1 August 2017 Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks
Author Affiliations +
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.



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 walking1,2 and standing3,4 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 variability56.7 or slower gait velocity.8,9 Moreover, lower whole-brain 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.1112.13 In older age, however, shrinking of those cortical structures14,15 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.1819.20.21 Likewise, MEG exhibits a high vulnerability for motion artifacts18 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.19,2526.27 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.2728.29 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 near-infrared 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 spectra19,20,32,33 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) noninvasively25,27,35,36 with a relatively good spatial and temporal resolution.1920.21.22 This benefit makes fNIRS systems suitable for the usage in special cohorts, such as children.18,20,22,3637.38.39.40 Moreover, fNIRS systems are applicable even during outdoor activities41 and could be used as a monitoring tool in neurorehabilitation settings.18,4243.44 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 layers36 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 exist17,42 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,4647.48 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.

Fig. 1

Search process and identification of relevant studies.



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.


Baseline Condition and Duration


Treadmill walking

Fifteen studies investigating cortical activation during treadmill walking assessed baseline brain activation during quiet standing.5354. In contrast, Eggenberger et al.68 chose slow walking (2  km/h) for 1 min as the baseline condition. The duration of baseline cortical activation used for further analyses varied between 2.555,56 and 20 s5960.61 (for an overview see Table 1).

Table 1

Overview about the population, study designs, and data processing steps of reviewed fNIRS studies (note that the number of trials is reported per condition).

First author– Population (n=number of participants; age in years±SD)1. Baseline condition1. DPF
• Conditions2. Baseline duration2. Data processing (filtering)
3. Number of trials and duration3. Final data processing
4. Rest phase duration4. Activation parameters
5. Time used for analysis
Al-Yahya et al.65– Stroke patients (n=19; 59.61±15.03)1. Quiet standing1. Age-dependent value (4.99+0.067×age0.814)
– Healthy old adults (n=20; 54.35±9.38)2. 25 to 45 s (randomized order)2. LPF at 0.67 Hz
• DTW vs. NW3. 5×; 30 s3. Baseline correction; averaging
4. 25 to 45 s (between trials / randomized order)4. Oxy- and deoxyHb
5. 6 to 16 s after task begin
Atsummori et al.69– Healthy young adults (n=6; 29.7±3.3)1. Quiet standing1. Constant value (no details reported)
• DTW vs. NW2. 5 s before task begin2. Not reported
3. 5× (DTW) / 6× (NW); 10 s3. Baseline correction; averaging
4. 20 s at beginning4. Oxy- and deoxyHb
5. 6 s after task begin/ending
Basso-Moro et al.98– Healthy young adults (n=16; 29±4.8)1. Quiet standing1. Age-dependent value (4.99+0.067×age0.814)
• Perturbations in semivirtual reality with increasing difficulty2. Last 30 s (of 2 min)2. LPF at 0.1 Hz
3. 7×; 45 s3. Averaging
4. 2 min after block4. Oxy- and deoxyHb
5. Last 10 s of perturbation
Beurskens et al.105– Healthy young adults (n=15; 24.5±3.3)1. Sitting on chair1. Constant value (6.0)
– Healthy old adults (n=10; 71.0±3.8)2. 30 s2. HRF-filter; wavelet MDL detrending algorithm
• DTW vs. NW3. 2×; 30 s3. Moving standard deviation and spline interpolation, baseline correction, canonical HRF
4. Not reported4. Oxy- and deoxyHb
5. Entire task time
Caliandro et al.70– Patients with ataxic gait (n=14; 27 to 71)1. Quiet standing1. Constant value (5.93)
– Healthy controls (n=20; 32 to 65)2. Last 10 s of standing2. LPF at 0.1 Hz
• Patients vs. HC3. 1×; 10 m3. Baseline correction; averaging
4. Not relevant4. OxyHb
5. Entire task time expect of first 5 s
Caliandro et al.71– Patients with ataxic gait (n=19; 31 to 70)1. Quiet standing1. Constant value (5.93)
– Healthy controls (n=15; 36 to 73)2. Last 10 s of standing2. LPF at 0.1 Hz
• Patients vs. HC3. 2×; 10 m3. Baseline correction; averaging
4. 30 min between trials4. OxyHb
5. Entire task time expect of first 5 s
Clark et al.66– Older persons with mobility and somatosensory deficits (n=14; 77.1±5.56)1. Quiet standing1. N/A
• Walking in normal shoes vs. texture insoles vs. barefoot vs. DTW2. 10 s immediately before task2. No filter
3. 5× walking laps with 18 m (overground); 60 to 120 s (treadmill)3. Averaging
4. 2 min after task4. TOI
5. Entire task phase
Clark et al.86– Older adults with mild mobility deficits (n=16; 77.2±5.6)1. Quiet standing1. N/A
• NW vs. DTW2. 10 s immediately before task2. No filter
3. 5× walking laps with 18 m3. Averaging
4. 2 min after task4. TOI
5. 10 s before task begin (preparation phase) and in steady phase/transition phase excluded
Doi et al.72– Adults with mild cognitive impairment (n=16; 75.4±7.2)1. Quiet standing1. N/A (arbitrary unit)
• NW vs. DTW2. 10 s before walking2. LPF at 0.05 Hz; linear fitting on baseline data
3. 3×; 20 s3. Averaging
4. 30 s between trials4. OxyHb
5. Entire task period
Eggenberger et al.68– Healthy old adults (dancing: n=19; 72.8±5.9; balance: n=14; 77.8±7.4)1. Walking at 2  km/h1. N/A (absolute values)
• Dancing vs. balancing (before and after intervention)2. Middle 40 s (of 1 min)2. 60 s moving average: motion artifact correction (oxyHb: >2.5 and <2.5  μM/deoxyHb: >1.5 and <1.5  μM excluded); visual inspection of data
3. 8×; 30 s3. Averaging
4. 30 s between trials (walking at 2  km/h)4. OxyHb
5. 1 to 7 s = acceleration phase; 10 to 25 s = steady state walking phase; 26 to 34 s = deceleration phase; 35 to 46 s = drop phase
Ferrari et al.99– Healthy, young adults (n=22; 26.5±4.0)1. Quiet standing1. Age-dependent value (4.99+0.067×age0.814)
• Balancing in semivirtual reality2. Last 30 s (of 2 min)2. LPF at 0.1 Hz
3. 2×; 9 min3. Averaging
4. 2 min after block4. Oxy- and deoxyHb
5. 30 s per task
Fraser et al.63– Healthy young adults (n=19; 21.83±1.92)1. Quiet standing1. Constant value (no details reported)
– Healthy old adults (n=14; 66.85±5.26)2. 5 s2. No filter
• NW vs. single cognitive task vs. easy DTW vs. hard DTW3. Walking: 2×; 2 min; single cognitive task: 4×; 75 s; DTW 4×; 2 min (for each dual-task condition)3. Averaging
4. 30 to 60 s between trials4. Oxy- and deoxyHb
5. Entire task period
Fujimoto et al.102- Patients with subcortical stroke (n=20; 60.2±9.5)1. Quiet standing1. N/A (arbitrary unit)
• Postural test before/after rehabilitation2. Time before next perturbation (ERD)2. HPF at 0.01 Hz; PCA
3. 15×; 1 s3. Two parameter gamma HRF
4. 5 to 15 s between trials (randomized)4. Oxy- and deoxyHb
5. Around perturbations
Fujita et al.101– Healthy, young adults (low span group: n=13; 24.0±3.1 / high span group: n=16; 22.5±3.6)1. Quiet standing1. N/A (arbitrary unit)
• Single- and dual-task mono- or bipedal standing2. 10 s2. LPF at 0.5 Hz; HPF at 0.01 Hz; 5 s moving average
3. 3×; 20 s3. Baseline normalization, baseline correction, averaging
4. 10 s between trials4. OxyHb
5. Entire task time
Harada et al.53– Healthy, old adults (n=15; 63±4)1. Quiet standing1. N/A (arbitrary unit)
• Low vs. high gait capacity group at different speeds2. 10 s before walking2. HPF at 0.03 Hz
3. 3×; 60 s3. Baseline normalization; averaging
4. 40 s between trials4. OxyHb
5. 20 s after target speed
Helmich et al.108– Young, concussed adults with persistent postconcussive symptoms (n=7; 29±15)1. N/A1. Constant value (6.0)
– Young, concussed adults with minor postconcussive symptoms (n=13; 26±7)2. N/A2. LPF at 0.1 Hz; HPF at 0.001 Hz; spline interpolation; visual inspection
– Healthy, young adults (n=10; 27±8)3. 10×; 10 s3. Normalization; averaging
• Comparison of three groups during standing on different surfaces (stable vs. instable) and sensory conditions (eyes closed vs. eyes open vs. blurred vision)4. No rest between trials4. Oxy- and deoxyHb
5. Entire task time
Hernandez et al.82– Healthy old adults (n=8; 61±4)1. Quiet standing1. Constant value (6.0)
– Patients with multiple sclerosis (n=8; 57±5)2. 10 s before walking (counting silently in steps of 1)2. LPF at 0.14 Hz; noisy channels excluded (dark current condition or saturation); visual inspected
• Comparison of healthy adults and patients with multiple sclerosis during NW and DTW3. 3× walking loops (= 6× straight walks a 14 ft.)3. Baseline normalization; averaging
4. At least 10 s after trial4. OxyHb
5. Entire task time
Herold et al.100– Healthy young adults (n=10; 25; 21 to 47)1. Quiet standing1. N/A (arbitrary unit)
• Standing vs. balancing on balance board2. 30 s before task2. 5.0 s moving average; LPF at 0.5 Hz; HPF at 0.01 Hz; PCA (r=0.25)
3. 3×; 30 s3. Averaging
4. 30 s after trial4. Oxy- and deoxyHb
5. Middle 20 s
Holtzer et al.73– Healthy, young adults (n=11; 19 to 29)1. Quiet standing1. Constant value (6.0)
– Healthy, old adults (n=11; 69 to 88)2. 5 s before walking2. LPF at 0.14 Hz; combined principal and independent component analysis
• DTW vs. NW vs. standing/comparison between cohorts3. 3× walking loops (= 6× straight walks a 15 ft.)3. Baseline normalization; averaging
4. Not reported4. OxyHb
5. Old 4 s / young 3.5 s
Holtzer et al.74– Nondemented older adults (n=318; 76.66±6.7)1. Quiet standing1. Constant value (6.0)
• DTW vs. NW vs. standing2. 10 s (counting silently forward in steps of 1)2. LPF at 0.14 Hz; noisy channels excluded (dark current condition or saturation); visual inspected
3. 3× walking loops (= 6× straight walks a 14 ft.) / standing for 30 s3. Baseline normalization; averaging
4. “Short break” reported4. OxyHb
5. Entire task time
Holtzer et al.84– Nondemented older adults (n=348; 76.8±6.8)1. Quiet standing1. Constant value (6.0)
– Older adults with low perceived stress (n=147; 76.72±6.87)2. 10 s (counting silently forward in steps of 1)2. LPF at 0.14 Hz; noisy channels excluded (dark current condition or saturation); visual inspected
– Older adults high perceived stress (n=171; 76.58±6.37)3. 3× walking loops (= 6× straight walks a 14 ft.) / standing for 30 s3. Baseline normalization; averaging
• DTW vs. NW vs. standing/comparison between cohorts4. “Short break” reported4. OxyHb
5. Entire task time
Holtzer et al.75– Nondemented older adults (total: n=236; 75.5±6.49)1. Quiet standing1. Constant value (6.0)
– Healthy older adults (n=167; 74.43±6.04)2. 10 s (counting silently forward in steps of 1)2. LPF at 0.14 Hz; noisy channels excluded (dark current condition or saturation); visual inspected
– Older adults with peripheral NGA (n=40; 77.03±6.27)3. 3× walking loops (= 6× straight walks a 14 ft.) / standing for 30 s3. Baseline normalization; averaging
– Older adults with central NGA (n=29; 79.59±7.38)4. “Short break” reported4. OxyHb
• DTW vs. NW vs. standing/comparison between cohorts5. Entire task time
Holtzer et al.85– Older adults with low perceived fatigue (n=160; 76.20±6.64)1. Quiet standing1. Constant value (6.0)
– Older adults with high perceived fatigue (n=154; 77.41±6.66)2. 10 s (counting silently forward in steps of 1)2. LPF at 0.14 Hz; noisy channels excluded (dark current condition or saturation); visual inspected
• DTW vs. NW vs. standing/comparison between cohorts3. 3× walking loops (= 6× straight walks a 14 ft.) / standing for 30 s3. Baseline normalization; averaging
4. “Short break” reported4. OxyHb
5. Entire task time
Huppert et al.91– Healthy young adults (n=10; 21 to 47)1. Quiet standing1. Not relevant (image reconstruction)
• Stepping reaction task2. Time before next trial (4 to 8 s, random order)2. Discrete cosinus transform term (01/120  Hz); visual inspected
3. 8× blocks a 32× trials3. Gamma-variant HRF; averaging
4. 4 to 8 s between trials (random order) / few minutes after 2 to 3 scans4. Oxy- and deoxyHb
5. Entire task phase
Karim et al.97– Healthy young adults (n=9; 18 to 42)1. Quiet standing1. Not relevant (image reconstruction)
• Video game with balance task2. 60 s (pre- and posttask)2. Cosinus transform term (0 to 1/120  Hz); visual inspected
3. 6× beginner / 8× advanced level; 30 to 60 s3. Boxcar HRF; averaging
4. 30 s between trials4. Oxy- and deoxyHb
5. Entire task phase
Karim et al.92– Healthy young adults (n=15; 28±9)1. Quiet standing1. Not relevant (image reconstruction)
• SOT conditions2. 45 s before trial2. Cosinus transform term (0 to 1/120  Hz)
3. 2×;45  s3. Gamma-variant HRF; averaging
4. 60 s after trial / 2 min after two scans4. Oxy- and deoxyHb
5. Entire task phase
Kim et al.106– Healthy young adults (n=14; 30.06±4.53)1. Not reported1. Not reported
• Stepping (ST) vs. Treadmill walking (TW) vs. robot-assisted walking (RAW)2. Not reported2. Gaussian smoothing; wavelet MDL algorithm
3. 5×; 30 s (ST, TW); 60 s (RAW)3. Canonical HRF
4. 15 s at begin and end; 30 s between trials (ST, TW) / 60 s at begin and end; 45 s between trials (RAW)4. OxyHb
5. Entire task time
Koenraadt et al.54– Healthy, young adults (n=11/23±4)1. Quiet standing1. N/A (arbitrary unit)
• Precision walking vs. NW2. 25 to 35 s2. LPF at 1.25 Hz; HPF at 0.01 Hz; superficial interference with LPF at 1 Hz; short separation channels (1 cm)
3. 10×; 35 s3. Baseline normalization; averaging
4. 25 to 35 s before/after trial / 3 min after 10 trials4. Oxy- and deoxyHb
5. 12.5 s in task phase
Kurz et al.55– Healthy, young adults (n=13; 23.7±1.4)1. Quiet standing1. N/A (arbitrary unit)
• Forward vs. backward walking2. 2.5 s before walking2. HPF at 0.01 Hz; 5 s moving average; PCA (r=0.25)
3. 10×; 30 s3. Baseline correction; averaging
4. 30 s between trials4. Oxy- and deoxyHb
5. Entire task phase
Kurz et al.56– Children with spastic diplegic cerebral palsy (n=4; 11.0±4)1. Quiet standing1. N/A (arbitrary unit)
– Healthy children (n=8; 13.2±3)2. 2.5 s before walking2. HPF at 0.01 Hz; 5 s moving average; PCA (r=0.25)
• Patients vs. HC3. 10×; 30 s3. Baseline correction; averaging
4. 30 s between trials4. OxyHb
5. Entire task phase
Lin et al.103– Healthy middle-aged adults (n=15; 46±11)1. Quiet standing1. N/A (image reconstruction)
– Healthy old adults (n=15; 73±5)2. 40 s before trial2. Autoregressive model with prewhitened iterative reweighted least squares algorithm
• Middle-aged vs. old adults (different balance conditions)3. 4×; 40 s3. HRF; averaging
4. 1 min between trials4. Oxy- and deoxyHb
5. Entire task phase
Lin and Lin79– Healthy young adults (n=24; 20 to 27)1. Quiet standing1. Age-dependent value (4.99+0.067×age0.814)
• DTW vs. NW2. 20 s2. LPF at 0.2 Hz
3. 1×; 60 s3. Baseline correction
4. 20 s before/after task / 2 min after two trials4. OxyHb
5. Entire task phase
Lu et al.76– Healthy young adults (n=17; 23.1±1.5)1. Quiet standing1. Constant value (6.0)
• DTW vs. NW2. 5 s before walking2. Bandpass filter (LPF at 0.01 Hz; HPF at 0.2 Hz); PCA; spike rejection (channels with > CV 15% rejected/channels with CV > 10% for further analysis)
3. 3×; 60 s3. Averaging
4. 60 s between trials4. Hbdiff (oxyHb–deoxyHb)
5. Early phase (5 to 20 s after task begin); late phase (21 to 50 s after task begin)
Mahoney et al.93– Healthy, nondemented older adults (n=126; 74.41±6.12)1. Quiet standing1. Constant value (6.0)
– Older adults wild mild Parkinson symptoms (n=117; 77.50±6.72)2. First 2 s2. LPF at 0.14 Hz; visual inspected
– Patients with Parkinson disease (n=26; 81.23±5.93)3. 10 s3. Baseline normalization; averaging
• Patients vs. HC (standing while counting silently in steps of 1)4. “Short break” reported4. OxyHb
5. Entire task phase
Maidan et al.90– Parkinson patients with FOG (n=11; 66.2±10.0)1. Walking1. Age-dependent value (4.99+0.067×age0.814)
– Healthy controls (n=11; 71.2±6.0)2. 6 s before FOG2. LPF at 0.14 Hz
• Patients vs. HC (walking; turning)3. 6 s walking with 180 deg turn3. Baseline correction; averaging
4. 2 min between tasks4. OxyHb
5. Defined time period around FOG event (prior=6 to 3  s / before=3 to 0 / during=0 to 3 s)
Maidan et al.80– Healthy, older adults (n=38; 70.4±0.9)1. Quiet standing1. Age-dependent value (4.99+0.067×age0.814)
– Parkinson patients (n=68; 71.7±1.1)2. 5 s before task2. Bandpass filter (LPF at 0.01 Hz and HPF at 0.14 Hz), wavelet filter; CBSI
• DTW vs. NW vs. obstacle negotiation3. 5×; 30 s3. Baseline correction; averaging
4. 20 s after trial / between trials on individual needs4. OxyHb
5. Entire task phase
McKendrick et al.88– Healthy, young adults (n=13; 22; 19 to 31)1. Sitting (for sitting condition) and standing (for walking condition)1. Constant value (5.94)
• Sitting vs. walking indoors vs. walking outdoors (all conditions while performing secondary task)2. 10 s2. LPF at 0.1 Hz; visual inspected
3. 16×; 120 s (sitting) / 8×; a 120 s (per walking condition)3. Baseline correction
4. 5 min between walking conditions4. Oxy- and deoxyHb
5. Entire task time
Meester et al.57– Young, healthy adults (n=17; 27.8±6.3)1. Quiet standing1. Age-dependent value (4.99+0.067×age0.814)
• DTW vs. NW2. Middle 10 s of rest2. LPF at 0.67 Hz; 4 s moving average; visual inspected
3. 5×; 30 s3. Averaging
4. 20 to 40 s between trials4. OxyHb
5. Middle 10 s of task
Metzger et al.64– Healthy young adults (n=12; 27.6; 19 to 39)1. Quiet standing1. N/A (arbitrary unit)
• DTW vs. NW2. 10 s at begin2. 5 s moving average; CBSI
3. 4×; 45 s3. Averaging; baseline correction
4. 15 s after trial4. Oxy- and deoxyHb
5. Entire task time
Mihara et al.58– Stroke patients with ataxic gait (n=12; 52.7±16.9, 12 to 74)1. Quiet standing1. N/A (arbitrary unit)
– Healthy controls (n=11; 42.6±11.6, 30 to 70)2. 6 s before walking2. Not reported
• Patients vs. HC3. 3×; 60 s (HC); 30 s (patients)3. Baseline correction; averaging
4. 15 s before/after walking4. OxyHb
5. Acceleration phase = 6 s after starting treadmill; steady phase = 6 s during steady speed
Mihara et al.94– Healthy young adults (n=15; 29.4±6.7)1. Quiet standing1. N/A (arbitrary unit)
• Warned before perturbations vs. baseline; unwarned before perturbations vs. baseline; warned vs. unwarned2. Time before next perturbation (ERD)2. HPF at 0.05 Hz
3. 20 to 30×; 1 s3. Gaussian HRF; averaging
4. 5 to 20 s between trials (randomized) / 4 to 5 min after block4. OxyHb
5. Around perturbation
Mihara et al.95– Stroke patients (n=20; 61.6±11.9)1. Quiet standing1. N/A (arbitrary unit)
• Balance perturbations2. Time before next perturbation (ERD)2. HPF at 0.03 Hz
3. 15×; 1 s3. Two-parameter gamma HRF
4. 5 to 15 s between trials (randomized)4. OxyHb
5. Around perturbations
Mirelman et al.77– Young, healthy adults (n=23; 30.9±3.7)1. Quiet standing1. Age-dependent value (4.99+0.067×age0.814)
• Standing vs. DTS vs. NW vs. DTW2. 20 s before walking2. LPF at 0.14 Hz; continuous wavelet transform
3. 5×; 30 m3. Baseline correction; averaging
4. 20 s before/after trial4. OxyHb
5. Entire task phase
Miyai et al.107– Healthy young adults (n=8; 35±8, 24 to 56)1. Quiet standing1. N/A (arbitrary unit)
• NW vs. standing2. 30 s2. HPF at 0.03 Hz
3. 5×; 30 s3. Linear interpolation; averaging
4. 30 s between trials4. Oxy- and deoxyHb
5. Entire task phase
Miyai et al.61– Stroke patients (n=6; 57±13)1. Quiet standing1. N/A (arbitrary unit)
• Walking with mechanical assistance vs. walking with facilitation technique2. Middle 20 s2. HPF at 0.03 Hz
3. 4×; 30 s3. Linear interpolation; baseline correction; averaging
4. 30 s between trials4. OxyHb
5. Last 20 s of task phase
Miyai et al.60– Stroke patients (n=8; 57±12)1. Quiet standing1. N/A (arbitrary unit)
• Before/after 2 months rehabilitation2. Middle 20 s2. HPF at 0.03 Hz
3. 4×; 30 s3. Linear interpolation; baseline correction; averaging
4. 30 s between trials4. OxyHb
5. Last 20 s of task phase
Miyai et al.59– Stroke patients with hemiparesis (n=6; 57±6)1. Quiet standing1. N/A (arbitrary unit)
– Healthy controls (n=6, 53±11)2. Middle 20 s2. HPF at 0.03 Hz
• Walking with weight support vs. walking without weight support3. 4×; 30 s3. Linear interpolation; baseline correction; averaging
4. 30 s between trials4. OxyHb
5. Last 20 s of task phase
Nieuwhof et al.81– Parkinson patients (n=14; 71.2±5.4)1. Quiet standing1. Constant value (6.0)
• DTW (with different tasks)2. Last 5 s of standing2. LPF at 0.1 Hz; visual inspected
3. 5×; 40 s3. Baseline correction; averaging
4. 20 s between trials / 1 to 2 min between blocks4. OxyHb and deoxyHb
5. Entire task phase
Osofundiya et al.87– Obese old adults (n=10; 80.6±6.79)1. Quiet standing1. Constant value (6.0)
– nonobese old adults (n=10; 80.6±7.50)2. 10 s2. Not reported
• Quiet sitting vs. NW vs. precision walking vs. DTW3. 8× a 30 s3. Baseline correction; averaging
4. 10 s between trials4. OxyHb and HbT
5. Entire task phase
Saitou et al.78– Hemiplegic stroke patients (n=44; 66±9.3)1. Quiet standing1. Constant value (5.9)
• Different tasks (e.g., calculation, pulley, we only consider walking vs. baseline)2. 5 min2. Not reported
3. 1×; 5 min3. Averaging
4. 5 min4. OxyHb; CBV; COV
5. Entire task phase
Suzuki et al.62– Healthy, young adults (n=9; 28.1±7.4, 22 to 46)1. Quiet standing1. N/A (arbitrary unit)
• Walking at different speeds2. First 13 s2. HPF at 0.03 Hz
3. 3×; 90 s3. Linear interpolation; baseline correction; averaging
4. 30 s between trials4. Oxy- and deoxyHb; regional cortical activation ratio (oxy Hb of the specific channel divided by oxyHb of all 42 channels multiplied by 100)
5. 13.5 s in task phase
Suzuki et al.67– Healthy, young adults (n=7; 31.3±4.8, 24 to 45)1. Quiet standing1. Not relevant (arbitrary unit)
• Walking with vs. without verbal preadvice2. 10 s before walking2. HPF at 0.03 Hz
3. 4×; 40 s3. Baseline normalization; averaging
4. 10 to 25 s between trials (randomized order)4. Oxy- and deoxyHb
5. First 10 s of task phase
Takeuchi et al.89– Young healthy adults (n=16; 25.9±4.4, 20 to 33)1. Walking1. Constant value (no details reported)
– Healthy older adults (n=15; 71.7±3.3, 65 to 78)2. 30 s2. Spike rejection (artifact with more than 3 SD); 5 s moving average; bandpass filter (LPF at 0.5 Hz; HPF at 0.01 Hz)
• Walking vs. walking with smartphone3. 15×; 10 s3. Baseline normalization; averaging
4. No rest4. OxyHb
5. Entire task phase
Takakura et al.96– Healthy young adults (n=11; 33.4±7.4)1. Quiet standing1. Constant value (1.0)
• SOT conditions2. 20 s before task2. Bandpass Fourier filter (0.01 to 0.1 Hz)
3. 3×; 20 s3. Averaging
4. Few minutes after 3 trials4. OxyHb
5. Entire task phase
Verghese et al.83– Older adults (n=166; 75±6.1)1. Quiet standing1. Constant value (6.0)
– NW vs. DTW vs. standing2. 10 s (counting silently forward in steps of 1)2. LPF at 0.14 Hz; noisy channels excluded (dark current condition or saturation); visual inspected
3. 3× walking loops (= 6× straight walks a 14 ft.) / standing for 30 s)3. Baseline normalization; averaging
4. “Short break” reported4. OxyHb
5. Entire task phase
Wang et al.104– Healthy young adults (n=22; 24.4±1.6)1. Sitting (eyes closed)1. Age-dependent constant value (WL: 760  nm=5.91; WL: 850=5.40)
– Healthy older adults (n=39; 70.5±7.7)2. 20 min2. Bandpass filter (0.005 to 2 HZ)
• Standing connectivity differences healthy young and healthy old adults3. 1×; 10 min3. Wavelet phase coherence analysis
4. No rest4. OxyHb
5. Entire task time

Abbreviations: deoxyHb, deoxygenated hemoglobin; DTS, dual-task standing; DTW, dual-task walking; ft., feet; HC, healthy controls; HPF, high-pass filter; HRF, hemodynamic response function; LPF, low-pass filter; MDL, minimum description length; MRI, magnetic resonance imaging; N/A, not applicable; NW, normal walking; NGA, neurological gait abnormalities; oxyHb, oxygenated hemoglobin; PCA, principal component analysis; RAW, robot assisted walking; SOT, sensory organization test; ST, stepping; TOI, tissue oxygenation index; and vs., versus.


Overground walking

Twenty-one studies conducting overground walking, quantified baseline brain activation in a standing position.66,6970. In contrast, two studies assessed baseline brain activation while walking89 or during a predefined time period prior to a freezing of gait event (FOG; a sudden, brief inability to start movement or to continue rhythmic, repeated movements despite the internal intention to move).90 The duration to assess baseline brain activity ranged between 5 s69,73,76,81 and 5 min.78 Most studies used 10 s to quantify baseline brain activity.66,7071.72,74,75,8283. Interestingly, Holtzer et al.74,75,8384.85 asked their participants to conduct a simple counting task (in steps of 1) during the baseline condition (for an overview see Table 1).


Postural tasks

In postural research, 13 studies assessed baseline brain activity during quiet standing.9192. The temporal duration to quantify baseline brain activity ranged from 293 to 60 s.97 In most studies, data of 30 s9899.100 or a few seconds before starting the next trial91,94,95,102 were used to assess baseline brain activation. In addition, Wang et al.104 used 20 min quiet sitting to measure baseline connectivity (for an overview see Table 1).


Number and Duration of Trials and Rest Phases


Treadmill walking

The studies that used a treadmill for the walking condition5354.,105106.107 are shown in Table 1. Per task, a minimum of 2 trials105 and a maximum of 10 trials5455.56 were performed. Most studies used three to five trials to assess task-relevant cortical activity.53,57,5960.61.62,64,64,65,67,106,107 Task phases were set to 30 s in the majority of the studies,5556.,65,68,105106.107 but Harada et al.,53 Kim et al.,106 and Mihara et al.58 used 60 s, Koenraadt et al.54 used 35 s, Metzger et al.64 used 45 s, Suzuki et al.67 used 40 s, Suzuki et al.62 used 90 s, and Fraser et al.63 used 120 s. The time of the rest phases ranged in most studies between 25 and 60 s.5354.55.56.57,5960.61.62,65,68,106,107 Additionally, rest times of 15 s prior to58,64 and after each walking trial58 were reported while Suzuki et al.67 implemented 10 to 25 s between trials (for an overview see Table 1).


Overground walking

Twenty-three studies investigated cortical hemodynamic responses during overground walking.66,6970. For each condition, 1,70,79 3,76 4,87 5,66,69,71,77,80,81,86 6,7374.75,8283.84.85 and 15 walks were used.89 Either the time for each task phase ranged between 1069 and 120 s66 or the participants were asked to walk a predetermined distance ranging between 47374.75,82,83,85 and 90 m.86 The resting phases prior to and after each trial lasted 20 s77,79 or 60 s76 and 10 s87 or 30 s between the trials.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 used66,86,90 while one study allowed participants to rest 588 or 30 min between tasks71 (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 conducted103 which lasted 45,92 40,103 or 20 s.96 The participants of the three studies using mechanical perturbations performed 1595,102 to 30 trials94 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 1103 or 2 min was included.92,98,99 To avoid fatigue, resting times after some trials that lasted a few minutes were common91,92,94 (for an overview see Table 1).


Source–Detector Separation

The closest distances between the optodes (source and detector) were reported to be 1  cm, which was used as a short separation channel54 and was followed by an interoptode distance of 2.5 cm.7374.75,8283.84.85,93,105 Three studies used 3.2 cm,91,92,97 and two studies used 3.5 cm.80,81,90 Another seven studies used 4 cm.70,71,7879.80.81,104 One study used a different distance between source and detector (1, 3, and 4 cm)54 and another one used multidistance measurement (2.0, 2.5, 3.5, and 4.0 cm).68 The remaining 36 studies set the interoptode distances at 3 cm.53,5556.,6465.66.67,69,72,76,77,80,81,8687.88.89,9495.96,9899.,106107.108 An overview on used source–detector is shown in Fig. 2(a).

Fig. 2

Overview on (a) used source–detector separation and (b) DPF values in the reviewed studies.



Placement of the Optodes

The majority of studies used the international “10-20 EEG system” for the placement of the optodes.5354.55.56.57,5960.,6768.,79,8182.83,85,8788. In some studies, an additional three-dimensional (3-D)-digitizer was applied69,76,9495.96,106,108 or an MRI scan was conducted5960.61.62,67,90,9495.96,102,107 to coregister optode positions on the head. Other placement strategies (placing optodes on the forehead) were applied in four studies.58,66,80,86


Differential Path Length Factor

The differential path length factor (DPF) is a scaling factor that specifies how many times the detected light has traveled farther than the source–detector separation through the brain.109,110 In 21 studies, constant DPF values were used63,6970.71,7374.75.76,78,8182.83.84.85,8788.89,93,96,105,108 whereas nine studies used age-dependent DPF values.57,65,77,79,80,90,98,99,104

For constant DPF, values of 1.0,96 5.9,78 5.93,70,71 5.94,88 and 6.07374.75.76,8182.83.84.85,87,93,105,108 were used, while age-dependent DPF values were calculated according to the formula (DPF=4.99+0.067×age0.814).57,65,77,79,80,90,98,99 An overview on used DPF values is provided in Fig. 2(b). In other studies, arbitrary units,5354.55.56,5859.60.61.62,64,67,72,94,95,100101.102,107 tissue oxygenation index [TOI; the ratio of oxyHb to total hemoglobin (sum of oxy- and deoxyHb)],66,86 image reconstruction,91,92,97,103 or absolute values68 were used, which are not dependent on specific DPF values.


Data Processing: Signal Filtering and Movement Artifact Removal

Twenty-one studies applied a low-pass filter (LPF) to their data,54,57,65,7172.73.74.75,77,79,8182.83,85,88,90,93,9899.100.101 14 studies used a high-pass filter (HPF)53,55,56,5960.61.62,67,94,95,100101.102,107 and 5 studies used a bandpass filter.76,80,89,96,104 Most studies applied an LPF with a cut-off frequency around 0.1 Hz71,7374.75,77,8081.,88,90,93,98,99,108 whereas some studies used an LPF with a cut-off frequency at 0.05,72 0.67,57,65 0.5,100,101 and 1 Hz54 (for an overview see Table 1). Eight studies applied an HPF with a cut-off frequency at 0.03 Hz,53,5960.61.62,67,95,107 six studies at 0.01 Hz,5455.56,80,100,101 and one study at 0.0594 or 0.001 Hz.108 Furthermore, eight studies used the moving average method5556.57,64,68,89,100,101 to smooth their data. Filter methods based on principal component analysis (PCA) were conducted in six studies55,56,73,76,100,102 and a spike artifact correction was used in three studies.68,76,89 Few studies applied HRF filter,105 an autoregressive model with prewhitened iterative reweighted least square algorithms,103 wavelet filter,80,105,106 Gaussian smoothing,106 and correlation-based signal improvement (CBSI).64,80 A visual inspection of data was reported in 13 studies.68,74,75,8182.83.84.85,88,93,97,108,111


Data Processing: Correction for Physiological Artifacts

One study applied short separation channels54 and one study used multidistance measurements68 to correct for superficial blood flow. For multidistance measurements or short separation channels, normally lower source–detector separation (<1.5  cm) is chosen, which is used to probe extracerebral noise. Furthermore, the following additional physiological parameters were measured to take into account systemic physiological artifacts: (1) heart rate,53,57,5960.61.62,71,87,98,99,107 (2) blood pressure,53,54,57,5960.61.62,71,107 and (3) arterial oxygen saturation.5960.61.62,107 The usage of filter methods based on PCA, which can be useful for the correction of motion and physiological noise, was used in six studies.55,56,73,76,100,102


Data Processing: Final Data Processing and Statistical Analysis

Twenty-three of the reviewed studies used a baseline correction53,55,56,5859.60.61.62,64,65,6970.71,77,7980.81,87,90,96,100,101,105 and 14 studies conducted a baseline normalization.53,54,67,7374.75,8283.84.85,89,93,101,108 Furthermore, almost all studies computed an average of (1) all trials and (2) across the channels of a specific ROI.5354.,6869.,8081.,87,8990.91,9394.95.96,9899.100.101,103,107

In addition, linear interpolations were used in the studies of Miyai et al. and Suzuki et al.5960.61.62,107 A method based on moving standard deviation and spline interpolation was applied by Beurskens et al.105 Three studies applied discrete cosine transform terms.91,92,97

Canonical hemodynamic response function was conducted in two studies105,106 that examined cortical activation during walking. Studies researching postural tasks used either a gamma hemodynamic response function91,92,95,102,103 or a Gaussian hemodynamic response function.94 A wavelet coherence analysis was used in one study.104

Five studies divided their task phase in different time periods,58,68,76,86,90 18 studies used predetermined time intervals inside the task phase,53,54,57,5960.61.62,65,67,6970.71,73,93,94,9899.100 and 24 studies used the entire task phase for analysis.55,56,63,64,72,74,75,77,80,82,83,85,87,89,91,92,96,97,101102.103,106107.108

The statistical analysis was performed in 47 studies with parametric53,54,5657.,7374.75.76.77,79,80,8283.,9192.,103104. and in one study with nonparametric methods.81 Eight studies used parametric and nonparametric methods55,71,72,78,90,100101.102 (see Table 1).


Markers for the Assessment of Cortical Activation

The majority of reviewed studies used changes of oxyHb to assess brain activation.53,5758.,68,7071.,7778.79.80,8283.84.85,89,90,9394.95.96,101,104,106 Furthermore, 21 studies used both oxyHb and deoxyHb to quantify the activation of the region of interest.5455.56,6364.65,67,69,81,88,91,92,9798.99.100,102,103,105,107,108 Only Clark et al.66,86 used the TOI, which is the ratio of oxygenated to total tissue hemoglobin, to evaluate cortical activation. In addition, Lu et al.76 used Hb diff (oxyHb – deoxyHb) for the quantification of cortical activation. Furthermore, one study used a cortical activation ratio62 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 was associated with a higher activation of prefrontal cortex (PFC),53,54,57,58,67 presupplementary motor area,53,67 premotor cortex (PMC),53,106 supplementary motor area (SMA),5354.55,58,67,106,107 and sensorimotor cortex (SMC).53,58,67,106 A higher PFC activation was observed in persons with low gait capacity,53 high perceived stress,84 high perceived fatigue,85 high risk of falling,83 ataxic gait,70,71 and patients with Parkinson’s disease80,90 during walking. Moreover, higher activation of precentral gyrus (PrG), postcentral gyrus (PoG), and superior parietal lobule (SPL) was observed in children with cerebral palsy56 and in stroke patients in the nonaffected hemisphere in the PFC,58 SMA,58,61 and SMC.61 During dual-task walking (e.g., walking and solving an additional cognitive or motor task), the PFC exhibited an enhanced activation in stroke patients,65 patients with multiple sclerosis,82 patients with Parkinson’s disease,81 obese adults,87 older adults with mild cognitive impairments,72 old adults with mobility deficits,66,86 and healthy older63,65,7374.75,82 and young adults.57,63,64,73,76,77 In comparison to young adults, older adults exhibited a higher73 or similar63 PFC activation during dual-task walking. The activation of PFC during dual-task walking was associated with the performance in motor tasks,75,77,89,105 cognitive tasks,75,77,89 and neuropsychological tests.72 In single task walking, PFC activation positively correlated with the neuropsychological performance in healthy older persons68 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 task79,88 and in healthy seniors while solving a complex visual task.105 Interestingly, the activation of PFC in older adults is decreased after a motor intervention68 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 gyrus97 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 disease93 or in stroke patients’ in the affected95,102 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

During the SOT, different sensory information changes the functional connectivity of brain areas96,103 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 PFC95,102 and SMA was observed.95,100,102



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.


Discussion: Methodology


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 hours113 for instance during driving114,115 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 networks118 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 tasks121122. 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 processes128129.130.131 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 required134,135 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 depth27,34 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 regions141142.143 could influence the penetration depth and the sensitivity to hemodynamic changes in cortical layer.142143.144

Remarkably, a longer source–detector separation leads to a greater contribution of cerebral than extracerebral layer to obtain hemodynamics signals.145146.147.148 The penetration depth of light is less than half of the interoptode distance147 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 flow154, 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.110,155,158159.160 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,162163.164 or absolute values137,163,165 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 stimulus-dependent 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,169170.171 task-related component analysis,172173.174 CBSI,175 wavelet-based filters,171,176177.178.179 autoregressive algorithm-based filters,180 Kalman filter,181 and Wiener filter182 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 filter167,168 or spline technique.168 These filter methods were occasionally applied in reviewed studies80,105,106 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 signal189,190 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-related167 or physiological artifacts.169,170,192193.194.195 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 technique198199.200, 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 noise141,145,200,202203., which contaminates fNIRS signals.45,199,201,209210. The optimal distance between short separation channels varied across different cortex regions141,202 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 technique216 whereas simple statistics might also be appropriate (and commonly used192) especially in studies utilizing block designs.55,56,5960.61.62,107,217 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 data220221.222 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.10,224225.226 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 Ye192 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 signals30 and the decrease in deoxyHb is related to an increase in BOLD contrast obtained in fMRI231,232, 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.30,45,165


Discussion: Main Findings



Evidence from neuroimaging studies point out that two distinct supraspinal locomotor networks are responsible for the control of walking and standing1,233234.235.236.237 (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.233234.235.236.237 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,57,63,63,66,69,7273.74,77,86,89 (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 fatigue85 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 control239 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

Fig. 3

Schematic illustration of the indirect and direct locomotor pathways as a function of the degree of automaticity in motor control.


However, premotor areas and the SMA play a role in different cognitive processes241242.243 and were activated as a function of task difficulty in a variety of cognitive domains.244245.246 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 task105 or difficult working memory tasks during walking79,88 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 research248,249 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 patients59 and could be a hint that weight supports lower task complexity.250 Interestingly, a verbal preadvice67,94 or the usage of mechanical assistance during walking61,106 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 pathway251 which are shown in Fig. 3. Our results reveal that the PFC activation is enhanced in (1) neurological patients during standing93 or during postural perturbations95,102 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 measures95,100,102 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,252253.254 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



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 reduction261 and compensational recruitment.262


Postural Tasks

While fMRT is sensitive to motion artifacts,1819.20.21 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 board264265.266). 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.



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 falls132,267 or for cognitive decline268 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.

Table 2

Recommendations for future fNIRS studies.

• 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.



For further information about search strategy, cohort characteristics, study protocols, number of fNIRS channels, used wavelengths and sampling frequencies in the reviewed studies, we provide supplemental content which is available in Ref. 52, or can be requested by e-mail from the corresponding author.


The authors declare no conflict of interest.


1. D. Hamacher et al., “Brain activity during walking: a systematic review,” Neurosci. Biobehav. Rev. 57, 310–327 (2015). http://dx.doi.org/10.1016/j.neubiorev.2015.08.002 Google Scholar

2. R. Holtzer et al., “Neuroimaging of mobility in aging: a targeted review,” J. Gerontol. Ser. A: Biol. Sci. Med. Sci. 69(11), 1375–1388 (2014). http://dx.doi.org/10.1093/gerona/glu052 Google Scholar

3. J. V. Jacobs and F. B. Horak, “Cortical control of postural responses,” J. Neural Transm. 114(10), 1339–1348 (2007). http://dx.doi.org/10.1007/s00702-007-0657-0 Google Scholar

4. K. Takakusaki, “Neurophysiology of gait: from the spinal cord to the frontal lobe,” Mov. Disord.: Off. J. Mov. Disord. Soc. 28(11), 1483–1491 (2013). http://dx.doi.org/10.1002/mds.v28.11 Google Scholar

5. A. L. Rosso et al., “Higher step length variability indicates lower gray matter integrity of selected regions in older adults,” Gait Posture 40(1), 225–230 (2014). http://dx.doi.org/10.1016/j.gaitpost.2014.03.192 Google Scholar

6. O. Beauchet et al., “Higher gait variability is associated with decreased parietal gray matter volume among healthy older adults,” Brain Topogr. 27(2), 293–295 (2014).BRTOEZ0896-0267 http://dx.doi.org/10.1007/s10548-013-0293-y Google Scholar

7. Q. Tian et al., “Lower gray matter integrity is associated with greater lap time variation in high-functioning older adults,” Exp. Gerontol. 77, 46–51 (2016).EXGEAB0531-5565 http://dx.doi.org/10.1016/j.exger.2016.02.009 Google Scholar

8. C. Rosano et al., “Slower gait, slower information processing and smaller prefrontal area in older adults,” Age Ageing 41(1), 58–64 (2011).AANGAH0002-0729 http://dx.doi.org/10.1093/ageing/afr113 Google Scholar

9. M. L. Callisaya et al., “Global and regional associations of smaller cerebral gray and white matter volumes with gait in older people,” PLoS One 9(1), e84909 (2014).POLNCL1932-6203 http://dx.doi.org/10.1371/journal.pone.0084909 Google Scholar

10. M. P. Boisgontier et al., “Whole-brain grey matter density predicts balance stability irrespective of age and protects older adults from falling,” Gait Posture 45, 143–150 (2016). http://dx.doi.org/10.1016/j.gaitpost.2016.01.019 Google Scholar

11. M. Taubert et al., “Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections,” J. Neurosci. 30(35), 11670–11677 (2010).JNRSDS0270-6474 http://dx.doi.org/10.1523/JNEUROSCI.2567-10.2010 Google Scholar

12. M. Taubert et al., “Rapid and specific gray matter changes in M1 induced by balance training,” NeuroImage 133, 399–407 (2016).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2016.03.017 Google Scholar

13. M. Taubert, A. Villringer and P. Ragert, “Learning-related gray and white matter changes in humans: an update,” Neuroscientist 18(4), 320–325 (2012). http://dx.doi.org/10.1177/1073858411419048 Google Scholar

14. N. Raz et al., “Regional brain changes in aging healthy adults: general trends, individual differences and modifiers,” Cereb. Cortex 15(11), 1676–1689 (2005). http://dx.doi.org/10.1093/cercor/bhi044 Google Scholar

15. N. Raz et al., “Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter,” Cereb. Cortex 7(3), 268–282 (1997). http://dx.doi.org/10.1093/cercor/7.3.268 Google Scholar

16. R. D. Seidler et al., “Motor control and aging: links to age-related brain structural, functional, and biochemical effects,” Neurosci. Biobehav. Rev. 34(5), 721–733 (2010).NBREDE0149-7634 http://dx.doi.org/10.1016/j.neubiorev.2009.10.005 Google Scholar

17. S. Perrey, “Possibilities for examining the neural control of gait in humans with fNIRS,” Front. Physiol. 5, 282 (2014).FROPBK0301-536X http://dx.doi.org/10.3389/fphys.2014.00204 Google Scholar

18. S. Cutini and S. Brigadoi, “Unleashing the future potential of functional near-infrared spectroscopy in brain sciences,” J. Neurosci. Methods 232, 152–156 (2014).JNMEDT0165-0270 http://dx.doi.org/10.1016/j.jneumeth.2014.05.024 Google Scholar

19. S. C. Bunce et al., “Functional near-infrared spectroscopy,” IEEE Eng. Med. Biol. Mag. 25(4), 54–62 (2006).IEMBDE0739-5175 http://dx.doi.org/10.1109/MEMB.2006.1657788 Google Scholar

20. J. Saliba et al., “Functional near-infrared spectroscopy for neuroimaging in cochlear implant recipients,” Hear. Res. 338, 64–75 (2016).HERED30378-5955 http://dx.doi.org/10.1016/j.heares.2016.02.005 Google Scholar

21. S. Cutini, S. Moro and S. Bisconti, “Review: functional near infrared optical imaging in cognitive neuroscience: an introductory review,” J. Near Infrared Spectrosc. 20(1), 75 (2012). http://dx.doi.org/10.1255/jnirs.969 Google Scholar

22. S. Lloyd-Fox, A. Blasi and C. E. Elwell, “Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy,” Neurosci. Biobehav. Rev. 34(3), 269–284 (2010).NBREDE0149-7634 http://dx.doi.org/10.1016/j.neubiorev.2009.07.008 Google Scholar

23. J. Thompson, W. Sebastianelli and S. Slobounov, “EEG and postural correlates of mild traumatic brain injury in athletes,” Neurosci. Lett. 377(3), 158–163 (2005).NELED50304-3940 http://dx.doi.org/10.1016/j.neulet.2004.11.090 Google Scholar

24. M. Smith, “Shedding light on the adult brain: a review of the clinical applications of near-infrared spectroscopy,” Philos. Trans. R. Soc. A 369(1955), 4452–4469 (2011). http://dx.doi.org/10.1098/rsta.2011.0242 Google Scholar

25. D. R. Leff et al., “Assessment of the cerebral cortex during motor task behaviours in adults: a systematic review of functional near infrared spectroscopy (fNIRS) studies,” NeuroImage 54(4), 2922–2936 (2011).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2010.10.058 Google Scholar

26. A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci. 20(10), 435–442 (1997).TNSCDR0166-2236 http://dx.doi.org/10.1016/S0166-2236(97)01132-6 Google Scholar

27. S. Perrey, “Non-invasive NIR spectroscopy of human brain function during exercise,” Methods 45(4), 289–299 (2008). http://dx.doi.org/10.1016/j.ymeth.2008.04.005 Google Scholar

28. C. Huneau, H. Benali and H. Chabriat, “Investigating human neurovascular coupling using functional neuroimaging: a critical review of dynamic models,” Front. Neurosci. 9, e1002435 (2015).1662-453X http://dx.doi.org/10.3389/fnins.2015.00467 Google Scholar

29. L.-D. Liao et al., “Neurovascular coupling: in vivo optical techniques for functional brain imaging,” BioMed. Eng. OnLine 12(1), 38 (2013). http://dx.doi.org/10.1186/1475-925X-12-38 Google Scholar

30. H. Obrig and A. Villringer, “Beyond the visible imaging the human brain with light,” J. Cereb. Blood Flow Metab. 23(1), 1–18 (2003). http://dx.doi.org/10.1097/01.WCB.0000043472.45775.29 Google Scholar

31. H. Obrig et al., “Near-infrared spectroscopy. Does it function in functional activation studies of the adult brain?” Int. J. Psychophysiol. 35(2–3), 125–142 (2000).IJPSEE0167-8760 http://dx.doi.org/10.1016/S0167-8760(99)00048-3 Google Scholar

32. M. Izzetoglu et al., “Functional brain imaging using near-infrared technology,” IEEE Eng. Med. Biol. Mag. 26(4), 38–46 (2007). http://dx.doi.org/10.1109/MEMB.2007.384094 Google Scholar

33. F. Scholkmann et al., “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” NeuroImage 85, 6–27 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.05.004 Google Scholar

34. J. León-Carrión, U. León-Domínguez, “Functional near-infrared spectroscopy (fNIRS): principles and neuroscientific applications,” in Neuroimaging Methods, and P. Bright, Ed., pp. 48–74, INTECH Open Access Publisher, Rijeka, Croatia (2017). Google Scholar

35. M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” NeuroImage 63(2), 921–935 (2012).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2012.03.049 Google Scholar

36. V. Quaresima, S. Bisconti and M. Ferrari, “A brief review on the use of functional near-infrared spectroscopy (fNIRS) for language imaging studies in human newborns and adults,” Brain Lang. 121(2), 79–89 (2012). http://dx.doi.org/10.1016/j.bandl.2011.03.009 Google Scholar

37. Y. Minagawa-Kawai et al., “Optical imaging of infants’ neurocognitive development: recent advances and perspectives,” Dev. Neurobiol. 68(6), 712–728 (2008). http://dx.doi.org/10.1002/(ISSN)1932-846X Google Scholar

38. H. Shibasaki, “Human brain mapping: hemodynamic response and electrophysiology,” Clin. Neurophysiol. 119(4), 731–743 (2008).CNEUFU1388-2457 http://dx.doi.org/10.1016/j.clinph.2007.10.026 Google Scholar

39. T. Wilcox and M. Biondi, “fNIRS in the developmental sciences,” Wiley Interdiscip. Rev. Cognit. Sci. 6(3), 263–283 (2015). http://dx.doi.org/10.1002/wcs.1343 Google Scholar

40. J. Gervain et al., “Near-infrared spectroscopy: a report from the McDonnell infant methodology consortium,” Dev. Cognit. Neurosci. 1(1), 22–46 (2011). http://dx.doi.org/10.1016/j.dcn.2010.07.004 Google Scholar

41. S. K. Piper et al., “A wearable multi-channel fNIRS system for brain imaging in freely moving subjects,” NeuroImage 85, 64–71 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.06.062 Google Scholar

42. H. Obrig, “NIRS in clinical neurology—a ‘promising’ tool?” NeuroImage 85, 535–546 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.03.045 Google Scholar

43. M. Mihara and I. Miyai, “Review of functional near-infrared spectroscopy in neurorehabilitation,” Neurophotonics 3(3), 031414 (2016). http://dx.doi.org/10.1117/1.NPh.3.3.031414 Google Scholar

44. V. Gramigna et al., “Near-infrared spectroscopy in gait disorders. Is it time to begin?,” Neurorehabil. Neural Repair 31(5), 402–412 (2017). http://dx.doi.org/10.1177/1545968317693304 Google Scholar

45. I. Tachtsidis and F. Scholkmann, “False positives and false negatives in functional near-infrared spectroscopy: issues, challenges, and the way forward,” Neurophotonics 3(3), 030401 (2016). http://dx.doi.org/10.1117/1.NPh.3.3.030401 Google Scholar

46. E. Tanaka, S. Saegusa and L. Yuge, “Development of a whole body motion support type mobile suit and evaluation of cerebral activity corresponding to the cortical motor areas,” J. Adv. Mech. Des. Syst. Manuf. 7(1), 82–94 (2013). http://dx.doi.org/10.1299/jamdsm.7.82 Google Scholar

47. T. Sukal-Moulton et al., “Functional near infrared spectroscopy of the sensory and motor brain regions with simultaneous kinematic and EMG monitoring during motor tasks,” J. Visualized Exp. 5 (94), 52391 (2014). http://dx.doi.org/10.3791/52391 Google Scholar

48. P. Pinti et al., “Using fiberless, wearable fNIRS to monitor brain activity in real-world cognitive tasks,” J. Visualized Exp. 2(106), e53336 (2015). http://dx.doi.org/10.3791/53336 Google Scholar

49. S. Suzuki, F. Harashima and K. Furuta, “Human control law and brain activity of voluntary motion by utilizing a balancing task with an inverted pendulum,” Adv. Hum.–Comput. Interact. 2010(2), 1–16 (2010). http://dx.doi.org/10.1155/2010/215825 Google Scholar

50. S. Imaoka and E. Matubara, “Postural adjustment mechanisms of cerebrovascular disease patients: a comparison with healthy adults using near-infrared spectroscopy,” Rigakuryoho Kagaku 30(6), 981–985 (2015). http://dx.doi.org/10.1589/rika.30.981 Google Scholar

51. I. Naitou et al., “The dynamics of blood oxygen in the brain of healthy young adults in the performance of various walking styles,” Rigakuryoho Kagaku 28(4), 435–440 (2013). http://dx.doi.org/10.1589/rika.28.435 Google Scholar

52. F. Herold et al., Appendix to “Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks,”  https://www.researchgate.net/profile/Fabian_Herold (2017). Google Scholar

53. T. Harada et al., “Gait capacity affects cortical activation patterns related to speed control in the elderly,” Exp. Brain Res. 193(3), 445–454 (2009). http://dx.doi.org/10.1007/s00221-008-1643-y Google Scholar

54. K. L. Koenraadt et al., “Cortical control of normal gait and precision stepping: an fNIRS study,” NeuroImage 85, 415–422 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.04.070 Google Scholar

55. M. J. Kurz, T. W. Wilson and D. J. Arpin, “Stride-time variability and sensorimotor cortical activation during walking,” NeuroImage 59(2), 1602–1607 (2012).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2011.08.084 Google Scholar

56. M. J. Kurz, T. W. Wilson and D. J. Arpin, “An fNIRS exploratory investigation of the cortical activity during gait in children with spastic diplegic cerebral palsy,” Brain Dev. 36(10), 870–877 (2014).NTHAA70029-0831 http://dx.doi.org/10.1016/j.braindev.2014.01.003 Google Scholar

57. D. Meester et al., “Associations between prefrontal cortex activation and H-reflex modulation during dual task gait,” Front. Hum. Neurosci. 8, 78 (2014). http://dx.doi.org/10.3389/fnhum.2014.00078 Google Scholar

58. M. Mihara et al., “Sustained prefrontal activation during ataxic gait: a compensatory mechanism for ataxic stroke?” NeuroImage 37(4), 1338–1345 (2007).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2007.06.014 Google Scholar

59. I. Miyai et al., “Effect of body weight support on cortical activation during gait in patients with stroke,” Exp. Brain Res. 169(1), 85–91 (2006). http://dx.doi.org/10.1007/s00221-005-0123-x Google Scholar

60. I. Miyai et al., “Longitudinal optical imaging study for locomotor recovery after stroke,” Stroke 34(12), 2866–2870 (2003).SJCCA70039-2499 http://dx.doi.org/10.1161/01.STR.0000100166.81077.8A Google Scholar

61. I. Miyai et al., “Premotor cortex is involved in restoration of gait in stroke,” Ann. Neurol. 52(2), 188–194 (2002). http://dx.doi.org/10.1002/(ISSN)1531-8249 Google Scholar

62. M. Suzuki et al., “Prefrontal and premotor cortices are involved in adapting walking and running speed on the treadmill: an optical imaging study,” NeuroImage 23(3), 1020–1026 (2004).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2004.07.002 Google Scholar

63. S. A. Fraser et al., “Comparable cerebral oxygenation patterns in younger and older adults during dual-task walking with increasing load,” Front. Aging Neurosci. 8, 240 (2016). http://dx.doi.org/10.3389/fnagi.2016.00240 Google Scholar

64. F. G. Metzger et al., “Functional brain imaging of walking while talking—an fNIRS study,” Neuroscience 343, 85–93 (2017). http://dx.doi.org/10.1016/j.neuroscience.2016.11.032 Google Scholar

65. E. Al-Yahya et al., “Prefrontal cortex activation while walking under dual-task conditions in stroke: a multimodal imaging study,” Neurorehabil. Neural Repair 30(6), 591–599 (2016). http://dx.doi.org/10.1177/1545968315613864 Google Scholar

66. D. J. Clark et al., “Enhanced somatosensory feedback reduces prefrontal cortical activity during walking in older adults,” J. Gerontol. Ser. A: Biol. Sci. Med. Sci. 69(11), 1422–1428 (2014). http://dx.doi.org/10.1093/gerona/glu125 Google Scholar

67. M. Suzuki et al., “Activities in the frontal cortex and gait performance are modulated by preparation: an fNIRS study,” NeuroImage 39(2), 600–607 (2008).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2007.08.044 Google Scholar

68. P. Eggenberger et al., “Exergame and balance training modulate prefrontal brain activity during walking and enhance executive function in older adults,” Front. Aging Neurosci. 8, 66 (2016). http://dx.doi.org/10.3389/fnagi.2016.00066 Google Scholar

69. H. Atsumori et al., “Noninvasive imaging of prefrontal activation during attention-demanding tasks performed while walking using a wearable optical topography system,” J. Biomed. Opt. 15(4), 046002 (2010).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.3462996 Google Scholar

70. P. Caliandro et al., “Prefrontal cortex controls human balance during overground ataxic gait,” Restor. Neurol. Neurosci. 30(5), 397–405 (2012). http://dx.doi.org/10.3233/RNN-2012-120239 Google Scholar

71. P. Caliandro et al., “Prefrontal cortex as a compensatory network in ataxic gait: a correlation study between cortical activity and gait parameters,” Restor. Neurol. Neurosci. 33(2), 177–187 (2015). http://dx.doi.org/10.3233/RNN-140449 Google Scholar

72. T. Doi et al., “Brain activation during dual-task walking and executive function among older adults with mild cognitive impairment: a fNIRS study,” Aging Clin. Exp. Res. 25(5), 539–544 (2013). http://dx.doi.org/10.1007/s40520-013-0119-5 Google Scholar

73. R. Holtzer et al., “fNIRS study of walking and walking while talking in young and old individuals,” J. Gerontol. Ser. A: Biol. Sci. Med. Sci. 66A(8), 879–887 (2011). http://dx.doi.org/10.1093/gerona/glr068 Google Scholar

74. R. Holtzer et al., “Online fronto-cortical control of simple and attention-demanding locomotion in humans,” NeuroImage 112, 152–159 (2015).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2015.03.002 Google Scholar

75. R. Holtzer et al., “Neurological gait abnormalities moderate the functional brain signature of the posture first hypothesis,” Brain Topogr. 29(2), 334–343 (2016).BRTOEZ0896-0267 http://dx.doi.org/10.1007/s10548-015-0465-z Google Scholar

76. C.-F. Lu et al., “Maintaining gait performance by cortical activation during dual-task interference: a functional near-infrared spectroscopy study,” PLoS One 10(6), e0129390 (2015).POLNCL1932-6203 http://dx.doi.org/10.1371/journal.pone.0129390 Google Scholar

77. A. Mirelman et al., “Increased frontal brain activation during walking while dual tasking: an fNIRS study in healthy young adults,” J. Neuroeng. Rehabil. 11, 85 (2014). http://dx.doi.org/10.1186/1743-0003-11-85 Google Scholar

78. H. Saitou et al., “Cerebral blood volume and oxygenation among poststroke hemiplegic patients: effects of 13 rehabilitation tasks measured by near-infrared spectroscopy,” Arch. Phys. Med. Rehabil. 81(10), 1348–1356 (2000).APMHAI0003-9993 http://dx.doi.org/10.1053/apmr.2000.9400 Google Scholar

79. M.-I. B. Lin and K.-H. Lin, “Walking while performing working memory tasks changes the prefrontal cortex hemodynamic activations and gait kinematics,” Front. Behav. Neurosci. 10, 92 (2016). http://dx.doi.org/10.3389/fnbeh.2016.00092 Google Scholar

80. I. Maidan et al., “The role of the frontal lobe in complex walking among patients with Parkinson’s disease and healthy older adults: an fNIRS study,” Neurorehabil. Neural Repair 30(10), 963–971 (2016). http://dx.doi.org/10.1177/1545968316650426 Google Scholar

81. F. Nieuwhof et al., “Measuring prefrontal cortical activity during dual task walking in patients with Parkinson’s disease. Feasibility of using a new portable fNIRS device,” Pilot Feasibility Stud. 2(1), 918719 (2016). http://dx.doi.org/10.1186/s40814-016-0099-2 Google Scholar

82. M. E. Hernandez et al., “Brain activation changes during locomotion in middle-aged to older adults with multiple sclerosis,” J. Neurol. Sci. 370, 277–283 (2016).JNSCAG0022-510X http://dx.doi.org/10.1016/j.jns.2016.10.002 Google Scholar

83. J. Verghese et al., “Brain activation in high-functioning older adults and falls: prospective cohort study,” Neurology 88(2), 191–197 (2016).NEURAI0028-3878 http://dx.doi.org/10.1212/WNL.0000000000003421 Google Scholar

84. R. Holtzer et al., “Stress and gender effects on prefrontal cortex oxygenation levels assessed during single and dual-task walking conditions,” Eur. J. Neurosci. 45(5), 660–670 (2016). http://dx.doi.org/10.1111/ejn.13518 Google Scholar

85. R. Holtzer et al., “Interactions of subjective and objective measures of fatigue defined in the context of brain control of locomotion,” J. Gerontol. Ser. A Biol. Sci. Med. Sci. 72(3), 417–423 (2017). http://dx.doi.org/10.1093/gerona/glw167 Google Scholar

86. D. J. Clark et al., “Utilization of central nervous system resources for preparation and performance of complex walking tasks in older adults,” Front. Aging Neurosci. 6, 217 (2014). http://dx.doi.org/10.3389/fnagi.2014.00217 Google Scholar

87. O. Osofundiya et al., “Obesity-specific neural cost of maintaining gait performance under complex conditions in community-dwelling older adults,” Clin. Biomech. 35, 42–48 (2016). http://dx.doi.org/10.1016/j.clinbiomech.2016.03.011 Google Scholar

88. R. McKendrick et al., “Prefrontal hemodynamics of physical activity and environmental complexity during cognitive work,” Hum. Factors 59(1), 147–162 (2017).HUFAA60018-7208 http://dx.doi.org/10.1177/0018720816675053 Google Scholar

89. N. Takeuchi et al., “Parallel processing of cognitive and physical demands in left and right prefrontal cortices during smartphone use while walking,” BMC Neurosci. 17(1), 9 (2016).1471-2202 http://dx.doi.org/10.1186/s12868-016-0244-0 Google Scholar

90. I. Maidan et al., “Changes in oxygenated hemoglobin link freezing of gait to frontal activation in patients with Parkinson disease: an fNIRS study of transient motor-cognitive failures,” J. Neurol. 262(4), 899–908 (2015). http://dx.doi.org/10.1007/s00415-015-7650-6 Google Scholar

91. T. Huppert et al., “Measurement of brain activation during an upright stepping reaction task using functional near-infrared spectroscopy,” Hum. Brain Mapp. 34(11), 2817–2828 (2013). http://dx.doi.org/10.1002/hbm.22106 Google Scholar

92. H. Karim et al., “Functional brain imaging of multi-sensory vestibular processing during computerized dynamic posturography using near-infrared spectroscopy,” NeuroImage 74, 318–325 (2013).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.02.010 Google Scholar

93. J. R. Mahoney et al., “The role of prefrontal cortex during postural control in Parkinsonian syndromes a functional near-infrared spectroscopy study,” Brain Res. 1633, 126–138 (2016).BRREAP0006-8993 http://dx.doi.org/10.1016/j.brainres.2015.10.053 Google Scholar

94. M. Mihara et al., “Role of the prefrontal cortex in human balance control,” NeuroImage 43(2), 329–336 (2008).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2008.07.029 Google Scholar

95. M. Mihara et al., “Cortical control of postural balance in patients with hemiplegic stroke,” NeuroReport 23(5), 314–319 (2012).NERPEZ0959-4965 http://dx.doi.org/10.1097/WNR.0b013e328351757b Google Scholar

96. H. Takakura et al., “Cerebral hemodynamic responses during dynamic posturography. Analysis with a multichannel near-infrared spectroscopy system,” Front. Hum. Neurosci. 9, 620 (2015). http://dx.doi.org/10.3389/fnhum.2015.00620 Google Scholar

97. H. Karim et al., “Functional near-infrared spectroscopy (fNIRS) of brain function during active balancing using a video game system,” Gait Posture 35(3), 367–372 (2012). http://dx.doi.org/10.1016/j.gaitpost.2011.10.007 Google Scholar

98. S. Basso Moro et al., “A semi-immersive virtual reality incremental swing balance task activates prefrontal cortex: a functional near-infrared spectroscopy study,” NeuroImage 85(Pt 1), 451–460 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.05.031 Google Scholar

99. M. Ferrari et al., “Prefrontal cortex activated bilaterally by a tilt board balance task: a functional near-infrared spectroscopy study in a semi-immersive virtual reality environment,” Brain Topogr. 27(3), 353–365 (2014).BRTOEZ0896-0267 http://dx.doi.org/10.1007/s10548-013-0320-z Google Scholar

100. F. Herold et al., “Cortical activation during balancing on a balance board,” Hum. Mov. Sci. 51, 51–58 (2017).HMSCDO0167-9457 http://dx.doi.org/10.1016/j.humov.2016.11.002 Google Scholar

101. H. Fujita et al., “Role of the frontal cortex in standing postural sway tasks while dual-tasking: a functional near-infrared spectroscopy study examining working memory capacity,” BioMed. Res. Int. 2016(5), 1–10 (2016). http://dx.doi.org/10.1155/2016/7053867 Google Scholar

102. H. Fujimoto et al., “Cortical changes underlying balance recovery in patients with hemiplegic stroke,” NeuroImage 85, 547–554 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.05.014 Google Scholar

103. C.-C. Lin et al., “Functional near-infrared spectroscopy (fNIRS) brain imaging of multi-sensory integration during computerized dynamic posturography in middle-aged and older adults,” Exp. Brain Res. 235(4), 1247–1256 (2017).EXBRAP0014-4819 http://dx.doi.org/10.1007/s00221-017-4893-8 Google Scholar

104. B. Wang et al., “Posture-related changes in brain functional connectivity as assessed by wavelet phase coherence of NIRS signals in elderly subjects,” Behav. Brain Res. 312, 238–245 (2016).BBREDI0166-4328 http://dx.doi.org/10.1016/j.bbr.2016.06.037 Google Scholar

105. R. Beurskens et al., “Age-related changes in prefrontal activity during walking in dual-task situations: a fNIRS study,” Int. J. Psychophysiol. 92(3), 122–128 (2014).IJPSEE0167-8760 http://dx.doi.org/10.1016/j.ijpsycho.2014.03.005 Google Scholar

106. H. Y. Kim et al., “Best facilitated cortical activation during different stepping, treadmill, and robot-assisted walking training paradigms and speeds: a functional near-infrared spectroscopy neuroimaging study,” NeuroRehabilitation 38(2), 171–178 (2016). http://dx.doi.org/10.3233/NRE-161307 Google Scholar

107. I. Miyai et al., “Cortical mapping of gait in humans: a near-infrared spectroscopic topography study,” NeuroImage 14(5), 1186–1192 (2001).NEIMEF1053-8119 http://dx.doi.org/10.1006/nimg.2001.0905 Google Scholar

108. I. Helmich, A. Berger and H. Lausberg, “Neural control of posture in individuals with persisting postconcussion symptoms,” Med. Sci. Sports Exercise 48(12), 2362–2369 (2016).MSPEDA0195-9131 http://dx.doi.org/10.1249/MSS.0000000000001028 Google Scholar

109. G. Strangman, M. A. Franceschini and D. A. Boas, “Factors affecting the accuracy of near-infrared spectroscopy concentration calculations for focal changes in oxygenation parameters,” NeuroImage 18(4), 865–879 (2003).NEIMEF1053-8119 http://dx.doi.org/10.1016/S1053-8119(03)00021-1 Google Scholar

110. F. Scholkmann and M. Wolf, “General equation for the differential pathlength factor of the frontal human head depending on wavelength and age,” J. Biomed. Opt. 18(10), 105004 (2013).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.JBO.18.10.105004 Google Scholar

111. T. J. Huppert et al., “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt. 48(10), D280 (2009).APOPAI0003-6935 http://dx.doi.org/10.1364/AO.48.00D280 Google Scholar

112. R. E. Vanderwert and C. A. Nelson, “The use of near-infrared spectroscopy in the study of typical and atypical development,” NeuroImage 85(Pt 1), 264–271 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.10.009 Google Scholar

113. M. A. Killingsworth and D. T. Gilbert, “A wandering mind is an unhappy mind,” Science 330(6006), 932–932 (2010).SCIEAS0036-8075 http://dx.doi.org/10.1126/science.1192439 Google Scholar

114. G. Berthie et al., “The restless mind while driving: drivers’ thoughts behind the wheel,” Accid. Anal. Prev. 76, 159–165 (2015). http://dx.doi.org/10.1016/j.aap.2015.01.005 Google Scholar

115. C. Galera et al., “Mind wandering and driving: responsibility case–control study,” BMJ 345, e8105 (2012). http://dx.doi.org/10.1136/bmj.e8105 Google Scholar

116. C. T. Lin et al., “Mind-wandering tends to occur under low perceptual demands during driving,” Sci. Rep. 6, 21353 (2016). http://dx.doi.org/10.1038/srep21353 Google Scholar

117. M. Spronken et al., “Temporal focus, temporal distance, and mind-wandering valence: results from an experience sampling and an experimental study,” Conscious Cognit. 41, 104–118 (2016). http://dx.doi.org/10.1016/j.concog.2016.02.004 Google Scholar

118. M. F. Mason et al., “Wandering minds: the default network and stimulus-independent thought,” Science 315(5810), 393–395 (2007).SCIEAS0036-8075 http://dx.doi.org/10.1126/science.1131295 Google Scholar

119. K. C. Fox et al., “Dreaming as mind wandering: evidence from functional neuroimaging and first-person content reports,” Front. Hum. Neurosci. 7, 412 (2013). http://dx.doi.org/10.3389/fnhum.2013.00412 Google Scholar

120. G. Durantin, F. Dehais and A. Delorme, “Characterization of mind wandering using fNIRS,” Front. Syst. Neurosci. 9, 98 (2015). http://dx.doi.org/10.3389/fnsys.2015.00045 Google Scholar

121. P. Corbeil et al., “Perturbation of the postural control system induced by muscular fatigue,” Gait Posture 18(2), 92–100 (2003). http://dx.doi.org/10.1016/S0966-6362(02)00198-4 Google Scholar

122. P. A. Gribble and J. Hertel, “Effect of lower-extremity muscle fatigue on postural control,” Arch. Phys. Med. Rehabil. 85(4), 589–592 (2004).APMHAI0003-9993 http://dx.doi.org/10.1016/j.apmr.2003.06.031 Google Scholar

123. J. L. Helbostad et al., “Consequences of lower extremity and trunk muscle fatigue on balance and functional tasks in older people: a systematic literature review,” BMC Geriatr. 10, 56 (2010). http://dx.doi.org/10.1186/1471-2318-10-56 Google Scholar

124. T. Paillard, “Effects of general and local fatigue on postural control: a review,” Neurosci. Biobehav. Rev. 36(1), 162–176 (2012).NBREDE0149-7634 http://dx.doi.org/10.1016/j.neubiorev.2011.05.009 Google Scholar

125. M. Salavati et al., “Changes in postural stability with fatigue of lower extremity frontal and sagittal plane movers,” Gait Posture 26(2), 214–218 (2007). http://dx.doi.org/10.1016/j.gaitpost.2006.09.001 Google Scholar

126. J. A. Yaggie and S. J. McGregor, “Effects of isokinetic ankle fatigue on the maintenance of balance and postural limits,” Arch. Phys. Med. Rehabil. 83(2), 224–228 (2002).APMHAI0003-9993 http://dx.doi.org/10.1053/apmr.2002.28032 Google Scholar

127. A. E. Shortz et al., “The effect of cognitive fatigue on prefrontal cortex correlates of neuromuscular fatigue in older women,” J. NeuroEng. Rehabil. 12, 115 (2015). http://dx.doi.org/10.1186/s12984-015-0108-3 Google Scholar

128. M. L. Latash, J. P. Scholz and G. Schöner, “Motor control strategies revealed in the structure of motor variability,” Exercise Sport Sci. Rev. 30(1), 26–31 (2002).ESSRB80091-6331 http://dx.doi.org/10.1097/00003677-200201000-00006 Google Scholar

129. R. T. Harbourne and N. Stergiou, “Movement variability and the use of nonlinear tools: principles to guide physical therapist practice,” Phys. Ther. 89(3), 267–282 (2009). http://dx.doi.org/10.2522/ptj.20080130 Google Scholar

130. N. Stergiou, R. Harbourne and J. Cavanaugh, “Optimal movement variability: a new theoretical perspective for neurologic physical therapy,” J. Neurol. Phys. Ther. 30(3), 120–129 (2006). http://dx.doi.org/10.1097/01.NPT.0000281949.48193.d9 Google Scholar

131. N. Stergiou and L. M. Decker, “Human movement variability, nonlinear dynamics, and pathology: is there a connection?” Hum. Mov. Sci. 30(5), 869–888 (2011).HMSCDO0167-9457 http://dx.doi.org/10.1016/j.humov.2011.06.002 Google Scholar

132. D. Hamacher et al., “Kinematic measures for assessing gait stability in elderly individuals: a systematic review,” J. R. Soc. Interface 8(65), 1682–1698 (2011).1742-5689 http://dx.doi.org/10.1098/rsif.2011.0416 Google Scholar

133. N. König et al., “Revealing the quality of movement: a meta-analysis review to quantify the thresholds to pathological variability during standing and walking,” Neurosci. Biobehav. Rev. 68, 111–119 (2016).NBREDE0149-7634 http://dx.doi.org/10.1016/j.neubiorev.2016.03.035 Google Scholar

134. J. H. Hollman et al., “Number of strides required for reliable measurements of pace, rhythm and variability parameters of gait during normal and dual task walking in older individuals,” Gait Posture 32(1), 23–28 (2010). http://dx.doi.org/10.1016/j.gaitpost.2010.02.017 Google Scholar

135. F. Riva, M. C. Bisi and R. Stagni, “Gait variability and stability measures: minimum number of strides and within-session reliability,” Comput. Biol. Med. 50, 9–13 (2014).CBMDAW0010-4825 http://dx.doi.org/10.1016/j.compbiomed.2014.04.001 Google Scholar

136. A. F. Cannestra et al., “Refractory periods observed by intrinsic signal and fluorescent dye imaging,” J. Neurophysiol. 80(3), 1522–1532 (1998).JONEA40022-3077 Google Scholar

137. A. Pellicer and M. del Carmen Bravo, “Near-infrared spectroscopy: a methodology-focused review,” Semin. Fetal Neonat. Med. 16(1), 42–49 (2011). http://dx.doi.org/10.1016/j.siny.2010.05.003 Google Scholar

138. M. Ferrari, L. Mottola and V. Quaresima, “Principles, techniques, and limitations of near infrared spectroscopy,” Can. J. Appl. Physiol. 29(4), 463–487 (2004). http://dx.doi.org/10.1139/h04-031 Google Scholar

139. F. Orihuela-Espina et al., “Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation,” Phys. Med. Biol. 55(13), 3701–3724 (2010).PHMBA70031-9155 http://dx.doi.org/10.1088/0031-9155/55/13/009 Google Scholar

140. D. A. Benaron et al., “Transcranial optical path length in infants by near-infrared phase-shift spectroscopy,” J. Clin. Monit. Comput. 11(2), 109–117 (1995). http://dx.doi.org/10.1007/BF01617732 Google Scholar

141. S. Brigadoi and R. J. Cooper, “How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy,” Neurophotonics 2(2), 025005 (2015). http://dx.doi.org/10.1117/1.NPh.2.2.025005 Google Scholar

142. G. E. Strangman, Q. Zhang and Z. Li, “Scalp and skull influence on near infrared photon propagation in the Colin27 brain template,” NeuroImage 85(Pt 1), 136–149 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.04.090 Google Scholar

143. S. Wang et al., “Effects of spatial variation of skull and cerebrospinal fluid layers on optical mapping of brain activities,” Opt. Rev. 17(4), 410–420 (2010). http://dx.doi.org/10.1007/s10043-010-0076-6 Google Scholar

144. K. L. Perdue, Q. Fang and S. G. Diamond, “Quantitative assessment of diffuse optical tomography sensitivity to the cerebral cortex using a whole-head probe,” Phys. Med. Biol. 57(10), 2857–2872 (2012).PHMBA70031-9155 http://dx.doi.org/10.1088/0031-9155/57/10/2857 Google Scholar

145. T. Funane et al., “Quantitative evaluation of deep and shallow tissue layers’ contribution to fNIRS signal using multi-distance optodes and independent component analysis,” NeuroImage 85(Pt 1), 150–165 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.02.026 Google Scholar

146. T. Funane et al., “Greater contribution of cerebral than extracerebral hemodynamics to near-infrared spectroscopy signals for functional activation and resting-state connectivity in infants,” Neurophotonics 1(2), 025003 (2014). http://dx.doi.org/10.1117/1.NPh.1.2.025003 Google Scholar

147. A. V. Patil et al., “Experimental investigation of NIRS spatial sensitivity,” Biomed. Opt. Express 2(6), 1478–1493 (2011).BOEICL2156-7085 http://dx.doi.org/10.1364/BOE.2.001478 Google Scholar

148. S. Gunadi et al., “Spatial sensitivity and penetration depth of three cerebral oxygenation monitors,” Biomed. Opt. Express 5(9), 2896–2912 (2014).BOEICL2156-7085 http://dx.doi.org/10.1364/BOE.5.002896 Google Scholar

149. V. Toronov et al., “Near-infrared study of fluctuations in cerebral hemodynamics during rest and motor stimulation: temporal analysis and spatial mapping,” Med. Phys. 27(4), 801–815 (2000).MPHYA60094-2405 http://dx.doi.org/10.1118/1.598943 Google Scholar

150. S. Kohri et al., “Quantitative evaluation of the relative contribution ratio of cerebral tissue to near-infrared signals in the adult human head: a preliminary study,” Physiol. Meas. 23(2), 301–312 (2002).PMEAE30967-3334 http://dx.doi.org/10.1088/0967-3334/23/2/306 Google Scholar

151. D. P. Carmody et al., “A quantitative measure of myelination development in infants, using MR images,” Neuroradiology 46(9), 781–786 (2004). http://dx.doi.org/10.1007/s00234-004-1241-z Google Scholar

152. A. Giorgio et al., “Age-related changes in grey and white matter structure throughout adulthood,” NeuroImage 51(3), 943–951 (2010).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2010.03.004 Google Scholar

153. X. Wang et al., “Probabilistic MRI brain anatomical atlases based on 1,000 Chinese subjects,” PLoS One 8(1), e50939 (2013).POLNCL1932-6203 http://dx.doi.org/10.1371/annotation/bae9fc08-fbfa-45b5-9d1d-0b8254d6efd5 Google Scholar

154. K. L. Leenders et al., “Cerebral blood flow, blood volume and oxygen utilization: normal values and effect of age,” Brain: J. Neurol. 113(Pt 1), 27–47 (1990). http://dx.doi.org/10.1093/brain/113.1.27 Google Scholar

155. A. Duncan et al., “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39(5), 889–894 (1996).PEREBL0031-3998 http://dx.doi.org/10.1203/00006450-199605000-00025 Google Scholar

156. P. van der Zee et al., “Experimentally measured optical pathlengths for the adult head, calf and forearm and the head of the newborn infant as a function of inter optode spacing,” in Oxygen Transport to Tissue XIII, , T. K. Goldstick, M. McCabe and D. J. Maguire, Eds., Vol. 316, pp. 143–153, Springer, Boston, Massachusetts (1992). Google Scholar

157. M. Essenpreis et al., “Spectral dependence of temporal point spread functions in human tissues,” Appl. Opt. 32(4), 418–425 (1993).APOPAI0003-6935 http://dx.doi.org/10.1364/AO.32.000418 Google Scholar

158. H. Zhao et al., “Maps of optical differential pathlength factor of human adult forehead, somatosensory motor and occipital regions at multi-wavelengths in NIR,” Phys. Med. Biol. 47(12), 2075–2093 (2002).PHMBA70031-9155 http://dx.doi.org/10.1088/0031-9155/47/12/306 Google Scholar

159. Y. Hoshi, “Functional near-infrared spectroscopy: potential and limitations in neuroimaging studies,” Int. Rev. Neurobiol 66(5), 237–266 (2005). http://dx.doi.org/10.1016/S0074-7742(05)66008-4 Google Scholar

160. Y. Hoshi, “Functional near-infrared spectroscopy: current status and future prospects,” J. Biomed. Opt. 12(6), 062106 (2007).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.2804911 Google Scholar

161. A. Maki et al., “Spatial and temporal analysis of human motor activity using noninvasive NIR topography,” Med. Phys. 22(12), 1997–2005 (1995).MPHYA60094-2405 http://dx.doi.org/10.1118/1.597496 Google Scholar

162. M. M. Tisdall et al., “The effect on cerebral tissue oxygenation index of changes in the concentrations of inspired oxygen and end-tidal carbon dioxide in healthy adult volunteers,” Anesth. Analg. 109(3), 906–913 (2009).AACRAT0003-2999 http://dx.doi.org/10.1213/ane.0b013e3181aedcdc Google Scholar

163. M. Wolf, M. Ferrari and V. Quaresima, “Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications,” J. Biomed. Opt. 12(6), 062104 (2007).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.2804899 Google Scholar

164. V. Quaresima and M. Ferrari, “Muscle oxygenation by near-infrared-based tissue oximeters,” J. Appl. Physiol. 107(1), 371 (2009). http://dx.doi.org/10.1152/japplphysiol.00215.2009 Google Scholar

165. P. Ekkekakis, “Illuminating the black box: investigating prefrontal cortical hemodynamics during exercise with near-infrared spectroscopy,” J. Sport Exercise Psychol. 31(4), 505–553 (2009). http://dx.doi.org/10.1123/jsep.31.4.505 Google Scholar

166. M. L. Schroeter et al., “Age dependency of the hemodynamic response as measured by functional near-infrared spectroscopy,” NeuroImage 19(3), 555–564 (2003).NEIMEF1053-8119 http://dx.doi.org/10.1016/S1053-8119(03)00155-1 Google Scholar

167. S. Brigadoi et al., “Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data,” NeuroImage 85, 181–191 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.04.082 Google Scholar

168. R. J. Cooper et al., “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).1662-453X http://dx.doi.org/10.3389/fnins.2012.00147 Google Scholar

169. Y. Zhang et al., “Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging,” J. Biomed. Opt. 10(1), 011014 (2005).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.1852552 Google Scholar

170. H. Santosa et al., “Noise reduction in functional near-infrared spectroscopy signals by independent component analysis,” Rev. Sci. Instrum. 84(7), 073106 (2013). http://dx.doi.org/10.1063/1.4812785 Google Scholar

171. F. C. Robertson, T. S. Douglas and E. M. Meintjes, “Motion artifact removal for functional near infrared spectroscopy: a comparison of methods,” IEEE Trans. BioMed. Eng. 57(6), 1377–1387 (2010). http://dx.doi.org/10.1109/TBME.2009.2038667 Google Scholar

172. H. Tanaka, T. Katura and H. Sato, “Task-related oxygenation and cerebral blood volume changes estimated from NIRS signals in motor and cognitive tasks,” NeuroImage 94, 107–119 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2014.02.036 Google Scholar

173. H. Tanaka, T. Katura and H. Sato, “Task-related component analysis for functional neuroimaging and application to near-infrared spectroscopy data,” NeuroImage 64, 308–327 (2013).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2012.08.044 Google Scholar

174. M. A. Yucel et al., “Target principal component analysis: a new motion artefact correction approach for near-infrared spectroscopy,” J. Innovative Opt. Health Sci. 7(2), 1350066 (2014). http://dx.doi.org/10.1142/S1793545813500661 Google Scholar

175. X. Cui, S. Bray and A. L. Reiss, “Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics,” NeuroImage 49(4), 3039–3046 (2010).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2009.11.050 Google Scholar

176. B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33(2), 259–270 (2012).PMEAE30967-3334 http://dx.doi.org/10.1088/0967-3334/33/2/259 Google Scholar

177. K. E. Jang et al., “Wavelet minimum description length detrending for near-infrared spectroscopy,” J. Biomed. Opt. 14(3), 034004 (2009).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.3127204 Google Scholar

178. A. M. Chiarelli et al., “A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data,” NeuroImage 112, 128–137 (2015).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2015.02.057 Google Scholar

179. H. Sato et al., “Wavelet analysis for detecting body-movement artifacts in optical topography signals,” NeuroImage 33(2), 580–587 (2006).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2006.06.028 Google Scholar

180. J. W. Barker, A. Aarabi and T. J. Huppert, “Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS,” Biomed. Opt. Express 4(8), 1366–1379 (2013).BOEICL2156-7085 http://dx.doi.org/10.1364/BOE.4.001366 Google Scholar

181. M. Izzetoglu et al., “Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering,” BioMed. Eng. OnLine 9(1), 16 (2010). http://dx.doi.org/10.1186/1475-925X-9-16 Google Scholar

182. M. Izzetoglu et al., “Motion artifact cancellation in NIR spectroscopy using Wiener filtering,” IEEE Trans. Biomed. Eng. 52(5), 934–938 (2005).IEBEAX0018-9294 http://dx.doi.org/10.1109/TBME.2005.845243 Google Scholar

183. T. Nozawa, T. Kondo, “A comparison of artifact reduction methods for real-time analysis of fNIRS data,” in Human Interface and the Management of Information: Information and Interaction, and D. Hutchison, Ed., Vol. 5618, pp. 413–422, Springer, Berlin, Heidelberg (2009). Google Scholar

184. F. Scholkmann et al., “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).PMEAE30967-3334 http://dx.doi.org/10.1088/0967-3334/31/5/004 Google Scholar

185. F. Scholkmann et al., “End-tidal CO2: an important parameter for a correct interpretation in functional brain studies using speech tasks,” NeuroImage 66, 71–79 (2013).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2012.10.025 Google Scholar

186. F. Scholkmann, M. Wolf and U. Wolf, “The effect of inner speech on arterial CO2 and cerebral hemodynamics and oxygenation: a functional NIRS study,” Adv. Exp. Med. Biol. 789, 81–87 (2013).AEMBAP0065-2598 http://dx.doi.org/10.1007/978-1-4614-7411-1 Google Scholar

187. D. A. Boas, A. M. Dale and M. A. Franceschini, “Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy,” NeuroImage 23(Suppl. 1), S275–S288 (2004).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2004.07.011 Google Scholar

188. M. Caldwell et al., “Modelling confounding effects from extracerebral contamination and systemic factors on functional near-infrared spectroscopy,” NeuroImage 143, 91–105 (2016).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2016.08.058 Google Scholar

189. G. Gratton and P. M. Corballis, “Removing the heart from the brain: compensation for the pulse artifact in the photon migration signal,” Psychophysiology 32(3), 292–299 (1995).PSPHAF0048-5772 http://dx.doi.org/10.1111/psyp.1995.32.issue-3 Google Scholar

190. G. Morren et al., “Detection of fast neuronal signals in the motor cortex from functional near infrared spectroscopy measurements using independent component analysis,” Med. Biol. Eng. Comput. 42(1), 92–99 (2004).MBECDY0140-0118 http://dx.doi.org/10.1007/BF02351016 Google Scholar

191. L. Holper, F. Scholkmann and M. Wolf, “The relationship between sympathetic nervous activity and cerebral hemodynamics and oxygenation: a study using skin conductance measurement and functional near-infrared spectroscopy,” Behav. Brain Res. 270, 95–107 (2014).BBREDI0166-4328 http://dx.doi.org/10.1016/j.bbr.2014.04.056 Google Scholar

192. S. Tak and J. C. Ye, “Statistical analysis of fNIRS data: a comprehensive review,” NeuroImage 85, 72–91 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.06.016 Google Scholar

193. J. Virtanen, T. Noponen and P. Meriläinen, “Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals,” J. Biomed. Opt. 14(5), 054032 (2009).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.3253323 Google Scholar

194. X. Zhang, J. A. Noah and J. Hirsch, “Separation of the global and local components in functional near-infrared spectroscopy signals using principal component spatial filtering,” Neurophotonics 3(1), 015004 (2016). http://dx.doi.org/10.1117/1.NPh.3.1.015004 Google Scholar

195. G. Bauernfeind et al., “Separating heart and brain: on the reduction of physiological noise from multichannel functional near-infrared spectroscopy (fNIRS) signals,” J. Neural Eng. 11(5), 056010 (2014).1741-2560 http://dx.doi.org/10.1088/1741-2560/11/5/056010 Google Scholar

196. F. B. Haeussinger et al., “Reconstructing functional near-infrared spectroscopy (fNIRS) signals impaired by extra-cranial confounds: an easy-to-use filter method,” NeuroImage 95, 69–79 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2014.02.035 Google Scholar

197. A. R. Harrivel et al., “Dynamic filtering improves attentional state prediction with fNIRS,” Biomed. Opt. Express 7(3), 979–1002 (2016).BOEICL2156-7085 http://dx.doi.org/10.1364/BOE.7.000979 Google Scholar

198. R. B. Saager and A. J. Berger, “Direct characterization and removal of interfering absorption trends in two-layer turbid media,” J. Opt. Soc. Am. A 22(9), 1874–1882 (2005).JOAOD60740-3232 http://dx.doi.org/10.1364/JOSAA.22.001874 Google Scholar

199. E. Kirilina et al., “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” NeuroImage 61(1), 70–81 (2012).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2012.02.074 Google Scholar

200. F. Scholkmann, A. J. Metz and M. Wolf, “Measuring tissue hemodynamics and oxygenation by continuous-wave functional near-infrared spectroscopy—how robust are the different calculation methods against movement artifacts?” Physiol. Meas. 35(4), 717–734 (2014).PMEAE30967-3334 http://dx.doi.org/10.1088/0967-3334/35/4/717 Google Scholar

201. R. Saager and A. Berger, “Measurement of layer-like hemodynamic trends in scalp and cortex: implications for physiological baseline suppression in functional near-infrared spectroscopy,” J. Biomed. Opt. 13(3), 034017 (2008).JBOPFO1083-3668 http://dx.doi.org/10.1117/1.2940587 Google Scholar

202. L. Gagnon et al., “Short separation channel location impacts the performance of short channel regression in NIRS,” NeuroImage 59(3), 2518–2528 (2012).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2011.08.095 Google Scholar

203. L. Gagnon et al., “Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling,” NeuroImage 56(3), 1362–1371 (2011).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2011.03.001 Google Scholar

204. L. Gagnon et al., “Further improvement in reducing superficial contamination in NIRS using double short separation measurements,” NeuroImage 85(Pt 1), 127–135 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.01.073 Google Scholar

205. J. R. Goodwin, C. R. Gaudet and A. J. Berger, “Short-channel functional near-infrared spectroscopy regressions improve when source–detector separation is reduced,” Neurophotonics 1(1), 015002 (2014). http://dx.doi.org/10.1117/1.NPh.1.1.015002 Google Scholar

206. T. Sato et al., “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes,” NeuroImage 141, 120–132 (2016).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2016.06.054 Google Scholar

207. Y. Zhang et al., “Multiregional functional near-infrared spectroscopy reveals globally symmetrical and frequency-specific patterns of superficial interference,” Biomed. Opt. Express 6(8), 2786–2802 (2015).BOEICL2156-7085 http://dx.doi.org/10.1364/BOE.6.002786 Google Scholar

208. R. B. Saager, N. L. Telleri and A. J. Berger, “Two-detector corrected near infrared spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS,” NeuroImage 55(4), 1679–1685 (2011).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2011.01.043 Google Scholar

209. T. Miyazawa et al., “Skin blood flow influences cerebral oxygenation measured by near-infrared spectroscopy during dynamic exercise,” Eur. J. Appl. Physiol. 113(11), 2841–2848 (2013).EJAPFN1439-6319 http://dx.doi.org/10.1007/s00421-013-2723-7 Google Scholar

210. T. Takahashi et al., “Influence of skin blood flow on near-infrared spectroscopy signals measured on the forehead during a verbal fluency task,” NeuroImage 57(3), 991–1002 (2011).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2011.05.012 Google Scholar

211. L. Gagnon et al., “Quantification of the cortical contribution to the NIRS signal over the motor cortex using concurrent NIRS-fMRI measurements,” NeuroImage 59(4), 3933–3940 (2012).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2011.10.054 Google Scholar

212. P. G. Al-Rawi, P. Smielewski and P. J. Kirkpatrick, “Evaluation of a near-infrared spectrometer (NIRO 300) for the detection of intracranial oxygenation changes in the adult head,” Stroke 32(11), 2492–2500 (2001).SJCCA70039-2499 http://dx.doi.org/10.1161/hs1101.098356 Google Scholar

213. D. Canova et al., “Inconsistent detection of changes in cerebral blood volume by near infrared spectroscopy in standard clinical tests,” J. Appl. Physiol. 110(6), 1646–1655 (2011). http://dx.doi.org/10.1152/japplphysiol.00003.2011 Google Scholar

214. A. Vrana et al., “Different mechanosensory stimulations of the lower back elicit specific changes in hemodynamics and oxygenation in cortical sensorimotor areas: a fNIRS study,” Brain Behav. 6(12), e00575 (2016). http://dx.doi.org/10.1002/brb3.2016.6.issue-12 Google Scholar

215. J. C. Ye et al., “NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy,” NeuroImage 44(2), 428–447 (2009).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2008.08.036 Google Scholar

216. M. M. Plichta et al., “Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: a parametric validation study,” NeuroImage 35(2), 625–634 (2007).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2006.11.028 Google Scholar

217. T. W. Wilson, M. J. Kurz and D. J. Arpin, “Functional specialization within the supplementary motor area: a fNIRS study of bimanual coordination,” NeuroImage 85, 445–450 (2014).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2013.04.112 Google Scholar

218. A. K. Singh et al., “Scope of resampling-based tests in fNIRS neuroimaging data analysis,” Stat. Sin. 18, 1519–1534 (2008).STSNEO Google Scholar

219. M. W. Fagerland, “t-tests, non-parametric tests, and large studies—a paradox of statistical practice?” BMC Med. Res. Method. 12, 78 (2012). http://dx.doi.org/10.1186/1471-2288-12-78 Google Scholar

220. S. Burke, “Missing values, outliers, robust statistics and non-parametric methods,” LC-GC Eur. Online Suppl. Stat. Data Anal. 2, 19–24 (2001). Google Scholar

221. C. Potvin and D. A. Roff, “Distribution-free and robust statistical methods: viable alternatives to parametric statistics,” Ecology 74(6), 1617–1628 (1993).ECGYAQ0094-6621 http://dx.doi.org/10.2307/1939920 Google Scholar

222. D. M. Erceg-Hurn and V. M. Mirosevich, “Modern robust statistical methods: an easy way to maximize the accuracy and power of your research,” Am. Psychol. 63(7), 591–601 (2008). http://dx.doi.org/10.1037/0003-066X.63.7.591 Google Scholar

223. M. P. Boisgontier and B. Cheval, “The ANOVA to mixed model transition,” Neurosci. Biobehav. Rev. 68, 1004–1005 (2016). http://dx.doi.org/10.1016/j.neubiorev.2016.05.034 Google Scholar

224. E. Aarts et al., “A solution to dependency: using multilevel analysis to accommodate nested data,” Nat. Neurosci. 17(4), 491–496 (2014).NANEFN1097-6256 http://dx.doi.org/10.1038/nn.3648 Google Scholar

225. C. M. Judd, J. Westfall and D. A. Kenny, “Treating stimuli as a random factor in social psychology: a new and comprehensive solution to a pervasive but largely ignored problem,” J. Pers. Social Psychol. 103(1), 54–69 (2012).JPSPB20022-3514 http://dx.doi.org/10.1037/a0028347 Google Scholar

226. B. M. Bolker et al., “Generalized linear mixed models: a practical guide for ecology and evolution,” Trends Ecol. Evol. 24(3), 127–135 (2009). http://dx.doi.org/10.1016/j.tree.2008.10.008 Google Scholar

227. M. A. Kamran, M. M. N. Mannan and M. Y. Jeong, “Cortical signal analysis and advances in functional near-infrared spectroscopy signal: a review,” Front. Hum. Neurosci. 10, 261 (2016). http://dx.doi.org/10.3389/fnhum.2016.00261 Google Scholar

228. Y. Hoshi, “Functional near-infrared optical imaging: utility and limitations in human brain mapping,” Psychophysiology 40(4), 511–520 (2003).PSPHAF0048-5772 http://dx.doi.org/10.1111/psyp.2003.40.issue-4 Google Scholar

229. Y. Hoshi, N. Kobayashi and M. Tamura, “Interpretation of near-infrared spectroscopy signals: a study with a newly developed perfused rat brain model,” J. Appl. Physiol. 90(5), 1657–1662 (2001). Google Scholar

230. U. Lindauer et al., “Pathophysiological interference with neurovascular coupling— when imaging based on hemoglobin might go blind,” Front. Neuroenerg. 2, 25 (2010). http://dx.doi.org/10.3389/fnene.2010.00025 Google Scholar

231. A. Kleinschmidt et al., “Simultaneous recording of cerebral blood oxygenation changes during human brain activation by magnetic resonance imaging and near-infrared spectroscopy,” J. Cereb. Blood Flow Metab. 16(5), 817–826 (1996). http://dx.doi.org/10.1097/00004647-199609000-00006 Google Scholar

232. V. Toronov et al., “Investigation of human brain hemodynamics by simultaneous near-infrared spectroscopy and functional magnetic resonance imaging,” Med. Phys. 28(4), 521–527 (2001).MPHYA60094-2405 http://dx.doi.org/10.1118/1.1354627 Google Scholar

233. C. La Fougère et al., “Real versus imagined locomotion: a [F18]-FDG PET-fMRI comparison,” NeuroImage 50(4), 1589–1598 (2010).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2009.12.060 Google Scholar

234. A. Zwergal et al., “Aging of human supraspinal locomotor and postural control in fMRI,” Neurobiol. Aging 33(6), 1073–1084 (2012).NEAGDO0197-4580 http://dx.doi.org/10.1016/j.neurobiolaging.2010.09.022 Google Scholar

235. A. Zwergal et al., “Functional disturbance of the locomotor network in progressive supranuclear palsy,” Neurology 80(7), 634–641 (2013).NEURAI0028-3878 http://dx.doi.org/10.1212/WNL.0b013e318281cc43 Google Scholar

236. J. G. Nutt, F. B. Horak and B. R. Bloem, “Milestones in gait, balance, and falling,” Mov. Disord. 26(6), 1166–1174 (2011). http://dx.doi.org/10.1002/mds.23588 Google Scholar

237. A. Maillet, P. Pollak and B. Debu, “Imaging gait disorders in parkinsonism: a review,” J. Neurol. Neurosurg. Psychiatry 83(10), 986–993 (2012). http://dx.doi.org/10.1136/jnnp-2012-302461 Google Scholar

238. D. J. Clark, “Automaticity of walking: functional significance, mechanisms, measurement and rehabilitation strategies,” Front. Hum. Neurosci. 9, 246 (2015). http://dx.doi.org/10.3389/fnhum.2015.00246 Google Scholar

239. P. M. M. Young and J. B. Dingwell, “Voluntary changes in step width and step length during human walking affect dynamic margins of stability,” Gait Posture 36(2), 219–224 (2012). http://dx.doi.org/10.1016/j.gaitpost.2012.02.020 Google Scholar

240. E. Nordin et al., “Changes in step-width during dual-task walking predicts falls,” Gait Posture 32(1), 92–97 (2010). http://dx.doi.org/10.1016/j.gaitpost.2010.03.012 Google Scholar

241. G. Rizzolatti, L. Fogassi and V. Gallese, “Motor and cognitive functions of the ventral premotor cortex,” Curr. Opin. Neurobiol. 12(2), 149–154 (2002).COPUEN0959-4388 http://dx.doi.org/10.1016/S0959-4388(02)00308-2 Google Scholar

242. R. I. Schubotz and D. von Cramon, “Functional–anatomical concepts of human premotor cortex: evidence from fMRI and PET studies,” NeuroImage 20, S120–S131 (2003).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2003.09.014 Google Scholar

243. O. A. van den Heuvel et al., “Frontostriatal system in planning complexity: a parametric functional magnetic resonance version of tower of London task,” NeuroImage 18(2), 367–374 (2003).NEIMEF1053-8119 http://dx.doi.org/10.1016/S1053-8119(02)00010-1 Google Scholar

244. G. Cona and C. Semenza, “Supplementary motor area as key structure for domain-general sequence processing: a unified account,” Neurosci. Biobehav. Rev. 72, 28–42 (2017).NBREDE0149-7634 http://dx.doi.org/10.1016/j.neubiorev.2016.10.033 Google Scholar

245. J. R. Tregellas, D. B. Davalos and D. C. Rojas, “Effect of task difficulty on the functional anatomy of temporal processing,” NeuroImage 32(1), 307–315 (2006).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2006.02.036 Google Scholar

246. P. Nachev, C. Kennard and M. Husain, “Functional role of the supplementary and pre-supplementary motor areas,” Nat. Rev. Neurosci. 9(11), 856–869 (2008). http://dx.doi.org/10.1038/nrn2478 Google Scholar

247. A. Dietrich, “Transient hypofrontality as a mechanism for the psychological effects of exercise,” Psychiatry Res. 145(1), 79–83 (2006).PSRSDR http://dx.doi.org/10.1016/j.psychres.2005.07.033 Google Scholar

248. V. E. Kelly, A. J. Eusterbrock and A. Shumway-Cook, “Factors influencing dynamic prioritization during dual-task walking in healthy young adults,” Gait Posture 37(1), 131–134 (2013). http://dx.doi.org/10.1016/j.gaitpost.2012.05.031 Google Scholar

249. G. Yogev-Seligmann, J. M. Hausdorff and N. Giladi, “Do we always prioritize balance when walking? Towards an integrated model of task prioritization,” Mov. Disord. 27(6), 765–770 (2012).MOVDEA0885-3185 http://dx.doi.org/10.1002/mds.v27.6 Google Scholar

250. D. Carius et al., “Hemodynamic response alteration as a function of task complexity and expertise—an fNIRS study in jugglers,” Front. Hum. Neurosci. 10, 126 (2016). http://dx.doi.org/10.3389/fnhum.2016.00126 Google Scholar

251. E. Wittenberg et al., “Neuroimaging of human balance control: a systematic review,” Front. Hum. Neurosci. 11, 170 (2017). http://dx.doi.org/10.3389/fnhum.2017.00170 Google Scholar

252. T. Brandt et al., “Reciprocal inhibitory visual-vestibular interaction: visual motion stimulation deactivates the parieto-insular vestibular cortex,” Brain: J. Neurol. 121(Pt 9), 1749–1758 (1998). http://dx.doi.org/10.1093/brain/121.9.1749 Google Scholar

253. K. Jahn et al., “Brain activation patterns during imagined stance and locomotion in functional magnetic resonance imaging,” NeuroImage 22(4), 1722–1731 (2004).NEIMEF1053-8119 http://dx.doi.org/10.1016/j.neuroimage.2004.05.017 Google Scholar

254. S. Bense et al., “Multisensory cortical signal increases and decreases during vestibular galvanic stimulation (fMRI),” J. Neurophysiol. 85(2), 886–899 (2001).JONEA40022-3077 Google Scholar

255. H. O. Karnath, “New insights into the functions of the superior temporal cortex,” Nat. Rev. Neurosci. 2(8), 568–576 (2001).NRNAAN1471-003X http://dx.doi.org/10.1038/35086057 Google Scholar

256. A. Faraldo-García et al., “Influence of gender on the sensory organisation test and the limits of stability in healthy subjects,” Acta Otorrinolaringol. 62(5), 333–338 (2011). http://dx.doi.org/10.1016/j.otoeng.2011.03.006 Google Scholar

257. A. Dietrich, “Imaging the imagination: the trouble with motor imagery,” Methods 45(4), 319–324 (2008). http://dx.doi.org/10.1016/j.ymeth.2008.04.004 Google Scholar

258. J. L. Nasar and D. Troyer, “Pedestrian injuries due to mobile phone use in public places,” Accid. Anal. Prev. 57, 91–95 (2013). http://dx.doi.org/10.1016/j.aap.2013.03.021 Google Scholar

259. D. Stavrinos, K. W. Byington and D. C. Schwebel, “Distracted walking: cell phones increase injury risk for college pedestrians,” J. Saf. Res. 42(2), 101–107 (2011).JSFRAV0022-4375 http://dx.doi.org/10.1016/j.jsr.2011.01.004 Google Scholar

260. D. Hamacher et al., “The reliability of local dynamic stability in walking while texting and performing an arithmetical problem,” Gait Posture 44, 200–203 (2016). http://dx.doi.org/10.1016/j.gaitpost.2015.12.021 Google Scholar

261. R. Cabeza, “Hemispheric asymmetry reduction in older adults: the HAROLD model,” Psychol. Aging 17(1), 85–100 (2002).PAGIEL http://dx.doi.org/10.1037/0882-7974.17.1.85 Google Scholar

262. S. Heuninckx, N. Wenderoth and S. P. Swinnen, “Systems neuroplasticity in the aging brain: recruiting additional neural resources for successful motor performance in elderly persons,” J. Neurosci. 28(1), 91–99 (2008).JNRSDS0270-6474 http://dx.doi.org/10.1523/JNEUROSCI.3300-07.2008 Google Scholar

263. D. A. E. Bolton, “The role of the cerebral cortex in postural responses to externally induced perturbations,” Neurosci. Biobehav. Rev. 57, 142–155 (2015).NBREDE0149-7634 http://dx.doi.org/10.1016/j.neubiorev.2015.08.014 Google Scholar

264. V. M. Clark and A. M. Burden, “A 4-week wobble board exercise programme improved muscle onset latency and perceived stability in individuals with a functionally unstable ankle,” Phys. Ther. Sport 6(4), 181–187 (2005). http://dx.doi.org/10.1016/j.ptsp.2005.08.003 Google Scholar

265. J. U. Wester et al., “Wobble board training after partial sprains of the lateral ligaments of the ankle: a prospective randomized study,” J. Orthop. Sports Phys. Ther. 23(5), 332–336 (1996).JOSPDV http://dx.doi.org/10.2519/jospt.1996.23.5.332 Google Scholar

266. A. T. Onigbinde, T. Awotidebe and H. Awosika, “Effect of 6 weeks wobble board exercises on static and dynamic balance of stroke survivors,” Technol. Health Care 17(5–6), 387–392 (2009). http://dx.doi.org/10.3233/THC-2009-0559 Google Scholar

267. D. HamacherD. Hamacher and L. Schega, “Towards the importance of minimum toe clearance in level ground walking in a healthy elderly population,” Gait Posture 40(4), 727–729 (2014). http://dx.doi.org/10.1016/j.gaitpost.2014.07.016 Google Scholar

268. R. Morris et al., “Gait and cognition: mapping the global and discrete relationships in ageing and neurodegenerative disease,” Neurosci. Biobehav. Rev. 64, 326–345 (2016). http://dx.doi.org/10.1016/j.neubiorev.2016.02.012 Google Scholar


Fabian Herold received his BA degree in sport science from Otto von Guericke University in 2014. Currently, he is working in the Department of Sport Science at Otto von Guericke University as a research assistant, where he finished his MA thesis. His research interests include analyzing neuromotor control processes of gait and the application of functional near-infrared spectroscopy in motor control experiments.

Patrick Wiegel received his BA degree from Otto von Guericke University Magdeburg in 2014 and his MSc degree from Albert-Ludwigs-University Freiburg in 2016. Currently, he is a PhD student at Albert-Ludwigs-University Freiburg. His research focuses on the underlying neural processes of human motor control and motor learning.

Felix Scholkmann received his PhD at the University of Zurich, Switzerland, in 2014. As a postdoc at the Biomedical Optics Research Laboratory of the University Hospital Zurich and a research associate at the University of Bern, his research focuses on biomedical signal processing, biomedical optics (development and application of fNIRS for human optical neuroimaging), neuroscience, integrative physiology, and biophysics.

Angelina Thiers received her master’s degree in information technology from the University of Applied Science in Brandenburg in 2014. She is a researcher at the Chair Health and Physical Activity at Otto von Guericke University Magdeburg, Germany. Her research interests include biosignal analyses and the development of new applications.

Dennis Hamacher received his doctorate from Otto von Guericke University in 2017. At the Chair Health and Physical Activity, he is working as a research associate, where he analyzes the underlying mechanisms of gait stability in old and diseased cohorts.

Lutz Schega is a professor (full) and chair of the Department of Health and Physical Activity at Otto von Guericke University in Magdeburg. He received his PhD in sports science in 1994 and his habilitation (postdoctoral qualification) in sports and rehabilitation science in 2003 from the University of Leipzig. A major focus of his work is on investigating various aspects of human walking performance.

© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Fabian Herold, Fabian Herold, Patrick Wiegel, Patrick Wiegel, Felix Scholkmann, Felix Scholkmann, Angelina Thiers, Angelina Thiers, Dennis Hamacher, Dennis Hamacher, Lutz Schega, Lutz Schega, "Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks," Neurophotonics 4(4), 041403 (1 August 2017). https://doi.org/10.1117/1.NPh.4.4.041403 . Submission: Received: 4 March 2017; Accepted: 23 June 2017
Received: 4 March 2017; Accepted: 23 June 2017; Published: 1 August 2017


Optics In Automated Inspection
Proceedings of SPIE (October 19 1975)

Back to Top