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分段回波平面成像提高了大鼠脑皮质下功能连接网络的检测。

Segmented Echo Planar Imaging Improves Detection of Subcortical Functional Connectivity Networks in the Rat Brain.

机构信息

Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Corso Bettini 31, 38068, Rovereto, Italy.

Department of Molecular Biotechnologies and Health Sciences, University of Torino, Via Nizza 52, 10126, Torino, Italy.

出版信息

Sci Rep. 2019 Feb 4;9(1):1397. doi: 10.1038/s41598-018-37863-2.

Abstract

Susceptibility artifacts in the vicinity of aural and nasal cavities result in significant signal drop-out and image distortion in echo planar imaging of the rat brain. These effects may limit the study of resting state functional connectivity in deep brain regions. Here, we explore the use of segmented EPI for resting state fMRI studies in the rat, and assess the relative merits of this method compared to single shot EPI. Sequences were evaluated in terms of signal-to-noise ratio, geometric distortions, data driven detection of resting state networks and group level correlations of time series. Multishot imaging provided improved SNR, temporal SNR and reduced geometric distortion in deep areas, while maintaining acceptable overall image quality in cortical regions. Resting state networks identified by independent component analysis were consistent across methods, but multishot EPI provided a more robust and accurate delineation of connectivity patterns involving deep regions typically affected by susceptibility artifacts. Importantly, segmented EPI showed reduced between-subject variability and stronger statistical significance of pairwise correlations at group level over the whole brain and in particular in subcortical regions. Multishot EPI may represent a valid alternative to snapshot methods in functional connectivity studies, particularly for the investigation of subcortical regions and deep gray matter nuclei.

摘要

在大鼠脑的回波平面成像中,耳部和鼻腔附近的易感性伪影会导致信号明显下降和图像变形。这些影响可能会限制对深部脑区静息状态功能连接的研究。在这里,我们探索了分段 EPI 在大鼠静息状态 fMRI 研究中的应用,并评估了与单次激发 EPI 相比,这种方法的相对优点。根据信噪比、几何变形、数据驱动的静息状态网络检测以及时间序列的组水平相关性来评估序列。多激发成像在深部区域提供了更好的 SNR、时间 SNR 和降低的几何变形,同时在皮质区域保持可接受的整体图像质量。通过独立成分分析识别的静息状态网络在方法之间是一致的,但多激发 EPI 提供了更稳健和准确的连接模式描绘,包括通常受易感性伪影影响的深部区域。重要的是,分段 EPI 在整个大脑区域,特别是在皮质下区域,显示出较低的个体间变异性和更强的组水平成对相关性的统计学意义。多激发 EPI 可能是功能连接研究中快照方法的有效替代方法,特别是对于皮质下区域和深部灰质核的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e9dc/6362052/b836d7b1c060/41598_2018_37863_Fig1_HTML.jpg

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