again, these studies involved relatively small samples, and the patients were scanned after they had been treated with antipsychotic medication: it is thus unclear whether the neuroimaging findings predated treatment or were secondary to it. Most studies to date have related clinical outcomes to a single cross-sectional neuroimaging measure. Serial neuroimaging measurements provide data on how the brain changes over time within the same patient, and recent studies involving longitudinal scanning of patients suggest that measuring the progression of findings facilitates the prediction of outcome6. For example, longitudinal data from patients with first episode psychosis and from those with childhood-onset schizophrenia suggest that reductions in hippocampal volume over the first few years of illness are associated with poorer functioning at follow-up7. All of the studies mentioned above reported differences between groups of patients. However, in order for neuroimaging to be useful in a clinical setting, it must be able to facilitate outcome prediction using data from an individual patient. Multivariate statistical approaches such as machine learning provide a means of addressing this issue. For example, application of machine learning analyses to MRI data from patients with first episode psychosis showed that baseline neuroimaging data could predict a non-remitting course of illness over the subsequent six years with an accuracy of 72%8. Ongoing studies in this field are seeking to address the methodological issues that may have limited earlier work. Sample sizes can be increased through the involvement of multiple research sites. Although multi-centre studies are logistically challenging, and there are significant confounding factors associated with acquiring data on a variety of different scanners, these disadvantages are probably outweighed by the increased statistical power that results from having much larger samples. Similarly, serial neuroimaging studies are more difficult to carry out than those involving a single scan, but may provide more predictive power. Ongoing studies have also sought to enroll samples that are homogeneous with respect to stage of illness and previous treatment, and that are treated in a standardized way subsequent to scanning. A good example of this is OPTiMiSE (Optimization of Treatment and Management of Schizophrenia in Europe), a large multicenter study funded by the European Commission1. This involves a neuroimaging assessment of a large multi-centre sample of medication-na€ıve or minimally treated first episode patients, all of whom are then treated with amisulpride following a standardized protocol. Their clinical outcomes are evaluated prospectively.

Future studies may also benefit from using more than one modality of neuroimaging; there is some evidence that this may improve prediction of outcomes9, although other data do not support this10. Similarly, integrating neuroimaging data with non-imaging measures that have independently been linked with altered outcomes in psychosis, such as polygenic risk score, substance use, inflammatory markers and central nervous system autoantibodies, may enhance predictive power. However, although this may be a reasonable expectation, it has yet to be tested. Even if a neuroimaging measure is established as a robust statistical predictor of clinical outcomes, this does not necessarily mean that it can be translated into mainstream clinical practice. Financial and practical considerations will apply, such as the cost of scanning and the availability of the scanner. The development of tools that can be used in a clinical setting is likely to require neuroimaging measures that can be acquired without the need for highly specialized training or equipment. Some ongoing studies are explicitly focused on the development of such tools for psychosis (see, for instance, www.psyscan.eu). Given that psychotic disorders are pathophysiologically heterogeneous, it is reasonable to expect that neuroimaging techniques which can identify pathophysiological differences within patient samples may be useful in predicting clinical outcomes. However, at present, it is unclear which particular neuroimaging measures will be the most useful, and whether combining these with non-imaging biomarkers will enhance their ability to facilitate prediction of outcomes in psychosis. Philip McGuire, Paola Dazzan Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London; National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, UK 1. 2. 3. 4.

Dazzan P, Arango C, Fleischhacker W et al. Schizophr Bull 2015;41:574-83. Sharma T, Kerwin R. Br J Psychiatry 1996;169(Suppl. 31):5-9. Demjaha A, Egerton A, Murray RM et al. Biol Psychiatry 2014;75:e11-3. Palaniyappan L, Marques TR, Taylor H et al. JAMA Psychiatry 2013;23: 1031-40. 5. Reis Marques T, Taylor H, Chaddock C et al. Brain 2014;137:172-82. 6. Cahn W, van Haren NE, Hulshoff Pol HE et al. Br J Psychiatry 2006;189: 381-2. 7. Lappin JM, Morgan C, Chalavi S et al. Psychol Med 2014;44:1279-91. 8. Mourao-Miranda J, Reinders AA, Rocha-Rego V et al. Psychol Med 2012;42: 1037-47. 9. Suckling J, Barnes A, Job D et al. Hum Brain Mapp 2010;31:1183-95. 10. Reig S, Sanchez-Gonzalez J, Arango C et al. Hum Brain Mapp 2009;30: 355-68. DOI:10.1002/wps.20426

The role of expectations in mental disorders and their treatment Expectations are defined as cognitions which are futuredirected and focused on the incidence or non-incidence of a specific event or experience1. In the treatment of mental disorders, examining and modifying patients’ expectations is

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discussed as a central mechanism of change2,3. This focus on expectations does not disregard any past experiences, but considers them only of relevance if they determine predictions about future events.

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The relevance of expectations for clinical conditions and their treatment can be illustrated by the following example: temporary ear noises are no problem for most people, as long as the affected persons expect them to vanish promptly. However, the same experience is difficult to bear if affected people expect them to last forever. Analogously, it may be not the negative mood, the unpleasant stimulation, the adverse life event per se that determines whether exposed people develop a mental or psychosomatic disorder, but the expectation about the timeline of the aversive condition, expected future threats, expected curability, and expected competence to cope with the unpleasant experiences. Neurobiology and psychological sub-disciplines such as developmental psychology and social psychology have focused on expectations for decades. They provide us with detailed knowledge on how expectations are formed, under what circumstances they are modified, or when they persist despite contradictory experiences. Expectations lead to brain activities that sensitize for the expected experience4, and they are closely linked to affective reactions5. “Prediction error” paradigms and their association with dopaminergic activation, amygdala activation during aversive coding, and the role of contextual information in the generation of expectancies are just a few neurophysiological examples of how the topic has been investigated. Associative learning, influences via group norms and media, and the phenomenon of sticking to expectations despite expectation violations (cognitive “immunization”) are psychologically relevant concepts to better understand why specific expectations are present. We currently face the challenge of investigating the role of expectations from a clinical perspective and transferring this knowledge into psychotherapeutic and psychopharmacological practice. This approach may allow for a better understanding of the dynamics of mental and psychosomatic disorders, guiding the development of tailored interventions based on highly effective mechanisms. Moreover, focusing on expectations and their persistence helps to explain why some treatments fail. Some mental disorders are “expectation disorders” by definition. This is particularly so in the case of anxiety disorders, such as phobias, panic disorder and generalized anxiety disorder. In these cases, patients expect adverse consequences when being exposed to specific stimuli, situations, or experiences (e.g., the phobic stimulus, the experience of palpitations). In obsessivecompulsive disorder, the patient expects dreadful consequences if compulsive behaviors are prohibited. The role of expectations in post-traumatic stress disorder (PTSD) seems to be more complex. While most people feel secure and do not expect horrible events, this basic confidence in everyday life situations is violated if people suffer from trauma6. Some patients with PTSD do not want to talk about the trauma because they do not expect to be able to bear the emotions that will arise. In other mental disorders, expectations are not part of the diagnostic criteria, but are also of relevance. For example, indi-

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viduals suffering from depression show more depressionspecific negative expectations7. Even in general medical conditions, expectations and expectation-associated concepts (e.g., fear avoidance in chronic pain) have been shown to predict persistence and survival8. Expectations about treatment success are the most prominent predictor of outcome, both in psychopharmacological and psychological interventions, and they are considered to be a major determinant of placebo effects9. In most psychopharmacological trials, placebo responses represent a substantial proportion of the overall treatment effect. Optimizing treatment expectations can result in improved outcome and prevention of treatment side effects, while the induction of negative expectations can abolish the effects of highly effective medications10. If expectations are one of the most powerful predictors of outcome, interventions must maximally modify illness-specific expectations, and positive outcome expectations should be sufficiently established before treatment starts. One of the traditional psychological interventions that may be considered a powerful tool to change expectations is exposure therapy. However, traditional exposure therapy needs to be reformulated to better focus on the change of expectations (e.g., explicit comparison between pre-exposure expectations and post-exposure experiences)3. Expectation-focused psychological interventions (EFPI)7 place a strong focus on analyzing and summarizing disorder-specific expectations of the patient, developing situational tests to check the credibility of these expectations, and re-evaluating expectations by comparing pre-existing expectations with the experience during exposure. In addition to disorder-specific expectations, the baseline expectations about positive and negative effects of interventions should play an important role in treatment planning. If patients have negative attitudes about drug therapy, these attitudes should be addressed before starting medication. In psychological therapies, positive outcome expectations should be established before more challenging interventions are suggested. Considering the large effects that must be attributed to placebo mechanisms in psychiatry, expectations and their modification can be considered the most powerful mechanism for successful treatment. Therefore, there is an urgent need to utilize knowledge about expectations to improve treatment outcomes. Winfried Rief, Julia Anna Glombiewski Department of Clinical Psychology and Psychotherapy, Philipps-University of Marburg, Marburg, Germany 1.

Rief W, Glombiewski JA, Gollwitzer M et al. Curr Opin Psychiatry 2015;28: 378-85. 2. Craske MG, Treanor M, Conway CC et al. Behav Res Ther 2014;58:10-23. 3. Craske MG. Verhaltenstherapie 2015;25:134-44. 4. Koyama T, McHaffie JG, Laurienti PJ et al. PNAS 2005;102:12950-5. 5. Schwarz KA, Pfister R, Buchel C. Trends Cogn Sci 2016;20:469-80. 6. Janoff-Bulman R. Soc Cogn 1989;7:113-36. 7. Rief W, Glombiewski JA. Verhaltenstherapie 2016;26:47-54. 8. Barefoot JC, Brummett BH, Williams RB et al. Arch Intern Med 2011;171: 929-35. 9. Schedlowski M, Enck P, Rief W et al. Pharmacol Rev 2015;67:697-730. 10. Enck P, Bingel U, Schedlowski M et al. Nat Rev Drug Discov 2013;12:191-204. DOI:10.1002/wps.20427

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