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. 2021 Feb 22;31(7):3353–3362. doi: 10.1093/cercor/bhab016

Children with Cerebral Palsy Have Altered Occipital Cortical Oscillations during a Visuospatial Attention Task

Jacy R VerMaas 1,2, Brandon J Lew 3, Michael P Trevarrow 4, Tony W Wilson 5, Max J Kurz 6,✉
PMCID: PMC8196258  PMID: 33611348

Abstract

Dynamically allocating neural resources to salient features or objects within our visual space is fundamental to making rapid and accurate decisions. Impairments in such visuospatial abilities have been consistently documented in the clinical literature on individuals with cerebral palsy (CP), although the underlying neural mechanisms are poorly understood. In this study, we used magnetoencephalography (MEG) and oscillatory analysis methods to examine visuospatial processing in children with CP and demographically matched typically developing (TD) children. Our results indicated robust oscillations in the theta (4–8 Hz), alpha (8–14 Hz), and gamma (64–80 Hz) frequency bands in the occipital cortex of both groups during visuospatial processing. Importantly, the group with CP exhibited weaker cortical oscillations in the theta and gamma frequency bands, as well as slower response times and worse accuracy during task performance compared to the TD children. Furthermore, we found that weaker theta and gamma oscillations were related to greater visuospatial performance deficits across both groups. We propose that the weaker occipital oscillations seen in children with CP may reflect poor bottom-up processing of incoming visual information, which subsequently affects the higher-order visual computations essential for accurate visual perception and integration for decision-making.

Keywords: brain imaging, magnetoencephalography, MEG, vision, visual perception

Introduction

Cerebral palsy (CP) is a neurodevelopmental disorder that is primarily characterized by impaired motor functions resulting from brain lesions or anomalies that occur early in life (Rosenbaum et al. 2007). Although the treatments for CP are primarily motor centric, there is a growing body of literature that suggests visual impairments are also central to this disorder (Fazzi et al. 2009; Ego et al. 2015; VerMaas et al. 2019, 2020). The specific visual deficits that emerge are heterogeneous, potentially reflecting dysfunction of the ventral and dorsal visual pathways (Fazzi et al. 2009; van Genderen et al. 2012; Galli et al. 2018) that are involved in bottom-up sensory and top-down attention processes (Smith and Chatterjee 2008). Deficits in visuospatial processing are among the most common visual impairments reported in individuals with CP (Fazzi et al. 2009; Ortibus et al. 2009; Pueyo et al. 2009; Ego et al. 2015) and may be present despite normal or near-normal visual acuity (Akhutina et al. 2003; Ego et al. 2015).

Visuospatial dysfunctions noted in children with CP are multifactorial and include deficits in attention to visual stimuli in specific areas of space, as well as errors judging the relationship between spatial elements. Such visuospatial impairments are frequently attributed to the white matter lesions commonly observed in children born premature (Fazzi et al. 2004, 2009; van den Hout et al. 2004; Ortibus et al. 2011; Belmonti et al. 2015; Ego et al. 2015). The magnitude of these deficits have been linked to the extent of the white matter lesions (Guzzetta et al. 2001; Fazzi et al. 2004, 2009; Pavlova et al. 2007; Pavlova and Krägeloh-Mann 2013; Galli et al. 2018) and the severity of the accompanying sensorimotor impairments (Pavlova et al. 2007; Hawe et al. 2020). Visuospatial abilities appear to be unrelated to the subtype of CP (i.e., spastic, athetoid, ataxic, mixed), intellectual disability, side of motor impairment, or history of seizures (Ego et al. 2015). Furthermore, some children with CP that have no discernable insults on their MRIs also present with visuospatial deficits (Schenk-Rootlieb et al. 1994; Fazzi et al. 2009; Ortibus et al. 2009, 2011; van Genderen et al. 2012).

Visuospatial functioning is also often associated with spatial neglect and visual field dysfunction. Ickx et al. (2018) not only found nonlateralized visuospatial attentional deficits in children with right and left hemiplegic CP (HCP) but also found that children with left HCP tended to have a greater egocentric visuospatial impairment (lateralized neglect in reference to their body), while right HCP had greater allocentric impairment (lateralized neglect in reference to an object). Similar visuospatial deficits have been reported in children with bilateral brain injuries (Fazzi et al. 2009; Pueyo et al. 2009; Schmetz et al. 2019). One study also found that children with CP had difficultly shifting their eye gaze to attend to a new spatial target (Maioli et al. 2019). While these collective studies highlight the significance of visuospatial impairments, the underlying neural mechanisms generating this dysfunction remain unclear.

Several magnetoencephalographic (MEG) studies have begun to uncover how spectrally specific cortical oscillations serve the components of visuospatial processing and attention (Wiesman, Mills, et al. 2018a; Wiesman, O’Neill, et al. 2018b; Wiesman and Wilson 2019; Lew, Wiesman, et al. 2020b). These studies have revealed that there is an early increase in occipital theta (4–8 Hz) oscillations that is tied to object perception and the initial encoding of visual information. There is also a strong decrease in the strength of alpha (8–14 Hz) activity in the lateral occipital cortices during visual processing, which has been linked to the functional disinhibition of local visual circuits. Lastly, a concurrent increase in occipital gamma (50–90 Hz) activity has been widely observed and attributed to attending to the visual stimulus. Although these multispectral changes have been thoroughly examined in multiple studies, they have yet to be investigated in children with CP. If these oscillations are disturbed in those with CP, this could at least partially explain the aberrant visuospatial processing seen in children with CP. To this end, the objectives of this study were twofold. First, we aimed to determine whether occipital cortical oscillations during visuospatial processing differ between children with CP and demographically matched controls. Second, we sought to investigate a potential link between the strength of the spectrally specific cortical oscillations and visuospatial task performance. We hypothesized that children with CP would exhibit weaker occipital oscillations during task performance and respond more slowly and less accurately compared to the typically developing (TD) controls.

Materials and Methods

Participants

A total of 42 children participated in this study, including 21 diagnosed with spastic CP (age = 13.5 ± 3.0 years; 10 males) and 21 TD controls (age = 14.7 ± 3.2 years; 11 males). All participants had normal or corrected to normal binocular visual acuity and could focus at a distance of 1 m, the distance of the stimulus screen. The children with CP presented with spastic diplegia (n = 15) or hemiplegia (n = 6, left-side affected = 2) and had minimally impaired hand function as categorized by the Manual Abilities Classification System (MACS Levels I-II). Given that the prior literature had indicated that visuospatial abilities appear to be unrelated to the subtype of CP and side of motor impairment (cf., Ego et al. 2015), we felt that it was appropriate to enroll participants that had either a diplegic or hemiplegic presentation. Most participants with CP had mild to moderate motor impairments as categorized using the Gross Motor Functional Classification System (GMFCS). Five participants were classified as level I (walks independently), nine as level II (walks with some limitations), six as level III (walks using crutches or walker), and one was level IV (limited walking, uses wheelchair). The TD controls had no known neurological, developmental, or musculoskeletal impairments. All of the parents provided written consent and the children assented. The Institutional Review Board at the University of Nebraska Medical Center reviewed and approved the protocol for this investigation.

MEG Experimental Paradigm

Participants completed a visuospatial processing task during MEG data acquisition that is known to elicit spectrally specific neural responses (Wiesman et al. 2017). Briefly, the participants were seated upright in a magnetically shielded room with their head positioned within the helmet-shaped MEG sensor array. The stimulus images were displayed on a back-projected flat screen at eye level and approximately 1 m away. The participants were instructed to fixate on a centrally located crosshair that remained present throughout the paradigm. After a variable amount of time (ISI: 1900–2100 ms), an 8 × 8 high-contrast grid appeared for 800 ms in one of four locations: offset to the right or left, and above or below the fixation cross (Fig. 1). The left/right orientations were defined by a lateral offset of 75% of the grid from the center of the fixation. The participants were instructed to respond with a right-hand button press as soon as they determined whether the checkerboard was positioned more to the left (index finger) or right (middle finger) of the center cross. Prior to the task, each participant practiced the task to ensure comprehension. Each participant completed 240 trials, balanced across positions in a pseudo-randomized order.

Figure 1.

Figure 1

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Visuospatial processing and attention paradigm. Each trial began with a fixation period lasting about 2000 ms (variable interstimulus interval: 1900–2200 ms), with the final 400 ms of fixation (prior to the stimulus onset) serving as the baseline period. The high contrast stimulus appeared for 800 ms in one of four positions. Participants attended to and indicated the lateralized position of the stimulus relative to the central fixation cross by pressing a button with their index (left) or middle (right) finger.

MEG Data Acquisition and Coregistration

All recordings were conducted in a one-layer magnetically shielded room with active shielding engaged for environmental noise compensation. Neuromagnetic responses were sampled continuously at 1 kHz with an acquisition bandwidth of 0.1–330 Hz using an Elekta/MEGIN MEG system (Helsinki, Finland) with 306 magnetic sensors, including 204 planar gradiometers and 102 magnetometers. During data collection, the participants were monitored via real-time audio-video feeds from inside the shielded room. Each participant wore a custom-built head-stabilization device that consisted of inflatable airbags that surrounded the sides of the head and filled the void between the head and MEG helmet. This system stabilized the head and reduced the probability of any large head movements occurring during the data collections.

Prior to the MEG experiment, four coils were affixed to the head of each participant and were used for continuous head localization. The location of these coils, three fiducial points, and the scalp surface were digitized to determine their three-dimensional position (Fastrak 3SF0002, Polhemus Navigator Sciences, Colchester, VT, USA). During the MEG recording, an electric current with a unique frequency label (e.g., 322 Hz) was fed to each of the four coils. This induced a measurable magnetic field and allowed each coil to be localized 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 (including the fiducials and scalp surface points), each participant’s MEG data were coregistered with their structural T1-weighted MRI prior to source space analyses. T1-weighted images were acquired with a 3T Siemens Skyra scanner using a 32-channel head coil (TR: 2400 ms; TE: 1.94 ms; field of view: 256 mm; slice thickness: 1 mm with no gap; in-plane resolution: 1.0 × 1.0 mm). The MRI data were aligned parallel to the anterior and posterior commissures and transformed into standardized space using BESA MRI (Version 2.0; BESA GmbH, Gräfelfing, Germany).

MEG Preprocessing and Time-Frequency Transformation

Using the MaxFilter software (Elekta/MEGIN), each MEG data set was individually corrected for head motion that may have occurred during the visual processing experiment and was subjected to noise reduction using the signal space separation method with a temporal extension (Taulu and Simola 2006). Cardiac and blink artifacts were removed from the data using signal-space projection (SSP), which was accounted for during source reconstruction (Uusitalo and Ilmoniemi 1997; Ille et al. 2002). The continuous magnetic time series was divided into epochs of 2700 ms in duration, from −500 to +2200 ms, with the onset of the visual stimulus being defined as 0 ms and the baseline period defined as −400 to 0 ms. Artifact rejection was performed using a fixed threshold method and supplemented with visual inspection. Since only correct trials were used in the analysis and the children with CP had fewer correct responses, trials were randomly removed from the TD controls to achieve similar signal-to-noise ratios between the groups. This was accomplished by randomly rejecting trials (i.e., sections of the raw data) in the control group. An average of 74.6 correct trials per participant were removed from the TD children through this process and/or because they actually contained artifactual MEG signals, while 18.9 correct trails were removed for the children with CP because signals during these periods exceeded our artifact threshold (none were removed randomly). Ultimately, this process resulted in an average of 150 trials per participant that were used for further analysis, and the mean number of trials per participant did not significantly differ between groups (CP = 147 ± 57, TD = 154 ± 57; P = 0.71). The artifact-free epochs were transformed into the time-frequency domain using complex demodulation (Kovach and Gander 2016), and the resulting spectral power estimations per sensor were averaged over trials to generate time-frequency plots of the mean spectral density. These sensor-level data were normalized using the respective bin’s baseline power (i.e., mean power during the −400 to 0 ms time window).

The specific time-frequency windows used for imaging were determined by a fully data-driven approach that began with a statistical analysis of the sensor-level spectrograms across both groups and the entire array of gradiometers. Each data point in the spectrogram was initially evaluated using a mass univariate approach based on the general linear model. 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-sample t-tests (baseline vs. visual presentation) 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 across all participants. In stage two, time-frequency bins that survived the threshold were clustered with temporally and spectrally neighboring bins that were also above the threshold (P < 0.05), 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 (Maris and Oostenveld 2007). For each comparison, at least 1000 permutations were computed to build a distribution of cluster values. Based on these analyses, only the time-frequency windows that contained significant oscillatory events across all participants (described in the Results section) were subjected to the beamforming analysis. For a detailed description of these methods, see Wiesman and Wilson (2020).

MEG Source Imaging and Statistics

The Dynamic Imaging of Coherent Sources beamformer was employed to calculate the source power across the entire brain volume for the statistically defined time-frequency windows of interest (Gross et al. 2001; Hillebrand et al. 2005). The single images were derived from the cross-spectral densities of all combinations of MEG sensors, and the solution of the forward problem for each location on a grid specified by input voxel space. Following convention, the source power in these images was normalized per participant using a separately averaged prestimulus noise period of equal duration and bandwidth (Van Veen et al. 1997; Hillebrand et al. 2005). Such images are typically referred to as pseudo-t maps, with units (pseudo-t) that reflect noise-normalized power differences per voxel. The resulting beamformer images were 4.0 × 4.0 × 4.0 mm resolution and were transformed into standard space by using the transform that was previously applied to the structural MRI volume and then spatially resampled. MEG preprocessing and imaging was performed with the Brain Electrical Source Analysis software (BESA v6.0; Grafelfing, Germany). We subsequently averaged all output pseudo-t maps across participants per time frequency response (see below) to identify the peak voxels, which were used to identify group differences in the cortical oscillations of interest. Independent t-tests were then used to discern differences in the spectrally specific pseudo-t values of the respective groups. To qualitatively examine the neural dynamics, the virtual sensor (i.e., voxel time series) for the peak voxel per response were computed by applying the sensor-weighting matrix derived through the forward computation to the preprocessed signal vector, which yielded a time series for each source vector centered in the voxel of interest (Cheyne et al. 2006; Heinrichs-Graham et al. 2016; Heinrichs-Graham and Wilson 2016).

Visuomotor Behavioral Data

Behavioral assessment of the visuospatial task performance was quantified using accuracy and reaction time parameters. Reaction time was defined as the time difference between stimulus onset and when the button press occurred. To account for spurious reaction times, reaction times 2.5 standard deviations above or below the individual participant’s mean were excluded prior to averaging. The reaction times of the remaining trials were then averaged for each participant and across the group. The accuracy was calculated as the percentage of trials where the participant correctly identified the visual stimulus position. Independent t-tests were used to discern between group differences. Spearman rho correlations were performed between the MEG variables and the behavioral variables to identify relationships between the strength of cortical oscillations and visuomotor task performance. Spearman rho correlations were also performed between the reaction time and accuracy to assess whether there was a speed-accuracy trade off. Nonparametric correlations were selected to evaluate the relationships for the entire data set because Shapiro–Wilk statistical tests showed that some of the behavioral and MEG variables were not normally distributed.

Results

Visuospatial Task Performance

There were significant differences in visuospatial task performance between the children with CP and the TD group. The children with CP were significantly less accurate on the task (TD = 96 ± 8%, CP = 70 ± 26%, P < 0.001; Fig. 2A) and had slower reaction times (TD = 538 ± 120 ms, CP = 737 ± 162 ms, P < 0.001; Fig. 2B). Together these results suggest that the children with CP had more difficulty deciphering the spatial location of the high-contrast stimulus relative to the crosshair. Additionally, there was a negative relationship between accuracy and reaction time (rs = −0.51, P = 0.001; Fig. 2C) across all children, suggesting that children who were more accurate tended to respond more quickly (i.e., opposite of a speed/accuracy tradeoff). Evaluation of the respective groups showed that there was a negative correlation between accuracy and reaction time in the TD group (rs = −0.48, P = 0.042), but the same relationship was not significant in the group with CP (P > 0.05).

Figure 2.

Figure 2

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Visuospatial task performance. (A) Behavioral results for accuracy, with % correct on the y-axis and groups denoted on the x-axis. As shown, children with CP were significantly less accurate on the task. (B) Behavioral results for reaction time, with milliseconds (ms) on the y-axis and groups on the x-axis. As shown, children with CP were slower to identify the location of the high-contrast stimulus. (C) Rank order relationship between accuracy (in % correct) and reaction time (in ms) for the entire group of participants. The blue dots represent the TD children and the red dots represent the children with CP. The negative correlation implied that children who took longer to respond were also less accurate. ***P < 0.001.

Given that the behavioral responses were dependent on the ability to adequately control hand motor actions, we performed a follow-up analysis to determine if the reaction time and accuracy data were related to the GMFCS and MACS levels of each participant. This analysis indicated that there was no association (Ps > 0.05) between these variables, which suggests that the severity of the motor impairment in participants with CP was not related to their visuospatial task performance.

MEG Sensor-Level Analysis

Analysis of the sensor spectrograms collapsed across all participants revealed multiple, spectrally distinct oscillatory responses in sensors located over the occipital cortices (Fig. 3A; Ps < 0.001, corrected). These included a power increase in the theta (4–8 Hz, 50–300 ms) and gamma (64–80 Hz, 400–550 ms) bands, along with a power decrease in the alpha band (8–14 Hz, 400–550 ms). Additionally, a significant power decrease emerged in the beta band (18–24 Hz) that was strongest in sensors over the left fronto-parietal region. Prior studies using this task have identified that the sensorimotor cortices are the source of the beta power decrease (Wiesman, Mills, et al. 2018a; Wiesman, O’Neill, et al. 2018b; Wiesman and Wilson 2019; Lew, O’Neill, et al. 2020a). Hence, we did not further examine the beta oscillations since the production of the motor action was not of primary interest in this study.

Figure 3.

Figure 3

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Time-frequency spectrogram and beamformer images. The visuospatial task-related neural responses were averaged across all participants per MEG sensor. (A) Each representative plot displays the oscillatory responses from a gradiometer sensor located over the occipital cortex that was representative of the significant neural responses. Time (in ms) is denoted on the x-axis, with 0 ms defined as the onset of the visual stimulus. Spectral power is expressed as the percent difference from the baseline period (−400 to 0 ms). Separate components of significantly increased power were seen across the gamma (64–80 Hz, 400–550 ms; top spectrogram) and theta (4–8 Hz, 50–300 ms; bottom spectrogram) frequency bands. A significant decrease in power was seen in the alpha band (8–14 Hz, 400–550 ms; bottom spectrogram). Permutation testing indicated that these three time-frequency periods were significant relative to baseline, P < 0.001, corrected. (B) These time-frequency windows were imaged separately in each participant and then grand-averaged across all participants. As shown, all three responses were centered in the occipital cortices. The pseudo-t color bar is shown to the right of the gamma (64–80 Hz; top image), slightly more lateralized alpha (8–14 Hz; middle image), and theta (4–8 Hz; bottom image) occipital cortical responses.

Anatomical Results

To identify the brain regions generating the responses noted in the sensor analysis, a beamformer was applied to each participant’s responses using the time-frequency windows identified in the sensor-level analyses and an equal time-frequency window from the prestimulus baseline period (theta: −300 to −50 ms; alpha and gamma: −250 to −100 ms). The resulting output images were averaged across all participants for each time-frequency component. The images of the theta (4–8 Hz, 50–300 ms) and gamma responses (64–80 Hz, 400–550 ms) revealed bilateral peaks of neural activity in the primary visual cortices (Fig. 3B), while the alpha activity (8–14 Hz, 400–550 ms) showed bilateral peaks in more lateral visual association cortices (Fig. 2B). We subsequently extracted the pseudo-t values from the peak voxel of the respective cortical oscillations. As we did not have hemisphere-specific hypotheses, each participant’s respective pseudo-t values from each hemisphere were averaged. The neural time courses were also extracted from the same peak voxels and averaged across hemispheres to qualitatively evaluate the temporal dynamics of each response.

Our statistical analysis revealed that the children with CP exhibited weaker cortical theta oscillations (P = 0.008; Fig. 4A). Inspection of the neural time course for this response showed that the children with CP had weaker theta oscillations shortly after stimulus presentation, and this difference extended until about 350 ms (Fig. 4B). The children with CP also had weaker gamma oscillations in medial occipital cortices compared to the TD children (P < 0.001; Fig. 4C). Evaluation of the gamma time series revealed an extended period of weaker activity in those with CP (Figs 3 and 4). In contrast, the occipital alpha oscillations were not significantly different between the respective groups (CP = 14.8 ± 15.2, TD = 25.1 ± 19.0; P = 0.060).

Figure 4.

Figure 4

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Theta and gamma oscillatory responses. (A) Theta (4–8 Hz, 50–300 ms) occipital cortical oscillations. The relative response values (pseudo-t) show a weaker response in children with CP (P = 0.008). (B) Theta peak voxel neural time course. The time series of the children with CP are plotted in red, while the TD participants are plotted in blue. Time (ms) is denoted on the x axis with relative amplitude (%) shown on the y-axis. The visual stimulus was presented at 0 ms (dotted line), and the time-frequency window imaged is shown in the grayed area. (C) Gamma (64–80 Hz; 400–550 ms) occipital cortical oscillations. Relative response values (pseudo-t) show a weaker response in children with CP (P < 0.001). (D) Gamma peak voxel neural time course. The time series is presented following the same format as in (B). **P = 0.008, ***P < 0.001.

Relationship between Cortical Oscillations and Task Performance

Next, we examined whether there was a relationship between the strength of visual cortical oscillations and task performance. These analyses revealed that the strength of theta and gamma oscillations were positively correlated with accuracy for the entire group of children (theta: rs = 0.57, P < 0.001, Fig. 5A; gamma: rs = 0.53, P < 0.001; Fig. 5B). Evaluation of the groups separately showed that there was a tight relationship between accuracy and the strength of the gamma (rs = 0.71, P < 0.001) and theta (rs = 0.74, P < 0.001) oscillations for the TD group, while the relationship between the respective cortical oscillations and reaction time was less apparent in the group with CP (P > 0.05).

Figure 5.

Figure 5

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Neurobehavioral correlations. Rank–order correlations between the strength of the occipital cortical oscillations and performance on the visuospatial processing task for the entire group of participants. The blue dots represent the TD children and the red dots represent the children with CP. The correlations revealed that stronger gamma (64–80 Hz) and theta (4–8 Hz) band oscillations within the occipital cortices were associated with greater accuracy (A, B) and faster responses (C, D) on the visuospatial processing task.

Significant negative correlations were also detected between reaction time and the strength of the theta (rs = −0.48, P = 0.001; Fig. 5C) and gamma (rs = −0.41, P = 0.006; Fig. 5D) cortical oscillations for the entire group of children. These findings suggest that children who had stronger theta and gamma occipital cortical oscillations responded both faster and more accurately on the visuospatial processing task. This relationship was not significant when the two groups were tested individually (both Ps > 0.05).

Discussion

Impairments in visuospatial processes are the most common visual-processing deficits found in children with CP (Fazzi et al. 2009; Ortibus et al. 2009; Pueyo et al. 2009; Ego et al. 2015). Nonetheless, we have little understanding of the neural aberrations that may contribute to the altered visuospatial functions seen in the clinic. This study is the first to use MEG to evaluate the oscillatory responses during visuospatial processing in children with CP. Our results showed that the theta and gamma cortical oscillations in the primary visual cortices were weaker in children with CP. Furthermore, the weaker theta and gamma cortical oscillations were linked with degraded performance on the visuospatial processing task. Altogether, these results indicate that altered theta and gamma cortical oscillations likely play a role in the impaired visuospatial processing seen in children with CP.

The occipital theta and gamma cortical oscillation seen in this investigation were consistent with other studies that have used a similar visuospatial processing and attention task (Wiesman, Mills, et al. 2018a; Wiesman, O’Neill, et al. 2018b; Wiesman and Wilson 2019; Lew, O’Neill, et al. 2020a). Prior research has suggested that such oscillations are linked with processing of visual information (Bastos et al. 2015; Michalareas et al. 2016; Takesaki et al. 2016; Saleem et al. 2017, and likely play fundamental roles in central nervous system communication, perception, and cognition (Başar et al. 2001). The theta activity is presumed to be representative of object perception and the initial coding of basic visual information (Başar et al. 2001; Makeig et al. 2002; Busch et al. 2009), while the gamma oscillations are thought to be important for attending to the stimulus and perceptual binding (Edden et al. 2009; Muthukumaraswamy and Singh 2013).

One of our most important findings was that, relative to controls, children with CP exhibited markedly blunted occipital theta and gamma cortical oscillations during performance of the visuospatial processing task. The altered theta oscillations imply that the neural populations involved in the perception and initial encoding of visual information were abnormal in the children with CP. Furthermore, we also found weaker gamma oscillations in the children with CP, which supports the notion of altered visual attention function and possibly insufficient parsing of fine visual features. Based on these data, we contend that these aberrant medial occipital oscillations play a prominent role in the visuospatial deficiencies that have been widely described in the clinical literature (Fazzi et al. 2009; Ortibus et al. 2009; Pueyo et al. 2009; Ego et al. 2015). This impression is supported by the behavioral results as well, which showed the children with CP had slower reaction times and made significantly more errors in perceiving the spatial location of the high-contrast visual stimuli. Furthermore, our correlational analysis enhances this argument as the participants with weaker theta and gamma occipital oscillations also tended be less accurate and took longer to identify the spatial location of the visual stimulus.

Visual processing plays a crucial role in movement planning, motor control, and error evaluation and feedback (Krigolson et al. 2015). Individuals with CP need precise information from their visual environment to adapt their movement patterns in order to complete functional activities such as reaching (Savelsbergh et al. 2013). If the bottom-up processing of the spatially relevant visual environment is aberrant, the downstream visual computations and motor planning decisions based on this degraded visual information are likely to result in visuomotor performance errors. In our study, we expected slower reaction times in the children with CP given the motor impairments. However, their task accuracy was also markedly reduced compared to the control group, and both behavioral responses were linked to weaker theta and gamma oscillations occurring during stimulus presentation. This finding is consistent with previous studies reporting a similar relationship between theta and gamma neural activity and improved visuospatial task performance (Busch et al. 2009; Edden et al. 2009; Wiesman et al. 2017; Wiesman, O’Neill, et al. 2018b) and suggests that deficits in visuospatial neural functions contribute to the motor performance impairments observed in children with CP. Of note, it is also conceivable that both the motor and visual impairments are hindering visuospatial functioning (Belmonti et al. 2015; Hawe et al. 2020). Motor impairments may disrupt the process of visuospatial development by limiting physical exploration of the visual environment (Smith and Chatterjee 2008; Pavlova and Krägeloh-Mann 2013; Thébault et al. 2018). This may also account for the enlarging gap seen in the visuospatial abilities of younger and older children with CP compared to TD controls (Schmetz et al. 2019).

Overall our results imply that children with CP have slower reaction times, but still have the potential to be somewhat accurate in their visuospatial decisions (~70% of time). The slower reaction times might reflect that children with CP need a longer time to process visual information in order to make accurate decisions, although caution is warranted as we did not observe a significant correlation between reaction time and accuracy in the CP group alone. Nonetheless, this concept is in line with prior studies that have shown children with CP require more time to plan their visuospatial motor actions (Kurz et al. 2014, 2017, 2020). Collectively, these results imply that therapeutic strategies should place greater emphasis on the planning stage in order to improve the fidelity of the motor decisions that are made based on visuospatial information.

This study was not without limitations. First, our children with CP were not homogenous, as we included both hemiplegic and diplegic presentations. Our decision to include both subtypes was based on a prior systematic review that indicated that the visuospatial deficiencies seen in children with CP were unrelated to the subtype of CP and side of motor impairment (Ego et al. 2015). It is possible that the MEG analysis employed in this investigation might be more sensitive to the potential differences in the visuospatial processing of the respective subtypes. However, we were unable to robustly test this notion since we had a limited number of participants with the respective subtypes and the groups were unbalanced. Secondarily, our experimental data does not provide a full clinical picture of the visual disturbances in the children, as a detailed neuro-ophthalmological evaluation was not performed. That being said, it should be recognized that the MEG assessments used here do provide a reliable and unbiased measure of visual cortical function in children with CP, as they rely on performance and not self-report. Lastly, our behavioral results relied on the participants being able to adequately fractionate their finger movements to press a button, which signified that the stimulus was shifted to the right or left. It is possible that the reaction times and/or accuracy might have been negatively affected by the reduced hand motor control. However, we are skeptical that this had a major impact on our results, as follow-up analyses indicated that the MACS levels of the participants were not significantly correlated with the reaction times and accuracy of the children with CP.

The results reported here add to the growing number of MEG studies that have shown that the occipital cortical oscillations are aberrant in children with CP. For example, these investigations have identified that children with CP have aberrant alpha (8–14 Hz) and beta (16–24 Hz) occipital cortical oscillations during performance of a visuomotor target matching task (Kurz et al. 2017). Studies have also identified that the V5/MT alpha-beta (8–20 Hz) oscillations are weaker in children with CP during perception of a moving stimulus (VerMaas et al. 2019). Likewise, the occipital alpha-beta (10–20 Hz) and gamma (40–72 Hz) oscillations are abnormal when children with CP view high-contrast spatial gratings (VerMaas et al. 2020). We contend that the neural mechanisms underlying visual processing impairments in children with CP deserve greater attention, as these deficits could be key contributors to the cognitive and motor deficits seen in the clinic and understood as defining features of CP. Furthermore, we stress that therapeutic approaches that are directed at improving visual processing in children with CP will likely have major, cascading beneficial effects on their motor actions.

Funding

National Institutes of Health (grant numbers 1R01-HD086245; R01-HD101833).

Notes

Conflict of Interest: None.

Contributor Information

Jacy R VerMaas, Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; Department of Physical Therapy, Munroe-Meyer Institute, University of Nebraska Medical Center, Omaha, NE 68198, USA.

Brandon J Lew, Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA.

Michael P Trevarrow, Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA.

Tony W Wilson, Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA.

Max J Kurz, Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA.

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