Placebo analgesia and nocebo hyperalgesia across species: direct neural comparisons between rats and humans

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While animal models are increasingly used to explore the underlying mechanisms of these phenomena, it remains unclear whether animals experience placebo and nocebo effects in a manner comparable to humans or whether the associated neurobiological pathways are conserved across species. In this study, we introduce a novel framework for comparing brain activity between humans and rodents during placebo analgesia and nocebo hyperalgesia. Using c-Fos immunohistochemistry in rats and fMRI in humans, we examined neural activity in 70 pain-related brain regions, identifying both species-specific and conserved connectivity changes. Functional connectivity analysis, refined by pruning connections based on anatomical pathways, revealed significant overlap in key regions, including the amygdala, anterior cingulate cortex, and nucleus accumbens, highlighting conserved circuits driving placebo and nocebo responses This cross-species methodology offers a powerful new approach for investigating the neurobiology of pain modulation, bridging the gap between animal models and human studies. Identifying these common connections validates the use of animal models and enables preclinical researchers to focus on circuits that are conserved across species, ensuring greater translational relevance when developing new and effective treatments for pain conditions. Biological sciences/Neuroscience/Neural circuits Health sciences/Anatomy/Nervous system/Brain Biological sciences/Neuroscience/Sensorimotor processing Biological sciences/Neuroscience Biological sciences/Psychology/Human behaviour c-Fos functional connectivity pain circuitry network analysis comparative neuroimaging fMRI pain modulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Chronic pain affects millions of individuals worldwide, and current treatments often provide inadequate relief, leading to significant physical, emotional, and economic burdens. Recent research has highlighted the potential utility of placebo analgesia as a powerful, non-pharmacological intervention that could augment chronic pain management by harnessing the brain’s endogenous pain modulation systems (Rossettini et al., 2023 ). Conversely, nocebo hyperalgesia—the phenomenon in which negative expectations worsen pain—may significantly contribute to both the development and persistence of chronic pain conditions (Buchel, 2023 ). Identifying ways to inhibit the nocebo effect could therefore be transformative in chronic pain therapy, potentially preventing its escalation and improving patient outcomes. Historically, studies on placebo analgesia and nocebo hyperalgesia have been primarily limited to humans, which have provided critical insights into the psychological and neurobiological mechanisms driving these effects. Only recently have researchers begun to develop and validate animal models to study these phenomena in more controlled experimental settings (Xu et al., 2018 ; Boorman & Keay, 2021 ; Boorman & Keay, 2023 ; Chen et al., 2024a ; Chen et al., 2024b ). Rodent models enable the experimental manipulation of neural circuits and molecular pathways that are not possible in human studies, allowing us to identify the cellular and synaptic mechanisms driving these pain modulatory processes. Moreover, rodent models provide an avenue for testing potential pharmacological or genetic interventions that could mitigate nocebo hyperalgesia or enhance placebo analgesia, which could lead to novel therapeutic strategies for pain conditions. Significant progress has been made in mapping the neurobiological substrates of placebo analgesia and nocebo hyperalgesia. Studies in humans have implicated key brain regions, including the prefrontal cortex, anterior cingulate cortex (ACC), insular cortex and midbrain periaqueductal gray (PAG), which are involved in top-down modulation of pain through cognitive and emotional regulation (Benedetti et al., 2005 ; Freeman et al., 2015 ). Similarly, in rodent models, structures including the frontal cortical regions, amygdala, ACC, and PAG have also been shown to play central roles in modulating pain responses during placebo and nocebo conditions (Zhang et al., 2013 ; Xu et al., 2018 ; Zeng et al., 2018 ; Chen et al., 2024a ; Chen et al., 2024b ). Furthermore, pathways involving the amygdala and nucleus accumbens (NAc), which regulate emotional processing and reward, have emerged as critical mediators in both placebo-induced analgesia and nocebo-induced hyperalgesia (Schafer et al., 2018 ). These circuits reflect the complex interplay between cognitive, emotional, and sensory systems in the modulation of pain, underscoring the necessity of cross-species studies to fully elucidate these processes. Despite these advances, there has yet to be a direct comparison of the neurobiological mechanisms underlying placebo analgesia or nocebo hyperalgesia between humans and animals. Our study addresses this critical gap by directly comparing the regional activation, functional connectivity and neural circuits activated during placebo and nocebo responses in both humans and rats. By doing so, we aim to establish a cross-species framework that will enhance our understanding of conserved and species-specific mechanisms of pain modulation. This comparison is particularly important as it will validate rodent models as translational tools for pain research, facilitating the development of new interventions that target placebo and nocebo pathways across species. Results Response conditioning elicits placebo analgesia and nocebo hyperalgesia in both humans and rats To elicit placebo analgesia and nocebo hyperalgesia in humans, we used a common response conditioning protocol. Low, moderate and high thermal stimuli were repeatedly paired with topical creams labelled Lidocaine (placebo), Vaseline (control) and Capsaicin (nocebo), respectively. During subsequent fMRI scans, all three creams were reapplied, but the moderate intensity stimulus was delivered to each cream site (Fig. 1 a-d). Permutation testing of pain ratings between the control cream and the Lidocaine or Capsaicin creams revealed placebo responder subpopulations (22/46; Fig. 1 e-g) and nocebo responder subpopulations (14/25; Figure h-j), respectively. A similar protocol was used to elicit placebo analgesia and nocebo hyperalgesia in rats. However, while for the human study we were able to employ a within-subject design, the study in rats was necessarily a between-subjects design. As such, separate groups of rats underwent a response conditioning procedure in which either a low, moderate or high intensity thermal stimulus was paired with contextual cues (Fig. 1 k-m). To assess the impact of cue saliency on the strength of conditioned responses, placebo and nocebo groups were tested either in a ‘minimal context’ or an ‘enhanced context’ setting (Fig. 1 n,o). Subsequently, all groups were tested in their same context at the moderate stimulus. Comparisons of pain behaviours between the control group and the low and high intensity conditioned groups revealed that this procedure produced strong placebo analgesia (Fig. 1 p,q) and nocebo hyperalgesia (Fig. 1 r,s) in the majority of rats tested. Firstly, to assess changes in overall regional brain activity during placebo and nocebo in humans, beta values representing the degree of correlation between the blood oxygen level dependent (BOLD) fMRI signal intensity and the thermal stimulation pattern was calculated for 70 regions of interest (ROIs) for each fMRI scan (Fig. 2 ). Beta values of each ROI for the placebo/nocebo scans were then compared to the control scan. To assess changes in overall regional brain activity during placebo and nocebo in rats, c-Fos expression density in 70 ROIs across the brain was quantified for each placebo and nocebo group and compared to the control group. These ROIs were chosen based on their known or putative involvement in the processing, transmission and modulation of pain and/or contextual cues. Given the different methodologies used to measure brain activity for humans and rats, to make direct comparisons between species we chose to use estimation statistics to identify the top 20 ROIs that showed the largest effect sizes between the placebo/nocebo conditions and controls. See Supplementary Table 1 for detailed results, and Supplementary Table 2 for complete list of ROIs and abbreviation. Human and rats show similar placebo-associated changes in activity across multiple brain regions In humans, the average effect size (Cohen’s d) between placebo and control for the top 20 ROIs was 0.36 (range 0.25–0.64). In rats, for the minimal context group, the average effect size in c-Fos expression density between placebo and control of the top 20 ROIs was 0.80 (range 0.53–2.14). For the enhanced context group, the average effect size of the top 20 ROIs was 0.64 (range 0.46–1.19). Similar patterns of activity were observed between the two rat groups. Placebo analgesia was associated with increases in c-Fos expression in 19/20 ROIs in the minimal context and 18/20 in the enhanced context, with 8 overlapping ROIs that showed similar effect sizes (the gracile nuclei, ACC, NAc, and basolateral amygdala [BLA]). However, the differences between these groups, most notably in the PAG, posterior insula cortex and paraventricular thalamus (PVT), likely reflects the effect of the enhanced context on regional brain activity. Remarkably, 8 regions were identified as having placebo-related changes in activity in both humans and rats. These were the contralateral primary somatosensory cortex (S1), ipsilateral cuneate/gracile, contralateral medial (PB), ipsilateral basolateral amygdala (BMA), the septal nuclei and the rostral and caudal PVT (Fig. 3 ). Human and rats show similar nocebo-associated changes in activity across brain regions In humans, the average effects size between the nocebo and control scans for the top 20 VOIs was 0.53 (range 0.39–0.95). In rats, for the minimal context group, the average effect size of the top 20 ROIs was 0.66 (range 0.49–1.12), while for the enhanced context group, the average effect size was 0.76 (range 0.6–1.15). Once again, similar overall patterns of activity were observed between these two rat groups, with 7 overlapping regions, each of which showed similar effect sizes. However, in stark contrast to the placebo groups, nearly all the top 20 ROIs in both nocebo groups had decreases in c-Fos expression compared to the control (16/20 in the minimal context and 19/20 in the enhanced context). Remarkably, 11 regions were identified as having nocebo-related changes in activity in both humans and rats. These were the ipsilateral primary motor cortex, caudal and rostral PVT, paraventricular hypothalamus, locus coeruleus, rostral ventromedial medulla, contralateral medial and lateral parabrachial nuclei, and the ipsilateral cuneate nucleus (Fig. 4 ). Functional connectivity and functional circuit networks We next determined which neural circuits were active during placebo analgesia and nocebo hyperalgesia using functional connectivity. Functional connectivity can be defined as the temporal coincidence of spatially distant neurophysiological events (Friston, 2011 ). That is, activity in one brain region/nucleus is correlated with and predictive of activity in another. For the rats, it is therefore possible to create functional connectivity maps for each group by correlating c-Fos expression between each of the ROIs. High or low correlations (Pearson’s r-values) between two regions indicates high or low functional connectivity, respectively. Indeed, c-Fos functional connectivity is now a widely used and accepted approach (Wheeler et al., 2013 ; Silva et al., 2016 ; Vetere et al., 2017 ; Terstege et al., 2022 ). However, as recently outlined by Terstege et al. ( 2022 ), adequate group sizes are essential (n = 8 or above) to produce reliable results. In order to better align our human data with the rat analysis, we used the beta values from the fMRI analyses to assess functional connectivity, rather than utilizing traditional fMRI time-series data. As such, the beta values, representing the overall level of neural activation in each region during noxious stimuli, were correlated between ROIs to establish functional connectivity. This method provides a comparable measure to the c-Fos correlations used in rats, allowing us to assess functional relationships across species using a similar framework. To further facilitate cross-species comparisons, we identified the top 100 strongest functional connections from the control group data in both rats and humans. These connections were selected based on their strength of correlation (Pearson’s r values) and plotted to visualize and compare connectivity patterns between the two species (Supplementary Fig. 1). Comparison of functional connectivity maps in the control groups revealed species-specific patterns. The top 100 human connections were dominated by left-right frontal cortical links, while rats exhibited stronger cortical-subcortical connectivity. Notably, 16 connections were shared between species, significantly higher than the 4.66 expected by chance, largely involving frontal cortical regions, the ACC, and middle cingulate cortex (Table 1 ). Graph metrics were comparable: the rat map showed an average path length of 3.145, the human map had an average path length of 3.479, reflecting similar network efficiency despite species-specific topologies (see Supplementary Table 2 for full graph metrics). Enhancing the context during conditioning increases brain-wide functional connectivity To assess changes in functional connectivity during placebo and nocebo responses, we applied the same r -value cut-offs as used in the control groups (rats: r = 0.879, humans: r = 0.699) (See Supplementary Fig. 4), with connections shared with controls excluded from this analysis to isolate only unique placebo-related and nocebo-related connections. For the rats, this analysis revealed a divergent pattern: minimal context rats exhibited a decrease or no change to their functional connectivity, with 69 connections for placebo and 105 for nocebo, whereas the enhanced context placebo and nocebo groups had 291 and 268 connections, respectively (Supplementary Figs. 2, 3). This would indicate that the enhanced context greatly increased overall functional connectivity of the brain, particularly between frontal cortical regions and the amygdala and olfactory areas. Conserved Functional Connectivity Patterns in Human and Rat Placebo Responses Human placebo responses also showed an increase in overall functional connectivity, expanding to 200 connections compared to controls. Notably, 47 of these connections were shared between humans and the two rat placebo groups (Supplementary Fig. 2; Table 2 ). This shared connectivity was concentrated in the amygdala, ACC, and NAc. These overlaps suggest a conserved set of circuits or networks across species that either accompany or drive the placebo analgesic response. Limited Cross-Species Overlap in Nocebo-Related Functional Connectivity Human nocebo responses showed a modest increase in overall functional connectivity, with 130 connections compared to controls. In contrast to the placebo condition, only 4 connections were shared between humans and minimal context rats, and 19 with enhanced context rats. The increased connectivity in enhanced context rats (268 connections), did not translate to significant overlap with humans. Importantly, the nocebo networks exhibited a distinct topology from placebo networks, particularly with reduced amygdala involvement in human nocebo responses. This suggests that nocebo hyperalgesia is driven by different neural circuits across species, highlighting the specificity of nocebo-related mechanisms. Neural Circuits Underlying Pain, Placebo Analgesia, and Nocebo Hyperalgesia A major caveat of functional connectivity analysis is that it only reflects statistical correlations, not direct anatomical or causal relationships between regions. To refine our analysis, we pruned the functional connections using anatomical data from the Rat Connectome Project of the University of Rostock (Schmitt & Eipert, 2012 ), which compiles tract-tracing studies in rodents. From this database, we extracted projection direction and strength for every functional connection in the isolated networks shown in Supplementary Figs. 2–4. If there was weak or no evidence for an anatomical connection, it was removed from the network. We first applied this pruning and assigned strength to the control groups, generating force-directed graphs for humans and rats (Fig. 5 ). Shared circuits are highlighted in red. Both networks showed similar overall patterns, with frontal cortical regions interconnected in both species, while subcortical areas like the PAG and other brainstem regions had primarily local connections. The overlapping circuits between species were related predominantly to prefrontal and PAG areas. Indeed, despite the differences in these control networks, the presence of conserved pathways suggests fundamental similarities in pain processing between humans and rats. Figure 6 shows the pruned placebo networks for humans and rats. Several key hub regions, defined as those with the highest weighted degree of connectivity, emerged across species and conditions. In humans, the PAG, posterior insular cortex, ACC, and BLA served as the primary hubs. The rat minimal context network, while more modular, shared similar hub regions, including the BLA, anterior insula, ACC, vlPAG, and orbitofrontal cortex. Interestingly, while the PAG in the minimal context rats formed an isolated, island-like network disconnected from the broader network, the human PAG was more integrated, with connections extending to other major hubs. In the enhanced context rats, hub activity shifted to regions such as the anterior and posterior insula, pyriform cortex, orbitofrontal cortex, and prelimbic cortex. The appearance of the pyriform cortex, anterior olfactory nucleus, and medial amygdala in this context likely reflects processing of the strong olfactory cues used during conditioning. Despite these differences, several core connections are shared across species, particularly in prefrontal and pain-modulatory regions, suggesting conserved pathways in placebo analgesia. All shared connections are detailed in Table 2, which includes the pruned connections, their direction, and the strength of these pathways. Figure 7 shows the pruned nocebo networks for humans and rats. Key hub regions were identified in both humans and rats, though with fewer shared connections compared to placebo. In humans, the primary hubs included the prelimbic cortex, motor cortex, anterior and middle cingulate cortex, and the PAG. Unlike the placebo network, the PAG in humans formed an isolated network, showing no integration with the broader nocebo network. In the rat minimal context, the main hubs were the prelimbic cortex, posterior insula, and ACC, but notably, there were no shared connections between this network and the human nocebo network. For the enhanced context, the primary hubs shifted to the anterior insula, ACC, vlPAG, and BLA. Although there were more shared connections between the enhanced context rats and humans, the overall overlap was still far less than that observed in the placebo networks. These differences, particularly the isolated PAG in humans and the more integrated PAG in the enhanced context rats, highlight distinct network organizations between placebo and nocebo conditions. The shared connections across species were limited, suggesting a more species-specific recruitment of circuits during nocebo hyperalgesia compared to placebo analgesia. All shared connections for nocebo groups are detailed in Table 3, which includes the pruned connections, their direction, and the strength of these pathways. Table 1 Shared ROI-ROI functional and anatomical connections between humans and rats in the control groups. See Pearson’s r Functional Connection Human Rat (minimal) Anatomical connection Strength(s) PrL(L)-PrL(R) 0.890 0.972 PrL(L) ↔ PrL(R) 3, 3 PrL(L)-ACC1(R) 0.702 0.966 PrL(L) → ACC1(R) 1 PrL(L)-ACC1(L) 0.705 0.952 PrL(L) → ACC1(L) 1 IL(L)-IL(R) 0.877 0.933 IL(L) ↔ IL(R) 3, 3 S1(L)-M1(L) 0.836 0.879 S1(L) → M1(L) 3 ACC1(L)-ACC1(R) 0.931 0.975 - ACC2(L)-ACC1(R) 0.876 0.939 - ACC2(L)-ACC2(R) 0.931 0.906 ACC2(L) ↔ ACC2(R) 2, 2 MCC1(L)-MCC1(R) 0.966 0.932 - MCC2(L)-MCC2(R) 0.973 0.959 - rVLPAG(L)-rLPAG(R) 0.704 0.934 rVLPAG(L) ↔ rLPAG(R) 3, 0.5 cVLPAG(L)-cDR 0.887 0.903 cVLPAG(L) ↔ cDR 3, 3 rLPAG(L)-rVLPAG(R) 0.863 0.883 rLPAG(L) ↔ rVLPAG(R) 0.5, 3 PVH(L)-PVH(R) 0.871 0.888 PVH(L) ↔ PVH(R) 1, 1 PrL(R)-ACC1(R) 0.710 0.913 PrL(R) → ACC1(R) 3 S1(R)-M1(R) 0.903 0.905 S1(R) → M1(R) 3 Table 2 Shared ROI-ROI functional and anatomical connections between humans and rats in the placebo groups Pearson’s r Functional Connection Human Rat (min) Rat (enh) Anatomical connection Strength(s) PrL(R)-ACC-1(R) 0.705 0.765 0.949 PrL(R) ↔ ACC-1(R) 1, 3 pINS(R)-ACC-2(R) 0.798 0.268 0.909 pINS(R) ↔ ACC-2(R) 1, 1 pINS(R)-MCC-1(R) 0.787 0.749 0.884 pINS(R) → MCC-1(R) 2 pINS(R)-MCC-2(R) 0.714 0.606 0.911 pINS(R) ← MCC-2(R) 3 pINS(R)-pINS(L) 0.789 0.714 0.958 pINS(R) ↔ pINS(L) 3, 3 pINS(R)-aINS(L) 0.756 0.869 0.908 pINS(R) ↔ aINS(L) 3, 3 pINS(R)-ACC-2(L) 0.718 0.339 0.898 pINS(R) ↔ ACC-2(L) 3, 1 pINS(R)-MCC-1(L) 0.769 0.305 0.935 - pINS(R)-MCC-2(L) 0.727 0.561 0.923 - pINS(R)-rLPAG(L) 0.772 0.904 -0.073 pINS(R) → rLPAG(L) 1 ACC-1(R)-Claus(L) 0.785 0.764 0.881 ACC-1(R) ↔ Claus(L) 2, 3 ACC-1(R)-MCC-1(L) 0.833 0.922 0.868 - ACC-1(R)-MCC-1(R) 0.807 0.709 0.951 ACC-1(R) ↔ MCC-1(R) 3, 1 ACC-2(R)-ACC-1(L) 0.872 0.902 0.808 ACC-2(R) ← ACC-1(L) 2 ACC-2(R)-MCC-1(L) 0.765 0.834 0.966 - ACC-2(R)-MCC-2(L) 0.866 0.698 0.899 - NAc(R)-NAc(L) 0.858 0.514 0.974 - NAc(R)-Nash(L) 0.823 0.594 0.895 - NAc(R)-CeA(L) 0.728 -0.116 0.946 - BLA(R)-NAc(L) 0.755 0.477 0.920 - BLA(R)-Nash(L) 0.784 0.456 0.935 BLA(R) ↔ Nash(L) 3, 3 BLA(R)-BLA(L) 0.904 0.952 0.877 BLA(R) ↔ BLA(L) 1, 1 BLA(R)-CeA(L) 0.811 0.619 0.957 - MeA(R)-Sept(L) 0.706 0.372 0.953 MeA(R) ↔ Sept(L) 1.5, 2 MeA(R)-MeA(L) 0.759 0.002 0.914 - rVPLAG(R)-rVPLAG(L) 0.896 0.918 0.864 rVPLAG(R) ↔ rVPLAG(L) 3, 3 rVPLAG(R)-rLPAG(R) 0.949 0.409 0.908 rVPLAG(R) ↔ rLPAG(R) 3, 0.5 cVPLAG(R)-cDR 0.843 0.521 0.889 - rLPAG(R)-cLPAG(R) 0.702 0.792 0.978 rLPAG(R) ↔ cLPAG(R) 2, 2 cLPAG(R)-cVLPAG(L) 0.750 0.898 0.943 cLPAG(R) ↔ cVLPAG(L) 0.5, 3 cLPAG(R)-cLPAG(L) 0.811 0.771 0.967 cLPAG(R) ↔ cLPAG(L) 2, 2 cLPAG(L)-rLPAG(L) 0.708 0.624 0.899 cLPAG(L) ↔ rLPAG(L) 2, 2 rLC(R)-rLC(L) 0.852 0.735 0.972 rLC(R) ↔ rLC(L) 3, 3 cLPAG(L)-cVLPAG(L) 0.901 0.832 0.987 cLPAG(L) ↔ cVLPAG(L) 3, 3 CeA(L)-Nash(L) 0.753 0.301 0.933 CeA(L) → Nash(L) 1.5 CeA-(L)-NAc(L) 0.721 0.112 0.974 CeA-(L) ← NAc(L) 0.5 Sept(L)-Nash(L) 0.791 0.684 0.887 Sept(L) → Nash(L) 2 Nash(L)-NAc(L) 0.914 0.824 0.908 Nash(L) ↔ NAc(L) 2, 2 Nash(L)-PrL(L) 0.747 0.909 0.500 Nash(L) ← PrL(L) 3 MCC-1(L)-MCC-2(L) 0.890 0.741 0.905 MCC-1(L) ↔ MCC-2(L) 4, 3 ACC-1(L)-ACC-2(L) 0.896 0.957 0.937 ACC-1(L) ↔ ACC-2(L) 0.5, 1 ACC-2(L)-PrL(L) 0.789 0.874 0.903 ACC-2(L) ↔ PrL(L) 1, 2 PrL(L)-IL(L) 0.759 0.640 0.976 PrL(L) ↔ IL(L) 3, 4 OFC(L)-Claus(L) 0.721 0.850 0.972 OFC(L) ↔ Claus(L) 3, 2 lPB(L)-mPB(L) 0.892 0.794 0.884 - rPVT(R)-cPVT(R) 0.821 0.320 0.883 - rPVT(L)-cPVT(L) 0.743 0.320 0.883 - Table 3 Shared ROI-ROI functional and anatomical connections between humans and rats in the nocebo groups Pearson’s r Functional Connection Human Rat (min) Rat (enh) Anatomical connection Strength(s) PrL(R)-S1(R) 0.783 0.771 0.991 PrL(R) ← S1(R) 1 PrL(R)-M1(R) 0.769 0.790 0.940 PrL(R) ↔ M1(R) 4, 2 PrL(R)-ACC-1(L) 0.711 0.703 0.879 PrL(R) ↔ ACC-1(L) 2, 2 S1(R)-PrL(L) 0.775 0.771 0.952 - S1(R)-M1(L) 0.712 0.810 0.921 S1(R) → M1(L) 2 M1(R)-M1(L) 0.818 0.912 0.981 - ACC-1(R)-NAc(L) 0.699 0.770 0.890 ACC-1(R) → NAc(L) 1 MCC-1(R)-rVLPAG(R) 0.699 0.568 0.904 MCC-1(R) → rVLPAG(R) 2 MCC-2(R)-PrL(L) 0.704 0.676 0.932 MCC-2(R) ↔ PrL(L) 0.5, 1 BNST(R)-Sept(R) 0.706 0.734 0.880 BNST(R) ↔ Sept(R) 3, 3 BLA(R)-CeA(R) 0.711 0.941 0.916 BLA(R) ↔ CeA(R) 1, 3 rVLPAG(R)-rVLPAG(L) 0.948 0.876 0.908 rVLPAG(R) ↔ rVLPAG(L) 3, 3 cVLPAG(R)-cLPAG(R) 0.936 0.896 0.763 cVLPAG(R) ↔ cLPAG(R) 3, 0.5 cLPAG(L)-cVLPAG(L) 0.845 0.945 0.768 cLPAG(L) ↔ cVLPAG(L) 3, 3 rLPAG(L)-M1(L) 0.746 0.211 0.888 rLPAG(L) ← M1(L) 1 MCC-2(L)-M1(L) 0.752 0.640 0.888 - MCC-2(L)-PrL(L) 0.728 0.823 0.916 MCC-2(L) ↔ PrL(L) 1, 1 M1(L)-aINS(L) 0.768 0.058 0.923 - aINS(L)-Claus(L) 0.828 0.419 0.967 aINS(L) ↔ Claus(L) 2.5, 2 Discussion This study presents a novel approach to cross-species analysis, utilizing c-Fos immunohistochemistry in rats and fMRI in humans to create comparable, species-specific maps of brain activity and neural circuitry. We propose an innovative framework for investigating conserved and distinct brain circuits underlying complex behaviours and physiological responses. Furthermore, this approach enables preclinical research to prioritize the neural pathways that are most likely conserved between species, accelerating translational efforts and refining focus on the circuits with the greatest relevance to humans. With respect to placebo analgesia and nocebo hyperalgesia, our findings revealed that while there are notable species-specific differences, certain key regions—such as the ACC, PAG, and amygdala—show conserved patterns of activation and connectivity in response to placebo and nocebo conditioning. However, given the novelty of our approach, when interpreting these findings, two critical considerations arise: 1) how well our results align with established literature on placebo and nocebo responses, and 2) the comparability of the methodologies used for each species. How do our findings compare to what is known about the neural activity during placebo analgesia and nocebo hyperalgesia? fMRI has now been extensively employed to investigate the neural basis of placebo analgesia, and, to a lesser extent, nocebo hyperalgesia. However, while many of these studies often identify different collections of brain regions responsive during placebo and nocebo manipulations, several key regions consistently emerge central to these responses. Notably, the meta-analysis by (Zunhammer et al., 2021 ) synthesized data from 603 participants across 20 fMRI studies, revealing common areas of activation associated with placebo analgesia. These included the mid-cingulate and insula cortices, frontoparietal regions and the thalamus. In our study, despite our different analysis pipeline —using beta values within 70 pre-defined regions of interest rather than a whole-brain voxel-based analysis—we observed activation patterns that generally align with previous research. The top 20 brain regions in humans that showed the largest changes during placebo analgesia in our study (see Fig. 3 ) fell into three categories. Firstly, regions that have previously been shown to be involved in placebo analgesia. These were the majority of the regions identified by our analysis: the dorsolateral prefrontal cortex (dlPFC; (Tu et al., 2021 ; Crawford et al., 2023b )), the medial prefrontal cortex (mPFC; (Wager et al., 2004 )), the rostral ventromedial medulla (RVM), paraventricular hypothalamus (PVH; (Eippert et al., 2009 )), the amygdala (Petrovic et al., 2005 ), S1, and the thalamus (Zunhammer et al., 2021 ). Secondly, brainstem regions that are known to be involved in pain modulation but currently have scarce direct evidence for their involvement in placebo analgesia (see (Crawford et al., 2022 ) for review). These were the medial PB (mPB), the locus coeruleus (LC), the subnucleus reticularis dorsalis (SRD) and the cuneate nucleus ipsilateral to the stimulus. Thirdly, regions that have not been previously associated with placebo analgesia. These were the septal nuclei and the bed nucleus of the stria terminalis (BNST). Similarly, the top 20 brain regions in humans that showed the largest changes during nocebo hyperalgesia (see Fig. 4 ) fell into the same three categories. While considerably fewer studies have investigated the neural correlates of nocebo hyperalgesia, half of the regions identified by our analysis have previously been reported. These were the orbitofrontal cortex (OFC; (Kong et al., 2008 ; Freeman et al., 2015 ); Schiene et al., 2018), ACC (Tinnermann et al., 2017 )), NAc, and PVH (Freeman et al., 2015 ). Several pain modulatory brainstem regions were also identified. These were the LC, RVM, medial and lateral PB, and the cuneate nucleus. Finally, the regions identified by our analysis that have yet to be associated with nocebo hyperalgesia were specifically the primary motor cortex (M1), PVT, BLA and the CeA. With respect to preclinical research, only a handful of studies have explored the neurobiological correlates of placebo analgesia, yet once again, several brain regions have emerged as central. For instance, the rostral rACC (Zhang et al., 2013 ; Lee et al., 2015 )), ventral tegmental area (VTA; (Lee et al., 2015 )), ventrolateral PAG (vlPAG), medial prefrontal cortex, NAc (Xu et al., 2018 ; Zeng et al., 2018 )) have all been shown to have altered activity during the expression of placebo analgesia. Additionally, a recent study by (Chen et al., 2024b ) identified an ACC-pontine circuit projecting into the cerebellum as both necessary and sufficient for producing placebo analgesia; while (Chen et al., 2024a ) found that direct activation of central amygdala (CeA) neurons can condition placebo responses, but these neurons do not re-activate during the expression of placebo analgesia. Our study identified many of these same regions as having large changes in neural activity, including the vlPAG, ACC, CeA, and NAc, highlighting a remarkable consistency with this limited existing literature. It is also worth noting that many regions included in our study—such as specific brainstem and subcortical nuclei—have not been previously investigated in animal models of placebo analgesia. While there have been three recent studies that have developed animal models of nocebo hyperalgesia (Martin et al., 2019 ; Trask et al., 2022 ; Boorman & Keay, 2023 ), to our knowledge this is the first study to investigate the neural basis of these effects. However, interestingly, (Zhang et al., 2024 ) also recently found that the ACC, BLA, and PVT had increased c-Fos expression in their rat model of nocebo nausea. Methodological Comparability: Bridging Human and Animal Models Comparing neural activity across humans and rats in the context of placebo and nocebo effects introduces multiple layers of complexity. To meaningfully interpret our findings, we must consider the comparability of our approaches across three key levels: (i) the behavioural protocols used to induce placebo and nocebo responses in each species, (ii) the methodologies employed to assess neural activity (i.e., c-Fos immunohistochemistry in rats and fMRI in humans), and (iii) the analytical techniques applied to extract functional connectivity and circuit-level insights from these data. At the behavioural model level, both human and rodent paradigms employ response conditioning, in which the intensity of a thermal stimulus was repeatedly paired with contextual cues to produce learned associations. While there are obvious differences between the procedures (for instance, in humans these associations are reinforced with verbal instructions), ultimately both paradigms are designed to produce expectancies and predictions about an upcoming stimulus. It therefore seems likely that each would engage the same neural mechanisms, and indeed the limited research in rodents suggests this is the case (Xu et al., 2018 ; Chen et al., 2024b ). Further, the inclusion of the enhanced context group was designed to capture the multisensory cues inherent in human conditioning protocols. By incorporating a combination of olfactory, auditory, and visual stimuli, this group aimed to amplify the salience of the contextual pairings, thereby mimicking the cognitive and sensory complexity of human expectancy formation. At the methodological level, prima facie , c-Fos expression and fMRI appear to be fundamentally different approaches, with c-Fos expression being a cellular-level marker and fMRI being a regional, hemodynamic-based measurement. Additionally, while fMRI offers essentially a real-time measure of regional activity, the delayed nature of c-Fos production means that it represents a cumulative cellular response over the preceding 90–120 minutes from perfusion and fixation. Further, the reliance on post-mortem tissue for c-Fos quantification precludes a within-subjects design as was used in humans. Interestingly, both methodologies share the notable limitation of being unable to distinguish whether the observed changes in signal arise from excitatory or inhibitory neurons. However, ultimately, both techniques serve as proxies to capture population-level neural activation, with each signal presumed to reflect increased neuronal firing of action potentials, albeit with different spatiotemporal resolution. At the analysis level, we chose to use fMRI beta values, which collapses the signal across the scan into a single value for each region, thus providing the closest approximation to the cumulative nature of c-Fos expression. This approach also ensured that the functional connectivity maps derived from fMRI aligned more closely with the static connectivity patterns inferred from c-Fos densities. We also carefully selected and defined our brain ROIs to ensure that they were homologous across species, relying on known histological, functional, and anatomical similarities. Only 4 out of the 70 ROIs from each species lacked a clear equivalent in the other. In humans, the dlPFC has no discrete homologue in rodents (Preuss, 1995 ; Wise, 2008 ; Carlen, 2017 ; Laubach et al., 2018 ). Conversely, in rodents, the anterior olfactory nucleus and piriform cortex did not have a clear human counterpart, given the fundamental differences in olfactory processing across species (Johnston, 2003 ; Arakawa et al., 2008 ; Maresh et al., 2008 ). We included these regions due to their known or putative involvement in conditioning and placebo responses in each species. Furthermore, by focusing on the top 20 ROIs with the largest changes in activity and the top 100 functional connections (or the relative differences from controls) in each species, we aimed to capture the most relevant neural changes during placebo and nocebo responses, while also accommodating for species-specific architecture. Finally, the pruning of the functional connectivity maps to retain only anatomically plausible connections was a key step in our analysis. This process identified 58 directional connections for placebo and 25 for nocebo (with variable strengths) that were shared by humans and rats, thus narrowing the focus to conserved pathways most likely involved in these responses. It is important to note that unlike the approach taken by many recent studies that focus on a single critical circuit, our aim was to provide a foundation for future research by identifying a small collection of specific, biologically plausible connections for functional interrogation. As such, these findings offer a starting point for defining the constellation of neural circuits necessary and sufficient for placebo and nocebo responses. It is our hope that our study will therefore act as a springboard for advancing translational efforts and refining our mechanistic understanding of these phenomena. Conclusion This study offers a novel framework to investigate cross-species comparisons of the brain regions and neural circuits underlying placebo analgesia and nocebo hyperalgesia, revealing both conserved and species-specific patterns. By integrating functional connectivity with anatomical pruning, we identified a number of shared circuits likely responsible for placebo and nocebo responses across both rats and humans. Our findings underscore the translational relevance of rodent models for studying human pain processes and targeting these shared neural circuits may hold promise for developing new therapies to treat pain conditions. Methods Overall experimental design This study sought to directly compare the neural activity and circuits underlying placebo analgesia and nocebo hyperalgesia in both rats and humans, with the goal of identifying conserved neural circuits that could be targeted in the development of treatments for pain modulation. Rats provide a highly controlled model for dissecting specific neural mechanisms, while human data allows us to establish the relevance of these findings in a clinical context. To achieve this, we first quantified neural activity across 70 ROIs in the brain using c-Fos expression in rats and fMRI beta values in humans. Once neural activity was measured for each ROI, estimation statistics were applied to compare overall activity levels between control and placebo and nocebo conditions, identifying the regions with the most significant changes. This allowed for a direct comparison of neural activity patterns between species. Next, to define the neural circuits driving these responses, we examined functional connectivity—specifically, connections that were highly active during placebo and nocebo conditions. These functional connections were then pruned and weighted using known anatomical pathways from the Rat Connectome Project, ensuring that the resulting networks reflected biologically plausible circuits. All graphs were created in either GraphPad Prism v9.3.1 or Gephi software 0.10.1 (Gephi Consortium, https://gephi.org/ ) and imported into Adobe Illustrator 2021 (v25.2) to create the figurework. Rat experiments Animals and housing The experimental protocols used in this study were approved by the University of Sydney Animal Care and Ethics Committee (Project Number 1165). All procedures followed the guidelines outlined by the NHMRC's 'Code for the Care and Use of Animals in Research' and the NSW Animal Research Act (2007). The principles of the three R's (replacement, reduction, and refinement) were strictly applied to minimize pain and discomfort, and the study adhered to the IASP's ‘Ethical Guidelines for Investigations of Experimental Pain in Conscious Animals.’ Six-week-old male (n = 50) Sprague-Dawley rats (ARC, Perth, WA, Australia), weighing 170-220g upon arrival were used for these experiments. Rats were housed in groups of four in individually ventilated cages, with ad libitum access to standard chow and water. Both the housing and testing rooms were kept at a controlled temperature (22 ± 1°C) and humidity (40–70%). To align with the rats' active period, the housing room operated on a reversed 12-hour light/dark cycle, with lights off at 07:00. Eliciting placebo analgesia and nocebo hyperalgesia The experimental design, behavioural procedures and behavioural results have been reported previously (Boorman & Keay, 2023 ). A response conditioning protocol was used to induce placebo analgesia and nocebo hyperalgesia in rats with a chronic constriction injury (CCI) of the sciatic nerve. Six days post-injury, rats underwent conditioning using a hot/cold plate analgesiometer, with 10 trials conducted over 5 consecutive days (5 morning and 5 afternoon sessions). Placebo groups were conditioned at a non-noxious thermoneutral temperature (30°C), while nocebo groups were conditioned at a noxious cold temperature (4°C). On Test Day, both groups were exposed to a mildly noxious cold stimulus (20°C), and differences in hind paw withdrawal responses were measured to assess conditioned placebo or nocebo effects. A 20°C natural history control group, which did not undergo conditioning, was also tested for comparison. To assess the impact of contextual salience on placebo and nocebo effects, an enhanced context group was conditioned with additional sensory cues. Rats were randomly assigned to each group, and all data, including video recordings, are available upon request. All response-conditioning sessions took place in a temperature- and humidity-controlled room (21 ± 1°C, 40–75% humidity) separate from the housing room. The control group and the minimal context groups were tested in a context designed to minimize the strength and saliency of sensory cues, with rats tested under dim red lighting (~ 10 lux) in a quiet, odour-free environment. Enhanced context groups were conditioned in a custom-built chamber (60cm x 45cm x 30cm) under medium-level white lighting (~ 50 lux), wideband white noise (75–80 dB), and a strong vanilla scent from a plug-in diffuser (Fig. 1 o). The chamber was cleaned with an apple-scented disinfecting wipe between tests to maintain olfactory consistency. To prevent the possibility of lingering odours from affecting the minimal context groups, enhanced context groups were always tested at the end of the testing period. Rats were tested 10 times over 5 consecutive days, with 5 morning and 5 afternoon sessions. Testing began approximately + 2 hr from lights off for morning sessions and approximately + 8 hr from lights off for afternoon sessions, with each cage of rats tested in the same sequence at ~ 5-minute intervals. Perfusions were performed 90 minutes after testing under deep pentobarbital anaesthesia, using 400 ml of cold heparinised saline followed by 400 ml of cold 4% paraformaldehyde in sodium acetate-borate buffer, pH 9.6. Chronic constriction injury (CCI) surgery All rats underwent a unilateral chronic constriction injury (CCI) of the sciatic nerve, following the method initially detailed by (Bennett & Xie, 1988 ). At the time of surgery, the rats weighed between 240 and 280 grams. The procedure began with anaesthesia induced and maintained using isoflurane (5% for induction and 2.5% for maintenance) delivered in 100% oxygen at a flow rate of 1.5 L/min. After achieving a surgical plane of anaesthesia, the right hind limb was shaved and sterilized with povidone-iodine. A 2 cm incision was made parallel to the femur, and blunt dissection of the biceps femoris was performed to expose the sciatic nerve. Four chrome catgut ligatures (5 − 0, Johnson & Johnson) were loosely tied around the nerve, spaced 1 mm apart, just proximal to its trifurcation. Care was taken to ensure that the ligatures compressed the nerve without obstructing epineural blood flow. The nerve was then repositioned, and the incision was closed with Michel clips. A few drops of lignocaine (20 mg/ml) were applied to the incision site, followed by a dusting of topical antibiotic powder (Tricin®, Jurox). To prevent licking of the wound, a mixture of petroleum jelly and quinine was applied. Post-surgery, the rats were placed in individual cages and allowed to recover until they regained mobility and alertness (approximately 30 minutes) before being returned to their home cages. c-Fos Immunohistochemistry The brains of 8 rats per group were selected and sectioned at 50 µm using a cryostat (Leica CM1950) into a 1 in 8 series. One series from each brain was stained for c-Fos. Free-floating sections were first washed in 0.1 M PBS (3 x 10 minutes) at room temperature. Sections were then permeabilized in 50% ethanol for 30 minutes, followed by quenching of endogenous peroxidase activity with 3% hydrogen peroxide in 50% ethanol for 30 minutes. After further washes (3 x 10 minutes in PBS), sections were incubated in 10% normal horse serum (NHS) in 0.1 M PBS for 30 minutes to block non-specific binding. Sections were then incubated overnight at 4°C with polyclonal rabbit anti-c-Fos IgG (1:3000 in 2% NHS/PBS; Abcam, RRID:AB_2737414). The following day, sections were washed in PBS and incubated with horse anti-rabbit IgG (1:500 in 2% NHS/PBS; Vector Laboratories, RRID:AB_2336201) for 2 hours at room temperature. After additional washes, sections were incubated in ExtrAvidin Peroxidase (1:1000 in PBS; Sigma-Aldrich) for 2.5 hours. c-Fos immunoreactivity was visualized using 3,3’-diaminobenzidine tetrahydrochloride (DAB) as the chromogen, with the glucose oxidase method producing a brown precipitate. The reaction was carried out on ice and stopped after ~ 15 minutes with PBS washes (4 x 10 minutes), once optimal contrast was achieved. Sections were mounted on gelatin-coated slides, allowed to dry for 48 hours, dehydrated through ascending ethanol series, cleared in histolene, and coverslipped with DPX mounting medium. The brain ROIs were carefully selected based on their involvement in key processes such as pain transmission and modulation, as well as the processing of contextual information, including olfactory cues. A number of regions, such as orbitofrontal cortex, NAc and claustrum were also included due to their critical roles in integrating sensory information, motivational states, and decision-making processes—elements central to both placebo and nocebo responses. In total, 70 brain regions were analyzed to capture the full scope of these mechanisms. All images were captured using a Gryphax Kapella camera (Jenoptik, Jena, Germany). ROIs were identified based on the cytoarchitectural features of the tissue, with the stereotaxic rat atlas of Paxinos and Watson (2005) serving as a guide. Each microscope slide was coded to blind the experimenter (DB) during both image capture and analysis. For each ROI, 1, 2, or 3 slices per brain were analyzed and averaged, depending on the region. ROIs were manually outlined using ImageJ software, and c-Fos density was calculated for each ROI in each brain. Human experiments Participants and apparatus All experimental procedures were approved through the University of Sydney Human Research Ethics Committee (HREC:2019/037), consistent with the Declaration of Helsinki. 46 healthy control participants were recruited for this study, with all 46 undergoing the placebo component, and 25 participants undergoing the nocebo component in addition to placebo, providing written informed consent on arrival. Participants were provided with an emergency buzzer whilst inside the scanner so that they could stop the experiment at any time. Before exiting the study, participants were informed as to the necessary deception and true methodology of the experiment both verbally and through a written statement. Noxious thermal stimuli were delivered throughout the protocol using a Peltier-element thermode device (Medoc LTD Advanced Medical Systems, Rimat Yishai, Israel), connected to a thermal sensory analyser which delivered eight stimuli at a pre-programmed temperature for 15-seconds (2.5 second ramp up, 10 second plateau, 2.5 second ramp down), each separated by a 15-second inter-stimulus interval at a baseline temperature of 32 degrees Celsius (°C). Functional magnetic resonance imaging (fMRI) sequences were acquired using a whole-body Siemens MAGNETOM 7 Tesla (7T) MRI system (Siemens Healthcare, Erlangen, Germany) with a combined single-channel transmit and 32-channel receive head coil (Nova Medical, Wilmington MA, USA). located at the Melbourne Brain Centre Imaging Unit in Melbourne, Victoria. Placebo and Nocebo Conditioning On entering the study participants were shown three creams: a control cream described as Vaseline, a placebo cream labelled and described to contain the analgesic additive Lidocaine, and a nocebo cream labelled and described to contain the topical irritant, Capsaicin. The three creams were placed in adjacent locations on participants’ right forearm and remained applied for five minutes to “take effect”. During this time, a thermal calibration protocol was conducted, with the thermode attached to participants’ left forearm, delivering a randomized series of temperatures between 44-48.5°C in the timings described above. Participants were informed this protocol was conducted to establish their moderate pain intensity – a temperature eliciting between 4 to 5 out of 10 on a 10-point scale (0 = no pain; 10 = worst pain imaginable), and that this moderate intensity would be used for the remainder of the study applied to each of the three creams. In reality, three temperatures were recorded, one eliciting a low pain (2–3/10 VAS), one eliciting a high pain (6–7/10 VAS), as well as the initially described moderate intensity. The three creams were then removed from participants’ forearms with the experimenter wearing gloves to enhance belief the cream’s contained active ingredients, and a conditioning protocol was conducted. Throughout two rounds of a response conditioning protocol (Fig. 1 a-d), different intensity noxious stimuli were applied to each of the three cream sites (low intensity to placebo, moderate intensity to control, high intensity to nocebo), despite informing participants that all three cream sites would be receiving identical temperature and intensity moderate stimuli. Participants recorded their pain in real time using a visual analogue scale, building belief that the respective placebo and nocebo creams were taking effect relative to the control cream. The order of stimulation and locations of the placebo and nocebo creams proximal or distal relative to control was counterbalanced between participants to reduce ordering or sensitivity effects. Once two rounds of conditioning were complete, participants exited the session and returned the following day for an fMRI scanning session. Before scanning, a brief reinforcement protocol was conducted where each cream site, now applied to the left forearm received four noxious stimuli at the same individually calibrated low- moderate- and high-intensity as during conditioning. This was conducted to have participants remember the relative effect of the creams from the prior day, and re-establish the correct moderate intensity for testing. Once complete, the three creams were then applied to the right forearm, and participants entered into the MRI scanner. Placebo and Nocebo Test Over the course of four independent fMRI series, each of the cream sites were stimulated a total of eight times in the timings described above (15-second stimulus, 15-second inter-stimulus interval). Dissimilar to conditioning, during the test protocol all three cream sites received identical intensity moderate stimuli, with the mean difference between VAS scores recorded during control-site stimulation relative to the placebo-site encoding their placebo analgesia response, and vice versa for the nocebo-site and their nocebo hyperalgesia response. The control site was stimulated twice to provide a “pre” and “post” measurement for both placebo analgesia and nocebo hyperalgesia, and this order was kept counterbalanced in the same order as during conditioning (i.e. 50% of participants received control- and placebo-site series first and vice versa ). During this test phase, participants recorded their ongoing pain in real time during a digital and MR-compatible VAS, reflected on a screen above with the position of a slider controlled using a two-button button box. Determining placebo and nocebo responders Using test phase VAS ratings, participants’ mean pain to each of the eight stimuli delivered to either the control- and placebo cream sites, or the control- and nocebo cream sites were entered into a bootstrapped permutation model, where 10,000 artificial samples were generated, sampled with replacement, encoding both their mean control and placebo, or control and nocebo responses. These responses were significance tested with a significant reduction of pain during placebo relative to control, or significant enhancement of pain during nocebo relative to control indicating a placebo or nocebo responder, respectively. From our sample, 22/46 participants were identified as placebo responders (46%), and 14/25 participants identified as nocebo responders (56%). Whilst fMRI series were collected on the entire sample and are published elsewhere (Crawford et al., 2021 ; Crawford et al., 2023a ), this investigation involves analyses conducted solely on these two responder cohorts. MRI acquisition and preprocessing parameters All image series were acquired with participants positioned supine within the head coil, with sponges inserted to support the head and minimise lateral and translational movement. A T1-weighted anatomical image set covering the whole brain was collected (repetition time = 5000 ms, echo time = 3.1ms, raw voxel size = 0.73x0.73x0.73mm, 224 sagittal slices, scan time = 7mins). Each of the four fMRI acquisitions consisted of 134 gradient echo echo-planar measurements using BOLD contrast covering the entire brain. Images were acquired interleaved with a multi-band factor of four and an acceleration factor of three (repetition time = 2500ms, echo time = 26ms; raw voxel size = 1.0x1.0x1.2mm, 124 axial slices, scan time = 6:25mins). Image preprocessing and statistical analyses were performed using SPM12 (Friston, 2003) and custom software (Diedrichsen, 2006 ). The first five volumes of each scan were removed from the model due to excessive signal saturation from the scanner. The remaining 129 functional images were slice-time and motion corrected and the resulting 6 directional movement parameters were inspected to ensure that all fMRI scans had no greater than 1mm of linear movement or 0.5 degrees of rotation movement in any direction. Images were then linearly detrended to remove global signal changes, physiological noise relating to cardiac and respiratory frequency was removed using the DRIFTER toolbox (Sarkka et al., 2012 ), and the 6-parameter movement related signal changes were modelled and removed using a linear modelling of realignment parameters procedure (Macey et al., 2004 ). From this point, two image sets were created: one wholebrain and one brainstem-isolated functional image set. For wholebrain images, each individual’s fMRI image sets were resliced to 1mm isotropic voxels and co-registered to their own T1-weighted anatomical. The T1 was then spatially normalized to the MNI152 template in Montreal Neurological Institute (MNI) space using the computational anatomy toolbox (CAT) (Gaser et al., 2024 ), and these parameters applied to the fMRI image sets. The normalized fMRI images were then spatially smoothed using a 6mm full-width at half maximum Gaussian filter. For brainstem-isolated images, the Spatially Unbiased Infratentorial Template (SUIT) toolbox was used to first isolate in the T1 image series a section of the image encompassing the brainstem and cerebellum. Manual made masks were then generated covering the rostrocaudal extent of the brainstem and cerebellum in both the brainstem isolated T1 and wholebrain fMRI image series, resliced into 0.5mm isotropic voxels. Both image series were cropped using the manual masks, and the T1 normalized to the SUIT template in MNI space using the SUIT normalize function, with these parameters applied to the cropped fMRI series. The normalized fMRI images were then spatially smoothed using a 1mm FWHM gaussian filter. In both the wholebrain and brainstem-isolated image series, signal intensity changes were determined by applying a repeating boxcar model encoding scan volumes where noxious stimuli were present was applied, convolved with a canonical hemodynamic response function. This analysis resulted in brain maps in which each voxels value represented the magnitude of signal intensity changes during each noxious stimulus period. VOI generation and beta-value extraction Individual VOIs were generated for each cortical and brainstem pain-responsive site, derived from the Human Connectome Project atlas extended (HCPex) (Huang et al., 2022 ) for the cortex and subcortex, and hand drawn with reference to Mai and Paxinos’ Atlas of the Human Brain (Mai et al., 2015 ). In total, 71 VOIs were generated and are listed in Supplementary Table 1. A measurement of average signal intensity, that is, relative increases or decreases in neuronal activation during the application of noxious stimuli relative to baseline periods of the fMRI scan, were extracted from each of the matched control and placebo or control and nocebo contrast images. These values were then recorded in a matrix table for further analyses. Placebo and Nocebo-Induced Changes in Regional Activity For the calculation of placebo and nocebo-related changes in overall regional activity, we used estimation statistics to quantify the differences between the placebo and control groups and the nocebo and control groups. The top 20 regions showing the largest changes were identified for both placebo and nocebo conditions (Figs. 3 , 4 ). Estimation statistics were computed using the web application built by Hung Nguyen (estimationstats.com), which utilizes the Python code developed by (Ho et al., 2019 ). For each test, effect sizes (Cohen's d) and their associated 95% confidence intervals were calculated using bias-corrected and accelerated bootstrap resampling with replacement, with 5000 bootstrap samples applied per test. We opted for estimation statistics over classical significance testing due to their ability to offer richer, more nuanced insights into the magnitude and precision of the observed changes in neural activity. By focusing on effect sizes, this method captures the magnitude and uncertainty of neural activity changes, highlighting the most relevant patterns without being restricted by arbitrary significance thresholds. This approach was crucial for comparing species-specific and shared patterns of neural connectivity, ensuring that we prioritized meaningful biological differences over potentially spurious statistical results. c-Fos and beta-value functional connectivity and neural circuitry For both the c-Fos density and the beta-values, a correlation matrix was generated for each group (control, placebo, and nocebo) using GraphPad Prism 9. Each matrix represented the pairwise correlations between the 70 ROIs, capturing the strength of connectivity based on Pearson’s r-values. For the control groups, we isolated the top 100 strongest r-values in each species to create an adjacency matrix (created in Microsoft Excel), representing the most robust functional connections. These connections were visualized in network plots for both rat and human control groups. To maintain consistency, the same r-value cut-off that isolated the top 100 connections in the control groups was applied to the placebo and nocebo groups. This allowed us to determine changes in overall connectivity during placebo analgesia and nocebo hyperalgesia, and the connectivity patterns were also plotted for each group. We then identified shared connections between the rat and human groups (Tables 1 – 3 ) to highlight species-conserved connections. To visualize the functional connectivity graphs, we used Gephi software v0.10.1. The adjacency matrices, which were calculated from the correlation matrices, were imported into Gephi as undirected connections. We utilized the circular layout, with nodes arranged by their ID. The darkness of each node was scaled according to the number of connections it held, allowing for easy identification of hub regions. After generating the graphs, they were exported into Adobe Illustrator for further refinement. Shared connections between rats and humans were manually highlighted in red to visualize conserved pathways across species. Finally, we pruned these networks by cross-referencing with anatomical data from the Rat Connectome Project, ensuring that only connections with known anatomical pathways remained in the analysis. The connectome provided further details about the strength and direction of each connection, assigning connection strengths ranging from 0.5 (very light) to 4 (very strong), forming the basis of the final neural circuitry model. This pruning step allowed us to refine the functional networks into anatomically grounded circuits, providing insights into the neural circuits underlying placebo analgesia and nocebo hyperalgesia across species. Similar to the functional connectivity analysis, adjacency matrices were generated from the correlation matrices and imported into Gephi as directed connections. We applied the Force Atlas layout with the following parameters: inertia = 0.1, repulsion strength = 1000, attraction strength = 10, maximum displacement = 1000, auto stabilize function = on, autostab strength = 80, autostab sensibility = 0.2, gravity = 3000, attraction distribution = off, adjust by sizes = on, and speed = 1.0. Node size was determined by the degree of connectivity, where more connected nodes appeared larger, while the size of the arrows represented the strength of the anatomical projection. This method allowed us to generate directed graphs that visually emphasize the most strongly connected nodes and circuits, reflecting the overall structure of the neural networks across species. Declarations Acknowledgements and Conflicts of Interest Statement We wish to thank the many volunteers in this study. The authors acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability, at Monash University. This work was funded by the National Health and Medical Research Council of Australia Grant 1130280 and the NWG Macintosh Memorial Grant, University of Sydney, Australia. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author contributions Conceptualization: DB, LC, LH, KK. Methodology: DB, LC, LH, KK. Software: DB, LC, LH. Validation: DB, LC, LH, KK. Formal analysis: DB, LC. Investigation: DB, LC. Resources: LH, KK. Data curation: DB, LC. Writing – original draft: DB, LC, LH, KK. Writing – Reviewing & editing: DB, LC, LH, KK. Visualization: DB. Supervision: LH, KK. Project administration: DB, LC, LH, KK. Funding acquisition: LH, KK. 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Xu, L, Wan, Y, Ma, L, Zheng, J, Han, B, Liu, FY, Yi, M & Wan, Y (2018), 'A Context-Based Analgesia Model in Rats: Involvement of Prefrontal Cortex', Neurosci Bull, vol. 34, no. 6, pp. 1047-1057. Zeng, Y, Hu, D, Yang, W, Hayashinaka, E, Wada, Y, Watanabe, Y, Zeng, Q & Cui, Y (2018), 'A voxel-based analysis of neurobiological mechanisms in placebo analgesia in rats', Neuroimage, vol. 178, pp. 602-612. Zhang, RR, Zhang, WC, Wang, JY & Guo, JY (2013), 'The opioid placebo analgesia is mediated exclusively through mu-opioid receptor in rat', Int J Neuropsychopharmacol, vol. 16, no. 4, pp. 849-56. Zhang, Y, Huang, W, Shan, Z, Zhou, Y, Qiu, T, Hu, L, Yang, L, Wang, Y & Xiao, Z (2024), 'A new experimental rat model of nocebo-related nausea involving double mechanisms of observational learning and conditioning', CNS Neurosci Ther, vol. 30, no. 2, pp. e14389. Zunhammer, M, Spisak, T, Wager, TD, Bingel, U & Placebo Imaging, C (2021), 'Meta-analysis of neural systems underlying placebo analgesia from individual participant fMRI data', Nat Commun, vol. 12, no. 1, pp. 1391. Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTable1EstimationStats.xlsx Supplementary Table 1 SupplementaryMaterialBoormanetal2024.pdf Supplementary Figures and Tables Cite Share Download PDF Status: Published Journal Publication published 05 Apr, 2025 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5590281","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":390665343,"identity":"5830b817-c7f4-4e77-a6e6-6e0f1f1d1265","order_by":0,"name":"Damien Boorman","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-2819-1948","institution":"University of Toronto Mississauga","correspondingAuthor":true,"prefix":"","firstName":"Damien","middleName":"","lastName":"Boorman","suffix":""},{"id":390665344,"identity":"c1ca89cb-3e75-4a9a-9f60-258dd3df8c9d","order_by":1,"name":"Lewis Crawford","email":"","orcid":"","institution":"University of 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07:05:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8889195,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5590281/v1/eba46147-0861-4cd8-b808-c8bdf0e570aa.pdf"},{"id":78824999,"identity":"21f578c9-c7c8-401f-9edd-d0a524cc0f1e","added_by":"auto","created_at":"2025-03-19 12:21:36","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23150,"visible":true,"origin":"","legend":"Supplementary Table 1","description":"","filename":"SupplementaryTable1EstimationStats.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5590281/v1/f9613113137b03db62d7405f.xlsx"},{"id":78826060,"identity":"4edf29b5-18e2-469e-a4f7-56b851260b68","added_by":"auto","created_at":"2025-03-19 12:37:36","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":579240,"visible":true,"origin":"","legend":"Supplementary Figures and Tables","description":"","filename":"SupplementaryMaterialBoormanetal2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5590281/v1/f56ce64870387720cbaac9dc.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Placebo analgesia and nocebo hyperalgesia across species: direct neural comparisons between rats and humans","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic pain affects millions of individuals worldwide, and current treatments often provide inadequate relief, leading to significant physical, emotional, and economic burdens. Recent research has highlighted the potential utility of placebo analgesia as a powerful, non-pharmacological intervention that could augment chronic pain management by harnessing the brain\u0026rsquo;s endogenous pain modulation systems (Rossettini et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, nocebo hyperalgesia\u0026mdash;the phenomenon in which negative expectations worsen pain\u0026mdash;may significantly contribute to both the development and persistence of chronic pain conditions (Buchel, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Identifying ways to inhibit the nocebo effect could therefore be transformative in chronic pain therapy, potentially preventing its escalation and improving patient outcomes.\u003c/p\u003e \u003cp\u003eHistorically, studies on placebo analgesia and nocebo hyperalgesia have been primarily limited to humans, which have provided critical insights into the psychological and neurobiological mechanisms driving these effects. Only recently have researchers begun to develop and validate animal models to study these phenomena in more controlled experimental settings (Xu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Boorman \u0026amp; Keay, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Boorman \u0026amp; Keay, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Rodent models enable the experimental manipulation of neural circuits and molecular pathways that are not possible in human studies, allowing us to identify the cellular and synaptic mechanisms driving these pain modulatory processes. Moreover, rodent models provide an avenue for testing potential pharmacological or genetic interventions that could mitigate nocebo hyperalgesia or enhance placebo analgesia, which could lead to novel therapeutic strategies for pain conditions.\u003c/p\u003e \u003cp\u003eSignificant progress has been made in mapping the neurobiological substrates of placebo analgesia and nocebo hyperalgesia. Studies in humans have implicated key brain regions, including the prefrontal cortex, anterior cingulate cortex (ACC), insular cortex and midbrain periaqueductal gray (PAG), which are involved in top-down modulation of pain through cognitive and emotional regulation (Benedetti et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Freeman et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, in rodent models, structures including the frontal cortical regions, amygdala, ACC, and PAG have also been shown to play central roles in modulating pain responses during placebo and nocebo conditions (Zhang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Furthermore, pathways involving the amygdala and nucleus accumbens (NAc), which regulate emotional processing and reward, have emerged as critical mediators in both placebo-induced analgesia and nocebo-induced hyperalgesia (Schafer et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These circuits reflect the complex interplay between cognitive, emotional, and sensory systems in the modulation of pain, underscoring the necessity of cross-species studies to fully elucidate these processes.\u003c/p\u003e \u003cp\u003eDespite these advances, there has yet to be a direct comparison of the neurobiological mechanisms underlying placebo analgesia or nocebo hyperalgesia between humans and animals. Our study addresses this critical gap by directly comparing the regional activation, functional connectivity and neural circuits activated during placebo and nocebo responses in both humans and rats. By doing so, we aim to establish a cross-species framework that will enhance our understanding of conserved and species-specific mechanisms of pain modulation. This comparison is particularly important as it will validate rodent models as translational tools for pain research, facilitating the development of new interventions that target placebo and nocebo pathways across species.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResponse conditioning elicits placebo analgesia and nocebo hyperalgesia in both humans and rats\u003c/h2\u003e \u003cp\u003eTo elicit placebo analgesia and nocebo hyperalgesia in humans, we used a common response conditioning protocol. Low, moderate and high thermal stimuli were repeatedly paired with topical creams labelled Lidocaine (placebo), Vaseline (control) and Capsaicin (nocebo), respectively. During subsequent fMRI scans, all three creams were reapplied, but the moderate intensity stimulus was delivered to each cream site (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-d). Permutation testing of pain ratings between the control cream and the Lidocaine or Capsaicin creams revealed placebo responder subpopulations (22/46; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee-g) and nocebo responder subpopulations (14/25; Figure h-j), respectively. A similar protocol was used to elicit placebo analgesia and nocebo hyperalgesia in rats. However, while for the human study we were able to employ a within-subject design, the study in rats was necessarily a between-subjects design. As such, separate groups of rats underwent a response conditioning procedure in which either a low, moderate or high intensity thermal stimulus was paired with contextual cues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ek-m). To assess the impact of cue saliency on the strength of conditioned responses, placebo and nocebo groups were tested either in a \u0026lsquo;minimal context\u0026rsquo; or an \u0026lsquo;enhanced context\u0026rsquo; setting (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003en,o). Subsequently, all groups were tested in their same context at the moderate stimulus. Comparisons of pain behaviours between the control group and the low and high intensity conditioned groups revealed that this procedure produced strong placebo analgesia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ep,q) and nocebo hyperalgesia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003er,s) in the majority of rats tested.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFirstly, to assess changes in overall regional brain activity during placebo and nocebo in humans, beta values representing the degree of correlation between the blood oxygen level dependent (BOLD) fMRI signal intensity and the thermal stimulation pattern was calculated for 70 regions of interest (ROIs) for each fMRI scan (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Beta values of each ROI for the placebo/nocebo scans were then compared to the control scan. To assess changes in overall regional brain activity during placebo and nocebo in rats, c-Fos expression density in 70 ROIs across the brain was quantified for each placebo and nocebo group and compared to the control group. These ROIs were chosen based on their known or putative involvement in the processing, transmission and modulation of pain and/or contextual cues. Given the different methodologies used to measure brain activity for humans and rats, to make direct comparisons between species we chose to use estimation statistics to identify the top 20 ROIs that showed the largest effect sizes between the placebo/nocebo conditions and controls. See Supplementary Table\u0026nbsp;1 for detailed results, and Supplementary Table\u0026nbsp;2 for complete list of ROIs and abbreviation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHuman and rats show similar placebo-associated changes in activity across multiple brain regions\u003c/h3\u003e\n\u003cp\u003eIn humans, the average effect size (Cohen\u0026rsquo;s d) between placebo and control for the top 20 ROIs was 0.36 (range 0.25\u0026ndash;0.64). In rats, for the minimal context group, the average effect size in c-Fos expression density between placebo and control of the top 20 ROIs was 0.80 (range 0.53\u0026ndash;2.14). For the enhanced context group, the average effect size of the top 20 ROIs was 0.64 (range 0.46\u0026ndash;1.19). Similar patterns of activity were observed between the two rat groups. Placebo analgesia was associated with increases in c-Fos expression in 19/20 ROIs in the minimal context and 18/20 in the enhanced context, with 8 overlapping ROIs that showed similar effect sizes (the gracile nuclei, ACC, NAc, and basolateral amygdala [BLA]). However, the differences between these groups, most notably in the PAG, posterior insula cortex and paraventricular thalamus (PVT), likely reflects the effect of the enhanced context on regional brain activity. Remarkably, 8 regions were identified as having placebo-related changes in activity in both humans and rats. These were the contralateral primary somatosensory cortex (S1), ipsilateral cuneate/gracile, contralateral medial (PB), ipsilateral basolateral amygdala (BMA), the septal nuclei and the rostral and caudal PVT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eHuman and rats show similar nocebo-associated changes in activity across brain regions\u003c/h3\u003e\n\u003cp\u003eIn humans, the average effects size between the nocebo and control scans for the top 20 VOIs was 0.53 (range 0.39\u0026ndash;0.95). In rats, for the minimal context group, the average effect size of the top 20 ROIs was 0.66 (range 0.49\u0026ndash;1.12), while for the enhanced context group, the average effect size was 0.76 (range 0.6\u0026ndash;1.15). Once again, similar overall patterns of activity were observed between these two rat groups, with 7 overlapping regions, each of which showed similar effect sizes. However, in stark contrast to the placebo groups, nearly all the top 20 ROIs in both nocebo groups had decreases in c-Fos expression compared to the control (16/20 in the minimal context and 19/20 in the enhanced context). Remarkably, 11 regions were identified as having nocebo-related changes in activity in both humans and rats. These were the ipsilateral primary motor cortex, caudal and rostral PVT, paraventricular hypothalamus, locus coeruleus, rostral ventromedial medulla, contralateral medial and lateral parabrachial nuclei, and the ipsilateral cuneate nucleus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eFunctional connectivity and functional circuit networks\u003c/h3\u003e\n\u003cp\u003eWe next determined which neural circuits were active during placebo analgesia and nocebo hyperalgesia using functional connectivity. Functional connectivity can be defined as the temporal coincidence of spatially distant neurophysiological events (Friston, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). That is, activity in one brain region/nucleus is correlated with and predictive of activity in another. For the rats, it is therefore possible to create functional connectivity maps for each group by correlating c-Fos expression between each of the ROIs. High or low correlations (Pearson\u0026rsquo;s r-values) between two regions indicates high or low functional connectivity, respectively. Indeed, c-Fos functional connectivity is now a widely used and accepted approach (Wheeler et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Silva et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vetere et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Terstege et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, as recently outlined by Terstege et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), adequate group sizes are essential (n\u0026thinsp;=\u0026thinsp;8 or above) to produce reliable results.\u003c/p\u003e \u003cp\u003eIn order to better align our human data with the rat analysis, we used the beta values from the fMRI analyses to assess functional connectivity, rather than utilizing traditional fMRI time-series data. As such, the beta values, representing the overall level of neural activation in each region during noxious stimuli, were correlated between ROIs to establish functional connectivity. This method provides a comparable measure to the c-Fos correlations used in rats, allowing us to assess functional relationships across species using a similar framework. To further facilitate cross-species comparisons, we identified the top 100 strongest functional connections from the control group data in both rats and humans. These connections were selected based on their strength of correlation (Pearson\u0026rsquo;s r values) and plotted to visualize and compare connectivity patterns between the two species (Supplementary Fig.\u0026nbsp;1). Comparison of functional connectivity maps in the control groups revealed species-specific patterns. The top 100 human connections were dominated by left-right frontal cortical links, while rats exhibited stronger cortical-subcortical connectivity. Notably, 16 connections were shared between species, significantly higher than the 4.66 expected by chance, largely involving frontal cortical regions, the ACC, and middle cingulate cortex (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Graph metrics were comparable: the rat map showed an average path length of 3.145, the human map had an average path length of 3.479, reflecting similar network efficiency despite species-specific topologies (see Supplementary Table\u0026nbsp;2 for full graph metrics).\u003c/p\u003e\n\u003ch3\u003eEnhancing the context during conditioning increases brain-wide functional connectivity\u003c/h3\u003e\n\u003cp\u003eTo assess changes in functional connectivity during placebo and nocebo responses, we applied the same \u003cem\u003er\u003c/em\u003e-value cut-offs as used in the control groups (rats: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.879, humans: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.699) (See Supplementary Fig.\u0026nbsp;4), with connections shared with controls excluded from this analysis to isolate only unique placebo-related and nocebo-related connections. For the rats, this analysis revealed a divergent pattern: minimal context rats exhibited a decrease or no change to their functional connectivity, with 69 connections for placebo and 105 for nocebo, whereas the enhanced context placebo and nocebo groups had 291 and 268 connections, respectively (Supplementary Figs.\u0026nbsp;2, 3). This would indicate that the enhanced context greatly increased overall functional connectivity of the brain, particularly between frontal cortical regions and the amygdala and olfactory areas.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConserved Functional Connectivity Patterns in Human and Rat Placebo Responses\u003c/h2\u003e \u003cp\u003eHuman placebo responses also showed an increase in overall functional connectivity, expanding to 200 connections compared to controls. Notably, 47 of these connections were shared between humans and the two rat placebo groups (Supplementary Fig.\u0026nbsp;2; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This shared connectivity was concentrated in the amygdala, ACC, and NAc. These overlaps suggest a conserved set of circuits or networks across species that either accompany or drive the placebo analgesic response.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLimited Cross-Species Overlap in Nocebo-Related Functional Connectivity\u003c/h3\u003e\n\u003cp\u003eHuman nocebo responses showed a modest increase in overall functional connectivity, with 130 connections compared to controls. In contrast to the placebo condition, only 4 connections were shared between humans and minimal context rats, and 19 with enhanced context rats. The increased connectivity in enhanced context rats (268 connections), did not translate to significant overlap with humans. Importantly, the nocebo networks exhibited a distinct topology from placebo networks, particularly with reduced amygdala involvement in human nocebo responses. This suggests that nocebo hyperalgesia is driven by different neural circuits across species, highlighting the specificity of nocebo-related mechanisms.\u003c/p\u003e\n\u003ch3\u003eNeural Circuits Underlying Pain, Placebo Analgesia, and Nocebo Hyperalgesia\u003c/h3\u003e\n\u003cp\u003eA major caveat of functional connectivity analysis is that it only reflects statistical correlations, not direct anatomical or causal relationships between regions. To refine our analysis, we pruned the functional connections using anatomical data from the Rat Connectome Project of the University of Rostock (Schmitt \u0026amp; Eipert, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which compiles tract-tracing studies in rodents. From this database, we extracted projection direction and strength for every functional connection in the isolated networks shown in Supplementary Figs.\u0026nbsp;2\u0026ndash;4. If there was weak or no evidence for an anatomical connection, it was removed from the network.\u003c/p\u003e \u003cp\u003eWe first applied this pruning and assigned strength to the control groups, generating force-directed graphs for humans and rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Shared circuits are highlighted in red. Both networks showed similar overall patterns, with frontal cortical regions interconnected in both species, while subcortical areas like the PAG and other brainstem regions had primarily local connections. The overlapping circuits between species were related predominantly to prefrontal and PAG areas. Indeed, despite the differences in these control networks, the presence of conserved pathways suggests fundamental similarities in pain processing between humans and rats.\u003c/p\u003e\u003cp\u003eFigure 6\u0026nbsp;shows the pruned placebo networks for humans and rats. Several key hub regions, defined as those with the highest weighted degree of connectivity, emerged across species and conditions. In humans, the PAG, posterior insular cortex, ACC, and BLA served as the primary hubs. The rat minimal context network, while more modular, shared similar hub regions, including the BLA, anterior insula, ACC, vlPAG, and orbitofrontal cortex. Interestingly, while the PAG in the minimal context rats formed an isolated, island-like network disconnected from the broader network, the human PAG was more integrated, with connections extending to other major hubs. In the enhanced context rats, hub activity shifted to regions such as the anterior and posterior insula, pyriform cortex, orbitofrontal cortex, and prelimbic cortex. The appearance of the pyriform cortex, anterior olfactory nucleus, and medial amygdala in this context likely reflects processing of the strong olfactory cues used during conditioning. Despite these differences, several core connections are shared across species, particularly in prefrontal and pain-modulatory regions, suggesting conserved pathways in placebo analgesia. All shared connections are detailed in Table 2, which includes the pruned connections, their direction, and the strength of these pathways.\u003c/p\u003e\n\u003cp\u003eFigure 7\u0026nbsp;shows the pruned nocebo networks for humans and rats. Key hub regions were identified in both humans and rats, though with fewer shared connections compared to placebo. In humans, the primary hubs included the prelimbic cortex, motor cortex, anterior and middle cingulate cortex, and the PAG. Unlike the placebo network, the PAG in humans formed an isolated network, showing no integration with the broader nocebo network. In the rat minimal context, the main hubs were the prelimbic cortex, posterior insula, and ACC, but notably, there were no shared connections between this network and the human nocebo network. For the enhanced context, the primary hubs shifted to the anterior insula, ACC, vlPAG, and BLA. Although there were more shared connections between the enhanced context rats and humans, the overall overlap was still far less than that observed in the placebo networks. These differences, particularly the isolated PAG in humans and the more integrated PAG in the enhanced context rats, highlight distinct network organizations between placebo and nocebo conditions. The shared connections across species were limited, suggesting a more species-specific recruitment of circuits during nocebo hyperalgesia compared to placebo analgesia. All shared connections for nocebo groups are detailed in Table 3, which includes the pruned connections, their direction, and the strength of these pathways.\u003c/p\u003e\n \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShared ROI-ROI functional and anatomical connections between humans and rats in the control groups. See\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePearson\u0026rsquo;s r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional Connection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRat (minimal)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnatomical connection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrength(s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrL(L)-PrL(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e 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align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIL(L)\u003cb\u003e\u0026harr;\u003c/b\u003eIL(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1(L)-M1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS1(L)\u003cb\u003e\u0026rarr;\u003c/b\u003eM1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC2(L)-ACC2(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACC2(L)\u003cb\u003e\u0026harr;\u003c/b\u003eACC2(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCC1(L)-MCC1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCC2(L)-MCC2(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erVLPAG(L)-rLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003erVLPAG(L)\u003cb\u003e\u0026harr;\u003c/b\u003erLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3, 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecVLPAG(L)-cDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecVLPAG(L)\u003cb\u003e\u0026harr;\u003c/b\u003ecDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erLPAG(L)-rVLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003erLPAG(L)\u003cb\u003e\u0026harr;\u003c/b\u003erVLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVH(L)-PVH(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePVH(L)\u003cb\u003e\u0026harr;\u003c/b\u003ePVH(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1, 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrL(R)-ACC1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrL(R)\u003cb\u003e\u0026rarr;\u003c/b\u003eACC1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1(R)-M1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS1(R)\u003cb\u003e\u0026rarr;\u003c/b\u003eM1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShared ROI-ROI functional and anatomical connections between humans and rats in the placebo groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003ePearson\u0026rsquo;s r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional Connection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eRat (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRat (enh)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAnatomical connection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStrength(s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrL(R)-ACC-1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrL(R)\u003cb\u003e\u0026harr;\u003c/b\u003eACC-1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epINS(R)-ACC-2(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003epINS(R)\u003cb\u003e\u0026harr;\u003c/b\u003eACC-2(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1, 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epINS(R)-MCC-1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003epINS(R)\u003cb\u003e\u0026rarr;\u003c/b\u003eMCC-1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epINS(R)-MCC-2(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003epINS(R)\u003cb\u003e\u0026larr;\u003c/b\u003eMCC-2(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epINS(R)-pINS(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003epINS(R)\u003cb\u003e\u0026harr;\u003c/b\u003epINS(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epINS(R)-aINS(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003epINS(R)\u003cb\u003e\u0026harr;\u003c/b\u003eaINS(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epINS(R)-ACC-2(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003epINS(R)\u003cb\u003e\u0026harr;\u003c/b\u003eACC-2(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epINS(R)-MCC-1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epINS(R)-MCC-2(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epINS(R)-rLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003epINS(R)\u003cb\u003e\u0026rarr;\u003c/b\u003erLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC-1(R)-Claus(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eACC-1(R)\u003cb\u003e\u0026harr;\u003c/b\u003eClaus(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC-1(R)-MCC-1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC-1(R)-MCC-1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eACC-1(R)\u003cb\u003e\u0026harr;\u003c/b\u003eMCC-1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC-2(R)-ACC-1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eACC-2(R)\u003cb\u003e\u0026larr;\u003c/b\u003eACC-1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC-2(R)-MCC-1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC-2(R)-MCC-2(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAc(R)-NAc(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAc(R)-Nash(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAc(R)-CeA(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLA(R)-NAc(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLA(R)-Nash(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBLA(R)\u003cb\u003e\u0026harr;\u003c/b\u003eNash(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLA(R)-BLA(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBLA(R)\u003cb\u003e\u0026harr;\u003c/b\u003eBLA(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1, 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLA(R)-CeA(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeA(R)-Sept(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMeA(R)\u003cb\u003e\u0026harr;\u003c/b\u003eSept(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeA(R)-MeA(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erVPLAG(R)-rVPLAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003erVPLAG(R)\u003cb\u003e\u0026harr;\u003c/b\u003erVPLAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erVPLAG(R)-rLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003erVPLAG(R)\u003cb\u003e\u0026harr;\u003c/b\u003erLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecVPLAG(R)-cDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erLPAG(R)-cLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003erLPAG(R)\u003cb\u003e\u0026harr;\u003c/b\u003ecLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecLPAG(R)-cVLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecLPAG(R)\u003cb\u003e\u0026harr;\u003c/b\u003ecVLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecLPAG(R)-cLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecLPAG(R)\u003cb\u003e\u0026harr;\u003c/b\u003ecLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecLPAG(L)-rLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecLPAG(L)\u003cb\u003e\u0026harr;\u003c/b\u003erLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erLC(R)-rLC(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003erLC(R)\u003cb\u003e\u0026harr;\u003c/b\u003erLC(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecLPAG(L)-cVLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecLPAG(L)\u003cb\u003e\u0026harr;\u003c/b\u003ecVLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeA(L)-Nash(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCeA(L)\u003cb\u003e\u0026rarr;\u003c/b\u003eNash(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCeA-(L)-NAc(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCeA-(L)\u003cb\u003e\u0026larr;\u003c/b\u003eNAc(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSept(L)-Nash(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSept(L)\u003cb\u003e\u0026rarr;\u003c/b\u003eNash(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNash(L)-NAc(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNash(L)\u003cb\u003e\u0026harr;\u003c/b\u003eNAc(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNash(L)-PrL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNash(L)\u003cb\u003e\u0026larr;\u003c/b\u003ePrL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCC-1(L)-MCC-2(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMCC-1(L)\u003cb\u003e\u0026harr;\u003c/b\u003eMCC-2(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC-1(L)-ACC-2(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eACC-1(L)\u003cb\u003e\u0026harr;\u003c/b\u003eACC-2(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5, 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC-2(L)-PrL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eACC-2(L)\u003cb\u003e\u0026harr;\u003c/b\u003ePrL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrL(L)-IL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrL(L)\u003cb\u003e\u0026harr;\u003c/b\u003eIL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOFC(L)-Claus(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOFC(L)\u003cb\u003e\u0026harr;\u003c/b\u003eClaus(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elPB(L)-mPB(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erPVT(R)-cPVT(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erPVT(L)-cPVT(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShared ROI-ROI functional and anatomical connections between humans and rats in the nocebo groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003ePearson\u0026rsquo;s r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional Connection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eRat (min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eRat (enh)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAnatomical connection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStrength(s)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrL(R)-S1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrL(R)\u003cb\u003e\u0026larr;\u003c/b\u003eS1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrL(R)-M1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrL(R)\u003cb\u003e\u0026harr;\u003c/b\u003eM1(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrL(R)-ACC-1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrL(R)\u003cb\u003e\u0026harr;\u003c/b\u003eACC-1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1(R)-PrL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS1(R)-M1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eS1(R)\u003cb\u003e\u0026rarr;\u003c/b\u003eM1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1(R)-M1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC-1(R)-NAc(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eACC-1(R)\u003cb\u003e\u0026rarr;\u003c/b\u003eNAc(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCC-1(R)-rVLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMCC-1(R)\u003cb\u003e\u0026rarr;\u003c/b\u003erVLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCC-2(R)-PrL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMCC-2(R)\u003cb\u003e\u0026harr;\u003c/b\u003ePrL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.5, 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNST(R)-Sept(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBNST(R)\u003cb\u003e\u0026harr;\u003c/b\u003eSept(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLA(R)-CeA(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBLA(R)\u003cb\u003e\u0026harr;\u003c/b\u003eCeA(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erVLPAG(R)-rVLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003erVLPAG(R)\u003cb\u003e\u0026harr;\u003c/b\u003erVLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecVLPAG(R)-cLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecVLPAG(R)\u003cb\u003e\u0026harr;\u003c/b\u003ecLPAG(R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecLPAG(L)-cVLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecLPAG(L)\u003cb\u003e\u0026harr;\u003c/b\u003ecVLPAG(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erLPAG(L)-M1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003erLPAG(L)\u003cb\u003e\u0026larr;\u003c/b\u003eM1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCC-2(L)-M1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCC-2(L)-PrL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMCC-2(L)\u003cb\u003e\u0026harr;\u003c/b\u003ePrL(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1, 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1(L)-aINS(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eaINS(L)-Claus(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eaINS(L)\u003cb\u003e\u0026harr;\u003c/b\u003eClaus(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.5, 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThis study presents a novel approach to cross-species analysis, utilizing c-Fos immunohistochemistry in rats and fMRI in humans to create comparable, species-specific maps of brain activity and neural circuitry. We propose an innovative framework for investigating conserved and distinct brain circuits underlying complex behaviours and physiological responses. Furthermore, this approach enables preclinical research to prioritize the neural pathways that are most likely conserved between species, accelerating translational efforts and refining focus on the circuits with the greatest relevance to humans. With respect to placebo analgesia and nocebo hyperalgesia, our findings revealed that while there are notable species-specific differences, certain key regions\u0026mdash;such as the ACC, PAG, and amygdala\u0026mdash;show conserved patterns of activation and connectivity in response to placebo and nocebo conditioning. However, given the novelty of our approach, when interpreting these findings, two critical considerations arise: 1) how well our results align with established literature on placebo and nocebo responses, and 2) the comparability of the methodologies used for each species.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHow do our findings compare to what is known about the neural activity during placebo analgesia and nocebo hyperalgesia?\u003c/b\u003e \u003c/p\u003e \u003cp\u003efMRI has now been extensively employed to investigate the neural basis of placebo analgesia, and, to a lesser extent, nocebo hyperalgesia. However, while many of these studies often identify different collections of brain regions responsive during placebo and nocebo manipulations, several key regions consistently emerge central to these responses. Notably, the meta-analysis by (Zunhammer et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) synthesized data from 603 participants across 20 fMRI studies, revealing common areas of activation associated with placebo analgesia. These included the mid-cingulate and insula cortices, frontoparietal regions and the thalamus. In our study, despite our different analysis pipeline \u0026mdash;using beta values within 70 pre-defined regions of interest rather than a whole-brain voxel-based analysis\u0026mdash;we observed activation patterns that generally align with previous research.\u003c/p\u003e \u003cp\u003eThe top 20 brain regions in humans that showed the largest changes during placebo analgesia in our study (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) fell into three categories. Firstly, regions that have previously been shown to be involved in placebo analgesia. These were the majority of the regions identified by our analysis: the dorsolateral prefrontal cortex (dlPFC; (Tu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Crawford et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e)), the medial prefrontal cortex (mPFC; (Wager et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)), the rostral ventromedial medulla (RVM), paraventricular hypothalamus (PVH; (Eippert et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)), the amygdala (Petrovic et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), S1, and the thalamus (Zunhammer et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Secondly, brainstem regions that are known to be involved in pain modulation but currently have scarce direct evidence for their involvement in placebo analgesia (see (Crawford et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for review). These were the medial PB (mPB), the locus coeruleus (LC), the subnucleus reticularis dorsalis (SRD) and the cuneate nucleus ipsilateral to the stimulus. Thirdly, regions that have not been previously associated with placebo analgesia. These were the septal nuclei and the bed nucleus of the stria terminalis (BNST).\u003c/p\u003e \u003cp\u003eSimilarly, the top 20 brain regions in humans that showed the largest changes during nocebo hyperalgesia (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) fell into the same three categories. While considerably fewer studies have investigated the neural correlates of nocebo hyperalgesia, half of the regions identified by our analysis have previously been reported. These were the orbitofrontal cortex (OFC; (Kong et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Freeman et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); Schiene et al., 2018), ACC (Tinnermann et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)), NAc, and PVH (Freeman et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Several pain modulatory brainstem regions were also identified. These were the LC, RVM, medial and lateral PB, and the cuneate nucleus. Finally, the regions identified by our analysis that have yet to be associated with nocebo hyperalgesia were specifically the primary motor cortex (M1), PVT, BLA and the CeA.\u003c/p\u003e \u003cp\u003eWith respect to preclinical research, only a handful of studies have explored the neurobiological correlates of placebo analgesia, yet once again, several brain regions have emerged as central. For instance, the rostral rACC (Zhang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)), ventral tegmental area (VTA; (Lee et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)), ventrolateral PAG (vlPAG), medial prefrontal cortex, NAc (Xu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)) have all been shown to have altered activity during the expression of placebo analgesia. Additionally, a recent study by (Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) identified an ACC-pontine circuit projecting into the cerebellum as both necessary and sufficient for producing placebo analgesia; while (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) found that direct activation of central amygdala (CeA) neurons can condition placebo responses, but these neurons do not re-activate during the expression of placebo analgesia. Our study identified many of these same regions as having large changes in neural activity, including the vlPAG, ACC, CeA, and NAc, highlighting a remarkable consistency with this limited existing literature. It is also worth noting that many regions included in our study\u0026mdash;such as specific brainstem and subcortical nuclei\u0026mdash;have not been previously investigated in animal models of placebo analgesia.\u003c/p\u003e \u003cp\u003eWhile there have been three recent studies that have developed animal models of nocebo hyperalgesia (Martin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Trask et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Boorman \u0026amp; Keay, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), to our knowledge this is the first study to investigate the neural basis of these effects. However, interestingly, (Zhang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also recently found that the ACC, BLA, and PVT had increased c-Fos expression in their rat model of nocebo nausea.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMethodological Comparability: Bridging Human and Animal Models\u003c/h2\u003e \u003cp\u003eComparing neural activity across humans and rats in the context of placebo and nocebo effects introduces multiple layers of complexity. To meaningfully interpret our findings, we must consider the comparability of our approaches across three key levels: (i) the behavioural protocols used to induce placebo and nocebo responses in each species, (ii) the methodologies employed to assess neural activity (i.e., c-Fos immunohistochemistry in rats and fMRI in humans), and (iii) the analytical techniques applied to extract functional connectivity and circuit-level insights from these data.\u003c/p\u003e \u003cp\u003eAt the behavioural model level, both human and rodent paradigms employ response conditioning, in which the intensity of a thermal stimulus was repeatedly paired with contextual cues to produce learned associations. While there are obvious differences between the procedures (for instance, in humans these associations are reinforced with verbal instructions), ultimately both paradigms are designed to produce expectancies and predictions about an upcoming stimulus. It therefore seems likely that each would engage the same neural mechanisms, and indeed the limited research in rodents suggests this is the case (Xu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Further, the inclusion of the enhanced context group was designed to capture the multisensory cues inherent in human conditioning protocols. By incorporating a combination of olfactory, auditory, and visual stimuli, this group aimed to amplify the salience of the contextual pairings, thereby mimicking the cognitive and sensory complexity of human expectancy formation.\u003c/p\u003e \u003cp\u003eAt the methodological level, \u003cem\u003eprima facie\u003c/em\u003e, c-Fos expression and fMRI appear to be fundamentally different approaches, with c-Fos expression being a cellular-level marker and fMRI being a regional, hemodynamic-based measurement. Additionally, while fMRI offers essentially a real-time measure of regional activity, the delayed nature of c-Fos production means that it represents a cumulative cellular response over the preceding 90\u0026ndash;120 minutes from perfusion and fixation. Further, the reliance on post-mortem tissue for c-Fos quantification precludes a within-subjects design as was used in humans. Interestingly, both methodologies share the notable limitation of being unable to distinguish whether the observed changes in signal arise from excitatory or inhibitory neurons. However, ultimately, both techniques serve as proxies to capture population-level neural activation, with each signal presumed to reflect increased neuronal firing of action potentials, albeit with different spatiotemporal resolution.\u003c/p\u003e \u003cp\u003eAt the analysis level, we chose to use fMRI beta values, which collapses the signal across the scan into a single value for each region, thus providing the closest approximation to the cumulative nature of c-Fos expression. This approach also ensured that the functional connectivity maps derived from fMRI aligned more closely with the static connectivity patterns inferred from c-Fos densities. We also carefully selected and defined our brain ROIs to ensure that they were homologous across species, relying on known histological, functional, and anatomical similarities. Only 4 out of the 70 ROIs from each species lacked a clear equivalent in the other. In humans, the dlPFC has no discrete homologue in rodents (Preuss, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Wise, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Carlen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Laubach et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conversely, in rodents, the anterior olfactory nucleus and piriform cortex did not have a clear human counterpart, given the fundamental differences in olfactory processing across species (Johnston, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Arakawa et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Maresh et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). We included these regions due to their known or putative involvement in conditioning and placebo responses in each species. Furthermore, by focusing on the top 20 ROIs with the largest changes in activity and the top 100 functional connections (or the relative differences from controls) in each species, we aimed to capture the most relevant neural changes during placebo and nocebo responses, while also accommodating for species-specific architecture.\u003c/p\u003e \u003cp\u003eFinally, the pruning of the functional connectivity maps to retain only anatomically plausible connections was a key step in our analysis. This process identified 58 directional connections for placebo and 25 for nocebo (with variable strengths) that were shared by humans and rats, thus narrowing the focus to conserved pathways most likely involved in these responses. It is important to note that unlike the approach taken by many recent studies that focus on a single critical circuit, our aim was to provide a foundation for future research by identifying a small collection of specific, biologically plausible connections for functional interrogation. As such, these findings offer a starting point for defining the constellation of neural circuits necessary and sufficient for placebo and nocebo responses. It is our hope that our study will therefore act as a springboard for advancing translational efforts and refining our mechanistic understanding of these phenomena.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study offers a novel framework to investigate cross-species comparisons of the brain regions and neural circuits underlying placebo analgesia and nocebo hyperalgesia, revealing both conserved and species-specific patterns. By integrating functional connectivity with anatomical pruning, we identified a number of shared circuits likely responsible for placebo and nocebo responses across both rats and humans. Our findings underscore the translational relevance of rodent models for studying human pain processes and targeting these shared neural circuits may hold promise for developing new therapies to treat pain conditions.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003eOverall experimental design\u003c/h2\u003e \u003cp\u003eThis study sought to directly compare the neural activity and circuits underlying placebo analgesia and nocebo hyperalgesia in both rats and humans, with the goal of identifying conserved neural circuits that could be targeted in the development of treatments for pain modulation. Rats provide a highly controlled model for dissecting specific neural mechanisms, while human data allows us to establish the relevance of these findings in a clinical context. To achieve this, we first quantified neural activity across 70 ROIs in the brain using c-Fos expression in rats and fMRI beta values in humans. Once neural activity was measured for each ROI, estimation statistics were applied to compare overall activity levels between control and placebo and nocebo conditions, identifying the regions with the most significant changes. This allowed for a direct comparison of neural activity patterns between species. Next, to define the neural circuits driving these responses, we examined functional connectivity—specifically, connections that were highly active during placebo and nocebo conditions. These functional connections were then pruned and weighted using known anatomical pathways from the Rat Connectome Project, ensuring that the resulting networks reflected biologically plausible circuits. All graphs were created in either GraphPad Prism v9.3.1 or Gephi software 0.10.1 (Gephi Consortium, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gephi.org/\u003c/span\u003e\u003cspan address=\"https://gephi.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and imported into Adobe Illustrator 2021 (v25.2) to create the figurework.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRat experiments\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eAnimals and housing\u003c/h2\u003e \u003cp\u003eThe experimental protocols used in this study were approved by the University of Sydney Animal Care and Ethics Committee (Project Number 1165). All procedures followed the guidelines outlined by the NHMRC's 'Code for the Care and Use of Animals in Research' and the NSW Animal Research Act (2007). The principles of the three R's (replacement, reduction, and refinement) were strictly applied to minimize pain and discomfort, and the study adhered to the IASP's ‘Ethical Guidelines for Investigations of Experimental Pain in Conscious Animals.’ Six-week-old male (n = 50) Sprague-Dawley rats (ARC, Perth, WA, Australia), weighing 170-220g upon arrival were used for these experiments. Rats were housed in groups of four in individually ventilated cages, with ad libitum access to standard chow and water. Both the housing and testing rooms were kept at a controlled temperature (22 ± 1°C) and humidity (40–70%). To align with the rats' active period, the housing room operated on a reversed 12-hour light/dark cycle, with lights off at 07:00.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEliciting placebo analgesia and nocebo hyperalgesia\u003c/h2\u003e \u003cp\u003eThe experimental design, behavioural procedures and behavioural results have been reported previously (Boorman \u0026amp; Keay, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A response conditioning protocol was used to induce placebo analgesia and nocebo hyperalgesia in rats with a chronic constriction injury (CCI) of the sciatic nerve. Six days post-injury, rats underwent conditioning using a hot/cold plate analgesiometer, with 10 trials conducted over 5 consecutive days (5 morning and 5 afternoon sessions). Placebo groups were conditioned at a non-noxious thermoneutral temperature (30°C), while nocebo groups were conditioned at a noxious cold temperature (4°C). On Test Day, both groups were exposed to a mildly noxious cold stimulus (20°C), and differences in hind paw withdrawal responses were measured to assess conditioned placebo or nocebo effects. A 20°C natural history control group, which did not undergo conditioning, was also tested for comparison. To assess the impact of contextual salience on placebo and nocebo effects, an enhanced context group was conditioned with additional sensory cues. Rats were randomly assigned to each group, and all data, including video recordings, are available upon request.\u003c/p\u003e \u003cp\u003eAll response-conditioning sessions took place in a temperature- and humidity-controlled room (21 ± 1°C, 40–75% humidity) separate from the housing room. The control group and the minimal context groups were tested in a context designed to minimize the strength and saliency of sensory cues, with rats tested under dim red lighting (~ 10 lux) in a quiet, odour-free environment. Enhanced context groups were conditioned in a custom-built chamber (60cm x 45cm x 30cm) under medium-level white lighting (~ 50 lux), wideband white noise (75–80 dB), and a strong vanilla scent from a plug-in diffuser (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eo). The chamber was cleaned with an apple-scented disinfecting wipe between tests to maintain olfactory consistency. To prevent the possibility of lingering odours from affecting the minimal context groups, enhanced context groups were always tested at the end of the testing period. Rats were tested 10 times over 5 consecutive days, with 5 morning and 5 afternoon sessions. Testing began approximately + 2 hr from lights off for morning sessions and approximately + 8 hr from lights off for afternoon sessions, with each cage of rats tested in the same sequence at ~ 5-minute intervals. Perfusions were performed 90 minutes after testing under deep pentobarbital anaesthesia, using 400 ml of cold heparinised saline followed by 400 ml of cold 4% paraformaldehyde in sodium acetate-borate buffer, pH 9.6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eChronic constriction injury (CCI) surgery\u003c/h2\u003e \u003cp\u003eAll rats underwent a unilateral chronic constriction injury (CCI) of the sciatic nerve, following the method initially detailed by (Bennett \u0026amp; Xie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). At the time of surgery, the rats weighed between 240 and 280 grams. The procedure began with anaesthesia induced and maintained using isoflurane (5% for induction and 2.5% for maintenance) delivered in 100% oxygen at a flow rate of 1.5 L/min. After achieving a surgical plane of anaesthesia, the right hind limb was shaved and sterilized with povidone-iodine. A 2 cm incision was made parallel to the femur, and blunt dissection of the biceps femoris was performed to expose the sciatic nerve. Four chrome catgut ligatures (5 − 0, Johnson \u0026amp; Johnson) were loosely tied around the nerve, spaced 1 mm apart, just proximal to its trifurcation. Care was taken to ensure that the ligatures compressed the nerve without obstructing epineural blood flow. The nerve was then repositioned, and the incision was closed with Michel clips. A few drops of lignocaine (20 mg/ml) were applied to the incision site, followed by a dusting of topical antibiotic powder (Tricin®, Jurox). To prevent licking of the wound, a mixture of petroleum jelly and quinine was applied. Post-surgery, the rats were placed in individual cages and allowed to recover until they regained mobility and alertness (approximately 30 minutes) before being returned to their home cages.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ec-Fos Immunohistochemistry\u003c/h2\u003e \u003cp\u003eThe brains of 8 rats per group were selected and sectioned at 50 µm using a cryostat (Leica CM1950) into a 1 in 8 series. One series from each brain was stained for c-Fos. Free-floating sections were first washed in 0.1 M PBS (3 x 10 minutes) at room temperature. Sections were then permeabilized in 50% ethanol for 30 minutes, followed by quenching of endogenous peroxidase activity with 3% hydrogen peroxide in 50% ethanol for 30 minutes. After further washes (3 x 10 minutes in PBS), sections were incubated in 10% normal horse serum (NHS) in 0.1 M PBS for 30 minutes to block non-specific binding. Sections were then incubated overnight at 4°C with polyclonal rabbit anti-c-Fos IgG (1:3000 in 2% NHS/PBS; Abcam, RRID:AB_2737414). The following day, sections were washed in PBS and incubated with horse anti-rabbit IgG (1:500 in 2% NHS/PBS; Vector Laboratories, RRID:AB_2336201) for 2 hours at room temperature. After additional washes, sections were incubated in ExtrAvidin Peroxidase (1:1000 in PBS; Sigma-Aldrich) for 2.5 hours. c-Fos immunoreactivity was visualized using 3,3’-diaminobenzidine tetrahydrochloride (DAB) as the chromogen, with the glucose oxidase method producing a brown precipitate. The reaction was carried out on ice and stopped after ~ 15 minutes with PBS washes (4 x 10 minutes), once optimal contrast was achieved. Sections were mounted on gelatin-coated slides, allowed to dry for 48 hours, dehydrated through ascending ethanol series, cleared in histolene, and coverslipped with DPX mounting medium.\u003c/p\u003e \u003cp\u003eThe brain ROIs were carefully selected based on their involvement in key processes such as pain transmission and modulation, as well as the processing of contextual information, including olfactory cues. A number of regions, such as orbitofrontal cortex, NAc and claustrum were also included due to their critical roles in integrating sensory information, motivational states, and decision-making processes—elements central to both placebo and nocebo responses. In total, 70 brain regions were analyzed to capture the full scope of these mechanisms. All images were captured using a Gryphax Kapella camera (Jenoptik, Jena, Germany). ROIs were identified based on the cytoarchitectural features of the tissue, with the stereotaxic rat atlas of Paxinos and Watson (2005) serving as a guide. Each microscope slide was coded to blind the experimenter (DB) during both image capture and analysis. For each ROI, 1, 2, or 3 slices per brain were analyzed and averaged, depending on the region. ROIs were manually outlined using ImageJ software, and c-Fos density was calculated for each ROI in each brain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eHuman experiments\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eParticipants and apparatus\u003c/h2\u003e \u003cp\u003eAll experimental procedures were approved through the University of Sydney Human Research Ethics Committee (HREC:2019/037), consistent with the Declaration of Helsinki. 46 healthy control participants were recruited for this study, with all 46 undergoing the placebo component, and 25 participants undergoing the nocebo component in addition to placebo, providing written informed consent on arrival. Participants were provided with an emergency buzzer whilst inside the scanner so that they could stop the experiment at any time. Before exiting the study, participants were informed as to the necessary deception and true methodology of the experiment both verbally and through a written statement.\u003c/p\u003e \u003cp\u003eNoxious thermal stimuli were delivered throughout the protocol using a Peltier-element thermode device (Medoc LTD Advanced Medical Systems, Rimat Yishai, Israel), connected to a thermal sensory analyser which delivered eight stimuli at a pre-programmed temperature for 15-seconds (2.5 second ramp up, 10 second plateau, 2.5 second ramp down), each separated by a 15-second inter-stimulus interval at a baseline temperature of 32 degrees Celsius (°C). Functional magnetic resonance imaging (fMRI) sequences were acquired using a whole-body Siemens MAGNETOM 7 Tesla (7T) MRI system (Siemens Healthcare, Erlangen, Germany) with a combined single-channel transmit and 32-channel receive head coil (Nova Medical, Wilmington MA, USA). located at the Melbourne Brain Centre Imaging Unit in Melbourne, Victoria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePlacebo and Nocebo Conditioning\u003c/h2\u003e \u003cp\u003e On entering the study participants were shown three creams: a control cream described as Vaseline, a placebo cream labelled and described to contain the analgesic additive Lidocaine, and a nocebo cream labelled and described to contain the topical irritant, Capsaicin. The three creams were placed in adjacent locations on participants’ right forearm and remained applied for five minutes to “take effect”. During this time, a thermal calibration protocol was conducted, with the thermode attached to participants’ left forearm, delivering a randomized series of temperatures between 44-48.5°C in the timings described above. Participants were informed this protocol was conducted to establish their moderate pain intensity – a temperature eliciting between 4 to 5 out of 10 on a 10-point scale (0 = no pain; 10 = worst pain imaginable), and that this moderate intensity would be used for the remainder of the study applied to each of the three creams. In reality, three temperatures were recorded, one eliciting a low pain (2–3/10 VAS), one eliciting a high pain (6–7/10 VAS), as well as the initially described moderate intensity.\u003c/p\u003e \u003cp\u003eThe three creams were then removed from participants’ forearms with the experimenter wearing gloves to enhance belief the cream’s contained active ingredients, and a conditioning protocol was conducted. Throughout two rounds of a response conditioning protocol (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-d), different intensity noxious stimuli were applied to each of the three cream sites (low intensity to placebo, moderate intensity to control, high intensity to nocebo), despite informing participants that all three cream sites would be receiving identical temperature and intensity moderate stimuli. Participants recorded their pain in real time using a visual analogue scale, building belief that the respective placebo and nocebo creams were taking effect relative to the control cream. The order of stimulation and locations of the placebo and nocebo creams proximal or distal relative to control was counterbalanced between participants to reduce ordering or sensitivity effects.\u003c/p\u003e \u003cp\u003eOnce two rounds of conditioning were complete, participants exited the session and returned the following day for an fMRI scanning session. Before scanning, a brief reinforcement protocol was conducted where each cream site, now applied to the left forearm received four noxious stimuli at the same individually calibrated low- moderate- and high-intensity as during conditioning. This was conducted to have participants remember the relative effect of the creams from the prior day, and re-establish the correct moderate intensity for testing. Once complete, the three creams were then applied to the right forearm, and participants entered into the MRI scanner.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePlacebo and Nocebo Test\u003c/h2\u003e \u003cp\u003eOver the course of four independent fMRI series, each of the cream sites were stimulated a total of eight times in the timings described above (15-second stimulus, 15-second inter-stimulus interval). Dissimilar to conditioning, during the test protocol all three cream sites received identical intensity moderate stimuli, with the mean difference between VAS scores recorded during control-site stimulation relative to the placebo-site encoding their placebo analgesia response, and \u003cem\u003evice versa\u003c/em\u003e for the nocebo-site and their nocebo hyperalgesia response. The control site was stimulated twice to provide a “pre” and “post” measurement for both placebo analgesia and nocebo hyperalgesia, and this order was kept counterbalanced in the same order as during conditioning (i.e. 50% of participants received control- and placebo-site series first and \u003cem\u003evice versa\u003c/em\u003e). During this test phase, participants recorded their ongoing pain in real time during a digital and MR-compatible VAS, reflected on a screen above with the position of a slider controlled using a two-button button box.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eDetermining placebo and nocebo responders\u003c/h2\u003e \u003cp\u003eUsing test phase VAS ratings, participants’ mean pain to each of the eight stimuli delivered to either the control- and placebo cream sites, or the control- and nocebo cream sites were entered into a bootstrapped permutation model, where 10,000 artificial samples were generated, sampled with replacement, encoding both their mean control and placebo, or control and nocebo responses. These responses were significance tested with a significant reduction of pain during placebo relative to control, or significant enhancement of pain during nocebo relative to control indicating a placebo or nocebo responder, respectively. From our sample, 22/46 participants were identified as placebo responders (46%), and 14/25 participants identified as nocebo responders (56%). Whilst fMRI series were collected on the entire sample and are published elsewhere (Crawford et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Crawford et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e), this investigation involves analyses conducted solely on these two responder cohorts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eMRI acquisition and preprocessing parameters\u003c/h2\u003e \u003cp\u003eAll image series were acquired with participants positioned supine within the head coil, with sponges inserted to support the head and minimise lateral and translational movement. A T1-weighted anatomical image set covering the whole brain was collected (repetition time = 5000 ms, echo time = 3.1ms, raw voxel size = 0.73x0.73x0.73mm, 224 sagittal slices, scan time = 7mins). Each of the four fMRI acquisitions consisted of 134 gradient echo echo-planar measurements using BOLD contrast covering the entire brain. Images were acquired interleaved with a multi-band factor of four and an acceleration factor of three (repetition time = 2500ms, echo time = 26ms; raw voxel size = 1.0x1.0x1.2mm, 124 axial slices, scan time = 6:25mins).\u003c/p\u003e \u003cp\u003eImage preprocessing and statistical analyses were performed using SPM12 (Friston, 2003) and custom software (Diedrichsen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The first five volumes of each scan were removed from the model due to excessive signal saturation from the scanner. The remaining 129 functional images were slice-time and motion corrected and the resulting 6 directional movement parameters were inspected to ensure that all fMRI scans had no greater than 1mm of linear movement or 0.5 degrees of rotation movement in any direction. Images were then linearly detrended to remove global signal changes, physiological noise relating to cardiac and respiratory frequency was removed using the DRIFTER toolbox (Sarkka et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and the 6-parameter movement related signal changes were modelled and removed using a linear modelling of realignment parameters procedure (Macey et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). From this point, two image sets were created: one wholebrain and one brainstem-isolated functional image set.\u003c/p\u003e \u003cp\u003eFor wholebrain images, each individual’s fMRI image sets were resliced to 1mm isotropic voxels and co-registered to their own T1-weighted anatomical. The T1 was then spatially normalized to the MNI152 template in Montreal Neurological Institute (MNI) space using the computational anatomy toolbox (CAT) (Gaser et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and these parameters applied to the fMRI image sets. The normalized fMRI images were then spatially smoothed using a 6mm full-width at half maximum Gaussian filter. For brainstem-isolated images, the Spatially Unbiased Infratentorial Template (SUIT) toolbox was used to first isolate in the T1 image series a section of the image encompassing the brainstem and cerebellum. Manual made masks were then generated covering the rostrocaudal extent of the brainstem and cerebellum in both the brainstem isolated T1 and wholebrain fMRI image series, resliced into 0.5mm isotropic voxels. Both image series were cropped using the manual masks, and the T1 normalized to the SUIT template in MNI space using the SUIT normalize function, with these parameters applied to the cropped fMRI series. The normalized fMRI images were then spatially smoothed using a 1mm FWHM gaussian filter. In both the wholebrain and brainstem-isolated image series, signal intensity changes were determined by applying a repeating boxcar model encoding scan volumes where noxious stimuli were present was applied, convolved with a canonical hemodynamic response function. This analysis resulted in brain maps in which each voxels value represented the magnitude of signal intensity changes during each noxious stimulus period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eVOI generation and beta-value extraction\u003c/h2\u003e \u003cp\u003eIndividual VOIs were generated for each cortical and brainstem pain-responsive site, derived from the Human Connectome Project atlas extended (HCPex) (Huang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for the cortex and subcortex, and hand drawn with reference to Mai and Paxinos’ Atlas of the Human Brain (Mai et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In total, 71 VOIs were generated and are listed in Supplementary Table\u0026nbsp;1. A measurement of average signal intensity, that is, relative increases or decreases in neuronal activation during the application of noxious stimuli relative to baseline periods of the fMRI scan, were extracted from each of the matched control and placebo or control and nocebo contrast images. These values were then recorded in a matrix table for further analyses.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003ePlacebo and Nocebo-Induced Changes in Regional Activity\u003c/h2\u003e \u003cp\u003eFor the calculation of placebo and nocebo-related changes in overall regional activity, we used estimation statistics to quantify the differences between the placebo and control groups and the nocebo and control groups. The top 20 regions showing the largest changes were identified for both placebo and nocebo conditions (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Estimation statistics were computed using the web application built by Hung Nguyen (estimationstats.com), which utilizes the Python code developed by (Ho et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For each test, effect sizes (Cohen's d) and their associated 95% confidence intervals were calculated using bias-corrected and accelerated bootstrap resampling with replacement, with 5000 bootstrap samples applied per test.\u003c/p\u003e \u003cp\u003eWe opted for estimation statistics over classical significance testing due to their ability to offer richer, more nuanced insights into the magnitude and precision of the observed changes in neural activity. By focusing on effect sizes, this method captures the magnitude and uncertainty of neural activity changes, highlighting the most relevant patterns without being restricted by arbitrary significance thresholds. This approach was crucial for comparing species-specific and shared patterns of neural connectivity, ensuring that we prioritized meaningful biological differences over potentially spurious statistical results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003ec-Fos and beta-value functional connectivity and neural circuitry\u003c/h2\u003e \u003cp\u003eFor both the c-Fos density and the beta-values, a correlation matrix was generated for each group (control, placebo, and nocebo) using GraphPad Prism 9. Each matrix represented the pairwise correlations between the 70 ROIs, capturing the strength of connectivity based on Pearson’s r-values.\u003c/p\u003e \u003cp\u003eFor the control groups, we isolated the top 100 strongest r-values in each species to create an adjacency matrix (created in Microsoft Excel), representing the most robust functional connections. These connections were visualized in network plots for both rat and human control groups. To maintain consistency, the same r-value cut-off that isolated the top 100 connections in the control groups was applied to the placebo and nocebo groups. This allowed us to determine changes in overall connectivity during placebo analgesia and nocebo hyperalgesia, and the connectivity patterns were also plotted for each group. We then identified shared connections between the rat and human groups (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e–\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) to highlight species-conserved connections.\u003c/p\u003e \u003cp\u003eTo visualize the functional connectivity graphs, we used Gephi software v0.10.1. The adjacency matrices, which were calculated from the correlation matrices, were imported into Gephi as undirected connections. We utilized the circular layout, with nodes arranged by their ID. The darkness of each node was scaled according to the number of connections it held, allowing for easy identification of hub regions. After generating the graphs, they were exported into Adobe Illustrator for further refinement. Shared connections between rats and humans were manually highlighted in red to visualize conserved pathways across species.\u003c/p\u003e \u003cp\u003eFinally, we pruned these networks by cross-referencing with anatomical data from the Rat Connectome Project, ensuring that only connections with known anatomical pathways remained in the analysis. The connectome provided further details about the strength and direction of each connection, assigning connection strengths ranging from 0.5 (very light) to 4 (very strong), forming the basis of the final neural circuitry model. This pruning step allowed us to refine the functional networks into anatomically grounded circuits, providing insights into the neural circuits underlying placebo analgesia and nocebo hyperalgesia across species. Similar to the functional connectivity analysis, adjacency matrices were generated from the correlation matrices and imported into Gephi as directed connections.\u003c/p\u003e \u003cp\u003eWe applied the Force Atlas layout with the following parameters: inertia = 0.1, repulsion strength = 1000, attraction strength = 10, maximum displacement = 1000, auto stabilize function = on, autostab strength = 80, autostab sensibility = 0.2, gravity = 3000, attraction distribution = off, adjust by sizes = on, and speed = 1.0. Node size was determined by the degree of connectivity, where more connected nodes appeared larger, while the size of the arrows represented the strength of the anatomical projection. This method allowed us to generate directed graphs that visually emphasize the most strongly connected nodes and circuits, reflecting the overall structure of the neural networks across species.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAcknowledgements and Conflicts of Interest Statement\u003c/h2\u003e \u003cp\u003eWe wish to thank the many volunteers in this study. The authors acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability, at Monash University. This work was funded by the National Health and Medical Research Council of Australia Grant 1130280 and the NWG Macintosh Memorial Grant, University of Sydney, Australia. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eConceptualization: DB, LC, LH, KK. Methodology: DB, LC, LH, KK. Software: DB, LC, LH. Validation: DB, LC, LH, KK. Formal analysis: DB, LC. Investigation: DB, LC. Resources: LH, KK. Data curation: DB, LC. Writing \u0026ndash; original draft: DB, LC, LH, KK. Writing \u0026ndash; Reviewing \u0026amp; editing: DB, LC, LH, KK. Visualization: DB. Supervision: LH, KK. Project administration: DB, LC, LH, KK. Funding acquisition: LH, KK.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. This includes raw and processed imaging data, behavioural datasets, and analysis scripts used in this study. Due to ethical considerations, raw human data have been de-identified to ensure privacy and confidentiality in compliance with applicable guidelines and regulations. Access to these data may require approval from the appropriate ethics board and completion of a data-sharing agreement. Animal data, including c-Fos expression maps and functional connectivity matrices, as well as the custom analysis code, are available directly from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArakawa, H, Blanchard, DC, Arakawa, K, Dunlap, C \u0026amp; Blanchard, RJ (2008), \u0026apos;Scent marking behavior as an odorant communication in mice\u0026apos;, \u003cem\u003eNeurosci Biobehav Rev,\u003c/em\u003e vol. 32, no. 7, pp. 1236-48.\u003c/li\u003e\n\u003cli\u003eBenedetti, F, Mayberg, HS, Wager, TD, Stohler, CS \u0026amp; Zubieta, JK (2005), \u0026apos;Neurobiological mechanisms of the placebo effect\u0026apos;, \u003cem\u003eJ Neurosci,\u003c/em\u003e vol. 25, no. 45, pp. 10390-402.\u003c/li\u003e\n\u003cli\u003eBennett, GJ \u0026amp; Xie, YK (1988), \u0026apos;A peripheral mononeuropathy in rat that produces disorders of pain sensation like those seen in man\u0026apos;, \u003cem\u003ePain,\u003c/em\u003e vol. 33, no. 1, pp. 87-107.\u003c/li\u003e\n\u003cli\u003eBoorman, DC \u0026amp; 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While animal models are increasingly used to explore the underlying mechanisms of these phenomena, it remains unclear whether animals experience placebo and nocebo effects in a manner comparable to humans or whether the associated neurobiological pathways are conserved across species. In this study, we introduce a novel framework for comparing brain activity between humans and rodents during placebo analgesia and nocebo hyperalgesia. Using c-Fos immunohistochemistry in rats and fMRI in humans, we examined neural activity in 70 pain-related brain regions, identifying both species-specific and conserved connectivity changes. Functional connectivity analysis, refined by pruning connections based on anatomical pathways, revealed significant overlap in key regions, including the amygdala, anterior cingulate cortex, and nucleus accumbens, highlighting conserved circuits driving placebo and nocebo responses This cross-species methodology offers a powerful new approach for investigating the neurobiology of pain modulation, bridging the gap between animal models and human studies. Identifying these common connections validates the use of animal models and enables preclinical researchers to focus on circuits that are conserved across species, ensuring greater translational relevance when developing new and effective treatments for pain conditions.\u003c/p\u003e","manuscriptTitle":"Placebo analgesia and nocebo hyperalgesia across species: direct neural comparisons between rats and humans","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-19 12:21:31","doi":"10.21203/rs.3.rs-5590281/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7f0afb7e-a2fb-46a8-824f-9e3d20011253","owner":[],"postedDate":"March 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41634446,"name":"Biological sciences/Neuroscience/Neural circuits"},{"id":41634447,"name":"Health sciences/Anatomy/Nervous system/Brain"},{"id":41634448,"name":"Biological sciences/Neuroscience/Sensorimotor processing"},{"id":41634449,"name":"Biological sciences/Neuroscience"},{"id":41634450,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2025-04-06T07:05:40+00:00","versionOfRecord":{"articleIdentity":"rs-5590281","link":"https://doi.org/10.1038/s42003-025-07993-1","journal":{"identity":"communications-biology","isVorOnly":false,"title":"Communications Biology"},"publishedOn":"2025-04-05 04:00:00","publishedOnDateReadable":"April 5th, 2025"},"versionCreatedAt":"2025-03-19 12:21:31","video":"","vorDoi":"10.1038/s42003-025-07993-1","vorDoiUrl":"https://doi.org/10.1038/s42003-025-07993-1","workflowStages":[]},"version":"v1","identity":"rs-5590281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5590281","identity":"rs-5590281","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}