Ryali Srikanth, Shih Yen-Yu Ian, Chen Tianwen, Kochalka John, Albaugh Daniel, Fang Zhongnan, Supekar Kaustubh, Lee Jin Hyung, Menon Vinod
Stanford University School of Medicine, Stanford, USA.
University of North Carolina, Chapel Hill, USA.
Neuroimage. 2016 May 15;132:398-405. doi: 10.1016/j.neuroimage.2016.02.067. Epub 2016 Mar 2.
State-space multivariate dynamical systems (MDS) (Ryali et al. 2011) and other causal estimation models are being increasingly used to identify directed functional interactions between brain regions. However, the validity and accuracy of such methods are poorly understood. Performance evaluation based on computer simulations of small artificial causal networks can address this problem to some extent, but they often involve simplifying assumptions that reduce biological validity of the resulting data. Here, we use a novel approach taking advantage of recently developed optogenetic fMRI (ofMRI) techniques to selectively stimulate brain regions while simultaneously recording high-resolution whole-brain fMRI data. ofMRI allows for a more direct investigation of causal influences from the stimulated site to brain regions activated downstream and is therefore ideal for evaluating causal estimation methods in vivo. We used ofMRI to investigate whether MDS models for fMRI can accurately estimate causal functional interactions between brain regions. Two cohorts of ofMRI data were acquired, one at Stanford University and the University of California Los Angeles (Cohort 1) and the other at the University of North Carolina Chapel Hill (Cohort 2). In each cohort, optical stimulation was delivered to the right primary motor cortex (M1). General linear model analysis revealed prominent downstream thalamic activation in Cohort 1, and caudate-putamen (CPu) activation in Cohort 2. MDS accurately estimated causal interactions from M1 to thalamus and from M1 to CPu in Cohort 1 and Cohort 2, respectively. As predicted, no causal influences were found in the reverse direction. Additional control analyses demonstrated the specificity of causal interactions between stimulated and target sites. Our findings suggest that MDS state-space models can accurately and reliably estimate causal interactions in ofMRI data and further validate their use for estimating causal interactions in fMRI. More generally, our study demonstrates that the combined use of optogenetics and fMRI provides a powerful new tool for evaluating computational methods designed to estimate causal interactions between distributed brain regions.
状态空间多变量动态系统(MDS)(里亚利等人,2011年)和其他因果估计模型正越来越多地用于识别脑区之间的定向功能相互作用。然而,此类方法的有效性和准确性却鲜为人知。基于小型人工因果网络计算机模拟的性能评估在一定程度上可以解决这个问题,但它们往往涉及简化假设,从而降低了所得数据的生物学有效性。在这里,我们采用一种新颖的方法,利用最近开发的光遗传学功能磁共振成像(ofMRI)技术,在选择性刺激脑区的同时,同步记录高分辨率全脑功能磁共振成像数据。ofMRI能够更直接地研究从受刺激部位到下游激活脑区的因果影响,因此是体内评估因果估计方法的理想选择。我们使用ofMRI来研究功能磁共振成像的MDS模型是否能够准确估计脑区之间的因果功能相互作用。我们获取了两个队列的ofMRI数据,一个在斯坦福大学和加利福尼亚大学洛杉矶分校(队列1),另一个在北卡罗来纳大学教堂山分校(队列2)。在每个队列中,光刺激被施加到右侧初级运动皮层(M1)。一般线性模型分析显示,队列1中有显著的下游丘脑激活,队列2中有尾状核 - 壳核(CPu)激活。MDS分别准确估计了队列1中从M1到丘脑以及队列2中从M1到CPu的因果相互作用。正如预期的那样,在相反方向未发现因果影响。额外的对照分析证明了受刺激部位与目标部位之间因果相互作用的特异性。我们的研究结果表明,MDS状态空间模型能够准确可靠地估计ofMRI数据中的因果相互作用,并进一步验证了它们在功能磁共振成像中用于估计因果相互作用的用途。更一般地说,我们的研究表明,光遗传学和功能磁共振成像的联合使用为评估旨在估计分布式脑区之间因果相互作用的计算方法提供了一个强大的新工具。