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Frozen in Fear: Machine Learning Unveils Sex Differences in Rats' Freezing Postures and Defensive Reactions- [electronic resource]
Frozen in Fear: Machine Learning Unveils Sex Differences in Rats' Freezing Postures and Defensive Reactions- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101913
- ISBN
- 9798380363853
- DDC
- 150
- 서명/저자
- Frozen in Fear: Machine Learning Unveils Sex Differences in Rats Freezing Postures and Defensive Reactions - [electronic resource]
- 발행사항
- [S.l.]: : University of California, Los Angeles., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(148 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
- 주기사항
- Advisor: Fanselow, Michael S.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Females are twice as likely as males to be diagnosed with anxiety-related disorders, yet they are underrepresented in preclinical research. We investigated fear responses in female and male Long Evans rats to understand the boundaries surrounding fear. Anxiety and fear, as motivational factors, prompt animals to engage in defense responses that maximize chances for survival. Predatory Imminence Continuum (PIC) theory provides a framework for understanding defensive behavior and organizes behaviors according to threat proximity: pre-encounter, post-encounter, and circa-strike, representing anxiety, fear, and panic states. However, a limited understanding remains of the specific thresholds where anxiety transforms into fear and vice versa. In Experiment 1, we induced increased anxiety and fear, exposing female and male rats to varying foot shock intensities as punishment for freezing behavior to investigate reactions to threats. We hypothesized that rats experiencing lower fear levels would learn the avoidance response. Our findings showed male rats punished for freezing reduced freezing behavior compared to females at the same low intensity. We interpreted these findings as females did not reduce freezing because they were positioned higher on the PIC, specifically in the post-encounter mode, where defensive freezing dominates. Experiment 2 aimed to establish the gradation of fear in the post-encounter mode and determine its boundaries. Traditional fear assessment methods oversimplify freezing behavior as binary. Differentiating qualitative aspects of freezing postures can offer a deeper insight into fear, even when freezing durations are similar. We employed markerless pose estimation and developed a custom unsupervised machine learning (UML) algorithm to analyze freezing postures. We hypothesized the postures in which females freeze indicate their fear level. Our UML grouped freezing postures into eight distinct clusters; two were enriched with females punished for freezing. Female enrichment was not due to sexual dimorphism or shock reactivity. Analysis revealed that the animal's orientational, postural, and positional features also influenced clustering. Furthermore, animals engaged in different behaviors before and after each freezing bout, providing further insights into their fear levels. By understanding fear and its impact on females, we aim to focus on this understudied population and contribute to improving anxiety-related disorder diagnostics and treatment.
- 일반주제명
- Psychology.
- 일반주제명
- Behavioral sciences.
- 일반주제명
- Mental health.
- 키워드
- Anxiety
- 키워드
- Fear
- 키워드
- Female anxiety
- 키워드
- Freezing
- 기타저자
- University of California, Los Angeles Psychology 0780
- 기본자료저록
- Dissertations Abstracts International. 85-03B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798380363853
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■0820 ▼a150
■1001 ▼aSmith-Vickery, Nancy Jo.
■24510▼aFrozen in Fear: Machine Learning Unveils Sex Differences in Rats' Freezing Postures and Defensive Reactions▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of California, Los Angeles. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(148 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-03, Section: B.
■500 ▼aAdvisor: Fanselow, Michael S.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aFemales are twice as likely as males to be diagnosed with anxiety-related disorders, yet they are underrepresented in preclinical research. We investigated fear responses in female and male Long Evans rats to understand the boundaries surrounding fear. Anxiety and fear, as motivational factors, prompt animals to engage in defense responses that maximize chances for survival. Predatory Imminence Continuum (PIC) theory provides a framework for understanding defensive behavior and organizes behaviors according to threat proximity: pre-encounter, post-encounter, and circa-strike, representing anxiety, fear, and panic states. However, a limited understanding remains of the specific thresholds where anxiety transforms into fear and vice versa. In Experiment 1, we induced increased anxiety and fear, exposing female and male rats to varying foot shock intensities as punishment for freezing behavior to investigate reactions to threats. We hypothesized that rats experiencing lower fear levels would learn the avoidance response. Our findings showed male rats punished for freezing reduced freezing behavior compared to females at the same low intensity. We interpreted these findings as females did not reduce freezing because they were positioned higher on the PIC, specifically in the post-encounter mode, where defensive freezing dominates. Experiment 2 aimed to establish the gradation of fear in the post-encounter mode and determine its boundaries. Traditional fear assessment methods oversimplify freezing behavior as binary. Differentiating qualitative aspects of freezing postures can offer a deeper insight into fear, even when freezing durations are similar. We employed markerless pose estimation and developed a custom unsupervised machine learning (UML) algorithm to analyze freezing postures. We hypothesized the postures in which females freeze indicate their fear level. Our UML grouped freezing postures into eight distinct clusters; two were enriched with females punished for freezing. Female enrichment was not due to sexual dimorphism or shock reactivity. Analysis revealed that the animal's orientational, postural, and positional features also influenced clustering. Furthermore, animals engaged in different behaviors before and after each freezing bout, providing further insights into their fear levels. By understanding fear and its impact on females, we aim to focus on this understudied population and contribute to improving anxiety-related disorder diagnostics and treatment.
■590 ▼aSchool code: 0031.
■650 4▼aPsychology.
■650 4▼aBehavioral sciences.
■650 4▼aMental health.
■653 ▼aAnxiety
■653 ▼aFear
■653 ▼aFemale anxiety
■653 ▼aFreezing
■653 ▼aUnsupervised machine learning
■690 ▼a0621
■690 ▼a0602
■690 ▼a0800
■690 ▼a0347
■71020▼aUniversity of California, Los Angeles▼bPsychology 0780.
■7730 ▼tDissertations Abstracts International▼g85-03B.
■773 ▼tDissertation Abstract International
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16935274▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
