Tensorial independent component analysis reveals social and reward networks associated with major depressive disorder.
Tensorial independent component analysis reveals social and reward networks associated with major depressive disorder.
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张力独立的组件分析揭示了与重度抑郁症相关的社会和奖励网络。
DOI：
10.1002/hbm.26254
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发表时间：
2023-05
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
重度抑郁症（MDD）与功能性大脑连接的变化有关。然而，典型的功能连接分析，如静息状态数据的空间独立成分分析（伊卡），往往忽略了受试者间变异性的来源，这对于识别与MDD相关的功能连接模式至关重要。通常，空间伊卡等方法将识别单个组件来代表默认模式网络（DMN）等网络，即使数据中的组显示出差异DMN共激活。为了解决这一差距，该项目应用伊卡的张量扩展（张量伊卡）-明确纳入受试者之间的变异性-使用人类连接组项目（HCP）的功能性MRI数据来识别功能性连接的网络。来自HCP的数据包括诊断为MDD的个体、MDD家族史和进行赌博和社会认知任务的健康对照。基于将MDD与奖励和社会刺激的神经激活钝化相关联的证据，我们预测张量伊卡将识别与MDD中时空一致性降低和基于奖励的社交和奖励网络活动钝化相关的网络。在这两项任务中，张量伊卡确定了三个网络，显示出MDD的一致性下降。这三个网络都包括腹内侧前额叶皮层、纹状体和小脑，在各自的任务条件下表现出不同的激活。然而，MDD仅与一个网络中基于任务的激活与社交任务的差异相关。此外，这些结果表明，张量伊卡可能是一个有价值的工具，了解临床差异有关的网络激活和连接。以前的工作已经将重度抑郁症（MDD）与功能性大脑连接的变化联系起来，通常使用基于种子的方法或独立成分分析（伊卡）。我们选择应用张量扩展伊卡，以利用受试者之间的变异性，作为识别功能性大脑网络的方法，并将结果与依赖于空间伊卡的二元回归进行比较，这是一种更常用的分析。虽然传统的双回归分析没有揭示与MDD相关的全脑网络，但我们的张量伊卡方法确定了三个与MDD中对奖励和社会刺激的差异激活相关的网络。
Major depressive disorder (MDD) has been associated with changes in functional brain connectivity. Yet, typical analyses of functional connectivity, such as spatial independent components analysis (ICA) for resting‐state data, often ignore sources of between‐subject variability, which may be crucial for identifying functional connectivity patterns associated with MDD. Typically, methods like spatial ICA will identify a single component to represent a network like the default mode network (DMN), even if groups within the data show differential DMN coactivation. To address this gap, this project applies a tensorial extension of ICA (tensorial ICA)—which explicitly incorporates between‐subject variability—to identify functionally connected networks using functional MRI data from the Human Connectome Project (HCP). Data from the HCP included individuals with a diagnosis of MDD, a family history of MDD, and healthy controls performing a gambling and social cognition task. Based on evidence associating MDD with blunted neural activation to rewards and social stimuli, we predicted that tensorial ICA would identify networks associated with reduced spatiotemporal coherence and blunted social and reward‐based network activity in MDD. Across both tasks, tensorial ICA identified three networks showing decreased coherence in MDD. All three networks included ventromedial prefrontal cortex, striatum, and cerebellum and showed different activation across the conditions of their respective tasks. However, MDD was only associated with differences in task‐based activation in one network from the social task. Additionally, these results suggest that tensorial ICA could be a valuable tool for understanding clinical differences in relation to network activation and connectivity. Previous work has associated major depressive disorder (MDD) with changes in functional brain connectivity often using seed‐based methods or independent components analysis (ICA). We chose to apply tensorial extension ICA, in order to leverage the between subjects variability, as a method for identifying functional brain networks and compare results to dual‐regression relying on spatial ICA, a more commonly used analysis. Although conventional dual‐regression analyses did not reveal whole‐brain networks associated with MDD, our tensorial ICA approach identified three networks associated with differential activation in response to rewards and social stimuli in MDD.
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影响因子：
6.9
作者：
Hanson JL;Knodt AR;Brigidi BD;Hariri AR
通讯作者：
Hariri AR
缺乏半胱氨酸/谷氨酸抗植物的小鼠的皮质纹状体功能障碍和社会相互作用缺陷。
影响因子：
11
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Bentea E;Villers A;Moore C;Funk AJ;O'Donovan SM;Verbruggen L;Lara O;Janssen P;De Pauw L;Declerck NB;DePasquale EAK;Churchill MJ;Sato H;Hermans E;Arckens L;Meshul CK;Ris L;McCullumsmith RE;Massie A
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Massie A
影响因子：
82.9
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Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
通讯作者：
Liston C
影响因子：
4.8
作者：
Calhoun, VD;Pekar, JJ;Pearlson, GD
通讯作者：
Pearlson, GD
影响因子：
5.7
作者：
Beckmann, CF;Smith, SM
通讯作者：
Smith, SM