Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK.
Usher Institute, The University of Edinburgh, Edinburgh, UK.
Int J Stroke. 2023 Oct;18(9):1051-1062. doi: 10.1177/17474930231192214. Epub 2023 Aug 12.
Fatigue is a common and disabling symptom following stroke, but its underlying mechanisms are unknown. Associations with a number of imaging features have been proposed.
We aimed to assess whether neuroimaging parameters could better inform our understanding of possible causes of post-stroke fatigue (PSF) through systematic review and meta-analysis.
Using a predefined protocol registered with PROSPERO (ID: CRD42022303168), we searched EMBASE, MEDLINE, PubMed, and PsycInfo for studies assessing PSF and computerized tomography (CT), magnetic resonance (MR), positron emission tomography (PET) imaging, or diffusion tensor imaging (DTI). We extracted neuroimaging parameters and narratively analyzed study results to assess any association with PSF. Where there were 3+ similar studies, we carried out a meta-analysis using inverse-variance random-effects model to estimate the total association of each neuroimaging parameter on PSF. The risk of bias was assessed using the Newcastle and Ottawa Scale.
We identified 46 studies ( = 6543); in many studies, associations with fatigue were secondary or subanalyses (28.3%). Imaging parameters were assessed across eight variables: lesion lateralization, lesion location, lesion volume, brain atrophy, infarct number, cerebral microbleeds, white matter hyperintensities (WMHs), and network measures. Most variables showed no conclusive evidence for any association with fatigue. Meta-analysis, where possible, showed no association of the following with PSF; left lesion lateralization (OR: 0.88, 95% CI (0.64, 1. 22) ( 0.45)), infratentorial lesion location (OR: 1.83, 95% CI (0.63, 5.32) ( 0.27)), and WMH (OR: 1.21, 95% CI (0.84, 1.75) ( 0.29)). Many studies assessed lesion location with mixed findings; only one used voxel-symptom lesion-mapping (VSLM). Some small studies suggested an association between altered functional brain networks, namely frontal, fronto-striato-thalamic, and sensory processing networks, with PSF.
There was little evidence for the association between any neuroimaging parameters and PSF. Future studies should utilize advanced imaging techniques to fully understand the role of lesion location in PSF, while the role of altered brain networks in mediating PSF merits further research.
疲劳是中风后常见且使人致残的症状，但其潜在机制尚不清楚。已有研究提出了与多种影像学特征相关的假设。
我们旨在通过系统评价和荟萃分析评估神经影像学参数是否可以更好地帮助我们理解中风后疲劳（PSF）的可能原因。
使用在 PROSPERO 上预先设定的方案（ID：CRD42022303168），我们在 EMBASE、MEDLINE、PubMed 和 PsycInfo 中检索评估 PSF 和计算机断层扫描（CT）、磁共振（MR）、正电子发射断层扫描（PET）成像或弥散张量成像（DTI）的研究。我们提取了神经影像学参数，并对研究结果进行了叙述性分析，以评估与 PSF 的任何关联。如果有 3 项及以上相似的研究，我们将使用逆方差随机效应模型进行荟萃分析，以估计每个神经影像学参数对 PSF 的总关联。使用纽卡斯尔-渥太华量表评估偏倚风险。
我们确定了 46 项研究（ = 6543）；在许多研究中，疲劳相关性是次要的或子分析（28.3%）。在八个变量中评估了影像学参数：病变侧化、病变位置、病变体积、脑萎缩、梗死数量、脑微出血、脑白质高信号（WMHs）和网络测量。大多数变量没有明确证据表明与疲劳有任何关联。荟萃分析显示，以下因素与 PSF 无关：左侧病变侧化（OR：0.88，95%CI（0.64，1.22）（ 0.45））、幕下病变位置（OR：1.83，95%CI（0.63，5.32）（ 0.27））和 WMH（OR：1.21，95%CI（0.84，1.75）（ 0.29））。许多研究使用混合发现评估病变位置；只有一项研究使用了基于体素的症状-病变-映射（VSLM）。一些小型研究表明，改变的功能性脑网络（即额叶、额纹状体-丘脑和感觉处理网络）与 PSF 之间存在关联。
神经影像学参数与 PSF 之间的关联证据很少。未来的研究应利用先进的影像学技术来全面了解病变位置在 PSF 中的作用，而改变的大脑网络在介导 PSF 中的作用值得进一步研究。
