Chandra Noirrit Kiran, Sitek Kevin R, Chandrasekaran Bharath, Sarkar Abhra
The University of Texas at Dallas, Department of Mathematical Sciences, Richardson, TX 76010, USA.
Northwestern University, Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Evanston, IL 60208, USA.
Imaging Neurosci (Camb). 2024;2. doi: 10.1162/imag_a_00258. Epub 2024 Aug 12.
The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultrahigh-field functional magnetic resonance imaging (fMRI) acquisition have enabled novel noninvasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high-temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.
听觉系统由多个皮层下脑结构组成，这些结构沿着初级听觉通路处理和细化传入的声音信号。由于对脑内深处小结构成像的技术限制，我们对皮层下听觉系统的大部分了解都基于使用侵入性方法对动物模型的研究。超高场功能磁共振成像（fMRI）采集技术的进步使得对人类听觉皮层下结构进行新的非侵入性研究成为可能，包括听觉表征的基本特征，如音调拓扑和周期拓扑。然而，人类皮层下网络的功能连接性仍未得到充分探索，相关方法仍在不断发展。传统上，功能连接性是通过具有完全相关矩阵的fMRI数据来估计的。然而，偏相关揭示了在去除所有其他区域的影响后两个区域之间的关系，反映了更直接的连接性。偏相关分析在升序听觉系统中特别有前景，在该系统中，感觉信息以强制性方式从一个核传递到另一个核，沿着初级听觉通路向上传递，提供了听觉刺激的冗余但也越来越抽象的表征。虽然大多数基于偏相关学习条件依赖结构的现有方法都假设数据是独立同分布的高斯分布，但fMRI数据表现出与高斯性的显著偏差以及高时间自相关性。在本文中，我们开发了一种自回归矩阵 - 高斯耦合图形模型（ARMGCGM）方法来估计偏相关，从而推断听觉系统内的功能连接模式，同时适当考虑连续fMRI扫描之间的自相关性。我们的结果显示，每一侧（左侧和右侧）初级听觉通路中连续结构之间存在很强的正偏相关，包括听觉中脑和丘脑之间，以及初级和联合听觉皮层之间。当根据采集方案将数据分成两半并分别为每半数据计算偏相关时，以及在交叉验证折叠中，这些结果都非常稳定。相比之下，基于完全相关的分析识别出了一个丰富的相互连接网络，该网络并不特定于沿通路的相邻节点。总体而言，我们的结果表明，使用新颖的连接性方法可以恢复沿听觉通路的独特功能连接模式，并且我们的连接性方法在多次采集过程中是可靠的。
