解剖通路模型与苍白球、小脑和皮层脊髓通路迹图估计值的比较

IF 2.4 3区 医学 Q3 NEUROSCIENCES Brain connectivity Pub Date : 2023-05-01 Epub Date: 2023-03-24 DOI:10.1089/brain.2022.0068
Mikkel V Petersen, Cameron C McIntyre
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引用次数: 0

摘要

介绍:人脑结构连通性模型通常采用牵引法进行模拟。然而,将解剖学通路图谱非线性拟合到全新的受试者大脑是一种更简单的替代方法,据推测这种方法能提供更符合解剖学实际的结果。因此,本研究的目的是对同一受试者通过路径图谱拟合或牵引重建产生的流线估计值进行并排比较。研究方法我们的分析重点是利用人类连接组计划(HCP)的示例数据集重建皮质脊髓束(CST)、小脑丘脑(CBT)和苍白球丘脑(PT)通路。我们使用 MRtrix3 探索了全脑以及手动种子到目标的牵引成像方法。同时,我们使用高级归一化工具(ANTs)对每个 HCP 数据集的轴突通路图集进行了非线性拟合。结果:不同的方法对每个受试者的每条通路产生了明显不同的估计值。拟合的路径图谱高度定型,其流线轨迹的可变性较低。人工束描法得出的通路估计值与拟合图谱通路基本一致,但单条流线的可变性较高。通过全脑束描得出的通路重建结果变异程度最高,而且难以对 CBT 或 PT 通路进行解剖学上的真实描述。结论解剖学通路模型拟合的速度、简便性、可重复性和逼真性使其成为某些形式的人脑结构连通性建模的诱人选择。影响声明 轴突通路建模是脑深部刺激(DBS)研究的重要组成部分,该研究旨在确定刺激直接激活的大脑连接。皮质脊髓束、小脑(CBT)和苍白球(PT)通路与眼下DBS治疗帕金森病的研究特别相关。我们的研究结果表明,在研究丘脑下 DBS 时,将 CBT 和 PT 通路的解剖通路模型拟合到全新的受试者大脑中,是一种在解剖学上比束图法更现实的选择。
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Comparison of Anatomical Pathway Models with Tractography Estimates of the Pallidothalamic, Cerebellothalamic, and Corticospinal Tracts.

Introduction: Models of structural connectivity in the human brain are typically simulated using tractographic approaches. However, the nonlinear fitting of anatomical pathway atlases to de novo subject brains represents a simpler alternative that is hypothesized to provide more anatomically realistic results. Therefore, the goal of this study was to perform a side-by-side comparison of the streamline estimates generated by either pathway atlas fits or tractographic reconstructions in the same subjects. Methods: Our analyses focused on reconstruction of the corticospinal tract (CST), cerebellothalamic (CBT), and pallidothalamic (PT) pathways using example datasets from the Human Connectome Project (HCP). We used MRtrix3 to explore whole brain, as well as manual seed-to-target, tractography approaches. In parallel, we performed nonlinear fits of an axonal pathway atlas to each HCP dataset using Advanced Normalization Tools (ANTs). Results: The different methods produced notably different estimates for each pathway in each subject. The fitted atlas pathways were highly stereotyped and exhibited low variability in their streamline trajectories. Manual tractography resulted in pathway estimates that generally corresponded with the fitted atlas pathways, but with a higher degree of variability in the individual streamlines. Pathway reconstructions derived from whole-brain tractography exhibited the highest degree of variability and struggled to create anatomically realistic representations for either the CBT or PT pathways. Conclusion: The speed, simplicity, reproducibility, and realism of anatomical pathway model fits makes them an appealing option for some forms of structural connectivity modeling in the human brain. Impact statement Axonal pathway modeling is an important component of deep brain stimulation (DBS) research studies that seek to identify the brain connections that are directly activated by stimulation. The corticospinal tract, cerebellothalamic (CBT), and pallidothalamic (PT) pathways are specifically relevant to the study of subthalamic DBS for the treatment of Parkinson's disease. Our results suggest that anatomical pathway model fits of the CBT and PT pathways to de novo subject brains represent a more anatomically realistic option than tractographic approaches when studying subthalamic DBS.

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来源期刊
Brain connectivity
Brain connectivity Neuroscience-General Neuroscience
CiteScore
4.80
自引率
0.00%
发文量
80
期刊介绍: Brain Connectivity provides groundbreaking findings in the rapidly advancing field of connectivity research at the systems and network levels. The Journal disseminates information on brain mapping, modeling, novel research techniques, new imaging modalities, preclinical animal studies, and the translation of research discoveries from the laboratory to the clinic. This essential journal fosters the application of basic biological discoveries and contributes to the development of novel diagnostic and therapeutic interventions to recognize and treat a broad range of neurodegenerative and psychiatric disorders such as: Alzheimer’s disease, attention-deficit hyperactivity disorder, posttraumatic stress disorder, epilepsy, traumatic brain injury, stroke, dementia, and depression.
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