Yonsei Graduate Program in Cognitive Science, Yonsei University, Seoul, 03722, Republic of Korea.
Department of Counselling, Hannam University, Daejeon, 34430, Republic of Korea.
Sci Rep. 2024 Aug 26;14(1):19760. doi: 10.1038/s41598-024-70394-7.
Academic achievement is a critical measure of intellectual ability, prompting extensive research into cognitive tasks as potential predictors. Neuroimaging technologies, such as functional near-infrared spectroscopy (fNIRS), offer insights into brain hemodynamics, allowing understanding of the link between cognitive performance and academic achievement. Herein, we explored the association between cognitive tasks and academic achievement by analyzing prefrontal fNIRS signals. A novel quantum annealer (QA) feature selection algorithm was applied to fNIRS data to identify cognitive tasks correlated with CSAT scores. Twelve features (signal mean, median, variance, peak, number of peaks, sum of peaks, range, minimum, kurtosis, skewness, standard deviation, and root mean square) were extracted from fNIRS signals at two time windows (10- and 60-s) to compare results from various feature variable conditions. The feature selection results from the QA-based and XGBoost regressor algorithms were compared to validate the former's performance. In a two-step validation process using multiple linear regression models, model fitness (adjusted R) and model prediction error (RMSE) values were calculated. The quantum annealer demonstrated comparable performance to classical machine learning models, and specific cognitive tasks, including verbal fluency, recognition, and the Corsi block tapping task, were correlated with academic achievement. Group analyses revealed stronger associations between Tower of London and N-back tasks with higher CSAT scores. Quantum annealing algorithms have significant potential in feature selection using fNIRS data, and represents a novel research approach. Future studies should explore predictors of academic achievement and cognitive ability.
学业成就（academic achievement）是智力能力的重要衡量标准，促使人们广泛研究认知任务（cognitive tasks）作为潜在的预测指标。神经影像学技术（neuroimaging technologies），如功能近红外光谱（functional near-infrared spectroscopy，fNIRS），提供了对大脑血液动力学（brain hemodynamics）的深入了解，有助于理解认知表现（cognitive performance）与学业成就（academic achievement）之间的联系。在此，我们通过分析前额叶 fNIRS 信号（prefrontal fNIRS signals）来探索认知任务与学业成就之间的关联。我们应用了一种新颖的量子退火（quantum annealer，QA）特征选择算法（feature selection algorithm），对 fNIRS 数据进行分析，以确定与 CSAT 分数相关的认知任务。从两个时间窗口（10 秒和 60 秒）的 fNIRS 信号中提取了 12 个特征（signal mean、median、variance、peak、number of peaks、sum of peaks、range、minimum、kurtosis、skewness、standard deviation 和 root mean square），以比较来自不同特征变量条件的结果。基于 QA 和 XGBoost 回归器（regressor）算法的特征选择结果进行了比较，以验证前者的性能。在使用多元线性回归模型（multiple linear regression models）的两步验证过程中，计算了模型拟合度（adjusted R）和模型预测误差（RMSE）值。量子退火算法与传统机器学习模型的性能相当，并且特定的认知任务（包括言语流畅性、识别和 Corsi 块敲击任务）与学业成就相关。组分析（group analyses）揭示了伦敦塔（Tower of London）和 N 回（N-back）任务与更高的 CSAT 分数之间更强的关联。量子退火算法在使用 fNIRS 数据进行特征选择方面具有显著的潜力，代表了一种新的研究方法。未来的研究应该探索学业成就和认知能力的预测指标。
