Novel Oppositional Defiant Disorder or Conduct Disorder 24 Months After Traumatic Brain Injury in Children and Adolescents.

OBJECTIVE: The authors sought to identify predictive factors of new-onset or novel oppositional defiant disorder or conduct disorder assessed 24 months after traumatic brain injury (TBI). METHODS: Children ages 5 to 14 years who had experienced TBI were recruited from consecutive hospital admissions. Soon after injury, participants were assessed for preinjury characteristics, including psychiatric disorders, socioeconomic status (SES), psychosocial adversity, and family function, and the presence and location of lesions were documented by MRI. Psychiatric outcomes, including novel oppositional defiant disorder or conduct disorder, were assessed 24 months after injury. RESULTS: Of the children without preinjury oppositional defiant disorder, conduct disorder, or disruptive behavior disorder not otherwise specified who were recruited in this study, 165 were included in this sample; 95 of these children returned for the 24-month assessment. Multiple imputation was used to address attrition. The prevalence of novel oppositional defiant disorder or conduct disorder was 23.7 out of 165 (14%). In univariable analyses, novel oppositional defiant disorder or conduct disorder was significantly associated with psychosocial adversity (p=0.049) and frontal white matter lesions (p=0.016) and was marginally but not significantly associated with SES. In the final multipredictor model, frontal white matter lesions were significantly associated with novel oppositional defiant disorder or conduct disorder (p=0.021), and psychosocial adversity score was marginally but not significantly associated with the outcome. The odds ratio of novel oppositional defiant disorder or conduct disorder among the children with versus those without novel depressive disorder was significantly higher for girls than boys (p=0.025), and the odds ratio of novel oppositional defiant disorder or conduct disorder among the children with versus those without novel attention-deficit hyperactivity disorder (ADHD) was significantly higher for boys than girls (p=0.006). CONCLUSION: Approximately 14% of children with TBI developed oppositional defiant disorder or conduct disorder. The risk for novel oppositional defiant disorder or conduct disorder can be understood from a biopsychosocial perspective. Sex differences were evident for comorbid novel depressive disorder and comorbid novel ADHD.

Case report: Neural timing deficits prevalent in developmental disorders, aging, and concussions remediated rapidly by movement discrimination exercises.

Background: The substantial evidence that neural timing deficits are prevalent in developmental disorders, aging, and concussions resulting from a Traumatic Brain Injury (TBI) is presented. Objective: When these timing deficits are remediated using low-level movement-discrimination training, then high-level cognitive skills, including reading, attention, processing speed, problem solving, and working memory improve rapidly and effectively. Methods: In addition to the substantial evidence published previously, new evidence based on a neural correlate, MagnetoEncephalography physiological recordings, on an adult dyslexic, and neuropsychological tests on this dyslexic subject and an older adult were measured before and after 8-weeks of contrast sensitivity-based left-right movement-discrimination exercises were completed. Results: The neuropsychological tests found large improvements in reading, selective and sustained attention, processing speed, working memory, and problem-solving skills, never before found after such a short period of training. Moreover, these improvements were found 4 years later for older adult. Substantial MEG signal increases in visual Motion, Attention, and Memory/Executive Control Networks were observed following training on contrast sensitivity-based left-right movement-discrimination. Improving the function of magnocells using figure/ground movement-discrimination at both low and high levels in dorsal stream: (1) improved both feedforward and feedback pathways to modulate attention by enhancing coupled theta/gamma and alpha/gamma oscillations, (2) is adaptive, and (3) incorporated cycles of feedback and reward at multiple levels. Conclusion: What emerges from multiple studies is the essential role of timing deficits in the dorsal stream that are prevalent in developmental disorders like dyslexia, in aging, and following a TBI. Training visual dorsal stream function at low levels significantly improved high-level cognitive functions, including processing speed, selective and sustained attention, both auditory and visual working memory, problem solving, and reading fluency. A paradigm shift for treating cognitive impairments in developmental disorders, aging, and concussions is crucial. Remediating the neural timing deficits of low-level dorsal pathways, thereby improving both feedforward and feedback pathways, before cognitive exercises to improve specific cognitive skills provides the most rapid and effective methods to improve cognitive skills. Moreover, this adaptive training with substantial feedback shows cognitive transfer to tasks not trained on, significantly improving a person's quality of life rapidly and effectively.

EMG-projected MEG High-Resolution Source Imaging of Human Motor Execution: Brain-Muscle Coupling above Movement Frequencies.

Magnetoencephalography (MEG) is a non-invasive functional imaging technique for pre-surgical mapping. However, movement-related MEG functional mapping of primary motor cortex (M1) has been challenging in presurgical patients with brain lesions and sensorimotor dysfunction due to the large numbers of trails needed to obtain adequate signal to noise. Moreover, it is not fully understood how effective the brain communication is with the muscles at frequencies above the movement frequency and its harmonics. We developed a novel Electromyography (EMG)-projected MEG source imaging technique for localizing M1 during ~1 minute recordings of left and right self-paced finger movements (~1 Hz). High-resolution MEG source images were obtained by projecting M1 activity towards the skin EMG signal without trial averaging. We studied delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-90 Hz) bands in 13 healthy participants (26 datasets) and two presurgical patients with sensorimotor dysfunction. In healthy participants, EMG-projected MEG accurately localized M1 with high accuracy in delta (100.0%), theta (100.0%), and beta (76.9%) bands, but not alpha (34.6%) and gamma (0.0%) bands. Except for delta, all other frequency bands were above the movement frequency and its harmonics. In both presurgical patients, M1 activity in the affected hemisphere was also accurately localized, despite highly irregular EMG movement patterns in one patient. Altogether, our EMG-projected MEG imaging approach is highly accurate and feasible for M1 mapping in presurgical patients. The results also provide insight into movement related brain-muscle coupling above the movement frequency and its harmonics.

Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning.

Advancements in deep learning algorithms over the past decade have led to extensive developments in brain-computer interfaces (BCI). A promising imaging modality for BCI is magnetoencephalography (MEG), which is a non-invasive functional imaging technique. The present study developed a MEG sensor-based BCI neural network to decode Rock-Paper-scissors gestures (MEG-RPSnet). Unique preprocessing pipelines in tandem with convolutional neural network deep-learning models accurately classified gestures. On a single-trial basis, we found an average of 85.56% classification accuracy in 12 subjects. Our MEG-RPSnet model outperformed two state-of-the-art neural network architectures for electroencephalogram-based BCI as well as a traditional machine learning method, and demonstrated equivalent and/or better performance than machine learning methods that have employed invasive, electrocorticography-based BCI using the same task. In addition, MEG-RPSnet classification performance using an intra-subject approach outperformed a model that used a cross-subject approach. Remarkably, we also found that when using only central-parietal-occipital regional sensors or occipitotemporal regional sensors, the deep learning model achieved classification performances that were similar to the whole-brain sensor model. The MEG-RSPnet model also distinguished neuronal features of individual hand gestures with very good accuracy. Altogether, these results show that noninvasive MEG-based BCI applications hold promise for future BCI developments in hand-gesture decoding.

Assessing Pediatric Mild Traumatic Brain Injury and Its Recovery Using Resting-State Magnetoencephalography Source Magnitude Imaging and Machine Learning.

The objectives of this machine-learning (ML) resting-state magnetoencephalography (rs-MEG) study involving children with mild traumatic brain injury (mTBI) and orthopedic injury (OI) controls were to define a neural injury signature of mTBI and to delineate the pattern(s) of neural injury that determine behavioral recovery. Children ages 8-15 years with mTBI (n = 59) and OI (n = 39) from consecutive admissions to an emergency department were studied prospectively for parent-rated post-concussion symptoms (PCS) at: 1) baseline (average of 3 weeks post-injury) to measure pre-injury symptoms and also concurrent symptoms; and 2) at 3-months post-injury. rs-MEG was conducted at the baseline assessment. The ML algorithm predicted cases of mTBI versus OI with sensitivity of 95.5 ± 1.6% and specificity of 90.2 ± 2.7% at 3-weeks post-injury for the combined delta-gamma frequencies. The sensitivity and specificity were significantly better (p < 0.0001) for the combined delta-gamma frequencies compared with the delta-only and gamma-only frequencies. There were also spatial differences in rs-MEG activity between mTBI and OI groups in both delta and gamma bands in frontal and temporal lobe, as well as more widespread differences in the brain. The ML algorithm accounted for 84.5% of the variance in predicting recovery measured by PCS changes between 3 weeks and 3 months post-injury in the mTBI group, and this was significantly lower (p < 10-4) in the OI group (65.6%). Frontal lobe pole (higher) gamma activity was significantly (p < 0.001) associated with (worse) PCS recovery exclusively in the mTBI group. These findings demonstrate a neural injury signature of pediatric mTBI and patterns of mTBI-induced neural injury related to behavioral recovery.

Functional topologies of spatial cognition predict cognitive and motor progression in Parkinson's.

Background: Spatial cognition deteriorates in Parkinson's disease (PD), but the neural substrates are not understood, despite the risk for future dementia. It is also unclear whether deteriorating spatial cognition relates to changes in other cognitive domains or contributes to motor dysfunction. Objective: This study aimed to identify functional connectivity abnormalities in cognitively normal PD (PDCN) in regions that support spatial cognition to determine their relationship to interfacing cognitive functions and motor disability, and to determine if they predict cognitive and motor progression 2 years later in a PDCN subsample. Methods: Sixty-three PDCN and 43 controls underwent functional MRI while judging whether pictures, rotated at various angles, depicted the left or right hand. The task activates systems that respond to increases in rotation angle, a proxy for visuospatial difficulty. Angle-modulated functional connectivity was analyzed for frontal cortex, posterior cortex, and basal ganglia regions. Results: Two aberrant connectivity patterns were found in PDCN, which were condensed into principal components that characterized the strength and topology of angle-modulated connectivity. One topology related to a marked failure to amplify frontal, posterior, and basal ganglia connectivity with other brain areas as visuospatial demands increased, unlike the control group (control features). Another topology related to functional reorganization whereby regional connectivity was strengthened with brain areas not recruited by the control group (PDCN features). Functional topologies correlated with diverse cognitive domains at baseline, underscoring their influences on spatial cognition. In PDCN, expression of topologies that were control features predicted greater cognitive progression longitudinally, suggesting inefficient communications within circuitry normally recruited to handle spatial demands. Conversely, stronger expression of topologies that were PDCN features predicted less longitudinal cognitive decline, suggesting functional reorganization was compensatory. Parieto-occipital topologies (control features) had different prognostic implications for longitudinal changes in motor disability. Expression of one topology predicted less motor decline, whereas expression of another predicted increased postural instability and gait disturbance (PIGD) feature severity. Concurrently, greater longitudinal decline in spatial cognition predicted greater motor and PIGD feature progression, suggesting deterioration in shared substrates. Conclusion: These novel discoveries elucidate functional mechanisms of visuospatial cognition in PDCN, which foreshadow future cognitive and motor disability.

Two mechanisms facilitate regional independence between brain regions based on an examination of alpha-band activity in healthy control adult males.

BACKGROUND: At rest, 8 to 12 Hz alpha rhythms are the dominant rhythm in the brain, with a common peak alpha frequency (PAF = the frequency at which alpha generators show maximum power) observed across brain regions. Although a common PAF across brain regions should result in high between-region connectivity, especially connectivity measures assessing the phase-similarity between alpha generators, high inter-regional alpha connectivity has not been observed. The present study was conducted as an initial step toward identifying mechanisms that allow brain regions to maintain functional independence in the presence of a common PAF. METHODS: MEG data were obtained from 16 healthy control male adults (mean age = 24 years; range 21 to 30 years). A task requiring participants to alternate between a 10 s eyes-closed condition and a 5 s eyes-open condition was used to drive parietal-occipital alpha generators, with the 10 s eyes-closed condition eliciting large-amplitude alpha activity and thus providing alpha measures with good signal-to-noise ratio for source localization. Alpha source-space measures were obtained using Vector-based Spatial-Temporal Analysis using L1-minimum-norm. In each participant, the four strongest parietal-occipital alpha generators were identified. Connectivity between sources was assessed via a measure of phase-based connectivity called inter-site phase clustering (ISPC). RESULTS: Intra-class correlations (ICC) showed very high similarity in the average PAF (=computed using all eyes-closed data) between the four alpha sources (ICC single measure = 0.88, p < 0.001). Despite a common average PAF, across participants, significant ISPC was often observed no more than that expected by chance. Examination of the alpha time course data indicated that low ISPC was often due to instantaneous changes in alpha phase (phase slips). ISPC analyses removing data with phase slips indicated that low ISPC was also due to slight continuous changes in the alpha frequency, with frequency drift more likely in non-significant than significant ISPC trials. CONCLUSIONS: The present exploratory effort suggested two processes underlying the lack of observed inter-regional alpha phase coherence that may help maintain regional functional independence even in the presence of a common PAF. In particular, although the alpha generators were observed to oscillate at the same rate on average, across time each alpha generator oscillated a little slower or faster, and about every one and a half seconds an alpha generator abruptly lost the beat. Because of this, functional independence among alpha generators (and thus brain regions) was the rule rather than the exception. Studies replicating these processes that allow brain regions to maintain functional independence, using different source localization methods and in different conditions (e.g., a true resting state), are warranted. IMPACT STATEMENT: Using source localization to measure parietal-occipital alpha generator activity, two properties that limit between-region alpha functional connectivity are proposed. In particular, a model of alpha generator activity is offered where via transient phase slips occurring approximately every 1.5 s, as well as slight non-stationarity in the alpha frequency, brain regions retain a common alpha frequency while also maintaining regional identity and presumably functionality. Findings also suggest novel markers for use in studies examining changes in alpha activity across maturation as well as in studies examining alpha activity in patient populations where alpha abnormalities have been reported.

Magnetoencephalography Language Mapping Using Auditory Memory Retrieval and Silent Repeating Task.

PURPOSE: The study aims to (1) examine the spatiotemporal map of magnetoencephalography-evoked responses during an Auditory Memory Retrieval and Silent Repeating (AMRSR) task, and determine the hemispheric dominance for language, and (2) evaluate the accuracy of the AMRSR task in Wernicke and Broca area localization. METHODS: In 30 patients with brain tumors and/or epilepsies, the AMRSR task was used to evoke magnetoencephalography responses. We applied Fast VEctor-based Spatial-Temporal Analyses with minimum L1-norm source imaging method to the magnetoencephalography responses for localizing the brain areas evoked by the AMRSR task. RESULTS: The Fast-VEctor-based Spatial-Temporal Analysis found consistent activation in the posterior superior temporal gyrus around 300 to 500 ms, and another activation in the frontal cortex (pars opercularis and/or pars triangularis) around 600 to 900 ms, which were localized to the Wernicke area (BA 22) and Broca area (BA 44 and BA 45), respectively. The language-dominant hemispheric laterization elicited by the AMRSR task was comparable with the result from an Auditory Dichotic task result given to the same patient, with the exception that AMRSR is more sensitive on bilateral language laterization cases on finding the Wernicke and Broca areas. CONCLUSIONS: For all patients who successfully finished the AMRSR task, Fast-VEctor-based Spatial-Temporal Analysis could establish accurate and robust localizations of Broca and Wernicke area and determine hemispheric dominance. For subjects with normal auditory functionality, the AMRSR paradigm evaluation showed significant promise in providing reliable assessments of cerebral language dominance and language network localization.

Detection of Chronic Blast-Related Mild Traumatic Brain Injury with Diffusion Tensor Imaging and Support Vector Machines.

Blast-related mild traumatic brain injury (bmTBI) often leads to long-term sequalae, but diagnostic approaches are lacking due to insufficient knowledge about the predominant pathophysiology. This study aimed to build a diagnostic model for future verification by applying machine-learning based support vector machine (SVM) modeling to diffusion tensor imaging (DTI) datasets to elucidate white-matter features that distinguish bmTBI from healthy controls (HC). Twenty subacute/chronic bmTBI and 19 HC combat-deployed personnel underwent DTI. Clinically relevant features for modeling were selected using tract-based analyses that identified group differences throughout white-matter tracts in five DTI metrics to elucidate the pathogenesis of injury. These features were then analyzed using SVM modeling with cross validation. Tract-based analyses revealed abnormally decreased radial diffusivity (RD), increased fractional anisotropy (FA) and axial/radial diffusivity ratio (AD/RD) in the bmTBI group, mostly in anterior tracts (29 features). SVM models showed that FA of the anterior/superior corona radiata and AD/RD of the corpus callosum and anterior limbs of the internal capsule (5 features) best distinguished bmTBI from HCs with 89% accuracy. This is the first application of SVM to identify prominent features of bmTBI solely based on DTI metrics in well-defined tracts, which if successfully validated could promote targeted treatment interventions.

Internetwork Connectivity Predicts Cognitive Decline in Parkinson's and Is Altered by Genetic Variants.

In Parkinson's disease (PD) functional changes in the brain occur years before significant cognitive symptoms manifest yet core large-scale networks that maintain cognition and predict future cognitive decline are poorly understood. The present study investigated internetwork functional connectivity of visual (VN), anterior and posterior default mode (aDMN, pDMN), left/right frontoparietal (LFPN, RFPN), and salience (SN) networks in 63 cognitively normal PD (PDCN) and 43 healthy controls who underwent resting-state functional MRI. The functional relevance of internetwork coupling topologies was tested by their correlations with baseline cognitive performance in each group and with 2-year cognitive changes in a PDCN subsample. To disentangle heterogeneity in neurocognitive functioning, we also studied whether α-synuclein (SNCA) and microtubule-associated protein tau (MAPT) variants alter internetwork connectivity and/or accelerate cognitive decline. We found that internetwork connectivity was largely preserved in PDCN, except for reduced pDMN-RFPN/LFPN couplings, which correlated with poorer baseline global cognition. Preserved internetwork couplings also correlated with domain-specific cognition but differently for the two groups. In PDCN, stronger positive internetwork coupling topologies correlated with better cognition at baseline, suggesting a compensatory mechanism arising from less effective deployment of networks that supported cognition in healthy controls. However, stronger positive internetwork coupling topologies typically predicted greater longitudinal decline in most cognitive domains, suggesting that they were surrogate markers of neuronal vulnerability. In this regard, stronger aDMN-SN, LFPN-SN, and/or LFPN-VN connectivity predicted longitudinal decline in attention, working memory, executive functioning, and visual cognition, which is a risk factor for dementia. Coupling strengths of some internetwork topologies were altered by genetic variants. PDCN carriers of the SNCA risk allele showed amplified anticorrelations between the SN and the VN/pDMN, which supported cognition in healthy controls, but strengthened pDMN-RFPN connectivity, which maintained visual memory longitudinally. PDCN carriers of the MAPT risk allele showed greater longitudinal decline in working memory and increased VN-LFPN connectivity, which in turn predicted greater decline in visuospatial processing. Collectively, the results suggest that cognition is maintained by functional reconfiguration of large-scale internetwork communications, which are partly altered by genetic risk factors and predict future domain-specific cognitive progression.

Peripheral Nerve Magnetoneurography With Optically Pumped Magnetometers.

Electrodiagnosis is routinely integrated into clinical neurophysiology practice for peripheral nerve disease diagnoses, such as neuropathy, demyelinating disorders, nerve entrapment/impingement, plexopathy, or radiculopathy. Measured with conventional surface electrodes, the propagation of peripheral nerve action potentials along a nerve is the result of ionic current flow which, according to Ampere's Law, generates a small magnetic field that is also detected as an "action current" by magnetometers, such as superconducting quantum interference device (SQUID) Magnetoencephalography (MEG) systems. Optically pumped magnetometers (OPMs) are an emerging class of quantum magnetic sensors with a demonstrated sensitivity at the 1 fT/√Hz level, capable of cortical action current detection. But OPMs were ostensibly constrained to low bandwidth therefore precluding their use in peripheral nerve electrodiagnosis. With careful OPM bandwidth characterization, we hypothesized OPMs may also detect compound action current signatures consistent with both Sensory Nerve Action Potential (SNAP) and the Hoffmann Reflex (H-Reflex). In as much, our work confirms OPMs enabled with expanded bandwidth can detect the magnetic signature of both the SNAP and H-Reflex. Taken together, OPMs now show potential as an emerging electrodiagnostic tool.

Semantic Recollection in Parkinson's Disease: Functional Reconfiguration and MAPT Variants.

Decline in semantic cognition in early stages of Parkinson's disease (PD) is a leading risk factor for future dementia, yet the underlying neural mechanisms are not understood. The present study addressed this gap by investigating the functional connectivity of regions involved in semantic recollection. We further examined whether microtubule-associated protein tau (MAPT) risk variants, which may accelerate cognitive decline, altered the strength of regional functional connections. Cognitively normal PD and healthy elder controls underwent fMRI while performing a fame-discrimination task, which activates the semantic network. Analyses focused on disturbances in fame-modulated functional connectivity in PD for regions that govern semantic recollection and interrelated processes. Group differences were found in multiple connectivity features, which were reduced into principal components that reflected the strength of fame-modulated regional couplings with other brain areas. Despite the absence of group differences in semantic cognition, two aberrant connectivity patterns were uncovered in PD. One pattern was related to a loss in frontal, parietal, and temporal connection topologies that governed semantic recollection in older controls. Another pattern was characterized by functional reconfiguration, wherein frontal, parietal, temporal and caudate couplings were strengthened with areas that were not recruited by controls. Correlations between principal component scores and cognitive measures suggested that reconfigured frontal coupling topologies in PD supported compensatory routes for accessing semantic content, whereas reconfigured parietal, temporal, and caudate connection topologies were detrimental or unrelated to cognition. Increased tau transcription diminished recruitment of compensatory frontal topologies but amplified recruitment of parietal topologies that were unfavorable for cognition. Collectively, the findings provide a new understanding of early vulnerabilities in the functional architecture of regional connectivity during semantic recollection in cognitively normal PD. The findings also have implications for tracking cognitive progression and selecting patients who stand to benefit from therapeutic interventions.

Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.

Combat-related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active-duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep-learning neural network method, 3D-MEGNET, and applied it to resting-state magnetoencephalography (rs-MEG) source-magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat-deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All-frequency model, which combined delta-theta (1-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D-MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver-operator-characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta-theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta-theta and gamma-band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta-band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source-imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all-frequency model offered more discriminative power than each frequency-band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders.

Enhanced Fast-VESTAL for Magnetoencephalography Source Imaging: From Theory to Clinical Application in Epilepsy.

A novel magnetoencephalography source imaging approach called Fast Vector-based Spatio-Temporal Analysis (Fast-VESTAL) has been successfully applied in creating source images from evoked and resting-state data from both healthy subjects and individuals with neurological and/or psychiatric disorders, but its reconstructed source images may show false-positive activations, especially under low signal-to-noise ratio conditions. Here, to effectively reduce false-positive artifacts, we introduced an enhanced Fast-VESTAL (eFast-VESTAL) approach that adopts generalized second-order cone programming. We compared the spatiotemporal characteristics of the eFast-VESTAL approach to those of the popular distributed source approaches (e.g., the minimum L2-norm/ mixed-norm methods) using computer simulations and auditory experiments. More importantly, we applied eFast-VESTAL to the presurgical evaluation of epilepsy. Our results demonstrated that eFast-VESTAL exhibited a lower dipole localization error and/or a higher correlation coefficient (CC) between the estimated source time series and ground truth under various conditions of source waveforms. Experimentally, eFast-VESTAL displayed more focal activation maps and a higher CC between the raw and predicted sensor data in response to auditory stimulation. Notably, eFast-VESTAL was the most accurate method for noninvasively detecting the epileptic zones determined using more invasive stereo-electroencephalography in the comparison.

Magnetoencephalography Signal Processing, Forward Modeling, Magnetoencephalography Inverse Source Imaging, and Coherence Analysis.

Magnetoencephalography (MEG) is a noninvasive functional imaging technique for the brain. MEG directly measures the magnetic signal due to neuronal activation in gray matter with high spatial localization accuracy. The first part of this article covers the overall concepts of MEG and the forward and inverse modeling techniques. It is followed by examples of analyzing evoked and resting-state MEG signals using a high-resolution MEG source imaging technique. Next, different techniques for connectivity and network analysis are reviewed with examples showing connectivity estimates from resting-state and epileptic activity.

Presurgical Functional Mapping with Magnetoencephalography.

Noninvasive functional brain imaging with magnetoencephalography (MEG) is regularly used to map the eloquent cortex associated with somatosensory, motor, auditory, visual, and language processing before a surgical resection to determine if the functional areas have been reorganized. Most tasks can also be performed in the pediatric population. To acquire an optimal MEG study for any of these modalities, the patient needs to be well rested and attending to the stimulation.

Magnetoencephalography for Mild Traumatic Brain Injury and Posttraumatic Stress Disorder.

Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) are leading causes of sustained physical, cognitive, emotional, and behavioral deficits in the general population, active-duty military personnel, and veterans. However, the underlying pathophysiology of mTBI/PTSD and the mechanisms that support functional recovery for some, but not all individuals is not fully understood. Conventional MR imaging and computed tomography are generally negative in mTBI and PTSD, so there is interest in the development of alternative evaluative strategies. Of particular note are magnetoencephalography (MEG) -based methods, with mounting evidence that MEG can provide sensitive biomarkers for abnormalities in mTBI and PTSD.

Marked Increases in Resting-State MEG Gamma-Band Activity in Combat-Related Mild Traumatic Brain Injury.

Combat-related mild traumatic brain injury (mTBI) is a leading cause of sustained impairments in military service members and veterans. Recent animal studies show that GABA-ergic parvalbumin-positive interneurons are susceptible to brain injury, with damage causing abnormal increases in spontaneous gamma-band (30-80 Hz) activity. We investigated spontaneous gamma activity in individuals with mTBI using high-resolution resting-state magnetoencephalography source imaging. Participants included 25 symptomatic individuals with chronic combat-related blast mTBI and 35 healthy controls with similar combat experiences. Compared with controls, gamma activity was markedly elevated in mTBI participants throughout frontal, parietal, temporal, and occipital cortices, whereas gamma activity was reduced in ventromedial prefrontal cortex. Across groups, greater gamma activity correlated with poorer performances on tests of executive functioning and visuospatial processing. Many neurocognitive associations, however, were partly driven by the higher incidence of mTBI participants with both higher gamma activity and poorer cognition, suggesting that expansive upregulation of gamma has negative repercussions for cognition particularly in mTBI. This is the first human study to demonstrate abnormal resting-state gamma activity in mTBI. These novel findings suggest the possibility that abnormal gamma activities may be a proxy for GABA-ergic interneuron dysfunction and a promising neuroimaging marker of insidious mild head injuries.

Brain Amygdala Volume Increases in Veterans and Active-Duty Military Personnel With Combat-Related Posttraumatic Stress Disorder and Mild Traumatic Brain Injury.

OBJECTIVE: To identify amygdalar volumetric differences associated with posttraumatic stress disorder (PTSD) in individuals with comorbid mild traumatic brain injury (mTBI) compared with those with mTBI-only and to examine the effects of intracranial volume (ICV) on amygdala volumetric measures. SETTING: Marine Corps Base and VA Healthcare System. PARTICIPANTS: A cohort of veterans and active-duty military personnel with combat-related mTBI (N = 89). DESIGN: Twenty-nine participants were identified with comorbid PTSD and mTBI. The remaining 60 formed the mTBI-only control group. Structural images of brains were obtained with a 1.5-T MRI scanner using a T1-weighted 3D-IR-FSPGR pulse sequence. Automatic segmentation was performed in Freesurfer. MAIN MEASURES: Amygdala volumes with/without normalizations to ICV. RESULTS: The comorbid mTBI/PTSD group had significantly larger amygdala volumes, when normalized to ICV, compared with the mTBI-only group. The right and left amygdala volumes after normalization to ICV were 0.122% ± 0.012% and 0.118% ± 0.011%, respectively, in the comorbid group compared with 0.115% ± 0.012% and 0.112% ± 0.009%, respectively, in the mTBI-only group (corrected P < .05). CONCLUSIONS: The ICV normalization analysis performed here may resolve previous literature discrepancies. This is an intriguing structural finding, given the role of the amygdala in the challenging neuroemotive symptoms witnessed in casualties of combat-related mTBI and PTSD.