2.1.

Subjects

Eight children with hemiparetic CP were included in the study (subjects 1 to 8, six male and two female, $10\pm 1.7$ years old) along with eight typically developing children as control subjects (five male and three female, $9.5\pm 1.5$ years old). Each subject performed two tapping trials, one with each hand, making a total of 16 trials for the group of subjects with CP. Subjects 6, 7, and 8 did not exhibit mirror motions while performing the tasks. Excessive head motions and a failure to follow the protocol resulted in unreliable data for subject 5. Additionally, subjects 2 and 4 did not exhibit mirror motions while tapping with their unaffected hand. The GLM/ICA method was applicable to the six remaining trials where mirror motions were present. Three of the subjects were classified as level 2 on the Manual Ability Classification System (MACS),¹⁷ two of whom were right-hand affected (subjects 2 and 3) and one was left-hand affected (subject 1). One subject (subject 4) was classified as MACS level 1 and was right-hand affected. Anatomical T1-MPRAGE MRI images were available for all subjects recruited for this study, which enabled localizing their brain lesions. Subject 1 proved to be a unique case because at seven years of age, due to intractable epileptic seizures, he underwent a right functional peri-insular hemispherectomy involving removal of part of the right temporal lobe, a section of the corpus callosum (disconnection of both hemispheres), and subcortical deafferentation of the right frontal, parietal, and occipital lobes. This subject had a physical shift in the brain midline separating the two hemispheres, as verified by the anatomical MRI, and presented atypical sensorimotor activation patterns that are discussed in this work. These studies were performed under the approval of the University of Texas Southwestern Medical Center at Dallas institutional review board protocol (IRB No. 042007-064).

2.2.

Experimental Protocol and Measurement Setup

Each subject performed a finger-tapping task, once with their affected hand and once with their nonaffected hand, while the other hand was to remain at rest to the best of the subject’s ability. Children were instructed to tap only with four fingers (excluding the thumb) in unison and keep their wrists on the table while finger tapping. A program with an engaging animation made in-house on Adobe Flash (Adobe Systems Incorporated, San Jose, Calfornia) was used to prompt the children to tap at a frequency of 1 Hz. The experimental protocol for finger tapping with each arm consisted of eight 15-s tapping periods, each followed by 25 s of rest, with a 3-min rest period before the first tapping period. For the motion tracking of hands and arms during the finger tapping task, 5-mm-diameter hemispherical retroreflective targets (B&L Engineering, Santa Ana, California) were attached with tape on the finger nail bed and just above the proximal interphalangeal joint on each finger, excluding the thumb [Fig. 1(a)]. The three-dimensional coordinates of these retroreflective targets were tracked by a six-camera VICON Mx motion capture system (Vicon, Denver, Colorado) at a sampling rate of 120 Hz. Three of the six analyzed trials were performed with a seven-camera setup shown in Fig. 1(b), where the two cameras to the sides of the subject were $\sim 1.5\mathrm{m}$ high, and all others $\sim 3\mathrm{m}$ high. The remaining three trials employed a six-camera setup arranged in a semicircle around the subject with a distance of $\sim 3\mathrm{m}$ from the subject and heights ranging from 2 to 4 m. The reason that two different camera setups were used in this work was that after subjects 1 and 4 were measured, the study was moved to a different room, which was smaller and camera geometry accommodations had to be made to maintain consistent retroreflective marker tracking. The retroreflective target localization accuracy in both rooms was $\pm 1\mathrm{mm}$, which was much smaller than the finger tapping amplitude. More marker occlusions were observed in the smaller room, but due to the high number of markers on each hand, at least one marker provided sufficient trajectory data in any one measurement. Muscle movement was monitored by eight sets of wireless EMG electrodes (BTS FREEEMG 300, BTS, Garbagnate Milanese MI, Italy) placed on the finger flexor and extensor muscles, biceps, and triceps of both arms.

Fig. 1

The motion tracking and functional near-infrared spectroscopy measurement setups: (a) retroreflective target placement on a subject’s hand, (b) camera setup relative to the child’s position, and (c) location of sources and detectors on the head with covered cortical areas, premotor cortex (PMC), supplementary motor area (SMA), and primary motor/sensory area (M1/S1) circled in red.

A continuous wave fNIRS system (CW-6, Techen Inc., Milford, Massachusetts) was used to measure $\mathrm{\Delta }\mathrm{HbO}$ in the sensorimotor cortex during the finger tapping tasks. A custom headset was created with 16 source fiber bundles [Fig. 1(c), magenta circles] and 32 detector fiber bundles [Fig. 1(c), blue squares]. The headset was centered on the Cz position of the EEG International $10/20$ system¹⁸ and attached to the subject’s head with Velcro straps. Anatomical measurements of the C3, C4, F3, and F4 positions made before each fNIRS session were sufficient to localize the primary motor/sensory area (M1/S1), premotor cortex (PMC), and supplementary motor area (SMA) [Fig. 1(c)]. Each source bundle simultaneously emitted 690 and 830 nm light, which was received by up to six detectors located within a 3 cm radius giving a total of 84 source-detector pairs for each wavelength. Eight short (1.5 cm) source-detector pairs were employed to measure background $\mathrm{\Delta }\mathrm{HbO}$ fluctuations in the scalp and used to adaptively filter the fNIRS signals (Sec. 2.4). The detected light signal was sampled at a rate of 25 Hz, and the system utilized frequency modulation (6.4 to 12.6 kHz, with 200 Hz increment) to simultaneously monitor activation over all source-detector pairs for the duration of the trial. Data acquisition for the fNIRS, EMG, and motion tracking systems was manually started simultaneously at the beginning of each measurement protocol, which afforded a synchronicity of $\pm 0.5\mathrm{s}$ between modalities that is much shorter than the duration of elicited hemodynamic responses.

2.3.

Signal Processing

Systemic cerebral hemodynamic fluctuations are present in fNIRS signals due to physiological events, such as cardiac pulsation, respiration, and Mayer waves, which typically fall within 0.8 to 2.0, 0.1 to 0.33, and $\sim 0.1\mathrm{Hz}$ or lower frequency ranges, respectively.^19,20 Cortical hemodynamic responses to motor task protocols usually fall within the 0.01 to 0.4 Hz frequency range, so there is overlap between the evoked response and the physiological artifacts of respiration and Mayer waves.

To remove these artifacts, a combination of band-pass and adaptive filtering was used. Cardiac pulsation was removed by band-pass filtering all signals between 0.01 and 0.4 Hz. To remove respiration and Mayer waves, an adaptive least mean square filter was then applied using a short (1.5 cm) source-detector pair as a reference channel as previously reported.²¹ Additionally, data were visually inspected and any tapping/rest blocks with obvious motion artifacts were excluded from further analysis.

The acquired reflectance data were reconstructed into fNIRS activation images using the open-source HomER software implemented in MATLAB® (Mathworks, Natick, Massachusetts).²² HomER uses the Tikhonov perturbation solution to the photon diffusion equation, which employs a regularized Moore-Penrose inversion scheme^23,24 to reconstruct activation images ($20\times 21\text{pixels}$) that represent maps of $\mathrm{\Delta }\mathrm{HbO}$ on the cortical surface within the detectors’ field of view [Fig. 1(c)].

2.4.

GLM Analysis with a Boxcar Regressor

A standard GLM analysis was performed with a boxcar regressor using Eq. (1).

All terms in Eq. (2) were introduced in Eq. (1) except for $c$, which represents a contrast vector that allowed for hypothesis testing. A contrast vector of [$-1$, 1] was used to test the null hypothesis that activation was not above baseline, i.e., ${\beta }_1={\beta }_0$. With the application of the Bonferroni’s correction for multiple comparisons,²⁷ a pixel with $p<0.0001$ ($|t|>3.5$) was considered to have significant cortical activity relative to baseline conditions. Activation images were created with the significant ${\beta }_1$ values, which gave $\mathrm{\Delta }\mathrm{HbO}$ in $\mu \mathrm{Mol}$. To perform group analysis on the control subjects, the $\mathit{Y}$ matrix in Eq. (1) was created by concatenating the $\mathrm{\Delta }\mathrm{HbO}$ time-series data for each subject.

2.5.

GLM/ICA Method

3.1.

Identification of Brain Activation Areas due to Tapping and Mirror Motions Using Motion Tracking

3.1.1.

Determination that activation in M1/S1 seen in the boxcar image was due to tapping after removal of activation from the mirror image

The GLM/ICA method was first applied to subject 1 performing a tapping task with his unaffected right hand. Figures 2(a) and 2(b) show the vertical displacement recorded from the index finger distal target on the right and left hand, respectively. Figure 2(b) indicates that mirror motions were present during this trial, but they were lower in amplitude and sparser than the tapping hand motions seen in Fig. 2(a). Displacements were qualitatively similar in all other trials and are, therefore, not shown in subsequent figures. Figures 2(c) and 2(d) show the tapping (right hand) and mirror (left hand) images, respectively, produced by the GLM/ICA method. Figure 2(e) shows the activation image created using the standard boxcar regressor analysis for the right hand tapping trial, and Fig. 2(f) shows the standard boxcar image for subject 1’s left hand tapping trial. Note that in Figs. 2(c) to 2(f) and all subsequent figures, a dashed black line indicates the midline and gray color ovals indicate the locations of M1/S1, PMC functional areas in each brain hemisphere, and SMA shared between hemispheres, as described in Sec. 2.2 and Fig. 1(c) above. Comparing Fig. 2(c) to Fig. 2(d) revealed that the area of activation present in the right primary M1/S1 area of the tapping hand image [Fig. 2(c)] was absent from the mirror hand image [Fig. 2(d)], as indicated by the circles, but the images were otherwise similar. Overlap was seen in the right and left PMC areas where activation was present in both images [Figs. 2(c) and 2(d)], as well as the midline activation areas. These midline activations, which are atypical of hand tapping tasks, were specific to this subject and likely due to the shift in the left brain hemisphere mentioned in Sec. 2.1. Despite the spatial overlap of several activation areas between tapping and mirror images [Figs. 2(c) and 2(d)], the GLM/ICA method provided an advantage as it removed some ambiguity present in the standard boxcar image [Fig. 3(e)] that could not attribute any activation area solely to one type of motion. The method was also applied to subject 1’s left hand tapping task and similar results were obtained (not shown for brevity). It was interesting to see that the M1/S1 and part of the PMC areas activated for this subject during left hand tapping were slightly displaced relative to the corresponding areas activating for right hand tapping [compare Fig. 2(e) to Fig. 2(f) boxcar images].

Fig. 2

Comparison of activation images made with the general linear model/independent component analysis (GLM/ICA) method or a boxcar regressor for subject 1 performing right hand tapping. (a) Vertical position of nail target on tapping (right) hand and (b) on the mirror (left) hand. (c) Image created using the GLM/ICA method for the tapping (right) hand and (d) the mirror (left) hand. (e) Activation image made using a boxcar regressor for right hand tapping. (f) Activation image made using a boxcar regressor for left hand tapping. The black dashed lines indicate the shifted midline and the gray ovals indicate the M1/S1 and PMC of each hemisphere and the SMA shared between hemispheres.

Fig. 3

Comparison of activation images made with the GLM/ICA method or a boxcar regressor for subject 2 performing right hand tapping. (a) Image created using GLM/ICA for the tapping (right) hand and (b) the mirror (left) hand. (c) Activation image made using a boxcar regressor for right hand tapping. (d) Activation image made using a boxcar regressor for left hand tapping. The black dashed lines indicate the midline and the gray ovals indicate the M1/S1 and PMC of each hemisphere and the SMA shared between hemispheres.

3.1.3.

Determination that bilateral activation in M1/S1 seen in the boxcar image was due to tapping in one brain hemisphere and due to mirror motions in the other

The results of the GLM/ICA method applied to subject 3 performing a right (affected) hand tapping task are shown in Fig. 4. Figures 4(a) and 4(b) show the tapping (right hand) and mirror (left hand) activation images, respectively, and Figs. 4(c) and 4(d) show the boxcar images for the right and left hand trials, respectively. Notice that similar to the results for subject 2 (Sec. 3.2.2), the activation in the ipsilateral (right) primary M1/S1 area (red circles in Fig. 4) present in the right hand boxcar image [Fig. 4(c)] was mostly removed from the tapping image [Fig. 4(a)]. In addition, an activation area in the left SMA (gray circles in Fig. 4) was also removed from the tapping image [Fig. 4(a)]. Both areas were assigned to the mirror hand image [Fig. 4(b)], indicating that mirror (left hand) motions were primarily responsible for activation in these areas.

Fig. 4

Comparison of activation images made with the GLM/ICA method and a boxcar regressor for subject 3 performing right hand tapping. (a) Image created using the GLM/ICA method for the tapping (right) hand and (b) the mirror (left) hand. (c) Activation image made using a boxcar regressor for right hand tapping. (d) Activation image made using a boxcar regressor for left hand tapping. The black dashed lines indicate the midline and the gray ovals indicate the M1/S1 and PMC of each hemisphere and the SMA shared between hemispheres.

In addition, activation in the contralateral (left) M1/S1 area (blue circles in Fig. 4) was absent in the mirror image [Fig. 4(b)], but was present in the tapping and boxcar images [Figs. 4(a) and 4(c)]. In this case, the GLM/ICA method provided another cleaned-up activation image by removing contamination from mirror motions, demonstrating its superiority over standard boxcar analysis.

Subject 3 also exhibited mirror motions while tapping with his left (unaffected) hand, and Fig. 4(d) shows the boxcar image from this trial containing contralateral M1/S1 and PMC activation, similar to Fig. 4(b) for right hand tapping. The GLM/ICA results of this trial are not shown, because the method produced tapping and mirror images that looked nearly identical and, therefore, did not provide any useful information. This occurred because subject 3’s right (affected) hand mirror motions were highly correlated (0.93) with the left (unaffected) hand tapping motions. The effect of motion correlation on GLM/ICA results is further discussed in Sec. 4.4.

3.2.

Identification of Brain Activation Areas due to Tapping and Mirror Motions Using EMG

The GLM/ICA method was applied to subject 4 performing a right (affected) hand tapping task, but, unlike all other cases, the motion regressors were created using EMG signals. The reason was that subject 4 had no detectable mirror (left hand) motions, so no motion tracking regressors could be created. However, muscle activity was detected in the mirror (left) hand, indicating the subject was pressing the mirror (left) arm against the table while finger tapping with the other (right) hand. Figures 5(a) and 5(b) show the tapping (right hand) and mirror (left hand) images, respectively. Figures 5(c) and 5(d) show the corresponding boxcar images for the right and left hand trials, respectively. In this case, the boxcar [Fig. 5(c)] and tapping images [Fig. 5(a)] looked similar, with M1/S1 and PMC areas activating bilaterally during the finger tapping motion. Bilateral activation in sensorimotor areas is common in children with CP, but is not seen in all cases as activation patterns are variable in this population.^29,30 Interestingly, in the mirror hand image [Fig. 5(b)], the ipsilateral M1/S1 and contralateral PMC showed weak levels of activation consistent with the relatively sparse and low-amplitude mirror hand muscle activations. These areas were also active during the left hand tapping trial, but their size was larger and also involved activation of the ipsilateral (left) PMC [Fig. 5(d)]. The observed differences between the mirror hand image [Fig. 5(b)] and the left hand tapping one [Fig. 5(d)] were not surprising since these two activation images were not generated by the same type of motion. Nevertheless, Fig. 5(b) suggests that when the mirror hand was pressing against the table, a relatively small contribution was added to the activation image seen by boxcar analysis [Fig. 5(c)], though the latter would not have identified the origin of these mirror hand contributions.

Fig. 5

Comparison of activation images made with the GLM/ICA method and a boxcar regressor for subject 4 performing right hand tapping. (a) Image created using GLM/ICA method for the tapping (right) hand and (b) the mirror (left) hand. (c) Activation image made using a boxcar regressor. (d) Activation image made using a boxcar regressor for left hand tapping. The black dashed lines indicate the midline and the gray ovals indicate the M1/S1 and PMC of each hemisphere and the SMA area shared between hemispheres.

3.3.

Application of a One-Regressor GLM/ICA Method to a Typically Developing Child

The modified one-regressor GLM/ICA method was applied to the right hand trial of subject 9, who was a typically developing child. Figures 6(a) and 6(b) show the images created using the standard boxcar analysis and the modified one-regressor GLM/ICA method, respectively. Figure 6(c) shows the vertical displacement of the marker placed on the nail of the tapping index finger. The red arrows in Fig. 6(c) indicate that subject 9 moved during the resting portions of the protocol (only three tapping intervals are shown for clarity). Figure 6(d) shows the group analysis performed using the right hand tapping trials from six right-handed control subjects. Two of the eight control subjects were excluded from the group analysis, one who was left handed and one due to motion artifacts. The three circles in Figs. 6(a) and 6(b) indicate areas where the activation patterns differed in the two images. In the contralateral (left) M1/S1 area (blue circles), activation was increased in the GLM/ICA image [Fig. 6(b)], while in the contralateral (left) PMC, it was decreased (red circles). Additionally, an area of deactivation was increased in the ipsilateral (right) M1/S1 area (gray circles) in the GLM/ICA image [Fig. 6(b)]. In all three areas, the GLM/ICA image [Fig. 6(b)] more closely matched the group analysis image [Fig. 6(d)] than the boxcar image [Fig. 6(a)]. In this case, the GLM/ICA method provided an improved image by taking into account the unintended motions occurring during the rest periods. In the more typical case where a subject followed the protocol correctly without extraneous motions, no significant differences were seen between the boxcar and GLM/ICA images.

Fig. 6

Comparison of activation images made with the GLM/ICA method and a boxcar regressor for control subject 9 performing right hand tapping. (a) Activation image made using a boxcar regressor. (b) Activation image made with the modified GLM/ICA method. (c) Vertical position of nail target on tapping (right) hand. (d) Group analysis including six typically developing children as control subjects. The black dashed lines indicate the midline and the gray ovals indicate the M1/S1 and PMC of each hemisphere and the SMA shared between hemispheres.

4.1.

Overview

A shortcoming of current standard GLM analyses for fNIRS data is that they cannot distinguish whether sensorimotor cortex areas activate due to intended or unintended mirror motions. In this study, the GLM/ICA method was developed to help address this shortcoming. The method was applied to fNIRS data acquired during a hand tapping task performed by children with CP who exhibited unintended mirror motions. In some cases (subjects 2 and 3), activation areas present in the standard boxcar image were removed from the tapping hand image, thus providing a cleaned-up activation image with mirror motions contamination removed. In other cases (subjects 1 and 4), the tapping hand image was not very different from the boxcar one, but the mirror image offered additional information on how unintended motions were mapped onto the sensorimotor cortex. Regardless of which case was achieved, the GLM/ICA method produced additional interesting information that was not attainable by standard boxcar regressor analysis.

4.2.

Special Case of a Subject with Prior Brain Surgery

The special case of subject 1 merits further discussion. As mentioned in Sec. 2.1, subject 1 had prior brain surgery that resulted in a physical shift of his brain’s midline, as verified by anatomical MRI. fNIRS imaging for this subject indicated bilateral activation for tapping with either hand though, interestingly, the activation region in the right M1/S1 was in slightly different locations depending on which hand was tapping. Specifically, during left hand tapping by subject 1 [Fig. 2(f)], an adjacent but distinct activation area was located laterally and posterior to the right hand tapping activation in right M1/S1 seen in Figs. 2(c) and 2(e). The fact that portions of both the right and left M1/S1 areas activated for tapping performed by either hand likely caused each hand to act as the mirror one when the other hand was tapping. For this special case, the GLM/ICA method was especially useful in disentangling the relative contributions of tapping versus mirror motion in this subject with complicated cortical reorganization after surgery.

4.3.

Nonseparable Activation Areas and Motion Regressor Correlation

In all cases, there were some activation areas that appeared in both the tapping and mirror images, and therefore, the source of these activations remained ambiguous. Since the GLM/ICA method assigned ICs uniquely to the tapping or mirror images, the temporal hemodynamic patterns in these regions were different even though they were spatially overlapping. It is, therefore, possible that in some cases both tapping and mirror motions did contribute to activation in the same areas as plasticity after brain injury may have resulted in one brain area eliciting motion in both hands.³¹ However, it is also possible that some of the activation attributed to the same pixel locations in both images was due to only tapping or mirror motions and that the GLM/ICA method could not identify the responsible motion. This is because the tapping and mirror hand regressors were typically correlated to varying degrees. When two highly correlated regressors were used in the GLM analysis, the model could not uniquely assign the variance in the $\mathrm{\Delta }\mathrm{HbO}$ signals to one of the regressors, as applying a high weight to one regressor and low weight to the other produced a solution nearly identical to the opposite weight assignments. This could have resulted in false overlap of activation areas between the two images. In the case of subject 3’s left hand tapping trial (Sec. 3.1.3), where the correlation coefficient between motion capture regressors for tapping and mirror motions was high (0.93), the overlap was so severe that the images looked identical and no activation areas could be attributed to only one motion (not shown for brevity). However, this high correlation occurred in only one out of the five motion tracking data sets analyzed in this study. In the remaining four motion tracking data sets, the fraction of ICs rejected because they correlated with both regressors was in the 25 to 50% range, corresponding to correlation coefficients in the 0.59 to 0.78 range. In all four cases, meaningful information on mirror motion effects was attained despite this limitation.

4.4.

Motion Tracking Versus EMG Regressors

Since EMG systems are less expensive and require less setup time than a multicamera motion capture system, a valid question would be why use motion capture data when EMG signals could be used instead? The answer is that motion regressors created from EMG signals tended to be more correlated than motion capture data, with correlation coefficients in the 0.72 to 0.96 range. As discussed in Sec. 4.4, higher levels of correlation between the motion regressors resulted in less meaningful results. In all cases where both EMG and motion capture regressors were available, the EMG regressors had higher correlation coefficients than their corresponding motion capture regressors and were, therefore, inferior. However, in the unique case of subject 4 (Sec. 3.2), the EMG regressors had a very low correlation coefficient (0.08), so meaningful results could be obtained.

4.5.

Data Acquisition Limitations

In addition to the limitation of high correlation between the regressors (Sec. 4.4), this method is limited by issues common to all fNIRS and motion capture studies. In this work, motion artifacts were observed in several data sets. However, in the five subjects analyzed, motion artifacts did not cause any trials to be excluded. Also, motion capture data were not always reliable as occlusions sometimes occurred during the hand tapping task and the children occasionally moved their hands outside the camera system’s field of view. Fortunately, this did not occur in any trials for the five subjects analyzed in this study, but only in those excluded due to other reasons, such as the absence of mirror motions.