Functional MRI (fMRI) is a prominent imaging technique to probe brain function, however, a substantial proportion of noise from multiple sources influences the reliability and reproducibility of fMRI data analysis and limits its clinical applications. Extensive effort has been devoted to improving fMRI data quality, but in the last two decades, there is no consensus reached which technique is more effective. In this study, we developed a novel deep neural network for denoising fMRI data, named denoising neural network (DeNN). This deep neural network is 1) applicable without requiring externally recorded data to model noise; 2) spatially and temporally adaptive to the variability of noise in different brain regions at different time points; 3) automated to output denoised data without manual interference; 4) trained and applied on each subject separately and 5) insensitive to the repetition time (TR) of fMRI data. When we compared DeNN with a number of nuisance regression methods for denoising fMRI data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, only DeNN had connectivity for functionally uncorrelated regions close to zero and successfully identified unbiased correlations between the posterior cingulate cortex seed and multiple brain regions within the default mode network or task positive network. The whole brain functional connectivity maps computed with DeNN-denoised data are approximately three times as homogeneous as the functional connectivity maps computed with raw data. Furthermore, the improved homogeneity strengthens rather than weakens the statistical power of fMRI in detecting intrinsic functional differences between cognitively normal subjects and subjects with Alzheimer’s disease.

中文翻译：

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使用时间相关的深度神经网络解开脑组织之间的时间序列可提高 fMRI 数据质量：DeNN：用于 fMRI 去噪的人工智能

功能性 MRI (fMRI) 是探测大脑功能的重要成像技术，然而，来自多个来源的大量噪声影响了 fMRI 数据分析的可靠性和可重复性，并限制了其临床应用。一直致力于提高 fMRI 数据质量的广泛努力，但在过去的 20 年中，尚未就哪种技术更有效达成共识。在这项研究中，我们开发了一种新颖的深度神经网络，用于对 fMRI 数据进行去噪，称为去噪神经网络 (DeNN)。这种深度神经网络 1) 适用于不需要外部记录的数据来模拟噪声；2）在空间和时间上适应不同时间点不同大脑区域噪声的可变性；3) 自动输出去噪数据，无需人工干预；4) 对每个主题分别进行训练和应用 5) 对 fMRI 数据的重复时间 (TR) 不敏感。当我们将 DeNN 与许多用于去噪来自阿尔茨海默病神经影像学倡议 (ADNI) 数据库的 fMRI 数据去噪的干扰回归方法进行比较时，只有 DeNN 具有接近于零的功能不相关区域的连接性，并成功地识别了后扣带皮层种子和多个大脑之间的无偏相关性默认模式网络或任务正网络中的区域。使用 DeNN 去噪数据计算的全脑功能连接图大约是使用原始数据计算的功能连接图的三倍。此外，