ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de médecine translationnelle, Strasbourg, France.
INSERM U1114, Fédération de Médecine Translationnelle de Strasbourg, Strasbourg, France.
J Neurosci Methods. 2014 Feb 15;223:30-4. doi: 10.1016/j.jneumeth.2013.11.014. Epub 2013 Dec 4.
During the last years, many investigations focused on spontaneously active cerebral networks such as the default-mode network. A data-driven technique, the independent component analysis, allows segregating such spontaneous (co-)activity maps (SAM) from noise in functional magnetic resonance imaging (fMRI) time series. The inter-rater reliability of manual selection of not only the default-mode network but all SAMs remained to be assessed.
The current study was performed on 20 min (400 volumes) fMRI time series of 30 healthy participants. SAMs' selection criteria were first established on past experience and from the literature. The inter-rater reliability of SAMs vs non-SAMs manual selection was then investigated from 250 independent components per participant.
Inter-rater Kappa coefficient was of 0.89 ± 0.01 on whole analysis, and 0.88 ± 0.09 on participant per participant analysis.
Without focusing on specific and predetermined SAMs only, our criteria allow a reliable selection of all SAMs including the idiosyncratic networks.
The proposed SAM's selection criteria are reliable enough to allow scientific exploration of all SAMs at the single subject level.
在过去的几年中，许多研究都集中在自发性活跃的大脑网络上，如默认模式网络。一种数据驱动的技术，独立成分分析，允许从功能磁共振成像（fMRI）时间序列中的噪声中分离出这种自发（共同）活动图（SAM）。手动选择不仅默认模式网络，而且所有 SAM 的组内信度仍然需要评估。
本研究对 30 名健康参与者的 20 分钟（400 个卷）fMRI 时间序列进行了研究。首先根据以往的经验和文献确定了 SAM 的选择标准。然后，从每个参与者的 250 个独立分量中研究了 SAM 与非 SAM 手动选择的组内信度。
整体分析的组内 Kappa 系数为 0.89 ± 0.01，参与者内分析的组内 Kappa 系数为 0.88 ± 0.09。
我们的标准不仅关注特定的和预先确定的 SAM，还允许可靠地选择包括个体网络在内的所有 SAM。
所提出的 SAM 选择标准足够可靠，可以允许在单个受试者水平上对所有 SAM 进行科学探索。
