Department of English, Seijo University, Tokyo, 157-8511, Japan.
Department of Engineering Technology, University of Houston, Houston, TX, USA.
Exp Brain Res. 2022 Jun;240(6):1701-1711. doi: 10.1007/s00221-022-06365-z. Epub 2022 Apr 24.
The retrieval of phonological, lexical, semantic, or syntactic language information from long-term memory plays an important role in language processing. However, it remains unclear whether variability analysis of brain signals obtained using functional near-infrared spectroscopy (fNIRS) is able to separate language-related task conditions. This study employed multifractal detrended fluctuation (MFDF) analysis focusing on the width of the multifractal spectrum to elucidate whether high complexity tasks increase the fractal dynamics of brain activation signals compared to low complexity tasks. Nine Japanese college students participated in a long-term memory retrieval experiment using low (n ± 1) and high (n ± 2) complexity tasks. Our results showed that high complexity tasks induced a significantly larger multifractal spectral width in the posterior medial temporal gyri bilaterally, due to higher cognitive demands. These findings suggest that in addition to conventional techniques based on mean amplitude analysis and general linear modelling of fNIRS data, the use of MFDF analysis offers a powerful alternative methodology to gain a deeper understanding of long-term memory retrieval in language memory processing.
从长时记忆中检索语音、词汇、语义或句法语言信息在语言处理中起着重要作用。然而，目前尚不清楚使用近红外光谱功能成像（fNIRS）获得的脑信号的变异性分析是否能够分离与语言相关的任务条件。本研究采用多重分形去趋势波动分析（MFDF），重点分析多重分形谱的宽度，以阐明与低复杂性任务相比，高复杂性任务是否会增加大脑激活信号的分形动力学。9 名日本大学生参与了使用低（n ± 1）和高（n ± 2）复杂性任务的长时记忆检索实验。我们的结果表明，由于认知需求较高，高复杂性任务在双侧后内侧颞叶引起了显著更大的多重分形谱宽度。这些发现表明，除了基于 fNIRS 数据的平均幅度分析和广义线性模型的传统技术外，MFDF 分析的使用还提供了一种强大的替代方法，可以更深入地了解语言记忆处理中的长时记忆检索。
