Background: Cognitive performance is relatively well preserved during early cognitive impairment owing to compensatory mechanisms.Methods: We explored functional near-infrared spectroscopy (fNIRS) alongside a semantic verbal fluency task (SVFT) to investigate any compensation exhibited by the prefrontal cortex (PFC) in Mild Cognitive Impairment (MCI) and mild Alzheimer's disease (AD). In addition, a group of healthy controls (HC) was studied. A total of 61 volunteers (31 HC, 12 patients with MCI and 18 patients with mild AD) took part in the present study.Results: Although not statistically significant, MCI exhibited a greater mean activation of both the right and left PFC, followed by HC and mild AD. Analysis showed that in the left PFC, the time taken for HC to achieve the activation level was shorter than MCI and mild AD (p = 0.0047 and 0.0498, respectively); in the right PFC, mild AD took a longer time to achieve the activation level than HC and MCI (p = 0.0469 and 0.0335, respectively); in the right PFC, HC, and MCI demonstrated a steeper slope compared to mild AD (p = 0.0432 and 0. 0107, respectively). The results were, however, not significant when corrected by the Bonferroni-Holm method. There was also found to be a moderately positive correlation (R = 0.5886) between the oxygenation levels in the left PFC and a clinical measure [Mini-Mental State Examination (MMSE) score] in MCI subjects uniquely.Discussion: The hyperactivation in MCI coupled with a better SVFT performance may suggest neural compensation, although it is not known to what degree hyperactivation manifests as a potential indicator of compensatory mechanisms. However, hypoactivation plus a poorer SVFT performance in mild AD might indicate an inability to compensate due to the degree of structural impairment.Conclusion: Consistent with the scaffolding theory of aging and cognition, the task-elicited hyperactivation in MCI might reflect the presence of compensatory mechanisms and hypoactivation in mild AD could reflect an inability to compensate. Future studies will investigate the fNIRS parameters with a larger sample size, and their validity as prognostic biomarkers of neurodegeneration.

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Near-infrared spectroscopy (NIRS), one of the candidates to be used in a neurofeedback system or brain-computer interface (BCI), measures the brain activity by monitoring the changes in cerebral hemoglobin concentration. However, hemodynamic changes in the scalp may affect the NIRS signals. In order to remove the superficial signals when NIRS is used in a neurofeedback system or BCI, real-time processing is necessary. Real-time scalp signal separating (RT-SSS) algorithm, which is capable of separating the scalp-blood signals from NIRS signals obtained in real-time, may thus be applied. To demonstrate its effectiveness, two separate neurofeedback experiments were conducted. In the first experiment, the feedback signal was the raw NIRS signal recorded while in the second experiment, deep signal extracted using RT-SSS algorithm was used as the feedback signal. In both experiments, participants were instructed to control the feedback signal to follow a predefined track. Accuracy scores were calculated based on the differences between the trace controlled by feedback signal and the targeted track. Overall, the second experiment yielded better performance in terms of accuracy scores. These findings proved that RT-SSS algorithm is beneficial for neurofeedback.

Mismatch between mental workload and working memory capacity can cause mental underload or overload. Adopting the Yerkes-Dodson law as the framework, functional near-infrared spectroscopy adaptive cognitive training system (FACTS) has been developed, whereby the mental workload shall never exceed an individual's capacity, to prevent those unintended conditions. It works by monitoring mental workload in real time and performing dynamic difficulty adjustment accordingly. The feasibility study involved thirty-seven healthy participants undergoing mental arithmetic task with and without FACTS. Without FACTS, the participants not only showed higher NASA Task Load Index scores but also poorer task performance and a significant drop in DLPFC activation towards the end of the task, signifying more severe mental overload. Conversely, they continued to exhibit manifestation of productive learning with FACTS despite showing early signs of mental overload. The study results demonstrated that it is feasible to implement the concept of FACTS. The actual gains from cognitive training will be investigated in future longitudinal study. INDEX TERMS Functional near-infrared spectroscopy, dynamic difficulty adjustment, cognitive training, mental workload, cognitive underload, cognitive overload

The effect of stress on task performance is complex, too much or too little stress negatively affects performance and there exists an optimal level of stress to drive optimal performance. Task difficulty and external affective factors are distinct stressors that impact cognitive performance. Neuroimaging studies showed that mood affects working memory performance and the correlates are changes in haemodynamic activity in the prefrontal cortex (PFC). We investigate the interactive effects of affective states and working memory load (WML) on working memory task performance and haemodynamic activity using functional near-infrared spectroscopy (fNIRS) neuroimaging on the PFC of healthy participants. We seek to understand if haemodynamic responses could tell apart workload-related stress from situational stress arising from external affective distraction. We found that the haemodynamic changes towards affective stressor- and workload-related stress were more dominant in the medial and lateral PFC, respectively. Our study reveals distinct affective state-dependent modulations of haemodynamic activity with increasing WML in n-back tasks, which correlate with decreasing performance. The influence of a negative effect on performance is greater at higher WML, and haemodynamic activity showed evident changes in temporal, and both spatial and strength of activation differently with WML.

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