In-vivo-NMR Laboratory, Max Planck Institute for Neurological Research, Cologne, Germany.
Neuroimage. 2011 Feb 14;54(4):2828-39. doi: 10.1016/j.neuroimage.2010.10.053. Epub 2010 Oct 23.
Resting state functional MRI (rs-fMRI) of the brain has the potential to elicit networks of functional connectivity and to reveal changes thereof in animal models of neurological disorders. In the present study, we investigate the contribution of physiological noise and its impact on assessment of functional connectivity in rs-fMRI of medetomidine sedated, spontaneously breathing rats at ultrahigh field of 11.7 Tesla. We employed gradient echo planar imaging (EPI) with repetition times of 3s and used simultaneous recordings of physiological parameters. A model of linear regression was applied to quantify the amount of BOLD fMRI signal fluctuations attributable to physiological sources. Our results indicate that physiological noise - mainly originating from the respiratory cycle -dominates the rs-fMRI time course in the form of spatially complex correlation patterns. As a consequence, these physiological fluctuations introduce severe artifacts into seed-based correlation maps and lead to misinterpretation of corresponding connectivity measures. We demonstrate that a scheme of motion correction and linear regression can significantly reduce physiological noise in the rs-fMRI time course, remove artifacts, and hence improve the reproducibility of functional connectivity assessment. In conclusion, physiological noise can severely compromise functional connectivity MRI (fcMRI) of the rodent at high fields and must be carefully considered in design and interpretation of future studies. Motion correction should be considered the primary strategy for reduction of apparent motion related to respiratory fluctuations. Combined with subsequent regression of physiological confounders, this strategy has proven successful in reducing physiological noise and related artifacts affecting functional connectivity analysis. The proposed new and rigorous protocol now opens the potential of fcMRI to elicit the role of brain connectivity in pathological processes without concerns of confounding contributions from physiological noise.
静息态功能磁共振成像(rs-fMRI)有可能引出功能连接网络,并揭示神经疾病动物模型中功能连接的变化。在本研究中,我们研究了生理噪声的贡献及其对 11.7T 超高场下接受美托咪定镇静、自主呼吸大鼠 rs-fMRI 中功能连接评估的影响。我们采用重复时间为 3s 的梯度回波平面成像(EPI),并同时记录生理参数。我们应用线性回归模型来量化归因于生理源的 BOLD fMRI 信号波动的数量。我们的结果表明,生理噪声——主要来源于呼吸周期——以空间复杂相关模式的形式主导 rs-fMRI 时程。因此,这些生理波动会在种子相关性图谱中引入严重的伪影,并导致对相应连接性测量的错误解释。我们证明,运动校正和线性回归方案可以显著降低 rs-fMRI 时间序列中的生理噪声,去除伪影,从而提高功能连接评估的可重复性。总之,生理噪声会严重影响高场下啮齿动物的功能连接磁共振成像(fcMRI),在未来研究的设计和解释中必须谨慎考虑。运动校正应被视为减少与呼吸波动相关的明显运动的主要策略。结合随后对生理混杂因素的回归,该策略已被证明在降低影响功能连接分析的生理噪声和相关伪影方面是成功的。所提出的新的严格方案现在为 fcMRI 引出大脑连接在病理过程中的作用开辟了潜力,而不必担心生理噪声的混杂影响。