Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks

Abstract. Safe locomotion is a crucial aspect of human daily living that requires well-functioning motor control processes. The human neuromotor control of daily activities such as walking relies on the complex interaction of subcortical and cortical areas. Technical developments in neuroimaging systems allow the quantification of cortical activation during the execution of motor tasks. Functional near-infrared spectroscopy (fNIRS) seems to be a promising tool to monitor motor control processes in cortical areas in freely moving subjects. However, so far, there is no established standardized protocol regarding the application and data processing of fNIRS signals that limits the comparability among studies. Hence, this systematic review aimed to summarize the current knowledge about application and data processing in fNIRS studies dealing with walking or postural tasks. Fifty-six articles of an initial yield of 1420 publications were reviewed and information about methodology, data processing, and findings were extracted. Based on our results, we outline the recommendations with respect to the design and data processing of fNIRS studies. Future perspectives of measuring fNIRS signals in movement science are discussed.


Introduction
Safe locomotion is indispensable for human daily living and requires good functionality of motor control processes. The efficiency of motor control processes of daily motor activities such as walking 1,2 and standing 3,4 relies on complex neuronal networks encompassing subcortical and cortical brain structures. Studies show that a smaller gray matter volume is associated with lower gait performance indicated by increased gait variability [5][6][7] or slower gait velocity. 8,9 Moreover, lower wholebrain gray matter volume goes along with worse postural balance performance irrespective of age, 10 whereas the increase of gray matter volume is associated with balance improvements. [11][12][13] In older age, however, shrinking of those cortical structures 14,15 might diminish motor control capabilities. 16 The substantial body of literature suggests that cortical structures play an important role for the motor control of daily motor tasks. Therefore, the assessment of cortical activity while subjects are moving is a key factor to foster a better understanding of neuromotor control which, in turn, could help to improve rehabilitation strategies. 17 Brain activity can be measured by the following neuroimaging methods: functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), positron-emission-tomography (PET), electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS). While fMRI is considered as gold standard for the assessment of activity in cortical and subcortical structures, it suffers from the vulnerability for movement artifacts and the restricted range of motion in the scanner. [18][19][20][21] Likewise, MEG exhibits a high vulnerability for motion artifacts 18 while the use of PET does not allow repeated measurements due to the injection of radioactive tracers. 20 EEG puts out not only signals with high temporal resolution but also signals with a relatively weak spatial resolution. 18,22 Furthermore, EEG is vulnerable to artifacts, time consuming in preparation, 18,22,23 and the signals are hard to interpret for nonexperts. 24 Hence, fMRI, MEG, PET, and EEG suffer from specific restrictions that hamper a time-efficient evaluation of cortical activation in moving subjects.
fNIRS is a relatively new optical neuroimaging technique that uses the theory of neurovascular coupling. 19,[25][26][27] Neurovascular coupling results from the neuronal activity or glia activity that provokes an enhanced blood flow in an active brain region to satisfy energetic demands of the neuronal tissue. [27][28][29] Based on these hemodynamic responses of neuronal cortical tissues, the fNIRS technology allows an indirect evaluation of brain activation (such as fMRI). 18,19 Therefore, light with different wavelengths in the nearinfrared spectrum is emitted through the skull and undergoes some scattering and absorption processes inside the neuronal tissue. 27,30,31 In the neuronal tissue, the chromophores such as oxygenated (oxyHb) and deoxygenated hemoglobin (deoxyHb) absorb light at different spectra 19,20,32,33 whereas the nonabsorbed components of the scattered light leave the brain in a banana-shaped course. Those components are recorded by a detector on the head surface. 30,31,34 Based on the described neurovascular coupling, an enhanced brain activation induces an intensified blood flow in the active brain regions leading to an increase in oxyHb and decrease of deoxyHb. 27,30 As a consequence of the different absorption spectra of the chromophores, the activity-dependent concentration changes in oxy-and deoxyHb can be quantified with the modified Beer-Lambert law and used as an indicator of regional brain activation. 19,20,27,30,32 The advantage of optical neuroimaging using fNIRS is the possibility to measure cortical activity (quantified as changes in tissue oxygenation and blood perfusion, associated with neural activity) noninvasively 25,27,35,36 with a relatively good spatial and temporal resolution. [19][20][21][22] This benefit makes fNIRS systems suitable for the usage in special cohorts, such as children. 18,20,22,[36][37][38][39][40] Moreover, fNIRS systems are applicable even during outdoor activities 41 and could be used as a monitoring tool in neurorehabilitation settings. 18,[42][43][44] From this point of view, fNIRS is a promising tool to understand the contribution of cortical areas in the neuromotor control of gross motor skills, such as posture and walking. 17 However, the fNIRS technology also has some disadvantageous including a limited depth sensitivity that restricts the measurements of brain activity to cortical layers 36 and the vulnerability to systemic vascular changes that may contaminate the signal during strenuous physical tasks. 27,45 In addition, no standardized procedures regarding the usage of fNIRS with respect to measuring cortical activity in moving subjects exist 17,42 which clearly limits the comparability across existing studies.
This systematic review elucidates the application of fNIRS in neuromotor research and concentrates on two crucial motor tasks, namely locomotion and postural stability. In this context, we aim to give an overview about (a) the methodological approach of fNIRS and (b) the main findings of the fNIRS measurements reported in the literature.

Systematic Literature Search and Data Extraction
Two independent researchers performed a systematic literature search to identify all relevant studies applying fNIRS to investigate hemodynamic brain responses during walking and postural tasks on February 4, 2017. Therefore, we used the following search terms: gait OR walking OR posture OR "postural control" OR balance OR balancing OR sway AND NIRS OR fNIR OR fNIRS OR "functional near-infrared spectroscopy" OR "near-infrared spectroscopy" OR "functional near-infrared spectroscopic" OR "optical imaging system." All studies that used brain-computer interfaces, examined orthostatic regulation or animals, provided insufficient statistical methods, or used non-English language were excluded. During this procedure, six articles were excluded due to the lack of statistical analyses, [46][47][48] ineligible measurement condition, 49 and non-English language. 50,51 The search and screening process is shown in Fig. 1. From the included studies, data about cohort characteristics, fNIRS methodology, and main findings were extracted.

Results: Methodology Employed in the Studies
In the following, we will provide information about the methodological approaches of the reviewed studies. We focused on general aspects regarding the application, data processing, and data analyzing of fNIRS (e.g., study design, used filter methods, and statistical analysis). Further information about the cohorts, tasks, sampling frequencies, wavelengths, and numbers of channels can be requested by e-mail from the corresponding author or is available in Ref. 52.

Overground walking
Twenty-three studies investigated cortical hemodynamic responses during overground walking. 66 72 Two studies used 20 s of rest between successive trials and 1 to 2 min of rest between successive task blocks. 80,81 Furthermore, in three studies, a rest of 2 min was used 66,86,90 while one study allowed participants to rest 5 88 or 30 min between tasks 71 (for an overview see Table 1).

Postural tasks
Regarding the examination of brain activity during a sensory organization test (SOT; a balance test using quantitatively different visual, proprioceptive, and vestibular cues to assess the quality of postural stance stability), two trials, 92 three trials, 96 or four trials were conducted 103 which lasted 45, 92 40, 103 or 20 s. 96 The participants of the three studies using mechanical perturbations performed 15 95,102 to 30 trials 94 with a randomized perturbation duration of 5 to 20 s. 94,95,102 In semivirtual reality, seven trials with a task phase duration of 45 s were used. 98 The rest between task phases depended on the conducted tasks (see Table 1) and ranged between 4 and 20 s. 91,94,95,101 In other studies, a rest of 1 103 or 2 min was included. 92,98,99 To avoid fatigue, resting times after some trials that lasted a few minutes were common 91,92,94 (for an overview see Table 1).

Data Processing: Signal Filtering and Movement Artifact Removal
Twenty-one studies applied a low-pass filter (LPF) to their data, 54

Markers for the Assessment of Cortical Activation
The majority of reviewed studies used changes of oxyHb to assess brain activation. 53 76 used Hb diff (oxyHb -deoxyHb) for the quantification of cortical activation. Furthermore, one study used a cortical activation ratio 62 to measure brain activation (for an overview see Table 1).

Results: Main Findings of the Studies
In the following sections, we will provide an overview about the main findings of the reviewed studies. The results section is divided into outcomes of walking and postural tasks.

Walking
Walking was associated with a higher activation of prefrontal cortex (PFC), 53 72 In single task walking, PFC activation positively correlated with the neuropsychological performance in healthy older persons 68 and with motor performance in neurologically diseased persons. 70,71 A decrease in PFC activation was observed in younger adults while walking and solving a working memory task 79,88 and in healthy seniors while solving a complex visual task. 105 Interestingly, the activation of PFC in older adults is decreased after a motor intervention 68 and when textured insoles were used or barefoot walking was conducted. 66 In contrast, the inpatient intervention in stroke patients enhanced PMC activation during walking. 60 Additionally, an increase of motor complexity due to the increase in walking speed led to a pronounced activation of PFC, 62 SMA, 53 and Broca area, 64 whereas a decrease of motor complexity due to body weight support induced a decrease in SMC activation. 59

Postural Tasks
In balance tasks, the activation of PFC, 91,98,99 SMA, 101,102 and superior temporal gyrus 97 was modulated by task difficulty and by age-related processes. 104 Furthermore, an increased PFC activation was observed during standing in young adults with postconcussion symptoms, 108 in patients with Parkinson's disease 93 or in stroke patients' in the affected 95,102 and unaffected hemisphere. 95 Furthermore, stroke patients showed a stronger activation in PMC and parietal areas concerning the unaffected hemisphere. 95 After the rehabilitation program, the same patients showed a decreased activation of PMC and parietal areas but a bilateral increase in PFC and SMA activations. 102 Neurophotonics 041403-14 Oct-Dec 2017 • Vol. 4 (4) During the SOT, different sensory information changes the functional connectivity of brain areas 96,103 and induced activation changes especially in superior marginal gyrus, 92,96 operculum, 96 temporal-parietal areas, 103 and occipital regions. 103 Additionally, correlation between balance performance and the activation of PFC 95,102 and SMA was observed. 95,100,102 5 Discussion fNIRS is a relatively new neuroimaging technique that has attracted attention in scientists who examine neuromotor control. This resulted in a considerable magnitude of published studies. However, a summarization and evaluation that can help to improve future experimental protocols was still required. In the first part of the discussion section, we will discuss the findings about study designs, fNIRS configurations and data processing steps to come closer to more standardized protocols that are not available at this moment. 27,112 In the second part, the main findings of the reviewed studies are discussed.

Baseline condition and duration
The majority of studies with walking or postural tasks assessed baseline brain activation in quiet standing. Interestingly, Holtzer et al. 74,75 used a silent counting task to avoid mind wandering. Mind wandering occurs up to 50% of the waking hours 113 for instance during driving 114,115 especially when perceptual requirements are low. 116 Moreover, the wandering of the mind is characterized by the processing of task unrelated thoughts such as worrying about the past or future, 117 which evokes a stronger activation of default networks 118 and hence changes the activation in PFC areas. 119,120 In addition, it was shown by Durantin et al. 120 that fNIRS is sensitive to detect mind wandering. Based on these assumptions, it is possible that mind wandering influences the cortical activation during baseline (and maybe motor control) affecting further analyzation processes. Hence, it might be advantageous to use the approach of Holtzer et al., 74,75 which eventually minimizes the detrimental effect of mind wandering on cortical activation and leads to a more standardized baseline assessment. However, before the usage of this simple counting task can be recommended, further research should investigate its influence on cortical activation patterns including examination of enhanced reproducibility.

Number and duration of trials and rest phases
Our results revealed that the number of trials and their durations varied across the studies evaluating walking or postural tasks. The most common time interval was set to 30 s. However, we are unaware of a study investigating the influence of measurement strategy (e.g., required number of trials to achieve a sufficient reproducibility). Hence, further methodological investigations to optimize fNIRS measurement protocols are needed. Moreover, the duration and number of the trials depend on the aim of the study. Longer measurement durations may be useful to study the contribution of different areas in the temporal course of movement execution. In contrast, longer measurement durations could result in motor fatigue. Motor fatigue does diminish performance for example in postural tasks [121][122][123][124][125][126] and would hence change underlying motor control processes. This again could potentially evoke altered hemodynamic responses, which were observed after cognitive fatigue. 127 However, research examining the interplay between a specific gross motor task and hemodynamic responses as a function of physical fatigue level has not been conducted yet.
Another interesting point influencing the trial duration is the combination of analysis methods. From a movement scientific view, the analysis of gait features (especially gait variability and stability) gives an insight in the central organization of motor control processes [128][129][130][131] and those are useful to detect risk groups such as fallers. 132,133 To reliably assess gait variability or stability, a larger number of strides is required 134,135 and as a consequence, a sufficiently long time period (in which an adequate number of strides can be undertaken) of the trial duration has to be recorded. The rest phase durations in included studies have varying temporal ranges. In general, empirical evidence suggests that refraction time or time with reduced responsiveness lasted for almost the same duration as stimulus time. 136 Hence, we recommend to include intertrial rest intervals with at least the same duration as the task period, especially in block design studies.

Source-detector separation
The separation of source to detector is one important aspect for penetration depth 27,34 and the influence of extracerebral signals. 34,137 Our results indicated that 3 cm was the most commonly used distance in the reviewed studies. In the literature, different recommendations about optimal source-detector separation exist. While some authors recommend 4 cm, 34 other collectives recommend 3 cm. 138,139 In addition, especially in children or infants shorter interoptode distance (>2.0 cm) is recommended for usage. 22,139 The issue of the optimal separation between source and detector is a controversial debate because different third variables such as different colors of the participant's skin and/or hair used wavelengths and head size could influence penetration depth. 34,140 Furthermore, the varying thickness of scalps, skulls, and cerebrospinal fluids in individuals and cortical regions [141][142][143] could influence the penetration depth and the sensitivity to hemodynamic changes in cortical layer. [142][143][144] Remarkably, a longer source-detector separation leads to a greater contribution of cerebral than extracerebral layer to obtain hemodynamics signals. [145][146][147][148] The penetration depth of light is less than half of the interoptode distance 147 causing short channel distances to cover only signals from noncerebral compartments. 137,141 For instance, at the source-detector separation of 3 cm, the contribution of the gray matter to the light absorption is estimated to range from about 20% to 30%. 149 Moreover, Kohri et al. 150 observed that at source-detector separation of 2, 3, and 4 cm, the cerebral tissue contributes to 33%, 55%, and 69% to the optical signal. Hence, we recommend that the source-detector separation should be greater than 3 cm to enhance the contribution of cerebral cortical layer to the optical signal.

Placement of optodes
The majority of the studies used the 10 to 20 EEG systems to place the optodes. This standardized location system ensures the comparability among the different studies. The additionally used 3-D digitizer or individual MRI scan improves the registration of channels to specific brain areas. Based on the data we recommend for optode placement the usage of the 10 to 20 EEG systems to ensure the comparability among studies.

Differential path length factor
Our results show that most studies used constant DPF with a value of 6. The usage of a constant DPF value seems not always appropriate because the brain undergoes age-related changes of gray and white matter, 151,152 intracranial volume, 153 and cerebral volume as well as blood flow 154 , which may affect DPF. 155 Furthermore, methodological studies show that DPF values are (1) age-dependent and subject-specific, 110,155,156 (2) wavelength-dependent, 110,155,157 and (3) cortex region-dependent. 110,155,[158][159][160] Hence, it seems favorable to calculate specific DPF values to enhance the measurement accuracy in age-groups in which formulas to calculate age-specific DPF values are available (adults under 50 years). 110,155 Otherwise, "arbitrary units," 161 TOI, [162][163][164] or absolute values 137,163,165 could be used since those do not depend on a specific DPF value. In addition, it is suggested that the calculation of effect sizes is useful to deal with the DPF issue. 166 However, additional research is strongly needed that provides a formula to calculate DPF values for specific age-groups (adults older than 50 years) dependent on wavelength and cortex region. In our opinion, the optimal approach to quantify DPF, taking the dependency of DPF regarding subject, age, wavelength, and cortex region into account, is the direct quantification of DPF using frequency or time-domain NIRS.

Data processing: signal filtering and movement artifact removal
In sum, either LPFs or HPFs were commonly applied in the reviewed studies to remove noise and drifts. Most of the studies used a cut-off frequency for LPF around 0.1 Hz and HPF around 0.01 Hz. The reviews of Brigadoi et al., 167 Cooper et al., 168 and Gervain et al. 40 recommended to use a bandpass filter (consisting of both LPFs and HPFs) with cut-off frequencies at 0.5 (LPF) and 0.01 Hz (HPF). The bandpass filtering should be used carefully to avoid accidental removal of stimulusdependent hemodynamic response signals. 111 Hence, a higher cut-off frequency at 0.5 Hz (LPF) in conjunction with other more sophisticated filter methods is recommended to be used for the removal of movement and physiological noise. 111,167,168 Different methods such as PCA, [169][170][171] task-related component analysis, [172][173][174] CBSI, 175 wavelet-based filters, 171,176-179 autoregressive algorithm-based filters, 180 Kalman filter, 181 and Wiener filter 182 are proposed for the filtering of fNIRS data. Interestingly, Nozawa et al. 183 suggested that effectiveness of motion correction filter methods depends on subject and task. However, reviews comparing a variety of filter methods recommend the additional application of wavelet filter 167,168 or spline technique. 168 These filter methods were occasionally applied in reviewed studies 80,105,106 leaving potential to optimize the filtering processes in further studies. Based on these assumptions, we recommend the usage of a bandpass filter and wavelet filter to reduce motion artifacts. If there are sudden shifts in the data (baseline shift), the approach developed by Scholkmann et al. 184 can be useful to remove them.

Data processing: correction for physiological artifacts
Twelve studies recorded physiological signals such as heart rate, blood pressure, or arterial oxygenation saturation parallel to the fNIRS signals. Task-related systematic changes in heart rate, respiration rate, or blood pressure are known to influence the fNIRS signal and may cause false-positive results. 45 For instance, often unconsidered factors such as adding of speech as a task (e.g., in dual-task paradigms) lead to changes in partial pressure of end-tidal carbon dioxide, which influences cerebral hemodynamics and masked neuronal-induced activity changes. 185,186 Hence, to improve the accuracy of fNIRS, the recording and elimination of systemic physiological changes seems necessary. 45,187,188 The signals of additional physiological measures could be useful for filtering of fNIRS signal 189,190 or to ensure the absence of systematic physiological differences among the experimental conditions. 87 In addition, some measures such as heart rate variability could be used to study the interplay between the central (fNIRS) and the autonomic (e.g., heart rate variability) nervous system. 120,191 Furthermore, filter methods based on PCA and independent component analysis, which were applied in six studies, 55,56,73,76,100,102 could be used to remove movement-related 167 or physiological artifacts. 169,170,[192][193][194][195] In addition to the other filter methods, 196,197 a more "direct" approach to reduce extracerebral noise is the use of short separation channels or multidistance technique [198][199][200] , which were applied in only two of the reviewed studies. 54,68 Short separation channels have a small distance between source and detector to record extra cerebral signals, such as superficial blood flow. 141,198,201 These extracerebral signals are used to filter the remaining fNIRS data. Previous studies revealed that the application of short separation channels is powerful in reducing extracerebral noise 141,145,200,[202][203][204][205][206][207][208] , which contaminates fNIRS signals. 45,199,201,[209][210][211][212][213][214] The optimal distance between short separation channels varied across different cortex regions 141,202 but should be generally <1 cm for measurement on the head of adult humans. Hence, further development and implementation of short separation channels (multidistance technique) could enhance the accuracy of fNIRS measurements and have to be considered whenever technically possible.

Data processing: final data processing and statistical analysis
Most studies used baseline normalization and baseline correction to circumvent the influence of different path lengths factors. 166 Furthermore, averaging of channels across trials and in specified ROIs was common practice in the reviewed studies. Some studies divided their task phase in different time periods, which seems useful for studying the contribution of cortical areas in different temporal periods during task execution. Therefore, attention should be paid to the temporal delay of ∼2 to 5 s in hemodynamic response. 69,107,139 The majority of the reviewed studies used simple statistics based on processing mean values over the task period. This approach, however, tends to result in a loss of acquisition of information because it does not consider the temporal shape of the fNIRS signal. 192 Hence, some authors suggest that the analysis of fNIRS data with general linear models is more favorable. 192,215 However, the choice of the statistical analysis methods should depend on the research question and the experimental design. 216 For instance, in an event-related design, the application of a general linear model is a valid technique 216 whereas simple statistics might also be appropriate (and commonly used 192 ) especially in studies utilizing block designs. 55,56,[59][60][61][62]107,217 The majority of reviewed studies used parametric methods for statistical data analysis. In fNIRS studies, the assumptions for parametric tests are sometimes violated (e.g., normal distribution due to small sample size); therefore, nonparametric tests are a considerable option. 218,219 Moreover, nonparametric tests are more robust and less influenced by outliers or nonnormal distributed data [220][221][222] and are recommended to use in fNIRS studies. From another point of view, in neuroscience, multiple experimental conditions (crossed) or multiple observations per condition (nested) were used. 223,224 Furthermore, different categorical or continuous confounding variables have to be considered (e.g., gait speed, education, and gender) and/or data were unbalanced or incomplete, which makes it necessary to use advanced statistical methods. 223,225 To account for those problems, linear mixed-effect models can be used. 10,[224][225][226] However, statistical methods should be chosen carefully considering the experimental design and distribution of recorded data. A further description of statistical methods for fNIRS data is given in the reviews of Tak and Ye 192 and Kamran et al. 227

Markers for the assessment of cortical activation
The majority of reviewed studies used only oxyHb for the quantification of cortical activation since a change in oxyHb is assumed to be a more robust marker of changes in regional cerebral blood flow than changes in deoxyHb. 160,228,229 However, this procedure seems questionable because neuronal activity is not just mirrored in an increase of oxyHb but also in a decrease in deoxyHb in healthy adults. 30,230 Furthermore, an enhanced level of physiological noise is more prominent in oxyHb signals 30 and the decrease in deoxyHb is related to an increase in BOLD contrast obtained in fMRI 231,232 , which supports the validity of the evaluation of deoxyHb changes. In pathological states, neurovascular coupling might perhaps be impaired, which results in altered concentration changes in deoxyHb during neural activity. 230 Lindauer et al. 230 assumed that in some pathological states, an increase in deoxyHb may reflect neural activity. Based on the mentioned assumptions, it seems favorable to report at least oxyHb as well as deoxyHb to assess task-dependent activity. 30 Evidence from neuroimaging studies point out that two distinct supraspinal locomotor networks are responsible for the control of walking and standing 1,233-237 (see Fig. 3). The direct locomotor network consists of the primary motor cortex (M 1) and the cerebellar locomotor region and is potentially activated in the absence of pathologies or challenging situations. 235 In the indirect locomotor pathway, the neuronal commands are transmitted via PFC and SMA to the basal ganglia and subthalamic as well as mesencephalic locomotor regions. [233][234][235][236][237] The indirect locomotor pathway becomes activated when the automatic execution of walking is impaired (e.g., in challenging situations) and compensatory mechanisms are necessary. 44,238 This assumption is supported by findings of our reviewed fNIRS studies, which reported more pronounced activation in prefrontal structures in (1) in adults during dual-task walking, 57,63,63,66,69,[72][73][74]77,86,89 (2) in adults during fast walking, 53,64 (3) in obese persons, 87 (4) in individuals with low gait capacity during fast walking, 53 (5) in older adults with high level of perceived fatigue 85 or stress, 84 (6) in old adults with increased fall risk, 83 and (7) in neurological patients. 58,70,71,75,80,82,90 Remarkably, the PFC activation in neurological patients correlates with their step widths, 71 which again (1) is associated with balance control 239 and (2) serves as a predictor of falls. 240 Furthermore, correlations between cortical activation and motor performance, 55,56 especially obvious in dual-task walking conditions, 76,77,89,105 was observed. This reinforces the important role of cortical areas in motor control. Moreover, the reduction of PFC activity after a motor-cognitive intervention program (lasting 8 weeks) 68 perhaps originated from the shift toward a more automatic control of locomotion relying on the enhanced usage of direct locomotor pathway via M1, cerebellum, and spinal cord. 1,233,234,238 However, premotor areas and the SMA play a role in different cognitive processes [241][242][243] and were activated as a function of task difficulty in a variety of cognitive domains. [244][245][246] Hence, the phenomenon of a more pronounced activation of premotor areas (as part of indirect locomotor pathway) in diseased cohorts (or during challenging motor tasks) is perhaps not fully attributable to motor task complexity but partly also to general task complexity.
However, the decrease in PFC activity in a complex visual task 105 or difficult working memory tasks during walking 79,88 may not be induced by the shifts in locomotor pathways but rather originate from the prioritization of task-relevant areas as consequence of the limited resources of the brain. 247 While those three studies focused only on PFC activity, it is difficult to draw a final conclusion about potentially underlying cortical processes in other areas. Hence, to elucidate the mechanisms with respect to task prioritizations, we require further research 248,249 including the simultaneous assessment of more cortical structures (e.g., motor areas).
For the design and monitoring of rehabilitative interventions, fNIRS could be a promising tool. 42 For instance, the SMC activity decreases during weight-supported walking in stroke patients 59 and could be a hint that weight supports lower task complexity. 250 Interestingly, a verbal preadvice 67,94 or the usage of mechanical assistance during walking 61,106 increases central nervous load. These findings could be useful to create tailored rehabilitation programs that consider mental load as variable for workload assessment.

Postural tasks
As pointed out for walking, neural control of posture is realized via direct or indirect pathway 251 which are shown in Fig. 3. Our results reveal that the PFC activation is enhanced in (1) neurological patients during standing 93 or during postural perturbations 95,102 and (2) healthy adults during challenging balance tasks. 91,98,99 These findings and the observations that PFC activity and SMA are associated with balance measures 95,100,102 support the notion that indirect locomotor pathway is crucial for neuromotor processes in nonautomatized challenging situations.
Additionally, altered sensory information evoked by the execution of SOT induces a higher activation especially in STG. 92,96 The STG is associated with (1) the control of more difficult balance tasks, 97 (2) the integration of vestibular information, [252][253][254] and (3) the spatial orientation. 255 So far, the mentioned studies did include only young participants. 92,96 While aging changes the contribution of somatosensory, vestibular, and visual system in balance tasks, 256 it seems necessary to enlarge existing knowledge about cortical sensory integration processes.

Key Studies
In the following, we highlight one key study in the area of walking and balance. Those studies are of high practical relevance and cannot be performed in an fMRI since motor imagery is suggested not to be a satisfactory indicative of brain activation during motor execution. 257

Walking
The usage of a smartphone during walking causes serious injuries. 258,259 Hence, the understanding and the analysis of underlying motor control processes of walking while texting on a smartphone seems to be of high practical relevance. 260 The investigation of smartphone usage while recording the kinematics of gait is not possible in an fMRI-scanner but could be conducted with fNIRS. In the study of Takeuchi et al., 89 the influence of using a smartphone while walking was investigated in healthy old and young adults. Takeuchi et al. 89 observed that in young adults, the activation magnitude of left PFC is associated with dual-task cost (change between single-and dual-task performances) of gait acceleration and right PFC is related to the dual-task cost of the conducted cognitive smartphone task. In contrast, in the older adults middle PFC was associated with dual-task costs of step time and the activation of the left PFC is associated with dual-task costs of gait acceleration. 89 Furthermore, younger adults have lower dual-task costs in kinematic parameters. 89 In sum, these results point toward the effective lateralization in young adults, while in older adults more resources are needed to maintain gait performance which is in accordance with the theories of hemispheric asymmetry reduction 261 and compensational recruitment. 262

Postural Tasks
While fMRT is sensitive to motion artifacts, 18-21 the simultaneous recording of brain activity and the quantification of kinematic parameters of gross motor skills (e.g., dynamic whole-body balance task) are impossible. Remarkably, it is assumed that to increase our knowledge about neuromotor control processes, the simultaneous assessment of brain activity and kinematic parameters is necessary. 263 Furthermore, gross motor skills are, for example, an essential part of rehabilitative interventions (e.g., balancing on wobble board [264][265][266] ). The study of Herold et al. 100 used fNIRS to investigate the contribution of motor areas in online neuromotor control of balance performance on a wobble board and recorded simultaneous sway parameters via an inertial sensor. They observed (1) a pronounced activation of PrG, PoG, and SMA during balancing and (2) a strong negative correlation between the magnitude of SMA activation and sway in mediolateral direction during balancing. 100 The results of Herold et al. 100 allow a deeper understanding of the role of the SMA in online neuromotor control of balance movements and may be helpful to design tailored intervention programs or to monitor the intervention progress.

Conclusion
In sum, neuroimaging with the fNIRS technology seems to be a promising tool to shed light on the functioning of cortical areas in motor control. However, the absence of standardized study protocols limits the comparability among studies. Based on our findings, we deduce recommendations and potential future directions, which are shown in Table 2. Hopefully, those recommendations will lay foundations to improve the study protocols and data processing of fNIRS methodology encouraging further research to extend our existing knowledge about neuromotor control processes. This increase in knowledge might be helpful to develop tailored rehabilitation programs for clinical settings in, e.g., orthopedics and neurology. 42 Furthermore, combining the information we can derive from fNIRS signals with kinematic parameters which are risk factors for falls 132,267  cognitive decline 268 could perhaps support a more sensitive and effective early detection of persons with a high likelihood for falls or with a high risk to develop cognitive diseases. This, in turn, may allow an early onset of therapeutic interventions, an effective monitoring of intervention programs and it would support the decision making in health care units. Those potential applications could be beneficial for patients and the resources of the health care system. Recommendations: • Report all technical configuration details (source-detector separation, wavelengths, sampling frequency, number of measurement channels, DPF values with selection process, etc.) and design-related details (e.g., duration of task and rest phases).
• Optode placement should be based on the 10 to 20 EEG system.
• Additional measures (e.g., heart rate, blood pressure, respiration, skin conductance, etc.) should be used to monitor systematic changes.
• In order to process data, the use of bandpass filters and wavelet filters is recommended.
• DPF values should be calculated depending on age and cortex region or directly quantified via frequency-or time-domain NIRS.
• Physiological cofounders (e.g., scalp blood flow) should be reduced with the aid of PCA/ICA analyses or the usage of short separation channels.
• Baseline correction or baseline normalization should be applied.
• Averaging across channels of a ROI and trials seems to be favorable.
• The relative changes of both, oxyHb and deoxyHb, should be reported and used in the statistical analysis.