Abstract
Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitive impairment (MCI) from normal controls (NC) is still one of the most challenging problems. Dynamic functional connectivity (dFC), consisting of time-varying spatiotemporal dynamics, may characterize "chronnectome" diagnostic information for improving MCI classification. However, most of the current dFC studies are based on detecting discrete major brain status via spatial clustering, which ignores rich spatiotemporal dynamics contained in such chronnectome. We propose Deep Chronnectome Learning for exhaustively mining the comprehensive information, especially the hidden higher-level features, i.e., the dFC time series that may add critical diagnostic power for MCI classification. To this end, we devise a new Fully-connected Bidirectional Long Short-Term Memory Network (Full-BiLSTM) to effectively learn the periodic brain status changes using both past and future information for each brief time segment and then fuse them to form the final output. We have applied our method to a rigorously built large-scale multi-site database (i.e., with 164 data from NCs and 330 from MCIs, which can be further augmented by 25 folds). Our method outperforms other state-of-the-art approaches with an accuracy of 73.6% under solid cross-validations. We also made extensive comparisons among multiple variants of LSTM models. The results suggest high feasibility of our method with promising value also for other brain disorder diagnoses.
Abstract (translated)
从静息状态fMRI（RS-fMRI）中提取的脑功能连接（FC）已经成为疾病诊断的常用方法，其中从正常对照（NC）中区分具有轻度认知障碍（MCI）的受试者仍然是最具挑战性的问题之一。动态功能连接（dFC）由时变时空动态组成，可以表征“chronnectome”诊断信息，以改善MCI分类。然而，目前大多数dFC研究都是基于通过空间聚类检测离散的主要大脑状态，这忽略了这种慢性切除术中包含的丰富的时空动态。我们提出Deep Chronnectome Learning用于详尽挖掘综合信息，尤其是隐藏的更高级别的特征，即可以为MCI分类增加关键诊断能力的dFC时间序列。为此，我们设计了一个新的全连接双向长短期记忆网络（Full-BiLSTM），使用每个短时间段的过去和未来信息有效地学习周期性大脑状态变化，然后融合它们以形成最终输出。我们已经将我们的方法应用于严格建立的大型多站点数据库（即，来自NC的164个数据和来自MCI的330个数据，其可以进一步增加25倍）。在固体交叉验证下，我们的方法优于其他最先进的方法，准确度为73.6％。我们还对LSTM模型的多种变体进行了广泛的比较。结果表明我们的方法具有很高的可行性，对其他脑疾病的诊断具有前景。
URL
https://arxiv.org/abs/1808.10383
