Adaptive and Maladaptive Neural Network Responses to Inhibitory Challenges

自适应和适应不良神经网络对抑制性挑战的反应

基本信息

批准号：
10542339
负责人：
Naomi Samimi-Sadeh
金额：
$ 51.46万
依托单位：
UNIVERSITY OF DELAWARE
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10542339
关键词：
Adult Affective Alcohols Amygdaloid structure Antisocial Personality Disorder Behavior Benchmarking Biological Assay Biological Markers Brain Classification Clinical Clinical Treatment Cognitive Communication Compensation Complex Cues Data Desire for food Diagnostic Dimensions Disease Emotional Emotions Failure Functional Magnetic Resonance Imaging Goals Health Hypersensitivity Image Impulsivity Individual Informal Social Control Investigation Knowledge Learning Life Life Expectancy Light Link MRI Scans Magnetic Resonance Imaging Maps Measures Mental disorders Methods Modeling National Institute of Mental Health Nature Neurobiology Neurosciences Outcome Participant Pathologic Play Process Protocols documentation Psychiatric Diagnosis Psychopathology Public Health Reaction Research Research Domain Criteria Resources Rewards Risk Risk Behaviors Risk Factors Role Sampling Self-control as a personality trait Short-Term Memory Structure Substance Use Disorder Suicide System Taxes Translating Violence clinical diagnostics clinical risk distraction graph theory high risk behavior improved negative mood neural neural circuit neural model neural network neuroimaging novel promote resilience recruit resilience response reward processing substance use support network trait impulsivity

项目摘要

ABSTRACT Impulse control predicts a myriad of serious public health problems that substantially reduce life expectancy, including suicide, violence, substance use, and other risky behaviors. Although it is known that inhibitory control deficits confer risk for these clinical problems, one critical barrier to progress in this field is that much less is known about how this core regulatory process interacts with other cognitive and affective systems. Given that impulse control failures that occur in everyday life reflect the interaction of multiple cognitive and affective systems, this knowledge is critical for mapping impulse control failures onto neural circuits and accurately modeling the impulsivity that occurs in psychopathology as disruptions in neural systems. The objective of this application is to determine how functional brain networks supporting inhibitory control respond to cognitively- and affectively-challenging contexts and to evaluate the relevance of these networks for explaining clinical problems with impulsivity. We propose a novel investigation aimed at better understanding how three contexts known to challenge impulse control (cognitive resource depletion, competing appetitive cues, and negative mood induction) impact the functional brain networks that support successful inhibitory control and, ultimately, self-regulation in mental illness. First, healthy adults will undergo a thorough clinical diagnostic assessment and complete a battery of functional magnetic resonance imaging (fMRI) tasks in the MRI scanner that assess inhibitory control in different challenging contexts. These data are expected to contribute a precise working model of how functional brain networks compensate to meet the unique inhibitory control demands present in different challenging contexts. They will also add context as a level of analysis to existing neural models of inhibition, bringing them closer to capturing the multidimensional nature of inhibitory control failures in everyday life. Next, given that replication failures are common in neuroimaging research, a subset of participants will undergo a second MRI scan after a three-month period to establish the reliability of the novel functional brain metrics of inhibitory control we propose to investigate. Finally, a central goal of the NIMH RDoC initiative is to link clinical problems to neural systems (Cuthbert & Insel, 2013). Consistent with this initiative, we propose to examine disruptions in inhibitory control networks as potential transdiagnostic risk factors for externalizing disorders (e.g., alcohol/ substance use disorders, antisocial personality disorder) and as biomarkers for identifying subtypes of inhibition. This approach has the potential to create a more neuroscience-based classification of clinical problems related to impulsivity. Together, this research is expected to ultimately aid in the treatment of clinical impulsivity by leading to a deeper understanding of the brain networks that support successful inhibitory control.

抽象的冲动控制预示着许多严重的公共卫生问题，这些问题会大大缩短预期寿命，包括自杀、暴力、药物滥用和其他危险行为。尽管已知抑制控制缺陷会给这些临床问题带来风险，这一领域取得进展的一个关键障碍是人们对这个核心调节过程如何与其他认知和情感系统相互作用知之甚少。鉴于日常生活中发生的冲动控制失败反映了多种认知和认知的相互作用对于情感系统，这些知识对于将冲动控制失败映射到神经回路和准确地将精神病理学中发生的冲动建模为神经系统的破坏。这该应用程序的目的是确定支持抑制控制的功能性大脑网络如何响应认知和情感上具有挑战性的环境，并评估这些网络的相关性用冲动解释临床问题。我们提出一项新颖的调查，旨在更好地理解已知的三种环境如何挑战冲动控制（认知资源枯竭、竞争性食欲）线索和负面情绪诱导）影响支持成功抑制的功能性大脑网络控制并最终实现精神疾病的自我调节。首先，健康成年人将接受彻底的临床检查诊断评估并完成一系列功能磁共振成像 (fMRI) 任务 MRI 扫描仪可评估不同挑战性环境中的抑制控制。这些数据预计贡献一个精确的工作模型，说明功能性大脑网络如何补偿以满足独特的抑制作用不同挑战性环境中存在的控制需求。他们还将添加上下文作为分析级别现有的抑制神经模型，使它们更接近于捕捉抑制的多维性质控制日常生活中的失败。接下来，鉴于复制失败在神经影像研究中很常见，三个月后，部分参与者将接受第二次 MRI 扫描，以确定其可靠性我们建议研究抑制控制的新颖功能性大脑指标。最后，该计划的一个中心目标是 NIMH RDoC 计划旨在将临床问题与神经系统联系起来（Cuthbert & Insel，2013）。符合在这一举措中，我们建议将抑制控制网络的破坏视为潜在的跨诊断风险外化障碍的因素（例如酒精/药物滥用障碍、反社会人格障碍）以及作为识别抑制亚型的生物标志物。这种方法有可能创造更多基于神经科学的与冲动相关的临床问题的分类。总的来说，这项研究是预计将通过加深对临床冲动的理解来最终帮助治疗临床冲动支持成功的抑制控制的大脑网络。