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.

基于静息状态功能 MRI (rs-fMRI) 的脑网络分割受噪声影响，导致每个网络内的虚假小补丁和功能同质性降低。获得新生儿大脑的稳健和均匀分割更加困难，因为新生儿 rs-fMRI 与相对较高的噪声水平相关，并且没有来自功能性新生儿图谱的先验知识可作为空间约束。为了应对这些挑战，我们开发了一种新颖的数据驱动的正则化归一化切割 (RNcut) 方法。RNcut 是通过向传统的归一化切割 (Ncut) 方法添加两个正则化项（使用马尔可夫随机场的平滑项和小块去除项）来制定的。RNcut 和竞争方法使用具有已知基本事实的模拟数据集进行测试，然后应用于成人和新生儿 rs-fMRI 数据集。基于 RNcut 生成的分割网络，对网络内连接进行了量化。模拟数据集的测试结果表明，RNcut 方法对噪声更稳健 (p < 0.01)，并且可以描绘具有明显更好 (p < 0.01) 空间连续性和显着高于竞争方法 (p < 0.01) 的功能同质性的分割功能网络. RNcut 在新生儿和成人 rs-fMRI 数据集上的应用揭示了新生儿大脑与成人大脑不同的功能性大脑组织。总的来说，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。广泛的大脑网络应用和分析，尤其是新生儿和婴儿大脑分割，具有嘈杂和大数据集样本，可能会从这种 RNcut 方法中受益。