The Mind Research Network, Albuquerque, NM, USA; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.
The Mind Research Network, Albuquerque, NM, USA.
Neuroimage Clin. 2019;24:101970. doi: 10.1016/j.nicl.2019.101970. Epub 2019 Aug 5.
Studies have used resting-state functional magnetic resonance imaging (rs-fMRI) to examine associations between psychopathy and brain connectivity in selected regions of interest as well as networks covering the whole-brain. One of the limitations of these approaches is that brain connectivity is modeled as a constant state through the scan duration. To address this limitation, we apply group independent component analysis (GICA) and dynamic functional network connectivity (dFNC) analysis to uncover whole-brain, time-varying functional network connectivity (FNC) states in a large forensic sample. We then examined relationships between psychopathic traits (PCL-R total scores, Factor 1 and Factor 2 scores) and FNC states obtained from dFNC analysis. FNC over the scan duration was better represented by five states rather than one state previously shown in static FNC analysis. Consistent with prior findings, psychopathy was associated with networks from paralimbic regions (amygdala and insula). In addition, whole-brain FNC identified 15 networks from nine functional domains (subcortical, auditory, sensorimotor, cerebellar, visual, salience, default mode network, executive control and attentional) related to psychopathy traits (Factor 1 and PCL-R scores). Results also showed that individuals with higher Factor 1 scores (affective and interpersonal traits) spend more time in a state with weaker connectivity overall, and changed states less frequently compared to those with lower Factor 1 scores. On the other hand, individuals with higher Factor 2 scores (impulsive and antisocial behaviors) showed more dynamism (changes to and from different states) than those with lower scores.
研究使用静息态功能磁共振成像(rs-fMRI)来检查在选定的感兴趣区域以及覆盖整个大脑的网络中,精神病态与大脑连通性之间的关联。这些方法的局限性之一是,通过扫描持续时间,大脑连通性被建模为恒定状态。为了解决这个限制,我们应用组独立成分分析(GICA)和动态功能网络连通性(dFNC)分析来揭示大样本法医中全脑、时变功能网络连通性(FNC)状态。然后,我们检查了精神病态特征(PCL-R 总分、因子 1 和因子 2 得分)与 dFNC 分析获得的 FNC 状态之间的关系。FNC 在扫描持续时间内由五个状态而不是以前在静态 FNC 分析中显示的一个状态更好地表示。与先前的发现一致,精神病态与边缘区域(杏仁核和岛叶)的网络有关。此外,全脑 FNC 确定了来自九个功能域(皮质下、听觉、感觉运动、小脑、视觉、突显、默认模式网络、执行控制和注意力)的 15 个网络与精神病态特征(因子 1 和 PCL-R 得分)有关。结果还表明,因子 1 得分较高(情感和人际关系特征)的个体在整体连通性较弱的状态下花费的时间更多,并且与因子 1 得分较低的个体相比,状态变化的频率更低。另一方面,因子 2 得分较高(冲动和反社会行为)的个体比得分较低的个体表现出更多的动态性(从不同状态的变化)。