Longitudinal surface-based spatial Bayesian GLM reveals complex trajectories of motor neurodegeneration in ALS.
Longitudinal surface-based spatial Bayesian GLM reveals complex trajectories of motor neurodegeneration in ALS.
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DOI：
10.1016/j.neuroimage.2022.119180
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发表时间：
2022-07-15
期刊：
影响因子：
5.7
通讯作者：
Welsh, Robert C.
中科院分区：
文献类型：
--
作者：
Mejia, Amanda F.;Koppelmans, Vincent;Jelsone-Swain, Laura;Kalra, Sanjay;Welsh, Robert C.
纵向功能磁共振成像研究为神经退行性疾病、发育和衰老的研究带来了巨大的希望，但要实现其全部潜力，取决于随着时间的推移，在个体受试者中提取准确的基于功能磁共振成像的脑功能和组织测量。对于罕见、异质和/或快速进展的神经退行性疾病的研究尤其如此。这些通常涉及具有异质性功能特征的小样本，使得传统的组差异分析效用有限。一种这样的疾病是肌萎缩侧索硬化症（ALS），一种导致运动功能极度丧失并最终死亡的严重疾病。在这里，我们使用一种先进的个性化任务fMRI分析方法来分析一个丰富的纵向数据集，其中包含16名ALS患者的190个手紧握fMRI扫描（78次扫描）和22名年龄匹配的健康对照（112次扫描）具体来说，我们采用我们的基于皮层表面的空间贝叶斯一般线性模型（GLM），它具有高功率和精度来检测个体受试者的激活，并提出了一种新的纵向延伸，以利用跨访问共享的信息。我们在自然表面空间进行所有分析，以保留个体解剖和功能特征。使用混合效应模型随后研究激活大小与ALS疾病进展之间的关系，我们首次观察到运动激活的倒U形轨迹：在相对轻度的运动障碍时，我们观察到激活扩大，而在较高水平的运动障碍时，我们观察到激活严重减少，反映了运动功能完全丧失的进展。我们进一步观察到不同的轨迹取决于临床进展率，更快的进展者在残疾的早期阶段表现出更极端的变化。这些不同的轨迹表明，最初的超激活可能是由于抑制性神经元的损失，而不是功能补偿，如先前所假设的。这些发现大大推进了对ALS疾病过程的科学理解。这项研究还提供了第一个现实世界的例子，如何基于表面的空间贝叶斯分析任务功能磁共振成像可以进一步科学地理解神经退行性疾病和其他现象。基于表面的空间贝叶斯GLM在BayesfMRI R包中实现
Longitudinal fMRI studies hold great promise for the study of neurodegenerative diseases, development and aging, but realizing their full potential depends on extracting accurate fMRI-based measures of brain function and organization in individual subjects over time. This is especially true for studies of rare, heterogeneous and/or rapidly progressing neurodegenerative diseases. These often involve small samples with heterogeneous functional features, making traditional group-difference analyses of limited utility. One such disease is amyotrophic lateral sclerosis (ALS), a severe disease resulting in extreme loss of motor function and eventual death. Here, we use an advanced individualized task fMRI analysis approach to analyze a rich longitudinal dataset containing 190 hand clench fMRI scans from 16 ALS patients (78 scans) and 22 age-matched healthy controls (112 scans) Specifically, we adopt our cortical surface-based spatial Bayesian general linear model (GLM), which has high power and precision to detect activations in individual subjects, and propose a novel longitudinal extension to leverage information shared across visits. We perform all analyses in native surface space to preserve individua anatomical and functional features. Using mixed-effects models to subsequently study the relationship between size of activation and ALS disease progression, we observe for the first time an inverted U-shaped trajectory o motor activations: at relatively mild motor disability we observe enlarging activations, while at higher levels of motor disability we observe severely diminished activation, reflecting progression toward complete loss of motor function. We further observe distinct trajectories depending on clinical progression rate, with faster progressors exhibiting more extreme changes at an earlier stage of disability. These differential trajectories suggest that initial hyper-activation is likely attributable to loss of inhibitory neurons, rather than functional compensation as earlier assumed. These findings substantially advance scientific understanding of the ALS disease process. This study also provides the first real-world example of how surface-based spatial Bayesian analysis of task fMRI can further scientific understanding of neurodegenerative disease and other phenomena. The surface-based spatial Bayesian GLM is implemented in the BayesfMRI R package
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影响因子：
3.7
作者：
Cremers HR;Wager TD;Yarkoni T
通讯作者：
Yarkoni T
影响因子：
4.8
作者：
Stern, Emily R.;Welsh, Robert C.;Fitzgerald, Kate D.;Taylor, Stephan F.
通讯作者：
Taylor, Stephan F.
影响因子：
3.7
作者：
Eloyan A;Shou H;Shinohara RT;Sweeney EM;Nebel MB;Cuzzocreo JL;Calabresi PA;Reich DS;Lindquist MA;Crainiceanu CM
通讯作者：
Crainiceanu CM
影响因子：
14.5
作者：
Douaud, Gwenaelle;Filippini, Nicola;Turner, Martin R.
通讯作者：
Turner, Martin R.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y