Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States; Center for Neurotechnology in Mental Health Research, Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, United States.
Bernard and Irene Schwartz Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, 660 First Ave., New York City, NY, United States.
Neuroimage. 2023 Jun;273:120111. doi: 10.1016/j.neuroimage.2023.120111. Epub 2023 Apr 13.
Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.
弥散磁共振成像(dMRI)示踪技术为研究大脑回路及其与基因突变或神经疾病相关的行为之间的关系提供了有趣的见解,已经在多个物种中得到了广泛应用。然而,现有的示踪技术方法存在灵敏度和特异性有限的问题,导致对重建连接的解释不确定。因此,在本研究中,我们旨在优化成像和计算管道,以在小鼠大脑皮质丘脑网络中实现示踪和示踪剂轴突投射图谱之间尽可能好的空间重叠。我们开发了一种基于 dMRI 的小鼠前脑图谱,其中包含从 Allen 小鼠脑图谱(AMBA)导入的结构标签。使用图谱和 dMRI 示踪技术,我们首先使用不同的成像和示踪技术参数,重建了详细的节点到节点的小鼠大脑皮质丘脑结构连接矩阵。然后,我们通过量化示踪数据与 Allen 小鼠脑连接图谱(AMBCA)之间的相似性,研究了每种条件对皮质丘脑投射准确重建的影响。我们的结果表明,这些参数显著影响示踪技术的结果,并且我们的图谱可用于研究小鼠大脑中的宏观结构连接。此外,小鼠大脑灰质中的示踪仍然面临挑战,需要改进成像和示踪技术方法。