Resting-State Magnetoencephalography Source Imaging Pilot Study in Children with Mild Traumatic Brain Injury

Abstract

Mild traumatic brain injury (mTBI) accounts for the vast majority of all pediatric TBI. An important minority of children who have suffered an mTBI have enduring cognitive and emotional symptoms. However, the mechanisms of chronic symptoms in children with pediatric mTBI are not fully understood. This is in part due to the limited sensitivity of conventional neuroimaging technologies. The present study examined resting-state magnetoencephalography (rs-MEG) source images in 12 children who had mTBI and 12 age-matched control children. The rs-MEG exams were performed in children with mTBI 6 months after injury when they reported no clinically significant post-injury psychiatric changes and few if any somatic sensorimotor symptoms but did report cognitive symptoms. MEG source magnitude images were obtained for different frequency bands in alpha (8–12 Hz), beta (15–30 Hz), gamma (30–90 Hz), and low-frequency (1–7 Hz) bands. In contrast to the control participants, rs-MEG source imaging in the children with mTBI showed: 1) hyperactivity from the bilateral insular cortices in alpha, beta, and low-frequency bands, from the left amygdala in alpha band, and from the left precuneus in beta band; 2) hypoactivity from the bilateral dorsolateral prefrontal cortices (dlPFC) in alpha and beta bands, from the ventromedial prefrontal cortex (vmPFC) in beta band, from the ventrolateral prefrontal cortex (vlPFC) in gamma band, from the anterior cingulate cortex (ACC) in alpha band, and from the right precuneus in alpha band. The present study showed that MEG source imaging technique revealed abnormalities in the resting-state electromagnetic signals from the children with mTBI.

Introduction

Pediatric traumatic brain injury (TBI) is a major public health problem with at least 90% of those injured having mild traumatic brain injury (mTBI).^1–4 Even if only a small minority of pediatric mTBI patients have adverse behavioral or psychiatric outcomes, this would have important implications for public mental health. Extant research has revealed a number of positive predictors of adverse behavioral or psychiatric outcomes after pediatric mTBI.⁵ These include 1) hospitalization for mTBI; 2) multiple mTBIs; 3) higher rates of complications relative to healthy controls rather than injured controls; 4) shorter injury-to-assessment duration, suggesting that complications resolve over time; and 5) severity of injury within the limits of mTBI including the presence of magnetic resonance imaging (MRI) abnormalities.⁵

However, conventional neuroimaging including computed tomography (CT) and MRI are relatively insensitive in demonstrating abnormalities following mTBI⁶ even in individuals with persistent post-concussion symptoms and cognitive deficits. The quest for a more sensitive modality to document brain damage has included the use of magnetoencephalography (MEG). MEG may be more sensitive, in part, because it is able to detect cellular activity not detected by macroscopic structural neuroimaging.⁷ Resting state MEG (rs-MEG) has been shown in a number of adult mTBI studies to accompany persistent behavioral changes when conventional imaging modalities were negative.^8,9 Thus not only does rs-MEG appear to be sensitive to post-mTBI behavioral complications, but as a functional brain imaging tool it offers a window to understand mechanisms of such change. In particular, rs-MEG findings include an increase in slow wave (delta-theta) bands in frontal and temporal regions in symptomatic adults after mTBI.^8–11 This increase in slow wave bands is purported to be evidence of neurological damage^12–14 as well as of attempts at neurological repair.¹⁵ However, there are no data examining this phenomenon in children with mTBI.

The current investigation is the first published study to assess functional brain activity measured by rs-MEG in children and adolescents with mTBI compared with age-matched controls. We hypothesized that children and adolescents with mTBI will exhibit significant differences compared with controls with regard to rs-MEG activity, especially with respect to increased presence of slow waves in fronto-temporal regions.

Methods

The study protocol was approved by the combined institutional review boards of the University of California, San Diego and Rady Children's Hospital San Diego. All participants and parents/guardians gave written informed consent prior to study procedures. The informed consent followed the ethical guidelines of the Declarations of Helsinki (sixth revision, 2008).

Research participants

Twenty-four participants between the ages of 8 and 15 years were recruited for this pilot study as a supplement to already in-progress larger cohort pediatric TBI studies of consecutively treated children and adolescents through the Emergency Department at Rady Children's Hospital San Diego, with inclusion and exclusion criteria described below. A sample of 12 children with mTBI from this consecutively treated series of patients was recruited for the main study and its MEG supplement. A sample of 9 control participants was also recruited from unselected consecutively treated children and adolescents at the same Emergency Department after they presented with an orthopedic injury, but no history of mTBI. Three additional children with no history of TBI or orthopedic injury were recruited through word of mouth.

Based on parent/guardian and self-report, the control group had no previous history of TBI and the mTBI participants all had no previous history of TBI prior to this injury. The participants were selected to be comparable on age; see Table 1 displaying participants' demographic characteristics. The sample included 12 mTBI participants with a mean age of 12.58 (standard deviation [SD] = 1.83) years with an average duration of 6 months between the injury and the MEG exam. The control group mean age was 12.67 (SD = 2.27) years. Consistent with our selection of participants on age, there was no significant difference in age between groups. Both groups comprised predominantly males; the mTBI participant group included 10/12 (83%) males, and the control group included 11/12 (92%) males. Additionally, the demographics table displays Wechsler Abbreviated Scale of Intelligence-Second Edition (WASI-II)¹⁶ full scale IQ-2 subtest scores with no significant group difference found with respect to IQ scores (mTBI mean = 106.00, SD = 8.87, control mean = 108.50, SD = 14.53, p = 0.62). One of the mTBI participants was not included in the WASI-II IQ analysis due to administration error.

Table 1.

Demographic Information and Post-Concussive Symptom Scores in Study Participants

	mTBI (n = 12),	Controls (n = 12),	P
	mean (SD)	mean (SD)
Age	12.58 (1.83)	12.67 (2.27)	ns
WASI-II IQ	106.00 (8.87), n = 11	108.50 (14.53)	ns
Cognitive HBI parent	9.00 (5.12), n = 11	4.67 (4.95), n = 9	0.07
Somatic HBI parent	1.91 (1.70), n = 11	1.22 (1.86), n = 9	ns
HBI parent total	10.91 (6.02), n = 11	5.89 (5.97), n = 9	0.08
Cognitive HBI child	13.18 (5.67), n = 11	5.78 (4.71), n = 9	0.006
Somatic HBI child	4.36 (3.11), n = 11	2.78 (2.91), n = 9	ns
HBI child total	17.55 (7.52), n = 11	8.56 (5.62), n = 9	0.008

P-value significance based on t tests.

HBI, Health and Behavior Inventory; mTBI, mild traumatic brain injury; ns, not significant; SD, standard deviation; WASI-II, Wechsler Abbreviated Scale of Intelligence, Second Edition.

The mTBI group included children only if they have suffered a closed head injury that resulted in an observed loss of consciousness, a Glasgow Coma Scale (GCS)¹⁷ score of 13 or 14, or if a GCS score of 15 was noted there had to be at least two symptoms of concussion documented by the Emergency Department medical staff (e.g., transient neurological deficits, vomiting, nausea, headache, diplopia, dizziness). Hospitalization itself did not automatically exclude participation. Exclusion criteria included loss of consciousness greater than 30 min or a GCS score of less than 13. Other exclusion criteria for the mTBI group included the following: 1) associated injury that was deemed severe, as documented by an Abbreviated Injury Scale (AIS)¹⁸ score greater than 3; 2) associated injury that was likely to interfere with cognitive testing (e.g., injury to dominant upper limb); 3) hypoxia, hypotension, or shock associated with the injury; 4) alcohol or drug ingestion involved with the injury; 5) documented history of previous TBI meeting the above criteria for at least mTBI; 6) pre-injury neurological disorder, schizophrenia, autism spectrum disorder, or intellectual deficiency; 7) any medical contraindication to MRI and MEG; 8) any injury requiring neurosurgical intervention; and 9) currently taking medications (e.g., some sedative neuroleptics and hypnotics) known to alter the power of brain rhythms.¹⁹

Exclusion criteria for all controls included presence of: 1) documented history of previous TBI meeting the above criteria for at least mTBI; 2) pre-injury neurological disorder, schizophrenia, autism spectrum disorder, or intellectual deficiency; 3) any medical contraindication to MRI and MEG; and 4) taking medications known to alter the power of brain rhythms. Additionally, any participant was excluded if they had extensive metal dental hardware (e.g., braces and large metal dentures; fillings were acceptable) or other metal objects in the head, neck, or face areas that cause artifacts in the MEG data, and not removable during pre-processing.

Mechanism of injury in the mTBI group (n = 12) was a fall in 8 cases, head to head collision in soccer in 2 cases, helmet to helmet collision in football in 1 case, and passenger in a motor vehicle accident in 1 case. Injury in 5 of the cases was related to sports. Initial impact contact for the mTBI group was in the midline in 8 cases, left sided in 3 cases, and right sided in 1 case. Mechanism of injury in the orthopedic controls (n = 9) involved falls in 4 cases including 3 during sports activities, other sports injuries in 4 cases, and collision with an object (ran and hit hand on a car) in 1 case. None of the orthopedic controls hit their heads.

MEG data acquisition and signal pre-processing to remove artifacts

rs-MEG data (spontaneous recording for detecting MEG slow-wave signals) were collected at the University of California, San Diego MEG Center using the VectorView™ whole-head MEG system (Elekta-Neuromag, Helsinki, Finland) with 306 MEG channels. Participants sat inside a multi-layer magnetically shielded room (IMEDCO-AG).²⁰ Precautions were taken to ensure head stability; foam wedges were inserted between the participant's head and the inside of the unit, and a Velcro strap was placed under the participant's chin and anchored in superior and posterior axes. Head movement across different sessions was about 2–3 mm.

MEG recording was divided into three 5-min blocks with eyes closed, alternating with three 5-min blocks with eyes open. In the eyes-closed condition, the participant was instructed to keep his/her eyes closed and empty his/her mind. In the eyes-open condition, the participant was instructed to fix his/her eyes on a fixation point and empty his/her mind. The order of blocks was counter-balanced between participants. Data were sampled at 1000 Hz and were run through a high-pass filter with a 0.1 Hz cutoff, and a low-pass filter with a 300 Hz cutoff. The filter associated with MEG data acquisition is a first-order time-domain filter with 3 dB around the cutoff points. Eye blinks, eye movements, and heart signals were monitored. Because the MEG eyes-open data were contaminated with eye blinks in many participants, we focused on analyzing the eyes-closed data in the present study. Due to the non-effort dependent nature of the resting state exam, the data of the solitary participant that was involved in litigation were included.

Substantial effort was taken to ensure that participants were alert during the MEG recordings. Prior to all study sessions, participants completed a questionnaire about the number of hours they slept the previous night, how rested they felt, and if there was any reason that they might not be attentive and perform to the best of their abilities (due to headache, pain, etc.). During MEG recording, participants were viewed on camera while technicians also monitored alpha band oscillations, which are consistently associated with tonic alertness.²¹

Eyes-closed MEG data were first run through MaxFilter, also known as signal space separation,^22–24 to remove external sources of interference (e.g., magnetic artifacts due to metal objects, strong cardiac signals, environment noises, etc.) and to co-register the MEG data by removing the small head movements across the two 5-min eyes-closed sessions. Next, residual artifacts due to eye movements and residual cardiac signals were removed using Independent Component Analysis with Fast-ICA.^25,26

Structural MRI, MEG-MRI registration, BEM forward calculation

Structural MRI of the participant's head was collected using either a General Electric 3T MR 750 (in 21 participants) or a General Electric 1.5T Excite MRI scanner (in 3 participants). The acquisition from the GE 3T system contains a standard high-resolution anatomical volume (T1-weighted three-dimensional inversion recovery spoiled gradient [3D-IR-SPGR]) with a 1.2-mm slice thickness. The acquisition from the GE 1.5T system contains a standard high-resolution anatomical volume with a resolution of 0.94 × 0.94 × 1.2 mm³ using a T1-weighted three-dimensional inversion recovery fast spoiled gradient (3D-IR-FSPGR) pulse sequence. To co-register the MEG with MRI coordinate systems, three anatomical landmarks (i.e., left and right pre-auricular points, and nasion) were measured for each participant using the Probe Position Identification system (Polhemus). By using MRILAB (Elekta/Neuromag) to identify the same three points on the participant's MR images, a transformation matrix involving both rotation and translation between the MEG and MR coordinate systems was generated. To increase the reliability of the MEG-MR co-registration, ∼80 points on the scalp were digitized with the Polhemus system, in addition to the three landmarks, and those points were co-registered onto the scalp surface of the MR images.

The T1-weighted images were also used to extract the brain volume and innermost skull surface (SEGLAB software developed by Elekta/Neuromag). The Realistic Boundary Element Method (BEM) head model was used for MEG forward calculation.^27,28 The BEM mesh was constructed by tessellating the inner skull surface from the T1-weighted MRI into ∼6000 triangular elements with a size of ∼5 mm. A cubic source grid of 5 mm was used for calculating the MEG gain (i.e., lead-field) matrix, which leads to a grid with ∼10,000 nodes covering the whole brain. Other conventional MRI sequences typical for identifying structural lesions were also performed: 1) axial T2*-weighted; 2) axial fast spin echo T2-weighted; and 3) axial fluid attenuated inversion recovery (FLAIR). These conventional MRIs were reviewed by a board-certified neuroradiologist (JRH) to determine if participants had visible lesions on MRI.

MEG slow-wave source magnitude imaging using Fast-VESTAL

The voxel-wise MEG source magnitude images were obtained using our high-resolution Fast-VESTAL MEG source imaging method.^8,29 This approach requires the sensor waveform covariance matrix. Here, the second 5-min rs-MEG sensor-waveform data set was registered to the first 5-min resting-state data set using MaxFilter. The artifact-free, eyes-closed, rs-MEG sensor-waveform data sets were divided into 2.5-sec epochs. The data in each epoch were first Direct Current-corrected and then run through band-pass filters for the following frequency bands: alpha band (8–12 Hz), beta band (15–30 Hz), gamma band (30–80 Hz), high-gamma band (80–150 Hz), and low-frequency band (1–7 Hz) that combined delta (1–4 Hz) and theta bands (4–7 Hz). Notch filters at 60 Hz and 120 Hz were applied to remove the power line signals and their second harmonics. Frequency-domain band-pass filter with zero phase-shift via discrete Fourier transform was used. At each end of the band-pass filter, the transition of the Hanning window in the filter was selected to be at 10% of the associated cutoff frequency.

Waveforms from all 306 sensors including 204 planar-gradiometers and 102 magnetometers were used in the analysis. For each frequency band, sensor-waveform covariance matrices were calculated for individual epochs after the band-pass filtering, then the final sensor-waveform covariance matrix was obtained by averaging the covariance matrices across individual epochs for the 10-min resting-state data. Using such a covariance matrix, MEG slow-wave source magnitude images that cover the whole brain were obtained for each participant following the Fast-VESTAL procedure for a given frequency band.^29,30

Statistical analysis

In all participants, voxel-wise whole-brain MEG source magnitude images obtained from Fast-VESTAL were first spatially co-registered to the MNI-152³¹ brain-atlas template using a linear affine transformation program, FLIRT, in the FSL software package.^32,33 Then in MNI-152 space, the MEG source magnitude images were spatially smoothed using a Gaussian kernel with 5 mm full width half maximum (FWHM), followed by a logarithmic transformation using FSL. Next, voxel-wise statistical analyses were performed to assess the group differences between the children with mTBI and the control children.

For each frequency band, a voxel-wise, two-tailed t test was performed to examine the group differences (i.e., mTBI vs. control group). Family-wise error across voxels was corrected using standard cluster analysis for the t-value maps to control for family-wise errors at a corrected p < 0.01 level, using “3dFWHMx” and “3dClustSim” functions in the latest version of AFNI (http://afni.nimh.nih.gov). A mask that contained the statistically significant clusters was created, and then applied to the t-value maps to create the corrected group statistical maps for the MEG source magnitude images.

Post-concussion behavioral assessment and psychiatric assessment

As part of the larger cohort studies, at 6-months post-injury and coincident with the rs-MEG examination, mTBI participants and their primary caretaker rated their persistent post-concussion symptoms (PCS) on the Health and Behavior Inventory (HBI) scale. The HBI is a 20-item self-report measure of mTBI symptoms validated on youth ages 8–15 years and their parents.³⁴ The scale has two domains consisting of 11 cognitive symptoms and 9 somatic symptoms, where higher scores reflect higher report of symptoms. The cognitive symptoms include problems with attention, distractibility, concentration, memory, following directions, daydreaming, confusion, task completion, problem solving, and learning. The somatic symptoms include headaches, dizziness, vision problems, nausea, and fatigue. The ratings of one participant indicated a clear outlier associated with ongoing litigation. We therefore removed this participant's ratings from the mTBI group analysis. Nine orthopedic injury control participants' ratings were included in the control analysis, as the other 3 controls were uninjured and were recruited from a larger cohort study for pediatric TBI that did not include these measures. It is important to note that those 3 uninjured control participants and their parents were asked a series of questions regarding any PCS and denied having any symptoms.

Determination of the presence at 6-months post-injury of a new-onset psychiatric disorder after mTBI or after orthopedic injury, that is, coincident with the rs-MEG assessment, was made by combining data from a baseline psychiatric assessment that recorded pre-injury diagnoses and a 6-month psychiatric interview that recorded all diagnoses present at 6-months post-injury. The psychiatric assessment was accomplished through a baseline interview conducted by a trained-research assistant who was not blind to group affiliation (i.e., mTBI vs. control). The interviewer administered the Neuropsychiatric Rating Schedule (NPRS)³⁵ and the Schedule for Affective Disorders and Schizophrenia for School-aged Children, Present and Lifetime version (K-SADS-PL)³⁶ to generate Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) pre-injury diagnoses including post-traumatic stress disorder. The assessment was repeated at 6-months post-injury by a different research assistant who was blind to the participants' group. This procedure generated DSM-5 diagnoses that allowed for the documentation of new-onset post-injury psychiatric disorders including post-traumatic stress disorder. The K-SADS-PL was administered to the mTBI participants (n = 12) and the orthopedic injury controls (n = 9) but not to the uninjured controls (n = 3).

Results

Post-concussive symptoms and psychiatric disorders

As seen in Table 1, which demonstrates results of independent sample t test analyses, mTBI participants rated their cognitive symptoms significantly higher than control participants (p = 0.006), with the mTBI child HBI scores for cognitive domain, mean = 13.18 (SD = 5.67) significantly higher than the child HBI scores for controls, mean = 5.78 (SD = 4.71). Parents' rating of cognitive symptoms in child mTBI participants also trended toward significance (p = 0.07) with the parents of mTBI participants rating the cognitive symptoms on the HBI higher, mean 9.0 (SD = 5.12) than their control counterparts, mean = 4.67 (SD = 4.95). The somatic score rating of the HBI for the parents and children did not significantly differ between groups. Therefore, the HBI symptom rating total for mTBI children was significantly higher than the control group, p = 0.008, but this was largely driven by the cognitive symptom rating. The higher parent HBI symptom total rating also trended toward significance, p = 0.08, and this was also driven by the cognitive symptom rating.

The psychiatric diagnostic findings based on the NPRS and K-SADS-PL interviews found that none of the 12 mTBI participants had any lifetime pre-injury psychiatric disorders or any new-onset psychiatric disorders at the 6-month assessment. Among the 9 injured controls, 2 had a pre-injury lifetime psychiatric disorder (1 with “other specified disruptive, impulse control and conduct disorder; 1 with social phobia). Only the child with social phobia developed a new-onset psychiatric disorder (specific phobia of sleeping alone) that was present at post-injury follow-up.

Hyper- and hypoactivations revealed by MEG source magnitude images in pediatric mTBI

Figure 1 shows group differences between children with mTBI and controls in rs-MEG source magnitude for different frequency bands. MEG source magnitude images were obtained for different frequency bands in alpha, beta, gamma, and low-frequency delta-theta bands. In contrast to the control children, rs-MEG source imaging of the alpha band in the children with mTBI showed hyperactivity from anterior aspect of left inferior temporal lobe (ITL) near amygdala, left parahippocampal gyrus, and right posterior insular cortex, but hypoactivity from anterior cingulate cortex (ACC), left dorsolateral prefrontal cortices (dlPFC), right precuneus, left orbitofrontal cortex (OFC), and left cerebellum. For the beta band, children with mTBI showed hyperactivity from right temporal pole/anterior insular, left precuneus, and left cerebellum, but hypoactivity from bilateral dlPFC, left ventromedial prefrontal cortex (vmPFC), and bilateral inferior temporal lobe (ITL) areas. For the gamma band, the mTBI group showed hyperactivity from the right fusiform gyrus (FG), right visual cortex, and bilateral cerebella, but hypoactivity from right ventrolateral prefrontal cortex (vlPFC) and right ITL area. For the delta-theta band, mTBI group showed hyperactivity from bilateral superior temporal lobe (STL) areas, and hypoactivity from right superior frontal gyrus (SFG). These findings are summarized in Table 2.

FIG. 1.

Hyper- (warm color scale) and hypo-activity (cold color scale) in resting-state MEG source magnitude imaging in children with mTBI, compared with control children. MEG, magnetoencephalography; mTBI, mild traumatic brain injury.

Table 2.

Brain Areas Showing Increased (Hyper) or Decreases (Hypo) rs-MEG Activity in mTBI Group across Different Frequency Bands, Compared with Healthy Control Group

Brain regions	α	β	γ	δ-θ
L dlPFC	-	-
L vmPFC		-
L OFC	-
L ACC	-
L ITL	+	-
L STL				+
L parahippocampal gyrus	+
L precuneus		+
L cerebellum	-	+	+
R dlPFC		-
R vlPFC			-
R SFG				-
R insula, posterior div.	+
R ACC	-
R precuneus	-
R temporal pole		+
R ITL		-	-
R STL				+
R FG			+
R visual cortex			+
R cerebellum			+

“+” designates increased activity, whereas “-” designates decreased activity in the mTBI group.

ACC, anterior cingulate cortex; div., division; dl, dorsolateral; FG, fusiform gyrus; ITL, inferior temporal lobe; L, left; OFC, orbitofrontal cortex; MEG, magnetoencephalography; mTBI, mild traumatic brain injury; PFC, prefontal cortex; R, right; SFG, superior frontal gyrus; STL, superior temporal lobe; vl, ventrolateral; vm, ventromedial.

Discussion

Abnormal rs-MEG activity in children with mTBI

In the present study, we found abnormal rs-MEG activity in a variety of prefrontal, temporal, and parietal areas in the group of children with mTBI, compared with the control group. In the alpha band, the significant findings were the hypo-activity from cortical regions in PFC (i.e., dlPFC, OFC, ACC) and precuneus areas, and hyper-activity in ITL, parahippocampus, and insula. Neuronal modeling studies have shown that thalamo-cortical interactions are essential for the generation of alpha rhythms.^37–39 The observed rs-MEG alpha-band hypoactivity may suggest a deficit in thalamo-cortical interactions, which possibly leads to reduced functional inhibition in the cortical areas of PFC and precuneus in children with mTBI. Although it has been suggested that alpha-band activity is associated with functional inhibition,^40,41 the physiology origin of alpha rhythm generation is rather complicated and far from clear. Most of the studies cited in a recent review article⁴² showed that thelamo-cortical interactions are important in generating alpha rhythm, although some studies found cortico-cortical interactions can also generate alpha frequency oscillation. It was suggested the functional role of alpha might depend on physiological excitation as much as on physiological inhibition.⁴² This theory is supported by animal and human pharmacological work showing that GABAergic, glutamatergic, cholinergic, and serotonergic receptors in the thalamus and the cortex play a key role in the regulation of alpha rhythm.⁴²

In general, a normal amount of alpha activity is preferred in the resting-state, and reduced alpha-band power has been observed in individuals with Alzheimer's disease,^43,44 post-traumatic stress disorder (PTSD),⁴⁵ and schizophrenia.^46,47 The hyper-activity seen in ITL, parahippocampus, and insula of the present study may suggest functional over-inhibition in these regions, which may lead to deficits in memory function (ITL, parahippocampus) or poly-sensory information integration (insula).

In the present study, both hyper- and hypo-activities were observed in rs-MEG from a number of frontal, temporal, occipital, and cerebella regions in beta and gamma bands. Activity in the beta band is thought to be associated with communication between remote brain structures, whereas gamma synchrony promotes local computations.^48,49 Although the gamma band electromagnetic signals are generated locally, non-local brain areas can still show significant functional connectivity as measured by coherence related to the gamma band signals. Using combined electrophysiological and functional MRI measurements, studies in both humans and animals have shown that gamma-band power exhibits spatial coherence over long timescales with the strongest coherence between functionally related areas that are not necessarily local.^50–54 Our findings of abnormal rs-MEG in beta and gamma bands suggest abnormal communication patterns in remote and well as local networks. It is also shown that GABA-ergic inhibition is essential in the generation of synchronized gamma rhythm.^55–59 The damage to the vulnerable GABA-ergic inter-neurons due to mTBI may contribute to the abnormal gamma activity in our mTBI group. The finding of gamma-band hyper-activity in bilateral cerebella was consistent with such mechanism, owing to the situation that basket and stellate cells, rich in cerebellum, are GABA-ergic and provide inhibitions to Purkinje cells.^60,61

Our study found increased low-frequency (delta-theta band) rs-MEG activity from bilateral temporal lobes. Theta-band signals have been reported in previous EEG studies, although these signals were predominantly task-activated (e.g., problem solving).^19,62–65 Increased low-frequency brain rhythms in delta band were often seen in individuals with neurological disorders, for example, epilepsy and traumatic brain injury.^{8–11,66–71} When examining the mechanism of abnormal delta rhythms, electrophysiological studies in animals show that abnormal delta activity is from gray matter neurons that have experienced deafferentation due to blockage or limitation in the cholinergic pathways.^12–14,72 More recent animal and human studies have also shown the close link between delta wave generation and metabolic clearance of toxic proteins such as beta amyloid (A_β), which commonly exist in the brain of patients with TBI or Alzheimer's disease.^73–75 It is reported that the increase of delta waves may suggest ongoing neural healing at the sites of the injury in mTBI.¹⁵ Such a healing hypothesis was consistent with the deafferentation mechanism that is believed to be an important process of isolating the brain injured area from the rest of the system.⁷⁶ However, more animal and human studies are needed to fully address this healing hypothesis with the low-frequency rhythm.

Limitations

There are several important limitations of this study. This is a small pilot study that included a sample of unselected consecutively treated children from a larger previously started study of consecutive ER-treated children with mTBI. Despite small sample size, rs-MEG appears to be highly sensitive to group differences. The absence of new-onset psychiatric disorder in the mTBI group precluded analyses of the relationship of psychiatric complications and rs-MEG correlates. The small mTBI sample influenced our decision to delay reporting analyses of the change in PCS ratings (from before injury to after injury) and their rs-MEG correlates. Not all participants had cognitive symptom data, standardized cognitive assessments, or psychiatric assessments and this precluded related additional mTBI versus control analyses. The age range of participants was relatively wide and there are known brain changes with development and age. We attempted to mitigate this by successfully matching the mTBI and controls by age. Finally, it is possible that children who suffer injuries may be more immature than age-matched peers. We attempted to manage this problem by recruiting 9 of the controls after an orthopedic injury and the 3 uninjured controls were originally recruited as controls for sports-related mTBI and were thus exposed to sports and potential injury.

Conclusion

This pilot study demonstrated significant differences in rs-MEG activity between participants with mTBI and a comparison group. These differences were evident across alpha, beta, gamma, and slow waves (delta-theta) bands. The slow wave findings resemble those in adult mTBI studies, but the findings in other bands may be unique to pediatric mTBI. None of the participants with mTBI had a pre-injury psychiatric disorder nor did any develop a new-onset psychiatric disorder post-injury. Nevertheless, in addition to rs-MEG differences there was a statistically significant, but not clinically significant, youth-rated cognitive post-concussion symptom score between participants with mTBI and controls. It is possible that rs-MEG is sufficiently sensitive to detect subtle evidence of neurological injury from a mTBI and also sensitive to potentially related subtle behavioral symptom changes. Further, it is intriguing to consider whether the current rs-MEG findings provide evidence that after a single mTBI, the pediatric brain may be vulnerable to additional, clinically significant behavioral or psychiatric disturbance should the child suffer additional mTBIs. There is evidence that repeated mTBI may be associated with greater risk of PCS,⁷⁷ although the underlying pathophysiology, which may be clarified with rs-MEG, is unknown.

We plan to recruit a larger sample that has sufficient statistical power to potentially delineate the relationship between post-injury subclinical change in symptomatology and specific brain regions that show functional rs-MEG differences between mTBI and other-injured controls. This work may lead to an enhanced understanding of neurobiological mechanisms of post-injury behavioral and psychiatric change and guide potential treatments such as transcranial magnetic stimulation to normalize brain function.¹⁵

Funding Information

This work was supported in part by the National Institute of Child Health and Development grants 1R01HD068432-01A1 and 1R-01 HD088438-01A1 (PI: J.E. Max), and by Merit Review Grants from the Department of Veterans Affairs (PI: M.X. Huang): I01-CX000499, I01-RX001988, MHBA-010-14F, NURC-022-10F, and NEUC-044-06S.

Author Disclosure Statement

Dr. Max provides expert testimony in cases of traumatic brain injury on an ad hoc basis for plaintiffs and defendants on a more or less equal ratio. This activity constitutes approximately 10% of Dr. Max's professional activity.