Interactions between the central nervous system (CNS) and autonomic nervous system (ANS) play a crucial role in modulating perception, cognition, and emotion production. Previous studies on CNS–ANS interactions, or heart–brain coupling, have often used heart rate variability (HRV) metrics derived from electrocardiography (ECG) recordings as empirical measurements of sympathetic and parasympathetic activities. Functional near-infrared spectroscopy (fNIRS) is a functional brain imaging modality that is increasingly used in brain and cognition studies. The fNIRS signals contain frequency bands representing both neural activity oscillations and heartbeat rhythms. Therefore, fNIRS data acquired in neuroimaging studies can potentially provide a single-modality approach to measure task-induced responses in the brain and ANS synchronously, allowing analysis of CNS–ANS interactions. In this proof-of-concept study, fNIRS was used to record hemodynamic changes from the foreheads of 20 university students as they each played a round of multiplayer online battle arena (MOBA) game. From the fNIRS recordings, neural and heartbeat frequency bands were extracted to assess prefrontal activities and short-term pulse rate variability (PRV), an approximation for short-term HRV, respectively. Under the experimental conditions used, fNIRS-derived PRV metrics showed good correlations with ECG-derived HRV golden standards, in terms of absolute measurements and video game playing (VGP)-related changes. It was also observed that, similar to previous studies on physical activity and exercise, the PRV metrics closely related to parasympathetic activities recovered slower than the PRV indicators of sympathetic activities after VGP. It is concluded that it is feasible to use fNIRS to monitor concurrent brain and ANS activations during online VGP, facilitating the understanding of VGP-related heart–brain coupling.

Hemiplegia after stroke has become a major cause of the world’s high disabilities, and it is vital to enhance our understanding of post-stroke neuroplasticity to develop efficient rehabilitation programs. This study aimed to explore the brain activation and network reorganization of the motor cortex (MC) with functional near-infrared spectroscopy (fNIRS). The MC hemodynamic signals were gained from 22 stroke patients and 14 healthy subjects during a shoulder-touching task with the right hand. The MC activation pattern and network attributes analyzed with the graph theory were compared between the two groups. The results revealed that healthy controls presented dominant activation in the left MC while stroke patients exhibited dominant activation in the bilateral hemispheres MC. The MC networks for the two groups had small-world properties. Compared with healthy controls, patients had higher transitivity and lower global efficiency (GE), mean connectivity, and long connections (LCs) in the left MC. In addition, both MC activation and network attributes were correlated with patient’s upper limb motor function. The results showed the stronger compensation of the unaffected motor area, the better recovery of the upper limb motor function for patients. Moreover, the MC network possessed high clustering and relatively sparse inter-regional connections during recovery for patients. Our results promote the understanding of MC reorganization during recovery and indicate that MC activation and network could provide clinical assessment significance in stroke patients. Given the advantages of fNIRS, it shows great application potential in the assessment and rehabilitation of motor function after stroke.

与传统的核磁共振、脑电图等脑功能成像方法相比,功能性近红外光谱(fNIRS)技术具有抗电磁干扰和可直接检测血氧代谢信号等优势。本团队基于锁相光子计数技术发展了一套面向脑机接口(BCI)应用的便携式fNIRS拓扑成像系统,并开展了一系列仿体和在体实验,以评估该系统的性能。具体来说,仿体实验结果表明该系统具有良好的稳定性、线性度和抗串扰能力。在体实验采用了屏息和心算两种刺激范式,系统以4 Hz的采样频率进行同步测量,结果表明,该系统可以准确跟踪全局兴奋(屏息刺激)和局部兴奋(心算刺激)的时间变化曲线。本团队进一步对心算刺激下的测量结果进行了光学拓扑成像,结果显示,此刺激的兴奋区域大致位于前额叶左半部中央位置。此外,本系统进行了充分的小型化设计,可以应用于医疗、日常等情境。这一系统可以实现高灵敏度、全并行的微弱兴奋信号检测,为fNIRS-BCI的临床应用提供了一种实用、高效的测量手段。