相关的神经网络是情绪,记忆,动力过程的抑制的基础,这些过程是由数据驱动分析确定的
Karisa J Hunt1, Lindsay K Knight2,3, Brendan E Depue2
1Department of Psychological and Brain Sciences, University of Louisville, 2301 S, 3rd St., Louisville, KY, 40292, USA. Karisa.Hunt@Louisville.edu.
BMC neuroscience
|August 24, 2023
概括
共同的内在连接网络 (ICN) 对于在情绪,记忆和运动任务中的自我调节至关重要. 独立组件分析 (ICA) 成功识别了这些网络,突出了它们在抑制控制中的作用.
科学领域:
- 神经科学是一个神经科学.
- 认知神经科学 认知神经科学
- 精神病学研究 精神病学研究
背景情况:
- 目标导向的行为依赖于认知和情感过程的自我调节.
- 自律缺陷是精神疾病的特征,如PTSD和ADHD.
- 在自我监管中,研究内在连接网络 (ICN) 是至关重要的.
研究的目的:
- 确定常见的ICN是否参与调节情绪,运动和记忆过程.
- 评估独立组件分析 (ICA) 是否可以识别有助于抑制调节的ICN.
- 探索各种任务中自我调节的神经基础.
主要方法:
- 18名参与者完成了情绪调节,记忆抑制 (思考/不思考) 和运动抑制 (停止信号) 任务.
- 通过使用ICA (CONN; MATLAB) 来识别ICNs,分析了神经成像数据.
- ICNs与跨任务的行为表现相关.
主要成果:
- 国际航空航天局揭示了常见的中间前额头,条形和前额头对面的执行控制网络.
- 在特定任务的兴趣区域 (ROI) 观察到下调.
- 一种数据驱动的方法成功地确定了参与抑制调节的ICN.
结论:
- 常见的ICN在不同的抑制调节任务中始终参与.
- ICA是一种有效的数据驱动方法来识别这些共享的神经网络.
- 这些发现有助于理解自我调节及其功能障碍的神经基础.
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