Tierney Tim M, Weiss-Croft Louise J, Centeno Maria, Shamshiri Elhum A, Perani Suejen, Baldeweg Torsten, Clark Christopher A, Carmichael David W
Developmental Imaging and Biophysics, UCL Institute of Child Health, University College London, UK.
Cognitive Neuroscience and Neuropsychiatry, UCL Institute of Child Health, University College London, London, UK.
Neuroimage. 2016 Jan 1;124(Pt A):1009-1020. doi: 10.1016/j.neuroimage.2015.09.034. Epub 2015 Sep 28.
Different noise sources in fMRI acquisition can lead to spurious false positives and reduced sensitivity. We have developed a biophysically-based model (named FIACH: Functional Image Artefact Correction Heuristic) which extends current retrospective noise control methods in fMRI. FIACH can be applied to both General Linear Model (GLM) and resting state functional connectivity MRI (rs-fcMRI) studies. FIACH is a two-step procedure involving the identification and correction of non-physiological large amplitude temporal signal changes and spatial regions of high temporal instability. We have demonstrated its efficacy in a sample of 42 healthy children while performing language tasks that include overt speech with known activations. We demonstrate large improvements in sensitivity when FIACH is compared with current methods of retrospective correction. FIACH reduces the confounding effects of noise and increases the study's power by explaining significant variance that is not contained within the commonly used motion parameters. The method is particularly useful in detecting activations in inferior temporal regions which have proven problematic for fMRI. We have shown greater reproducibility and robustness of fMRI responses using FIACH in the context of task induced motion. In a clinical setting this will translate to increasing the reliability and sensitivity of fMRI used for the identification of language lateralisation and eloquent cortex. FIACH can benefit studies of cognitive development in young children, patient populations and older adults.
功能磁共振成像（fMRI）采集中的不同噪声源可能导致虚假的假阳性结果，并降低灵敏度。我们开发了一种基于生物物理学的模型（名为FIACH：功能图像伪影校正启发式算法），该模型扩展了当前fMRI中的回顾性噪声控制方法。FIACH可应用于一般线性模型（GLM）和静息态功能连接磁共振成像（rs-fcMRI）研究。FIACH是一个两步程序，包括识别和校正非生理性的大幅度时间信号变化以及高时间不稳定性的空间区域。我们在42名健康儿童执行包括已知激活的公开言语的语言任务时，证明了其有效性。与当前的回顾性校正方法相比，我们证明了FIACH在灵敏度上有很大提高。FIACH通过解释常用运动参数中未包含的显著方差，减少了噪声的混杂效应，并提高了研究的功效。该方法在检测颞下区域的激活方面特别有用，而这些区域已被证明对fMRI来说存在问题。我们已经表明，在任务诱发运动的情况下，使用FIACH可以提高fMRI反应的可重复性和稳健性。在临床环境中，这将转化为提高用于识别语言侧化和明确皮层的fMRI的可靠性和灵敏度。FIACH可以使幼儿、患者群体和老年人的认知发展研究受益。
