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PHARMACOLOGICAL REVIEWS
Copyright © 2015 by The American Society for Pharmacology and Experimental Therapeutics
http://dx.doi.org/10.1124/pr.114.009423
Pharmacol Rev 67:697–730, July 2015
ASSOCIATE EDITOR: MARTIN C. MICHEL
Neuro-Bio-Behavioral Mechanisms of Placebo and
Nocebo Responses: Implications for Clinical Trials and
Clinical Practice
Manfred Schedlowski, Paul Enck, Winfried Rief, and Ulrike Bingel
Institute of Medical Psychology and Behavioral Immunobiology (M.S.) and Department of Neurology (U.B.), University Clinic Essen, Essen,
Germany; Department of Internal Medicine VI, Psychosomatic Medicine, University Hospital Tübingen, Tübingen, Germany (P.E.); and
Department of Psychology, University of Marburg, Marburg, Germany (W.R.)
This work was supported by grants from the German Research Foundation (DFG) for the Research Unit FOR 1328 (BI 789/2-1,2; EN 50/
30-1; RI 574/21-1,2; RI 574-22-1; SCHE 341/17-1,2); and the German Federal Ministry of Education and Research (01GQ0808; to U.B.).
Address correspondence to: Dr. Manfred Schedlowski, Institute of Medical Psychology and Behavioral Immunobiology, University
Hospital Essen, 45122 Essen, Germany. E-mail: [email protected]
dx.doi.org/10.1124/pr.114.009423.
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Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 698
I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 698
II. The Origin of the Placebo Concept: From the Ancient Healer to Modern Medicine . . . . . . . . . . . 699
III. Placebo Effects and Effect Sizes in Clinical Trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701
A. Effect Sizes of Symptom Improvement across Different Medical Conditions. . . . . . . . . . . . . . 701
B. Influence of Patient Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702
C. Influence of Randomized Clinical Trial Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703
D. Head-to-Head Trials: No Placebo Arm but Even Stronger Placebo Effects . . . . . . . . . . . . . . . 703
IV. Neuro-Bio-Behavioral Mechanisms of Placebo Responses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704
A. Pain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704
1. Placebo Analgesia Involves Changes in the Pain Processing Network. . . . . . . . . . . . . . . . . 704
2. Placebo Analgesia Engages Descending Pain Modulatory Networks. . . . . . . . . . . . . . . . . . 705
3. Neurotransmitter Systems Involved in Placebo Analgesia. . . . . . . . . . . . . . . . . . . . . . . . . . . . 705
4. Unresolved Issues and Remaining Questions Regarding the Mechanisms of
Placebo Analgesia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 706
B. Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 706
C. Neuropsychiatric Diseases and Behavioral Disorders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 707
1. Depression. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 707
2. Schizophrenia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 707
3. Anxiety Disorders. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708
D. Immunologic Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708
1. Studies in Experimental Animals.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708
2. Human Studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709
3. Toward the Clinical Application of Learned Immune Responses. . . . . . . . . . . . . . . . . . . . . . 709
E. Neuroendocrine Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 710
F. Autonomic Organ Functioning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711
1. Cardiovascular Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 711
2. Pulmonary Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712
3. Nausea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712
G. Gastrointestinal System/Irritable Bowel Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713
H. Sleep Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714
V. Neuro-Bio-Behavioral Mechanisms of Nocebo Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715
A. Nocebo Effects in Clinical Trials and Clinical Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715
B. Psychologic Mechanisms Contributing to Nocebo Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715
C. Neurobiological Pathways of Nocebo Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715
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VI. The Effect of Placebo Responses on Pharmacological Treatments. . . . . . . . . . . . . . . . . . . . . . . . . . . . 716
A. Neural Mechanisms Underlying the Effect of Expectations on Drug Efficacy. . . . . . . . . . . . . 717
B. Additive versus Interactive Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718
VII. Predictors of Placebo and Nocebo Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718
VIII. Relevance and Implications of Placebo Responses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720
A. Randomized Clinical Trials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 720
B. Training Health Care Professionals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721
C. Health Care System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 722
IX. Placebo Research: What Next? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723
X. Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 723
Abstract——The placebo effect has often been considered a nuisance in basic and particularly clinical research.
This view has gradually changed in recent years due to
deeper insight into the neuro-bio-behavioral mechanisms
steering both the placebo and nocebo responses, the evil
twin of placebo. For the neuroscientist, placebo and
nocebo responses have evolved as indispensable tools
to understand brain mechanisms that link cognitive
and emotional factors with symptom perception as
well as peripheral physiologic systems and end organ functioning. For the clinical investigator, better
understanding of the mechanisms driving placebo and
nocebo responses allow the control of these responses
and thereby help to more precisely define the efficacy
of a specific pharmacological intervention. Finally, in
the clinical context, the systematic exploitation of these
mechanisms will help to maximize placebo responses
and minimize nocebo responses for the patient’s benefit.
In this review, we summarize and critically examine the
neuro-bio-behavioral mechanisms underlying placebo
and nocebo responses that are currently known in
terms of different diseases and physiologic systems. We
subsequently elaborate on the consequences of this
knowledge for pharmacological treatments of patients
and the implications for pharmacological research, the
training of healthcare professionals, and for the health
care system and future research strategies on placebo
and nocebo responses.
I. Introduction
such as regression to the mean (Fig. 1). These factors
can be distinguished from the actual placebo response
that is mediated via three interdependent factors:
patients’ expectations about treatment benefits, the
quality and quantity of doctor-patient communication,
and associative (conditioning) learning processes (Fig.
2). These psychologic factors trigger complex neurobiological phenomena distinctly involving the central
nervous system (CNS) as well as system-specific peripheral physiologic and end-organ changes. Confusion
persists even within the scientific community about
how to define the terms “placebo,” “placebo effect,” and
“placebo response.” In this review, we will refer to the
term placebo responses, thereby focusing on the effects
of the neuropsychological mechanisms of expectation,
communication, and conditioning that drive the placebo response (Table 1).
The past two decades have witnessed groundbreaking advances in the understanding of neurobiological and neuropsychological mechanisms of placebo
and nocebo responses in various medical conditions
(Moerman, 2002; Brody, 2008; Benedetti, 2008, 2011).
This knowledge is not only pivotal for more detailed
analyses of the neuropsychological mechanisms driving
More than 55 years ago, Stewart Wolf (Wolf, 1959)
published a paper in this journal entitled, “The
Pharmacology of Placebo,” in which he stated that
the chosen title itself may present a “picturesque
contradiction,” because by definition pharmacology is
concerned with the chemical properties of drugs and
their effects on biologic mechanisms. It was common
knowledge at that time that the pharmacological
action of drugs as well as “other forces at play and
the circumstances surrounding their administration”
contribute to the overall treatment effect (Wolf, 1950).
Empirical data revealed that placebo effects that
endow inert agents with potency or modify the treatment effects of active pharmacological substances are
not only subjective in nature but that they can also be
associated with measurable and thus objective changes
in end organ functions. However, the underlying mechanisms remained largely unclear.
The placebo effect itself—the symptom improvement
after inert treatments in clinical trials—is composed of
different factors, such as the natural history of a
disease or fluctuation of symptoms, response biases,
effects of cointerventions, or statistical phenomena,
ABBREVIATIONS: ANS, autonomic nervous system; BMJ, British Medical Journal; CCK, cholecystokinin; CER, comparative effectiveness
research; CNS, central nervous system; CS, conditioned stimulus; CsA, cyclosporine A; EEG, electroencephalography; fMRI, functional
magnetic resonance imaging, IBS, irritable bowel syndrome; IFN, interferon; IL, interleukin; PAG, periaqueductal gray; PET, positron
emission tomography; PSG, polysomnographic assessments; rACC, rostral anterior cingulate cortex; RCT, randomized clinical trials; SOL,
sleep onset latency; TST, total sleep time; US, unconditioned stimulus; WASO, wake after sleep onset.
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
699
Fig. 1. Unspecific or placebo effects from any medical treatment entail a number of subeffects such as the natural history of disease and spontaneous
symptom variation, statistical phenomenon such as regression to the mean, response biases and false positive responses, contextual factors, and effects
of cointerventions as well as the actual placebo response. The placebo response itself is mediated by associative learning or conditioning processes, as
well as cognitive factors such as a patient’s expectation of a benefit from a treatment and the quality of the doctor-patient communication. The equal
parts are not meant to indicate a statistically balanced contribution.
the placebo response—it also paves the way for systematic exploitation of this knowledge with the aim to minimize placebo responses in clinical trials and in the
context of drug development and to maximize the
placebo response in clinical practice to improve treatment
outcomes and patient benefit (Jubb and Bensing, 2013).
In this review we will particularly emphasize the
neuro-bio-behavioral mechanisms of placebo and nocebo
responses, their effects on pharmacological treatments,
and potential predictors of interindividual differences in
these effects. On this basis, we will outline the relevance
of placebo responses on the assay sensitivity in randomized clinical trials (RCTs) and on therapeutic outcomes
in clinical practice. Finally, we will discuss implications
for the training of health care professionals and the
health care systems themselves and propose future
research directions.
II. The Origin of the Placebo Concept: From the
Ancient Healer to Modern Medicine
Since the dawn of experimental placebo research in
the 1990s, many papers (Fig. 3) and books (Spiro, 1986;
Harrington, 1999; Thompson, 2005; Benedetti, 2008;
Shapiro and Shapiro, 2010) have referred to the history
of the placebo concept and its origins long before
evidence-based medicine became the gold standard in
Western medicine. In the following section, we briefly
refer to three aspects of these historical roots of the
placebo concept: etymological considerations, methodological aspects, and a “meaning” aspect (Moerman and
Jonas, 2002).
i. The origin of the word “placebo” from the Latin
verb “placere” (pleasing), in the sense of “I may
please” or “it may please,” has been greatly
Fig. 2. Placebo responses are primarily mediated by cognitive factors such as patients’ expectations of treatment benefits, by behavioral conditioning
(associative learning) with pharmacological stimuli, and the quality of doctor-patient communication. These psychologic factors trigger complex
neurobiological phenomena involving distinct CNS as well as system-specific peripheral physiologic and end-organ alterations.
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Schedlowski et al.
TABLE 1
Placebo/nocebo terminology
Terminology
Placebo
Placebo effect
Placebo response
Active placebo
Nocebo
The word placebo is the Latin term for “I shall please.”
It is used to indicate sham treatments or inert
substances such as sugar pills or saline infusions.
The placebo effect is defined as any improvement in
a symptom or physiologic condition of subjects after
placebo treatment. There are different mechanisms
underlying this phenomenon, including spontaneous
remission, regression to the mean, natural course of
a disease, biases, and placebo responses.
The placebo response refers to the outcome caused by
a placebo manipulation. It reflects the neurobiological
and psychophysiological response of an individual to
an inert substance or sham treatment and is
mediated by various factors within the treatment
context. Importantly, placebo responses are not
restricted to placebo treatments—they can also
modulate the outcome of any active treatment.
An active placebo is a substance or treatment that
mimics the side effects of the active compound being
tested and is thus by definition not an inert
substance. In clinical trials, active placebos are
administered to avoid un-blinding due to the different
side-effect profiles of drugs and placebo treatments.
The term nocebo (I shall harm) was introduced in
contrast to “placebo” to distinguish the positive from
the noxious effects of placebos, when an inert
substance is given within a negative context, inducing
negative expectations about the outcome.
stressed as the background of a “placebo” application that helps without any underlying scientific background. There is evidence that an even
earlier use of the word referred to “singing
a placebo” as someone mourning at a funeral as
a paid service (Shapiro, 1964). The term placebo
remains stuck with this negative connotation,
because it is ethically regarded as deception of
the patient and is thus only allowed under
restricted circumstances, e.g., those of the Declaration of Helsinki (World Medical Association,
2013). The use of placebos is still predominantly
an issue of pharmacological research and is
inevitably associated with clinical trials, although
the establishment of placebo-controlled trials was
certainly not the drug industry’s “invention.”
ii. Long before “placebo controls” became standard
in pharmacological research in the 1940s, the
idea of a placebo control was already in the mind
of doctors and researchers, even those now
with a questionable scientific reputation. William
Cullen used the term for the first time in 1772,
when he gave a patient a dose of mustard powder
and wrote “… that I did not trust much to it, but I
Fig. 3. Number of genuine placebo (solid line) and nocebo publications (broken line) in PubMed per year between 1950 and 2013 (from Weimer et al.,
2015b, with permission).
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
gave it because it is necessary to give a medicine,
and as what I call a placebo” (Cullen, 1772, cited
in Jütte, 2013, pp 1). Later, Samuel Hahnemann,
founder of homeopathy in the 18th century,
frequently used treatments such as milk sugar
(lactose), which he deemed ineffective: “In the
meantime, until the second medicament is given,
one can soothe the patient’s mind and desire for
medicine with something inconspicuous such as
a few teaspoons a day of raspberry juice or sugar
of milk” (Hahnemann, 1814, cited in Jutte, 2014,
pp 210). Although he never used the term placebo,
Hahnemann was obviously aware of its clinical
“potency.” Similarly, the famous “Nuremberg Salt
Test” of 1835 (Stolberg, 2006) (designed to disprove Hahnemann’s homeopathic concept) used
“pure snow water” as a control for the “saline
treatment” that was a popular homeopathic treatment around that time, also without mentioning
the term placebo. For more details/information on
this and other examples, we refer the interested
reader to Ted Kaptchuk’s article on the history of
blind assessment and placebo controls in medicine
(Kaptchuk, 1998).
iii. A recent publication in the British Medical
Journal (BMJ) sought to investigate the “meaning” of the term placebo appearing in articles
published in the BMJ in the early days of evidencebased medicine between 1840 and 1899, made
possible thanks to the fact that the BMJ’s complete archive has been digitized (Raicek et al.,
2012). They identified 71 articles mentioning
the term “placebo,” of which 47 (66%) were in
specific sections in the BMJ such as “Correspondence” (10%), “Original communications” (10%),
and “Reports of societies” (4%), with the remaining 42% distributed among 23 other categories.
Twenty-four of the citations (34%) were in nonspecified sections. They assigned the use of the
term placebo to nine categories: no effect or
pejorative, (31%), natural history (25%), satisfy
patient (20%), medical performance (10%), buy
time (4%), financial gain (4%), placebo control
(3%), has clinical effect (1%), and unclear (1%).
Taken together, only two articles (of 1886 and
1889) used placebos as controls to test the effects
of a medical treatment, and only one mentioned
its being clinically effective. According to the
authors, all placebo applications were deceptive
and did not debrief the patients after completion
of the study.
Our brief history of placebo use in medicine illustrates
the three aspects that are still evident in today’’s use of
placebos: the negative connotation of the term placebo,
the suspicion that the use of placebos implies the deception of patients, and the speculation that placebos
701
may be ineffective in helping patients. Experimental
and clinical data from the last two decades has clearly
demonstrated that it is about time these assumptions
were overcome.
III. Placebo Effects and Effect Sizes in
Clinical Trials
A. Effect Sizes of Symptom Improvement across
Different Medical Conditions
When compared with the effect of drugs in randomized placebo-controlled trials, placebo effects can vary
substantially, ranging from under 10% to over 60%,
even within single clinical entities. Requiring a 50%
symptom improvement to qualify as a treatment responder resulted in 26% placebo responders in diabetic
neuropathic pain (Arakawa et al., 2015) but this was
lower in other pain conditions (e.g., dental pain: 16%;
Averbuch and Katzper, 2001). Similar response rates
were reported in migraine (29%; Macedo et al., 2006),
fibromyalgia (45%; Hauser et al., 2011), and pancreatic
pain (20%; Capurso et al., 2012) investigations.
It is, however, almost impossible to compare the
placebo response data from different meta-analyses of
RCTs in various clinical conditions. RCTs with a binary
outcome that allow for the differentiation of placebo
responders and nonresponders usually report the
percentage of responders in the placebo arm, and the
meta-analyses of such trials report the “pooled placebo
response” (Weimer and Enck, 2014). Most RCTs with
gastroenterological patients take this approach, revealing pooled placebo response rates ranging between
25% and 45% (Weimer et al., 2013a; Elsenbruch and
Enck, 2015). Trials with continuous outcome measures
(symptom scale improvements, e.g., in depression,
schizophrenia, Parkinson’s disease, attention deficit
hyperactivity syndrome, etc.) usually report effect sizes
in the placebo arm, but their interpretation depends on
a clinically meaningful grading of the scale that can
vary according to the condition. In sleep disorders, for
instance, the placebo response accounts for 60% of the
response in the drug arm of respective trials when
assessed by polysomnographic measures (Winkler and
Rief, 2015).
Placebo effects are more pronounced when assessed
via patient-reported outcomes (symptoms, symptom
ratings, quality of life measures) compared with
biomarkers (Meissner, 2005) or disease markers. However, even with biologic indicators of disease activity
such as the Crohn’s Disease Activity Index or endoscopic
assessment of disease activity, the placebo response can
remain as high as 25% (Su et al., 2004). Biomarkers
such as the “forced expiratory volume” in asthma,
however, display very weak placebo responses (Wang
et al., 2012), and they are known to correlate poorly with
patient-reported outcomes or clinical assessments of
disease severity (Wechsler et al., 2011). The highest
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(around 40%) and most homogeneous placebo response
rates are reported in psychiatric trials [e.g., in depression (Papakostas and Fava, 2009)] but also in
functional gastrointestinal disorders, such as irritable
bowel syndrome (IBS) or functional dyspepsia, disorders
known to coincide with depression and anxiety (Ford
and Moayyedi, 2010). In contrast, placebo responses are
known to be lower in neurologic disorders (epilepsy,
Parkinson’s disease) (Goetz et al., 2008; Rheims et al.,
2008) and in treating addiction, i.e., smoking cessation
(Moore and Aubin, 2012) (Tables 2 and 3).
One of the pitfalls when assessing placebo responses
in RCTs is the fact that they can be confounded by
spontaneous symptom improvements (Fig. 1) assumed
to be similar across all the trial’s arms and therefore
not controlled for. Whether the placebo response in
RCTs is still of a clinically relevant effect size after
ignoring the contribution of spontaneous symptom
variation is debatable (Hrobjartsson and Gotzsche,
2001, 2004). A meta-analysis of three-arm trials in 8
different clinical conditions including a “no treatment”
control group revealed that about 50% of the placebo
response could be explained by spontaneous remission/
variation (Krogsboll et al., 2009). Although only involving 10 trials, a similar meta-analysis in major
depression disorder (Rutherford et al., 2012) indicated
that “waiting” had an effect size of approximately 0.5
(Cohen’s d), the equivalence of a 4-point, clinically
relevant improvement on the Hamilton Depression
Scale. However, a waiting list is a poor means of “no
treatment control” (Weimer and Enck, 2014) because it
induces a dynamic (the expectation of being treated in
the near future) that differs from that in an observation study arm only (Relton et al., 2010). It has even
been suggested that “waiting list” is a “nocebo” intervention, although evidence for such a worsening
effect is limited to analyses that failed to take sample
size into account (Furukawa et al., 2014).
B. Influence of Patient Characteristics
There tends to be very broad interindividual variation in the placebo response of healthy individuals and
patients. Thus, to keep the placebo response low in
RCTs, the search for predictors of placebo responses in
RCTs applied post hoc reanalyses and sensitivity tests
of individual RCTs to identify putative predictors of the
placebo response that could then be used to identify
and exclude so called “placebo responders” from trials.
Among the patient characteristics frequently accused of
driving the placebo response are sex (with larger placebo
responses in women) and (younger) age. Although these
factors were shown to be relevant in some trials (Thijs
et al., 1990; Freeman and Rickels, 1999; Rheims et al.,
2008; Cohen et al., 2010; Yildiz et al., 2011; Agid et al.,
2013; Arakawa et al., 2015), a recent review (Weimer
et al., 2015a) of 75 systematic reviews and metaanalyses including more than 1500 trials, 150,000
patients, and 40 medical indications revealed that age
and sex were not significant predictors of placebo
responses in RCT, despite occasional evidence from
experimental research (Aslaksen et al., 2007; Weimer
et al., 2013b).
A more detailed meta-analysis of patient-related
factors driving the placebo effect in psychiatric disorders recently revealed consistent positive associations
between placebo effects with lower disease severity at
baseline and shorter disease duration in the treatmentnaive patients (Weimer et al., 2015b). Similarly, low
symptom severity at study entry was also found to
correlate positively with a placebo response in other
diseases such as fibromyalgia (Hauser et al., 2011), diabetic neuropathic pain (Hauser et al., 2011), binge-eating
TABLE 2
Systematic reviews and meta-analyses of the placebo response in RCTs in neurologic disorders, pain
syndromes, and in psychiatric disorders
Note that this list is incomplete, as it reports only the meta-analysis with the largest number of studies included for the
respective disease.
Study
Rheims et al., 2008
Fulda and Wetter, 2008
Goetz et al., 2008
Diener et al., 1999
Macedo et al., 2006
Hauser et al., 2011
Hauser et al., 2012
Capurso et al., 2012
Papakostas and Fava, 2009
Bridge et al., 2009
Agid et al., 2013
Yildiz et al., 2011
Cohen et al., 2010
Newcorn et al., 2009
Buitelaar et al., 2012
Blom et al., 2014
N*
Disease
PR Is Higher with…**
32
36
11*
15
98
72
70
7
182
12
50
38
40
10
2*
10*
Epilepsy (children, adults)
Restless Leg Syndrome
Parkinson’s Disease
Migraine
Migraine
Fibromyalgia syndrome
Diabetic neuropath pain
Pancreatitis
Depression, adults
Depression, children
Psychosis
Bipolar mania
MDD, OCD, ANX (children)
ADHD (children)
ADHD (adults)
Binge Eating Disorder
younger age
longer trial duration
higher baseline severity
higher drug probability
European studies
lower baseline severity
lower baseline severity
more study sites
higher drug probability
more study sites
younger age, more recent trials
more study sites, female sex
children than in adolescents
comorbid MDD, non-white
higher baseline severity
lower baseline severity
ADHD, attention deficit hyperactivity disorder; ANX, anxiety disorder; MDD, major depressive disorder; OCD,
obsessive compulsive disorder; PR, placebo response.
*Indicates availability of individual patient data; number of RCTs included into analysis.
**Only the most important influential variable listed.
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
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TABLE 3
Systematic reviews and meta-analyses of the placebo response (pooled PR, percent) in RCTs in various
diseases in internal medicine
Note that this list is incomplete, as it reports only the meta-analysis with the largest number of studies included for the
given disease.
Study
N*
Disease
PR Is Higher with… **
Ilnyckyj et al., 1997
Su et al., 2004
Ford et al., 2010
Talley et al., 2006
de Craen et al., 1999a
Cremonini et al., 2010
Thijs et al., 1990
Cho et al., 2005
Freeman et al., 1999
Lamel et al., 2012
Narkus et al., 2013
Wang et al., 2012
38
21
73
4*
79
24
1*
29
2*
31
6
34
Ulcerative colitis
Crohn’s Disease
IBS
FD
Duodenal ulcers#
Reflux disease
Hypertension
CFS
PMS
Psoriasis
Allergy
Asthma
higher number of visits
longer study duration
European studies
inconsistent symptoms
higher application frequency
nonerosive reflux disease
older age
high intervention intensity
younger age
higher drug probability
subcutaneous administration
lower baseline severity
CFS, chronic fatigue syndrome; FD, functional dyspepsia; PMS, premenstrual syndrome; PR, placebo response.
*Indicates availability of individual patient data; number of RCTs included into analysis.
**Only the most influential variable listed.
disorder (Blom et al., 2014), or asthma (Wang et al.,
2012), but not in attention deficit hyperactivity
disorder (Buitelaar et al., 2012) or Parkinson’s disease
(Goetz et al., 2008). Meta-analyses of more trials in
different medical conditions also failed to clearly identify
predictors of the placebo response, which can at least be
partially explained by limited access to individualized
data, as Tables 2 and 3 illustrate. Individual patient
data were available in only 6 of the listed 30 metaanalyses, usually in small data sets. Although more
recent experimental approaches have begun focusing on
identifying behavioral or genetic traits of placebo or
nocebo response differences, no consistent concept has
emerged so far (see section VI).
C. Influence of Randomized Clinical Trial
Characteristics
When it was first noted that the rate of placebo
responses had increased over the years in RCTs for
depression (Walsh et al., 2002; Bridge et al., 2009) and
schizophrenia (Agid et al., 2013; Rutherford et al.,
2014), this was interpreted as an indication of changes
in the characteristics of RCTs. Several RCT characteristics that increase placebo response rates have been
identified in individual re- and meta-analyses. These
include numerous study sites participating in multicenter studies (Bridge et al., 2009; Yildiz et al., 2011;
Capurso et al., 2012), longer trial duration (Su et al.,
2004; Fulda et al., 2007), and RCTs performed in
Europe rather than the United States (Macedo et al.,
2006; Ford and Moayyedi, 2010). These factors have
changed during the last decades and might account for
these time trends. More importantly, in depression, it
has been shown that the trend is only apparent when
the doctor rates treatment success but not when the
patient does (Rief et al., 2009b). The same study also
reported evidence for the increasing homogeneity of
investigated patient samples over the years, which also
contributes to larger effect sizes but limits the external
validity of results. Furthermore, today’s RCTs also
involve more frequent doctor-patient contacts that can
contribute to enhanced placebo responses (Ilnyckyj
et al., 1997; Cho, 2005), as can the drug application
frequency (de Craen et al., 1999b) and its application
route (Narkus et al., 2013).
Unbalanced randomization refers to any deviation
from a 50:50 drug:placebo randomization in RCTs. This
approach is often chosen to assign more patients to the
active drug in a two-arm trial for ethical reasons or to
test more drug dosages against placebo by adding more
study arms. As noted early on (Diener et al., 1999),
unbalanced randomization may increase the overall
placebo response in RCTs, as shown in trials on
depression (Papakostas and Fava, 2009; Sinyor et al.,
2010; Mancini et al., 2014), schizophrenia (Woods
et al., 2005; Mallinckrodt et al., 2010), and psychosis
(Agid et al., 2013). It is, however, unclear whether this
represents a specific effect in neuropsychiatric disorders, because it has not been observed in other
conditions, such as IBS (Ford and Moayyedi, 2010;
Elsenbruch and Enck, 2015).
D. Head-to-Head Trials: No Placebo Arm but Even
Stronger Placebo Effects
Because placebo responses are immanent in all
medical treatments, the omission of a placebo arm in
RCTs does not prevent placebo responses—it only
renders their systematic assessment more difficult. The
evaluation of enrichment trials, or unbalanced randomization in experiments and clinical trials (Weimer and
Enck, 2014), has shown that providing a 100% probability of receiving an active treatment increases
the response in both drug and placebo groups, as
opposed to the 50:50 probability in placebo-controlled
trials. In head-to-head trials, in which both treatment
arms receive an active component, it is impossible to
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Schedlowski et al.
specifically assess a placebo response. However, analyses of the treatment responses in head-to-head trials
compared with placebo-controlled trials enable us to
indirectly assess the contribution of expectancy/placebo
mechanisms to the efficacy of active treatment (see section VI).
A meta-analytic comparison of head-to-head trials
and placebo-controlled trials of the same drugs for
treating depression demonstrated that comparative
trials enhance the drug response compared with
placebo-controlled trials of the same compounds. This
effect is explained solely by the patient’s 100% assurance to receive a drug, and it resulted in an additional
15% “placebo response” to the established average of
40% from placebo-controlled drug trials for depression
(Rutherford et al., 2012). Similar data have been
reported in schizophrenia (Woods et al., 2005). This
creates an ethical dilemma: head-to-head trials need up
to four times more patients to statistically test “noninferiority” than conventional placebo-controlled trials
(Leon, 2012), a factor that contradicts the Declaration of
Helsinki request (World Medical Association, 2013) that
the minimum number of patients be included in RCTs.
These trials are also associated with substantially
higher trial costs, in particular when the appropriate
comparator drug selected is not the property of the
sponsoring company and needs to be produced and
the provision of double-dummy technology is needed
(Marusic and Ferencic, 2013). Finally, the comparator’s
selection may force substantial methodological considerations and concerns if more than one potential
comparator is available on the market (Dunn et al.,
2013).
In summary, the substantial effect sizes of placebo
treatments in RCTs are based on both patient-related
factors (e.g., age, sex, disease history, and severity),
most of which vary greatly in their contribution to the
placebo response depending on the clinical condition,
and by design-related factors that appear more homogeneous between conditions (e.g., unbalanced randomization, frequency of doctor-patient contacts, trial
duration) but lack controllability across different conditions. Finally, public and scientific access to previous
RCTs on the individual patient-data level are required
(Tudur Smith et al., 2014; Lo, 2015) to further explore
the contribution of these factors to the effect size of
placebos as long as placebo-controlled trials are the gold
standard in the evaluation of new drugs.
best characterized placebo response (Tracey, 2010a). The
advent of modern noninvasive neuroimaging techniques
has provided the unique opportunity to study the neural
mechanisms of placebo analgesia and other placebo
responses (Benedetti, 2014).
1. Placebo Analgesia Involves Changes in the Pain
Processing Network. One of the key questions that
has intrigued neuroscientists and clinicians alike is
whether the subjective reductions in pain ratings
during placebo analgesia are associated with activity
changes in the "pain processing network"—the set of
brain regions most closely associated with the experience of pain—and, if so, in which of its components?
The majority of neuroimaging studies addressing
placebo analgesia report that the reduced pain
ratings during placebo analgesia are accompanied
by decreased activity in the classic pain-processing
areas, including the thalamus, insula, somatosensory
cortex, and mid-cingulate regions (Wager et al., 2004;
Bingel et al., 2006; Eippert et al., 2009a; Lui et al.,
2010; Elsenbruch et al., 2012a; Geuter et al., 2013)
(Fig. 4). Electroencephalography (EEG) studies have
further confirmed that placebo analgesia is associated with reduced amplitudes of event-related potentials to experimental pain stimuli (Wager et al., 2006;
Watson et al., 2007; Aslaksen et al., 2011).
IV. Neuro-Bio-Behavioral Mechanisms of
Placebo Responses
A. Pain
Among all the placebo responses, it is placebo
analgesia (referring to the phenomenon of reduced
pain ratings after application of a sham treatment) that
is the most thoroughly examined and neurobiologically
Fig. 4. The CNS mechanisms initiating and mediating placebo responses
are best characterized for placebo analgesia and involve the descending
pain modulatory network, which includes the dorsolateral prefrontal
cortex (DLPFC), the anterior cingulate cortex (ACC), the amygdala (Am),
and the PAG. Similar regions of the brain have been shown to contribute
to emotional placebo responses. The shared and distinct contributions of
different brain networks in other types of placebo responses are currently
unknown.
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
Evidence from spinal cord forced magnetic resonance
imaging (fMRI), which only recently became technically feasible in both humans and animals, has revealed
that pain-related activity in the ipsilateral dorsal horn,
corresponding to painful stimulation, is substantially
reduced under placebo (Eippert et al., 2009b) and rises
under expectations of increased pain (nocebo) (Geuter
and Büchel, 2013). Together these studies support the
notion that altered pain experience during placebo
analgesia is not simply the result of report bias but
can, at least in part, result from the active inhibition of
nociceptive activity, even involving very early stages of
neural processing. However, this does not exclude other
brain mechanisms’ relevance and contribution to placebo analgesia.
2. Placebo Analgesia Engages Descending Pain
Modulatory Networks. These observations have raised
the question which brain processes mediate changes in
pain processing and perception. The pioneering positron
emission tomography (PET) study on placebo analgesia
by Petrovic et al. (2002) first revealed a shared neural
network in the rostral anterior cingulate cortex (rACC)
and the brain stem underlying both opioid and placebo
analgesia. The relevance of this network for placebo
analgesia has been substantiated by several subsequent
studies using various procedures to induce placebo
analgesia, including placebo analgesic creams, sham
acupuncture, and others (Wager et al., 2004, 2007;
Zubieta et al., 2005; Bingel et al., 2006; Kong et al.,
2006; Eippert et al., 2009a). These studies showed that
placebo analgesia involves the activation of cingulofrontal brain regions together with subcortical structures such as the midbrain periaqueductal gray (PAG),
hypothalamus, and amygdala. Connectivity analyses
further revealed that the behavioral placebo analgesic
effect is related to enhanced functional coupling of the
rACC with brain stem areas, such as the PAG (Wager
et al., 2004, 2007; Eippert et al., 2009a), and that
individual activity and connectivity changes within this
network predict the behavioral analgesic effect (Wager
et al., 2004; Eippert et al., 2009a).
These studies support the notion that the top-down
activation of endogenous analgesic activity via the
descending modulatory system represents one mechanism of placebo analgesia. The prefrontal cortex seems
to play a crucial role in this mechanism. Activity in the
dorsolateral prefrontal cortex was found in the period
preceding noxious stimulation, which correlated with
activity in the PAG and the subsequent placebo
analgesic response (Wager et al., 2004; Eippert et al.,
2009a; Lui et al., 2010). Intriguingly, both temporary
functional lesions in the prefrontal cortex by repetitive
transcranial magnetic stimulation (Krummenacher
et al., 2010) as well as degenerated and disconnected
frontal lobes in Alzheimer’s disease (Benedetti et al.,
2006) are associated with a reduction in or complete
loss of verbally induced placebo analgesic responses.
705
These findings implicate the prefrontal cortex as
a brain region critical to the initiation of expectancyrelated effects on pain perception.
3. Neurotransmitter Systems Involved in Placebo
Analgesia. Levine et al. (1978) first demonstrated
that placebo analgesia can be antagonized by naloxone,
suggesting the involvement of endogenously released
opioids. Since then, the contribution of opioidergic
neurotransmission in placebo analgesia has been
corroborated by indirect pharmacological approaches
using opioid antagonists and PET studies using in vivo
receptor binding approaches with opioidergic ligands
(Amanzio and Benedetti, 1999; Zubieta et al., 2005;
Wager et al., 2007; Eippert et al., 2009a). These PET
studies have substantiated that the placebo-related
activity and connectivity changes within cingulofrontal and subcortical networks including the PAG
observed with fMRI involve m-opioidergic neurotransmission (for review, see Peciña and Zubieta, 2015). The
contribution of opioidergic neurotransmission is further substantiated by pharmacological fMRI studies,
i.e., by showing that naloxone blocks placebo-related
activity in the PAG and rostral ventromedial medulla
and PAG-rACC connectivity along with impaired
behavioral analgesia (Eippert et al., 2009a), as well
as in seminal studies linking variability in genes
modulating opioidergic mechanisms with an individual’s placebo analgesic response (Hall et al., 2012;
Peciña et al., 2013a,b, 2014, 2015).
The endogenous opioid system is not the only one
involved in placebo analgesia. Functional molecular
imaging investigating changes in the binding potential
of 11C-labeled raclopride has shown increased dopaminergic neurotransmission in the nucleus accumbens,
putamen, and caudate nucleus that correlated with the
individual placebo analgesic response (Scott et al.,
2007, 2008). Furthermore, Schweinhardt et al. (2009)
reported a close positive relationship between gray
matter density in the ventral striatum and the magnitude of placebo analgesia, as well as dopamine-related
personality traits (e.g., novelty, fun, and sensation
seeking) using voxel-based morphometry, whereas
others have directly linked reward responsiveness and
placebo analgesia experimentally (Scott et al., 2007).
These findings suggest a potentially relevant role of the
dopaminergic system and the striatum in particular
in placebo analgesia. It is, however, unclear whether
striatal dopaminergic activity is causally involved
in generating analgesia or rather reflects reward processes associated with pain relief. The expectation of
dopamine release enhanced reward learning and modulated learning-related signals in the striatum and
ventromedial prefrontal cortex (Schmidt et al., 2014).
In a recent fMRI study on placebo analgesia, the administration of the D2/3 antagonist haloperidol blocked
placebo-related activity in the striatum but had no effect
on analgesia or on activity in brain regions believed to
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Schedlowski et al.
encode pain intensity (Wrobel et al., 2014). Taken together, although these data suggest endogenous dopaminergic pathways somehow contributing to the individual
placebo analgesic response, the distinct role of the dopaminergic system in placebo analgesia requires further
investigation (Peciña et al., 2013a).
Placebo analgesia was also recently linked to the
cannabinoid system (Benedetti et al., 2011). This system
seems to underlie placebo analgesia after pharmacological conditioning with the nonsteroidal anti-inflammatory
drug ketorolac. In this case, placebo analgesic responses
were reversed by the cannabinoid 1 receptor antagonist
rimonabant, indicating that the effects elicited by nonsteroidal anti-inflammatory drug conditioning are partially mediated by the endogenous release of cannabinoids
(Benedetti et al., 2011).
Overall, these findings support the notion that the
neurobiological effects of placebo analgesia are related
to distinct brain mechanisms and neurochemical pathways that are activated in various contexts contributing to placebo analgesia.
4. Unresolved Issues and Remaining Questions Regarding the Mechanisms of Placebo Analgesia. Although the aforementioned lines of evidence support
the view that descending pain modulatory mechanisms,
even those involving the spinal cord, are a critical
pathway underlying placebo analgesia, there is also
evidence supporting the relevance of intracortical mechanisms to placebo analgesia. Recent novel methodological approaches including multivariate pattern analysis
and meta-analyses of brain-imaging studies suggest
that most behavioral variance in placebo analgesia is
explained by activity changes in intracortical, emotionrelated circuitry, rather than changes in sensory brain
areas (Wager et al., 2011), as one would expect were
placebo analgesia determined solely by descending inhibition. Defending this view, a recent laser-evoked
potential study reported that effective behavioral placebo
analgesia can occur without changes in N1 amplitude,
which represents the earliest recordable in vivo cortical
response to afferent spinothalamic input (Martini et al.,
2015).
However, in contrast to the latest perspectives that
either highlight descending or intracortical pathways,
it is conceivable that several not mutually exclusive
pathways can contribute to placebo analgesia, depending on the individual disposition and the context in
which placebo analgesia is induced. Further research
needs to explore the interactions between the different
systems involved in placebo analgesia under physiologic and pathologic conditions (e.g., acute and chronic
pain).
B. Parkinson’s Disease
Marked improvements in Parkinson symptoms have
been observed in the placebo arm of clinical trials
testing pharmacological and surgical treatments for
Parkinson’s disease (Shetty et al., 1999; Goetz et al.,
2002, 2008; McRae et al., 2004; Diederich and Goetz,
2008). Although placebo rates in clinical trials do not
allow for dissociating between “true” placebo responses
(induced by positive expectation, doctor-patient communication, or prior experience) and natural fluctuations in the underlying disease unless a “no treatment”
arm is included, these high placebo response rates in
clinical trials of Parkinson’s disease have motivated
deeper examination of the neurobiological mechanisms
underlying clinical improvements following placebo
treatments in Parkinson’s disease.
De la Fuente-Fernandez et al. (2001) were the first to
study the involvement of the endogenous dopamine
system for placebo responses in Parkinson patients by
using raclopride-PET. After the administration of
a placebo that patients believed to be apomorphine (a
powerful anti-Parkinsonian treatment), the authors
observed increasing dopaminergic neurotransmission
in the striatum. The release of dopamine in the dorsal
striatum was greater in those patients who reported
clinical improvement, suggesting a relationship between placebo-induced changes in dopaminergic neurotransmission in the dorsal striatum and individual
clinical benefit. Interestingly, no such association was
observed between clinical benefit and changes in
dopaminergic neurotransmission in the ventral striatum, which was linked with patients’ expectations of
symptom improvement that can also be considered
reward anticipation. The contribution of the dopaminergic system to placebo responses in Parkinson’s
disease was later corroborated by two other dopamineligand PET studies on placebo responses in Parkinson
patients (Strafella et al., 2006; Lidstone et al., 2010).
The latter study by Lidstone and colleagues specifically
investigated how the strength of expectation of clinical
improvement influences the changes in dopaminergic
neurotransmission in the striatum in response to
a placebo treatment. Parkinson patients were told they
had a specific probability (25%, 50%, 75%, or 100%) of
receiving an active dopaminergic medication, but they
in fact received a placebo. Intriguingly, significant
dopamine release was only documented in those
patients with the 75% probability expectation. The
response to prior medication appeared to be a major
determinant of placebo-induced dopamine release in the
motor (dorsal) striatum, which might be explained by
a preconditioning phenomenon. In contrast, the expectation of clinical improvement was additionally required
to trigger dopamine release in the ventral striatum
(Lidstone et al., 2010), which likely reflects rewardrelated processes associated with the expectation of
clinical improvement (de la Fuente-Fernandez et al.,
2004).
Single cell recordings in patients undergoing surgery
for implantation of a deep brain stimulator (stimulation of the nucleus subthalamicus) provided the unique
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
opportunity to directly investigate any neuronal changes
in the basal ganglia underlying placebo responses in
Parkinson’s disease. Benedetti et al. (2004) were the first
to record activity from single neurons in the subthalamic
nucleus in awake Parkinson’s disease patients before
and after placebo administration following a pharmacological preconditioning procedure using apomorphine
(Fig. 2). They reported a significant decrease in the firing
rate and bursting activity after the placebo intervention
compared with baseline that was associated with the
patients’ subjective report of well-being and a reduction
in muscle rigidity at the wrist, as rated by a blinded
neurologist. In their later study, those observations
were expanded by recordings from thalamic nuclei and
the substantia nigra (Benedetti et al., 2009), showing
complex changes in the entire subthalamic-nigral-thalamic
motor circuitry associated with the improvements following placebo administration (for a review, see Frisaldi et al.,
2014).
Taken together, several lines of evidence support the
notion that placebo responses in Parkinson’s disease
are associated with changes in neuronal activity and
dopaminergic neurotransmission in the brain circuitry
involved in the pathophysiology of the disease itself (for
review, see Murray and Stoessl, 2013). These findings
have important implications for the interpretation of
clinical trials and the clinical care of Parkinson
patients.
How reward-related and potential disease-specific
processes associated with the placebo response in
Parkinson’s disease combine and interact and how long
these short-term experimental observations last and
contribute to clinical improvements over the longer
term (weeks to months) will have to be determined in
future studies.
C. Neuropsychiatric Diseases and Behavioral
Disorders
Numerous clinical studies and meta-analyses provide compelling evidence for pronounced placebo
effects in psychiatric conditions such as depression,
schizophrenia, or anxiety disorders (Cavanna et al.,
2007; Murray and Stoessl, 2013; Weimer et al., 2015b).
Compared with pain or Parkinson’s disease, however,
the neurobiological mechanisms underlying placebo
responses in these disorders are much less well
understood, which is partly because of the lack of
experimental models suitable to investigate such
mechanisms in healthy volunteers and patients.
1. Depression. Comprehensive reviews and metaanalysis indicate that the drug-placebo difference in
clinical studies of depression are relatively small,
reporting placebo response rates up to 70% (Kirsch
and Sapirstein, 1998; Kirsch et al., 2008; Rief et al.,
2009a; Gueorguieva et al., 2011; Mora et al., 2011) (see
section III). In addition, 79% of placebo responders
remained well within the continuation phase in the
707
studies lasting over 12 weeks, suggesting a long-lasting
effect of the antidepressive placebo response (Khan
et al., 2008). The neurobiological mechanisms underlying these responses have been analyzed by employing
quantitative electroencephalography (Leuchter et al.,
2002). Placebo responders showed a significant increase
in prefrontal brain activity that was not observed in
placebo nonresponders or in patients responding or not
responding to the medication.
Changes in glucose metabolism in depressive male
patients were analyzed after 6 weeks’ treatment with
the antidepressant fluoxetine or placebo via PET
(Mayberg et al., 2002). The placebo response was
associated with metabolic increases in prefrontal,
anterior cingulate, premotor, parietal, and posterior
cingulate cortex and decreases in the subgenual
cingulate, parahippocampus, and thalamus. These
changes in brain activity revealed at least some shared
neurobiological pathways for placebo responses in pain
and depression in conjunction with expectation and
emotion- and reward-related circuitry involvement
(Benedetti et al., 2005; Wernicke and Ossanna, 2010;
Murray and Stoessl, 2013). These neurobiological
findings are accompanied by observations that increased subjective reward experience affects treatment
efficacy with antidepressive medication (Wichers et al.,
2009). In addition, the expectancy of improvement is
affected by the probability of receiving active antidepressant medication, which in turn seems to influence
the antidepressive response (Rutherford et al., 2013).
These neuropsychological post hoc data justify administering active placebos in clinical trials analyzing the
effectiveness of antidepressive treatment (Salamone,
2000; Moncrieff et al., 2004; Edward et al., 2005; Enck
et al., 2013; Weimer et al., 2015b).
2. Schizophrenia. Placebo responses reported in
clinical trials in schizophrenia are similar in magnitude, quality, and impact to those observed in depression trials (Kinon et al., 2011). The difference in
responder rates between antipsychotic and placebo
treatments in psychosis is reported to be 18% (Leucht
et al., 2009, 2013). Results suggest significant variability in the magnitude of the placebo response due to
patient characteristics and trial design factors, with
the magnitude of placebo effects increasing over time
(Agid et al., 2013; Rutherford et al., 2014) (see section
III).
To date, the underlying neurobiological mechanisms
steering placebo responses in schizophrenic patients
have not been studied; they are difficult to investigate
mainly because there is no generally accepted experimental model for schizophrenia. Moreover, schizophrenia’s symptoms are classified into three groups:
positive symptoms, such as hallucinations and delusions, negative symptoms, such as apathy and social
withdrawal, and cognitive symptoms, such as poor
executive functioning and working memory (Murray
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Schedlowski et al.
and Stoessl, 2013; Kingwell, 2014), all of which could
react differently to placebo mechanisms. Experimental
approaches in this field might be further complicated
by the fact that patients’ expectations may vary considerably because of a false interpretation of the term
placebo, and positive psychotic symptoms may be classified as rewarding (Dunn et al., 2006).
There is thus an urgent need to characterize the
neuropsychological mechanisms underlying placebo
responses in schizophrenia to ensure continued
support for investment and progress in CNS drug
development.
3. Anxiety Disorders. For anxiety disorders such as
generalized anxiety, panic disorder, or social phobia,
placebo effects in clinical trials have been reported
ranging between 10% and 60% (Loebel et al., 1986;
Mavissakalian, 1988; Mellergard and Rosenberg, 1990;
Piercy et al., 1996; Huppert et al., 2004; Khan et al.,
2005; Stein et al., 2006). Moreover, in RCTs investigating the anxiolytic effect of the benzodiazepine
alprazolam, improvement in the placebo arm remained
stable after the pill intake was discontinued, whereas
patients in the drug arm suffered relapses to baseline
levels (Ballenger et al., 1988; Pecknold et al., 1988),
indicating that placebo responses in anxiety disorders
can indeed be long lasting and clinically relevant. The
substantial and robust placebo responses observed in
clinical studies of anxiety disorders motivated experimental investigations in their neurobiological underpinnings and inspired the search for an individual’s
predictors of placebo responses, although the results so
far have been vague (Dager et al., 1990; Woodman
et al., 1994; Feltner et al., 2009).
PET analyses showed that placebo responses in
patients with social anxiety disorders were accompanied by attenuated amygdala activity, a brain region
crucial for emotional processing. Intriguingly, this only
applied to those subjects homozygous for the long allele
of the serotonin transporter–linked polymorphic region
or the G variant of the G-703T polymorphism in the
tryptophan-hydroxilase-2 gene promoter. In the same
study, the tryptophan-hydroxilase-2 polymorphism
significantly predicted placebo responses, whereby
homozygosity for the G allele was associated with
more pronounced improvement in anxiety symptoms
(Furmark et al., 2008). Likewise, anxiety-relieving
placebo responses in healthy subjects decreased activity in emotionally responding brain areas such as the
amygdala, insula dorsal ACC, and increased activity in
the subgenual anterior cingulate cortex and ventral
striatum, indicating a reward-related response induced
by cognitive factors such as volunteers’ expectations
(Zhang et al., 2011). In patients with social anxiety,
connectivity changes between the amygdala and
dorsolateral prefrontal cortex and rACC areas have
been shown to be associated with the individual
anxiolytic response to SSRI treatment and placebo,
as demonstrated in PET. These observations support
the notion that similar cognitive or expectancy-related
mechanisms are involved in improving emotion regulation in patients taking anxiolytic drugs and placebo
responders (Faria et al., 2014).
Placebo responses in neuropsychiatric disorders
have been documented in both RCTs and experimental
studies (Weimer et al., 2015b). A few experimental
studies have reported at least some shared neurobiological pathways for placebo responses in neuropsychiatric disorders and pain with the involvement of
expectation and emotion- and reward-related circuitry.
A thorough understanding of the mechanisms steering
these often profound placebo responses will be essential to exploiting this knowledge and to maximizing
these unspecific treatment effects in daily clinical care
for the patient’s benefit and to minimize placebo
responses for the development of effective drugs (Enck
et al., 2013).
D. Immunologic Responses
Meta-analyses and reviews have demonstrated the
susceptibility to “inert” treatments of various immunerelated pathologic conditions such as ulcerative colitis
(Ilnyckyj et al., 1997), duodenal ulcer (de Craen et al.,
1999b), Crohn’s disease (Su et al., 2004), or multiple sclerosis (La Mantia et al., 1996). However, few
attempts have been made to specifically investigate
placebo responses and the underlying mechanisms
directly modulating peripheral immune functions in
human subjects (Pacheco-Lopez et al., 2006). These
experimental data indicate that peripheral immune
functions seem to be predominantly affected by
associative learning or conditioning paradigms rather
than by cognitive factors alone, such as the expectation
of subjects or patients of a given treatment’s benefit.
Learned placebo responses in immune functions can
be demonstrated by employing associative learning
paradigms in experimental animals, healthy humans,
and patients (Enck et al., 2008; Schedlowski and
Pacheco-Lopez, 2010). A prerequisite for the classic
conditioning of immune functions is intense bidirectional communication between the CNS and the peripheral immune system (Meisel et al., 2005; Tracey,
2009, 2010b) that constantly exchange information on
efferent and afferent pathways. The classic conditioning of immune responses is one of the most impressive
examples of communication between these major
physiologic systems (Exton et al., 2001; Ader, 2003;
Pacheco-Lopez et al., 2006; Schedlowski and PachecoLopez, 2010).
1. Studies in Experimental Animals. Most insights
into the neurobiological mechanisms steering learned
placebo responses in immune functions came from
studies in experimental animals. The majority of
experiments in rodents usually employ the so-called
taste aversion paradigm (Garcia et al., 1985) in which
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
a novel taste (conditioned stimulus [CS]) is paired with
the administration of an immunomodulating drug (unconditioned stimulus [US]) during acquisition. When the
CS is re-presented at a subsequent time point during
the evocation or memory phase, animals demonstrate
a modification of immune parameters as a conditioned
response that generally mimic the actual drug (US)
effect. Experimental evidence over the last 3 decades
has demonstrated behaviorally conditioned stimulatory
or inhibitory effects in rodents, both in humoral and
cellular immunity, with behavioral conditioning able to
re-enlist changes in lymphocyte circulation and proliferation, cytokine production, natural killer cell activity, or endotoxin tolerance (reviewed in Exton et al.,
2001; Hucklebridge, 2002; Ader, 2003; Pacheco-Lopez
et al., 2006; Riether et al., 2008; Schedlowski and
Pacheco-Lopez, 2010).
The underlying central, efferent, and afferent neurobiological mechanisms steering learned immunosuppressive placebo responses are the best understood to
date and are thus described here in greater detail.
Employing the immunosuppressants cyclophoshamide
or cyclosporine A (CsA) as a US demonstrated that the
insular cortex and amygdala are central key structures
in the behaviorally conditioned suppression of antibody
production (Ramirez-Amaya et al., 1996), lymphocyte
activity, as well as cytokine production [interleukin
(IL)-2, interferon (IFN)-g] and cytokine mRNA expression (Pacheco-Lopez et al., 2005; Schedlowski and
Pacheco-Lopez, 2010). Employing the CsA-taste conditioning paradigm, the learned immunosuppressive
responses are mediated on the peripheral efferent
arm via the splenic nerve via noradrenaline and
adrenoceptor-dependent mechanisms (Exton et al.,
1998, 1999, 2002; Pacheco-Lopez et al., 2009; Riether
et al., 2011). However, this neuroanatomical pathway with the splenic nerve mediating the learned
immunosuppression appears to be just one of many
efferent neural routes mobilized during learned immunosuppression, because the learned inhibition of the
contact-hypersensitivity reaction (employing the identical CsA-taste paradigm) was independent of sympathetic splenic innervation (Exton et al., 2000).
Other approaches analyzed afferent mechanism(s)
enabling the CNS to receive information regarding
specific drug-induced immune changes or pharmacological effects induced by the drug employed as a US.
Acute peripheral CsA administration induced behavioral changes such as decreased ambulatory activity in
the open field 6 hours after CsA injection (von Horsten
et al., 1998). These changes coincide with increased
neuronal activity detected in the insular cortex and
amygdala 1 to 6 hours after intraperitoneal CsA
injection, as evident in intracortical EEG telemetry,
as well as c-Fos expression and increased noradrenaline levels in the amygdala as determined by microdialysis (Doenlen et al., 2011; Pacheco-Lopez et al.,
709
2013). These immediate alterations appeared to be
a direct effect of CsA and not indirectly mediated via
the vagus nerve, because a vagal deafferentation before
CsA injection did not prevent the increased neural
activity. However, CsA levels were detected in the
cerebellum, insular cortex, and amygdala 2 to 4 hours
after CsA administration (Pacheco-Lopez et al., 2013).
Overall, at present it is incompletely understood how
the CNS detects the changes induced by different
substances and drugs employed in paradigms of
learned immune responses, and we still need to
elucidate which molecules are the messengers that
activate the brain during the acquisition or learning
phase of a conditioning protocol (Hadamitzky et al.,
2013).
2. Human Studies. The data from experimental
animals together with deeper understanding of the
mechanisms responsible for learned immune functions
formed the basis for studying this interesting placebo phenomenon in humans (Vits et al., 2011). The
CsA-taste-immune paradigm was expanded from just
being applied in experimental animals to being applied in healthy humans, revealing the behaviorally
conditioned significant suppression of T-cell function
detected via impaired cytokine (IL-2 and IFN-g) production, reduced cytokine mRNA expression, and
inhibited T-cell proliferation (Goebel et al., 2002). This
learned immunosuppression can be repeatedly recalled
by exposing the conditioned subjects to the CS again
after an 11-day break (Wirth et al., 2011). Moreover,
plasma noradrenaline concentration, state anxiety,
and baseline levels of IL-2 predicted nearly 60% of
the conditioned immunosuppressive response (Ober
et al., 2012), providing evidence for biologic and psychologic predictors of conditioned placebo responses in
general and learned immune responses in particular.
Further studies employing the CsA-taste conditioning
paradigm in humans showed that immunosuppression
could not be induced by just manipulating the expectancy of test subjects (Albring et al., 2012). In addition,
experimental evidence confirms previous observations
in rodents (Niemi et al., 2007), namely that inducing
a pronounced learned response in immune functions
requires multiple CS-US combinations during acquisition and evocation (Goebel et al., 2005; Albring et al.,
2012; Grigoleit et al., 2012).
3. Toward the Clinical Application of Learned Immune Responses. A number of studies in rodents have
meanwhile demonstrated the potential clinical relevance of learned responses in immune functions.
Specifically, the morbidity and mortality of animals with
autoimmune disease was abated via the learned immunosuppressive response (Ader and Cohen, 1982;
Klosterhalfen and Klosterhalfen, 1983, 1990; Jones
et al., 2008). In addition, asthma-like symptoms, anaphylactic shock (Noelpp and Noelpp-Eschenhagen, 1951a,b;
Djuric et al., 1988; Palermo-Neto and Guimaraes,
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Schedlowski et al.
2000), histamine release (Peeke et al., 1987; Irie et al.,
2001, 2002, 2004), or delayed-type hypersensitivity
response (Bovbjerg et al., 1987; Roudebush and
Bryant, 1991; Exton et al., 2000) were affected by
learned immunosuppressive responses. Behavioral
conditioning as supportive therapy has also been
studied in the context of experimental cancer therapies
(Hiramoto et al., 1991; Spector, 1996; Bovbjerg, 2003),
in which the learned immune response reduced tumor
growth and prolonged survival time in conditioned
tumor-bearing mice (Ghanta et al., 1985, 1988, 1990).
Another example of the potential clinical relevance of
the behaviorally conditioned immune response is
illustrated by grafting experiments (Gorczynski et al.,
1982). Behaviorally conditioned CsA-immunosuppressive
effects prolonged the survival time of heterotopically
transplanted heart allografts in rats (Exton et al., 1998;
Grochowicz et al., 1991). Moreover, combining the
conditioning procedure with the administration of subtherapeutic doses of the immunosuppressive drug CsA
together with daily re-exposure to the CS led to longterm graft survival in 20–30% of the animals (Exton
et al., 1999), indicating synergistic effects via a combination of behaviorally conditioned immunosuppression and
subtherapeutic dosing of an immunosuppressive drug.
Early observations in humans, namely, that allergic
symptoms can be induced in affected patients despite
the absence of allergens, support the notion that
learning mechanisms contribute to asthma’s pathophysiology (Turnbull, 1962). This assumption is supported by a study in patients with allergic rhinitis,
whereby elevated measures of mast cell tryptase in
mucosa were conditioned behaviorally (Gauci et al.,
1994). Similarly, allergic subjects re-exposed to an
olfactory cue (CS) formerly paired with a grass-allergen
challenge displayed increased histamine release (Barrett
et al., 2000). Reversely, the antihistaminergic properties of the H1-receptor antagonist desloratadine was
behaviorally conditioned in patients suffering from
allergic house-dust-mite rhinitis, whereby the learned
placebo response significantly reduced subjective
symptoms, the allergic response to the skin prick test,
and basophile activation (Goebel et al., 2008). That
study was recently confirmed and expanded upon by
demonstrating reproducible placebo responses in the
allergic response induced by both expectation and
learning (Vits et al., 2013). The effectiveness of the
conditioning procedure on another type of allergic
reaction (delayed-type hypersensitivity response) was
tested in healthy volunteers undergoing monthly
tuberculin skin tests. All subjects presented significantly blunted symptom severity as a result of the
conditioning process (Smith and McDaniel, 1983).
However, employing a similar approach, those results
could not be replicated (Booth et al., 1995). The
efficiency of learned immune responses was also tested
in patients with multiple sclerosis who receiving
cyclophosphamide infusions continuously paired with
a novel taste during the learning phase. Re-exposure
to the CS alone during evocation significantly reduced
peripheral leukocyte numbers (Giang et al., 1996).
Furthermore, by pairing subcutaneous IFN-g injections with a strongly flavored drink (CS), elevated
levels of neopterin and quinolinic acid serum were
induced after re-exposing healthy volunteers to the CS
(Longo et al., 1999).
Together, these experimental data form a “proof of
principle that associative learning protocols may be
taken seriously as supportive treatment options during
immune pharmacological regimens (Schedlowski and
Pacheco-Lopez, 2010; Doering and Rief, 2012; Enck
et al., 2013). However, as with other learning processes, behaviorally conditioned immunosuppression is
subject to extinction, that is, the learned immunosuppressive response gradually decreases over time. This
constitutes a considerable problem for the systematic
application of conditioning paradigms as a treatment
option supporting immunopharmacological regimens.
Attempting to overcome this drawback, a recent study
demonstrated that extinction of the learned immunosuppressive response (IL-2 production and IL-2 mRNAexpression) in healthy humans can be inhibited by
combining the CS re-exposure during evocation with
administration of subtherapeutic doses of CsA (Albring
et al., 2014). Similarly, a recent partial reinforcement
schedule in which patients received a full dose of
medication 25 to 50% of the time and placebo medication the other times significantly reduced the amount of
corticosteroid needed to treat cutaneous lesions in
psoriasis patients (Ader et al., 2010).
Although the clinical exploitation of conditioned
immune responses is still in a very early stage, these
preliminary results provide evidence that extinction
processes might be overcome by systematically modifying learning protocols such as partial reinforcement
strategies (Ader et al., 2010; Doering and Rief, 2012;
Hadamitzky et al., 2013; Au Yeung et al., 2014).
E. Neuroendocrine Responses
The experimental designs employed in studies investigating learned placebo responses in neuroendocrine functions basically resemble the conditioning
protocols used to induce learned placebo responses in
immune functions. During acquisition, the conditioning group receives the pairing of a CS (e.g., stimulus
compound, injection procedure, novel tasting drink, or
novel olfactory stimulus) and a US (e.g., administration of adrenaline, insulin, dexamethasone, glucose,
IFN-b-1a, sumatriptan), which induces alterations in
neuroendocrine responses. The experimental group is
then re-exposed to the CS during evocation and
alterations in neuroendocrine functions (e.g., concentrations of adrenaline, glucose, cortisol, insulin, norepinephrine, glucagon, vasopressin, ACTH, somatropin)
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
are analyzed, reflecting the conditioned response.
Although learned placebo responses in neuroendocrine
functions have been demonstrated in experimental
animals (Ader, 1976; Buske-Kirschbaum et al., 1996;
Janz et al., 1996; Pacheco-Lopez et al., 2004), there are
few studies reporting these effects in humans, and
those that do mainly employed insulin as a US
measuring blood glucose or insulin levels as a conditioned response (Fehm-Wolfsdorf et al., 1993;
Stockhorst et al., 1999, 2004, 2011; Klosterhalfen
et al., 2000; reviewed in Wendt et al., 2014b). Two
human studies reported conditioned changes in plasma
cortisol concentrations. One study observed an increase
in plasma cortisol levels by re-exposing subjects to
a novel tasting drink (CS) that had been paired with
an injection of dexamethasone (US) (Sabbioni et al.,
1997). A decrease in cortisol and an increase in growth
hormone were observed when sumatriptan was used as
a US during the conditioning procedure (Benedetti et al.,
2003). These changes in cortisol and growth hormone
levels were induced via the associative learning protocol
but not via mere expectation, resembling the observations in learned immune responses. However, there are
currently no data on the neurobiological mechanisms
mediating these conditioned neuroendocrine responses,
which might explain why we are unaware of any
such protocols that have been transferred to clinical
conditions.
This experimental evidence demonstrates the potential applicability of such behavioral conditioning protocols in clinical practice. However, future studies will
have to analyze the kinetics of the behaviorally conditioned endocrine response and to elucidate whether and
to what extent these conditioned responses can be
reconditioned on multiple occasions. Only with this
information and more detailed knowledge of the mechanisms driving the CNS-endocrine system interaction
will it be possible to design conditioning protocols that
can be employed in clinical situations to the patients’
advantage.
F. Autonomic Organ Functioning
The innervation of most peripheral organs by
sympathetic and the parasympathetic branches of
the autonomic nervous system (ANS) forms the anatomic and neurophysiological basis of placebo responses
in end-organ functioning (Jänig, 2006). The ANS is
under inhibitory and excitatory control of a number of
cortical and subcortical structures such as the anterior
and mid-cingulate cortices, the insula, dorsolateral
prefrontal cortex, amygdala, hippocampal formation,
and the hypothalamus (Beissner et al., 2013), brain
areas known to mediate placebo responses in pain,
nausea, and Parkinson’s disease. Among the ANSregulated functions, cardiovascular, pulmonary, and
intestinal functions have been subjected to placebo
investigations.
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1. Cardiovascular Functions. A drop in blood
pressure is regularly observed in the placebo groups
of clinical trials of hypertension (Preston et al., 2000;
Weber, 2008). However, the inclusion of no-treatment
control groups have revealed that these effects seem to
be largely due to confounding factors such as spontaneous fluctuation and regression to the mean of
habituation effects (Hrobjartsson and Gotzsche, 2010).
In studies of placebo analgesia, pain relief is accompanied by a reduction in heart rate and heart rate
variability (Pollo et al., 2003). This placebo response in
pain perception and heart rate was naloxone reversible
but was unaffected by muscarinic blockade with
atropine. In contrast, b-adrenoceptor blockade with
propranolol did not affect placebo analgesia but did
antagonize a pain-induced heart rate increase, suggesting that placebo analgesia is independent from
heart rate changes (Pollo et al., 2003). This notion is
supported by observations where placebo analgesia
was associated with a reduction in subjective stress
levels, heart rate, and heart rate variability. The
reduction in sympathetic activity appeared to be a
factor involved in anticipatory placebo analgesia rather
than simply a direct consequence of pain reduction
(Aslaksen and Flaten, 2008; Aslaksen et al., 2011).
These placebo responses reflected in the heart rate
appear to be mediated via the prefrontal cortex, because the impaired connectivity of the prefrontal lobes
in Alzheimer patients has been associated with both
absent placebo analgesia and a heart rate reduction
(Benedetti et al., 2006). Whether placebo responses
evident in cardiovascular functions in placebo analgesia studies are a direct cause of the placebo instruction
or secondary to the altered pain perception is still not
clear.
Placebo responses on cardiovascular functions outside the context of pain-related changes in sympathetic
activity have been reported in experimental settings,
but they are inconsistent and scarce. Placebo-induced
drops in systolic but not diastolic blood pressure in
normo- and hypertensive patients have been reported
(Agras et al., 1982; Suchman and Ader, 1992; Amigo
et al., 1993). More recently, verbally induced expectation reduced systolic blood pressure without affecting
diastolic blood pressure or other autonomic responses,
such as skin conductance and sympathetic and parasympathetic components of heart rate variability in
healthy volunteers (Meissner and Ziep, 2011). Another
study reported stress responses induced by a placebospray administered with the suggestion that it would
either raise or lower blood pressure. Within the total
study sample, blood pressure was significantly higher
after the placebo spray independent of the associated suggestions (Zimmermann-Viehoff et al., 2013).
A systolic drop in blood pressure was evident after
placebo intake plus verbal suggestion but vanished
when participants believed that the medication had
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been switched from a trademark to a generic drug
(Faasse et al., 2013). Finally, verbal suggestions have
been shown to affect coronary diameter in chest pain
patients. Surprisingly, the verbal suggestion of vasodilatation induced significant vasoconstriction but was
associated with lower pain ratings in this patient group
without affecting stress ratings, heart rate, or blood
pressure (Ronel et al., 2011).
Employing the open versus hidden infusion paradigm, the b-blocker propranolol was more effective in
reducing heart rate when given by the doctor (open)
compared with the computer-infusion (hidden) condition
(Colloca and Benedetti, 2005) (for details, see section
VI). Similarly, the acetylcholine muscarinic antagonist
atropine induced a more pronounced increase in heart
rate when administered overtly compared with the
hidden condition (Benedetti et al., 2003).
Taken together, there is evidence of placebo responses
on systolic blood pressure, yet other effects on the
cardiovascular system require further clarification.
2. Pulmonary Functions. The autonomic nervous
system regulates smooth muscle constriction in the
respiratory tract; sympathetic activation via catecholaminergic mechanisms induces bronchodilation,
whereas bronchoconstriction is predominantly mediated via vagal efferents (Canning and Fischer, 2001).
Several studies reported bronchoconstriction in asthmatic patients induced by the use of a fake bronchoconstrictor containing pure saline solution (Isenberg et al.,
1992a,b). Verbally induced expectations of bronchoconstriction were antagonized by anticholinergic agents,
suggesting these nocebo responses are mediated via
enhanced vagal activation of lung functions (Luparello
et al., 1968; Butler and Steptoe, 1986). More recently, in
an experimental approach, a placebo bronchodilator
significantly reduced nonspecific airway hyperresponsiveness in asthmatic patients (Kemeny et al., 2007). In
contrast, another study identified clear and pronounced
placebo responses in subjective asthma symptoms but
observed no placebo responses on objective outcome
measures as measured with spirometry (Wechsler et al.,
2011). Taken together, whether and to what extent
subjective placebo and nocebo responses are associated
with physiologic lung function parameters will have to
be analyzed in experimental approaches and clinical
investigations.
Both heart rate and respiratory responses appear to be
affected during placebo analgesia. Patients treated with
the opioid buprenorphine for pain relief after surgery
presented a reduced respiratory response as one common
side effect of the opioid administration. When injected
with NaCl instead of the opioid, patients reported
placebo-induced pain relief and displayed a reduced
respiratory response that was antagonized with the
opioid antagonist naloxone (Benedetti et al., 1998, 1999).
3. Nausea. Nausea was known to demonstrate high
placebo response rates as early as 1955, when Beecher
(1955) cited a study with average placebo response
rates of 40% with seasickness in individuals working
on seagoing vessels (Gay and Carliner, 1949). The
emetogetic properties of ipecac, a herbal drug to induce
mild nausea in intoxication, was completely blocked
by verbal suggestions of nausea relief in fistulated
individuals (Wolf, 1950). A recent review on laboratory
experiments in healthy volunteers addressing placebo
responses in nausea concludes that nausea symptoms
can be alleviated with placebo interventions based on
the principles of Pavlovian conditioning, manipulating
expectancies, or a combination of both (Quinn and
Colagiuri, 2015).
Attempts to relieve nausea by performing Pavlovian conditioning build on laboratory and clinical evidence that anticipatory nausea, the urge to vomit, taste
aversion, and rotation tolerance can be classically
conditioned. Experimental techniques aiming to reduce
conditioned responding and/or to enhance extinction
may constitute promising tools leading to effective
interventions. Studies in healthy volunteers confirm
that anticipatory nausea is reduced by repetitive preexposure to the nausea-inducing environment (“latent
inhibition”) (Klosterhalfen et al., 2005) and by providing a different, salient beverage before nausea
induction (“overshadowing”) (Stockhorst et al., 2014).
Only a single study to date has conducted conditioningbased interventions in a clinical setting to reduce
nausea in cancer patients undergoing chemotherapy
(Stockhorst et al., 1998), although more work is underway in this promising field (Geiger and Wolfgram,
2013).
Changing expectations about nausea has been accomplished in laboratory experiments by the intake of
placebo pills (Levine et al., 2006) or by delivering
positive suggestions associated with distinct gustatory
stimuli (Klosterhalfen et al., 2009; Weimer et al.,
2012). Other studies attempting to modify expectations
have provided counterintuitive, i.e., reverse effects of
suggestions (Levine et al., 2006) or negative results
(Williamson et al., 2004). These inconsistencies may in
part be explained by sex differences (Klosterhalfen
et al., 2009; Weimer et al., 2012) and/or complex
interactions between the participant’s sex and that of
the experimenter (Aslaksen et al., 2007; Weimer et al.,
2012). Ultimately, interventions combining the principles of conditioning with optimized expectations may
be most promising for effective alleviation of nausea
symptoms. A combination of positive instructions and
surreptitiously reduced rotation speed in preceding
trials (i.e., conditioning) detected significantly alleviated symptoms, fewer nauseogenic head movements,
and longer rotation tolerance in a subsequent trial
(Horing et al., 2013).
Exploring the central mechanisms of nausea relief
with placebo (and drugs) is difficult given the risk of
aspiration during brain-scanner investigations with
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
experimentally induced nausea in supine position
(Kowalski et al., 2006). However, recent progress has
been made in a virtual-reality environment (Farmer
et al., 2015). Also very recent has been the introduction
of a novel experimental model that permits greater
freedom during nausea experiments inside and outside
brain scanners than the rotation paradigms currently in
use (Quinn et al., 2015). Galvanic vestibular stimulation
induced a sensory mismatch that proved highly effective
in conditioning nausea; it was also independent of other
experimental context variables. This phenomenon will
allow a more in-depth analysis of the central pathways
mediating the placebo response in nausea in the future.
Investigating the peripheral mechanisms underlying
placebo responses in nausea appears much easier.
Studies have suggested that expectancies affect both
the behavioral effects of nausea and induce changes in
gastric slow-wave rhythm as measured by electrogastrography (Williamson et al., 2004). However, there is
a paucity of findings and many are somewhat contradictory (Levine et al., 2006; Weimer et al., 2012; Horing
et al., 2013).
Efforts have been made to characterize peripheral
neuroendocrine and immune mediators associated
with stress responses, such as cortisol and cytokines
(Stockhorst et al., 1998, 2014; Klosterhalfen et al.,
2005; Meissner, 2009). However, replication and extension to healthy volunteers and patients is needed
before the putative role of the hypothalamic-pituitaryadrenal axis and immune systems in placebo responses
for nausea can be clarified.
The evidence is accumulating that nausea in cancer
patients undergoing chemotherapy can be predicted by
pretreatment expectancies (Colagiuri et al., 2011). To
date, the few interventional studies designed to improve
nausea in the context of chemotherapy by enhancing
positive expectations have yielded conflicting results
(Shelke et al., 2008; Roscoe et al., 2010). This is in line
with similar results originating in efforts to prevent (or
reduce) seasickness symptoms by manipulating expectations (Eden and Zuk, 1995).
G. Gastrointestinal System/Irritable Bowel Syndrome
Of all the gastrointestinal disorders, functional bowel
diseases of the IBS type represent the largest subgroup
with a population prevalence of around 15% in most
Western countries (Choung and Locke, 2011). Its major
clinical characteristic is abdominal pain in the absence
of macroscopically identifiable organic causes. The
pathophysiology of IBS is largely unknown, but the
search for biomarkers includes immunologic, endocrinological, and genetic contributions (Elsenbruch, 2011).
Experimental and clinical studies suggest that experimental visceral pain is affected by placebo interventions (Enck et al., 2012; Elsenbruch, 2014).
A recent meta-analysis reported that in 73 eligible
RCTs including 8364 patients with IBS allocated to
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placebo, the pooled placebo response rate across all
RCTs was 37.5% (Ford and Moayyedi, 2010). These
observations are confirmed by experimental data in
IBS patients in which augmented practitioner-patient
communication increases the beneficial effect of placebo acupuncture on symptom severity and quality of
life (Kaptchuk et al., 2008a) and in which an open-label
placebo produced significantly higher global-improvement
scores in these patients (Kaptchuk et al., 2010).
Our understanding of the neurobiological and neuropsychological mechanisms steering placebo responses
in visceral pain is much more limited compared
with somatic pain (Zhou and Verne, 2014). This is also
due to differences in the processing of visceral and
somatosensory signals in the periphery and the brain
(Aziz et al., 2000; Eickhoff et al., 2006). Most experimental placebo studies in the visceral-pain field employ a rectal distension model in which pressure- or
volume-controlled distension of the gastrointestinal
compartments is carried out, resulting in reliable
pain models. Verbal suggestions for pain relief induced
a significant reduction in rectal distension-induced
pain intensity in IBS patients (Vase et al., 2003). These
placebo responses in visceral pain seemed to correlate
negatively with anxiety and negative emotions but
were not associated with endogenous opioidergic mechanisms, because naloxone did not affect the placebo
response (Vase et al., 2005). The brain responses of IBS
patients were analyzed employing glucose-PET imaging before and 3 weeks after a placebo intervention,
revealing that prefrontal regions (the right ventrolateral
prefrontal cortex) predicted self-reported symptom
improvement in these patients with this relationship
meditated by changes in the dorsal anterior cingulate
cortex (Lieberman et al., 2004). Similarly, an fMRI
study reported that decreases in activity in pain-related
brain regions in the placebo condition were related to
verbal suggestion and "habituation, attention, and
conditioning" (Craggs et al., 2007, 2008; Price et al.,
2007).
A series of studies analyzed the role of expectation
in visceral placebo analgesia in healthy volunteers
(Benson et al., 2012; Elsenbruch et al., 2012a,b; Kotsis
et al., 2012; Schmid et al., 2013, 2015; Theysohn et al.,
2014). When visceral pain stimuli were delivered with
a cover story of receiving with varying likelihood (0%,
50%, 100%) an intravenous pain killer, the volunteers
reported a “dose-dependent” pain reduction (Elsenbruch
et al., 2012a). This placebo analgesia was associated
with activity changes in the thalamus, prefrontal,
and somatosensory cortices, especially in the painanticipation phase, consistent with findings in conditioned esophagal placebo analgesia (Lu et al., 2010). In
addition, in the “50% condition,” the placebo-induced
pain relief was more pronounced in those subjects who
believed they were in the active-treatment group
(Kotsis et al., 2012). A direct comparison in the placebo
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Schedlowski et al.
response to visceral pain between IBS patients and
healthy volunteers demonstrated similar placebo
responses at the behavioral level (magnitude of placebo
analgesia) but a more pronounced neural response in
affective and cognitive brain regions (insula, cingulate
cortex, prefrontal cortex), suggesting altered neural
processing of placebo-induced changes in pain perception in IBS and in patients with ulcerative colitis in
remission (Lee et al., 2012; Schmid et al., 2015).
Together, these experimental data demonstrate that
placebo responses in visceral pain on the behavioral
and a corresponding neural level are induced predominantly by expectation in healthy volunteers and
patients with IBS, particularly those with gastrointestinal disorders (Zhou and Verne, 2014). Interestingly, only one study thus far has reported successful
conditioning of visceral (esophageal) analgesia in
healthy volunteers (Lu et al., 2010), whereas placebo
analgesia studies with lower gastrointestinal pain (as
in IBS) are still lacking. Until cross-modality comparisons of placebo effects involving different pain models
and clinical conditions are available, it is impossible
to conclusively determine whether placebo effects in
visceral and somatic pain are similar or whether they
involve different pathways. In the meantime, there is
evidence to support “specificity” for the visceral domain
(Eickhoff et al., 2006).
H. Sleep Disorders
Placebo responses in insomnia can be assessed on
the subjective and objective level. Objectively assessed
sleep parameters derived from polysomnographic
assessments (PSG) are total sleeping time (TST), sleep
onset latency (SOL; time in bed before sleep onset),
wake after sleep onset (WASO), and sleep quality
(quotient between sleeping time and time in bed).
Subjective and objective improvements in sleep
quality in placebo arms of insomnia trials were very
recently subjected to meta-analyses (Winkler and Rief,
2015). Results in the placebo groups accounted for 64%
of the subjective benefits reported in the drug groups
and for 54% of physiologically assessed improvements.
The effect sizes for objective versus subjective outcome
parameters in the placebo groups did not differ (e.g.,
TST d = 0.43 derived from diaries; d = 0.42 derived
from PSG). No predictors for strong placebo responses
were identified, although other studies reported large
baseline variance in SOL and TST and longer SOL to
predict placebo responses (Ogawa et al., 2011). These
meta-analytically described placebo responses are in
accordance with a re-analysis of placebo arms in four
drug trials, also indicating the long-term persistence
and robustness of positive effects in the placebo arms
(Perlis et al., 2005). Other meta-analyses reported
a modest difference between drug arms (Z-drugs) and
placebo arms in insomnia trials, and the differences
in some objective variables (TST, WASO) were even
nonsignificant (Huedo-Medina et al., 2012). To further
disentangle the effects of expectation, regression to the
mean, and social desirability on subjective outcomes in
placebo arms of insomnia studies, studies directly
examining these influences have been conducted (McCall
et al., 2011) that found evidence for the contribution of
all three effects (however, with varying degrees) on
different outcome variables. Significant improvements
in subjective TST were also reported for the comparison between placebo arms and waiting list groups/no
treatment groups (McCall et al., 2005; Belanger et al.,
2007).
Although clinical trials only enable limited conclusions about the underlying mechanisms, several experimental approaches addressed placebo responses
in insomnia directly. In a pilot study, patients with
insomnia received a placebo pill for the second night
after baseline assessments of the first night. Although
this study did not control the so-called “first-night
effect” (reduced sleeping time during the first night in
the sleep laboratory), the improvements during the
second night were larger than would be expected, with
a TST increase of 1.3 hours and a WASO reduction to
57% of the first-night assessments. Additionally, the
working group reported a trend toward longer rapid
eye movement sleep periods after placebo intake
(Rogev and Pillar, 2013). Their result is in line with
an experiment examining whether the first-night effect
can be reduced when placebo pills are administered.
Using this laboratory model for insomnia in healthy
controls, the investigators showed that the first-night
effect is associated with rapid eye movement sleep
reductions and that this effect can be counteracted by
taking placebo pills (Suetsugi et al., 2007). Further
studies investigating healthy controls with experimentally balanced designs confirmed the positive effects of
placebo pill intake versus no treatment on WASO on
objective and subjective parameters (Fratello et al.,
2005). Placebo application can also stimulate alterations in circadian rhythms (Reinberg et al., 1994).
Learning has also been shown to influence sleep
quality. Alpha/theta neurofeedback was used to learn
the transition into presleep states. This transition is
accompanied by reduced activity in the brain areas
responsible for external monitoring of sensory input
(Kinreich et al., 2014), contributing to a reduction in
physiologic arousal.
The neurobiological underpinnings of placebo responses in insomnia are poorly understood. Further
investigation is needed to assess which functional and
neurophysiological aspects of sleep regulation beyond
PSG/EEG parameters are affected by placebo mechanisms. We need to discover which biologic system and
physiologic processes in sleep regulation are vulnerable to expectation and conditioning influences (e.g., the
ascending neuronal system including the reticular
formation, thalamus, and forebrain neurons; the
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
descending neuronal pathways encompassing neuronal
activities in the hypothalamus, brain stem, and spinal
cord; neurotransmitter systems such as the glutamatergic system, GABA, noradrenaline, dopamine, and
serotonin sensitive receptors; behavioral arousal and
muscle tone (Jones, 2011). Sleep is closely associated
with the restoration of physiologic systems, such as
nocturnal blood pressure dipping and changes in immune
functions (Rief et al., 2010; Euteneuer et al., 2014), but it
is also relevant for processes such as recreation and
memory consolidation (Weber et al., 2014), and placebo
responses in these sleep-associated factors also need to be
addressed.
V. Neuro-Bio-Behavioral Mechanisms of
Nocebo Responses
Nocebo responses describe negative treatment effects that are not directly attributable to the drug’s
pharmacokinetics. Two variants of these nocebo
responses exist: one is characterized by new symptoms
or a symptom aggravation associated with drug or
placebo intake, although the chemical agent itself is
not able to trigger these symptoms. Another variation
of nocebo responses is the reduced efficacy of clinical
interventions due to negative expectations or prior
experiences. The underlying mechanisms steering
these two types of nocebo responses are not usually
distinguished.
A. Nocebo Effects in Clinical Trials and Clinical
Practice
In double-blinded RCTs, the rate of unwanted side
effects reported in placebo groups is notoriously high,
although these symptoms obviously cannot be the
direct consequences of drug effects. Many patients
discontinue drug intake because of unwanted side
effects, although being in the placebo arm, or because
the reported adverse effects are in discordance with the
pharmacokinetic of the drug (Rief et al., 2006, 2009a;
de la Cruz et al., 2010; Stathis et al., 2013).
Nocebo mechanisms are also considered responsible
for epidemic waves of reports of adverse events. In New
Zealand, public blaming of a thyroxine product on
television led to a dramatic increase in adverse events
reported to the authorities responsible for drug
monitoring (Faasse et al., 2012). The typical problems
observed when switching from a brand to a generic
drug are related to nocebo mechanisms as documented
in antihypertensive treatments with placebo pills in
counterbalanced experimental approaches (Faasse et al.,
2013).
Several studies have confirmed that the unwanted
side-effect profiles of the placebo arms “mimic” the
expected side-effect profiles of the drug arms. The side
effects reported by placebo arms in multiple sclerosis
treatments have been substantially higher when disease-
715
modifying drugs were investigated compared with trials
investigating symptomatic treatments (Papadopoulos
and Mitsikostas, 2010). Similar effects were reported
from different groups of migraine drugs (Amanzio et al.,
2009) and antidepressants (Rief et al., 2009a). Such
a mimicking effect can hamper adequate detection of
drug-related adverse events.
B. Psychologic Mechanisms Contributing to
Nocebo Responses
Negative expectations are powerful determinants
and predictors of side effect development and drug
tolerability (Nestoriuc et al., 2010). Research paradigms therefore use verbal suggestion to manipulate
expectations, frequently amplifying this effect through
negative pre-experiences via Pavlovian conditioning.
Although conditioning paradigms are more powerful in
triggering placebo effects, both verbal suggestion and
learning induce similar effects on nocebo development
(Colloca et al., 2008).
Observational learning such as watching and/or
listening to others who report serious problems after
taking a specific drug is another powerful tool for
symptom development, sometimes even more powerful than the mere verbal suggestion of side effects
(Vögtle et al., 2013). The social dissemination of
somatic symptoms was demonstrated elegantly in
a recent study (Benedetti et al., 2014). The authors
informed just one person in a group about hypoxiainduced headache before the group was to be exposed
to hypobaric conditions in the Alps. This symptom
report “infected” other group members depending on
the intensity of social contacts with that particular
individual.
C. Neurobiological Pathways of Nocebo Responses
Neuroimaging studies have confirmed that the mere
expectation of symptoms can already activate the brain
structures responsible for symptom perception before
any sensory stimulation has occurred, thus sensitizing
the person to the perception of discomfort (Koyama
et al., 2005; Keltner et al., 2006). Pain expectation is
associated with significant activations in nociceptive
regions in the thalamus, second somatosensory cortex,
and insular cortex. When people expect highly intense,
noxious thermal stimuli, significant differences appear
in the caudal anterior cingulate cortex, head of the
caudate, cerebellum, and contralateral nucleus cuneiformis compared with the expectation of pain stimuli of
low intensity (Keltner et al., 2006).
Furthermore, although repeated pain stimulation
leads to habituation, this effect can be blocked via
verbal instructions such as: “with every assessment
day, pain intensity will increase,” inducing negative
expectations. This blockage of habituation is associated
with diverse activities in the brain’s right parietal
operculum (Rodriguez-Raecke et al., 2010).
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Schedlowski et al.
Different descriptions of a cream as being either
sensitizing for pain perception or neutral has revealed
different activities even on the spinal level (Geuter and
Büchel, 2013). The authors demonstrated that activation was higher under the nocebo instruction at the
level of stimulated dermatomes C5/C6 in the spinal
cord. Pain-related activity in the ipsilateral dorsal horn
of the spinal cord was enhanced. The effect of expectancy, stimulus processing, and perception has been
demonstrated across different sensory modalities. These
effects are displayed on a behavioral and neuronal level
(Summerfield and de Lange, 2014). Because expectation
facilitates the perception of a specific sensation and of
stimulus categories, this effect helps clarify why side
effects often occur as a cluster of multiple symptoms.
From a neurochemical point of view, nocebo
responses have been best characterized in the field
of pain. Nocebo responses have been associated with
variations in the dopaminergic, opiodergic and cholecystokinin (CCK) systems (Benedetti et al., 2007). CCK
is involved in the activation of descending pronociceptive pathways from the midbrain PAG in mediating
anxiety-induced hyperalgesia as well as in the development and maintenance of hyperalgesia associated
with peripheral neuropathy (Lovick, 2008). Accordingly, blockage of CCK pathways via the CCK antagonist proglumide leads to the abolition of the nocebo
response (Benedetti et al., 1997).
In the study investigating nocebo effects in highaltitude conditions, Benedetti’s working group reported a higher frequency of headache and examined
biologic trajectories of the nocebo via observation
effect. They identified significantly increased prostaglandin levels (prostaglandin E2, prostaglandin F2) in
the verbally “infected” group that mediated vasodilatation and subsequently the occurrence of headache
(Benedetti et al., 2014).
After examining pain relief caused by a placebo
mechanism versus pain amplification based on nocebo
effects, Scott et al. (2008) postulated that these may
just be different facets of the same pathways. However,
the very same person can suffer simultaneously from
nocebo responses while experiencing benefits from
placebo mechanisms. Even more, nocebo effects can
be (mis-)interpreted as signs of potent drugs, thus
amplifying placebo effects. Consequently, moderate
drug-onset effects can amplify placebo responses in
analgesic therapies (Rief and Glombiewski, 2012). This
shift in the meaning of somatic symptoms from disturbing side effect to indexing a powerful treatment is
associated with the activation of opioid and cannabinoid systems, especially in pain symptoms (Benedetti
et al., 2013). However, considering the broad range of
potential physiologic systems and drugs’ side effects,
various exchanges between placebo and nocebo mechanisms may be occurring for each symptom and
physiologic system (Enck et al., 2008). A recent study
reported distinct patterns of neural activation induced
by positive or negative expectancies, however, that
resulted in a correlated placebo and nocebo behavioral
response (Freeman et al., 2015).
Taken together, the underlying mechanisms of
nocebo responses are much less well understood than
those of placebo responses. In particular, the contribution of similar overlapping and distinct trajectories
mediating nocebo versus placebo responses require
further investigation.
VI. The Effect of Placebo Responses on
Pharmacological Treatments
Placebo responses contribute significantly to clinical
outcome in most if not all medical treatments including pharmacotherapy. Patients’ expectations are a key
factor that codetermines treatment efficacy. The
crucial role of expectation in the therapeutic outcome
is best illustrated in the so-called open/hidden drug
paradigm. In this paradigm, identical concentrations of
the same drug are administered under two conditions:
an open condition in which the patient is aware of the
time point at which the medication is administered by
a health care provider and of the intended treatment
outcome (e.g., analgesia) and a hidden condition in
which the patient is unaware of the medication being
administered by a computer-controlled infusion. This
paradigm enables the dissociation of the treatment’s
genuine pharmacodynamic effect (hidden treatment)
from the additional benefit of the psychosocial context
in which the treatment is provided. Studies based on
an open/hidden paradigm have revealed that psychosocial factors such as the awareness of a drug being
given can considerably enhance its analgesic effect
(Levine and Gordon, 1984). Conversely, the hidden
administration attenuates the analgesic effect of nonsteroidal anti-inflammatory drugs to nonsignificance,
and even the effects of opioids are substantially reduced
by hidden application (Colloca et al., 2004). Because of
its hidden application, the drug dosage had to be doubled
to achieve the same result as during open application, a
fact highlighting the economic relevance of placebo
responses.
This phenomenon is not limited to analgesics, because similar pharmacotherapeutic effects have also
been reported in other domains, such as motor function
in Parkinson’s disease and anxiety-related disorders
(Amanzio et al., 2001; Colloca et al., 2004). Findings
from these studies using the open/hidden drug paradigm are supported by investigations that explicitly
modulated the expectancy concerning a given drug by
verbal instructions (Lyerly et al., 1964; Kirk et al.,
1998; Metrik et al., 2009). The detrimental influence of
negative expectations on the drug response became, for
instance, apparent in a behavioral experimental study
by Dworkin et al. (1983), who reported a reversal of
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
analgesia by nitrous oxide in dental pain when
participants expected the drug to increase awareness
of bodily sensations. Although this is not the focus
of this review, it should be noted that substantial
expectation effects are also observed in nonpharmacological interventions such as acupuncture (Linde et al.,
2007).
Similarly, learning effects modulate the response to
pharmacological treatments (see above). The effect of
prior experience on treatment outcome is evident in
results from crossover studies (i.e., drug trials in
which subjects receive an active drug and a placebo
in randomized order), showing that the order in which
the placebo or active treatment is given substantially
modulates the treatment response. Generally speaking, the response to the active treatment is stronger
when active treatment is given first and weaker
when it follows the placebo treatment. This can be
explained by the fact that the treatment experience,
which can be assumed to be less substantial during
placebo, carries over into the active treatment and
vice versa. For instance, in a double-blind crossover
study on the effect of an antihypertensive drug, the
placebo did not affect the patients’ hypertension when
administered first (Suchman and Ader, 1992). Yet,
when placebos were administered after a week-long
use of atenolol, they produced a significantly greater
antihypertensive response than the "no treatment"
condition. Sequence effects of drug efficacy have also
been proposed in conjunction with analgesic therapies
for musculoskeletal pain (Batterman and Lower,
1968), indicating that not only was the placebo
medication more effective after active and effective
treatment but also that the active drug was less
effective when it followed an ineffective placebo
treatment. Similar sequence effects were recently
reported in conjunction with the analgesic effect of
active compared with sham repetitive transcranial
magnetic stimulation for chronic pain (Andre-Obadia
et al., 2011). Observations from clinical trials were
recently corroborated experimentally in an analysis
of whether prior experience with one treatment
can influence the response to a second treatment by
employing experimental models of two analgesic
treatments in healthy volunteers. In comparison
with those subjects with a positive treatment history,
participants who experienced no analgesia from the
first treatment continued to show a substantially
reduced response to a second, different analgesic
treatment. These behavioral effects were substantiated
in neuroimaging data showing significant activation
differences in brain regions coding pain and analgesia between groups (Kessner et al., 2014). In sum,
behavioral studies have proven that the effect of drug
treatment is determined by the drug’s pharmacological
profile and (at least partially) by expectancy and learning mechanisms.
717
A. Neural Mechanisms Underlying the Effect of
Expectations on Drug Efficacy
Recent studies have begun exploring the effects of
expectation and prior experience on opioid analgesia by
using functional brain imaging. Analgesia was studied
in response to the opioid remifentanil under three
conditions: without expecting analgesia (hidden application), expecting a positive analgesic effect, and not
expecting analgesia, i.e., the expectation of hyperalgesia (Bingel et al., 2011). Results show that the
positive treatment expectancy doubled the analgesic
benefit of remifentanil, whereas the negative treatment expectation interfered with the analgesic potential of remifentanil so severely that its analgesic effect
was completely abolished. Importantly, these changes
in pain perception were accompanied by significant
alterations in the neural response to noxious thermal
stimulation in core brain regions of the pain and
opioid-sensitive brain networks such as the thalamus,
mid-cingulate cortex, and primary somatosensory
cortex, brain areas that have consistently displayed
correlations with the intensity of nociceptive input and
resultant pain perception (Apkarian et al., 2005;
Tracey and Mantyh, 2007), and may therefore serve
as an objective index of analgesic efficacy (Bingel et al.,
2011). With respect to underlying mechanisms Bingel
et al. observed that the individual benefit from positive
treatment expectancy during remifentanil analgesia
was associated with activity in the descending pain
modulatory system, including cingulofrontal and subcortical brain areas, resembling mechanisms of placebo
analgesia. In contrast, the negative expectancy that
abolished the opioid’s analgesic effect was selectively
associated with increased activity in the hippocampus
and medial prefrontal cortex. These brain areas have
been implicated in the exacerbation of pain by mood
and anxiety in patients and healthy controls (Ploghaus
et al., 2001; Schweinhardt et al., 2008).
These initial experimental data on the expectancy
modulation of opioid analgesia substantiate the significant contribution of cognitive factors to the overall
benefit from pharmacological treatments. Similar
interactions between pharmacodynamic and psychologic effects on regulatory brain mechanisms have been
reported in conjunction with methylphenidate administration in cocaine-addicted patients (Volkow et al.,
2003). Together with evidence from experimental
placebo and nocebo studies, these findings reveal that
the effects of expectancy and prior experience converge
with pharmacological effects within the very same
biologic systems, involving distinct CNS and peripheral physiologic mechanisms. However, despite the
unequivocal effect of expectancy and learning on pharmacological treatments, research into its underlying
mechanisms remains at a very early stage, leaving many
unanswered questions.
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Schedlowski et al.
B. Additive versus Interactive Effects
One of the crucial questions still unanswered is
whether cognitive effects and pharmacologically induced analgesia combine in an additive or interactive
manner (Fig. 5). This is the crucial basic assumption
behind double-blinded RCTs, postulating that the
difference between drug and placebo arms reveals the
“real” drug effect. However, depending on the drug,
exogenous, pharmacologically induced mechanisms
and endogenous cascades triggered by expectancy,
learning, and their combination may combine in an
additive manner with one substance but combine
interactively with another. One approach to investigate additive versus interactive effects of drug and placebo is to adopt a balanced placebo design (Rohsenow
and Marlatt, 1981), in which the factors expectancy
and drug alternate in a 2 2 factorial manner.
Employing just such a design, Atlas et al. (2012) observed additive rather than interactive effects in conjunction with the influence of positive expectation on
remifentanil analgesia. Their results were substantiated by a complementary fMRI study investigating
pain-related responses during the open and hidden
administration of varying remifentanil dosages, also
allowing inferences about potential interactions: no
interactive effects of drug and expectation were observed at the neural level. This finding does not exclude the possibility that other drugs, i.e., nonopioid
analgesics show interactive analgesic effects with
expectation-induced analgesia. Future neuroscientific
investigations involving neuroimaging of the influence
of expectation and learning mechanisms on pharmacological treatments constitute a new and promising
avenue of research. Instead of studying the effect of one
of them in isolation by controlling the other, it is time
we unraveled how both mechanisms combine on the
neurobiological level. Deeper neurobiological understanding of their potential interaction promises to
ultimately optimize treatment outcomes, encouraging
the development of personalized treatment strategies.
VII. Predictors of Placebo and Nocebo Responses
The presence of placebo responses varies tremendously in both healthy volunteers and patients in
experimental and clinical settings. The individual
placebo response can range from no effect ("nonresponders") to profound changes in symptom or disease
severity ("responders"). Given that placebo responses
contribute so substantially to the overall treatment
outcome, knowledge about the individual magnitude of
their occurrence would not just guide therapeutic
decisions to optimize treatment outcomes in clinical
practice, it could also help to clarify poorly understood
inconsistencies in clinical trials (Enck et al., 2013).
How knowledge of an individual’s placebo responsiveness may inform clinical decision making is
highlighted in an open/hidden study of local anesthesia
in patients suffering from Alzheimer’s dementia (Benedetti
et al., 2006). These patients, neurobiologically characterized by impaired connectivity of the prefrontal lobes
(known to be placebo-relevant brain areas) presented
reduced pain relief from the open compared with the
hidden application of lidocaine, a local anesthetic. Loss
of their ability to form expectations reduced overall
treatment efficacy, and they needed dose increases to
experience adequate analgesia. This study illustrates
that the individual contribution of placebo mechanisms
to therapeutic outcome is, at least in part, determined
by an individual’s neurobiological make-up; it highlights
the necessity to adjust drug treatment approaches
depending on the individual predisposition for placebo
responses.
Seeking deeper knowledge of individual traits and
states susceptible to placebo responses, currently major
effort is undertaken to identify any psychologic or
Fig. 5. The additive model assumes unspecific effects of equal size in both the placebo and drug arm or groups of studies or experiments. The
interactive model, however, suggests that drug-specific effects interact with the placebo responses and will result in unequal placebo effects in the two
study arms or experimental groups. (Modified from Enck et al., 2013, with permission.)
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
physiologic, neurobiological, or genetic variables that
moderate individual placebo responsiveness over and
above the known established contribution of expectancy
and learning mechanisms in different physiologic systems
and diseases.
Along with the cumulative knowledge about the
underlying mechanisms, interindividual differences
are also best examined in conjunction with placebo
analgesia. Recent evidence supports the putative relevance of several psychosocial variables for an individual’s placebo responsiveness. These include anxiety and
fear (Flaten et al., 2011; Lyby et al., 2011), hypnotic
suggestibility (De Pascalis et al., 2002), locus of control
and self-efficacy (Horing et al., 2015), hostility (Peciña
et al., 2013a), coping abilities (Schneider et al., 2006),
dispositional optimism (Morton et al., 2009; Geers
et al., 2010), and empathy for socially induced placebo
analgesia (Colloca and Benedetti, 2009; Hunter et al.,
2014). However, no consistent personality profile has
emerged so far across different clinical conditions
(Horing et al., 2014).
Genetic traits are also being increasingly explored
regarding their contribution to an individual’s placebo analgesic responsiveness, a factor documented
in patients with IBS (Hall et al., 2012) assigned to one
of three treatment arms: no treatment (waiting list),
placebo treatment with limited patient-health practitioner interaction, and placebo treatment with
augmented supportive patient-health practitioner
interaction. The primary outcome, namely the change
from baseline in the IBS-Symptom Severity Scale
after 3 weeks of treatment, correlated with the
number of methionine alleles in the catechol-Omethytransferase gene Val158Met polymorphism
(rs4633), which strongly influences endogenous dopaminergic and opioidergic pathways. Patients who were
Met/Met homozygotes revealed the strongest placebo
analgesic effect under the augmented placebo arm,
whereas those who were Val/Val homozygotes were
less responsive to warm and caring physicians and
thus benefited minimally from placebo responses (Hall
et al., 2012). An earlier search for endocrine and
immune biomarkers of the placebo response in the
same data set yielded inconsistent findings. Of the 10
biomarkers tested, only one marker proved significant
for the placebo response (i.e., osteoprotegerin), but
because the analysis did not correct for multiple
testing, this result may reflect a Type I error (Kokkotou
et al., 2010).
Peciña et al. (2014) demonstrated an association in
healthy volunteers between the polymorphism in the
m-opioid receptor gene (OPRM1) A118G and placeboinduced changes in m-opioid binding potential (measured with opioid-ligand PET) in the anterior insula,
amygdala, nucleus accumbens, thalamus, and the
brain stem as well as placebo-induced changes in mood.
Intriguingly, genetic variation in the endocannabinoid
719
system (in the gene coding fatty acid amide hydrolase,
the major degrading enzyme of endocannabinoids) was
also linked to the placebo analgesic response in the
same dataset (Peciña et al., 2014).
Furthermore, an individual’s brain anatomy (including structural and functional measures) predicts
the capacity for placebo analgesic responses in healthy
subjects. Stein et al. (2012) indicated that white matter
integrity in the dorsolateral prefrontal and rostral
anterior cingulate cortices and their pathways to the
periaqueductal gray are positively associated with
individual placebo analgesic responses in healthy
subjects. This finding supports the importance of
structural brain connectivity in determining the individual ability to form placebo (analgesic) responses.
Along these lines, resting-state functional connectivity
between prefrontal and insular/parietal cortices is
also known to predict individual placebo analgesic
responses in patients suffering from chronic low back
pain (Hashmi et al., 2012) and the expectancy-related
modulation of pain in healthy volunteers (Kong et al.,
2013).
Psychologic, physiologic, and neurobiological predictors of placebo responses have also been identified for
placebo responses in other physiologic systems and
diseases. Placebo responses in anxiety disorders and
depression, for instance, have been correlated with
psychologic and genetic traits. An association with
the locus of control has been suggested for placebo
responses in depression (Reynaert et al., 1995), whereas
cultural variations seem to influence placebo responses
in anxiety disorders (Moerman, 2000). Serotonin-related
gene polymorphisms have been found to influence the
individual placebo response in social anxiety at the
behavioral and neural level (Furmark et al., 2008), and
genetic polymorphisms modulating monoaminergic tone
have been related to degree of placebo responsiveness in
major depressive disorder (Leuchter et al., 2009).
Regarding learned placebo responses on immune functions, state anxiety and plasma noradrenaline levels
were identified as predictors for a learned placebo
response in cytokine release (Ober et al., 2012) (see also
section IV).
Beyond the known contribution of psychologic factors
to nocebo responses (e.g., general drug sensitivity,
negative drug effects in the past, anxiety, etc.), little is
known about the neurobiological predictors of nocebo
responses. Wendt et al. (2014a) reported more drugspecific and drug-unspecific side effects in healthy
male subjects with Val/Val variations of the Val158Met
polymorphisms of the catechol-O-methytransferase
gene. Their observations further confirm a link between the dopaminergic system and the occurrence of
nocebo responses, as the valine genotype is associated with a 3–4 times higher catabolic rate of brain
dopamine than the methionine variant (Lachman
et al., 1996).
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Schedlowski et al.
The search of predictor variables in placebo and
nocebo responses remains in a very preliminary stage.
Studies have often enrolled small cohorts, which might
explain the heterogeneous results (Kaptchuk et al.,
2008b). Furthermore, it is statistically complicated to
sufficiently dissociate the contribution of state from
trait variables. Finally and most importantly, we do
not know whether and, if so, how placebo responsiveness in one system influences the placebo responsiveness in another; large placebo responses in one
condition do not necessarily predict large placebo
responses in other conditions as well (Whalley et al.,
2008). A deeper and more detailed understanding of
the neurobiological and physiologic mechanisms of
placebo responses will help unravel the shared and
distinct pathways and predictors for placebo responses
across different system and conditions (Zhang et al.,
2011).
VIII. Relevance and Implications of
Placebo Responses
A. Randomized Clinical Trials
The use of placebos in RCTs is without a doubt
necessary to develop new drugs, despite the current
preference of drug-approval authorities for head-tohead trials comparing novel compounds to drugs already
on the market (comparative effectiveness research
[CER]). CER requires that more patients be included
for noninferiority statistics (Gardiner et al., 2000).
However, CER trials raise the placebo response
without being able to assess it (Weimer and Enck,
2014). Some propose that future drug trials be
designed as three-arm trials: the drug under investigation, a comparator (the best drug available or on the
market), and a placebo (Hida and Tango, 2011). This
would increase the likelihood of patients receiving
effective treatment, in case of 1:1:1 randomization, to
a 66% drug chance compared with conventional 50% in
1:1 drug:placebo randomized RCTs. Such “enrichment
design,” however, may increase the drug and placebo
responses in some conditions (depression, schizophrenia, migraine) (Diener et al., 1999; Papakostas and
Fava, 2009; Rutherford et al., 2009) but not in others
such as IBS (Ford and Moayyedi, 2010; Elsenbruch and
Enck, 2015). At the same time, it accommodates ethical
arguments that more patients require and receive
effective medication (World Medical Association, 2013).
Two major critical issues arise from the use of
placebos in RCTs that may also apply to CER: the risk
of unblinding and the control for the spontaneous
variation of symptoms.
The traditional concept to reduce between-subject
data variance is the use of crossover designs that carry
the intrinsic risk of unblinding because patients may
identify the drug and the placebo phase by directly comparing side-effect profiles rather than effects
(Boutron et al., 2006; Machado et al., 2008), whereas in
parallel-group designs, this risk is much lower. At the
same time, crossover studies risk results being affected
by conditioning (learning) effects (Suchman and Ader,
1992; Colloca, 2014).
In the early 21th Century, RCTs occasionally
employed active placebos that mimic adverse events
(e.g., in treatment trials of depression) to prevent or
reduce such unblinding (Moncrieff et al., 2004), and
such unblinding caused by adverse events substantially
determines treatment results (Rief and Glombiewski,
2012). However, overall poorer discrimination between
drug and active placebo will result in larger patient
numbers to prove a drug’s superiority over placebo,
making them expensive. As an alternative, parallel
group designs are currently the gold standard (Weimer
and Enck, 2014).
Onset and offset effects may likewise enable the
unblinding of study-arm allocation (Bello et al., 2014),
an effect that is minimized by randomized run-in and/
or withdrawal designs where patients are enrolled and/
or withdrawn from active treatment with variable (but
double-blinded) periods of placebo application before
and thereafter, respectively (Ivanova and Tamura,
2011; Enck et al., 2013).
Another more recently reported risk for RCT
unblinding are social networks where patients enrolled
in the same or other studies communicate their experience. Via communicating effects and adverse event
profiles of the drugs under investigation, patients may
easily determine the treatment arm to which they have
been allocated (Lipset, 2014). It has been suggested that
rather than allowing this in uncontrolled manner,
investigators should try to incorporate network communication into their patient management strategy in RCTs
(Nicholas et al., 2013).
A meta-analysis of RCTs of IBS patients demonstrated that the higher overall adverse event profiles of
some drugs (e.g., spasmolytics, tricyclic antidepressants) in comparison with others (e.g., local antibiotics,
5-HT3 antagonists) resulted in higher overall drug
efficacy (drug minus placebo). These observations
argue for studies to be unblinded—for the investigators, but not for individual patients (Shah et al., 2014),
something that clearly calls for improved blinding
methods, e.g., the re-implementation of “active placebos”
(Edward et al., 2005; Rief and Glombiewski, 2012).
Finally, placebo responses may also be driven by
pretrial beliefs in the drug being tested: In the mock
informed-consent forms of three putative trials testing
either an antidepressant (desipramine), a prokinetic
(alosetron), or a local antibiotic (rifaximin), IBS patients
expected the highest efficacy from treatments with
which they were familiar (i.e., antibiotics), which the
authors called a “pre-cebo” effect, i.e., preconceived
notions from being familiar with the drug class (Kim
et al., 2012).
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
To assess the placebo response in RCTs, it would be
important to know how much of the overall placebo
effect is attributable to spontaneous symptom variation and the disease’s natural course (Enck et al.,
2013). Attempts have been made to estimate this
contribution by comparing placebo controls to “no
treatment controls” in trials where patients have been
allocated to a waiting list. Such a comparison is quite
common under conditions where a placebo control
group is not readily available for methodological
reasons, e.g., in psychotherapy trials. When waiting
control and placebo groups are compared, about 50% of
the placebo effect is attributable to spontaneous
variation of symptoms, at least in the clinical conditions tested such as depression or nausea (Krogsboll
et al., 2009). However, in most cases, especially in
cases of severe disease, a “no treatment” or waiting
control group would be unacceptable for ethical reasons
(World Medical Association, 2013). Waiting controls
also develop their own dynamics; on the one hand,
patients may improve while waiting because of being
assured of receiving active treatment soon (Beck et al.,
2015), similar to the effects seen in placebo run-in
phases (Enck et al., 2009). On the other hand, it may be
regarded as a punishment for patients not to be
included in the active treatment condition (Furukawa
et al., 2014). If waiting list controls are included,
a “step-wedge” design may be superior, because it
allows a “dose-response” assessment of waiting for
treatment (Fig. 6) (De Allegri et al., 2008; Weimer and
Enck, 2014).
Despite these complex considerations it remains
crucial to assess and dissociate the spontaneous courses
of disease from placebo and drug responses. The “cohort
multiple randomized controlled trial” (Relton et al.,
2010) is a reinvention of the “Zelen design” (Zelen,
1979) that recruits patients for a monitoring study (e.g.,
via a patient registry) before recruiting and randomizing a subgroup of the same patients for a placebocontrolled or CER study (Fig. 6). A number of approaches
have been published (Cockayne et al., 2014). Finally,
initial attempts have been made to avoid involving
patients that receive placebo and instead use historic
placebo controls from large trial databases (Desai et al.,
2013).
Growing knowledge about the mechanisms of placebo and nocebo responses is accompanied by a number
of critical consequences concerning the conduct of
RCTs, of which the two discussed above are major.
First, if trials are not truly blinded for both patients
and doctors, the development of novel therapies is
jeopardized. Second, if the natural courses of disease
symptoms are not controlled, the validity of such novel
therapies is questionable. Third and most importantly,
knowledge about the moderating and mediating factors
of placebo and nocebo responses enables us to asses,
control, and homogenize these responses in a clinical
721
Fig. 6. (A) The so-called step-wedge design is a modified waiting-list
control strategy. Patients are randomized to more than one waiting arm
with variable waiting periods, enabling assessment of the dose-response
function of waiting for treatment. (Modified from Enck et al., 2013, with
permission.) (B) The so-called Zelen design separates the recruitment of
patients for an observational study from recruitment for an interventional study, enabling the natural course of disease to be monitored
without randomizing patients to a no treatment control group. (Modified
from Enck et al., 2013, with permission.)
trial setting and thereby promises to improve assay
sensitivity. For further details regarding these strategies, see Enck et al. (2013).
B. Training Health Care Professionals
Given the crucial contribution of placebo and nocebo
effects to treatment outcomes, health care professionals must be trained to comprehend the mechanisms of
action of therapeutic interventions. Careful assessment
of patients’ expectations and pretreatment experiences
should be incorporated when documenting medical
histories. The physician should be able to judge whether
treatment expectations are helpful or dysfunctional and
whether pretreatment experiences might limit the
treatment response and/or make nocebo effects likely.
Special emphasis must be given to patients who fulfill
one of these risk factors for a suboptimal treatment
response.
722
Schedlowski et al.
At present, very few psychologic interventions have
been evaluated that aim to optimize patient’s expectations and to minimize the risk of nocebo development. One successful approach assessed how to optimize
patients’ expectations after experiencing myocardial
infarction, replicated with an additional tool targeting
partners’ treatment expectation as well (Petrie et al.,
2002; Broadbent et al., 2009). An optimal therapeutic
relationship seems to provide a solid basis from which to
benefit from placebo responses and to avoid nocebo
responses (Kaptchuk et al., 2008a). Of note, even these
relationship effects are becoming better understood
thanks to neuroimaging: there is evidence that pain
stimuli are better tolerated in the presence of pictures of
sympathetic people and that this effect is associated
with increased activity in the ventromedial prefrontal
cortex (Eisenberger et al., 2011).
The effects of treatment value (Waber et al., 2008;
Espay et al., 2015) and open applications reveal the
necessity to express the expected positive effects of
the intervention and to draw patient’s attention to the
visible cues of open treatment applications. Finally, the
role of observational learning can be used more
systematically. Role models (e.g., per video clips) could
be presented to patients, expressing some pretreatment concerns but then confirming positive treatment
effects. These models for observational learning could
be selected according to mechanisms that facilitate
observational learning (e.g., similarities in age, gender,
general attitudes).
The systematic use of Pavlovian learning to improve
medical interventions needs further evaluation, despite initially promising results (Ader et al., 2010). The
use of placebo-controlled drug reduction (Doering and
Rief, 2012) in particular promises to maintain drug
efficacy despite a systematic reduction in drug dosage
(Albring et al., 2014). Applying effective pretreatments
with low side effects is another option to further
optimize patients’ treatment expectations.
In addition to optimizing treatment effects for the
patient’s benefit, we now know that authentic and
empathic doctor-patient communication protects from
unwanted side effects. For instance, medical jargon is
likely to cause misunderstandings and trigger fear in
patients. A patient-centered communication style is
therefore required when explaining diagnostic procedures, their results, and the rationale and implementation of any intervention (Barsky et al., 2002;
Colloca and Finniss, 2012).
Nocebo research has clinical implications for the
prevention of side effects. Health care professionals
should be trained to offer balanced information on
expected positive treatment outcome and the risk of
side effects. Moreover, the patient’s own ability to cope
with side effects (should they develop) should be
emphasized. Although internet portals usually offer
threatening information, new Web-based information
systems should be developed and approved that offer
valuable and accurate information (see below). Finally,
if withdrawal symptoms are likely, “hidden withdrawal” procedures could be used to minimize negative
expectation effects (for further recommendations, see
Colloca and Finniss, 2012; Bingel, 2014).
Taken together, knowledge about placebo and nocebo
responses and their underlying mechanisms should be
integral elements in the curricula for health care
professionals. Furthermore, possessing the skills to
maximize placebo responses while minimizing nocebo
responses is essential from a clinical application
perspective.
C. Health Care System
The past decades have seen groundbreaking advances in the development of new diagnostic tools and
treatments. However, this progress has also led to
increased specialization and shorter consultation time
and thus to frequently low therapeutic alliance due
to an inadequate doctor-patient communication, as
reflected in the report that in the United States, 50% of
patients leave their doctor’s office without having
adequately understood what their physician told them
(Bodenheimer, 2008). In addition, patients explaining
their problem to a physician were interrupted after an
average of 23 seconds (Bodenheimer, 2008); similar
numbers have been reported for primary care practices
in European countries (Deveugele et al., 2002). These
disturbing findings are certainly not due to uncommunicative practitioners in general but because doctors
are often overstressed by having to see too many
patients in too little time. In addition, medical care
compensation systems such as the “diagnosis-related
group” raise pressure on clinicians by often leaving
them with too little time for adequate doctor-patient
communication. Serious communication takes time,
which calls for a fundamental reorganization of reimbursement structures in medical care to significantly upgrading the time spent communicating with
the patient.
In addition to improving the basic conditions for
adequate communication, improved patient information
systems such as drug information leaflets (package
inserts) should be designed to reduce negative expectations regarding unwanted treatment side effects
(Bingel, 2014). At present, all potential adverse events
must be listed for legal reasons and in standardized
terminology, although the empirical evidence of a
causal link between drug and unwanted side effects
is notoriously weak. Instead, lay and patient-oriented
language and the description of positive and unwanted
effects should be an integral part of any information
leaflet. This should include ways to convey abstract
information (such as the probability of the occurrence
of side effects) in an intuitive fashion—a future
challenge for the drug authorities.
Neuro-Bio-Behavioral Mechanisms of Placebo and Nocebo Responses
Harnessing placebo mechanisms strategically within
health care systems promises to fundamentally improve the effectiveness of treatment strategies by
optimizing drug treatment regimens, drug efficacy,
drug adherence, and the context characteristics within
the overall medical setting. Ultimately, these approaches
should lower health care costs and improve patient care.
723
term goal of learned pharmacological responses: to
employ these learning paradigms in clinical situations
as supportive therapy together with standard pharmacological regimen, the aim being to maximize the
therapeutic outcome (Doering and Rief, 2012; Wendt
et al., 2014b). Finally, another fascinating scientific
question is why did placebo responses develop during
evolution (Kaptchuk, 2002, 2011)?
IX. Placebo Research: What Next?
The advances during the last two decades in our
understanding in the neurobiological and neuropsychological mechanisms of placebo and nocebo responses
have been made possible by the employment of sophisticated experimental designs and tools, such as neuroimaging, in vivo receptor binding, and single-neuron
recording in awake subjects and patients. However, our
knowledge about the mechanisms underlying these
responses remains limited and several key issues need
to be addressed in future research. We have to learn
which medical conditions and physiologic systems are
affected by which placebo mechanisms and whether
these effects are clinically relevant. For example,
somatic placebo analgesia can be mediated by cognitive
factors such as patients’ expectation about a treatment’s
benefit, as well as by associative learning processes
(Colloca et al., 2008; Tracey, 2010a; Colloca, 2014),
whereas visceral placebo analgesia seems less responsive to conditioning. In contrast, neuroendocrine or
immune function appears to be affected primarily by
conditioning and not by patient expectation (Wendt
et al., 2014b). We also need to discover when placebo
and nocebo responses occur and to analyze the specific
situational circumstances and patient characteristics
that are particularly amenable to placebo or nocebo
responses (Kaptchuk et al., 2008b). This knowledge
about genetic and/or psychologic predictors will form
the basis from which to exploit placebo responses and
avoiding nocebo responses in daily clinical routine.
How placebo responses work remains a key issue,
because we need to better understand the brain
mechanisms at both the macroscopic level (in particular, the brain regions and their interactions with
peripheral physiologic functions), as well as on the
microscopic (cellular and molecular) levels involved.
The data demonstrating that placebo responses can
affect pharmacological treatments introduces fascinating clinical perspectives. It will be important to better
understand the interactions between placebo mechanisms and pharmacological effects to exploit this
phenomenon in daily clinical care for the patient’s
benefit.
Similarly, thorough knowledge of the basic mechanisms steering the behavioral conditioning of pharmacological responses will be essential, not just to better
understand the brain mechanisms involved in these
learning processes but especially to achieve the long-
X. Conclusion
Placebo and nocebo responses are mediated by
expectations, associative learning processes, and the
quality of the patient-physician interaction. They
modulate fundamentally symptom perception, the
course of diseases, and the efficacy and tolerability of
medical treatment. Converging evidence from experimental and clinical studies has demonstrated that these
positive and negative effects on health outcomes are
based on complex neurobiological phenomena involving
the contribution of distinct CNS as well as peripheral
physiologic mechanisms.
Recent insights into the psychologic, neurobiolological, and peripheral-physiologic processes underlying placebo and nocebo responses provide the
unique opportunity to systematically modulate these
responses depending on the context in which they
occur. In clinical care, the systematic use of placebo
mechanisms is a promising target to improve health
outcomes. In the context of RCTs, placebo and nocebo
responses represent a substantial risk of bias that
hampers drug development and assay sensitivity.
Strategies to homogenize and control placebo responses and to prevent nocebo responses in a strategybased manner promise to improve drug development
by increasing the sensitivity to detect drug-specific
effects and improve the efficacy and tolerability of
drug treatment regimens, drug adherence, and context characteristics within the general medical
setting.
The full potential of these strategies critically
depends on continued characterization of the neurobiological and peripheral-physiologic mechanisms underlying placebo and nocebo responses and, importantly,
identification of the predictor variables that influence an
individual’s placebo and nocebo response in a contextand disease-specific manner. This knowledge will build
the foundation for the exploitation of placebo and nocebo
responses based on mechanism-based and personalized
clinical decisions.
Authorship Contributions
Wrote or contributed to the writing of the manuscript: Schedlowski,
Enck, Rief, Bingel.
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