人类小脑通路的分裂对于促进我们对人脑的理解至关重要。现有的扩散磁共振成像纤维束分割方法已成功定义了主要的小脑纤维束,而仅仅依靠纤维束结构。然而,每个纤维束可以传递与小脑的多种认知和运动功能相关的信息。因此,考虑纤维束对个体运动和认知功能表现测量的潜在重要性,这可能是有益的。在这项工作中,我们提出了一种多模态数据驱动的小脑通路分割方法,其中包括微观结构和连通性的度量,以及个人功能绩效的衡量标准。我们的方法涉及首先训练多任务深度网络,以从一组纤维束结构特征中预测各种认知和运动测量。然后计算预测每个功能度量的每个结构特征的重要性,产生一组聚集到小脑小细胞通路的结构-功能显著性值。我们将我们的方法称为深度多模态显著性分组(DeepMSP),因为它计算预测认知和运动功能表现的结构测量的显著性,这些显著性被应用于分割任务。将DeepMSP应用于HumanConnectomeProject年轻成人研究的大规模数据集(n=1065),我们发现,识别多个小脑通路包裹是可行的,这些小脑通路包裹具有独特的结构-功能显著性模式,这些小脑通路包裹在训练褶皱间是稳定的.我们彻底试验了DeepMSP管道的所有阶段,包括网络选择,结构-功能显著性表示,聚类算法,和群集计数。我们发现,一维卷积神经网络架构和变压器网络架构在多任务耐久性预测方面表现相当。力量,读取解码,和词汇理解,这两种架构都优于完全连接的网络架构。定量实验表明,提出的低维显著性表示具有明确的运动与认知类别偏差的度量,可获得最佳的分割结果。而根据标准集群质量指标,包裹计数为4是最成功的。我们的结果表明,运动和认知显著性分布在小脑白质通路中。对最终k=4分组的检查显示,最高显著性部分对于运动和认知表现得分的预测都是最显著的,并且包括小脑中段和上段的部分。我们提出的基于显着性的分割框架,DeepMSP,启用多模态,数据驱动的纤维束成像分割。通过利用结构特征和功能性能度量,这种分割策略可能有可能增强对小脑通路结构-功能关系的研究.
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging
tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven
tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.