Impact of Affect Reactivity and Regulation on Breast Cancer Treatment Decisions

情绪反应性和调节对乳腺癌治疗决策的影响

基本信息

  • 批准号:
    9146318
  • 负责人:
  • 金额:
    $ 62.34万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-09-18 至 2020-08-31
  • 项目状态:
    已结题

项目摘要

 DESCRIPTION (provided by applicant): Women diagnosed with breast cancer are choosing bilateral mastectomy (BLM) at increasing rates, currently 14.3%, and 33% of those under 40. This is happening despite evidence that there is no survival benefit from BLM, along with surgical complications and other serious medical and personal costs, compared with more conservative approaches. Women's anxiety about recurrence is critical to this decision, so their choice may in large part reflect the way they experience and regulate affect. To understand the neurobiological and affective determinants of the choice of BLM, and thereby identify future opportunities for new interventions, we propose to examine the relationship between affect reactivity and regulation and women's decisions regarding BLM after initial diagnosis of breast cancer. We will also examine the impact of affect management and treatment decisions on subsequent psychosocial functioning. The study will involve recruiting a sample of 150 women recently diagnosed with breast cancer after their decision about treatment (75 who have elected BLM and 75 demographically and medically similar women who have decided not to have BLM), as well as a matched control group of 50 women without breast cancer. Affective reactivity to negative non-cancer and cancer- related stimuli will be studied using functional magnetic resonance imagining (fMRI). Likewise, affective regulation will be assessed with fMRI probes of both explicit (i.e. conscious, deliberate) and implicit (i.e. nonconscious, automatic) regulation o negative non-cancer and cancer-related stimuli. Psychosocial functioning will be assessed using self-report measures of anxiety, depression, well-being and functional status at 6, 12, and 18 months post-decision. Informational (e.g. awareness of influential people who have undergone BLM), and demographic variables (age, race, SES) will also be assessed. A physiological stress response measure, diurnal salivary cortisol slope, will be obtained at baseline and all follow-ups. This measure has been shown be associated with expression of negative affect, and to predict breast cancer progression. Our Specific Aims are to: 1) Examine affect reactivity and regulation among women with a recent diagnosis of breast cancer in comparison to healthy controls; 2) Relate affect reactivity and regulation to choice of BLM; and 3) Assess long term functional consequences of BLM decision and affect reactivity and regulation. This study will provide an empirical basis for better assisting patients in making difficult but important choices regarding breast cancer treatment alternatives.
 描述(由申请人提供):被诊断患有乳腺癌的女性选择双侧乳房切除术 (BLM) 的比例不断增加,目前为 14.3%,在 40 岁以下女性中选择双侧乳房切除术 (BLM) 的比例为 33%。尽管有证据表明,与更保守的方法相比,BLM 不会带来生存获益,而且还会产生手术并发症和其他严重的医疗和个人费用,但这种情况仍在发生。女性对复发的焦虑对于这一决定至关重要,因此她们的选择可能在很大程度上反映了她们体验和调节情感的方式。为了了解选择 BLM 的神经生物学和情感决定因素,从而确定未来新干预措施的机会,我们建议检查情感反应和调节与女性在乳腺癌初步诊断后关于 BLM 的决定之间的关系。我们还将研究情感管理和治疗决策对后续心理社会功能的影响。该研究将招募 150 名最近在决定接受治疗后被诊断患有乳腺癌的女性作为样本(其中 75 名选择了 BLM,75 名人口和医学上相似但决定不接受 BLM 的女性),以及由 50 名未患有乳腺癌的女性组成的匹配对照组。将使用功能磁共振成像(fMRI)研究对负面非癌症和癌症相关刺激的情感反应。同样,情感调节将通过功能磁共振成像探针对消极的非癌症和癌症相关刺激的显性(即有意识的、有意的)和隐性的(即无意识的、自动的)调节进行评估。将在做出决定后 6、12 和 18 个月使用自我报告的焦虑、抑郁、幸福感和功能状态指标来评估心理社会功能。还将评估信息变量(例如,对经历过 BLM 的有影响力人士的认识)和人口变量(年龄、种族、社会经济地位)。将在基线和所有后续行动中获得生理应激反应测量,即每日唾液皮质醇斜率。 该指标已被证明与负面情绪的表达相关,并可预测乳腺癌的进展。我们的具体目标是: 1) 与健康对照组相比,检查最近诊断出乳腺癌的女性的影响反应和调节; 2) 将影响反应性和调节与 BLM 的选择联系起来; 3) 评估 BLM 决策的长期功能后果并影响反应性和监管。这项研究将为更好地帮助患者就乳腺癌治疗方案做出困难但重要的选择提供经验基础。

项目成果

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Amit Etkin其他文献

Amit Etkin的其他文献

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{{ truncateString('Amit Etkin', 18)}}的其他基金

Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
验证基于机器学习的脑电图治疗抑郁症生物标志物
  • 批准号:
    10009501
  • 财政年份:
    2020
  • 资助金额:
    $ 62.34万
  • 项目类别:
Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
验证基于机器学习的脑电图治疗抑郁症生物标志物
  • 批准号:
    10116492
  • 财政年份:
    2020
  • 资助金额:
    $ 62.34万
  • 项目类别:
Validating of Machine Learning-Based EEG Treatment Biomarkers in Depression
验证基于机器学习的脑电图治疗抑郁症生物标志物
  • 批准号:
    10366060
  • 财政年份:
    2020
  • 资助金额:
    $ 62.34万
  • 项目类别:
Assessing an electroencephalography (EEG) biomarker of response to transcranial magnetic stimulation for major depression
评估重度抑郁症对经颅磁刺激反应的脑电图 (EEG) 生物标志物
  • 批准号:
    9933192
  • 财政年份:
    2020
  • 资助金额:
    $ 62.34万
  • 项目类别:
A "Circuits-First" Platform for Personalized Neurostimulation Treatment
用于个性化神经刺激治疗的“电路优先”平台
  • 批准号:
    10214488
  • 财政年份:
    2019
  • 资助金额:
    $ 62.34万
  • 项目类别:
A "Circuits-First" Platform for Personalized Neurostimulation Treatment
用于个性化神经刺激治疗的“电路优先”平台
  • 批准号:
    10000142
  • 财政年份:
    2019
  • 资助金额:
    $ 62.34万
  • 项目类别:
A "Circuits-First" Platform for Personalized Neurostimulation Treatment
用于个性化神经刺激治疗的“电路优先”平台
  • 批准号:
    10019435
  • 财政年份:
    2019
  • 资助金额:
    $ 62.34万
  • 项目类别:
A Circuit Approach to Mechanisms and Predictors of Topiramate Response
托吡酯反应机制和预测因子的电路方法
  • 批准号:
    10473684
  • 财政年份:
    2018
  • 资助金额:
    $ 62.34万
  • 项目类别:
A Circuit Approach to Mechanisms and Predictors of Topiramate Response
托吡酯反应机制和预测因子的电路方法
  • 批准号:
    10237286
  • 财政年份:
    2018
  • 资助金额:
    $ 62.34万
  • 项目类别:
A “Circuits-First” Platform for Personalized Neurostimulation Treatment
用于个性化神经刺激治疗的“电路优先”平台
  • 批准号:
    9552929
  • 财政年份:
    2017
  • 资助金额:
    $ 62.34万
  • 项目类别:

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Perinatal Affective Symptoms, Neuroactive Steroids, and GABA Receptor Plasticity in Women of Color
有色人种女性的围产期情感症状、神经活性类固醇和 GABA 受体可塑性
  • 批准号:
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使用键盘动力学对情感症状和认知进行不引人注目的监测
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使用键盘动力学对情感症状和认知进行不引人注目的监测
  • 批准号:
    10320061
  • 财政年份:
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使用键盘动力学对情感症状和认知进行不引人注目的监测
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使用键盘动力学对情感症状和认知进行不引人注目的监测
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使用高场个性化大脑映射在跨诊断样本中将童年威胁和剥夺与应激生理学和情感症状联系起来的内脏神经回路
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Visceral neural circuits linking childhood threat and deprivation with stress physiology and affective symptoms in a transdiagnostic sample using high-field personalized brain mapping
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Visceral neural circuits linking childhood threat and deprivation with stress physiology and affective symptoms in a transdiagnostic sample using high-field personalized brain mapping
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