Reddy Neha A, Zvolanek Kristina M, Moia Stefano, Caballero-Gaudes César, Bright Molly G
Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
Department of Biomedical Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, IL, United States.
Imaging Neurosci (Camb). 2024;2. doi: 10.1162/imag_a_00057. Epub 2024 Jan 5.
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.
运动任务功能磁共振成像（fMRI）在多种临床病症的研究中至关重要，包括中风和帕金森病。然而，任务相关的头部运动使运动任务fMRI变得复杂，这种运动在临床人群中可能会被放大，并混淆运动激活结果。一种可能缓解此问题的方法是多回波独立成分分析（ME-ICA），该方法已被证明能够将头部运动的影响与所需的血氧水平依赖（BOLD）信号分离，但尚未在具有大量运动的运动任务数据集中进行测试。在本研究中，我们从健康人群中收集了fMRI数据集，这些人在执行手部抓握任务时，有和没有与任务相关的放大头部运动，以模拟运动受损人群。我们使用三种模型分析了这些数据：单回波（SE）、多回波最佳组合（ME-OC）和ME-ICA。我们在个体水平和组水平上比较了这些模型在减轻头部运动影响方面的性能。在个体水平上，ME-ICA能更好地将头部运动的影响与BOLD信号分离并降低噪声。两种ME模型都导致脑运动区域的t统计量增加。与SE相比，在运动水平较高的扫描中，ME-ICA还减轻了伪影并提高了β系数估计的稳定性。在组水平上，所有三种模型在低运动和高运动扫描中均在预期的运动区域产生激活簇，这表明组水平平均也可能充分解决因个体而异的运动伪影。这些发现表明，ME-ICA是对具有高水平任务相关头部运动的运动任务数据进行个体水平分析的有用工具。ME-ICA带来的改进对于提高临床人群个体水平激活图的可靠性至关重要，在这些人群中，组水平分析可能不可行或不合适，例如，在中风部位和组织损伤程度各不相同的慢性中风队列中。
