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The location of a sensory cortex for temperature perception remains a topic of substantial debate. Both the parietal–opercular (SII) and posterior insula have been consistently implicated in thermosensory processing, but neither region has yet been identified as the locus of fine temperature discrimination. Using a perceptual learning paradigm in male and female humans, we show improvement in discrimination accuracy for subdegree changes in both warmth and cool detection over 5 d of repetitive training. We found that increases in discriminative accuracy were specific to the temperature (cold or warm) being trained. Using structural imaging to look for plastic changes associated with perceptual learning, we identified symmetrical increases in gray matter volume in the SII cortex. Furthermore, we observed distinct, adjacent regions for cold and warm discrimination, with cold discrimination having a more anterior locus than warm. The results suggest that thermosensory discrimination is supported by functionally and anatomically distinct temperature-specific modules in the SII cortex. SIGNIFICANCE STATEMENT We provide behavioral and neuroanatomical evidence that perceptual learning is possible within the temperature system. We show that structural plasticity localizes to parietal–opercular (SII), and not posterior insula, providing the best evidence to date resolving a longstanding debate about the location of putative “temperature cortex.” Furthermore, we show that cold and warm pathways are behaviorally and anatomically dissociable, suggesting that the temperature system has distinct temperature-dependent processing modules.

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants ("traveling subjects") visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty ('associability') signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief.

Tonic pain after injury characterises a behavioural state that prioritises recovery. Although generally suppressing cognition and attention, tonic pain needs to allow effective relief learning to reduce the cause of the pain. Here, we describe a central learning circuit that supports learning of relief and concurrently suppresses the level of ongoing pain. We used computational modelling of behavioural, physiological and neuroimaging data in two experiments in which subjects learned to terminate tonic pain in static and dynamic escape-learning paradigms. In both studies, we show that active relief-seeking involves a reinforcement learning process manifest by error signals observed in the dorsal putamen. Critically, this system uses an uncertainty (‘associability’) signal detected in pregenual anterior cingulate cortex that both controls the relief learning rate, and endogenously and parametrically modulates the level of tonic pain. The results define a self-organising learning circuit that reduces ongoing pain when learning about potential relief., eLife digest Chronic pain lasting longer than three months is a common problem that affects about 1 in 5 people at some point in their lives. The lack of effective treatments has led to widespread use of a group of drugs called opioids – the best-known example is morphine. Opioids work by activating the brain’s natural painkilling system and are useful to relieve short-term pain, for example in trauma or surgery, or in end-of-life care. Unfortunately, long-term use of opioids can cause many undesirable effects, including drug dependency. Misuse of opioids combined with the widespread availability of prescription drugs have contributed to the current crisis of opioid addiction and overdose. A better understanding of how the brain’s natural painkilling system works could help scientists develop painkillers that offer relief without the harmful side effects of opioids. While unpleasant, pain is important for survival. After an injury, for example, pain saps motivation and forces people to rest and preserve their energy as they are healing. In a way, this sort of pain is healthy because it promotes recovery. There may be times when the brain might want to turn off pain, such as when an individual is seeking new ways to relieve or manage pain. For example, by finding a way to cool a burn. Now, Zhang et al. show that the brain reduces pain while individuals are trying to find relief. In the experiments, a metal probe was attached to the arm of healthy volunteers and heated until it became painful but not hot enough to burn the skin. Then, the volunteers were asked to play a game in which they had to find out which button on a small keypad cooled down the probe. Sometimes it was easy to turn off the heat, sometimes it was difficult. During the game, volunteers reported how much pain they felt and Zhang et al. used brain imaging to see what happened in their brains. When the subjects were actively trying to work out which button they should press, pain was reduced. But when the subjects knew which button to press, it was not. Next, Zhang et al. found that a part of the brain called the pregenual cingulate cortex was responsible for making decisions about when to turn off pain and may so trigger the brain’s natural pain killing system. A next step will be to see how this part of the brain decides to turn off pain and if it also controls opioid-like or other chemicals. This could improve the use of opioids, or even help to discover alternative treatments for chronic pain.