[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122393-en":3,"doc-seo-122393-105":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},122393,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Rage against the machine - advancing aggression ethology through machine learning - dissertation","Aggression is a conserved behavior spanning adaptive and maladaptive forms, with maladaptive escalation linked to neuropsychiatric disorders such as autism spectrum disorders, PTSD, and intermittent explosive disorder. Current treatments remain limited or burdened by significant side effects. In this dissertation, aggression is modeled preclinically by developing machine learning approaches for high-throughput, consistent behavioral scoring. Using SimBA with explainability methods, the work quantifies and shares aggression classifiers. Results show sex-specific appetitive aggression in males, with whole-brain c-fos mapping implicating inhibitory circuitry in females and lateral septum modulation in males.","Rage against the machine: advancing aggression ethology through machine learning  \nNastacia L. Goodwin  \nA dissertation  \nsubmitted in partial fulfillment of the  \nrequirements for the degree of  \nDoctor of Philosophy  \nUniversity of Washington  \n2024  \nReading Committee:  \nSam Golden, Chair  \nSusan Ferguson  \nLarry Zweifel  \nProgram Authorized to Offer Degree:  \nNeuroscience  \n©Copyright 2024  \nNastacia L. Goodwin  \nUniversity of Washington  \nAbstract  \nRage against the machine: advancing aggression ethology through machine learning  \nNastacia L. Goodwin  \nChair of the Supervisory Committee:  \nSam Golden  \nBiological Structure  \nAggression is a highly conserved behavior and exists along a spectrum from adaptive to maladaptive. Adaptive aggression can serve to protect mates, territory, and resources. Maladaptive aggression, however, can present as escalated and uncontrolled, and can occur comorbid with neuropsychiatric disorders including autism spectrum disorders, post-traumatic stress disorder, and intermittent explosive disorder. Inappropriate aggression seeking is detrimental to both individuals and society, and current treatment options are largely ineffective, or associated with significant side effects (Coccaro et al. 2009; Carlson et al. 2010; Frogley et al. 2012; Khushu and Powney 2016). In the clinical literature, aggression is typically delineated into instrumental, reactive (fight or flight), and appetitive (rewarding) phenotypes. Preclinically, thereis a long history of research involving reactive aggression, but a much smaller body of work only in males examining the neurobiology of appetitive aggression. The goal of this dissertation was to further develop preclinical models of appetitive aggression in mice by understanding the different behavioral and whole-brain activation patterns between the sexes, and by directly comparing appetitive and reactive aggression phenotypes. A significant portion of my work in this arena has involved developing a machine learning based platform for high throughput and consistent scoring of aggression behaviors-Simple Behavioral Analysis (SimBA) . Importantly, Iposit that machine learning based behavioral detection paired with artificial intelligence explainability techniques allows users to objectively quantify and share behavioral classifiers inan RRID-like fashion. Using this platform, I have discovered that while both males and females  \nexhibit reactive aggression, males but not females show appetitive aggression. I examined the neural correlates ofthis behavioral sex difference using whole-brain c-fos activity mapping, identifying a potential network inhibiting appetitive aggression in females. In males, I further identified the lateral septum as a potential locus of differential control of reactive and appetitive aggression. Ultimately, this dissertation indicates that reactive and appetitive aggression areneurally dissociable processes, with an inhibitory network in females gating appetitive aggression.  \nTable of Contents  \nGratitudes ............................................................................................................................................ xvii  \nChapter 1 : Introduction .............................................................................................................. 18  \nMachine learning solutions to behavioral annotation .........................................................................18  \nExplainable machine learning ................................................................................................................2  \nSex differences in appetitive aggression .................................................................................................4  \nAppetitive aggression circuitry ...............................................................................................................4  \nWhole-brain specific experiments .....................................................................","cbCaicUs4Vm879Fi","https://ap.wps.com/l/cbCaicUs4Vm879Fi","pdf",13880758,1,232,"English","en",105,"# Gratitudes\n# Chapter 1 : Introduction\n## Machine learning solutions to behavioral annotation\n## Explainable machine learning\n## Sex differences in appetitive aggression\n## Appetitive aggression circuitry\n## Whole-brain specific experiments\n## Conclusions\n# Chapter 2 : Rage Against the Machine: Advancing the study of aggression ethology via machine learning\n## 2A. Winners like to win: revisiting aggression reward\n## 2B. Individual variability in inbred and outbred lines\n## 2C. Unconditioned vs. conditioned aggression\n## 2D. Addiction-like aggression behavior and relapse\n## 2E. Conclusions\n# Chapter 3A. Embracing machine learning\n## 3B. Supervised versus unsupervised learning\n## 3C. Common classifying algorithms for supervised learning\n## Neural Networks (Hopfield 1982; LeCun et al. 1989)\n## Random Forests (Breiman 2001; Liaw and Wiener 2002)\n## Gradient Boosting Machines (Freund and Schapire 1997; Friedman et al. 2000; Friedman 2001)\n## Support Vector Machines (Cortes and Vapnik 1995)","[{\"question\":\"How does the dissertation define adaptive and maladaptive aggression and why does it matter clinically?\",\"answer\":\"Aggression is described as a spectrum from adaptive to maladaptive. Maladaptive escalation can appear alongside neuropsychiatric disorders, and current treatments are largely ineffective or have significant side effects.\"},{\"question\":\"What is SimBA, and what problem does it address in aggression research?\",\"answer\":\"SimBA is a machine learning based platform for high-throughput, consistent scoring of aggression behaviors. It supports objective quantification and sharing of behavioral classifiers using explainability approaches.\"},{\"question\":\"What sex differences in appetitive aggression did the research find in mice?\",\"answer\":\"While both males and females exhibit reactive aggression, males show appetitive aggression whereas females do not. Whole-brain mapping suggests an inhibitory network in females that gates appetitive aggression.\"}]","Rage against the machine - advancing aggression ethology through machine learning - dissertation | PDF",1785810400,585,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"rage-against-the-machine-advancing-aggression-ethology-through-machine-learning-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/rage-against-the-machine-advancing-aggression-ethology-through-machine-learning-dissertation/122393/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the dissertation define adaptive and maladaptive aggression and why does it matter clinically?","Question",{"text":75,"@type":76},"Aggression is described as a spectrum from adaptive to maladaptive. Maladaptive escalation can appear alongside neuropsychiatric disorders, and current treatments are largely ineffective or have significant side effects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is SimBA, and what problem does it address in aggression research?",{"text":80,"@type":76},"SimBA is a machine learning based platform for high-throughput, consistent scoring of aggression behaviors. It supports objective quantification and sharing of behavioral classifiers using explainability approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"What sex differences in appetitive aggression did the research find in mice?",{"text":84,"@type":76},"While both males and females exhibit reactive aggression, males show appetitive aggression whereas females do not. 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