Milardi Demetrio, Basile Gianpaolo Antonio, Faskowitz Joshua, Bertino Salvatore, Quartarone Angelo, Anastasi Giuseppe Pio, Bramanti Alessia, Ciurleo Rosella, Cacciola Alberto
Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy.
Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy.
Neuroimage. 2022 Apr 15;250:118959. doi: 10.1016/j.neuroimage.2022.118959. Epub 2022 Feb 3.
The subthalamic nucleus (STN) is commonly used as a surgical target for deep brain stimulation in movement disorders such as Parkinson's Disease. Tractography-derived connectivity-based parcellation (CBP) has been recently proposed as a suitable tool for non-invasive in vivo identification and pre-operative targeting of specific functional territories within the human STN. However, a well-established, accurate and reproducible protocol for STN parcellation is still lacking. The present work aims at testing the effects of different tractography-based approaches for the reconstruction of STN functional territories. We reconstructed functional territories of the STN on the high-quality dataset of 100 unrelated healthy subjects and on the test-retest dataset of the Human Connectome Project (HCP) repository. Connectivity-based parcellation was performed with a hypothesis-driven approach according to cortico-subthalamic connectivity, after dividing cortical areas into three groups: associative, limbic and sensorimotor. Four parcellation pipelines were compared, combining different signal modeling techniques (single-fiber vs multi-fiber) and different parcellation approaches (winner takes all parcellation vs fiber density thresholding). We tested these procedures on STN regions of interest obtained from three different, commonly employed, subcortical atlases. We evaluated the pipelines both in terms of between-subject similarity, assessed on the cohort of 100 unrelated healthy subjects, and of within-subject similarity, using a second cohort of 44 subjects with available test-retest data. We found that each parcellation provides converging results in terms of location of the identified parcels, but with significative variations in size and shape. All pipelines obtained very high within-subject similarity, with tensor-based approaches outperforming multi-fiber pipelines. On the other hand, higher between-subject similarity was found with multi-fiber signal modeling techniques combined with fiber density thresholding. We suggest that a fine-tuning of tractography-based parcellation may lead to higher reproducibility and aid the development of an optimized surgical targeting protocol.
丘脑底核（STN）通常被用作帕金森病等运动障碍深部脑刺激的手术靶点。基于纤维束成像的基于连接性的脑区划分（CBP）最近被提议作为一种合适的工具，用于在体内非侵入性地识别和术前定位人类STN内的特定功能区域。然而，目前仍缺乏一种成熟、准确且可重复的STN脑区划分方案。本研究旨在测试不同基于纤维束成像的方法对STN功能区域重建的影响。我们在100名无亲属关系的健康受试者的高质量数据集以及人类连接体项目（HCP）数据库的重测数据集中重建了STN的功能区域。在将皮质区域分为三组：联合、边缘和感觉运动后，根据皮质-丘脑底核连接性，采用假设驱动的方法进行基于连接性的脑区划分。比较了四种脑区划分流程，它们结合了不同的信号建模技术（单纤维与多纤维）和不同的脑区划分方法（赢家通吃脑区划分与纤维密度阈值化）。我们在从三种不同的、常用的皮质下图谱获得的STN感兴趣区域上测试了这些程序。我们使用100名无亲属关系的健康受试者队列评估了各流程在受试者间相似性方面的表现，并使用44名有重测数据的受试者队列评估了受试者内相似性。我们发现，每种脑区划分方法在识别出的脑区位置方面都提供了趋同的结果，但在大小和形状上存在显著差异。所有流程在受试者内相似性方面都获得了非常高的结果，基于张量的方法优于多纤维流程。另一方面，多纤维信号建模技术与纤维密度阈值化相结合时，受试者间相似性更高。我们建议，对基于纤维束成像的脑区划分进行微调可能会提高可重复性，并有助于开发优化的手术靶点定位方案。
