[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117963-en":3,"doc-seo-117963-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117963,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Identifying Neural Signatures of Dopamine Signaling with Machine Learning","New imaging tools for neurotransmitters, neuromodulators, and neuropeptides have expanded insight into how neurochemistry shapes brain development and cognition, but extracting meaning from this information is still difficult. This study images dopamine modulation in striatal tissue slices using near-infrared catecholamine nanosensors (nIRCat) and applies machine learning to identify features that uniquely reflect stimulation strength and distinct neuroanatomical regions. Support vector machine and random forest models reliably discriminate dopamine release in dorsolateral versus dorsomedial striatum, outperforming conventional statistical analysis. The most predictive signals involve features of dopamine modulatory activity, including unique release site counts and peak dopamine per event.","UC Berkeley  \nUC Berkeley Previously Published Works  \nTitle  \nIdentifying Neural Signatures of Dopamine Signaling with Machine Learning.  \nPermalink  \n[https://escholarship.org/uc/item/585413fn](https://escholarship.org/uc/item/585413fn)  \nJournal  \nACS chemical neuroscience, 14(12)  \nISSN  \n1948-7193  \nAuthors  \nSorooshyari, Siamak K  \nOuassil, Nicholas Yang, Sarah Jet al.  \nPublication Date  \n2023-06-01  \nDOI  \n10.1021/acschemneuro.3c00001  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n[pubs.acs.org/chemneuro](pubs.acs.org/chemneuro)  Research Article   \nIdentifying Neural Signatures of Dopamine Signaling with Machine Learning  \nSiamak K. Sorooshyari, Nicholas Ouassil, Sarah J. Yang, and Markita P. Landry*  \n Cite This: ACS Chem. Neurosci. 2023, 14, 2282−2293  \nRead Online  \n\n|  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |  | *sı Supporting Information |\n\nDownloaded via UNIV OF CALIFORNIA BERKELEY on July 19, 2023 at 19:16:57 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nABSTRACT: The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with nearinfrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions. We trained a support vector machine and a random forest classifier to decide whether the recordings were made from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS) and find that machine learning is able to accurately distinguish dopamine release that occurs in DLS from that occurring in DMS in a manner unachievable with canonical statistical analysis. Furthermore, our analysis determines that dopamine modulatory signals including the number of unique dopamine release sites and peak dopamine released per stimulation event are most predictive of neuroanatomy. This is in light of integrated neuromodulator amount being the conventional metric used to monitor neuromodulation in animal studies. Lastly, our study finds that machine learning discrimination of different stimulation strengths or neuroanatomical regions is only possible in adult animals, suggesting a high degree of variability in dopamine modulatory kinetics during animal development. Our study highlights that machine learning could become a broadly utilized tool to differentiate between neuroanatomical regions or between neurotypical and disease states, with features not detectable by conventional statistical analysis.  \nKEYWORDS: dopamine, machine learning, nanosensors, striatum  \n■ INTRODUCTION  \nRecent advances in the ability to image neuromodulators from single neurons,1 in acute brain slices2 and in vivo,3,4 have enabled insights into the role of neurochemical communication in the neurotypical and diseased brain. The newly accessible neurochemical signals could greatly advance neuroimaging by providing an additional dimension of information regarding the role of neuromodulation in regulating brain circuits and the central role of neuromodulators in psychiatric and neurodegenerative disease. Specifically, several dopamine probes have been developed in the past few years that have achieved imaging of dopamine at spatiotemporal scales commensurate with endogenous neurochemical signaling. A class of genetically encoded probes have enabled cell-specific expression of protein-  \nbased reporters that fluoresce when dopamine is bound.3,4 Additionally, synthetic ","cbCaikmDtal1eSjz","https://ap.wps.com/l/cbCaikmDtal1eSjz","pdf",5104898,1,13,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"How does the study use machine learning to analyze dopamine imaging data?\",\"answer\":\"It images dopamine modulation in striatal slices with nIRCat and uses machine learning classifiers (including a support vector machine and a random forest) to infer which dopamine features correspond to stimulation strength and neuroanatomical region.\"},{\"question\":\"Which brain regions are distinguished in the analysis?\",\"answer\":\"The models distinguish recordings from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS).\"},{\"question\":\"What dopamine-related features are most predictive of neuroanatomy?\",\"answer\":\"Signals such as the number of unique dopamine release sites and the peak dopamine released per stimulation event best predict neuroanatomy.\"}]","Identifying Neural Signatures of Dopamine Signaling with Machine Learning | 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does the study use machine learning to analyze dopamine imaging data?","Question",{"text":75,"@type":76},"It images dopamine modulation in striatal slices with nIRCat and uses machine learning classifiers (including a support vector machine and a random forest) to infer which dopamine features correspond to stimulation strength and neuroanatomical region.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which brain regions are distinguished in the analysis?",{"text":80,"@type":76},"The models distinguish recordings from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS).",{"name":82,"@type":73,"acceptedAnswer":83},"What dopamine-related features are most predictive of neuroanatomy?",{"text":84,"@type":76},"Signals such as the number of unique dopamine release sites and the peak dopamine released per stimulation event best predict 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