Fairclough Stephen H, Burns Christopher, Kreplin Ute
Liverpool John Moores University, School of Natural Sciences and Psychology, Liverpool, Merseyside, United Kingdom.
University of Warwick, Warwick Manufacturing Group, Experiential Engineering, Coventry, United Kingdom.
Neurophotonics. 2018 Jul;5(3):035001. doi: 10.1117/1.NPh.5.3.035001. Epub 2018 Jul 12.
Previous research has demonstrated changes in neurovascular activation of the prefrontal cortex to increased working memory load. The primary purpose of the current paper was to investigate overload of working memory capacity using functional near-infrared spectroscopy (fNIRS) within the framework of motivational intensity theory. A secondary goal was to explore the influence of the correlation-based signal improvement (CBSI) as a method for correcting the influence of systemic variables. In study one, 30 participants (15 female, mean age = 21.09 years, s.d. = 2.9 years) performed a verbal version of the -back working memory task under four levels of demand (easy, hard, very hard, and impossible). In contrast to the raw data, CBSI-transformed fNIRS data indicated that neurovascular coupling was highest at hard demand when the task was challenging but success was possible. The second study ( ; 15 female, mean age = 22.4 years, s.d. = 5.3) replicated the working memory manipulation with the addition of low versus high levels of financial reward. Analyses of CBSI-transformed levels of oxygenated (HbO) and deoxygenated (HHb) hemoglobin replicated the first study at right lateral regions of the prefrontal cortex (BA46). HHb_CBSI data were significantly reduced at impossible demand for participants receiving the higher level of financial reward. The study is the first to support predictions from the motivational intensity model using neurovascular data. In addition, the application of CBSI to fNIRS data was found to improve the sensitivity of HbO and Hbb to the independent variables.
先前的研究表明,前额叶皮质的神经血管激活会随着工作记忆负荷的增加而发生变化。本文的主要目的是在动机强度理论的框架内,使用功能近红外光谱技术(fNIRS)来研究工作记忆容量的过载情况。第二个目标是探索基于相关性的信号改善(CBSI)作为一种校正系统变量影响的方法所产生的影响。在研究一中,30名参与者(15名女性,平均年龄 = 21.09岁,标准差 = 2.9岁)在四个需求水平(容易、困难、非常困难和不可能)下执行了言语版的n-back工作记忆任务。与原始数据相比,经CBSI转换的fNIRS数据表明,当任务具有挑战性但有可能成功时,在困难需求水平下神经血管耦合最高。第二项研究(n = 15;15名女性,平均年龄 = 22.4岁,标准差 = 5.3)重复了工作记忆操作,并增加了低水平与高水平的金钱奖励。对经CBSI转换的氧合血红蛋白(HbO)和脱氧血红蛋白(HHb)水平的分析在前额叶皮质右侧区域(BA46)重复了第一项研究的结果。对于接受较高水平金钱奖励的参与者,在不可能的需求水平下,HHb_CBSI数据显著降低。该研究首次使用神经血管数据支持了动机强度模型的预测。此外,研究发现将CBSI应用于fNIRS数据可提高HbO和Hbb对自变量变化的敏感性。