Accurate diagnosis of the level of consciousness in patients with prolonged disorders of consciousness (DOC) is critical for designing therapeutic plans, determining rehabilitative services, and predicting prognosis. The DOC^{1 – 4} mainly include coma (unwakefulness and reflex behavior only), unresponsive wakefulness syndrome (UWS), and minimally conscious state (MCS). Patients in UWS⁵ are clinically awake but unaware of themselves and the surrounding environment. Unlike UWS, patients in MCS⁶ present minimal, inconsistent, but reproducible purposeful evidence of awareness. The clinical evaluation of awareness and diagnosis of these two groups of patients is challenging. At present, subjective bedside behavioral assessments are still regarded as the clinical reference standard for evaluating the awareness of patients with DOC.⁷ However, it has been reported that the misdiagnosis rate was $\sim 40\%$.³ In fact, accurate evaluation of brain function and detection of residual awareness in patients with DOC is complicated, time-consuming, and challenging due to the cognitive, motor, and sensory impairments of the patients, as well as to other factors, such as the examiner and environmental conditions.

Recent advances in neuroimaging technologies have provided insights into detecting residual consciousness and assessing cognitive abilities in patients with disorders of consciousness (DOC). Functional near-infrared spectroscopy (fNIRS) is non-invasive and portable and can be used for longitudinal bedside monitoring, making it uniquely suited for evaluating brain function in patients with DOC at appropriate spatiotemporal resolutions. In this pilot study, an active command-driven motor imagery (MI) paradigm based on fNIRS was used to detect residual consciousness in patients with prolonged DOC. A support vector machine (SVM) classifier was used to classify yes-or-no responses. The results showed that relatively reliable responses were detected from three out of five patients in a minimally consciousness state (MCS). One of the patients answered all the questions accurately when assessed according to this method. This study confirmed the feasibility of using portable fNIRS technology to detect residual cognitive ability in patients with prolonged DOC by active command-driven motor imagery. We hope to detect the exact level of consciousness in DOC patients who may have a higher level of consciousness.

Background: Advances in neuroimaging have significantly enhanced our understanding of brain function, providing critical insights into the diagnosis and management of disorders of consciousness (DoC). Functional near-infrared spectroscopy (fNIRS), with its real-time, portable, and noninvasive imaging capabilities, has emerged as a promising tool for evaluating functional brain activity and nonrecovery potential in DoC patients. This review explores the current applications of fNIRS in DoC research, identifies its limitations, and proposes future directions to optimize its clinical utility.Aim: This review examines the clinical application of fNIRS in monitoring DoC. Specifically, it investigates the potential value of combining fNIRS with brain-computer interfaces (BCIs) and closed-loop neuromodulation systems for patients with DoC, aiming to elucidate mechanisms that promote neurological recovery.Methods: A systematic analysis was conducted on 155 studies published between January 1993 and October 2024, retrieved from the Web of Science Core Collection database.Results: Analysis of 21 eligible studies on neurological diseases involving 262 DoC patients revealed significant findings. The prefrontal cortex was the most frequently targeted brain region. fNIRS has proven crucial in assessing brain functional connectivity and activation, facilitating the diagnosis of DoC. Furthermore, fNIRS plays a pivotal role in diagnosis and treatment through its application in neuromodulation techniques such as deep brain stimulation (DBS) and spinal cord stimulation (SCS).Conclusion: As a noninvasive, portable, and real-time neuroimaging tool, fNIRS holds significant promise for advancing the assessment and treatment of DoC. Despite limitations such as low spatial resolution and the need for standardized protocols, fNIRS has demonstrated its utility in evaluating residual brain activity, detecting covert consciousness, and monitoring therapeutic interventions. In addition to assessing consciousness levels, fNIRS offers unique advantages in tracking hemodynamic changes associated with neuroregulatory treatments, including DBS and SCS. By providing real-time feedback on cortical activation, fNIRS facilitates optimizing therapeutic strategies and supports individualized treatment planning. Continued research addressing its technical and methodological challenges will further establish fNIRS as an indispensable tool in the diagnosis, prognosis, and treatment monitoring of DoC patients.

With the continuous advancements in critical medical care and neuroimaging technologies, there has been a significant improvement in the survival rate of patients who have suffered from brain injuries, including conditions such as cerebrovascular disease, stroke, anoxia, and traumatic brain injury (TBI).¹ Among them, a considerable number of severe cases have been diagnosed with disorders of consciousness (DOCs). DOCs are characterized by alterations in wakefulness and/or awareness,² mainly including coma, defined by the complete absence of wakefulness and awareness; unresponsive wakefulness syndrome (UWS), previously known as vegetative state (VS), which is characterized by the presence of wakefulness but without awareness of themselves and the external environment; and minimally conscious state (MCS), characterized by minimal, inconsistent, yet reproducible signs of awareness.^1,3 The accurate and prompt diagnosis of DOC is imperative for patients as it directly influences the design of individualized therapeutic strategies, the determination of prognostic outcomes, and even the decision-making process regarding end-of-life care.⁴

Background: Operating an aircraft is associated with a large mental workload; however, knowledge of the mental workload of ROV operators is limited. The purpose of this study was to establish a digital system for assessing the mental workload of remotely operated vehicle (ROV) operators using hemodynamic parameters, and compare results of different groups with different experience levels. Method: Forty-one trainee pilots performed flight tasks once daily for 5 consecutive days in a flight simulation. Forty-five pilots experienced pilots and 68 experienced drivers were also included. Hemodynamic responses were measured by functional near-infrared spectroscopy (fNIRS). Results: The median duration of peak oxyhemoglobin was 147.13 s (interquartile range [IQR] 21.97, 401.70 s) in the left brain and 180.74 s (IQR 34.37, 432.01 s) in the right brain in the experienced pilot group, and 184.42 s (IQR 3.41, 451.81 s) on day 5 in the left brain and 160.30 s (IQR 2.62, 528.20 s) in the right brain in the trainee group. Conclusion: Navigation training reduces peak oxyhemoglobin duration, and may potentially be used as a surrogate marker for mental workload of ROV operators. Peak oxyhemoglobin concentration during s task may allow development of a simplified scheme for optimizing flight performance based on the mental workload of a pilot.