Luckett Patrick H, Lee John J, Park Ki Yun, Raut Ryan V, Meeker Karin L, Gordon Evan M, Snyder Abraham Z, Ances Beau M, Leuthardt Eric C, Shimony Joshua S
Division of Neurotechnology, Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States.
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States.
Front Neurol. 2023 Jan 12;13:1055437. doi: 10.3389/fneur.2022.1055437. eCollection 2022.
Resting state functional MRI (RS-fMRI) is currently used in numerous clinical and research settings. The localization of resting state networks (RSNs) has been utilized in applications ranging from group analysis of neurodegenerative diseases to individual network mapping for pre-surgical planning of tumor resections. Reproducibility of these results has been shown to require a substantial amount of high-quality data, which is not often available in clinical or research settings.
In this work, we report voxelwise mapping of a standard set of RSNs using a novel deep 3D convolutional neural network (3DCNN). The 3DCNN was trained on publicly available functional MRI data acquired in = 2010 healthy participants. After training, maps that represent the probability of a voxel belonging to a particular RSN were generated for each participant, and then used to calculate mean and standard deviation (STD) probability maps, which are made publicly available. Further, we compared our results to previously published resting state and task-based functional mappings.
Our results indicate this method can be applied in individual subjects and is highly resistant to both noisy data and fewer RS-fMRI time points than are typically acquired. Further, our results show core regions within each network that exhibit high average probability and low STD.
The 3DCNN algorithm can generate individual RSN localization maps, which are necessary for clinical applications. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.
静息态功能磁共振成像（RS-fMRI）目前被广泛应用于众多临床和研究场景。静息态网络（RSNs）的定位已被应用于从神经退行性疾病的群体分析到肿瘤切除术前规划的个体网络映射等各种应用中。这些结果的可重复性已被证明需要大量高质量的数据，而这些数据在临床或研究环境中并不常见。
在这项工作中，我们报告了使用一种新型深度三维卷积神经网络（3DCNN）对一组标准RSNs进行体素级映射。该3DCNN在2010名健康参与者公开可用的功能磁共振成像数据上进行训练。训练后，为每个参与者生成代表体素属于特定RSN概率的图谱，然后用于计算平均概率图谱和标准差（STD）概率图谱，并将其公开。此外，我们将我们的结果与先前发表的静息态和基于任务的功能映射进行了比较。
我们的结果表明，该方法可应用于个体受试者，并且对噪声数据和比通常采集的更少的RS-fMRI时间点具有高度抗性。此外，我们的结果显示了每个网络内的核心区域，这些区域具有高平均概率和低标准差。
3DCNN算法可以生成个体RSN定位图谱，这对于临床应用是必要的。3DCNN映射结果与基于任务的功能磁共振成像反应之间的相似性支持了特定功能任务与RSNs之间的关联。
