Decreased thalamocortical connectivity in resolved Rolandic epilepsy.

OBJECTIVE: Median nerve somatosensory evoked fields (SEFs) conduction times reflect the integrity of neural transmission across the thalamocortical circuit. We hypothesized median nerve SEF conduction time would be abnormal in children with Rolandic epilepsy (RE). METHODS: 22 children with RE (10 active; 12 resolved) and 13 age-matched controls underwent structural and diffusion MRI and median nerve and visual stimulation during magnetoencephalography (MEG). N20 SEF responses were identified in contralateral somatosensory cortices. P100 were identified in contralateral occipital cortices as controls. Conduction times were compared between groups in linear models controlling for height. N20 conduction time was also compared to thalamic volume and Rolandic thalamocortical structural connectivity inferred using probabilistic tractography. RESULTS: The RE group had slower N20 conduction compared to controls (p = 0.042, effect size 0.6 ms) and this difference was driven by the resolved RE group (p = 0.046). There was no difference in P100 conduction time between groups (p = 0.83). Ventral thalamic volume positively correlated with N20 conduction time (p = 0.014). CONCLUSIONS: Children with resolved RE have focally decreased Rolandic thalamocortical connectivity. SIGNIFICANCE: These results identify a persistent focal thalamocortical circuit abnormality in resolved RE and suggest that decreased Rolandic thalamocortical connectivity may support symptom resolution in this self-limited epilepsy.

Source EEG reveals that Rolandic epilepsy is a regional epileptic encephalopathy.

Rolandic epilepsy is the most common form of epileptic encephalopathy, characterized by sleep-potentiated inferior Rolandic epileptiform spikes, seizures, and cognitive deficits in school-age children that spontaneously resolve by adolescence. We recently identified a paucity of sleep spindles, physiological thalamocortical rhythms associated with sleep-dependent learning, in the Rolandic cortex during the active phase of this disease. Because spindles are generated in the thalamus and amplified through regional thalamocortical circuits, we hypothesized that: 1) deficits in spindle rate would involve but extend beyond the inferior Rolandic cortex in active epilepsy and 2) regional spindle deficits would better predict cognitive function than inferior Rolandic spindle deficits alone. To test these hypotheses, we obtained high-resolution MRI, high-density EEG recordings, and focused neuropsychological assessments in children with Rolandic epilepsy during active (n = 8, age 9-14.7 years, 3F) and resolved (seizure free for > 1 year, n = 10, age 10.3-16.7 years, 1F) stages of disease and age-matched controls (n = 8, age 8.9-14.5 years, 5F). Using a validated spindle detector applied to estimates of electrical source activity in 31 cortical regions, including the inferior Rolandic cortex, during stages 2 and 3 of non-rapid eye movement sleep, we compared spindle rates in each cortical region across groups. Among detected spindles, we compared spindle features (power, duration, coherence, bilateral synchrony) between groups. We then used regression models to examine the relationship between spindle rate and cognitive function (fine motor dexterity, phonological processing, attention, and intelligence, and a global measure of all functions). We found that spindle rate was reduced in the inferior Rolandic cortices in active but not resolved disease (active P = 0.007; resolved P = 0.2) compared to controls. Spindles in this region were less synchronous between hemispheres in the active group (P = 0.005; resolved P = 0.1) compared to controls; but there were no differences in spindle power, duration, or coherence between groups. Compared to controls, spindle rate in the active group was also reduced in the prefrontal, insular, superior temporal, and posterior parietal regions (i.e., "regional spindle rate", P < 0.039 for all). Independent of group, regional spindle rate positively correlated with fine motor dexterity (P < 1e-3), attention (P = 0.02), intelligence (P = 0.04), and global cognitive performance (P < 1e-4). Compared to the inferior Rolandic spindle rate alone, models including regional spindle rate trended to improve prediction of global cognitive performance (P = 0.052), and markedly improved prediction of fine motor dexterity (P = 0.006). These results identify a spindle disruption in Rolandic epilepsy that extends beyond the epileptic cortex and a potential mechanistic explanation for the broad cognitive deficits that can be observed in this epileptic encephalopathy.

Cortical signatures of auditory object binding in children with autism spectrum disorder are anomalous in concordance with behavior and diagnosis.

Organizing sensory information into coherent perceptual objects is fundamental to everyday perception and communication. In the visual domain, indirect evidence from cortical responses suggests that children with autism spectrum disorder (ASD) have anomalous figure-ground segregation. While auditory processing abnormalities are common in ASD, especially in environments with multiple sound sources, to date, the question of scene segregation in ASD has not been directly investigated in audition. Using magnetoencephalography, we measured cortical responses to unattended (passively experienced) auditory stimuli while parametrically manipulating the degree of temporal coherence that facilitates auditory figure-ground segregation. Results from 21 children with ASD (aged 7-17 years) and 26 age- and IQ-matched typically developing children provide evidence that children with ASD show anomalous growth of cortical neural responses with increasing temporal coherence of the auditory figure. The documented neurophysiological abnormalities did not depend on age, and were reflected both in the response evoked by changes in temporal coherence of the auditory scene and in the associated induced gamma rhythms. Furthermore, the individual neural measures were predictive of diagnosis (83% accuracy) and also correlated with behavioral measures of ASD severity and auditory processing abnormalities. These findings offer new insight into the neural mechanisms underlying auditory perceptual deficits and sensory overload in ASD, and suggest that temporal-coherence-based auditory scene analysis and suprathreshold processing of coherent auditory objects may be atypical in ASD.

A computational paradigm for real-time MEG neurofeedback for dynamic allocation of spatial attention.

BACKGROUND: Neurofeedback aids volitional control of one's own brain activity using non-invasive recordings of brain activity. The applications of neurofeedback include improvement of cognitive performance and treatment of various psychiatric and neurological disorders. During real-time magnetoencephalography (rt-MEG), sensor-level or source-localized brain activity is measured and transformed into a visual feedback cue to the subject. Recent real-time fMRI (rt-fMRI) neurofeedback studies have used pattern recognition techniques to decode and train a brain state to link brain activities and cognitive behaviors. Here, we utilize the real-time decoding technique similar to ones employed in rt-fMRI to analyze time-varying rt-MEG signals. RESULTS: We developed a novel rt-MEG method, state-based neurofeedback (sb-NFB), to decode a time-varying brain state, a state signal, from which timings are extracted for neurofeedback training. The approach is entirely data-driven: it uses sensor-level oscillatory activity to find relevant features that best separate the targeted brain states. In a psychophysical task of spatial attention switching, we trained five young, healthy subjects using the sb-NFB method to decrease the time necessary for switch spatial attention from one visual hemifield to the other (referred to as switch time). Training resulted in a decrease in switch time with training. We saw that the activity targeted by the training involved proportional changes in alpha and beta-band oscillations, in sensors at the occipital and parietal regions. We also found that the state signal that encodes whether subjects attend to the left or right visual field effectively switches consistently with the task. CONCLUSION: We demonstrated the use of the sb-NFB method when the subject learns to increase the speed of shifting covert spatial attention from one visual field to the other. The sb-NFB method can target timing features that would otherwise also include extraneous features such as visual detection and motor response in a simple reaction time task.

Distinct Regional Oscillatory Connectivity Patterns During Auditory Target and Novelty Processing.

Auditory attention allows us to focus on relevant target sounds in the acoustic environment while maintaining the capability to orient to unpredictable (novel) sound changes. An open question is whether orienting to expected vs. unexpected auditory events are governed by anatomically distinct attention pathways, respectively, or by differing communication patterns within a common system. To address this question, we applied a recently developed PeSCAR analysis method to evaluate spectrotemporal functional connectivity patterns across subregions of broader cortical regions of interest (ROIs) to analyze magnetoencephalography data obtained during a cued auditory attention task. Subjects were instructed to detect a predictable harmonic target sound embedded among standard tones in one ear and to ignore the standard tones and occasional unpredictable novel sounds presented in the opposite ear. Phase coherence of estimated source activity was calculated between subregions of superior temporal, frontal, inferior parietal, and superior parietal cortex ROIs. Functional connectivity was stronger in response to target than novel stimuli between left superior temporal and left parietal ROIs and between left frontal and right parietal ROIs, with the largest effects observed in the beta band (15-35 Hz). In contrast, functional connectivity was stronger in response to novel than target stimuli in inter-hemispheric connections between left and right frontal ROIs, observed in early time windows in the alpha band (8-12 Hz). Our findings suggest that auditory processing of expected target vs. unexpected novel sounds involves different spatially, temporally, and spectrally distributed oscillatory connectivity patterns across temporal, parietal, and frontal areas.

Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG.

Magnetoencephalography (MEG) and electroencephalography (EEG) use non-invasive sensors to detect neural currents. Since the contribution of superficial neural sources to the measured M/EEG signals are orders-of-magnitude stronger than the contribution of subcortical sources, most MEG and EEG studies have focused on cortical activity. Subcortical structures, however, are centrally involved in both healthy brain function as well as in many neurological disorders such as Alzheimer's disease and Parkinson's disease. In this paper, we present a method that can separate and suppress the cortical signals while preserving the subcortical contributions to the M/EEG data. The resulting signal subspace of the data mainly originates from subcortical structures. Our method works by utilizing short-baseline planar gradiometers with short-sighted sensitivity distributions as reference sensors for cortical activity. Since the method is completely data-driven, forward and inverse modeling are not required. In this study, we use simulations and auditory steady state response experiments in a human subject to demonstrate that the method can remove the cortical signals while sparing the subcortical signals. We also test our method on MEG data recorded in an essential tremor patient with a deep brain stimulation implant and show how it can be used to reduce the DBS artifact in the MEG data by ~ 99.9% without affecting low frequency brain rhythms.

MNE Scan: Software for real-time processing of electrophysiological data.

BACKGROUND: Magnetoencephalography (MEG) and Electroencephalography (EEG) are noninvasive techniques to study the electrophysiological activity of the human brain. Thus, they are well suited for real-time monitoring and analysis of neuronal activity. Real-time MEG/EEG data processing allows adjustment of the stimuli to the subject's responses for optimizing the acquired information especially by providing dynamically changing displays to enable neurofeedback. NEW METHOD: We introduce MNE Scan, an acquisition and real-time analysis software based on the multipurpose software library MNE-CPP. MNE Scan allows the development and application of acquisition and novel real-time processing methods in both research and clinical studies. The MNE Scan development follows a strict software engineering process to enable approvals required for clinical software. RESULTS: We tested the performance of MNE Scan in several device-independent use cases, including, a clinical epilepsy study, real-time source estimation, and Brain Computer Interface (BCI) application. COMPARISON WITH EXISTING METHOD(S): Compared to existing tools we propose a modular software considering clinical software requirements expected by certification authorities. At the same time the software is extendable and freely accessible. CONCLUSION: We conclude that MNE Scan is the first step in creating a device-independent open-source software to facilitate the transition from basic neuroscience research to both applied sciences and clinical applications.

Differences in cortical response to acupressure and electroacupuncture stimuli.

BACKGROUND: FMRI studies focus on sub-cortical effects of acupuncture stimuli. The purpose of this study was to assess changes in primary somatosensory (S1) activity over the course of different types of acupuncture stimulation. We used whole head magnetoencephalography (MEG) to map S1 brain response during 15 minutes of electroacupuncture (EA) and acupressure (AP). We further assessed how brain response changed during the course of stimulation. RESULTS: Evoked brain response to EA differed from AP in its temporal dynamics by showing clear contralateral M20/M30 peaks while the latter demonstrated temporal dispersion. Both EA and AP demonstrated significantly decreased response amplitudes following five minutes of stimulation. However, the latency of these decreases were earlier in EA (~30 ms post-stimulus) than AP (> 100 ms). Time-frequency responses demonstrated early onset, event related synchronization (ERS), within the gamma band at ~70-130 ms and the theta band at ~50-200 ms post-stimulus. A prolonged event related desynchronization (ERD) of alpha and beta power occurred at ~100-300 ms post-stimulus. There was decreased beta ERD at ~100-300 ms over the course of EA, but not AP. CONCLUSION: Both EA and AP demonstrated conditioning of SI response. In conjunction with their subcortical effects on endogenous pain regulation, these therapies show potential for affecting S1 processing and possibly altering maladaptive neuroplasticity. Thus, further investigation in neuropathic populations is needed.

Effects of mindfulness meditation training on anticipatory alpha modulation in primary somatosensory cortex.

During selective attention, ∼7-14 Hz alpha rhythms are modulated in early sensory cortices, suggesting a mechanistic role for these dynamics in perception. Here, we investigated whether alpha modulation can be enhanced by "mindfulness" meditation (MM), a program training practitioners in sustained attention to body and breath-related sensations. We hypothesized that participants in the MM group would exhibit enhanced alpha power modulation in a localized representation in the primary somatosensory neocortex in response to a cue, as compared to participants in the control group. Healthy subjects were randomized to 8-weeks of MM training or a control group. Using magnetoencephalographic (MEG) recording of the SI finger representation, we found meditators demonstrated enhanced alpha power modulation in response to a cue. This finding is the first to show enhanced local alpha modulation following sustained attentional training, and implicates this form of enhanced dynamic neural regulation in the behavioral effects of meditative practice.

Mapping the signal-to-noise-ratios of cortical sources in magnetoencephalography and electroencephalography.

Although magnetoencephalography (MEG) and electroencephalography (EEG) have been available for decades, their relative merits are still debated. We examined regional differences in signal-to-noise-ratios (SNRs) of cortical sources in MEG and EEG. Data from four subjects were used to simulate focal and extended sources located on the cortical surface reconstructed from high-resolution magnetic resonance images. The SNR maps for MEG and EEG were found to be complementary. The SNR of deep sources was larger in EEG than in MEG, whereas the opposite was typically the case for superficial sources. Overall, the SNR maps were more uniform for EEG than for MEG. When using a noise model based on uniformly distributed random sources on the cortex, the SNR in MEG was found to be underestimated, compared with the maps obtained with noise estimated from actual recorded MEG and EEG data. With extended sources, the total area of cortex in which the SNR was higher in EEG than in MEG was larger than with focal sources. Clinically, SNR maps in a patient explained differential sensitivity of MEG and EEG in detecting epileptic activity. Our results emphasize the benefits of recording MEG and EEG simultaneously.

Bayesian inverse analysis of neuromagnetic data using cortically constrained multiple dipoles.

A recently introduced Bayesian model for magnetoencephalographic (MEG) data consistently localized multiple simulated dipoles with the help of marginalization of spatiotemporal background noise covariance structure in the analysis [Jun et al., (2005): Neuroimage 28:84-98]. Here, we elaborated this model to include subject's individual brain surface reconstructions with cortical location and orientation constraints. To enable efficient Markov chain Monte Carlo sampling of the dipole locations, we adopted a parametrization of the source space surfaces with two continuous variables (i.e., spherical angle coordinates). Prior to analysis, we simplified the likelihood by exploiting only a small set of independent measurement combinations obtained by singular value decomposition of the gain matrix, which also makes the sampler significantly faster. We analyzed both realistically simulated and empirical MEG data recorded during simple auditory and visual stimulation. The results show that our model produces reasonable solutions and adequate data fits without much manual interaction. However, the rigid cortical constraints seemed to make the utilized scheme challenging as the sampler did not switch modes of the dipoles efficiently. This is problematic in the presence of evidently highly multimodal posterior distribution, and especially in the relative quantitative comparison of the different modes. To overcome the difficulties with the present model, we propose the use of loose orientation constraints and combined model of prelocalization utilizing the hierarchical minimum-norm estimate and multiple dipole sampling scheme.

Distributed current estimates using cortical orientation constraints.

Distributed source models of magnetoencephalographic (MEG) and electroencephalographic (EEG) data employ dense distributions of current sources in a volume or on a surface. Previously, anatomical magnetic resonance imaging (MRI) data have been used to constrain locations and orientations based on cortical geometry extracted from anatomical MRI data. We extended this approach by first calculating cortical patch statistics (CPS), which for each patch corresponding to a current source location on the cortex comprise the area of the patch, the average normal direction, and the average deviation of the surface normal from its average. The patch areas were then incorporated in the forward model to yield estimates of the surface current density instead of dipole amplitudes at the current locations. The surface normal data were employed in a loose orientation constraint (LOC), which allows some variation of the current direction from the average normal. We employed this approach both in the l(2) minimum-norm estimates (MNE) and in the more focal l(1) minimum-norm solutions, the minimum-current estimate (MCE). Simulations in auditory and somatosensory areas with current dipoles and 10- or 20-mm diameter cortical patches as test sources showed that applying the LOC can increase localization accuracy. We also applied the method to in vivo auditory and somatosensory data.

Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain.

This paper presents a computationally efficient source estimation algorithm that localizes cortical oscillations and their phase relationships. The proposed method employs wavelet-transformed magnetoencephalography (MEG) data and uses anatomical MRI to constrain the current locations to the cortical mantle. In addition, the locations of the sources can be further confined with the help of functional MRI (fMRI) data. As a result, we obtain spatiotemporal maps of spectral power and phase relationships. As an example, we show how the phase locking value (PLV), that is, the trial-by-trial phase relationship between the stimulus and response, can be imaged on the cortex. We apply the method to spontaneous, evoked, and driven cortical oscillations measured with MEG. We test the method of combining MEG, structural MRI, and fMRI using simulated cortical oscillations along Heschl's gyrus (HG). We also analyze sustained auditory gamma-band neuromagnetic fields from MEG and fMRI measurements. Our results show that combining the MEG recording with fMRI improves source localization for the non-noise-normalized wavelet power. In contrast, noise-normalized spectral power or PLV localization may not benefit from the fMRI constraint. We show that if the thresholds are not properly chosen, noise-normalized spectral power or PLV estimates may contain false (phantom) sources, independent of the inclusion of the fMRI prior information. The proposed algorithm can be used for evoked MEG/EEG and block-designed or event-related fMRI paradigms, or for spontaneous MEG data sets. Spectral spatiotemporal imaging of cortical oscillations and interactions in the human brain can provide further understanding of large-scale neural activity and communication between different brain regions.