Department of Computer Science, University of Warwick, Coventry, United Kingdom; Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.
Department of Computer Science, University of Warwick, Coventry, United Kingdom; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Feb;3(2):187-197. doi: 10.1016/j.bpsc.2017.10.004. Epub 2017 Oct 23.
Resting-state functional connectivity reflects correlations in the activity between brain areas, whereas effective connectivity between different brain areas measures directed influences of brain regions on each other. Using the latter approach, we compare effective connectivity results in patients with major depressive disorder (MDD) and control subjects.
We used a new approach to the measurement of effective connectivity, in which each brain area has a simple dynamical model, and known anatomical connectivity is used to provide constraints. This helps the approach to measure the effective connectivity between the 94 brain areas parceled in the automated anatomical labeling (AAL2) atlas, using resting-state functional magnetic resonance imaging. Moreover, we show how the approach can be used to measure the differences in effective connectivity between different groups of individuals, using as an example effective connectivity in the healthy brain and in individuals with depression. The first brainwide resting-state effective-connectivity neuroimaging analysis of depression, with 350 healthy individuals and 336 patients with major depressive disorder, is described.
Key findings are that the medial orbitofrontal cortex, implicated in reward and subjective pleasure, has reduced effective connectivity from temporal lobe input areas in depression; that the lateral orbitofrontal cortex, implicated in nonreward, has increased activity (variance) in depression, with decreased effective connectivity to and from cortical areas contralateral to language-related areas; and that the hippocampus, implicated in memory, has increased activity (variance) in depression and increased effective connectivity from the temporal pole.
Measurements of effective connectivity made using the new method provide a new approach to causal mechanisms in the brain in depression.
静息态功能连接反映了大脑区域之间活动的相关性,而不同大脑区域之间的有效连接则测量了大脑区域之间的定向影响。我们使用后一种方法比较了患有重度抑郁症(MDD)和对照组患者的有效连接结果。
我们使用了一种新的有效连接测量方法,其中每个大脑区域都有一个简单的动力学模型,并且使用已知的解剖连接来提供约束。这有助于该方法使用静息态功能磁共振成像来测量自动解剖标记(AAL2)图谱中 94 个大脑区域之间的有效连接。此外,我们展示了如何使用该方法来测量不同个体组之间的有效连接差异,以健康大脑和抑郁症患者的有效连接为例。描述了对 350 名健康个体和 336 名患有重度抑郁症患者的首次全脑静息态有效连接神经影像学分析。
主要发现是,内侧眶额皮层,涉及奖励和主观愉悦,在抑郁症中,从颞叶输入区域的有效连接减少;外侧眶额皮层,涉及非奖励,在抑郁症中活动(方差)增加,与语言相关区域对侧的皮质区域的有效连接减少;海马体,涉及记忆,在抑郁症中活动(方差)增加,与颞极的有效连接增加。
使用新方法进行的有效连接测量为抑郁症中大脑的因果机制提供了一种新方法。