A method to mitigate spatio-temporally varying task-correlated motion artifacts from overt-speech fMRI paradigms in aphasia.
A method to mitigate spatio-temporally varying task-correlated motion artifacts from overt-speech fMRI paradigms in aphasia.
复制标题
一种减轻空间与任务相关的运动伪像的方法，来自公开的语音磁磁盘范式。
DOI：
10.1002/hbm.25280
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发表时间：
2021-03
影响因子：
4.8
通讯作者：
Crosson B
中科院分区：
文献类型：
--
作者：
Krishnamurthy V;Krishnamurthy LC;Meadows ML;Gale MK;Ji B;Gopinath K;Crosson B
量化准确的功能性磁共振成像（fMRI）激活图可能会受到某些任务范例中时空变化的任务相关运动（TCM）伪影的抑制（例如，公开演讲）。这些真实的任务与表征卒中后纵向脑重组相关，去除中医伪影对于改善临床解释和翻译至关重要。在这项研究中，我们开发了一种新的独立成分分析（伊卡）为基础的方法去噪时空变化的中医伪影在14名失语症患者参加了公开的语言功能磁共振成像范例。我们将新方法与其他现有方法进行了比较，例如“标准”体积配准、非选择性运动校正伊卡包（即，AROMA），并将新方法与AROMA相结合。结果表明，所提出的方法在消除TCM相关的假阳性活动（即，改进的检测能力），具有高的空间特异性。所提出的方法也有效地保持了去除TCM相关试验间变异性和信号保留之间的平衡。最后，我们发现中医伪影与临床指标有关，如语音流畅性和失语症的严重程度，并讨论了中医去噪对这种关系的影响。总的来说，我们的工作表明，常规的基于舱壁运动的去噪包不能有效地解释时空变化的TCM。此外，提出的TCM去噪方法需要一次性的前端工作来手动标记和训练分类器，这些分类器可以经济有效地用于对大型临床数据集进行去噪。量化准确的功能性磁共振成像（fMRI）激活图可能会受到某些任务范例中时空变化的任务相关运动（TCM）伪影的抑制（例如，公开演讲）。在这项研究中，我们开发了一种新的独立成分分析（伊卡）为基础的方法去噪时空变化的中医伪影在14名失语症患者谁参加了公开的语言功能磁共振成像范例。结果表明，所提出的方法在消除TCM相关的假阳性活动（即，改进的检测能力），具有高的空间特异性。所提出的方法也有效地保持了去除TCM相关试验间变异性和信号保留之间的平衡。最后，我们发现中医伪影与临床指标有关，如语音流畅性和失语症的严重程度，并讨论了中医去噪对这种关系的影响。
Quantifying accurate functional magnetic resonance imaging (fMRI) activation maps can be dampened by spatio‐temporally varying task‐correlated motion (TCM) artifacts in certain task paradigms (e.g., overt speech). Such real‐world tasks are relevant to characterize longitudinal brain reorganization poststroke, and removal of TCM artifacts is vital for improved clinical interpretation and translation. In this study, we developed a novel independent component analysis (ICA)‐based approach to denoise spatio‐temporally varying TCM artifacts in 14 persons with aphasia who participated in an overt language fMRI paradigm. We compared the new methodology with other existing approaches such as “standard” volume registration, nonselective motion correction ICA packages (i.e., AROMA), and combining the novel approach with AROMA. Results show that the proposed methodology outperforms other approaches in removing TCM‐related false positive activity (i.e., improved detectability power) with high spatial specificity. The proposed method was also effective in maintaining a balance between removal of TCM‐related trial‐by‐trial variability and signal retention. Finally, we show that the TCM artifact is related to clinical metrics, such as speech fluency and aphasia severity, and the implication of TCM denoising on such relationship is also discussed. Overall, our work suggests that routine bulkhead motion based denoising packages cannot effectively account for spatio‐temporally varying TCM. Further, the proposed TCM denoising approach requires a one‐time front‐end effort to hand label and train the classifiers that can be cost‐effectively utilized to denoise large clinical data sets. Quantifying accurate functional magnetic resonance imaging (fMRI) activation maps can be dampened by spatio‐temporally varying task‐correlated motion (TCM) artifacts in certain task paradigms (e.g., overt speech). In this study, we developed a novel independent component analysis (ICA)‐based approach to denoise spatio‐temporally varying TCM artifacts in fourteen persons with aphasia who participated in an overt language fMRI paradigm. Results show that the proposed methodology outperforms other approaches in removing TCM‐related false positive activity (i.e., improved detectability power) with high spatial specificity. The proposed method was also effective in maintaining a balance between removal of TCM‐related trial‐by‐trial variability and signal retention. Finally, we show that the TCM artifact is related to clinical metrics, such as speech fluency and aphasia severity, and the implication of TCM denoising on such relationship is also discussed.
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