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The neural mechanisms of placebo analgesia are not fully understood. Here the authors conducted a large scale meta-analysis of individual data from fMRI studies of pain and placebo conditions.

Craving—the urge to use a drug or to eat—is a core feature of substance use disorders. Koban et al. present an fMRI-based and machine-learning-based neuromarker that predicts the intensity of drug and food craving and separates drug users from non-users.

Whether the brain processes different types of pain similarly or differently remains unknown. The authors show that an established neurologic pain signature responds to five different types of visceral and somatic pain; they also develop a new classifier that reliably discriminates between both pain modalities.

The brain systems underlying fear experience are debated. Here the authors develop an fMRI-based neural signature for fear and show that fear is represented in distributed brain systems rather than isolated ‘fear centers’.

Losin et al. use neuroimaging to identify a brain mechanism underlying increased pain sensitivity in African Americans. This mechanism correlated with racial discrimination and implicated brain systems involved in context-based pain evaluation.

Rumination, which is the tendency to dwell on negative internal states repetitively, is a well-known cognitive style associated with depression. The authors developed a predictive model of rumination and observed that the dorsomedial prefrontal cortex plays an important role in rumination.

The mechanisms underlying placebo analgesia are not fully understood. Here the authors show that a placebo treatment inducing analgesia in healthy participants reduced fMRI activity in systems related to evaluative and affective processes, but not systems related to nociceptive pain processing.

Two fMRI studies (n = 358) show that cognitive regulation of negative emotion alters cortical activity but not amygdala or other subcortical areas. Regulation-related activity overlaps with emotion generation systems but also involves distinct areas.

Botvinik-Nezer et al. identify robust preference effects on updating of fraud beliefs related to the 2020 US election, across both political parties. Computational models explain these effects as rational updates from a system of biased prior beliefs.

Using multiple types of negative affect stimuli, functional magnetic resonance imaging and predictive modeling, Čeko et al. show that the brain integrates generalized and stimulus-type-specific representations of aversive events to jointly predict subjective experience.

The functional magnetic resonance imaging connectivity pattern of tonic experimental orofacial pain can be used as a quantitative and unbiased biomarker of clinical pain.

Chronic pain is the greatest source of disability globally and claims related to chronic pain feature in many insurance and medico-legal cases. In this Consensus Statement, a presidential task force of the International Association for the Study of Pain examines the capabilities of brain imaging in the diagnosis of chronic pain, and the ethical and legal implications of such uses of brain imaging.

A new study identifies four distinct 'biotypes' of depression on the basis of fMRI resting-state functional connectivity in a diverse sample of more than 1,000 individuals. The biotypes are diagnostic of depression and predict treatment response.

Neuroimaging methods are beginning to provide promising ways of understanding the functional organization of the brain across species.

Pain is affected by cerebral processes in addition to afferent nociceptive input. Here the authors develop an fMRI-based signature that predicts pain independent of the intensity of nociceptive signals and mediates the pain-modulating effects of several cognitive interventions.

This study uses fMRI in humans to find that prediction errors about pain are encoded in the periaqueductal gray. Modeling inter-area connectivity suggests that the ventromedial prefrontal cortex and the putamen pass on a value-related signal to this midbrain structure, which then conveys predictor error signals to prefrontal regions that regulate behavior.

Pain experience is highly individual, but its individual-specific brain features remain unclear. The authors identify brain regions with consistent versus variable representations of pain across a large sample of individuals.

Placebo effects are positive effects on health that arise from the response of the brain to the contextual information that accompanies the delivery of a treatment. In this Review, Wager and Atlas examine the neural mechanisms that underlie such effects, focusing on placebo analgesia.

Physical pain and social rejection are believed to be processed by common neural substrates in the brain. Here Woo et al.combine brain imaging with pattern analysis to show that, in fact, pain and rejection are processed by distinct neural substrates that are located in similar anatomical brain regions.

Assessing person-level human brain maps across 18 fMRI studies, the authors identify separable representations of pain, cognitive control, and negative emotion in the medial frontal cortex that generalize across different studies and tasks.

Shohamy and colleagues found that administration of a placebo can enhance learning from positive outcomes in Parkinson disease's patients. An analysis of fMRI signals recorded during behavior indicated that the drug and placebo both enhanced the representation of expected value in the ventromedial prefrontal cortex and attenuated prediction error signals in the ventral striatum. These results suggest that the mere expectation of reward can drive learning.

There is controversy about whether placebos without deception cause real psychobiological benefits. Here, the authors show that the positive effects of placebos without deception are more than response bias by providing evidence they can reduce self-report and neural measures of emotional distress.

A framework and web interface for the large-scale and automated synthesis of human neuroimaging data extracted from the literature is presented. It is used to generate a large database of mappings between neural and cognitive states and to address long-standing inferential problems in the neuroimaging literature.

Our experience of pain can be affected by our expectations about how much pain we will feel. Here, the authors show that both social information-driven expectations, and those based on personal experience, are both able to modulate pain, but by different neural pathways.

Jepma and colleagues provide evidence that prior beliefs about pain influence perceived intensity of pain, and the degree of learning about pain intensity. This finding helps to explain why beliefs are often resistant to updating with experience.

Here we demonstrate that patients’ pain experiences are directly modulated by providers’ expectations of treatment outcomes in a simulated clinical interaction, providing evidence of a socially transmitted placebo effect.

In this Perspective, Koban, Gianaros, Kober and Wager describe neural systems that construct models of the ‘self-in-context’. Such models endow events with personal meaning and enable predictive control over behaviour and peripheral physiology — with implications for health and disease.

Neuroimaging and pattern recognition are being combined to develop brain models of clinical disorders. Such models yield biomarkers that can be shared and validated across populations, narrowing the gap between neuroscience and clinical applications. The authors summarize 475 translational modeling studies, highlighting challenges and ways to improve biomarker development.

Neuroimaging-based machine-learning models should be interpretable to neuroscientists and users in applied settings. This protocol describes how to assess the interpretability of models based on fMRI.

Perszyk et al. identify the neural basis for odour-imagery ability and show that it indirectly predicts weight-gain susceptibility through a mechanism that is dependent on food-cue reactivity.

Although chronic pain is one of the most important medical problems facing society, there has been very limited progress in the development of novel therapies for this condition. Here, we discuss high-impact research priorities to reduce the number of people transitioning from acute to chronic intractable pain.

In 2018, the Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened to discuss strategies to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Consistent failure over the past few decades to reduce the high prevalence of stress-related disorders has motivated a search for alternative research strategies. Resilience refers to the phenomenon of many people maintaining mental health despite exposure to psychological or physical adversity. Instead of aiming to understand the pathophysiology of stress-related disorders, resilience research focuses on protective mechanisms that shield people against the development of such disorders and tries to exploit its insights to improve treatment and, in particular, disease prevention. To fully harness the potential of resilience research, a critical appraisal of the current state of the art — in terms of basic concepts and key methods — is needed. We highlight challenges to resilience research and make concrete conceptual and methodological proposals to improve resilience research. Most importantly, we propose to focus research on the dynamic processes of successful adaptation to stressors in prospective longitudinal studies.

In this Perspective, the authors propose a model in which an imbalance of threat and soothing systems leads to hyperactivation of the brain’s salience network, which, in conjunction with other mechanisms, contributes to fibromyalgia.

Comparison of resilient, trauma-exposed healthy participants and trauma-exposed individuals presenting with psychopathology reveals a resilience network connectivity signature in the right executive control network during generalization learning.