Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging During Rehabilitation Following Stroke: A Review

Introduction

Stroke is a common neurological disease characterized by vascular blockage, with high morbidity, disability, and mortality rates, and is currently the second leading cause of death and disability worldwide [1,2]. Stroke patients experience a range of functional deficits, including varying degrees of motor dysfunction [3], cognitive dysfunction [4], swallowing dysfunction [5], and linguistic dysfunction [6]. In addition to affecting the patient’s ability to carry out daily activities, it may also have long-term psychological effects [7]. Evidence-based medicine confirms [8] that timely and effective rehabilitation after stroke is the most effective way to reduce the disability rate and is an indispensable and critical part of the organized stroke management model. Consequently, early diagnosis of brain function abnormalities and dysfunctions in stroke patients and rehabilitation is a pressing social issue that requires attention.

Strokes are typically classified as either ischemic or hemorrhagic, and the resultant dysfunction is primarily caused by inadequate blood supply and cortical damage from cerebral oxygenation [9]. Therefore, monitoring changes in cerebral blood flow and oxygenation can provide a timely indication of rehabilitation and recovery as well as the effectiveness of treatment. Neuroimaging is an effective tool for monitoring and therapy, for evaluating the evolution of neural activity, stroke rehabilitation, and recovery [10]. Common methods for assessing cerebral hemodynamic activity [11] include position emission tomography (PET), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). PET requires the injection of radioisotope drugs and is limited in the range of motion and constraints. The main disadvantage of fMRI is its poor temporal resolution, which makes it difficult to assess brain function under motor states or magnetotherapy. In contrast, fNIRS has become popular due to its good temporal resolution (about 10 Hz) [12] and spatial sampling rate (10–20 mm) [13], as well as its low cost, safety, non-invasiveness, lack of radiation exposure, an unlimited monitoring environment, low motion sensitivity, and long-term detection capabilities.

Functional near-infrared spectroscopy is an emerging non-invasive optical imaging technology that measures the concentration of oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin, as well as distribution and changes in blood volume and oxygen saturation in the cerebral cortex to reflect the activity level of the brain [14]. Due to its potential portability and insensitivity to motion during data acquisition, in combination with behavioral analysis, fNIRS can monitor cortical activation in each region of interest related to cortical neuronal activity and the connectivity of each brain network [15,16]. This provides a visual monitoring tool for post-stroke rehabilitation and a more comprehensive understanding of the mechanisms involved in the recovery of dysfunction after stroke. Furthermore, fNIRS has significant potential as a therapeutic tool [17]. Near-infrared spectroscopy is a promising and effective method. We briefly review the imaging principles of fNIRS and summarize its application in post-stroke rehabilitation, as well as the advantages and limitations. This article aims to review the applications of functional near-infrared spectroscopy (fNIRS) neuroimaging during rehabilitation following stroke.

Imaging Principles of fNIRS

In 1977, Jöbsis and colleagues [14] introduced near-infrared spectroscopy (NIRS), which demonstrated that brain tissue transparency is high in the near-infrared range (700–900 nm). Furthermore, changes in oxyhemoglobin, deoxyhemoglobin, and total hemoglobin can be detected in real-time and non-invasively with fNIRS, prompting interest in this technique within the field of brain function research. The light source emits near-infrared light into a specific region of the brain through a light-emitting diode or a fiber-optic bundle that is adjusted to the subject’s head size. The light scatters in a banana-shaped path, and a light detector situated at a distance from the beam collects the light that the tissue has scattered back. The typical distance between the light source and detector for clinical purposes is 3 cm. There are 3 primary categories of NIRS systems [18]: (1) Continuous wave spectral systems; (2) Time domain spectral systems; and (3) Frequency domain spectral systems. The continuous wave system is extensively utilized in current research, in which NIRS light is radiated onto the cerebral cortex at a steady wavelength, and it calculates the relative concentration of hemoglobin. This system is favored due to its simplicity, portability, and low cost, despite the deficiencies in wave attenuation measurement [19,20].

Physical Foundation

The blood oxygen level in human tissues varies depending on the physiological condition and activity of the body. Such fluctuations impact the optical characteristics of the tissues. During physical activity, increased blood circulation can cause the cheeks to become rosy, while reduced blood circulation due to hypothermia can cause the nails to turn blue. Nervous system activity is likewise accompanied by changes in blood oxygenation, which can be detected by near-infrared light. Physiological tissues have 2 responses to light: absorption and scattering. As the amount of scattering in various parts of the head cortex does not vary with neural activity, the attenuation caused by scattering in cortical tissues remains constant. Therefore, alterations in attenuation, measured during cognitive activity, are attributed to changes in absorption.

The primary constituents of tissues that absorb NIR light are water and hemoglobin. In the spectral range of NIRS light from 600 to 900 nm, water has a very low absorption rate, whereas HbO and HbR have different light sensitivities [21]. HbO2 is sensitive to the oxygenated state, while HbR is sensitive to the deoxygenated state. These 2 compounds have distinguishable light-absorbing properties, which are predominantly manifested through the higher absorption coefficient of HbO in wavelengths larger than 800 nm. On the other hand, it must be noted that the absorption coefficient of HbR is greater at wavelengths below 800 nm [22], leading to changes in oxyhemoglobin and deoxyhemoglobin in the brain’s active tissues being the primary cause of the attenuation changes.

Neurovascular Coupling

Neuronal activity necessitates an energy supply of glucose and oxygen. Consequently, augmented neuronal activity results in concurrent elevation in cerebral oxygen metabolism. In situations where specific brain regions are active and involved in performing a task, the brain’s need for oxygen and glucose increases. This, in turn, leads to blood oxygen consumption to produce energy, ultimately causing HbO concentration to decline and that of HbR to increase. Oxygen consumption in the brain stimulates local vasodilation, increasing capillary blood flow. This, in turn, leads to an increase in local cerebral blood flow (CBF) and cerebral blood volume (CBV), which increase at much faster rates than the local rate of oxygen depletion. Ultimately, this manifests itself as an increase in the concentration of HbO and a decrease in the concentration of HbR in activated areas of the brain tissue [21,23] (Figure 1). These changes are estimated from alterations in light attenuation measured using fNIRS.

From the correlation between light attenuation and chromophore concentration changes in tissues [24], it is possible to use the modified Beer-Lambert law to calculate the changes in relative concentrations of oxyhemoglobin and deoxyhemoglobin in the brain during neural activity. This can allow for speculation on the associated brain regions and their interrelationships.

NIRS as a Monitoring Tool

UPPER LIMB MOVEMENT:

Upper-limb motor dysfunction is a common problem after stroke and its recovery process can be slow and challenging, with complex neural mechanisms, which has garnered considerable attention. Real-time monitoring of hemodynamic changes in the brain after stroke using fNIRS is a novel means of obtaining information on cortical activation and can provide insight into the brain activation pattern associated with stroke dysfunction.

Yang et al [25] analyzed the cortical activation of upper limb movements in healthy subjects using fNIRS and showed that there was extensive activation of the motor cortex and sensory cortex bilaterally during shoulder abduction and finger extension tasks, with greater activation in the contralateral hemisphere compared to the ipsilateral side. Sui et al [26] investigated resting-state functional connectivity with fNIRS in subacute stroke patients with varying degrees of upper-limb motor deficits, noting that reorganization of the ipsilateral hemisphere was more important for motor recovery, further validating the mechanism of healthy-side compensation during stroke rehabilitation, where recovery from hemiparesis could be improved by functional reorganization that encompasses the ipsilateral motor cortex. This mode of stimulation has also been confirmed in electroencephalogram (EEG) studies [27]. Zhang et al [28] conducted a study to evaluate cortical activation during a right-hand shoulder-touch task in 22 stroke patients and 14 healthy controls. The results indicated that healthy controls had a lateralization index (LI) of 0.268, displaying left hemisphere dominance in activation. In contrast, the stroke group had an LI of -0.009, demonstrating bilateral activation in the motor cortex. This shows that stroke patients exhibit right hemispheric involvement in compensation when compared to healthy individuals. A study investigating brain function in patients with different levels of motor dysfunction [29] discovered that patients with severe dysfunction displayed considerable hemispheric functional connectivity in the upper-limb motor assistance mode, bilaterally involving prefrontal, motor, and occipital areas, in contrast to patients with moderate dysfunction. Furthermore, the study revealed a significant increase in the involvement of ipsilateral assistive motor areas in the functional brain network.

Studies [30,31] have shown that there is cerebral hemisphere asymmetry after a stroke, and the activation pattern is compensated for by the healthy hemisphere. However, some researchers hold a different view on the compensatory role of the healthy hemisphere, suggesting that it is activated before the affected side, and that activation of the affected side improves the balance of hemispheric activity. The recovery of motor function in stroke patients leads to improvement in symmetry of the primary motor areas of the right and left hemispheres. Delorme et al [32] utilized fNIRS to evaluate primary sensorimotor cortex (SM1) activation progression in the cortex between both hemispheres during unilateral arm movements of stroke patients. Their findings showed a positive correlation between Fugl-Meyer scores and lateralization index in stroke patients, indicating that the cortical activation pattern transitioned from the healthy to the affected hemisphere upon recovery of motor function. Ni et al [33] examined cortical activation in stroke patients who underwent repetitive transcranial magnetic stimulation (rTMS) and discovered that the HbO2 level decreased significantly in the healthy sensorimotor cortex (SMC) region and increased significantly in the impacted SMC region after treatment. The changes were more pronounced in the rTMS group, suggesting that the brain activity pattern progressively normalized as motor function recovered in stroke patients. Functional magnetic resonance imaging (FMRI) results also confirm that activation changes are shifted from ipsilateral to contralateral processes [34,35]. Further studies are required to investigate the effect of interhemispheric activation on motor function using fNIRS to determine whether any activation differences are related to stroke severity, length of the rehabilitation cycle, or spontaneous recovery.

LOWER LIMB MOVEMENT:

With over 50% of patients experiencing multiple falls within 1 year after a stroke [36] and being unable to walk independently in the community [37], gait restoration has become a critical priority for stroke survivors. Investigating the neural correlates of walking is a crucial initial step toward understanding how brain activation can serve as a valuable biomarker or indicator of neuroplasticity throughout the rehabilitation process. A systematic review of real-time brain activity during walking in stroke patients [38] demonstrated that brain activation is greater in those who have had a stroke than in young adults without stroke. Additionally, hyperactivation and asymmetric activation of the brain after a stroke are reduced with walking interventions and improvements in walking ability. He et al [39] used fNIRS to investigate the mechanisms of cortical activation during walking in 8 healthy people and 6 hemiplegic patients and found that hemiplegic patients showed more sensorimotor cortex (SMC), supplementary motor area (SMA), and pre-motor cortex (PMC) cortical activation during walking than healthy subjects, and that activation in the unaffected (right) hemisphere was higher in hemiplegic patients during walking than during in healthy subjects. The functional electrical stimulation (FES)-assisted walking task increased activation in the affected hemisphere.

Dual-tasking involves performing 2 tasks simultaneously. Dual-task walking is an effective approach for examining walking function following a stroke [40,41]. Lim et al [42] discovered heightened activity in prefrontal, pre-motor, and posterior parietal cortical brain areas during dual-task walking. However, activation did not increase further with task difficulty. There is also evidence that brain activity increases with task complexity [43,44]. Liu et al [45] observed stroke patients’ brain activity during 3 tasks: single walking (SW), walking while performing the cognitive task (WCT), and walking while performing the motor task (WMT) at a self-selected speed. Their findings demonstrated that gait performance deteriorated during both dual tasks. Furthermore, hemoglobin difference indices substantially increased in the SMA and most channels of the bilateral PMC during WCT and WMT. SMA and particularly PMC were vital in cognitive and motor dual-task walking after stroke. However, the prefrontal cortex (PFC) is the most frequently investigated region [46,47], and it is widely acknowledged [48,49] that there are enhanced levels of PFC activity when performing dual-task walking. The mechanism may be related to the brain’s need for additional executive functions to resolve inter-task interference during dual-tasking and the loss of walking automaticity in stroke patients [50]. Based on the above studies, a dual-task training strategy, either motor or cognitive, could be implemented to enhance plasticity in the motor cortex, leading to improved recovery of motor function. However, Colett et al [51] reported contrasting findings, demonstrating decreased PFC activation in dual-task as opposed to single-task performance. This variability in results among studies could be attributed to their distinct types of dual-tasking.

Balance problems often manifest themselves in unsteady sitting and standing postures and reduced walking ability, which affects the ability to carry out daily activities. Mihara [52] used fNIRS to assess changes in PFC, SMA, and parietal activation induced by external postural perturbations in 2 scenarios: 1 in which information about external perturbations was provided, and 1 in which no information about external perturbations was provided. They then evaluated cortical activation linked to external postural perturbations prompted by swift forward and backward movements of the platform among 20 stroke patients [53]. The outcomes revealed marked rises in HbO in the bilateral PFC, as well as in pre-motor and parietal cortical regions in the unaffected hemisphere. A longitudinal study of cortical activation for balance recovery after stroke[54] showed a significant increase in postural perturbation-related activation in the bilateral SMA after intensive rehabilitation and a significant positive correlation between the HbO signal of the SMA in the healthy hemisphere and scores on the Berg Balance Scale. Herold’s fNIRS study similarly confirmed that the SMA plays a crucial role in recovery of balance function after stroke, particularly in controlling the mid-lateral sway state [55]. The PFC was significantly activated when subjects performed a 5-minute incremental tilt board balance task in a semi-immersive virtual reality environment, and prefrontal activation increased with increasing difficulty [56]. Motor areas such as the SMA and primary motor cortex (M1) also play an important role in neuromotor balance control, according to fMRI studies [57,58].

NIRS as an Assessment Tool

REHABILITATION ROBOTS:

Kim et al [59] conducted a study comparing 3 modes of walking training – conventional stepping walking (SW), treadmill walking (TW), and robot-assisted walking (RW) – in healthy participants. The results from fNIRS topographic mapping analyses indicated an increase in network activation in the SMC, PMC, and SMA regions for both training modes. However, during RW, more global motion network activations were observed. Li et al [60] discovered through fNIRS that patients with upper-limb dysfunction exhibited elevated levels of HbO in the affected PFC and ipsilateral M1, along with increased functional connectivity between the cortices, in contrast to patients undergoing upper-limb suspension training, while receiving upper-limb intelligent robot training. Network asymmetry associated with motor control was improved in chronic stroke patients walking with a portable hip-assisted robot [61]. Furthermore, there was a statistically significant increase in the activation of SMA, SMC, and PMC on the affected side solely in the end-effector robot-assisted gait training group [62]. Robot-assisted walking additionally stimulates sensory input, leading to enhanced variability in gait and raised brain activity in the regions of motor and sensory control [63]. All of these studies demonstrate the efficacy of robotic rehabilitation training. fNIRS provides an objective foundation for the future use of rehabilitation robots in rehabilitation of stroke patients.

Shi et al [64] found, using fNIRS, that cortical activation was more pronounced in patients undertaking a non-resistance exercise circle-drawing task with an upper-limb rehabilitation training system. They also found that activation was greater in the SMA and pre-motor area (PMA) during resistance exercises than in non-resistance exercises. Bae et al [65] recruited 23 post-stroke hemiplegic subjects and used fNIRS to monitor activation level of the right wrist during passive movement of the upper-limb rehabilitation robot at different speeds, and found that HbO levels in the left SMC area were significantly increased at medium speed (0.5 Hz). Zheng et al [66] investigated the effects of different training speeds on cortical activation in active and passive modes of an upper-limb rehabilitation robot and found that at slower movement speeds, active training promotes better cortical activation related to cognition and motor control than passive training. Therefore, we hypothesized that the choice of active, moderate-velocity resistance training using a rehabilitation robot would be more beneficial for functional recovery. However, there are currently no established standards for achieving optimal brain activation during the use of rehabilitation robots. Further research is required to produce better data and protocols for robotic rehabilitation.

TRANSCRANIAL MAGNETIC STIMULATION:

Transcranial magnetic stimulation (TMS) can alter cortical activation by enhancing or decreasing cortical excitability, dependent on the stimulation parameters. As a result, it is extensively utilized in post-stroke rehabilitation. Nonetheless, the precise mechanism underlying TMS therapy remains uncertain. Changes in cortical excitability stimulated by TMS are quantifiable through fluctuations in HbO concentration using fNIRS.

A randomized controlled trial [67] discovered that, following 1 Hz rTMS rehabilitation in the healthy hemisphere, cortical activation in the healthy hemisphere’s SMC area decreased. This indicates that rTMS inhibitory therapy can lower cortical activation in the healthy hemisphere, establishing a balance between the 2 cerebral hemispheres while also promoting the affected limb’s upper-limb motor function recovery. Zhang and colleagues [68] conducted an fNIRS test on individuals with mild cognitive dysfunction following a stroke who received high-frequency repetitive transcranial magnetic stimulation. They observed therapeutic efficacy and found that rTMS significantly increased frontal HbO concentration, resulting in improved cognitive function. Post-stroke cognitive impairment (PSCI) has been linked to decreased blood oxygen metabolism and functional activity in the brain areas of the dorsolateral prefrontal cortex (DLPFC), PMC, and SM1. Following a single high-frequency TMS intervention, it was observed that the functional connectivity between the left DLPFC, right PMC, and right SM1 brain regions had a significant positive effect on their activation [69]. No consensus currently exists regarding the superiority of either conventional rTMS or iTBS for treating patients with PSCI. Therefore, we aimed to examine the variations between iTBS and rTMS in PSCI patients [70], using fNIRS to study the neural activity changes induced by each intervention. In doing so, we aimed to establish a theoretical foundation for clinical application.

OTHER MEANS OF REHABILITATION:

The fNIRS study has identified disparities in the degree and form of cerebral activation associated with various physical training regimens. Research [71] indicates that the functional strength of the affected upper limb and hand functional areas in stroke patients is enhanced significantly more by unilateral upper limb training than by bilateral training. Additionally, the functional connectivity between some of the areas of interest is also improved. However, contrasting findings emerged in another study [72], indicating that bilateral training of post-stroke patients led to heightened activation of primary and supplementary motor cortical areas, as well as primary somatosensory cortical areas in both hemispheres of the brain. A meta-analysis [73] has revealed that bilateral upper-limb training enhances dyskinesia improvement in stroke patients. Nevertheless, these methods have not led to significant differences in stroke patients’ functional performance in comparison to unilateral upper-limb training. Therefore, additional investigations are necessary to validate these findings.

Several innovative therapeutic techniques have emerged to enhance functional recovery in patients. The use of fNIRS can provide a dependable means of verifying the effectiveness of certain methodologies. Wei et al [74] suggested a treatment called “ Remind-to-Move “ whereby a vibrating wristwatch is attached to the affected hand to prompt the patient to use the affected arm in daily activities. The study discovered through fNIRS assessment that the therapy augmented activity of the primary motor cortex and dorsolateral prefrontal cortex in stroke patients during the chronic phase. Novel robotic mirror therapy is shown to induce greater and more symmetrical neural activation in the motor cortex, as observed by fNIRS [75].

NIRS as a Therapeutic Tool

IMAGINE FEEDBACK:

Mental training based on the motor imagery model has been incorporated into stroke rehabilitation programs, with encouraging results [76,77]. Motor imagery restores damaged motor conduction pathways and stimulates motor cortex networks. There is compelling evidence [78–80] that motor imagery and motor execution share the same neural networks related to motor functions. As direct monitoring of motor imagery is challenging, researchers have enhanced the efficacy of motor imagery training by furnishing subjects with real-time feedback on the targeted cortical activity.

Fujiwara and colleagues [81] used fNIRS to examine variations in motor imagery and cortical hemodynamics in pure motor imagery and reverse video motor imagery of observing either one’s own or somebody else’s hand movement. The findings indicated that reverse video motor imagery of one’s own hand evoked notably greater activation in the motor-related cortex. We have additionally proven [82] the curative effectiveness of this technique in advancing functional recuperation following a stroke in a dual-center, double-blind, randomized controlled test of 54 patients experiencing mild-to-moderate walking problems induced by subcortical stroke who were registered in the study. Six neurofeedback interventions targeting the SMA were conducted on patients using functional near-infrared spectroscopy-mediated neurofeedback (fNIRS-NFB). The patients were randomly assigned to 1 of 2 groups: the true group, which received information regarding their own SMA cortical activity, and the false group, which received pre-recorded activity signals from other participants. Patients in the actual group demonstrated greater improvement in the 3-meter Timed Up and Go test than those in the sham-operated group, with a statistically significant association between the group and time. Notably, image-related activation in the SMA was significantly amplified only with genuine feedback, and resting-state connectivity between the SMA and the ventrolateral pre-motor area was heightened. In agreement with the above evidence, Ota et al [83] reported that fNIRS-based motor imagery training, using neurofeedback in the prefrontal cortex, enhances somatosensory-motor activity, ultimately ameliorating manual dexterity. Furthermore, Kober et al [84] also reported the effect of NIRS-mediated neurofeedback on cortical activation patterns using motor imagery tasks. In a study examining feedback of motor imagery when gripping a ball with the right hand, authentic neurofeedback leads to substantial activation of the left motor regions with particular specificity and focus, but false neurofeedback produced widespread cortical activation patterns that did not yield notable learning effects in a specific area. Although further studies are needed to confirm their clinical efficacy, these findings suggest the feasibility and potential effectiveness of a real-time NIRS-mediated neurofeedback system in motor imagery.

BRAIN-COMPUTER INTERFACE:

Brain-computer interface (BCI) aims to bypass the peripheral nervous system and muscles by using technology that directly translates signals from the central nervous system (CNS) into commands for external device control or communication [85,86]. The CNS signal employed in BCI can be captured using various recording methods. Among them, NIRS has gained much attention from researchers owing to its resistance to external electromagnetic interference and its resilience to motion artifacts. BCI constructed using fNIRS (fNIRS-BCI) have potential applications [87].

Patients who suffer from severe stroke and do not initially benefit from active exercise therapy but only receive passive exercise therapy can benefit from BCI technology [88–90]. This can allow them to interact with the outside world, improving their quality of life. It also provides new human-computer interaction options for healthy individuals [91]. Asgher et al [92] reported that hemiplegic patients supported by a BCI system can control a robotic exoskeleton hand to perform grasping tasks using two-state Mental Workload signals acquired by portable functional near-infrared spectroscopy. Implementing fNIRS technology can enhance walking capabilities through the decoding of subjects’ dynamic gait adjustments [93]. The technology assists walking devices in conjunction with a BCI. However, the fNIRS technique has certain limitations [94], including restricted spatial localization ability and vulnerability to physiological noise due to factors like respiration and heartbeat. To address the limitations of NIRS-based BCI systems and enhance the accuracy of classification, the integration of EEG-fNIRS through multimodal fusion techniques has emerged as a crucial approach for BCI advancement [95,96]. Incorporating EEG-fNIRS-based multimodality resulted in an 8–10% increase in classification accuracy, compared to solely using EEG-based BCI [97]. The hybrid EEG-fNIRS system demonstrated a recognition rate of up to 89%.

RTMS-TARGETED NAVIGATION POSITIONING:

There is ample evidence of motor improvement in stroke patients following rTMS intervention [98]. However, accurate positioning of the affected hemisphere poses a challenge. The predominant approach involves locating the motor hotspot with the highest amplitude of motor evoked potentials (MEP), which is the greatest amplitude of the MEP induced by the lowest intensity of magnetic output. Nevertheless, this is typically unobservable in the affected hemisphere of stroke patients owing to cortical necrosis or a motion-evoked threshold that outstrips the maximum instrument output threshold [99,100]; therefore, developing an innovative approach to positioning is of utmost importance.

In 2017, Hara and colleagues [101] pioneered the use of fNIRS to localize areas of language activation in 8 stroke patients with chronic-phase aphasia. These patients then underwent selective rTMS therapy and intensive speech therapy, guided by fNIRS. The study’s results indicate that the combination of these therapies resulted in significant improvements in language function. Another study investigated the efficacy of using fNIRS as a topographical guide for administering high-frequency rTMS to the ipsilateral lesion hemisphere in stroke patients before treatment [102]. The research comprised 51 post-stroke hemiparesis patients who underwent fNIRS-induced rTMS, MEP-induced rTMS, and sham stimulation. The findings indicate that the group induced with fNIRS developed motor hotspots even in some patients with undetectable MEP-induced hotspots. Furthermore, there was a significant statistical difference in muscle strength and FMA scores between the 2 groups compared to the sham-stimulation group, and elbow function showed greater improvement in the fNIRS group than in the other 2 groups. The above evidence suggests that fNIRS could serve as a dependable instrument to navigate motor neuromodulation hotspots in stroke patients. fNIRS possesses high sensitivity and spatial resolution for identifying areas of motion, offsetting the limitations of conventional techniques. Furthermore, it has cost-effectiveness and dependable navigation, which aid in personalized precision rehabilitation planning.

FUTURE DIRECTIONS:

The application of fNIRS in stroke rehabilitation has been widely studied. fNIRS has been used to monitor cerebral blood oxygenation and cerebral blood flow at stroke onset and throughout treatment and rehabilitation, to explore the relationship between changes in cortical activation and cerebral connectivity after stroke and functional improvement, and to guide the direction and intensity of rehabilitation training. fNIRS also has several potential advantages over techniques particularly relevant to rehabilitation, such as PET and fMRI. For instance, in the rehabilitation population, it is common for individuals to have a history of multiple exposures to ionizing radiation from X-rays or other imaging procedures due to complex and often long-term issues related to injury or illness. However, repeated monitoring using fNIRS is safe and feasible because the NIR light source is non-ionizing and does not exceed the energy accumulated by the brain on a sunny day. Additionally, it is not harmful to tissues and is relatively inexpensive and simple to operate. Furthermore, fNIRS is a non-invasive assessment that can be employed in patients who are unable to undergo magnetic resonance imaging (MRI) [103], such as those with metal implants or claustrophobia. Therefore, fNIRS enables the safe study of individuals who do not meet the prerequisites of other assessment methods. Most importantly, fNIRS can monitor brain activity while the individual is performing actual functional activities [104], as the ultimate goal of rehabilitation is to help the individual return to the highest possible level of functional independence.

While use of fNIRS in stroke rehabilitation is promising, there are still challenges and limitations to its clinical application. Firstly, the spatial resolution of fNIRS is low, which limits its ability to detect activity in deep brain tissue [105]. Additionally, clinical studies often have small sample sizes and focus on individual brain regions. Secondly, various manufacturers produce fNIRS devices with different configurations. The literature shows differences in the use of oxyhemoglobin, deoxyhemoglobin, and total hemoglobin as parameters of cortical activation. Thirdly, there is no consensus on which concentration changes of signals are used by fNIRS to assess brain activity [106,107]. Moreover, despite the wide range of fNIRS preprocessing and analysis procedures and free software available to the community, there has been no agreement on or guidelines for analysis of fNIRS data, unlike other well-established techniques such as fMRI [108]. Fourthly, the fNIRS signal acquisition process is susceptible to the subject’s body temperature, heartbeat, and external stimuli. These factors may cause inaccuracies in the data and impair the signal-to-noise ratio. Finally, it is important to note that hemodynamics and neural activity occur asynchronously. Changes in extracerebral and systemic blood flow may affect the accuracy of study results.

Therefore, in future studies, fNIRS technology can be combined with EEG and fMRI to form a multimodal brain function examination to conduct a deeper study of the brain. In clinical studies, fNIRS technology can be used to conduct studies with larger sample sizes and more comprehensive and complex studies involving the whole brain as a way to understand the activation of brain function, reorganization of structures, and connectivity of brain regions after stroke in a comprehensive and detailed manner. In addition, more clinical studies and experience are needed to verify the effectiveness and safety of fNIRS technology in stroke rehabilitation.

Conclusions

In conclusion, fNIRS, as a non-invasive imaging technology for real-time dynamic monitoring of brain function, fills gaps in the study of post-stroke neural remodeling mechanisms with its multiple advantages of safety, portability, integrality, and integrality. fNIRS has great potential in the monitoring, assessment, and treatment of post-stroke rehabilitation. As an objective and effective indicator, fNIRS is expected to have wider clinical applications in the field of post-stroke rehabilitation. This will promote the enrichment, scientific development, and individualization of post-stroke assessment and treatment protocols.