Metabolic connectivity mapping reveals effective connectivity in the resting human brain
Metabolic connectivity mapping reveals effective connectivity in the resting human brain
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DOI：
10.1073/pnas.1513752113
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发表时间：
2016-01-12
影响因子：
11.1
通讯作者：
Sorg, Christian
中科院分区：
文献类型：
--
作者：
Riedl, Valentin;Utz, Lukas;Sorg, Christian
大脑区域之间信号的方向性提供了有关人类认知和疾病状态的重要信息。然而，单独使用功能性磁共振成像（fMRI）来评估大脑状态之间的有效连接（EC）已经被证明是困难的。我们提出了一种新的措施EC，称为代谢连接映射（MCM），集成无向功能连接（FC）与局部能量代谢的功能磁共振成像和正电子发射断层扫描（PET）同时采集的数据。该方法基于神经元通信所需的大部分能量在突触后消耗的概念，即，在目标神经元上。我们调查了MCM和EC的生理范围内可能的变化，使用“睁眼”与“闭眼”的健康受试者的条件。独立的条件下，MCM可靠地检测到稳定和双向通信之间的早期和更高的视觉区域。此外，我们发现了稳定的自上而下的信号从额顶叶网络，包括额眼领域。相比之下，我们发现额外的自上而下的信号从所有主要集群的显着性网络的早期视觉皮层只有在眼睛睁开的条件。MCM揭示了贯穿整个皮层的一致的双向和单向信号，沿着两个简单大脑状态之间网络交互的显著变化。我们提出MCM作为一种新的方法来推断EC神经元的能量代谢，非常适合于研究在大脑中的信号层次和大脑疾病的缺陷。
Directionality of signaling among brain regions provides essential information about human cognition and disease states. Assessing such effective connectivity (EC) across brain states using functional magnetic resonance imaging (fMRI) alone has proven difficult, however. We propose a novel measure of EC, termed metabolic connectivity mapping (MCM), that integrates undirected functional connectivity (FC) with local energy metabolism from fMRI and positron emission tomography (PET) data acquired simultaneously. This method is based on the concept that most energy required for neuronal communication is consumed postsynaptically, i.e., at the target neurons. We investigated MCM and possible changes in EC within the physiological range using "eyes open" versus " eyes closed" conditions in healthy subjects. Independent of condition, MCM reliably detected stable and bidirectional communication between early and higher visual regions. Moreover, we found stable top-down signaling from a frontoparietal network including frontal eye fields. In contrast, we found additional top-down signaling from all major clusters of the salience network to early visual cortex only in the eyes open condition. MCM revealed consistent bidirectional and unidirectional signaling across the entire cortex, along with prominent changes in network interactions across two simple brain states. We propose MCM as a novel approach for inferring EC from neuronal energy metabolism that is ideally suited to study signaling hierarchies in the brain and their defects in brain disorders.