Koiso Kenshu, Müller Anna K, Akamatsu Kazuaki, Dresbach Sebastian, Wiggins Christopher J, Gulban Omer Faruk, Goebel Rainer, Miyawaki Yoichi, Poser Benedikt A, Huber Laurentius
Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, NL.
Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan.
Apert Neuro. 2023 Sep;3. doi: 10.52294/001c.87961. Epub 2023 Sep 15.
Cortical depth-dependent functional magnetic resonance image (fMRI), also known as layer-fMRI, has the potential to capture directional neural information flow of brain computations within and across large-scale cortical brain networks. E.g., layer-fMRI can differentiate feedforward and feedback cortical input in hierarchically organized brain networks. Recent advancements in 3D-EPI sampling approaches and MR contrast generation strategies have allowed proof-of-principle studies showing that layer-fMRI can provide sufficient data quality for capturing laminar changes in functional connectivity. These studies have however not shown how reliable the signal is and how repeatable the respective results are. It is especially unclear whether whole-brain layer-fMRI functional connectivity protocols are widely applicable across common neuroscience-driven analysis approaches. Moreover, there are no established preprocessing fMRI methods that are optimized to work for whole-brain layer-fMRI datasets. In this work, we aimed to serve the field of layer-fMRI and build tools for future routine whole-brain layer-fMRI in application-based neuroscience research. We have developed publicly available sequences, acquisition protocols, and processing pipelines for whole-brain layer-fMRI. These protocols are validated across 60 hours of scanning in nine participants. Specifically, we identified and exploited methodological advancements for maximizing tSNR efficiency and test-retest reliability. We are sharing an extensive multi-modal whole-brain layer-fMRI dataset (20 scan hours of movie-watching in a single participant) for the purpose of benchmarking future method developments: The Kenshu dataset. With this dataset, we are also exemplifying the usefulness of whole brain layer-fMRI for commonly applied analysis approaches in modern cognitive neuroscience fMRI studies. This includes connectivity analyses, representational similarity matrix estimations, general linear model analyses, principal component analysis clustering, etc. We believe that this work paves the road for future routine measurements of directional functional connectivity across the entire brain.
皮层深度依赖性功能磁共振成像（fMRI），也称为层fMRI，有潜力捕捉大脑大规模皮层网络内及跨网络的大脑计算的定向神经信息流。例如，层fMRI可以区分分层组织的大脑网络中的前馈和反馈皮层输入。3D-EPI采样方法和磁共振对比生成策略的最新进展使得原理验证研究得以进行，表明层fMRI可以提供足够的数据质量来捕捉功能连接中的层流变化。然而，这些研究尚未表明信号的可靠性如何以及各自结果的可重复性如何。尤其不清楚全脑层fMRI功能连接协议是否能广泛应用于常见的神经科学驱动分析方法。此外，还没有经过优化以适用于全脑层fMRI数据集的既定fMRI预处理方法。在这项工作中，我们旨在为层fMRI领域服务，并为基于应用的神经科学研究中未来的常规全脑层fMRI构建工具。我们已经开发了用于全脑层fMRI的公开可用序列、采集协议和处理流程。这些协议在九名参与者60小时的扫描中得到了验证。具体而言，我们识别并利用了方法学进展，以最大化tSNR效率和重测可靠性。我们正在共享一个广泛的多模态全脑层fMRI数据集（一名参与者20小时的观影扫描数据），用于对未来方法发展进行基准测试：Kenshu数据集。通过这个数据集，我们还举例说明了全脑层fMRI在现代认知神经科学fMRI研究中常用分析方法的有用性。这包括连接性分析、表征相似性矩阵估计、一般线性模型分析、主成分分析聚类等。我们相信这项工作为未来对全脑定向功能连接的常规测量铺平了道路。
