Regional brain oxygenation during phasic and tonic painful stimulation

J. Sandkithler, B. Bromm and G.F. Gebhart (Eds.) Pmgress in Brain Research, Vol. 129 0 2000 Elsevier Science B.V. All rights reserved

CHAPTER 21

Regional brain oxygenation during phasic and tonic painful stimulation C. Forster *, R. Ringler and H.O. Handwerker Institutfiir

Physiologie

und experimentelk

Pathophysiologie, 91054 Erlangen,

Introduction In 1990 Ogawa et al. (Ogawa et al., 1990) described the principle of the BOLD effect (blood oxygenation level dependent contrast). It is now widely used for detecting regional changes in cerebral blood flow in functional magnetic resonance imaging (fMRI). Increased neuronal activity leads to vasodilatation in the affected brain region which is followed by an increase of the blood oxygenation level at the venous side. Due to the diamagnetic properties of oxygenated hemoglobin the MRI signal from the activated cortical areas increases slightly, and the increase can be used as a marker of activity changes. In the last few years the use of fMRI has developed rapidly and numerous papers have been published describing brain regions which are involved in the processing of various motor and sensory tasks. This technique has also been used for studying which brain regions are possibly involved in pain processing (see also Casey, 2000, this volume). In connection with fMRI acute, experimental pain was most often induced by electrical stimulation of peripheral nerves (Davis et al., 199.5, 1997) or by thermal stimulation (Berman et al., 1998; Apkarian et al., 1999; Becerra et al., 1999). The reports *Corresponding author: C. Forster, Institut fir Physiologie und experimentelle Pathophysiologie, Universittit Erlangen/Ntirnberg, Universititsstrasse 17, 91054 Erlangen, Germany. Tel.: +49-9131-8522492; Fax: +49-91318522497; E-mail: [email protected]

Universittit Germany

ErlangenjNiinzberg,

Universitiitsstrasse

17,

on brain areas activated in response to pain stimuli varied between different groups, probably because the different stimulation techniques produced pain perceptions and reactions varying in several dimensions, e.g. quality, intensity, spatial and temporal properties, and the affective component of pain (see also Bromm et al., 2000, this volume; Casey, 2000, this volume; Flor, 2000, this volume). Therefore, for a better understanding of the different brain regions involved in sensory processing of painful and nonpainful stimuli, different stimuli, which have some, but not all dimensions in common, should be applied in the same experiment. This can be achieved by using various forms of mechanical stimulation. A pneumatic device has been developed in our laboratory for delivering impact stimuli to pre-selected skin spots (Kohlloffel et al., 1991). By tuning of the impact velocity the quality can be changed from a non-painful to a painful tactile sensation, by changing the impact frequency burning or stinging pain can be induced. Another form of mechanical stimulation is tonic pressure stimuli which evoke deep dull pain sensations in the skin which increase with stimulus repetition. In this case the pain persists even after the end of the pressure. These stimuli can be used to determine brain areas which are similarly or differentially activated and thus are probably involved in the sensory discriminative aspects of perception. Increasing the intensity of the pain level should activate in addition cortical areas which are related to the affective and unpleasant components of pain.

304

Under pathophysiological conditions stimuli which are non-painful when applied to normal skin can induce distinct pain. An example will be shown of a patient suffering from acute Complex Regional Pain Syndrome (CRPS) in one hand, who perceived tactile stimuli as painful, and in whom there was activation of a brain region not normally involved in processing of tactile perceptions. Subjects Seven right-handed male volunteers (age: 22 to 55 years) took part in this study, all of them free from any neurological or systemic disorders. In addition, seven patients (six female, one male; age: 27 to 68) suffering from CRPS (diagnosed according to the IASP criteria; Stanton et al., 1995) were studied. Only three of the females, however, showed clear signs of hyperalgesia in the affected left arms. The study was approved by the local ethics committee, and all subjects gave their informed consent. All subjects were informed that they could stop the stimulation and imaging procedure at any time by pressing the alarm balloon. During the imaging procedure their heads were fixed and their ears closed with plugs to minimize interference from the noises of the MRI scanner. Each subject underwent a brief training session several days before the MRI measurement to become familiar with the experimental protocol and the type of stimuli. The individual pain thresholds for the phasic and tonic stimuli were determined; in the patient group this was done separately both for the affected and for the non-affected limb. Data acquisition Imaging was performed with a 1.5 tesla Magnetom Vision MRI scanner (Siemens, Erlangen, Germany). For each subject a global shimming procedure was performed to adjust the field homogeneity. Then a three-dimensional MPRAGE (magnetization prepared rapid gradient echo) data set of the individual brain was acquired which was later used to identify the affected brain regions. Functional T2*-weighted images were obtained using a multi-slice echo planar imaging technique (EPI) with the following parameters: 16 axial slices

parallel to a line from the top of the orbital cavity to the auditory meatus; repetition time TR = 114 ms; echo time TE = 62 ms; flip angle = 90”; scan time = 2.5 s per 16 slices; slice thickness = 5 mm; field of view = 220 x 220 mm; data matrix = 128 x 128 pixels which gave volume elements (voxels) with a resolution of 1.72 x 1.72 x 4 mm3; the interslice gap was 1 mm. High-resolution Tl-weighted anatomic images (TR = 600 ms; TE = 15 ms; flip = 90”; resolution = 0.86 x 0.86 x 5 mm3) with the same orientation as the EPI slices were collected from each subject. They were used to align the functional slices into the three-dimensional data set and to identify the anatomical structures. The slice orientation was chosen so as to get a maximum representation of the sensory cortices and limbic systems (Andersson et al., 1997). Experimental

protocol

In each experiment four sequences of measurements were performed with the stimulus conditions ‘finger tapping’, ‘impact pain’, ‘tonic pain’ and ‘touch’. ‘Finger tapping’ is an active motor task widely used in fMR1 studies. In this task the subjects are asked to tap the four finger tips in rapid succession to the tip of the thumb, which serves as a control for the experimental conditions and helps to determine the position of the motor cortex. In the CRPS patients each of the four stimulus paradigms were applied first to the non-affected hand and then to the affected one. All healthy volunteers were stimulated to the right hand. The minimum time between succeeding sequences was about 5 min during which neither stimulation nor imaging was done. During each test sequence 78 blocks of functional MRI data were obtained consisting of 16 axial slices and lasting 195 s. During each sequence four stimulation periods were performed, each of them lasting 15 s (six blocks of slices). Stimulation started at acquisition time 30 s (after block 12) 70 s (block 28) 120 s (block 48), and 150 s (block 60) (Fig. 1). Pain rating. Subjects were instructed to assessthe intensity of the pain perception on a scale from 0 (no pain or only touch sensation) to 10 (most intense pain imaginable). Ratings were given verbally at the end of each stimulus sequence.

305

et al., 1994; Kleinschmidt et al., 1995). All calculations were done with a software package developed by us and running under Windows NT. The first two image blocks of each sequence were discarded to eliminate transients that appear at the beginning of the EPI measurement. The time series of each voxel was correlated with a reference wave form representing the on-off pattern of the stimulus (Fig. 1). To consider possible delays between the onset of a stimulus and the change in the BOLD signal (Davis et al., 1998) the stimulus pattern was shifted stepwise from one to six blocks to the right. The maximum correlation coefficient Y of each voxel was used for the ongoing analysis. Detection of clusters Fig. 1. Method of fMR1. The middle trace shows an example of the fMRI signal of a selected region during the course of an experiment. This signal is correlated with the rest-activation sequence according to the experimental setup (lower trace). To detect the delay of the BOLD effect to the stimulus response the rest-activation sequence is shifted stepwise up to six times. The resulting maximum correlation map for each axial slice is the basis of the cluster algorithm.

Mechanical stimulation A device driven by pressurized air was employed to deliver mechanical impact stimuli to the dorsal side of the index or middle fingers. The system contains no ferromagnetic parts in order to be compatible to the MRI environment. This stimulus apparatus has been described in detail elsewhere (Kohlloffel et al., 1991). It consisted of a guiding barrel in which a plastic cylinder with a diameter of 5 mm was accelerated towards the skin. The velocity of the object defines the power of the impact and hence the strength of the stimulus. During a stimulation period of 15 s the impacts were delivered at a rate of l/s. Another stimulation device was used for the tonic pressure application: a plastic cylinder (diameter: 3 mm) was pinched with a constant force (2.5 N to 4.5 N) to the back of the second phalanx of the index or middle fingers. Data analysis The analysis was performed using standard methods which are based on a correlation technique (Friston

Since Y has to be calculated for a large number of voxels (up to 512 x 512) the probability increases of marking voxels as activated when they do not represent an activated area (false positive). Usually these faulty markers appeared at single voxels with no or only a small number of markers in the surrounding region, while real activation seemed to form clusters of voxels with high correlation. Based on this characteristic a cluster detection algorithm was constructed to discriminate between real activation and statistical noise: each cluster consisted of a starting voxel, which had a correlation r > i&h, and a surrounding region with voxels which had correlations r > rgg (Fig. 2). rr,@, and rg5 were chosen such that 0.5% of all voxels had r > rhgh while r > rgs was true for 5%. In addition, the voxels of the surrounding region had to be direct neighbors of the starting voxel or another voxel of the cluster. The cluster size had to pass the minimum size of k = 12 marked voxels. k was determined empirically: the size and shape of the clusters hardly changed if k was set between 10 and 15. Identification

of activated brain regions

The affected brain regions were identified using the individual 3-D data set. The Tl-weighted single slices, which had the same orientation as the functional images, were registered into the individual 3-D data set (Hastreiter et al., 1996). Briefly: at least four pairs of corresponding landmarks, which could

“T A

0

no stimulus 0.1

0.2 0.3 0.4 0.5 correlation coefficient

1 0.6

Fig. 2. Cluster algorithm. Left side: normalized histogram of the maximum correlation map for all activated voxels. The determined thresholds rhish and r95, 0.5% and 5%, respectively, of the most significant VOX&, are shown for a sequence of painful stimuli (dotted line) compared to an experiment without stimulation (solid line). Right side: illustration of the two-threshold-based cluster algorithm. A point s with T(S) 2 rhtsh is used as a starting point for the cluster algorithm. If the number of voxels found with r(R) 1 rss in the surrounding region R equals or is above the predelined number of voxels k necessary for a cluster then this cluster counts as an activated region.

be found in the Tl-data set as well as in the 3-D data set, were manually selected. These pairs were used to initiate a process which performed transformations and rotations of the Tl slices until their differences to the corresponding part of the 3-D data set were minimized. The same transformations were then used to fit the clusters into the three-dimensional data set. Slices of any orientation through the individual brain could now be produced to be compared with a printed brain atlas. Some measurements were done without the MPRAGE data set. In these cases a computerized brain atlas was used (Thurfjell et al., 1995). Each individual brain was fitted into the standard brain of this atlas by means of translation, rotation, linear and non-linear scaling. This was interactively repeated until markers of the atlas brain best fitted the corresponding structures in the brain under investigation. The quality of the approximation was visually controlled using the contours of the brain surface, succinct sulci, and the ventricular system. Statistics For the analysis of pain-related areas six brain regions were chosen in which most of the active clusters were found. These regions had been described before in fMR1, PET, and MEG studies employ-

ing noxious and motor stimulation (Talbot et al., 1991; Coghill et al., 1994; Bromm and Chen, 1995; Svensson et al., 1997; Tiille et al., 1999; Treede et al., 1999). (1) Primary motor cortex Ml: Brodmann area (BA) 4. (2) Other motor areas: BA 6, 8, 9. (3) Primary somatosensory cortex Sl: BA 1, 2, 3. (4) Secondary somatosensory cortex S2: supramarginal gyrus BA 40, 43. (5) Anterior cingulate cortex (ACC) and medial frontal cortex BA 24, 32. (6) Areas of the frontal lobe: BA 46, 50. The total number of activated voxels that were assigned to clusters was computed and the percentage of them in each region of interest was determined. Cortical areas activated by painful stimuli Impacts and tonic pressure, the two forms of painful stimuli, activated cortical areas which had been found in previous studies to be involved in pain processing, namely the somatosensory areas SI and SII, the insular region and the ACC (Bromm and Chen, 1995; Bushnell et al., 1999; Coghill et al., 1999; Treede et al., 1999; see also Bromm et al., 2000, this volume; Casey, 2000, this volume). In addition, we found activation of motor areas, MI and supplementary motor cortex, and of frontal areas. In many cases the activations were bilateral. Different patterns of activation were observed in different

307 subjects. Fig. 3 shows an example of activations in one healthy subject to whom phasic and tonic pain stimuli were applied to the right hand.

In this specimen record, phasic pain induced contralateral activation of SI and SII cortex, bilateral activation of the insular region and of the ACC. The tonic pain stimulus also induced some activation in the contralateral SI cortex, activated SII and the insular region bilaterally, and also the ACC contralaterally. Temporal dynamics of the BOLD signal The correlation method was used in this study to detect brain areas which were activated during the stimulations. Since this was done with a simple onoff pattern (see Fig. 1) the correlation coefficients in this case do not reflect a common variance of the time course of the stimulus and the real BOLD signal. In particular a delay between this search pattern and the BOLD signal is to be expected. The correlation coefficients of the starting points of the clusters found by this method were 0.737 =t 0.087 (mean f Std. Dev.) during the tapping tasks, 0.538 f 0.087 during touch, 0.63 f 0.076 during phasic pain, and 0.494f0.103 during tonic pain. The mean phase shift to reach the maximum r was two blocks during all stimulus conditions which means that the BOLD signal had a mean shift of 5 s as compared with the rest-stimulation pattern to get a maximum correlation. This should not be interpreted as the delay between stimulus onset and activation of a certain brain area; instead this mainly was an effect of the cross-correlation: the BOLD signal started to increase immediately at the beginning of a stimulation but it lasted several image blocks to reach its maximum. The differences of the phase

Fig. 3. Activation during phasic and tonic pain stimuli. Activated functional areas (Z-3) during painful stimulation found by the evaluation algorithm described above. 1 = cingulate; 2 = somatosensory cortex (Sl); 3 = S2 and insula region. Upper row: axial slices through the Sl region (left) and the S2 and insula region (right). Lower row: coronal slice showing insula and S2 area (left) and sagittal view of the pre- and post-central region including S2 area (right). (A) Phasic pain with activated regions in the cingulate and the Sl contralateral to the stimulated finger as well as a bilateral activation of the S2 and insula region. (B) Tonic pain with some activation in the cingulate and in the ipsilateral S2 and insula region.

308

shifts of the voxels within a single cluster was fl block and was probably caused by the sampling process (jitter). Differential tonic pain

activation patterns during phasic and

For quantitative comparisons the cluster areas in each region were calculated as percentage of total activation in each subject. These data are summarized in Fig. 4 from which it is clear that tonic and phasic pain led to differential activation patterns. Phasic pain activated mainly the somatosensory projection areas SI and SII in the contralateral hemisphere; however, activation was also observed in the ipsilateral somatosensory projection areas. A significant proportion of the total activation was found in the frontal cortex. In contrast, tonic pain stimuli

A

contralateral

medial

ipsilateral

20 % 15% 10% 5 %

induced only minor activation in the somatosensory projection areas. Those stimuli which were generally regarded to be more unpleasant than the phasic stimuli induced stronger activation in the ACC and frontal cortex. However, the pain ratings were similar after both types of stimuli (median rating of tonic stimulus: 7; phasic stimulus: 8). Activation pain

of cortical areas during pathological

The same battery of stimuli was applied to subjects suffering from Complex Regional Pain Syndrome (CRPS) in one hand. Three of these subjects were in the acute phase of the disease and showed strong hyperalgesia of the affected hand. The painful tonic and phasic stimuli were adjusted in these subjects according to the individual pain thresholds. That means that stimuli applied to the affected and to the contralateral side were of different intensities. Under these conditions activation in SI, SII and insular region was not remarkably different from that observed in healthy subjects. However, a patient suffering from strong mechanical hyperalgesia who felt pain even from light touch stimuli (allodynia) showed pronounced activation of the ACC, an area which was never activated in healthy subjects during touch stimulation (Fig. 5). Conclusions

B

20%{-

Fig. 4. Distribution of clusters activated during painful stimulation. Percent of activation in different areas proportional to the total number of activated voxels. The main functional anatomical areas that were activated during phasic pain (A) and tonic pain (B) are shown. Ipsilateral, medial, and contralareral indicate the hemispheres in view of the stimulation side.

Our results show that painful stimuli activate various brain areas in healthy subjects, as described earlier (Coghill et al., 1994, 1999; Bushnell et al., 1999; Treede et al., 1999). Not only the known somatosensory areas were involved, but also areas related to motor activity and more integrative areas. It has been discussed that this multiple activation pattern is a strong indication of parallel processing of nociceptive input to the cerebral cortex (Porro et al., 1998; Gelnar et al., 1999). Besides the lateral somatosensory system which receives input from the contralateral body side, ipsilateral cortical areas are involved, in particular SII and the insular cortex, the frontal cortex and midline structures, e.g. the ACC. Phasic and tonic pain stimuli activate these regions differentially. The lateral projection system, and in particular the contralateral SI cortex, is more

Fig. 5. Activation due to allodynia in a patient with CRPS: Activated areas are found in the cingulate (I), the contralateral

a touch stimulus on the affected right hand leads to a painful somatosensory cortex S 1 (2) and S2/insula region (3).

strongly activated by phasic than by tonic pain stimuli though the latter are usually regarded as more painful by the subjects. This system seems to be mainly concerned with stimulus transients. In contrast, the tonic pain stimuli used in this study were activating the ACC and frontal cortical areas more efficiently than the somatosensory areas. This is in agreement with the notion that the ACC is important for the unpleasant affective component of pain (Davis et al., 1997; Tolle et al., 1999). This conclusion is also supported by our findings on a patient suffering from allodynia within the context of a CRPS. Upon touch stimulation this patient did not show a striking activation pattern in the lateral projection system, but a strong activation of ACC (Fig. 5). We conclude from these results that the processing of nociceptive stimuli is not dependent on a single brain area. It is apparently bilateral, in particular in the somatosensory areas SI and SII, in the insular region, in midline structures and in the frontal cortices. It is well conceivable that perception of the various dimensions of painful stimuli depends on activation of all these areas. This notion is supported by findings from patients in whom parts of the cerebral cortex have been removed. Discrete injuries of the contralateral SI cortex or removal of the cingulate cortex did not abolish the ability of the patients to perceive painful stimuli (Davis et al., 1994; Kuroda et al., 1995; Fuchs et al., 1996; Pastoriza et al., 1996; Peyron et al., 2000). The nociceptive input to these

sensation.

areas is probably transmitted via several pathways in the CNS. This is supported by electrophysiological findings (Willis-WD, 1985; Kenshalo-DR et al., 1988; Kalliomaki et al., 1993) and clinical observations (Berthier et al., 1988; Greenspan and Winfield, 1992; Knecht et al., 1996) which showed that parallel transmission seems to be a major principle in the processing of painful stimuli. In summary there is obviously no ‘master region’ for pain processing, a fact which ensures the ability of the organism to detect tissue injury even if parts of the CNS fail. Acknowledgements This work was supported by the Deutsche Forschungsgemeinschaft (DFG), SFB 353. References Andersson, J.L., Lilja, A., Hartvig, P., Langstrom, B., Gordh, T., Handwerker, H.O. and Torebjork, E. (1997) Somatotopic organization along the central sulcus, for pain localization in humans, as revealed by positron emission tomography. Exp. Bruin Rex, 117: 192-199. Apkarian, A.V., Darbar, A., Krauss, B.R., Gelnar, PA. and Szeverenyi, N.M. (1999) Differentiating cortical areas related to pain perception from stimulus identification: temporal analysis of fMRI activity. J. Neurophysiol., 8 1: 2956-2963. Becerra, L.R., Breiter, H.C., Stojanovic, M., Fishman, S., Edwards, A., Comite, A.R., Gonzalez, R.G. and Borsook, D. (1999) Human brain activation under controlled thermal stim-

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