Fourteen adults with CP (Age = 33.1 ± 8.6 years, Range = 20 – 47 yrs, MACS = I – III, Females = 8) and sixteen healthy controls (HC) (Age = 33.3 ± 9.8 yrs, Range = 19 – 49 yrs, Females = 9) participated in this experimental investigation. The participants with CP had Manual Ability Classification System (MACS) levels between I-III. MACS level of I indicates that the participant can easily handle most objects, while a MACS level of III indicates that an individual has difficulty handling objects and requires some assistance. None of the participants had a prior history of epilepsy or seizure activity. Participants were excluded according to MEG/MRI exclusionary criteria such as metal implants, dental braces or permanent retainers, or other metallic or otherwise magnetic non-removable devices. Each participant provided written informed consent to participate in the investigation. The protocol for this investigation was approved by University of Nebraska Medical Center’s Institutional Review Board and in compliance with the Code of Ethics of the World Medical Association. Effectively, all of the methods used in this investigation were performed in accordance with the relevant guidelines and regulations.
During MEG recording, participants were seated in a nonmagnetic chair within the magnetically-shielded room with their right hand positioned on a custom-made five-finger button pad. Each button press sent a unique signal (i.e., TTL pulse/trigger code) to the MEG acquisition computer, and thus behavioral responses were temporally synced with the MEG data. The participants completed an arrow-based version of the Eriksen flanker task55,56. Each trial began with a fixation cross that was presented for an interval of 1500 ± 50 ms. A row of five arrows was then presented for 2500 ms and the participants were instructed to respond about the direction of the middle target arrow with their second (left arrow) or third (right arrow) digit of the right hand using the custom 5-button pad (Figure 1). Visual presentation consisted of either a series of flanking arrows that had directions that were congruent (i.e., same direction) or incongruent (i.e., opposite direction) of the middle target arrow. The task stimuli were visually projected onto a screen that was approximately one meter from the participant. A total of 200 trials were presented, making the overall MEG recording time about 14 minutes. Trials were equally split and pseudo-randomized between congruent and incongruent conditions, with left and right pointing arrows being equally represented in each condition. Only correct responses were included for further analysis.
MEG Acquisition Parameters and Coregistration with MRI
All recordings were conducted in a one-layer magnetically-shielded room with active shielding engaged. Neuromagnetic responses were sampled continuously at 1 kHz with an acquisition bandwidth of 0.1–330 Hz using an MEGIN/Elekta MEG system with 306 magnetic sensors (Helsinki, Finland). Using MaxFilter (v2.2; Elekta), MEG data from each subject were individually corrected for head motion and subjected to noise reduction using the signal space separation method with a temporal extension 57,58.
Prior to starting the MEG experiment, four coils were attached to the subject’s head and localized, together with the three fiducial points and scalp surface, with a 3-D digitizer (Fastrak 3SF0002, Polhemus Navigator Sciences, Colchester, VT, USA). Once the subject was positioned for MEG recording, an electric current with a unique frequency label (e.g., 322 Hz) was fed to each of the coils. This induced a measurable magnetic field and allowed for each coil to be localized in reference to the sensors throughout the recording session. Since the coil locations were also known in head coordinates, all MEG measurements could be transformed into a common coordinate system. With this coordinate system, each participant’s MEG data were coregistered with structural T1-weighted MRI data prior to source space analyses using BESA MRI (Version 2.0). Structural T1-weighted MRI images were acquired using a Siemens Prisma 3-Tesla MRI scanner with a 64-channel head/neck coil and a sequence with the following parameters: TR = 2400 ms; TE = 1.96 ms; flip angle = 8°; FOV = 256 mm; slice thickness = 1 mm (no gap); voxel size = 1 x 1 x 1 mm. These data were aligned in parallel to the anterior and posterior commissures and transformed into standardized space. Each participant’s 4.0 x 4.0 x 4.0 mm MEG functional images were transformed into standardized space using the transform that was previously applied to the structural MRI volume and spatially resampled.
MEG Time-Frequency Transformation and Statistics
Cardiac artifacts were removed from the data using signal-space projection (SSP), which was accounted for during source reconstruction 59. The continuous magnetic time series was divided into epochs of 4000 ms duration from -2000 ms to 2000 ms, with the baseline being defined as -1600 to -800 ms and 0.0 ms being movement onset (i.e., button press). Epochs containing artifacts (e.g., eye blinks, muscle artifacts, etc.) were rejected based on a fixed-threshold method using individual amplitude and gradient thresholds, supplemented with visual inspection. The number of trials used in the final analyses did not statistically differ by group or condition (Ps > 0.05).
Artifact-free epochs were transformed into the time-frequency domain using complex demodulation, and the resulting spectral power estimations per sensor were averaged over trials to generate time-frequency plots of mean spectral density. These sensor-level data were normalized using the respective bin’s baseline power, which was calculated as the mean power during the –1600 to -800 ms time period. The specific time-frequency windows used for imaging were determined by statistical analysis of the sensor-level spectrograms across the entire array of gradiometers. To reduce the risk of false positive results while maintaining reasonable sensitivity, a two-stage procedure was followed to control for Type 1 error. In the first stage, paired t-tests against baseline were conducted on each data point and the output spectrogram of t-values was thresholded at P < 0.05 to define time-frequency bins containing potentially significant oscillatory deviations relative to baseline across all participants. In stage two, time-frequency bins that survived the threshold were clustered with temporally and/or spectrally neighboring bins that were also above the (P < 0.05) threshold, and a cluster value was derived by summing all of the t-values of all data points in the cluster. Nonparametric permutation testing was then used to derive a distribution of cluster-values, and the significance level of the observed clusters (from stage one) were tested directly using this distribution 60,61. For each comparison, at least 1,000 permutations were computed to build a distribution of cluster values. Based on these analyses, the time-frequency windows that corresponded to events of a priori interest (i.e., the beta ERD, PMBR, and gamma ERS) and contained significant oscillatory events across all participants and conditions were subjected to the beamforming analysis.
MEG Imaging & Statistics
Cortical networks were imaged using an extension of the dynamic imaging of coherent sources (DICS) beamformer 62,63, which employs spatial filters in the time-frequency domain to calculate source power for the entire brain volume. The single images were derived from the cross-spectral densities of all combinations of MEG gradiometers averaged over the time-frequency range of interest, and the solution of the forward problem for each location on a grid specified by input voxel space. Following convention, we computed noise-normalized, source power per voxel in each participant using active (i.e., task) and passive (i.e., baseline) periods of equal duration and bandwidth 62,64. Such images are typically referred to as pseudo-t maps, with units (pseudo-t) that reflect noise-normalized power differences (i.e., active vs. passive) per voxel. MEG pre-processing and imaging used the Brain Electrical Source Analysis (BESA version 6.1) software.
Normalized differential source power was computed for the statistically-selected time-frequency bands (see below) over the entire brain volume per participant at 4.0 x 4.0 x 4.0 mm resolution. The resulting 3D maps of brain activity were averaged across participants to assess the neuroanatomical basis of significant oscillatory responses identified through the sensor-level analysis. We then extracted virtual sensors (i.e., voxel time series) for the peak voxel of each oscillatory response. To compute the virtual sensors, we applied the sensor weighting matrix derived through the forward computation to the preprocessed signal vector, which yielded a time series corresponding to the location of interest. Note that this virtual sensor extraction was done per participant, once the coordinates of interest (i.e., one per cluster) were known. Once these virtual sensors were extracted, the magnitude of the beta ERD, PMBR, and gamma ERS were calculated as the minimum (for the beta ERD) and maximum (for the PMBR and gamma ERS) amplitude within the target period of interest.
Motor Behavioral Data
The output of the button pad was simultaneously collected at 1 kHz along with the MEG data. Accuracy was defined as the number of correct responses divided by the total number of trials. The time the participant took to decide the direction of the target arrow (i.e., reaction time) was calculated based on the time from when the arrow array was presented to when the button was pressed.
Spinal Cord MRI Processing
A portion of this investigation follows up on an MRI spinal cord project that was published previously 54. The parameters of each sequence are fully described in this previous study. In brevity, cervical-thoracic spinal cord MRI scans were acquired with a Siemens Prisma 3T scanner equipped with a 64-channel head/neck coil. The calculated total CSA across C6 – T3 was extracted from the T2 images and the T2* was used for gray and white matter extraction. Furthermore, the PAM50 template was registered to the diffusion-weighted images and MT images after motion correction, and the diffusion tensors for the respective spinal cord tracts were calculated. As the task was performed with the right hand, the fractional anisotropy (FA) and magnetization transfer ratio (MTR) values from the right corticospinal tract (CST) and cuneatus tracts were subsequently calculated from the diffusion-weighted images. These respective values were used to evaluate the relationship between the strength of the cortical oscillations and the integrity of the spinal cord microstructure. Complete details of the spinal cord imaging acquisition parameters and processing pipelines are detailed in Trevarrow et al. (2021).54
We performed 2 x 2 x 2 mixed model ANOVAs with condition (congruent and incongruent) as a within subjects factor, group (CP and HC) as a between subjects factor, and sex (male and female) as a between subjects factor in order to determine group, condition, and sex differences and identify possible interactions with respect to reaction time and accuracy within the task. Sex was included as an exploratory variable in the model since CP has been reported to be more common in males than females, but presentation differences are less understood 65. For the motor-related oscillations, we utilized 2 x 2 x 2 ANOVAs with condition (congruent and incongruent) as a within subjects factor, group (CP and HC) as a between subjects factor, and sex (male and female) as a between subjects factor in order to determine any group, condition, sex main effects, as well as interactions with respect to motor-related oscillatory activity. Lastly, Pearson correlations were used to determine the relationship between the behavioral data and strength of the beta responses, as well as the spinal cord structural MRI measures and the MEG/behavioral measures. All statistical analyses were conducted at a 0.05 alpha level. Results are reported as mean ± standard error of the mean.