Conceived and designed the experiments: MM IM NH YI KK. Performed the experiments: MM IM MH HY TK MO ND. Analyzed the data: MM AI YI MO ND. Contributed reagents/materials/analysis tools: MM AI YI. Wrote the paper: MM MI.
Two authors (AI and YI) are working for the manufacturer of the NIRS system (Shimadzu Corp., Kyoto, Japan) used in this study. But no financial support was provided for this study from the company. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials.
Accumulating evidence indicates that motor imagery and motor execution share common neural networks. Accordingly, mental practices in the form of motor imagery have been implemented in rehabilitation regimes of stroke patients with favorable results. Because direct monitoring of motor imagery is difficult, feedback of cortical activities related to motor imagery (neurofeedback) could help to enhance efficacy of mental practice with motor imagery. To determine the feasibility and efficacy of a real-time neurofeedback system mediated by near-infrared spectroscopy (NIRS), two separate experiments were performed. Experiment 1 was used in five subjects to evaluate whether real-time cortical oxygenated hemoglobin signal feedback during a motor execution task correlated with reference hemoglobin signals computed off-line. Results demonstrated that the NIRS-mediated neurofeedback system reliably detected oxygenated hemoglobin signal changes in real-time. In Experiment 2, 21 subjects performed motor imagery of finger movements with feedback from relevant cortical signals and irrelevant sham signals. Real neurofeedback induced significantly greater activation of the contralateral premotor cortex and greater self-assessment scores for kinesthetic motor imagery compared with sham feedback. These findings suggested the feasibility and potential effectiveness of a NIRS-mediated real-time neurofeedback system on performance of kinesthetic motor imagery. However, these results warrant further clinical trials to determine whether this system could enhance the effects of mental practice in stroke patients.
Motor imagery is a dynamic state during which a subject mentally simulates a specific movement without any overt movement
Previous neuroimaging studies have revealed that several cortical areas, including the premotor area, sensorimotor cortex, and inferior parietal area, were more greatly activated using kinesthetic (first-person perspective) motor imagery compared with visual (third-person perspective) motor imagery
Although the concept of neurofeedback is not novel, the technique has recently attracted a great deal of attention with regard to a “brain-computer interface”
The near-infrared spectroscopy (NIRS) system, which is another neurofeedback system candidate, could be useful in a clinical setting, because NIRS noninvasively measures regional hemodynamic changes in oxygenated and deoxygenated hemoglobin (OxyHb and DeoxyHb) associated with neuronal activation
The first aim of the study was to determine whether the system reliably estimated task-related cortical activation. Subsequently, whether neurofeedback could enhance cortical activation associated with motor imagery was analyzed. Sequential finger movements from the right hand were utilized for the motor imagery task; hemoglobin signal changes from the left motor cortex were evaluated as “real” feedback, and sham information irrelevant to cortical signals served as control or “sham” feedback. A small feasibility study was initially conducted using actual finger movements for the task. The main purpose of this initial experiment was to confirm that the real-time signal analysis method was consistent with off-line analyses, as well as to determine which hemoglobin oxygenation parameters – Oxy- or Deoxy-Hb signals – would be suitable for the feedback signal. Based on findings from the first experiment, a second experiment was conducted in which self-assessment scales of motor imagery and cortical activation mapping were compared between the two feedback conditions to determine whether real feedback significantly affected motor imagery quality and related cortical activation.
In accordance with the Declaration of Helsinki, written informed consent was obtained from each subject, who participated in the present study. The study was approved by the Ethics Committee of Morinomiya Hospital (Osaka, Japan).
A total of five healthy, right-handed subjects were recruited to test consistency of the neurofeedback system. Handedness of the subjects was measured by the self-report of the side of their hand used in writing and eating, and no subjects had history of correction of handedness. The mean (± SD) age of participants was 36.8 (±13.5) years, with one female subject. Written informed consent was obtained from each subject.
Self-paced, sequential movements of the right fingers were used for the motor task. Participants were asked to sequentially fold their right fingers from the thumb to the little finger, and then to unfold them from the little finger to the thumb. They repeated these movements during the 5-s task period. The experiment consisted of 15 executions of the motor task, with randomized inter-task rest periods ranging from 8–15 s (
The NIRS-mediated neurofeedback system consisted of the NIRS system, a data-processing computer, and a monitor to display feedback information. A schematic overview of this system is illustrated in
It was assumed that NIRS detects hemoglobin signal changes derived from local vascular reactions coupled with neuronal activation at the cortical surface
According to the fiber arrangement shown in
NIRS measures task-related changes in OxyHb and DeoxyHb signals on the cortical surface. In the present study, the OxyHb signal was primarily utilized as a cortical activation marker and feedback signal source. Previous studies demonstrated that OxyHb signals exhibit superior sensitivity in task-related signal changes and a greater correlation with blood oxygen level–dependent signals in functional magnetic resonance imaging (fMRI)
A two-parameter gamma hemodynamic response function (HRF), which was utilized in fMRI data analysis, served as the predictor for task-related hemoglobin signal changes
As a reference standard for cortical activation of each task, an off-line task-by-task GLM analysis was performed using all data points for the dataset. In this analysis, the time series was divided by task, and each individual task was represented by three box-car functions together with three basis sets for HRF. The design matrix consisted of 52 columns: a constant for collecting offsets, 3×15 columns (3 basis functions×15 repetitions of the task) for task data, and 6 columns for discrete cosine transform functions as high-pass filters with a cut-off frequency of 0.0125 Hz to remove baseline drifts (
To analyze real-time assessment, the referenced cortical signals calculated from the task-by-task analysis were compared with feedback signals calculated from the sliding-window GLM analysis. By comparing reference signals,
A total of 24 healthy, right-handed subjects were recruited, with no history of neurological or psychological disease. As Experiment 1, handedness of the subjects was measured by the self-report of the side of their hand used in writing and eating, and no subjects had history of correction of handedness. Written informed consent was obtained from each subject. Only two subjects, who participated in Experiment 1, were included.
Participants were asked to perform two sessions of the motor imagery task. Each session consisted of 15 sets in which participants performed imagery of right-finger movements, without physical movement, for 5 s. Environmental settings were similar to those in Experiment 1. Subjects were asked to imagine self-paced and sequential folding of the right fingers similar to the movements in Experiment 1. The subjects were also asked to kinesthetically imagine movements rather than visually (
During the motor imagery task, participants were instructed to watch the monitor where feedback information was displayed as vertical bar height and color (
After each session, the participants were asked to evaluate a self-assessment scale of motor imagery quality. They were asked to image finger-folding and to score how well they kinesthetically imagined the finger movements. If the subject experienced a vivid kinesthetic feeling that he/she had performed the task physically, then the 11-point scale scored a performance of 10, while the worst performance was scored as zero.
To estimate the effect of neurofeedback on motor imagery quality, self-assessment scale scores under real and sham feedback conditions were compared using a two-tailed paired
As a first-level analysis, the effect of neurofeedback on cortical activation maps associated with motor imagery was analyzed, and the contrast between motor imagery task and baseline in real and sham feedback conditions was estimated. In addition, intra-subject contrasts between the two conditions were evaluated. Accordingly, three beta-values were calculated from four different contrasts, including real feedback
Oxy- and DeoxyHb signal changes were analyzed. For each contrast, a positive beta-value indicated an increase, and a negative beta value indicated a decrease in hemoglobin signals in the former condition compared to the latter condition. In the design matrix, discrete cosine transform functions were included as high-pass filters with a cut-off frequency of 0.0125 Hz to remove baseline drifts. Averaged signal changes from 50 channels were included for eliminating global effects, such as autonomic responses relevant to motor imagery. To adjust for the auto-correlated error term, an autoregressive model of order 1 was used
In addition to statistical analysis using GLM, a timeline analysis of OxyHb and DeoxyHb signals from the left lateral premotor cortex (channels 3), left sensorimotor cortex (channel 4), and left parietal association cortex (channel 25) was also performed. In each channel, averaged data from 315 trials (15 trials×21 subjects) under real feedback and sham feedback conditions were plotted from 1 s before to 12 s after task onset to validate GLM analysis.
In the feasibility study, which utilized NIRS-mediated real-time neurofeedback system data from five healthy right-handed subjects, all subjects completed the right-finger folding task without obvious head motion. Correlation analyses between real-time feedback signals derived from sliding-window GLM analyses (TSWA) and results from conventional task-by-task GLM analyses (TRef) in five subjects are shown in
Subject | Correlation coefficient | p-value |
|
||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||
|
|
|
|
|
|
3 | r = 0.262 | p = 0.3455 |
|
|
|
5 | r = 0.0328 | p = 0.9075 |
Although 24 healthy, right-handed subjects were recruited to analyze the effects of neurofeedback on motor imagery task-related cortical activation, data from three subjects were excluded due to overt finger movements during the motor imagery task. Subsequent analyses comprised data from the remaining 21 subjects. The mean (± SD) age of the 21 subjects was 34.3 (±10.3), with 4 female subjects. The average (± SD) self-assessment scale scores for kinesthetic motor imagery, which were assessed after real- and sham-feedback conditions, were 5.0 (±1.6) and 4.1 (±1.8), respectively (
Paired
Self assessment for motor imagery | Average feedback presentation | ||||||
Subject | Gender | Age | Real | Sham | Real | Sham | Order of real feedback |
1 | F | 24 | 5 | 8 | 1.31±0.67 | 1.13±0.29 | First |
2 | M | 45 | 4 | 3 | 1.38±0.88 | 1.11±0.28 | Second |
3 | F | 37 | 5 | 4 | 1.73±1.41 | 1.18±0.27 | First |
4 | M | 47 | 5 | 3 | 2.68±1.96 | 1.17±0.27 | Second |
5 | M | 24 | 4 | 3 | 1.79±0.99 | 1.19±0.30 | First |
6 | M | 29 | 6 | 2 | 1.67±0.98 | 1.18±0.27 | Second |
7 | M | 42 | 2 | 1 | 1.22±0.67 | 1.18±0.24 | First |
8 | M | 48 | 6 | 5 | 0.88±1.69 | 1.21±0.26 | Second |
9 | M | 39 | 6.5 | 5 | 3.12±3.12 | 1.11±0.27 | First |
10 | M | 49 | 5 | 6 | 1.28±0.73 | 1.14±0.25 | Second |
11 | M | 35 | 4 | 6 | 1.31±0.67 | 1.18±0.26 | First |
12 | M | 24 | 6.5 | 3.5 | 0.97±0.76 | 1.11±0.28 | Second |
13 | F | 24 | 2 | 1 | 1.42±1.36 | 1.13±0.27 | First |
14 | M | 27 | 7 | 5 | 1.63±0.76 | 1.16±0.28 | Second |
15 | M | 38 | 5 | 4 | 2.01±2.00 | 1.11±0.30 | First |
16 | M | 55 | 4 | 2 | 2.38±1.82 | 1.11±0.28 | Second |
17 | M | 23 | 7 | 6 | 1.38±0.88 | 1.17±0.26 | First |
18 | M | 36 | 3 | 3 | 1.92±1.48 | 1.18±0.26 | Second |
19 | M | 26 | 5 | 5 | 1.88±0.84 | 1.16±0.29 | First |
20 | M | 25 | 8 | 5 | 1.58±0.90 | 1.11±0.27 | Second |
21 | F | 23 | 5.5 | 6 | 1.26±0.91 | 1.20±0.25 | First |
M: male F: female.
Cortical activation mapping with OxyHb signals revealed significantly increased motor imagery-related signals in the left sensorimotor and bilateral prefrontal cortex under real feedback conditions (
Results from second-level random effect analysis of comparisons between real feedbacks
CH | MNI coordinates (X/Y/Z) | Cortical region | BA | |
|
||||
Left sensorimotor cortex | ||||
4 | −47/−8/57 | PreCG | 4/6 | 3.70 |
Left prefrontal cortex | ||||
1 | −38/51/30 | MFG | 46 | 3.83 |
2 | −45/36/43 | MFG | 9/46 | 3.23 |
Right prefrontal cortex | ||||
47 | 41/47/29 | MFG | 46 | 3.53 |
48 | 45/28/42 | MFG | 9/46 | 3.80 |
40 | 31/51/35 | MFG | 9/46 | 3.54 |
|
||||
Left prefrontal cortex | ||||
1 | −38/51/30 | MFG | 46 | 3.89 |
2 | −45/36/43 | MFG | 9/46 | 3.87 |
5 | −26/56/35 | MFG | 9/46 | 3.28 |
12 | −19/57/37 | SFG | 9 | 2.98 |
13 | −18/40/50 | SFG | 9 | 3.54 |
Left parietal association cortex | ||||
25 | −9/−61/72 | Precuneus/SPL | 5/7 | 2.67 |
Right prefrontal cortex | ||||
40 | 31/51/35 | MFG | 9/46 | 3.26 |
Right parietal association cortex | ||||
32 | 6/−61/70 | Precuneus/SPL | 5/7 | 2.55 |
|
||||
Left premotor cortex | ||||
3 | −46/11/53 | MFG | 6 | 2.93 |
|
||||
Left parietal association cortex | ||||
25 | −9/−61/72 | Precuneus/SPL | 5/7 | 2.60 |
Right parietal association cortex | ||||
32 | 6/−61/70 | Precuneus/SPL | 5/7 | 2.55 |
CH: channel number; BA: Brodmann area; PreCG: precentral gyrus; SFG: superior frontal gyrus; MFG: middle frontal gyrus; SMA: supplementary motor area; SPL: superior parietal lobule.
CH | MNI coordinates (X/Y/Z) | Cortical region | BA | |
|
||||
Left parietal association cortex | ||||
25 | −9/−61/72 | Precuneus/SPL | 5/7 | 2.90 |
|
||||
Left parietal association cortex | ||||
25 | −9/−61/72 | Precuneus/SPL | 5/7 | 3.06 |
CH: channel number; BA: Brodmann area; SPL: superior parietal lobule.
In the left PMC, task-related OxyHb signal changes increased under real feedback conditions. In the left parietal association cortex, task-related OxyHb signal changes increased under sham feedback conditions. OxyHb signal changes were comparable between feedback conditions in the left SMC. In the left parietal association cortex, task-related DeoxyHb signals decreased only under sham feedback conditions.
Results from the present study demonstrated that the NIRS system can be used to detect real-time task-related hemoglobin signal changes, and this system can be reliably used as a neurofeedback tool. During motor learning processes, correct information feedback about performance (“knowledge of result”) is known to be effective in healthy subjects and in stroke patients
Results from Experiment 1 revealed that hemoglobin signal changes were detected by the sliding-window GLM analysis with a hemodynamic delay of several seconds. Results from real-time processing in this system represented task-related cortical hemoglobin signal changes. Compared with other neuroimaging modalities, the NIRS-mediated neurofeedback system exhibits several advantages for clinical application, including relative robustness of subject motion, shorter attachment time, and less subject constraint. However, one main technical flaw of the NIRS measurements is the delay of several seconds between neuronal activation and hemoglobin signal changes. Simultaneous measurement of NIRS and EEG, which measures direct neuronal activation and has greater temporal resolution, could be a possible solution for methodological limitations
The present results demonstrated that OxyHb was more robust under real-time assessment conditions for task-related cortical activation. However, it remains to be determined which hemoglobin parameters are more suitable for measuring cortical activation. Although the current theoretical framework for blood level-dependent (BOLD) signals in fMRI suggests that decreased DeoxyHb concentrations correlate with greater BOLD signals
Results from Experiment 2 demonstrated that NIRS-mediated neurofeedback enhanced motor imagery-related cortical activation in the contralateral premotor cortex. These findings were consistent with previous findings from a fMRI-mediated neurofeedback study, which showed rostral enlargement of motor imagery-related motor cortical activation
Results from the present study also demonstrated significantly greater cortical activation in the parietal association cortex under sham feedback conditions. Previous studies have suggested that the medial parietal association cortex is involved in memory-related visual imagery
Bilateral prefrontal cortex and right premotor cortex were activated by the motor imagery task, regardless of type of provided feedback (real or sham). Because motor imagery requires much attention and concentration, prefrontal activation could be related to cognitive processes involved in motor imagery. Indeed, previous reports have consistently documented greater cortical activation in the bilateral premotor cortex and prefrontal cortex during motor imagery tasks compared with motor execution tasks
Because cognitive processes, including planning, inhibition, and motor imagery, could evoke changes in heart and respiratory rate
Sham information served as feedback information for the control condition. In previous neurofeedback studies, several kinds of signals, including background signals with random fMRI noise
There were several limitations in this study. First, because the study included only healthy subjects, the effect of neurofeedback on stroke patients remains unclear. Although motor imagery in stroke patients is generally not impaired
In conclusion, results from the present study demonstrated the feasibility of a NIRS-mediated neurofeedback system and revealed the modulative effect of this system on motor imagery-related cortical activation. Results suggested that neurofeedback could facilitate individual skills for kinesthetic motor imagery. The NIRS-mediated neurofeedback system could be a promising tool, which could be applied in widespread areas, including neurorehabilitation. However, further clinical trials are needed to determine whether this system could enhance mental practice in stroke patients.