Encoding and Decoding of Pain Relief in the Human Brain

The studies in this thesis explored how pain and its relief are represented in the human brain. Pain and relief are important survival signals that motivate escape from danger and search for safety, however, they are often evaluated by subjective descriptions only. Studying how humans learn and adapt to pain and relief allows objective investigation of the information processing and neural circuitry underlying these internal experiences. My research set out to use computational learning models to provide mechanistic explanations for the behavioural and functional neuroimaging data collected in pain/relief learning experiments with independent groups of healthy human participants. With a Pavlovian acute pain conditioning task in Experiment 1, I found that 'associability' (a form of uncertainty signal) had a crucial role in controlling the learning rates of different conditioned responses, and can be used to anatomically dissociate underlying neural systems. Experiment 2 focused on relief learning of terminating a tonic pain stimulus, in which the priority for relief-seeking is in conflict with the general suppression of cognition and attention. I showed that associability during active learning not only controls the relief learning rate, but also correlates with endogenously modulated (reduced) ongoing pain. This finding was confirmed in Experiment 3 using an independent active relief learning paradigm in a complex dynamic environment. Critically, both experiments showed that associability was correlated with responses in the pregenual anterior cingulate cortex (pgACC), a brain region previously implicated in aspects of endogenous pain control related to attention and controllability. This provided a potential computational account of an information-sensitive endogenous analgesic mechanism. In Experiment 4, I explored the implications of endogenous controllability for technology-based pain therapeutics. I designed an adaptive closed-loop system that learned to control pain stimulation using decoded real-time pain representations from the brain. Subjects were shown to actively enhance the discriminability of pain only in the pgACC, and uncertainty during learning again correlated with endogenously modulated pain and were associated with pgACC responses. Together, these studies (i) show the importance of uncertainty in controlling learning during both acute and tonic pain, (ii) describe how uncertainty also flexibly modulates pain to maximise the impact of learning, (iii) illustrate a central role for the pgACC in this process, and (iv) reveal the implications for future technology-based therapeutic systems.Financial support was generously provided by the WD Armstrong Fund and the Cambridge Trust

Dissociable Learning Processes Underlie Human Pain Conditioning.

Pavlovian conditioning underlies many aspects of pain behavior, including fear and threat detection [1], escape and avoidance learning [2], and endogenous analgesia [3]. Although a central role for the amygdala is well established [4], both human and animal studies implicate other brain regions in learning, notably ventral striatum and cerebellum [5]. It remains unclear whether these regions make different contributions to a single aversive learning process or represent independent learning mechanisms that interact to generate the expression of pain-related behavior. We designed a human parallel aversive conditioning paradigm in which different Pavlovian visual cues probabilistically predicted thermal pain primarily to either the left or right arm and studied the acquisition of conditioned Pavlovian responses using combined physiological recordings and fMRI. Using computational modeling based on reinforcement learning theory, we found that conditioning involves two distinct types of learning process. First, a non-specific "preparatory" system learns aversive facial expressions and autonomic responses such as skin conductance. The associated learning signals-the learned associability and prediction error-were correlated with fMRI brain responses in amygdala-striatal regions, corresponding to the classic aversive (fear) learning circuit. Second, a specific lateralized system learns "consummatory" limb-withdrawal responses, detectable with electromyography of the arm to which pain is predicted. Its related learned associability was correlated with responses in ipsilateral cerebellar cortex, suggesting a novel computational role for the cerebellum in pain. In conclusion, our results show that the overall phenotype of conditioned pain behavior depends on two dissociable reinforcement learning circuits.Research was supported by National Institute for Information and Communications Technology (Japan), the Japanese Society for the Promotion of Science (JSPS) and The Wellcome Trust (UK). S.Z. was supported by the WD Armstrong Fund and the Cambridge Trust. G.G. was partially supported by the Kakenhi Research Grant B #13380602 from the Japan Society for the Promotion of Science. We thank the imaging team at the Center for Information and Neural Networks for their help in performing the study. The authors declare that there are no conflicts of interest.This is the final version of the article. It was first available from Elsevier via http://dx.doi.org/10.1016/j.cub.2015.10.06

Metabolic characterization of different-aged Monascus vinegars via HS-SPME-GC-MS and CIL LC-MS approach

Yongchun Monascus vinegar is one of famous Chinese vinegar types because of its unique flavor and special bioactivity. Aging process has been regarded as crucial for enhancing the flavor and quality of vinegar. However, changes in the metabolites along the aging of the vinegar are still poorly understood. In this study, a combination of headspace solid-phase micro-extraction gas chromatography-mass spectrometry and chemical isotope labeling liquid chromatography-mass spectrometry methods was used for investigating the metabolomes of one-year-old, five-year-old and thirty-year-old YMVs. DPPH radical-scavenging activity, total phenolics content, and total flavonoids content correlated positively with the aging time. The metabolite compositions in the different-aged vinegars were clearly separated in the PCA analysis. A total of 1133 volatile and non-volatile metabolites changed along the aging; 392 metabolites were in common whereas 126, 84, and 54 changed metabolites were unique to one-to-five year, one-to-thirty year, and five-to-thirty year-old vinegar comparisons, respectively. Organic acids and dipeptides, exhibiting taste characteristics, with constant increase or decrease with aging time and correlated with antioxidant capacities could be used as biomarkers for differentiating the different-aged vinegars. The results revealed aging time related changes in volatile and non-volatile metabolites in YMVs, providing useful knowledge for improving their quality.Peer reviewe

Discriminant Analysis of Jiang-Flavor Baijiu of Different Grades by Gas Chromatography-Mass Spectrometry and Electronic Tongue

Gas chromatography-mass spectrometry (GC-MS) and electronic tongue were used to quantitatively determine the volatile compounds and taste indices of 21 Jiang-flavor baijiu samples of different grades. These samples were differentiated by chemometrics, and key differential compounds among grades were identified. Finally, a discriminant model was established by machine learning. The results showed that there were differences in the contents of volatile compounds in Jiang-flavor baijiu of three grades, indicating the feasibility of further discriminant analysis. The total content of flavor compounds in second-grade baijiu (4 908 mg/L) was significantly lower than that in premium-grade (6 583 mg/L) and first-grade baijiu (8 254 mg/L), while the proportion of several esters responsible for floral and fruity aromas in total esters showed a decreasing trend as the grade decreased. Partial least squares-discriminant analysis (PLS-DA) identified 16 key differential compounds represented by ethyl palmitate and acetic acid. The results of electronic tongue showed that the taste indexes of premium-grade baijiu were more consistent, with lower bitterness and astringency aftertaste. The taste indexes of second-grade baijiu showed significant intersample differences. Principal component analysis (PCA) showed clear discrimination of Jiang-flavor baijiu of different grades according to their taste indexes. The above results provide a basis for the establishment of Jiang-flavor baijiu quality system. Four discriminant models were established based on 25 differential compounds and taste indexes identified. The accuracy of all models was higher than 90%, and the support vector machine (SVM) model performed best, with an accuracy of 100%

Effect of Spatial Heterogeneity on the Microbial Community of Daqu, a Fermentation Starter for Chinese Baijiu

The effect of spatial heterogeneity on the microbial community and physicochemical properties during the primary fermentation of Daqu were investigated by high-throughput sequencing technology and conventional detection methods. Nongxiangxing baijiu Daqu inoculated with the unique ripe starter obtained by gradually culturing and expanding Daqu treated by cosmic rays was used. The results showed that although the intensity of change in driving factors varied among layer, their trends were the same. The liquefying, saccharifying and esterifying power of Daqu were higher in the bottom layer than in the upper and middle layers at the same fermentation time and the fluctuation was small. The microbial community of Daqu was composed of 12 dominant bacterial genera, including Lactobacillus, Weissella, Bacillus, Kosakonia, Staphylococcu and Thermoactinomyces, and seven dominant fungal genera, such as Pichia, Thermoascus, and Rhizomucor. Principal co-ordinates analysis and hierarchical clustering analysis showed significant differences in the bacterial and fungal community structure among fermentation stages and layers. Procrustes analysis and Mantel test showed that moisture had a significant effect on the bacterial community in Daqu, and acidity had a significant effect on the bacterial community in the middle and bottom layers of Daqu. Moreover, moisture had a significant effect on the fungal community in the upper and middle layers of Daqu. Redundancy analysis showed that moisture and acidity were positively correlated with Lactobacillus and Pichia, while driving factors had different influences on the microbial communities in different layers of Daqu. Therefore, the interaction and co-occurrence patterns of microbial genera in Daqu could change due to the differences in driving factors among different layers of Daqu. These results suggested that regulating driving factors during the Daqu making process is an effective way to improve the microbial community structure and quality of Daqu