Neurofeedback using real-time near-infrared spectroscopy enhances motor imagery related cortical activation

Abstract

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.

Figure 1. Configuration and testing of the neurofeedback system using NIRS.

A, Representation of time course of the experiment. Subjects were asked to perform 15 repetitions of a 5-s task with randomized inter-task rest periods between 8–15 s. The total length of one experimental session was no longer than 250 s. B, Schematic figure of the NIRS-mediated neurofeedback system. Task-related cortical hemoglobin signal changes were transferred to a data-processing computer, and the evaluated cortical activation was visually fed back in real-time. Cortical activation was represented by bar height and color. C, The NIRS-mediated neurofeedback system in use. Subjects were seated in an armchair, and the heads were fixed to the headrest to avoid excessive head movement during experimentation.

Figure 2. Cortical placement of NIRS channels.

A, Fiber arrangement for the 50-channel NIRS system. The light source at the center of the third row was placed at the subject's vertex (Cz). B, Estimated location of each NIRS channel, which was defined as the midpoint of the line between the corresponding light source-detector pair. C, Estimated cortical area covered by channels 4, 9, and 17, which was set as the neurofeedback source of cortical activation related to the motor imagery task.

Figure 3. Design matrices for real-time processing and off-line processing.

A, The design matrix for real-time sliding-window GLM analysis. The time window was 80 data points wide. The matrix consisted of one constant column, three columns for task and rest phases, respectively, and one linear term (L). Task-related signal changes were estimated as a beta value, comparing task data with resting data. B, The design matrix for off-line task-by-task GLM analysis. The matrix consisted of a constant column, columns for each task, and a high-pass filter with a cut-off frequency of 0.0125 Hz.

Figure 4. Comparison of calculated t-values from real-time processing and off-line processing.

A, B, Scatter plot of calculated t-values from real-time processing and off-line processing in five subjects using (B) OxyHb signal data and (C) DeoxyHb signal data. Using OxyHb signal data, all five subjects exhibited significant correlations between real-time assessments of cortical activation calculated from sliding-window GLM analysis (T_SWA) and reference cortical activation calculated from task-by-task GLM analysis (T_Ref). However, correlations between T_SWA and T_Ref were less with DeoxyHb data. C, The dynamic change of cortical activation feedback, as calculated from sliding-window GLM analysis (black line) and reference cortical activations calculated from the task-by-task GLM analysis (gray bar) in Experiment 1 (data from a representative subject).

Figure 5. Self-assessment scale scores for kinesthetic motor imagery under real and sham feedback conditions.

Paired t-test revealed increased self-assessment scores for kinesthetic motor imagery under real feedback conditions.

Figure 6. Cortical mapping of motor imagery–related activation.

Results from second-level random effect analysis of comparisons between real feedbacks vs. baseline (A), between sham feedback vs. baseline (B), real vs. sham feedback (C), and sham vs. real feedback (D). Within-subject comparison between feedback conditions revealed significantly increased cortical activation in the left lateral premotor cortex under real feedback conditions compared with sham feedback conditions, as well as significantly increased activation in the bilateral parietal association cortex under sham feedback conditions compared with real feedback conditions.

Figure 7. Average hemoglobin signal changes in the left SMC, left PMC, and left parietal association cortex.

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.