Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China; School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China.
Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China; School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China.
Comput Biol Med. 2022 Nov;150:106078. doi: 10.1016/j.compbiomed.2022.106078. Epub 2022 Sep 10.
Resting-state Magnetic resonance imaging-based parcellation aims to group the voxels/vertices non-invasively based on their connectivity profiles, which has achieved great success in understanding the fundamental organizational principles of the human brain. Given the substantial inter-individual variability, the increasing number of studies focus on individual parcellation. However, current methods perform individual parcellations independently or are based on the group prior, requiring expensive computational costs, precise parcel alignment, and extra group information. In this work, an efficient and flexible parcellation framework of individual cerebral cortex was proposed based on a region growing algorithm by merging the unassigned and neighbor vertex with the highest-correlated parcel iteratively. It considered both consistency with prior atlases and individualized functional homogeneity of parcels, which can be applied to a single individual without parcel alignment and group information. The proposed framework was leveraged to 100 unrelated subjects for functional homogeneity comparison and individual identification, and 186 patients with Parkison's disease for symptom prediction. Results demonstrated our framework outperformed other methods in functional homogeneity, and the generated parcellations provided 100% individual identification accuracy. Moreover, the default mode network (DMN) exhibited higher functional homogeneity, intra-subject parcel reproducibility and fingerprinting accuracy, while the sensorimotor network did the opposite, reflecting that the DMN is the most representative, stable, and individual-identifiable network in the resting state. The correlation analysis showed that the severity of the disease symptoms was related negatively to the similarity of individual parcellation and the atlases of healthy populations. The disease severity can be correctly predicted using machine learning models based on individual topographic features such as parcel similarity and parcel size. In summary, the proposed framework not only significantly improves the functional homogeneity but also captures individualized and disease-related brain topography, serving as a potential tool to explore brain function and disease in the future.
静息态磁共振成像的脑区划分旨在根据其连接模式对体素/顶点进行非侵入性分组，这在理解人类大脑的基本组织原则方面取得了巨大成功。鉴于个体间存在大量的可变性，越来越多的研究集中在个体划分上。然而，当前的方法要么独立地进行个体划分，要么基于群体先验，这需要昂贵的计算成本、精确的划分配准和额外的群体信息。在这项工作中，我们提出了一种基于区域增长算法的高效灵活的个体大脑皮质划分框架，通过迭代地将未分配的和与最高相关的邻域顶点与最高相关的包裹体合并。该框架同时考虑了与先验图谱的一致性和包裹体的个体化功能同质性，无需包裹体配准和群体信息即可应用于单个个体。该框架被应用于 100 个无关个体进行功能同质性比较和个体识别，以及 186 名帕金森病患者进行症状预测。结果表明，我们的框架在功能同质性方面优于其他方法，生成的划分提供了 100%的个体识别准确率。此外，默认模式网络 (DMN) 表现出更高的功能同质性、个体内包裹体可重复性和指纹识别准确性，而感觉运动网络则相反，这反映出 DMN 是静息状态下最具代表性、最稳定和最可个体化识别的网络。相关性分析表明，疾病症状的严重程度与个体划分与健康人群图谱的相似性呈负相关。基于个体拓扑特征，如包裹体相似性和包裹体大小，使用机器学习模型可以正确预测疾病的严重程度。总之，该框架不仅显著提高了功能同质性，而且还捕获了个体和与疾病相关的脑拓扑，是未来探索大脑功能和疾病的潜在工具。
