个人级别的功能连接性预测了认知控制效率
Benjamin L Deck1, Apoorva Kelkar1, Brian Erickson1
1Department of Psychological and Brain Sciences, Drexel University, 3201 Chestnut Street, Philadelphia, 19104, PA, USA.
NeuroImage
|October 11, 2023
概括
认知控制 (CC) 依赖于大脑网络的相互作用. 这项研究表明,像前端-双侧控制网络 (FPCN-B) 和侧面默认模式网络 (L-DMN) 等网络之间的功能连接预测了个体的切换和抑制能力.
科学领域:
- 神经科学是一个神经科学.
- 认知心理学 认知心理学
- 脑部成像 脑部成像
背景情况:
- 认知控制 (CC) 对于日常解决问题至关重要,并且在各种疾病中受损.
- 三方模型将切换,抑制和更新确定为关键的CC行为.
- 特定的大脑网络,包括前侧双侧控制网络B (FPCN-B),背侧注意网络 (DAN), cingulo-opercular网络 (CON) 和横向默认模式网络 (L-DMN),都与CC行为有关.
研究的目的:
- 研究特定大脑网络之间的相互作用,预测认知切换和抑制的个体差异.
- 确定FPCN-B,DAN,CON和L-DMN内部和之间的功能连接模式是否可以预测CC行为.
主要方法:
- 参与者在执行需要认知切换和抑制的任务时接受了fMRI扫描.
- 支持向量回归 (SVR) 模型被用来预测行为表现,使用个人估计的功能连接.
- 分析了FPCN-B,DAN,CON和L-DMN之间的连接.
主要成果:
- 发现网络间的功能连接可以预测抑制和切换行为中的个体差异.
- 确定横向默认模式网络 (L-DMN) 在切换和抑制任务中起着重要作用.
- 个人估计的网络间连接可以作为CC行为可靠的标记.
结论:
- 大脑网络之间的相互作用,特别是涉及L-DMN,是认知控制的基础.
- 功能连接模式为评估和理解CC缺陷提供了潜在的生物标志物.
- 认知切换和抑制能力来自分布式神经网络之间的动态相互作用.
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