Connectivity-based segmentation of the thalamic motor region for deep brain stimulation in essential tremor: A comparison of deterministic and probabilistic tractography

IF 3.4 2区 医学 Q2 NEUROIMAGING Neuroimage-Clinical Pub Date : 2024-01-01 DOI:10.1016/j.nicl.2024.103587
Evangelia Tsolaki , Alon Kashanian , Kevin Chiu , Ausaf Bari , Nader Pouratian
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Abstract

Objective

Deep brain stimulation (DBS) studies have shown that stimulation of the motor segment of the thalamus based on probabilistic tractography is predictive of improvement in essential tremor (ET). However, probabilistic methods are computationally demanding, requiring the need for alternative tractography methods for use in the clinical setting. The purpose of this study was to compare probabilistic vs deterministic tractography methods for connectivity-based targeting in patients with ET.

Methods

Probabilistic and deterministic tractography methods were retrospectively applied to diffusion-weighted data sets in 36 patients with refractory ET. The thalamus and precentral gyrus were selected as regions of interest and fiber tracking was performed between these regions to produce connectivity-based thalamic segmentations, per prior methods. The resultant deterministic target maps were compared with those of thresholded probabilistic maps. The center of gravity (CG) of each connectivity map was determined and the differences in spatial distribution between the tractography methods were characterized. Furthermore, the intersection between the connectivity maps and CGs with the therapeutic volume of tissue activated (VTA) was calculated. A mixed linear model was then used to assess clinical improvement in tremor with volume of overlap.

Results

Both tractography methods delineated the region of the thalamus with connectivity to the precentral gyrus to be within the posterolateral aspect of the thalamus. The average CG of deterministic maps was more medial-posterior in both the left (3.7 ± 1.3 mm3) and the right (3.5 ± 2.2 mm3) hemispheres when compared to 30 %-thresholded probabilistic maps. Mixed linear model showed that the volume of overlap between CGs of deterministic and probabilistic targeting maps and therapeutic VTAs were significant predictors of clinical improvement.

Conclusions

Deterministic tractography can reconstruct DBS thalamic target maps in approximately 5 min comparable to those produced by probabilistic methods that require > 12 h to generate. Despite differences in CG between the methods, both deterministic-based and probabilistic targeting were predictive of clinical improvement in ET.

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基于连接性的丘脑运动区分割,用于深度脑刺激治疗本质性震颤:确定性和概率性束线图的比较
目的深部脑刺激(DBS)研究表明,根据概率性束图对丘脑运动区段进行刺激可预测本质性震颤(ET)的改善情况。然而,概率方法对计算要求很高,因此需要在临床环境中使用其他束描方法。本研究的目的是比较概率性与确定性牵引成像方法在基于连接的ET患者靶向治疗中的应用。方法将概率性和确定性牵引成像方法回顾性地应用于36名难治性ET患者的扩散加权数据集。根据先前的方法,丘脑和前中央回被选为研究区域,并在这些区域之间进行纤维追踪,以产生基于连接的丘脑分割。结果确定性目标图与阈值概率图进行了比较。确定了每个连接图的重心(CG),并描述了牵引成像方法之间空间分布的差异。此外,还计算了连接图和重心图与治疗激活组织体积(VTA)之间的交集。结果两种牵引成像方法都将丘脑与中央前回的连接区域划定在丘脑的后外侧。与阈值为30%的概率图相比,确定性图的平均CG在左半球(3.7 ± 1.3 mm3)和右半球(3.5 ± 2.2 mm3)更偏向于内后方。混合线性模型显示,确定性靶向图和概率性靶向图的CG与治疗性VTA之间的重叠体积是临床改善的重要预测因素。尽管两种方法的CG存在差异,但基于确定性和概率性的靶图都能预测ET的临床改善。
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来源期刊
Neuroimage-Clinical
Neuroimage-Clinical NEUROIMAGING-
CiteScore
7.50
自引率
4.80%
发文量
368
审稿时长
52 days
期刊介绍: NeuroImage: Clinical, a journal of diseases, disorders and syndromes involving the Nervous System, provides a vehicle for communicating important advances in the study of abnormal structure-function relationships of the human nervous system based on imaging. The focus of NeuroImage: Clinical is on defining changes to the brain associated with primary neurologic and psychiatric diseases and disorders of the nervous system as well as behavioral syndromes and developmental conditions. The main criterion for judging papers is the extent of scientific advancement in the understanding of the pathophysiologic mechanisms of diseases and disorders, in identification of functional models that link clinical signs and symptoms with brain function and in the creation of image based tools applicable to a broad range of clinical needs including diagnosis, monitoring and tracking of illness, predicting therapeutic response and development of new treatments. Papers dealing with structure and function in animal models will also be considered if they reveal mechanisms that can be readily translated to human conditions.
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