Saha Debbrata K, Bohsali Anastasia, Saha Rekha, Hajjar Ihab, Calhoun Vince D
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303.
University of Texas Southwestern Dallas, TX 75390.
bioRxiv. 2025 Jan 15:2024.01.10.575131. doi: 10.1101/2024.01.10.575131.
Positron emission tomography (PET) and magnetic resonance imaging (MRI) are both widely used neuroimaging techniques to study brain functional and molecular connectivity. Although whole brain resting functional MRI (fMRI) connectomes (a matrix describing the inter-regional connectivity patterns) are widely used, the integration or association of whole brain molecular connectomes with PET data are rarely done. This likely stems from the fact that PET data is typically analyzed by using a region of interest approach, while whole brain spatial networks and their connectivity (covariation) receive much less attention. As a result, to date, there have been little focus on directly comparing whole brain PET and fMRI connectomes. In this study, we present a method that uses spatially constrained independent component analysis (scICA) (utilizing fMRI components as spatial priors) to estimate corresponding (Amyloid) PET and fMRI connectomes and examine the relationship between them using datasets that include individuals with mild cognitive impairment (MCI). Our results demonstrate highly modularized PET connectome patterns that complement those identified from resting fMRI. In particular, fMRI showed strong intra-domain connectivity with interdomain anticorrelation in sensorimotor and visual domains as well as default mode network. PET amyloid data showed similar strong intra-domain effects, but showed much higher correlations within cognitive control and default mode domains, as well as anticorrelation between cerebellum and other domains. The estimated fMRI informed PET networks have similar, but not identical, network spatial patterns to the resting fMRI networks, with the fMRI informed PET networks being slightly smoother and, in some cases, showing variations in subnodes. To further compare the two modalities, we also analyzed the differences between individuals with MCI receiving medication versus a placebo. Results show both common and modality specific treatment effects on fMRI and PET connectomes. From our fMRI analysis, we observed higher connectivity differences in various regions, such as the connection between the thalamus and middle occipital gyrus, as well as the insula and right middle occipital gyrus. Meanwhile, the PET analysis revealed increased activation between the anterior cingulate cortex and the left inferior parietal lobe, along with other regions, in individuals who received medication versus placebo. In sum, our novel approach identifies corresponding whole-brain fMRI informed PET and fMRI networks and connectomes. While we observed common patterns of network connectivity, our analysis of the MCI treatment and placebo groups revealed that each modality captures modality and group specific information about brain networks, highlighting differences between the two groups in both network expression and network connectivity.
正电子发射断层扫描(PET)和磁共振成像(MRI)都是广泛用于研究脑功能和分子连接性的神经成像技术。尽管全脑静息功能MRI(fMRI)连接组(一种描述区域间连接模式的矩阵)被广泛使用,但全脑分子连接组与PET数据的整合或关联却很少进行。这可能源于这样一个事实,即PET数据通常采用感兴趣区域方法进行分析,而全脑空间网络及其连接性(协变)受到的关注要少得多。因此,迄今为止,很少有人专注于直接比较全脑PET和fMRI连接组。在本研究中,我们提出了一种方法,该方法使用空间约束独立成分分析(scICA)(将fMRI成分用作空间先验)来估计相应的(淀粉样蛋白)PET和fMRI连接组,并使用包括轻度认知障碍(MCI)个体的数据集来检验它们之间的关系。我们的结果表明,PET连接组模式高度模块化,对静息fMRI识别出的模式起到补充作用。具体而言,fMRI在感觉运动和视觉领域以及默认模式网络中显示出强烈的域内连接和域间反相关。PET淀粉样蛋白数据显示出类似的强烈域内效应,但在认知控制和默认模式域内显示出更高的相关性,以及小脑与其他域之间的反相关性。估计的基于fMRI的PET网络与静息fMRI网络具有相似但不完全相同的网络空间模式,基于fMRI的PET网络略显平滑,在某些情况下,子节点存在差异。为了进一步比较这两种模态,我们还分析了接受药物治疗与接受安慰剂治疗的MCI个体之间的差异。结果显示,fMRI和PET连接组存在共同的和模态特异性的治疗效果。从我们的fMRI分析中,我们观察到不同区域存在更高的连接性差异,例如丘脑与枕中回之间的连接,以及脑岛与右侧枕中回之间的连接。同时,PET分析显示,与接受安慰剂治疗的个体相比,接受药物治疗的个体在前扣带回皮质与左下顶叶以及其他区域之间的激活增加。总之,我们的新方法识别出了相应的全脑基于fMRI的PET和fMRI网络及连接组。虽然我们观察到了网络连接的共同模式,但我们对MCI治疗组和安慰剂组的分析表明,每种模态都捕获了关于脑网络的模态和组特异性信息,突出了两组在网络表达和网络连接方面存在的差异。