Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.
Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA.
复制标题
使用多回波 ICA 对运动任务 fMRI 数据中与任务相关的头部运动进行去噪。
DOI：
10.1101/2023.07.19.549746
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Bright,MollyG
中科院分区：
文献类型：
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作者：
Reddy,NehaA;Zvolanek,KristinaM;Moia,Stefano;Caballero-Gaudes,César;Bright,MollyG
运动任务功能磁共振成像（fMRI）在研究包括中风和帕金森病在内的几种临床疾病中至关重要。然而，运动任务功能磁共振成像是复杂的任务相关的头部运动，这可以放大在临床人群和混淆运动激活的结果。可以缓解这个问题的一种方法是多回波独立分量分析（ME-ICA），其已经被证明可以将头部运动的影响与期望的血氧水平依赖（BOLD）信号分离，但是尚未在具有大量运动的运动任务数据集中进行测试。在这项研究中，我们收集了一个健康人群的功能磁共振成像数据集，他们进行了手抓任务，有和没有任务相关的放大头部运动，以模拟运动受损的人口。我们使用三种模型分析这些数据：单回波（SE），多回波最佳组合（ME-OC）和ME-ICA。我们比较了模型在减轻头部运动对受试者水平和组水平的影响方面的表现。在受试者层面上，ME-ICA更好地将头部运动的影响与BOLD信号分离，并降低了噪声。两种ME模型导致大脑运动区域的t统计量增加。在高运动水平的扫描中，与SE相比，ME-ICA还减轻了伪影并提高了beta系数估计的稳定性。在组水平上，所有三个模型在具有低运动和高运动的扫描中在预期运动区域中产生激活集群，这表明组水平平均也可以充分解决因受试者而异的运动伪影。这些研究结果表明，ME-ICA是一个有用的工具，主题水平的分析与任务相关的头部运动的运动任务数据。ME-ICA提供的改进对于提高临床人群受试者水平激活图的可靠性至关重要，其中组水平分析可能不可行或不适当，例如，在具有不同卒中位置和组织损伤程度的慢性卒中队列中。
Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson’s disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired blood oxygenation level dependent (BOLD) signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models’ performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example, in a chronic stroke cohort with varying stroke location and degree of tissue damage.