consistent with the recent findings of Devor et al. (2003) and Sheth et al. (2004). Within the mid range of stimuli, the data is well approximated with a linear function. The use of such mid-range stimuli, producing neural and haemodynamic changes within this linear region, would facilitate the interpretation of imaging data.
Acknowledgments This work was supported by MRC project grant G0100538 (MJ and JB), NIH grant ROINSS445671 (JM), and a Sheffield University PhD studentship (NH-S) obtained with the help of Dr. Simon Whiteley. We gratefully acknowledge the Centre for Neural Communication Technology (University of Michigan) and the NIH grant that supports them (NIH NIBIB grant P41-RR09754) for the supply of, and assistance with, multichannel electrophysiology probes. The authors would also like to thank Mr. Chris Martin, Mr. Carlos Gias, and Drs. Ying Zheng and David Johnston for their input, and the technical staff of the Psychology Department; Marion Simkins, Natalie Walton, and Malcom Benn for their assistance with this work.
References 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, 921 – 930. 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, 670 – 679. Birn, R.M., Saad, Z.S., Bandettini, P.A., 2001. Spatial heterogeneity of the nonlinear dynamics in the FMRI BOLD response. NeuroImage 14, 817 – 826. Buxton, R.B., Wong, E.C., Frank, L.R., 1998. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn. Reson. Med. 39, 855 – 864. Caesar, K., Gold, L., Lauritzen, M., 2003. Context sensitivity of activitydependent increases in cerebral blood flow. Proc. Natl. Acad. Sci. U. S. A. 100, 4239 – 4244. Chapin, J.K., Lin, C.S., 1984. Mapping the body representation in the SI cortex of anesthetized and awake rats. J. Comp. Neurol. 229, 199 – 213. Cox, S.B., Woolsey, T.A., Rovainen, C.M., 1993. Localised 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, 899 – 913. Devor, A., Dunn, A.K., Andermann, M.L., Ulbert, I., Boas, D.A., Dale, A.M., 2003. Coupling of total hemoglobin concentration, oxygen-
573
ation, and neural activity in rat somatosensory cortex. Neuron 39, 353 – 359. Duong, T.Q., Kim, D.S., Ugurbil, K., Kim, S.G., 2001. Localized cerebral blood flow response at submillimeter columnar resolution. Proc. Natl. Acad. Sci. U. S.A. 98, 10904 – 10909. Friston, K.J., Mechelli, A., Turner, R., Price, C.J., 2000. Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. NeuroImage 12, 466 – 477. Golanov, E.V., Yamamoto, S., Reis, D.J., 1994. Spontaneous waves of cerebral blood flow associated with a pattern of electrocortical activity. Am. J. Physiol. 266, R204 – R214. Grubb, R.L., Raichle, M.E., Eichling, J.O., Ter-Pergossian, M.M., 1974. The effects of changes in PACO2 on cerebral blood volume, blood flow and vascular mean transit time. Stroke 5, 630 – 639. Heeger, D.J., Huk, A.C., Geisler, W.S., Albrecht, D.G., 2000. Spikes versus BOLD: what does neuroimaging tell us about neuronal activity? Nat. Neurosci. 3, 631 – 633. 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, 9403 – 9408. 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, 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, 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, 956 – 965. Kwong, K.K., Belliveau, J.W., Chesler, D.A., Goldberg, I.E., Weisskoff, R.M., Poncelet, B.P., Kennedy, D.N., Hoppel, B.E., Cohen, M.S., Turner, R., et al., 1992. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. U. S. A. 89, 5675 – 5679. Logothetis, N.K., 2003. MR imaging in the non-human primate: studies of function and of dynamic connectivity. Curr. Opin. Neurobiol. 13, 630 – 642. Logothetis, N.K., Wandell, B.A., 2004. Interpreting the BOLD signal. Annu. Rev. Physiol. 66, 735 – 769. Mandeville, J.B., Marota, J.J., Ayata, C., Zaharchuk, G., Moskowitz, M.A., Rosen, B.R., Weisskoff, R.M., 1999a. Evidence of a cerebrovascular post-arteriole windkessel with delayed compliance. J. Cereb. Blood Flow Metab. 19, 679 – 689. Mandeville, J.B., Marota, J.J.A., Ayata, C., Moskowitz, M.A., Weisskoff, R.M., Rosen, B., 1999b. MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation. Magn. Reson. Med. 42, 944 – 951. Marota, J.J., Ayata, C., Moskowitz, M.A., Weisskoff, R.M., Rosen, B.R., Mandeville, J.B., 1999. Investigation of the early response to rat forepaw stimulation. Magn. Reson. Med. 41, 247 – 252. 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, 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, 546 – 555. Mayhew, J., Johnston, D., Martindale, J., Jones, M., Berwick, J., Zheng, Y., 2001. Increased oxygen consumption following activation of brain: footnotes using spectroscopic analysis of neural activity in barrel cortex. NeuroImage 13, 975 – 987. Mitzdorf, U., 1985. Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol. Rev. 65, 37 – 100. Molgedey, L., Schuster, H.G., 1994. Separation of a mixture of
574
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independent signals using time delayed correlations. Phys. Rev. Lett. 72, 3634 – 3637. Nakagawa, H., Matsumoto, N., 2000. Current source density analysis of ON/OFF channels in the frog optic tectum. Prog. Neurobiol. 61, 1 – 44. Nakai, M., Maeda, M., 1999. Scopolamine-sensitive and resistant components of increase in cerebral cortical blood flow elicited by periaqueductal gray matter of rats. Neurosci. Lett. 270, 173 – 176. 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, 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, 221 – 228. Nicholson, C., Freeman, J.A., 1975. Theory of current source-density analysis and determination of conductivity tensor for anuran cerebellum. J. Neurophysiol. 38, 356 – 368. Nielsen, A., Lauritzen, M., 2001. Coupling and uncoupling of activitydependent increases of neuronal activity and blood flow in rat somatosensory cortex. J. Physiol. 533, 773 – 785. Nilsson, G.E., 1984. Signal processor for laser Doppler tissue flowmeters. Med. Biol. Eng. Comput. 22, 343 – 348. Ogawa, S., Tank, D.W., Menon, R., Ellerman, J.M., Kim, S., Merkle, H., Ugurbil, K., 1992. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. U. S. A. 89, 5951 – 5955. Ogawa, S., Lee, T.M., Stepnoski, R., Chen, W., Zhu, X.H., Ugurbil, K., 2000. An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds. Proc. Natl. Acad. Sci. U. S. A. 97, 11026 – 11031. Patel, U., 1983. Non-random distribution of blood vessels in the posterior region of the rat somatosensory cortex. Brain Res. 289, 65 – 70. Rapoport, S.I., Ohno, K., Pettigrew, K.D., 1979. Drug entry into the brain. Brain Res. 172, 354 – 359. Rappelsberger, P., Pockberger, H., Petsche, H., 1981. Current source
density analysis: methods and application to simultaneously recorded field potentials of the rabbit’s visual cortex. Pfluegers Arch. 389, 159 – 170. Schwartz, C.E., Wright, C.I., Shin, L.M., Kagan, J., Rauch, S.L., 2003. Inhibited and uninhibited infants bgrown upQ: adult amygdalar response to novelty. Science 300, 1952 – 1953. 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, 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, 347 – 355. Silva, A.C., Koretsky, A.P., 2002. Laminar specificity of functional MRI onset times during somatosensory stimulation in rat. Proc. Natl. Acad. Sci. U. S. A. 99, 15182 – 15187. Silva, A.C., Lee, S.-P., Iadecola, C., Kim, S.-G., 2000. Early temporal characteristics of CBF and deoxyhemoglobin changes during somatosensory stimulation. J. Cereb. Blood Flow Metab. 20, 201 – 206. Simons, D.J., 1985. Temporal and spatial integration in the rat SI vibrissa cortex. J. Neurophysiol. 54, 615 – 635. 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, 10765 – 10770. Soltysik, D.A., Peck, K.K., White, K.D., Crosson, B., Briggs, R.W., 2004. Comparison of hemodynamic response nonlinearity across primary cortical areas. NeuroImage 22, 1117 – 1127. Zheng, Y., Johnston, D., Berwick, J., Mayhew, J., 2001. Signal source separation in the analysis of neural activity in brain. NeuroImage 13, 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, 617 – 637.
Further nonlinearities in neurovascular coupling in rodent barrel cortex Nicola Hewson-Stoate,* Myles Jones, John Martindale, Jason Berwick, and John Mayhew Neural Imaging Research Unit, Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, UK Received 24 June 2004; revised 30 July 2004; accepted 30 August 2004 Available online 23 November 2004 An essential prerequisite for the accurate interpretation of noninvasive functional brain imaging techniques, such as blood oxygen level dependent (BOLD) fMRI, is a thorough understanding of the coupling relationship between neural activity and the haemodynamic response. The current study investigates this relationship using rat barrel cortex as a model. Neural input was measured by applying current source density (CSD) analysis to multi-laminar field potentials to remove ambiguities regarding the origin of the signal inherent in single electrode recordings. Changes in cerebral blood flow (CBF) were recorded with a laser Doppler flowmetry probe. The magnitude of neural and CBF responses were modulated over a large range by altering both the intensity and frequency of electrical whisker pad stimulation. Consistent with previous findings [Devor, A., et al., 2003. Neuron 39, 353–359; Sheth, S.A., et al., 2004. Neuron 42, 347–355] a power law function well described the relationship between neural activity and haemodynamics. Despite the nonlinearity of the coupling over the whole data set, the relationship was very well approximated by a linear function over mid-range stimuli. Altering the frequency of stimulation at 1.2 mA shifted the neural activity and corresponding haemodynamic response along this linear region, reconciling recent reports of a nonlinear relationship [Devor, A., et al., 2003. Neuron 39, 353–359; Jones, M., et al., 2004. NeuroImage 22, 956–965; Sheth, S.A., et al., 2004. Neuron 42, 347–355] with previous work that found a linear coupling relationship when altering stimulation frequency [Martindale, J., et al., 2003. J. Cereb. Blood Flow Metab. 23, 546– 555; Ngai, A.C., et al., 1999. Brain Res. 837, 221–228; Sheth, S., et al., 2003. NeuroImage 19, 884–894]. Using stimuli within this linear range in imaging studies would simplify the interpretation of findings. D 2004 Elsevier Inc. All rights reserved. Keywords: Nonlinearities; Neurovascular coupling; Barrel cortex; Rat
Introduction Many noninvasive functional brain imaging techniques, such as BOLD fMRI (Kwong et al., 1992; Ogawa et al., 1992), infer neural * Corresponding author. Fax: +44 114 276 6515. E-mail address: [email protected] (N. Hewson-Stoate). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2004.08.040
activity from changes in haemodynamics. It is therefore essential to have a thorough understanding of neurovascular coupling for the optimal design and subsequent interpretation of studies using such imaging methodologies. The BOLD signal results from changes in the concentration of deoxy-haemoglobin, and is based on the balance between the local changes in blood flow (CBF), blood volume (CBV), and oxygen consumption (CMRO2), which accompany neural activity. The relationship between changes in CBF and CBV has been described (Grubb et al., 1974; Jones et al., 2001, 2002, Mandeville et al., 1999a), as has the relationship between CBF and CMRO2 (Hoge et al., 1999; Jones et al., 2001; Mayhew et al., 2001). It is now necessary to quantitatively characterise the coupling between neural activity and CBF, to advance biophysical models of the BOLD signal (Friston et al., 2000; Zheng et al., 2002). In addition, the interpretation of perfusion-based fMRI, which is able to provide information at the spatial resolution of cortical columns (Duong et al., 2001), relies upon an understanding of the coupling between neural activity and CBF. If changes in CBF are not directly proportional to changes in neural activity, inaccurate inferences may be made on the basis of stimulus-induced haemodynamic changes (Schwartz et al., 2003). There is discrepancy within recent work investigating the neuro-haemodynamic coupling relationship, both within and between research groups. When manipulating stimulation frequency, a linear relationship has been reported (Martindale et al., 2003; Ngai et al., 1999; Sheth et al., 2003). However, studies in which stimulus intensity is also modulated report a nonlinear neurovascular coupling relationship (Devor et al., 2003; Jones et al., 2004; Sheth et al., 2004). Nemoto et al. (2004) suggest that the linearity of the coupling relationship is dependent on the stimulation parameter being manipulated, and report significant differences in the coupling relationship when intensity of stimulation is manipulated compared to when the number of stimuli is manipulated. In an attempt to reconcile this discrepancy, we proposed that within a certain stimulus range, the relationship can be well approximated by a linear function, and that altering stimulation frequency serves to modulate the amount of integrated neural activity along this linear portion of the neurohaemodynamic relationship. This hypothesis was explicitly tested
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in the current study, which extends our previous work (Jones et al., 2004; Martindale et al., 2003) by systematically altering both stimulation frequency and stimulation intensity, therefore encompassing a more complete range of physiological stimuli within the rat barrel system. A 1.2-mA stimulus in the anaesthetised rat is believed to elicit responses typical of those in the unanaesthetised rat following stimulation at 0.2 mA, which produces whisker movement that appears to be just above the threshold of behavioral relevance (C. Martin, PhD Thesis). The range of stimuli used in the current study is therefore believed to be relevant to the awake behaving rat. When investigating the coupling relationship between neural activity and haemodynamics, it is first necessary to discern the most appropriate metric of neural activity to take. Previous studies have examined the relationship with both local field potential (LFP) recordings (Sheth et al., 2003, 2004) and spiking activity (Heeger et al., 2000; Smith et al., 2002). Despite the dfortuitous correlationT (Logothetis, 2003) between spiking output and haemodynamics, both CBF and BOLD responses may correlate with LFPs even in the absence of spiking output (Caesar et al., 2003; Logothetis and Wandell, 2004). Furthermore, we suggested that the greater spatial concordance of synaptic input with CBF compared to spiking output implies that CBF more accurately reflects metabolic demand resulting from neural input to the cortex, as opposed to the spiking output of the region (Jones et al., 2004). LFPs, which are commonly taken as a measure of neural input to a region (Nielsen and Lauritzen, 2001; Sheth et al., 2004), are generated by current sinks (active process of ions flowing into a cell to generate excitatory post-synaptic potentials), and current sources (the passive outflow of ions), which summate over the region of interest, rendering the interpretation of data from a single electrode inherently ambiguous. The measurement of LFPs at many depths throughout the cortex enables the application of CSD analysis, which disambiguates the neural signal by revealing the location and magnitude of the underlying current sinks and sources (Mitzdorf, 1985; Nakagawa and Matsumoto, 2000; Nicholson and Freeman, 1975). We find that in barrel cortex the results of CSD analysis are dominated by a prominent active current sink (~450 Am below the cortical surface), which corresponds to the site of thalamic input, and therefore believe that the magnitude of this sink is the optimal metric to take for the investigation of haemodynamic coupling. In the current study, the rat barrel cortex was used as a model to investigate the neural–CBF relationship following electrical stimulation of the whisker pad at several intensities (0–1.6 mA, in 0.2 mA steps) and frequencies (1, 3, 5, 7 Hz). This animal model is well suited to such an investigation because somatosensory stimulation produces spatially discrete neural and haemodynamic responses due to the highly ordered spatial topography (Chapin and Lin, 1984) and well-defined microvasculature (Cox et al., 1993; Patel, 1983). The magnitude of the measured neural input was compared with CBF data from a laser Doppler flowmetry probe positioned adjacent to the electrode. Consistent with previous work (Devor et al., 2003; Sheth et al., 2004), a nonlinear relationship was found between neural activity and the haemodynamic response, which is well described by a power law function ( y = ax c). As hypothesised (Jones et al., 2004), there is a portion of this relationship that can be well explained with a linear function, and at a mid-range stimulus intensity (1.2 mA), altering stimulus frequency shifts data points along this linear portion of the neural
activity–CBF relationship, reconciling this work with previous studies which demonstrate a linear neurovascular coupling (Martindale et al., 2003; Ngai et al., 1999; Sheth et al., 2003).
Materials and methods Experimental overview In anaesthetised rats, the skull overlying the barrel cortex was thinned to translucency, and the barrels were located using single wavelength optical imaging following electrical stimulation of the whisker pad. A small hole was made in the skull and dura within the active barrel region to allow the insertion of a 16-channel multielectrode orthogonal to the cortical surface. A laser Doppler flowmetry probe was positioned adjacent to the electrode. Electrophysiology and CBF data were collected while the whisker pad was electrically stimulated at different frequencies (3, 5, and 7 Hz) and intensities (0–1.6 mA in 0.2-mA steps). The 1-Hz data is taken from Jones et al. (2004). Animal preparation Female hooded Lister rats (total n = 15, n = z6 for each stimulus condition) weighing between 250 and 400 g were kept in a 12-h dark/light cycle environment with food and water ad libitum. Animals were anaesthetised with urethane (1.25 g/kg i.p.), and depth of anaesthesia was monitored periodically by assessing the toe pinch withdrawal reflex. Rectal temperature was maintained at 378C throughout surgical and experimental procedures using a homeothermic blanket (Harvard). A tracheotomy was performed to allow artificial ventilation and the monitoring of end-tidal CO2. Ventilation parameters were adjusted for individual animals to maintain blood-gas measurements and end-tidal CO2 within physiological limits. The femoral artery and vein were cannulated to allow the measurement of mean arterial blood pressure and infusion of drugs, respectively. Phenylephrine (0.13–0.26 mg/h) was infused to maintain blood pressure within physiological limits (MABP, 100–110 mm Hg) (Golanov et al., 1994; Nakai and Maeda, 1999). Animals were placed in a stereotaxic frame (Kopf Instruments), and the skull overlying the right somatosensory cortex thinned to translucency using a dental drill. Saline was used to cool the area throughout. A plastic dwellT was secured around the thinned skull with dental cement, and filled with saline (378C) to reduce specularities from the skull surface. All procedures were carried out in accordance with Home Office regulations. Localisation of barrel cortex using single wavelength optical imaging Somatosensory cortex was monochromatically illuminated (590 nm, narrow bandwidth interference filter, F5 nm), and images were recorded using a 12-bit CCD camera (SMD 1M60). Barrel cortex was activated by electrically stimulating the whisker pad (1.2 mA, 5 Hz, 1 s). In each trial, data were recorded for 23 s. Stimulus onset was at 8 s, and there was a 25-s inter-stimulus interval. Thirty trials were collected at a sampling frequency of 15 Hz. Images were analysed using a modified version of a signal source separation algorithm (Molgedey and Schuster, 1994) as previously described (Zheng et al., 2001). This procedure has been
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shown on numerous occasions to produce spatially discrete activations 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 electrophysiology and LDF probes (Fig. 1). Electrophysiology To measure neural responses throughout the depth of the cortex, 16 electrodes linearly mounted on a single probe (100-Am spacing, area of each site 177 Am2, impedance 1.5–2.7 MV, probe width: 33-Am tip, 123 Am at uppermost electrode; CNCT, University of Michigan) were inserted normal to the cortical surface. To allow the insertion of this probe, a small hole was made in the skull and dura overlying the active region as located by single wavelength optical imaging. The probe was inserted under micromanipulator control to a depth of 1550 Am (uppermost electrode approximately 50 Am from cortical surface). The electrodes were coupled to a data acquisition device (Medusa Bioamp, TDT, FL) with a custom written Matlabk (the Mathworks Inc, USA) interface. Recordings were averaged for each experimental session (20 trials in each condition), with stimulus onset djitteredT within a 20-ms window to minimise any effects of 50 Hz mains noise. The resultant evoked field potentials were sampled at 6 kHz with 12-bit resolution. The data were subjected to current source density analysis (CSD) as described previously (Jones et al., 2004; Martindale et al., 2003, see also Mitzdorf, 1985; Rappelsberger et al., 1981 for more general discussions). Assuming homogeneity and anisotropy of the rat barrel cortex, a 1-D CSD equation can be applied to depth-resolved field potentials in order to estimate underlying current sinks and sources. The data were subsequently interpo-
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lated and filtered to give an apparent spatial resolution of 50 Am (Fig. 2). The mean of the time series through the current sink was obtained for each data set, and normalised by dividing the time series by the amplitude of the response to the first dpulseT in the stimulus train for the 1.2-mA condition. This removes the influence of inter-animal variability. The absolute minima of the normalised time series following each pulse in the stimulus train was taken, and averaged across same condition trials in each animal, before the data for all the animals were collated, and averaged for each experimental condition. Laser Doppler flowmetry (LDF) To enable concurrent measurement of cerebral blood flow (CBF), an LDF probe (Peri-Flux 5010, Perimed, Stockholm, 780 nm illumination, fiber separation 0.25 mm) was positioned over the active area normal to the cortical surface, adjacent to the electrode. The maximum distance between the electrode and the LDF probe was 100 Am. Visual inspection of the cortical surface allowed large vessels to be avoiding during LDF probe placement. The signal from the LDF probe was analysed by an LDF spectrometer, which includes a proprietary linearisation algorithm (Nilsson, 1984) to reduce errors caused by changes in blood volume. The LDF signal was digitised continuously using a 1401plus (CED Ltd, UK). The time constant of the LDF recordings was 0.2 s, and the bandwidth was 12 kHz. Data were trial averaged within animals for each stimulus condition recorded (z40 trials per stimulus condition). All CBF data were then normalised by the peak CBF response elicited by an additional stimulus condition (1.2 mA, 1 Hz, 3 s) to both compensate for inter-animal variability and facilitate comparison between the present study and our previous work (Jones et al., 2004). The
Fig. 1. Single-wavelength imaging of barrel cortex. (A) Thin cranial window under 590-nm illumination. (B) Region of activation following electrical stimulation of the contralateral whisker pad (1.2 mA, 5 Hz, 1 s). These maps were used to guide placement of the 16-channel silicon electrode (C), and the laser Doppler flowmetry probe (not shown).
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Fig. 2. Multilaminar analysis of local field potentials. (A) Local field potential recordings in rodent barrel cortex from a 16-channel multielectrode from a representative animal following electrical stimulation of the whisker pad at 1.2 mA (first pulse in the stimulus train). (B) The spatial distribution of the current sinks and sources relative to cortical depth following current source density (CSD) analysis on the same data set.
normalised CBF responses were then averaged across animals, with the peak response from each experimental condition taken for comparison with the neural data. Stimulus presentation and paradigms All stimulus presentation was controlled through a 1401plus (CED Ltd) running custom-written code with stimulus onset time locked to the electrophysiology recording unit. Electrical stimulation of the entire whisker pad was delivered via stainless-steel electrodes (Plastics One Inc) inserted subcutaneously in a rostral– caudal direction. With the exception of the previously reported 1-Hz data (Jones et al., 2004), stimulus duration was 2 s. The intensity of stimulation varied between 0 and 1.6 mA in 0.2-mA steps. Increasing the intensity of the stimulation resulted in a larger rostral–caudal deflection of the whiskers. No changes in MABP, heart rate, or PCO2 were observed at any of the stimulus intensities, suggesting that CBF values were not contaminated by systemic physiological changes. Each experimental run consisted of 20 trials at each intensity, randomly interleaved, with a 15-s inter-stimulus interval to allow the haemodynamic response to return to baseline between successive trials. The stimulus paradigm was presented at either 3, 5, or 7 Hz and in the majority of cases z40 trials of data were collected at each intensity at two different frequencies for an animal.
Results Neural responses and the resulting changes in cerebral blood flow were measured in rat barrel cortex following electrical
stimulation of the whisker pad. The amount of elicited neural activity was modulated by systematically varying both the intensity (0–1.6 mA in 0.2-mA steps) and frequency (1, 3, 5, 7 Hz) of whisker stimulation. A probe consisting of 16 linearly mounted electrodes inserted normal to the cortical surface was used to measure local field potentials throughout the cortical depth (Fig. 1). Current source density analysis of the local field potentials recorded throughout the cortical lamina revealed the location of current sinks and sources (Fig. 2). A prominent current sink was consistently found at ~450 Am below the cortical surface and most likely reflects the input from thalamus. An example of the time series through this dprimaryT current sink is shown in Figs. 3A–D. Changes in CBF were measured by a laser Doppler flowmetry probe positioned b1 mm from the cortical surface (to which the probe was orthogonal) adjacent to the electrode. The neural response occurs on a millisecond time scale, resulting in an individual response to each pulse in the stimulus train. Due to the intrinsic difference in temporal resolution, responses to individual pulses were not resolvable in the CBF data, where a single response is observed for the entire stimulus train (Fig. 3G). To investigate the relationship between these two processes, a single metric was taken for neural activity and CBF. For the neural activity, the absolute value of the minimum of the normalized time series through the current sink following each pulse was calculated for each stimulus condition. Both the mean and sum of the neural activity were determined from this measure. The peak of the normalised CBF response was taken as a measure of the haemodynamic response to compare with the neural activity. The 1-Hz data is taken from Jones et al. (2004), and is shown again here for comparison with the higher frequency data. In all data plots, error bars denote standard errors.
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Fig. 3. Neuronal and haemodynamic responses to stimuli of varying frequency. (A–D) Normalised neural responses to 1.2 mA stimuli from a representative animal. With increasing stimulation frequency there is increased attenuation of the response following the initial pulse. (E, F) Mean and summed neural activity, respectively, averaged across all subjects. Mean neural activity decreases with increasing stimulation frequency, whereas summed neural activity increases up to 5 Hz, and subsequently decreases. (G, H) CBF responses to stimulation of varying frequency averaged across all subjects. Response size increases up to 5 Hz, and decreases at 7 Hz.
Effect of manipulating stimulus parameters on neural activity and CBF To demonstrate the effect of increasing stimulus frequency, the normalised time series through the primary current sink (1, 3, 5, 7 Hz at 1.2 mA) is shown from a representative animal (Figs. 3A–D). At 1 Hz, the pulses in the stimulus train are equivalent in size. As stimulation frequency increases to 3 Hz and above, attenuation of the second and subsequent pulses is observed. Previous work has shown that stimuli separated by less than 600 ms results in such attenuation of responses (Martindale et al., 2003; Ogawa et al., 2000), likely to be due to inhibitory interactions (Simons, 1985).
At 3 Hz and above, stimuli are separated by less than 600 ms; hence the observed attenuation that results in a decrease of mean neural activity with increasing stimulus frequency (Fig. 3E). At 1.2 mA, summed neural activity increases up to 5 Hz, and subsequently decreases at 7 Hz, as does the resulting CBF response (Figs. 3F–H), consistent with previous findings (Martindale et al., 2003; Ngai et al., 1999; Sheth et al., 2004). At a stimulation frequency of 5 Hz, increasing stimulation intensity from 0 to 1.6 mA results in a monotonic increase in both mean and summed neural activity (Figs. 4A,B) and in the magnitudes of the CBF responses (Figs. 4C,D). This monotonic increase with regard to stimulus intensity is also apparent at the
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Fig. 4. Neuronal and haemodynamic response to stimuli of varying intensity. (A, B) Mean and summed neural activity averaged over all animals to a 5-Hz stimulus of varying intensity (0–1.6 mA in 0.2-mA steps). Both mean and summed neural responses increase monotonically with increasing stimulus intensity. (C) Mean time series of the CBF response to the 5-Hz stimulus averaged over all the animals for each intensity. (D) Peak CBF response similarly increases monotonically with increasing stimulus intensity.
other frequencies considered (1, 3, 7 Hz), although at 1 Hz the neural and CBF responses begin to plateau at the top of the intensity range, a trend which is not observed in the higher frequency data (Figs. 5A–C). The observation that increasing stimulus frequency from 1 to 7 Hz results in a decrease in mean neural activity (Fig. 3E) is evident at all stimulus intensities (Fig. 5A). However, the trend for summed neural activity to increase up to 5 Hz, and decrease at 7 Hz (Fig. 3F) is only apparent above a stimulus intensity of 0.8 mA. Below this stimulation intensity, frequency has little effect of summed neural activity. The CBF responses follow a very similar pattern to that of summed neural activity (Figs. 5B,C). Relationship between neural activity and CBF The peak of the normalised CBF response was plotted against the summed normalised neural activity (Fig. 6). As summed neural activity increases, so does the CBF response. The ability of a linear and two nonlinear models to describe the data was evaluated. The linear model was constrained to pass through zero ( y = ax, dlinearT). The first nonlinear model did not have this constraint, therefore allowing the indication of a threshold effect ( y = ax + b, dthresholdT, e.g., Nielsen and Lauritzen, 2001). A positive x-
intercept (and equivalently, a negative y-intercept) would indicate the presence of a threshold of neural activity, which must be exceeded before a haemodynamic response is elicited. The final model evaluated was a power law ( y = ax c , dpower lawT). This relationship was found by Devor et al. (2003), and also well described the data collected by Sheth et al. (2004). The three models were optimised using a nonlinear least-squares algorithm (Levenberg Marquardt algorithm, MATLABk function dlsqnonlinT) and compared to the original data (Fig. 7). A chi-square dgoodness of fitT test was used to assess the quality of fit for each model, using the summed neural activity as the independent variable. As a rule of thumb, the greater the difference between the data and the model predictions, the larger the chisquare value. From Table 1, it can be seen that the linear and threshold models have the largest chi-square values (48.8, df 30; and 46.1, df 29, respectively), and the power law model has the smallest chi-square (25.8, df 29). Thus, the power law provides the best fit to the data. The critical chi-square values corresponding to the P = 0.05 level for the appropriate degrees of freedom are shown in Table 1. It can be seen that the chi-square values for the linear and threshold models are greater than these critical values, hence significantly different from the data. In contrast, the chi-square value for the power law model is much smaller than the critical
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Fig. 5. Haemodynamic and neural responses to stimuli of differing intensity (0–1.6 mA) and frequency (1–7 Hz). (A) Mean neural activity increases monotonically with increasing stimulus intensity, and decreases with increasing stimulation frequency. (B) Summed neural activity increases with increasing stimulus intensity, and above 0.8 mA, increases with increasing stimulation frequency up until 5 Hz. At 7 Hz, there is a decrease in summed neural activity. (C) CBF response increases monotonically with stimulation intensity and increases with stimulation frequency up until 5 Hz, after which the CBF response decreases.
value with a P value of 0.6. Clearly, this model provides the most acceptable fit to the data, consistent with the findings of Sheth et al. (2004).
and CBF both increase, and at 7 Hz both neural activity and CBF are shifted back down, due to the increased inhibition of pulses at the higher frequency being akin to decreasing stimulus intensity.
Reconciling the nonlinear relationship with previous research Over the large range of physiological stimuli presented in the current study, the relationship between neural activity and the haemodynamic response was best fit with a power law function. However, inspection of the data indicates the presence of a linear relationship within the mid-range of stimuli presented (0.6–1.4 mA, 3, 5 Hz, chi-square 0.91, df = 9, P = 0.99). Further analysis revealed that at an intensity of 1.2 mA, increasing the stimulation frequency shifts the neural activity along this linear portion of the neural–CBF relationship. From 1 to 5 Hz, the neural activity
Fig. 6. Coupling between neural activity and the haemodynamic response. Normalised CBF responses are plotted against normalised R neural activity for the mean across animals for each stimulation condition. CBF increases monotonically with increasing R neural activity.
Discussion Through concurrent electrophysiology and recording of changes in CBF in rat barrel cortex following somatosensory stimulation, the present study confirms and extends previous findings of a nonlinear power law relationship between neural activity and the haemodynamic response (Devor et al., 2003; Sheth et al., 2004). The stimulation parameters used are comparable to those used in fMRI experiments (Mandeville et al., 1999b; Marota et al., 1999; Ogawa et al., 2000; Silva and Koretsky, 2002; Silva et al., 2000), thereby rendering the findings relevant to models of BOLD fMRI. Although the findings of a power law coupling relationship in the current study do not directly inform models of metabolic-flow coupling (Buxton et al., 1998; Mandeville et al., 1999a), they may be used in the future to evaluate the validity of such models. The use of current source density analysis of LFPs removes the ambiguities inherent in single electrode recordings. As a result, it is possible to confirm that the nonlinear relationship is between CBF and neural input (as measured by activity within the dprimaryT current sink). The nature of the nonlinearities in the relationship between neural activity and haemodynamics differ from those formerly reported by our laboratory. We previously altered the intensity of stimulation (1 Hz, 3 s, 0–1.6 mA in steps of 0.2 mA), and measured neural activity and CBF simultaneously using methods identical to those presented here. With this range of stimuli, we found an inverse sigmoid relationship to well describe the neurovascular coupling relationship (Jones et al., 2004). This discrepancy highlights the need for many stimulation conditions to elicit a large range of neural and haemodynamic responses to ensure that the most accurate representation of the relationship is determined. By looking at the effect of altering stimulus frequency at a midrange intensity (1.2 mA), we have been able to reconcile the observed nonlinear relationship with findings of a linear relation-
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Fig. 7. Neurovascular coupling models. Comparison of the model fits to the data; y = ax (linear, A) y = ax + b (linear with no constraint, B), and y = ax c (power law, C).
ship in previous work in which stimulation frequency was altered in order to modulate neural activity and the corresponding haemodynamic response (Martindale et al., 2003; Nemoto et al., 2004; Ngai et al., 1999; Sheth et al., 2003). We hypothesised that there is a linear portion of the neural–CBF relationship, and that at a mid-range stimulus intensity, frequency-induced changes in neural activity would shift the neural data along this linear region of the neural–CBF relationship (Jones et al., 2004). Observation of the data indicates the presence of such a linear portion of the relationship when taking the data from the 0.6–1.4 mA, at 3–5-Hz stimulation conditions. Evidence was found to suggest that manipulating frequency at 1.2 mA moves the neural activity and haemodynamic response within this linear region of the coupling relationship. It is only when intensity and frequency are modulated outside this range that the neural activity and haemodynamic response are moved into the nonlinear extremes. The presence of nonlinearities in neurovascular coupling has implications for both the design and interpretation of imaging studies. If the relationship between neural activity and the haemodynamic response was found to be linear, accurate interpretation of imaging signals with respect to neural activity would be possible. In this study, the linear model provided the least good fit to the data (indicated by the largest chi-square value). The threshold model, which does not constrain the model to pass through the origin, provided a slightly better fit (a slightly smaller chi-square value). The intercept for the threshold model
Table 1 Evaluating the neurovascular coupling models
a b c v2 df * Critical v 2** P value
Linear constrained through 0
Linear with intercept
Power law
y = ax
y = ax + b
y = ax c
0.47 – – 48.79 30 43.77 0.017 (b0.05)
0.49 0.0718 – 46.05 29 42.56 0.023 (b0.05)
0.28 – 1.37 25.80 29 42.56 0.636 (N0.05)
The best-fit parameters are shown, along with the chi-square goodness of fit statistic for each of the models. * df = no. of data points no. of parameters in model. ** At the P = 0.05 level for the corresponding df.
was negative with respect to the y-axis, suggesting that a threshold level of neural activity must be exceeded before a haemodynamic response is reliably elicited, consistent with previous findings (Nielsen and Lauritzen, 2001; Sheth et al., 2004). The implication of this is that the absence of a haemodynamic response does not necessarily preclude the existence of neural activity. We note that the threshold found in the present study is small, however, and may represent a difference in the sensitivity of the methods employed to measure the haemodynamic response compared to that used to measure neural activity. The power law model predictions provided the best fit to the data, and it is concluded that this model is the most acceptable of those tested to describe the relationship between neural activity and the haemodynamic response, consistent with others (Devor et al., 2003; Sheth et al., 2004). The nature of this nonlinear relationship means that at the upper end of the scale, small changes in neural activity elicit disproportionately large increases in CBF, and at the lower end of the relationship, large changes in neural activity elicit only small changes in the CBF. Therefore, if researchers assume a linear coupling relationship, there is a danger of over estimating the amount of neural activity from haemodynamic signals if strong stimuli are employed, and a danger of under estimating the amount of neural activity from haemodynamic signals if weak stimuli are employed. Consequently, when designing imaging studies, it is advisable to choose stimuli that will elicit responses in the mid range of the relationship, where neurovascular coupling is approximately linear, as this will make subsequent interpretation of the data more simple. The present work uses the rat barrel cortex as a model. Recent work in humans has reported differences in the haemodynamic response across auditory, visual, and motor cortices (Birn et al., 2001; Soltysik et al., 2004). This highlights the need for regionspecific research into the neurohaemodynamic coupling relationship before noninvasive brain imaging techniques can fulfil their potential in allowing the accurate inference of neural activity from haemodynamic changes. The principal difference between brain regions with regard to haemodynamics is the baseline blood perfusion and volume (Rapoport et al., 1979). Whether neurovascular coupling is similar at different baseline perfusion rates is under investigation in our laboratory. In addition, the further complexities in neurovascular coupling for long duration stimuli (Ances et al., 2000) are currently being explored with multielectrode recording techniques (Martindale et al, in preparation).
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The important issue of the possible effect of anaesthesia on the neurovascular coupling relationship will be investigated in due course in the awake-rat preparation (Berwick et al., 2002; Martin et al., 2002).
Conclusion The magnitude of neural and haemodynamic responses within rat barrel cortex were modulated over a large range by varying the intensity and frequency of electrical whisker pad stimulation. Across this broad range, the relationship between neural activity and CBF is well described by a power law, consistent with the recent findings of Devor et al. (2003) and Sheth et al. (2004). Within the mid range of stimuli, the data is well approximated with a linear function. The use of such mid-range stimuli, producing neural and haemodynamic changes within this linear region, would facilitate the interpretation of imaging data.
Acknowledgments This work was supported by MRC project grant G0100538 (MJ and JB), NIH grant ROINSS445671 (JM), and a Sheffield University PhD studentship (NH-S) obtained with the help of Dr. Simon Whiteley. We gratefully acknowledge the Centre for Neural Communication Technology (University of Michigan) and the NIH grant that supports them (NIH NIBIB grant P41-RR09754) for the supply of, and assistance with, multichannel electrophysiology probes. The authors would also like to thank Mr. Chris Martin, Mr. Carlos Gias, and Drs. Ying Zheng and David Johnston for their input, and the technical staff of the Psychology Department; Marion Simkins, Natalie Walton, and Malcom Benn for their assistance with this work.
References 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, 921 – 930. 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, 670 – 679. Birn, R.M., Saad, Z.S., Bandettini, P.A., 2001. Spatial heterogeneity of the nonlinear dynamics in the FMRI BOLD response. NeuroImage 14, 817 – 826. Buxton, R.B., Wong, E.C., Frank, L.R., 1998. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn. Reson. Med. 39, 855 – 864. Caesar, K., Gold, L., Lauritzen, M., 2003. Context sensitivity of activitydependent increases in cerebral blood flow. Proc. Natl. Acad. Sci. U. S. A. 100, 4239 – 4244. Chapin, J.K., Lin, C.S., 1984. Mapping the body representation in the SI cortex of anesthetized and awake rats. J. Comp. Neurol. 229, 199 – 213. Cox, S.B., Woolsey, T.A., Rovainen, C.M., 1993. Localised 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, 899 – 913. Devor, A., Dunn, A.K., Andermann, M.L., Ulbert, I., Boas, D.A., Dale, A.M., 2003. Coupling of total hemoglobin concentration, oxygen-
573
ation, and neural activity in rat somatosensory cortex. Neuron 39, 353 – 359. Duong, T.Q., Kim, D.S., Ugurbil, K., Kim, S.G., 2001. Localized cerebral blood flow response at submillimeter columnar resolution. Proc. Natl. Acad. Sci. U. S.A. 98, 10904 – 10909. Friston, K.J., Mechelli, A., Turner, R., Price, C.J., 2000. Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. NeuroImage 12, 466 – 477. Golanov, E.V., Yamamoto, S., Reis, D.J., 1994. Spontaneous waves of cerebral blood flow associated with a pattern of electrocortical activity. Am. J. Physiol. 266, R204 – R214. Grubb, R.L., Raichle, M.E., Eichling, J.O., Ter-Pergossian, M.M., 1974. The effects of changes in PACO2 on cerebral blood volume, blood flow and vascular mean transit time. Stroke 5, 630 – 639. Heeger, D.J., Huk, A.C., Geisler, W.S., Albrecht, D.G., 2000. Spikes versus BOLD: what does neuroimaging tell us about neuronal activity? Nat. Neurosci. 3, 631 – 633. 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, 9403 – 9408. 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, 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, 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, 956 – 965. Kwong, K.K., Belliveau, J.W., Chesler, D.A., Goldberg, I.E., Weisskoff, R.M., Poncelet, B.P., Kennedy, D.N., Hoppel, B.E., Cohen, M.S., Turner, R., et al., 1992. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. U. S. A. 89, 5675 – 5679. Logothetis, N.K., 2003. MR imaging in the non-human primate: studies of function and of dynamic connectivity. Curr. Opin. Neurobiol. 13, 630 – 642. Logothetis, N.K., Wandell, B.A., 2004. Interpreting the BOLD signal. Annu. Rev. Physiol. 66, 735 – 769. Mandeville, J.B., Marota, J.J., Ayata, C., Zaharchuk, G., Moskowitz, M.A., Rosen, B.R., Weisskoff, R.M., 1999a. Evidence of a cerebrovascular post-arteriole windkessel with delayed compliance. J. Cereb. Blood Flow Metab. 19, 679 – 689. Mandeville, J.B., Marota, J.J.A., Ayata, C., Moskowitz, M.A., Weisskoff, R.M., Rosen, B., 1999b. MRI measurement of the temporal evolution of relative CMRO2 during rat forepaw stimulation. Magn. Reson. Med. 42, 944 – 951. Marota, J.J., Ayata, C., Moskowitz, M.A., Weisskoff, R.M., Rosen, B.R., Mandeville, J.B., 1999. Investigation of the early response to rat forepaw stimulation. Magn. Reson. Med. 41, 247 – 252. 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, 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, 546 – 555. Mayhew, J., Johnston, D., Martindale, J., Jones, M., Berwick, J., Zheng, Y., 2001. Increased oxygen consumption following activation of brain: footnotes using spectroscopic analysis of neural activity in barrel cortex. NeuroImage 13, 975 – 987. Mitzdorf, U., 1985. Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena. Physiol. Rev. 65, 37 – 100. Molgedey, L., Schuster, H.G., 1994. Separation of a mixture of
574
N. Hewson-Stoate et al. / NeuroImage 24 (2005) 565–574
independent signals using time delayed correlations. Phys. Rev. Lett. 72, 3634 – 3637. Nakagawa, H., Matsumoto, N., 2000. Current source density analysis of ON/OFF channels in the frog optic tectum. Prog. Neurobiol. 61, 1 – 44. Nakai, M., Maeda, M., 1999. Scopolamine-sensitive and resistant components of increase in cerebral cortical blood flow elicited by periaqueductal gray matter of rats. Neurosci. Lett. 270, 173 – 176. 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, 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, 221 – 228. Nicholson, C., Freeman, J.A., 1975. Theory of current source-density analysis and determination of conductivity tensor for anuran cerebellum. J. Neurophysiol. 38, 356 – 368. Nielsen, A., Lauritzen, M., 2001. Coupling and uncoupling of activitydependent increases of neuronal activity and blood flow in rat somatosensory cortex. J. Physiol. 533, 773 – 785. Nilsson, G.E., 1984. Signal processor for laser Doppler tissue flowmeters. Med. Biol. Eng. Comput. 22, 343 – 348. Ogawa, S., Tank, D.W., Menon, R., Ellerman, J.M., Kim, S., Merkle, H., Ugurbil, K., 1992. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. U. S. A. 89, 5951 – 5955. Ogawa, S., Lee, T.M., Stepnoski, R., Chen, W., Zhu, X.H., Ugurbil, K., 2000. An approach to probe some neural systems interaction by functional MRI at neural time scale down to milliseconds. Proc. Natl. Acad. Sci. U. S. A. 97, 11026 – 11031. Patel, U., 1983. Non-random distribution of blood vessels in the posterior region of the rat somatosensory cortex. Brain Res. 289, 65 – 70. Rapoport, S.I., Ohno, K., Pettigrew, K.D., 1979. Drug entry into the brain. Brain Res. 172, 354 – 359. Rappelsberger, P., Pockberger, H., Petsche, H., 1981. Current source
density analysis: methods and application to simultaneously recorded field potentials of the rabbit’s visual cortex. Pfluegers Arch. 389, 159 – 170. Schwartz, C.E., Wright, C.I., Shin, L.M., Kagan, J., Rauch, S.L., 2003. Inhibited and uninhibited infants bgrown upQ: adult amygdalar response to novelty. Science 300, 1952 – 1953. 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, 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, 347 – 355. Silva, A.C., Koretsky, A.P., 2002. Laminar specificity of functional MRI onset times during somatosensory stimulation in rat. Proc. Natl. Acad. Sci. U. S. A. 99, 15182 – 15187. Silva, A.C., Lee, S.-P., Iadecola, C., Kim, S.-G., 2000. Early temporal characteristics of CBF and deoxyhemoglobin changes during somatosensory stimulation. J. Cereb. Blood Flow Metab. 20, 201 – 206. Simons, D.J., 1985. Temporal and spatial integration in the rat SI vibrissa cortex. J. Neurophysiol. 54, 615 – 635. 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, 10765 – 10770. Soltysik, D.A., Peck, K.K., White, K.D., Crosson, B., Briggs, R.W., 2004. Comparison of hemodynamic response nonlinearity across primary cortical areas. NeuroImage 22, 1117 – 1127. Zheng, Y., Johnston, D., Berwick, J., Mayhew, J., 2001. Signal source separation in the analysis of neural activity in brain. NeuroImage 13, 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, 617 – 637.