Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
Artif Intell Med. 2020 Jun;106:101872. doi: 10.1016/j.artmed.2020.101872. Epub 2020 May 12.
Brain network parcellation based on resting-state functional MRI (rs-fMRI) is affected by noise, resulting in spurious small patches and decreased functional homogeneity within each network. Obtaining robust and homogeneous parcellation of neonate brain is more difficult, because neonate rs-fMRI is associated with relatively higher level of noise and no prior knowledge from a functional neonate atlas is available as spatial constraints. To meet these challenges, we developed a novel data-driven Regularized Normalized-cut (RNcut) method. RNcut is formulated by adding two regularization terms, a smoothing term using Markov random fields and a small-patch removal term, to conventional normalized-cut (Ncut) method. The RNcut and competing methods were tested with simulated datasets with known ground truth and then applied to both adult and neonate rs-fMRI datasets. Based on the parcellated networks generated by RNcut, intra-network connectivity was quantified. The test results from simulated datasets demonstrated that the RNcut method is more robust (p < 0.01) to noise and can delineate parcellated functional networks with significantly better (p < 0.01) spatial contiguity and significantly higher (p < 0.01) functional homogeneity than competing methods. Application of RNcut to neonate and adult rs-fMRI dataset revealed distinctive functional brain organization of neonate brains from that of adult brains. Collectively, we developed a novel data-driven RNcut method by integrating conventional Ncut with two regularization terms, generating robust and homogeneous functional parcellation without imposing spatial constraints. A broad range of brain network applications and analyses, especially neonate and infant brain parcellation with noisy and large sample of datasets, can potentially benefit from this RNcut method.
基于静息态功能磁共振成像（rs-fMRI）的脑网络分割受到噪声的影响，导致虚假的小斑块和每个网络内的功能均匀性降低。获得新生儿脑的稳健和均匀分割更加困难，因为新生儿 rs-fMRI 与相对较高水平的噪声相关联，并且没有来自功能新生儿图谱的先验知识作为空间约束。为了应对这些挑战，我们开发了一种新的数据驱动正则化归一化切割（RNcut）方法。RNcut 通过向常规归一化切割（Ncut）方法添加两个正则化项来构建，一个使用马尔可夫随机场的平滑项和一个小斑块去除项。RNcut 和竞争方法使用具有已知真实值的模拟数据集进行了测试，然后应用于成人和新生儿 rs-fMRI 数据集。基于由 RNcut 生成的分割网络，量化了网络内连接。模拟数据集的测试结果表明，RNcut 方法对噪声更稳健（p<0.01），并且可以描绘出具有明显更好（p<0.01）的空间连续性和明显更高（p<0.01）的功能均匀性的分割功能网络比竞争方法。将 RNcut 应用于新生儿和成人 rs-fMRI 数据集揭示了新生儿大脑和成人大脑之间独特的功能组织。总之，我们通过将传统的 Ncut 与两个正则化项相结合，开发了一种新的数据驱动的 RNcut 方法，在不施加空间约束的情况下生成稳健且均匀的功能分割。广泛的脑网络应用和分析，特别是具有噪声和大量数据集的新生儿和婴儿脑分割，可能受益于这种 RNcut 方法。
