The injection pipette was not removed until 10 min after the end of the infusion to allow diffusion of the virus. Subjects for the behavioral experiment were injected with virus as described above, and dual fiberoptic cannulae (Doric Lenses) were implanted in order to have the tip of the fiberoptic cannulae (200 μm, 0.22 NA) above the left and the right LHb (A-P: −3.6 mm from bregma; M-L: ±0.75 mm;

D-V: −4.0 mm from dura) (see Figure S2) and were secured to the skull with screws and dental cement. Rats were injected subcutaneously with 5 mg/kg carprofen (NSAID) after surgery. Rats (n = 7 in ChR2-YFP group, n = 5 in mCherry control group) used for directed place preference (DPP) underwent surgery at 4–7 weeks old, and behavior experiments were conducted at least 3 weeks after LBH589 cell line surgery. DPP was carried out in a shuttle box (50 cm wide × 25 cm deep × 30 cm high; Coulbourne Instrument) equipped with a door separating the two halves and photocell detectors. Walls were modified in order to present different patterns to provide contextual differences. Photocell Selleck JNK inhibitor detectors allowed automatic monitoring of rat location in the cage for the duration

of testing. Optical activation of ChR2-YFP-expressing axons was performed by using an optical fiber coupled to a 473 nm solid-state laser diode (OEM Laser Systems) with 20 mW of output from the 200 μm fiber. Directed place preference was designed in order to monitor preference/aversion induced by optical stimulation of the LHb. Throughout the full duration of the test, rats were free to explore both sides of the cage. The first 10 min allowed us to measure preference for either context without manipulation. No preference was found during this first 10 min.

After this 10 min baseline period, optical stimulation (continuous 20 Hz, 5 ms pulse duration) was delivered while the animal was in one context (defined as “context A”). For the next 30 min, optical stimulation of the LHb occurred whenever the rat was located in context A. Optical stimulation was stopped when the animal was in the other side of the cage (context B). Avoidance scores were measured Doxorubicin mouse by taking time spent in context B minus time spent in context A divided by total time (120 s). Student’s t test compared avoidance score from period 10–40 min to baseline (0–10 min period). In a different set of DPP testing, pairing of the optical stimulation with context A (20 min) was switched to context B for another 20 min and then paired again with context A for the last 10 min of the 1 hr session (see schematic in Figure 3). One ChR2-YFP-expressing rat lost its cannula before the DPP reversal test, so only six ChR2-YFP-expressing rats were tested for reversal of DPP. Student’s t test compared avoidance score from periods 10–30, 30–50, and 50–60 min to baseline period (0–10 min). Two weeks after surgery, rats were anesthetized with isoflurane before decapitation and brain removal.

Other theoretical frameworks stress the role of the hippocampus in spatial processing in general (Burgess et al., 2002 and O’Keefe and Nadel, 1978), a role that could be extended to perceptual judgments on scenes. Anticancer Compound Library datasheet It has also been argued that the hippocampus is necessary in perceptual tasks that

require binding of information (Warren et al., 2012). These ideas have been challenged by studies failing to find scene perception impairments in patients with hippocampal damage (Hartley et al., 2007, Kim et al., 2011 and Shrager et al., 2006). The current study suggests that the distinction between state- and strength- based perception can help to reconcile the conflict in the literature. In previous studies (Aly and Yonelinas, 2012), we found strong evidence that strength-based perception is affected by manipulations of global featural relationships, whereas state-based perception is disproportionately driven by detection of relatively local, item-level differences. For example, when the only difference between a pair of scenes was a specific feature (e.g., a window in one scene that is absent in the other), perceptual

decisions were based primarily on state-based perception. In contrast, when the featural relations within the scenes differed from one another, performance relied more heavily on strength-based perception. Moreover, individuals reported identifying specific, local details when responses were state-based, and generalized feelings of overall difference/sameness when responses were strength based. In the current fMRI study, the hippocampus

and parahippocampal cortex ALK signaling pathway were sensitive to strength-based perception, but, importantly, we also found that other regions of the brain were sensitive to state-based perception. For example, the posterior parietal cortex exhibited state-based, but not strength-based, effects (M.A., C.R., and A.P.Y., unpublished data). Viewed in the context of our previous studies, the present results suggest significant constraints on when and how the hippocampus would be expected to contribute to perception. We propose that the hippocampus is involved in perceptual discriminations that require a representation Iodothyronine deiodinase of relational or conjunctive information. Not only did the hippocampus track the perceived “strength” of perceptual change, the more basic finding of hippocampal adaptation (greater activation for “different” than “same” trials) suggests the hippocampus forms precise representations of visual scenes. The differences we introduced were subtle—on a given trial, the paired scenes are essentially identical with very small distortions. Thus, finding hippocampal adaptation for such small visual differences provides further evidence that the hippocampus represents precise relational information (Bakker et al., 2008 and Lacy et al., 2011). Because state-based perception plays a larger role in performance when perceptual manipulations involve discrete features (e.g.

25, 285 mOsm). With cortical neuron recording, the extracellular solution contained 1 μM TTX. Light intensity was measured with a calibrated photometer with an integrating sphere detector (International Light Technologies) placed on the objective. A glass slide with a semispherical lens was used to direct the light into the integrating sphere. The area of illumination was measured with a stage micrometer for illumination intensity. Organotypic hippocampal slices were prepared from 2 days old rat pups as described previously (Shi et al., 1999). The various constructs were expressed using rAAV virus in CA3 neurons in 2 DIV slice cultures.

Cells were allowed to express for 14–17 days before being used for recordings. CA3 region was surgically removed to prevent stimulus induced bursting. Recordings were done in ACSF containing 119 mM NaCl, www.selleckchem.com/products/Adriamycin.html 2.5 mM KCl, 26 mM NaHCO3, 1 mM NaH2PO4, 11 mM glucose, 4 mM MgCl2, and 4 mM CaCl2, 100 μM picrotoxin and 4 μM 2-chloroadenosine (pH 7.4) at 24°C–28°C. Organotypic hippocampal slices were placed in a recording chamber on an Olympus BX50WI microscope with 60× water immersion objective (Olympus). Extracellular field potential was recorded in stratum radiatum with glass electrodes (1–2 MΩ) filled

with the perfusion solution. Synaptic responses were evoked by stimulating two independent pathways using bipolar stimulating electrodes (Frederick Haer) placed 150–250 μm down the apical dendrites, 100 μm apart, and 150–200 μm laterally in opposite directions. Field EPSP was measured by averaging the response IPI-145 clinical trial over a 5 ms fixed window covering

the peak amplitude. Results from two pathways were averaged and analyzed as n = 1. Whole-cell patch-clamp recordings were done with intracellular solution containing 115 mM Cs-Methanesulfonate, 20 mM CsCl, 10 mM HEPES, 2.5 mM MgCl2, 4 mM Na2ATP, 0.4 mM Na3GTP, 10 mM Na-phosphocreatine, 0.6 mM EGTA (pH 7.25). Electrically evoked EPSCs and miniature EPSCs were recorded under voltage clamping (Vhold = −60 mV; junctional potential not corrected). Recording and analysis were done with IGOR Pro (WaveMetrics). For the analysis of mEPSC frequency, EPSCs that had the characteristic excitatory EPSC profile (Bekkers et al., 1990) was manually identified over 1 min period of recording. Light illumination Non-receptor tyrosine kinase was provided from a 100 W mercury arc lamp filtered through a eGFP filter set with 480/40 nm excitation filter (Olympus). The illumination area was 360 μm in diameter. Hippocampal organotypic slices infected with rAAV and Sindbis virus were imaged under low magnification with a MVX10 Macroview microscope (Olympus). Citrine fluorescence was imaged with the eGFP filter set and tdTomato was imaged with a Texas Red filter set. For high magnification, the slices were fixed with 4% paraformaldehyde and imaged with a Zeiss LSM 780 confocal microscope (Zeiss).

, 2008). This behavior is characterized by temporal structure over a wide range of timescales, i.e., the extent of individual whisking bouts on the 1–10 s timescale, changes in the envelope of vibrissae movement on the 1 s timescale, and the motion of the vibrissae on the 0.1 s period of rhythmic motion (Berg and Kleinfeld, 2003a, Carvell et al., 1991 and Hill et al., 2008). The presence of multiple timescales in whisking, together with the relatively

small number of degrees of freedom in vibrissa control, suggest that vibrissa primary motor (vM1) cortex is an ideal cortical region to elucidate multiple timescales in motor control. Past electrophysiological measurements establish that neurons in vM1 cortex can exert fast control DZNeP order over vibrissa motion. Stimulation of vM1 cortex in anesthetized animals can elicit either rapid deflections of individual vibrissae (Berg and Kleinfeld, 2003b and Brecht et al., 2004) or extended whisking bouts that outlast the original stimulation (Cramer and Keller, 2006 and Haiss

and Schwarz, 2005). Measurement of the local field potential in vM1 cortex in awake animals indicates that units with rhythmic neural activity can lock to whisking (Ahrens and Kleinfeld, 2004 and Castro-Alamancos, 2006). Complementary work established that the firing rate of neurons in vM1 cortex respond to sensory input (Chakrabarti et al., 2008, Ferezou et al., 2006 and Kleinfeld et al., PARP inhibitor 2002). The response Amine dehydrogenase is band-limited in the sense that only the fundamental frequency of a periodic pulsatile input is represented, reminiscent of a control signal used to stabilize the output of servo-motors (Kleinfeld et al., 2002). Yet, prior work did not address the critical issue of signaling of motor commands at different timescales, e.g., slow changes in amplitude over multiple whisk cycles, nor did it address the nature of single unit activity in directing motor output. We separated whisking behavior into components that vary on distinct timescales and asked: (1) Do individual single units preferentially code different components of the motion? (2) If so,

is this representation driven by activity from a central source or by peripheral reafference? (3) How many neurons are required to accurately represent vibrissa motion in real time? (4) Given the high connectivity between vM1 and vibrissa primary sensory (vS1) cortices (Hoffer et al., 2003 and Kim and Ebner, 1999), how does the representation of whisking behavior differ between these areas? Rats were trained to whisk either while head-fixed or while freely exploring a raised platform (Hill et al., 2008). In the head-fixed paradigm, vibrissa position was monitored via a high-speed camera and processed to determine the azimuthal angle, defined as the angle in the horizontal plane and denoted θ(t), versus time.

While a substantial number of studies have looked at predictive INCB024360 price effects of local oscillatory activity, studies on predictive effects of phase coupling on perception or task performance are relatively rare. Based on studies of auditory and language processing, delta- and theta-band ICMs have been associated with predictive timing (“predicting when”). Beta- and gamma-band ICMs, in contrast,

may be relevant for encoding predictions about the nature of upcoming stimuli (“predicting what”) (Arnal and Giraud, 2012). It has been postulated that beta-band ICMs may specifically be involved in predicting a maintenance of the current sensorimotor setting, while gamma-band ICMs may encode the prediction of a change in stimulation or cognitive set (Engel and Fries, 2010). Alpha-band ICMs have been implicated in the inhibition and disconnection of task-irrelevant areas (Jensen et al., 2012). A number of animal studies demonstrate predictive or modulatory effects of phase ICMs. Spike synchronization in monkey motor cortex was observed

to reflect the animal’s expectancy of an upcoming stimulus (Riehle et al., 1997). Similarly, beta-band ICMs were found to occur in cat visual and parietal cortex during expectation of a task-relevant stimulus (Roelfsema et al., 1997). In cat visual cortex, gamma-band coupling in prestimulus epochs was shown to predict first-spike synchrony during stimulation (Fries et al., 2001). Studies of monkey visual cortex indicate that fluctuations in gamma-band ICMs modulate

the speed at which animals can detect a behaviorally see more relevant stimulus change (Womelsdorf et al., 2006). EEG studies in humans provide convergent evidence that prestimulus fluctuations in phase ICMs can modulate target detection (Hanslmayr et al., 2007 and Kranczioch et al., 2007), suggesting that perception of a task-relevant stimulus is hampered by alpha-band but facilitated by beta- and gamma-band ICMs. Furthermore, intrinsic fluctuations of phase ICMs are associated with fluctuations in perceptual states in ambiguous stimulus settings. Fluctuations in a beta-band ICM have been shown to predict the perceptual state in an ambiguous audio-visual paradigm (Hipp et al., PASK 2011) (Figure 3B). Intrinsically generated fluctuations in a gamma-band ICM seem responsible for perceptual changes in a dynamic apparent motion stimulus (Rose and Büchel, 2005). Both studies demonstrate the relevance of intrinsically generated fluctuations in coupling that are present during the task and interact with the stimuli such that one perceptual interpretation is favored. Importantly, phase ICMs also closely relate to plasticity. In addition to being enabled by preceding learning and plasticity (see preceding section) phase ICMs are, in turn, important in triggering synaptic changes. During development, phase ICMs are involved in shaping the network structure (Weliky, 2000 and Uhlhaas et al., 2010).

A fraction of neurons was significantly modulated by both PFC 4 Hz and hippocampal theta oscillations (Figure 7B; PFC: 13.7%; CA1: 21.0%; VTA: 16.9%; p < 0.05; Rayleigh test). Plotting all significantly selleck chemicals jointly modulated neurons showed that the population of phase-locked units occupied a diagonal

(Figure 7C), similar to the joint phase distribution of 4 Hz and theta oscillations (Figure 7A, second panel). The diagonal distribution of the comodulation values is an indication of the interdependent nature of neuronal phase locking to both rhythms. Comparison between jointly modulated predicting and nonpredicting PFC pyramidal neurons revealed that predicting neurons were significantly more strongly comodulated by these rhythms than nonpredicting cells (Figure 7D; p < 0.05; Figure S6). In addition, we found that local gamma oscillations in PFC and hippocampus were modulated by both PFC 4 Hz and CA1 theta oscillations (Figure S7). These findings suggest that PFC neurons, which are active in the working memory part of the task, are temporally coordinated (Jones and Wilson, 2005, Benchenane et al., 2010 and Rutishauser et al., 2010) by 4 Hz and theta oscillations.

The expected results of such coordination are that the synchronously discharging predicting cells can exert a stronger impact on downstream targets that guide behavior, as compared to the less synchronous nonpredicting population. LBH589 in vitro Finally, we compared LFP activity PD184352 (CI-1040) in PFC and hippocampus during task behaviors and in the home cage during

waking immobility, rapid eye movement (REM) sleep, and slow-wave sleep (Figure S8). PFC 4 Hz power was high during nose poking and running in the central arm and wheel, i.e., during times when working memory was active. PFC 4 Hz power was low during immobility and sleep, including theta-dominated REM sleep. Hippocampal theta power was high during running behavior and REM sleep but low during nose poking, immobility, and slow-wave sleep (Figure S8). Slow-wave sleep in both PFC and hippocampus was dominated by a large 2 Hz peak, a reflection of slow oscillation of non-REM sleep (Steriade et al., 1993). This behavior-dependent dissociation of power changes demonstrates that theta and 4 Hz oscillations are distinct rhythms with characteristically different behavioral correlates and presumably different mechanisms. Our findings demonstrate a triple time control of neurons in the PFC-VTA-hippocampus axis (Figure 8). The 4 Hz rhythm is the dominant pattern in PFC-VTA circuits, effectively modulating both local gamma oscillations and neuronal firing, whereas synchrony of neuronal spikes in the hippocampus is largely under the control of theta oscillations. Through phase coupling, 4 Hz and theta oscillations jointly coordinate gamma oscillations and neuronal assembly patterns in a task-relevant manner.

Analysis of the data from session 2 indicated only a significant main effect of Treatment (p = 0.002),

and did not yield a significant Strain × Treatment interaction (p = 0.38). There were no significant differences between the SedCon groups from each genotype in acquisition (p = 0.390) and reversal (p = 0.371). Perusal of the discriminative component (Fig. 5) during acquisition and reversal revealed a strain-related difference in performance. Interestingly, the SedCon E4 mice learned the discriminative component of the active avoidance task taking less trials that the SedCon buy Epacadostat E3 ones. Furthermore, significant effects of Treatment were only observed in the E3 mice. In the acquisition session, the ExCon and ExEC mice took 32% less trials to reach the criterion compared to the SedCon E3 mice while it was only about 15% less trials for the E4 mice. Analysis OTX015 cost of the trials to the discriminative component for session 1 yielded main effects of Strain and Treatment (all p < 0.008) but did not reveal a significant interaction between Strain and Treatment

(p = 0.23). In the reversal session, the all treated E3 mice took 25%–42% less trials than the SedCon mice while the E4 treated mice improved only by 8%–16%. An analysis of the data during session 2 indicated significant main effects of Strain and Treatment as well as an interaction between Strain and Treatment (all p < 0.036). For the discriminative Ramoplanin component of the active avoidance, a one-way ANOVA yielded only a main effect during the reversal phase (p = 0.039), however this main effect was solely driven by the significant difference between E3 and E4. There were no significant differences between the genotypes in both phases. The effect of Strain and Treatment were analyzed in terms of percent

time spent in the closed arms and open arms of the plus maze (Fig. 6). In the E3 group, there was no effect of Treatment on either measure; however it seems that the supplementation with EC diet reduced the amount of time spent in the open arms by the E4 mice. Furthermore, overall the E4 mice spent more time in the open arms compared to the E3 ones. Analyses of the data revealed a significant main effect of Strain for percent time in open arms (p < 0.05), however no effect of Treatment or an interaction between Strain and Treatment were found (all p > 0.109). When comparing the SedCon treatments groups across wild-type, E3, and E4 genotype there was no difference in their time spent in open arms (p = 0.071) and closed arms (p = 0.052).

, 1992). The above findings suggest the following model: leptin binds directly

to LEPRs on AgRP and POMC neurons, inhibiting AgRP neurons and activating POMC neurons, and this accounts for its antiobesity actions. If this model is correct and if it is the sole mechanism by which leptin regulates energy balance, then deletion of LEPRs on AgRP and POMC neurons should result in massive obesity, similar to that seen in mice with total lack of leptin action (i.e., Lepob/ob mice and Leprdb/db mice). To investigate this, we ABT-888 generated mice that lack LEPRs on POMC neurons (i.e., Pomc-Cre, Leprlox/lox mice), on AgRP neurons (i.e., Agrp-Cre, Leprlox/lox mice), and on both POMC and AgRP neurons (i.e., Pomc-Cre, Agrp-Cre, Leprlox/lox mice) ( Balthasar et al., 2004, Hill et al., 2010 and van de Wall et al., 2008). Of note, mice lacking LEPRs on either POMC neurons or on AgRP neurons developed very

mild obesity (increase in body weight of ∼5 g at 2–3 months old) ( Balthasar et al., 2004 and van de Wall et al., 2008). This effect was much smaller than expected, especially when one compares this with the 26 g increase in body weight in 10-week-old mice with global deficiency of LEPRs ( van de Wall et al., 2008). One possible explanation for the smaller than expected effect is that deletion of LEPRs in one class of neurons (for example, the POMC neurons) might be compensated by increased leptin action on the other class of neurons (for example, the AgRP neurons), or vice versa. However, this was not this website the case because an additive and still much smaller

than expected effect was observed in mice lacking LEPRs on both POMC and AgRP neurons ( van de Wall et al., 2008). In total, the above findings suggest that direct leptin action on POMC and AgRP neurons plays a small role in controlling energy Histidine ammonia-lyase balance and that there are likely to be other first-order, leptin-responding neurons that contribute importantly to leptin’s antiobesity actions. Areas beyond the arcuate could mediate important actions of leptin. Of note, LEPRs are present in many sites outside the arcuate. Within the hypothalamus, LEPRs are found in the ventromedial hypothalamus (VMH), the dorsomedial hypothalamus (DMH), the lateral hypothalamus (LH), and the ventral premammillary nucleus (PMv) (in addition to the arcuate); within the midbrain in the ventral tegmental area and raphe; and within the brainstem, in the parabrachial nucleus, periaqueductal gray, and dorsal vagal complex (Elias et al., 2000, Figlewicz et al., 2003, Fulton et al., 2006, Grill et al., 2002, Hommel et al., 2006, Leinninger et al., 2009, Leshan et al., 2009, Mercer et al., 1996, Mercer et al., 1998, Münzberg, 2008 and Scott et al., 2009). Strong arguments have been made that neurons outside the arcuate are well-positioned to play important roles in regulating appetite (Berthoud, 2002 and Grill and Kaplan, 2002).

Specifically, Ngn2-iN cells expressed at high levels the telencephalic markers Brn-2, Cux1, and FoxG1, which are characteristic of layer 2/3 excitatory cortical neurons, but lacked other prominent forebrain transcription factors (e.g., Tbr1 and Fog2). iN cells consistently expressed AMPA-type glutamate receptors GluA1, A2, and A4, but lacked NMDA-type glutamate receptors 3 weeks after induction (Figure 3A). Moreover, nearly all iN cells expressed vGlut2, and approximately 20% of iN cells expressed vGlut1. iN cells highly expressed GABAA receptors but lacked the vesicular GABA transporter vGAT or the GABA-synthetic enzyme glutamate decarboxylase

(GAD). Ngn2 iN cells expressed all panneuronal see more markers tested, but lacked expression of markers for various glia cell types or for stem cells (Figures 3A and S3B). These measurements show that Ngn2 iN cells are relatively homogeneous and that they constitute excitatory neurons that express telencephalic markers suggestive of

cortical layers 2/3. Arguably the most important question in the production of iN cells—in fact, in the in vitro production of all human neurons—is reproducibility between lines. We therefore assessed this question for the Ngn2-based protocol in great detail. Comparison of the gene expression profiles between iN cells produced by forced differentiation of H1 ESCs and of two independent

lines of iPSCs revealed a striking concordance VE-821 nmr in expression patterns (Figures 3B and S3D). There was no major difference between stem cells in the expression of the genes tested. The highly similar transcriptional effects of Ngn2 indicate that forced expression of Ngn2 can override presumptive epigenetic differences between various pluripotent stem cell lines to induce differentiation of a single homogenous population of excitatory forebrain neurons. We next probed the ability of Ngn2-induced iN cells to differentiate into electrophysiologically active neurons and to form synapses. To promote synapse formation, we cocultured iN cells with mouse glial cells (Pang et al., 2011). The iN cells reliably produced robust action potentials, Megestrol Acetate and exhibited voltage-gated Na+ and K+ currents that were indistinguishable between iN cells derived from H1 ESC and different iPSC lines (Figures 4A–4C and S4A). iN cells exhibited massive spontaneous synaptic activity that was blocked by the AMPA-receptor antagonist CNQX (Figure 4D). Extracellular stimulation evoked EPSCs of large amplitudes, documenting abundant synapse formation (Figures 4E and 4F). The kinetics of evoked EPSCs were identical at −70 mV and +40 mV holding potentials, and EPSCs were blocked by CNQX at both holding potentials.

The individual PEDro items satisfied by fewer than half the trials were concealed allocation (five trials) and those related to blinding, which is discussed in more detail in the next Epigenetics inhibitor section. As identified by

the PEDro scale, GRADE assessment of risk of bias showed that only five trials blinded participants, 3, 21, 22, 23 and 24 two trials blinded therapists, 19 and 23 and four trials blinded assessors. 3, 19, 20 and 21 Acupressure and yoga were the only interventions where the available trials allowed good precision. No inconsistency, serious indirectness, or publication bias was identified. The completeness of outcome data for each outcome was adequately described in all the included studies. No other limitations, such as stopping early for benefit or use of unvalidated outcome measures, were identified in any of the included studies. The summary of findings and evidence profile are presented in Table 2. The overall grade of the evidence obtained for the outcome menstrual pain for acupuncture and acupressure NVP-BGJ398 trials was ‘moderate.’ Spinal manipulation and TENS trials obtained ‘very low’ grades, while heat therapy and yoga trials obtained ‘low’ grades. The sample sizes contributed by the included trials ranged from 20 to 144. The mean age of participants in the included trials ranged from 17 to 34 years. One trial2 compared the effectiveness of TENS to a placebo

pill, two trials20 and 21 compared the effect of spinal manipulation to sham manipulation, and one trial19 compared the effect of continuous low-level heat to a sham heat patch. One trial25 compared the effect of yoga to no treatment. Two trials3 and 23 each compared the effect of acupuncture to two controls: sham treatment (ie, applied to non-acupoints), and no treatment. Four trials investigated the effect of acupressure, with

two of these trials applying no treatment to the control group24 and 26 and two using sham acupressure as a control.22 and 27 Two trials measured pain intensity on a numerical rating scale, and nine trials Ketanserin measured the pain intensity on a visual analogue scale (VAS). Although some trials also measured composite scores of pain and other menstrual Libraries symptoms, none of the included trials measured a validated quality-of-life score. Data were pooled from two methodologically high-quality trials, providing moderate grade evidence comparing the effect of acupuncture with a no-treatment control.3 and 23 Both trials measured pain intensity on a VAS. The analysis showed a significant benefit of acupuncture in reducing pain compared to control immediately after treatment, with a weighted mean difference of 2.3 (95% CI 1.6 to 2.9), as presented in Figure 2. A more detailed forest plot is presented in Figure 3, which is available in the eAddenda. The same two trials also compared the analgesic effect of acupuncture with placebo.