Integrative Neuroscience Group, Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Institute for Research and Development in Bioengineering and Bioinformatics (IBB), National Scientific and Technical Research Council (CONICET) and National University of Entre Ríos (UNER), Oro Verde, Argentina.
Hum Brain Mapp. 2021 Jun 1;42(8):2461-2476. doi: 10.1002/hbm.25380. Epub 2021 Feb 19.
Pain arises from the integration of sensory and cognitive processes in the brain, resulting in specific patterns of neural oscillations that can be characterized by measuring electrical brain activity. Current source density (CSD) estimation from low-resolution brain electromagnetic tomography (LORETA) and its standardized (sLORETA) and exact (eLORETA) variants, is a common approach to identify the spatiotemporal dynamics of the brain sources in physiological and pathological pain-related conditions. However, there is no consensus on the magnitude and variability of clinically or experimentally relevant effects for CSD estimations. Here, we systematically examined reports of sample size calculations and effect size estimations in all studies that included the keywords pain, and LORETA, sLORETA, or eLORETA in Scopus and PubMed. We also assessed the reliability of LORETA CSD estimations during non-painful and painful conditions to estimate hypothetical sample sizes for future experiments using CSD estimations. We found that none of the studies included in the systematic review reported sample size calculations, and less than 20% reported measures of central tendency and dispersion, which are necessary to estimate effect sizes. Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. These results provide a comprehensive summary of the effect sizes observed using LORETA in pain research, and this information can be used by clinicians and researchers to improve settings and designs of future pain studies.
疼痛源于大脑中感觉和认知过程的整合,导致特定的神经振荡模式,可以通过测量大脑电活动来描述。从低分辨率脑电磁层析成像 (LORETA) 及其标准化 (sLORETA) 和精确 (eLORETA) 变体中估计电流源密度 (CSD) 是一种常见的方法,用于识别生理和病理疼痛相关条件下大脑源的时空动力学。然而,对于 CSD 估计,在临床或实验相关效应的幅度和可变性方面尚无共识。在这里,我们系统地检查了在 Scopus 和 PubMed 中包含关键字“疼痛”以及“LORETA”、“sLORETA”或“eLORETA”的所有研究报告的样本量计算和效应量估计。我们还评估了 LORETA CSD 估计在无痛和疼痛条件下的可靠性,以估计使用 CSD 估计的未来实验的假设样本量。我们发现,系统评价中包含的研究均未报告样本量计算,并且少于 20%的研究报告了中心趋势和离散度的度量,这是估计效应量所必需的。基于这些数据和我们的实验结果,我们确定了使用 CSD 估计在疼痛研究中常用的样本量适合检测交叉设计中的中等和大效应量,而仅在平行设计中检测大效应量。这些结果提供了使用 LORETA 在疼痛研究中观察到的效应量的综合总结,临床医生和研究人员可以使用这些信息来改进未来疼痛研究的设置和设计。