{"doi":"10.1101/2024.02.12.579946","title":"Reverse engineering placebo analgesia","abstract":"SUMMARY Placebo analgesia is a widely observed clinical phenomenon. Establishing a robust mouse model of placebo analgesia is needed for careful dissection of the underpinning circuit mechanisms. However, previous studies failed to observe consistent placebo effects in rodent models of chronic pain. We wondered whether strong placebo analgesia can be reverse engineered using general anesthesia-activated neurons in the central amygdala (CeA GA ) that can potently suppress pain. Indeed, in both acute and chronic pain models, pairing a context with CeA GA -mediated pain relief produced robust context-dependent analgesia, exceeding that induced by morphine in the same paradigm. We reasoned that if the analgesic effect was dependent on reactivation of CeA GA neurons by conditioned contextual cues, the analgesia would still be an active treatment, rather than a placebo effect. CeA GA neurons indeed receive monosynaptic inputs from temporal lobe areas that could potentially relay contextual cues directly to CeA GA . However, in vivo imaging showed that CeA GA neurons were not re-activated in the conditioned context, despite mice displaying a strong analgesic phenotype, supporting the notion that the cue-induced pain relief is true placebo analgesia. Our results show that conditioning with activation of a central pain-suppressing circuit is sufficient to engineer placebo analgesia, and that purposefully linking a context with an active treatment could be a means to harness the power of placebo for pain relief.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":490002,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9527,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":284610,"name":"Nitsan Goldstein","orcid":"0000-0003-0926-5366","position":1,"is_corresponding":false},{"id":1250269,"name":"Julia Dziubek","orcid":null,"position":2,"is_corresponding":false},{"id":63853,"name":"Shengli Zhao","orcid":"0009-0007-9432-0070","position":3,"is_corresponding":false},{"id":859418,"name":"Andrew Harrahill","orcid":"0000-0002-3133-6493","position":4,"is_corresponding":false},{"id":1250270,"name":"Akili Sundai","orcid":null,"position":5,"is_corresponding":false},{"id":1179713,"name":"Seonmi Choi","orcid":null,"position":6,"is_corresponding":false},{"id":859417,"name":"Vincent Prevosto","orcid":"0000-0001-8803-5918","position":7,"is_corresponding":false},{"id":284547,"name":"Fan Wang","orcid":"0000-0003-2988-0614","position":8,"is_corresponding":false},{"id":519044,"name":"Bin Chen","orcid":"0000-0002-8174-5279","position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:08:32.775003Z","pmid":"38405975","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}