Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing.

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.

MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks.

Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-blinks, saccades, and cardiac activity are three of the most common sources of non-neuronal artifacts. They can be measured by affixing eye proximal electrodes, as in electrooculography (EOG), and chest electrodes, as in electrocardiography (ECG), however this complicates imaging setup, decreases patient comfort, and can induce further artifacts from movement. This work proposes an EOG- and ECG-free approach to identify eye-blinks, saccades, and cardiac activity signals for automated artifact suppression. The contribution of this work is three-fold. First, using a data driven, multivariate decomposition approach based on Independent Component Analysis (ICA), a highly accurate artifact classifier is constructed as an amalgam of deep 1-D and 2-D Convolutional Neural Networks (CNNs) to automate the identification and removal of ubiquitous whole brain artifacts including eye-blink, saccade, and cardiac artifacts. The specific architecture of this network is optimized through an unbiased, computer-based hyperparameter random search. Second, visualization methods are applied to the learned abstraction to reveal what features the model uses and to bolster user confidence in the model's training and potential for generalization. Finally, the model is trained and tested on both resting-state and task MEG data from 217 subjects, and achieves a new state-of-the-art in artifact detection accuracy of 98.95% including 96.74% sensitivity and 99.34% specificity on the held out test-set. This work automates MEG processing for both clinical and research use, adapts to the acquired acquisition time, and can obviate the need for EOG or ECG electrodes for artifact detection.

Association of Epigenetic Metrics of Biological Age With Cortical Thickness.

Importance: Magnetic resonance imaging (MRI) studies of aging adults have shown substantial intersubject variability across various brain metrics, and some of this variability is likely attributable to chronological age being an imprecise measure of age-related change. Accurately quantifying one's biological age could allow better quantification of healthy and pathological changes in the aging brain. Objective: To investigate the association of DNA methylation (DNAm)-based biological age with cortical thickness and to assess whether biological age acceleration compared with chronological age captures unique variance in cortical thinning. Design, Setting, and Participants: This cross-sectional study used high-resolution structural brain MRI data collected from a sample of healthy aging adults who were participating in a larger ongoing neuroimaging study that began in May 2014. This population-based study accrued participants from the greater Omaha, Nebraska, metropolitan area. One hundred sixty healthy adults were contacted for the MRI component, 82 of whom participated in both DNAm and MRI study components. Data analysis was performed from March to June 2019. Main Outcomes and Measures: Vertexwise cortical thickness, DNAm-based biological age, and biological age acceleration compared with chronological age were measured. A pair of multivariable regression models were computed in which cortical thickness was regressed on DNAm-based biological age, controlling for sex in the first model and also controlling for chronological age in the second model. Results: Seventy-nine adult participants (38 women; mean [SD] age, 43.82 [14.50] years; age range, 22-72 years) were included in all final analyses. Advancing biological age was correlated with cortical thinning across frontal, superior temporal, inferior parietal, and medial occipital regions. In addition, biological age acceleration relative to chronological age was associated with cortical thinning in orbitofrontal, superior and inferior temporal, somatosensory, parahippocampal, and fusiform regions. Specifically, for every 1 year of biological age acceleration, cortical thickness would be expected to decrease by 0.024 mm (95% CI, -0.04 to -0.01 mm) in the left orbitofrontal cortex (partial r, -0.34; P = .002), 0.014 mm (95% CI, -0.02 to -0.01 mm) in the left superior temporal gyrus (partial r, -0.36; P = .001), 0.015 mm (95% CI, -0.02 to -0.01 mm) in the left fusiform gyrus (partial r, -0.38; P = .001), 0.015 mm (95% CI, -0.02 to -0.01 mm) in the right fusiform gyrus (partial r, -0.43; P < .001), 0.019 mm (95% CI, -0.03 to -0.01 mm) in the right inferior temporal sulcus (partial r, -0.34; P = .002), and 0.011 mm (95% CI, -0.02 to -0.01 mm) in the right primary somatosensory cortex (partial r, -0.37; P = .001). Conclusions and Relevance: To our knowledge, this is the first study to investigate vertexwise cortical thickness in relation to DNAm-based biological age, and the findings suggest that this metric of biological age may yield additional insight on healthy and pathological cortical aging compared with standard measures of chronological age alone.

Modulation of attention networks serving reorientation in healthy aging.

Orienting attention to behaviorally relevant stimuli is essential for everyday functioning and mainly involves activity in the dorsal and ventral frontoparietal networks. Many studies have shown declines in the speed and accuracy of attentional reallocation with advancing age, but the underlying neural dynamics remain less understood. We investigated this age-related decline using magnetoencephalography (MEG) and a Posner task in 94 healthy adults (22-72 years old). MEG data were examined in the time-frequency domain, and significant oscillatory responses were imaged using a beamformer. We found that participants responded slower when attention reallocation was needed (i.e., the validity effect) and that this effect was positively correlated with age. We also found age-related validity effects on alpha activity in the left parietal and beta in the left frontal-eye fields from 350-950 ms. Overall, stronger alpha and beta responses were observed in younger participants during attention reallocation trials, but this pattern was reversed in the older participants. Interestingly, this alpha validity effect fully mediated the relationship between age and behavioral performance. In conclusion, older adults were slower in reorienting attention and exhibited age-related alterations in alpha and beta responses within parietal and frontal regions, which may reflect increased task demands depleting their compensatory resources.

Prefrontal Multielectrode Transcranial Direct Current Stimulation Modulates Performance and Neural Activity Serving Visuospatial Processing.

The dorsolateral prefrontal cortex (DLPFC) is known to play a critical role in visuospatial attention and processing, but the relative contribution of the left versus right DLPFC remains poorly understood. We applied multielectrode transcranial direct-current stimulation (ME-tDCS) to the left and right DLPFC to investigate its net impact on behavioral performance and population-level neural activity. The primary hypothesis was that significant laterality effects would be observed in regard to behavior and neural oscillations. Twenty-five healthy adults underwent three visits (left, right, and sham ME-tDCS). Following stimulation, participants completed a visuospatial processing task during magnetoencephalography (MEG). Statistically significant oscillatory events were imaged, and time series were then extracted from the peak voxels of each response. Behavioral findings indicated differences in reaction time and accuracy, with left DLPFC stimulation being associated with slower responses and decreased accuracy compared to right stimulation. Left DLPFC stimulation was also associated with increases in spontaneous theta and decreases in gamma within occipital cortices relative to both right and sham stimulation, while connectivity among DLPFC and visual cortices was generally increased contralateral to stimulation. These data suggest spectrally specific modulation of spontaneous cortical activity at the network-level by ME-tDCS, with distinct outcomes based on the laterality of stimulation.

Numerical working memory alters alpha-beta oscillations and connectivity in the parietal cortices.

Although the neural bases of numerical processing and memory have been extensively studied, much remains to be elucidated concerning the spectral and temporal dynamics surrounding these important cognitive processes. To further this understanding, we employed a novel numerical working memory paradigm in 28 young, healthy adults who underwent magnetoencephalography (MEG). The resulting data were examined in the time-frequency domain prior to image reconstruction using a beamformer. Whole-brain, spectrally-constrained coherence was also employed to determine network connectivity. In response to the numerical task, participants exhibited robust alpha/beta oscillations in the bilateral parietal cortices. Whole-brain statistical comparisons examining the effect of numerical manipulation during memory-item maintenance revealed a difference centered in the right superior parietal cortex, such that oscillatory responses during numerical manipulation were significantly stronger than when no manipulation was necessary. Additionally, there was significantly reduced cortico-cortical coherence between the right and left superior parietal regions during the manipulation compared to the maintenance trials, indicating that these regions were functioning more independently when the numerical information had to be actively processed. In sum, these results support previous studies that have implicated the importance of parietal regions in numerical processing, but also provide new knowledge on the spectral, temporal, and network dynamics that serve this critical cognitive function during active working memory maintenance.

Local cortical thickness predicts somatosensory gamma oscillations and sensory gating: A multimodal approach.

Two largely distinct bodies of research have demonstrated age-related alterations and disease-specific aberrations in both local gamma oscillations and patterns of cortical thickness. However, seldom has the relationship between gamma activity and cortical thickness been investigated. Herein, we combine the spatiotemporal precision of magnetoencephalography (MEG) with high-resolution magnetic resonance imaging and surface-based morphometry to characterize the relationships between somatosensory gamma oscillations and the thickness of the cortical tissue generating the oscillations in 94 healthy adults (age range: 22-72). Specifically, a series of regressions were computed to assess the relationships between thickness of the primary somatosensory cortex (S1), S1 gamma response power, peak gamma frequency, and somatosensory gating of identical stimuli. Our results indicated that increased S1 thickness significantly predicted greater S1 gamma response power, reduced peak gamma frequency, and improved somatosensory gating. Furthermore, peak gamma frequency significantly and partially mediated the relationship between S1 thickness and the magnitude of the S1 gamma response. Finally, advancing age significantly predicted reduced S1 thickness and decreased gating of redundant somatosensory stimuli. Notably, this is the first study to directly link somatosensory gamma oscillations to local cortical thickness. Our results demonstrate a multi-faceted relationship between structure and function, and have important implications for understanding age- and disease-related deficits in basic sensory processing and higher-order inhibitory function.

Prefrontal theta modulates sensorimotor gamma networks during the reorienting of attention.

The ability to execute a motor plan involves spatiotemporally precise oscillatory activity in primary motor (M1) regions, in concert with recruitment of "higher order" attentional mechanisms for orienting toward current task goals. While current evidence implicates gamma oscillatory activity in M1 as central to the execution of a movement, far less is known about top-down attentional modulation of this response. Herein, we utilized magnetoencephalography (MEG) during a Posner attention-reorienting task to investigate top-down modulation of M1 gamma responses by frontal attention networks in 63 healthy adult participants. MEG data were evaluated in the time-frequency domain and significant oscillatory responses were imaged using a beamformer. Robust increases in theta activity were found in bilateral inferior frontal gyri (IFG), with significantly stronger responses evident in trials that required attentional reorienting relative to those that did not. Additionally, strong gamma oscillations (60-80 Hz) were detected in M1 during movement execution, with similar responses elicited irrespective of attentional reorienting. Whole-brain voxel-wise correlations between validity difference scores (i.e., attention reorienting trials-nonreorienting trials) in frontal theta activity and movement-locked gamma oscillations revealed a robust relationship in the contralateral sensorimotor cortex, supplementary motor area, and right cerebellum, suggesting modulation of these sensorimotor network gamma responses by attentional reorienting. Importantly, the validity difference effect in this distributed motor network was predictive of overall motor function measured outside the scanner and further, based on a mediation analysis this relationship was fully mediated by the reallocation response in the right IFG. These data are the first to characterize the top-down modulation of movement-related gamma responses during attentional reorienting and movement execution.

Load effects on spatial working memory performance are linked to distributed alpha and beta oscillations.

Increasing spatial working memory (SWM) load is generally associated with declines in behavioral performance, but the neural correlates of load-related behavioral effects remain poorly understood. Herein, we examine the alterations in oscillatory activity that accompany such performance changes in 22 healthy adults who performed a two- and four-load SWM task during magnetoencephalography (MEG). All MEG data were transformed into the time-frequency domain and significant oscillatory responses were imaged separately per load using a beamformer. Whole-brain correlation maps were computed using the load-related beamformer difference images and load-related accuracy effects on the SWM task. The results indicated that load-related differences in left inferior frontal alpha activity during encoding and maintenance were negatively correlated with load-related accuracy differences on the SWM task. That is, individuals who had more substantial decreases in prefrontal alpha during high-relative to low-load SWM trials tended to have smaller performance decrements on the high-load condition (i.e., they performed more accurately). The same pattern of neurobehavioral correlations was observed during the maintenance period for right superior temporal alpha activity and right superior parietal beta activity. Importantly, this is the first study to employ a voxel-wise whole-brain approach to significantly link load-related oscillatory differences and load-related SWM performance differences.

Rhythmic Spontaneous Activity Mediates the Age-Related Decline in Somatosensory Function.

Sensory gating is a neurophysiological process whereby the response to a second stimulus in a pair of identical stimuli is attenuated, and it is thought to reflect the capacity of the CNS to preserve neural resources for behaviorally relevant stimuli. Such gating is observed across multiple sensory modalities and is modulated by age, but the mechanisms involved are not understood. In this study, we examined somatosensory gating in 68 healthy adults using magnetoencephalography (MEG) and advanced oscillatory and time-domain analysis methods. MEG data underwent source reconstruction and peak voxel time series data were extracted to evaluate the dynamics of somatosensory gating, and the impact of spontaneous neural activity immediately preceding the stimulation. We found that gating declined with increasing age and that older adults had significantly reduced gating relative to younger adults, suggesting impaired local inhibitory function. Most importantly, older adults had significantly elevated spontaneous activity preceding the stimulation, and this effect fully mediated the impact of aging on sensory gating. In conclusion, gating in the somatosensory system declines with advancing age and this effect is directly tied to increased spontaneous neural activity in the primary somatosensory cortices, which is likely secondary to age-related declines in local GABA inhibitory function.

Neural dynamics of verbal working memory processing in children and adolescents.

Development of cognitive functions and the underlying neurophysiology is evident throughout childhood and adolescence, with higher order processes such as working memory (WM) being some of the last cognitive faculties to fully mature. Previous functional neuroimaging studies of the neurodevelopment of WM have largely focused on overall regional activity levels rather than the temporal dynamics of neural component recruitment. In this study, we used magnetoencephalography (MEG) to examine the neural dynamics of WM in a large cohort of children and adolescents who were performing a high-load, modified verbal Sternberg WM task. Consistent with previous studies in adults, our findings indicated left-lateralized activity throughout the task period, beginning in the occipital cortices and spreading anterior to include temporal and prefrontal cortices during later encoding and into maintenance. During maintenance, the occipital alpha increase that has been widely reported in adults was found to be relatively weak in this developmental sample, suggesting continuing development of this component of neural processing, which was supported by correlational analyses. Intriguingly, we also found sex-specific developmental effects in alpha responses in the right inferior frontal region during encoding and in parietal and occipital cortices during maintenance. These findings suggested a developmental divergence between males and females in the maturation of neural circuitry serving WM during the transition from childhood to adolescence.

Load modulates the alpha and beta oscillatory dynamics serving verbal working memory.

A network of predominantly left-lateralized brain regions has been linked to verbal working memory (VWM) performance. However, the impact of memory load on the oscillatory dynamics serving VWM is far less understood. To further investigate this, we had 26 healthy adults perform a high-load (6 letter) and low-load (4 letter) variant of a VWM task while undergoing magnetoencephalography (MEG). MEG data were evaluated in the time-frequency domain and significant oscillatory responses spanning the encoding and maintenance phases were reconstructed using a beamformer. To determine the impact of load on the neural dynamics, the resulting images were examined using paired-samples t-tests and virtual sensor analyses. Our results indicated stronger increases in frontal theta activity in the high- relative to low-load condition during early encoding. Stronger decreases in alpha/beta activity were also observed during encoding in bilateral posterior cortices during the high-load condition, and the strength of these load effects increased as encoding progressed. During maintenance, stronger decreases in alpha activity in the left inferior frontal gyrus, middle temporal gyrus, supramarginal gyrus, and inferior parietal cortices were detected during high- relative to low-load performance, with the strength of these load effects remaining largely static throughout maintenance. Finally, stronger increases in occipital alpha activity were observed during maintenance in the high-load condition, and the strength of these effects grew stronger with time during the first half of maintenance, before dissipating during the latter half of maintenance. Notably, this was the first study to utilize a whole-brain approach to statistically evaluate the temporal dynamics of load-related oscillatory differences during encoding and maintenance processes, and our results highlight the importance of spatial, temporal, and spectral specificity in this regard.

The impact of type 1 diabetes on neural activity serving attention.

Type 1 diabetes has been associated with alterations in attentional processing and other cognitive functions, and previous studies have found alterations in both brain structure and function in affected patients. However, these previous neuroimaging studies have generally examined older patients, particularly those with major comorbidities known to affect functioning independent of diabetes. The primary aim of the current study was to examine the neural dynamics of selective attention processing in a young group of patients with type 1 diabetes who were otherwise healthy (i.e., without major comorbidities). Our hypothesis was that these patients would exhibit significant aberrations in attention circuitry relative to closely matched controls. The final sample included 69 participants age 19-35 years old, 35 with type 1 diabetes and 34 matched nondiabetic controls, who completed an Eriksen flanker task while undergoing magnetoencephalography. Significant group differences in flanker interference activity were found across a network of brain regions, including the anterior cingulate, inferior parietal cortices, paracentral lobule, and the left precentral gyrus. In addition, neural activity in the anterior cingulate and the paracentral lobule was correlated with disease duration in patients with type 1 diabetes. These findings suggest that alterations in the neural circuitry underlying selective attention emerge early in the disease process and are specifically related to type 1 diabetes and not common comorbidities. These findings highlight the need for longitudinal studies in large cohorts to clarify the clinical implications of type 1 diabetes on cognition and the brain.

tDCS modulates behavioral performance and the neural oscillatory dynamics serving visual selective attention.

Transcranial direct-current stimulation (tDCS) is a noninvasive method for modulating human brain activity. Although there are several hypotheses about the net effects of tDCS on brain function, the field's understanding remains incomplete and this is especially true for neural oscillatory activity during cognitive task performance. In this study, we examined whether different polarities of occipital tDCS differentially alter flanker task performance and the underlying neural dynamics. To this end, 48 healthy adults underwent 20 min of anodal, cathodal, or sham occipital tDCS, and then completed a visual flanker task during high-density magnetoencephalography (MEG). The resulting oscillatory responses were imaged in the time-frequency domain using beamforming, and the effects of tDCS on task-related oscillations and spontaneous neural activity were assessed. The results indicated that anodal tDCS of the occipital cortices inhibited flanker task performance as measured by reaction time, elevated spontaneous activity in the theta (4-7 Hz) and alpha (9-14 Hz) bands in prefrontal and occipital cortices, respectively, and reduced task-related theta oscillatory activity in prefrontal cortices during task performance. Cathodal tDCS of the occipital cortices did not significantly affect behavior or any of these neuronal parameters in any brain region. Lastly, the power of theta oscillations in the prefrontal cortices was inversely correlated with reaction time. In conclusion, anodal tDCS modulated task-related oscillations and spontaneous activity across multiple cortical areas, both near the electrode and in distant sites that were putatively connected to the targeted regions.

The strength of alpha and gamma oscillations predicts behavioral switch costs.

Cognitive flexibility is often examined using task-switch paradigms, whereby individuals either switch between tasks or repeat the same task on successive trials. The behavioral costs of switching in terms of accuracy and reaction time are well-known, but the oscillatory dynamics underlying such costs are poorly understood. Herein, we examined 25 healthy adults who performed a task-switching paradigm during magnetoencephalography (MEG). All MEG data were transformed into the time-frequency domain and significant oscillatory responses were imaged separately per condition (i.e., switch, repeat) using a beamformer. To determine the impact of task-switching on the neural dynamics, the resulting images were examined using paired-samples t-tests. Whole-brain correlations were also computed using the switch-related difference images (switch - repeat) and the switch-related behavioral data (i.e., switch costs). Our key results indicated stronger decreases in alpha and beta activity, and greater increases in gamma activity in nodes of the cingulo-opercular and fronto-parietal networks during switch relative to repeat trials. In addition, behavioral switch costs were positively correlated with switch-related differences in right frontal and inferior parietal alpha activity, and negatively correlated with switch effects in anterior cingulate and right temporoparietal gamma activity. In other words, participants who had a greater decrease in alpha or increase in gamma in these respective regions had smaller behavioral switch costs, which suggests that these oscillations are critical to supporting cognitive flexibility. In sum, we provide novel data linking switch effects and gamma oscillations, and employed a whole-brain approach to directly link switch-related oscillatory differences with switch-related performance differences.

Beta Oscillatory Dynamics in the Prefrontal and Superior Temporal Cortices Predict Spatial Working Memory Performance.

The oscillatory dynamics serving spatial working memory (SWM), and how such dynamics relate to performance, are poorly understood. To address these topics, the present study recruited 22 healthy adults to perform a SWM task during magnetoencephalography (MEG). The resulting MEG data were transformed into the time-frequency domain, and significant oscillatory responses were imaged using a beamformer. Voxel time series data were extracted from the cluster peaks to quantify the dynamics, while whole-brain partial correlation maps were computed to identify regions where oscillatory strength varied with accuracy on the SWM task. The results indicated transient theta oscillations in spatially distinct subregions of the prefrontal cortices at the onset of encoding and maintenance, which may underlie selection of goal-relevant information. Additionally, strong and persistent decreases in alpha and beta oscillations were observed throughout encoding and maintenance in parietal, temporal, and occipital regions, which could serve sustained attention and maintenance processes during SWM performance. The neuro-behavioral correlations revealed that beta activity within left dorsolateral prefrontal control regions and bilateral superior temporal integration regions was negatively correlated with SWM accuracy. Notably, this is the first study to employ a whole-brain approach to significantly link neural oscillations to behavioral performance in the context of SWM.

Altered Brain Dynamics in Patients With Type 1 Diabetes During Working Memory Processing.

It is now generally accepted that diabetes increases the risk for cognitive impairment, but the precise mechanisms are poorly understood. A critical problem in linking diabetes to cognitive impairment is that patients often have multiple comorbidities (e.g., obesity, hypertension) that have been independently linked to cognitive deficits. In the study reported here we focused on young adults with and without type 1 diabetes who were virtually free of such comorbidities. The two groups were matched on major health and demographic factors, and all participants completed a verbal working memory task during magnetoencephalographic brain imaging. We hypothesized that patients would have altered neural dynamics in verbal working memory processing and that these differences would directly relate to clinical disease measures. Accordingly, we found that patients had significantly stronger neural responses in the superior parietal cortices during memory encoding and significantly weaker activity in parietal-occipital regions during maintenance compared with control subjects. Moreover, disease duration and glycemic control were both significantly correlated with neural responses in various brain regions. In conclusion, young healthy adults with type 1 diabetes already have aberrant neural processing relative to their peers without diabetes, using compensatory responses to perform the task, and glucose management and duration may play a central role.

Oscillatory dynamics in the dorsal and ventral attention networks during the reorienting of attention.

The ability to reorient attention within the visual field is central to daily functioning, and numerous fMRI studies have shown that the dorsal and ventral attention networks (DAN, VAN) are critical to such processes. However, despite the instantaneous nature of attentional shifts, the dynamics of oscillatory activity serving attentional reorientation remain poorly characterized. In this study, we utilized magnetoencephalography (MEG) and a Posner task to probe the dynamics of attentional reorienting in 29 healthy adults. MEG data were transformed into the time-frequency domain and significant oscillatory responses were imaged using a beamformer. Voxel time series were then extracted from peak voxels in the functional beamformer images. These time series were used to quantify the dynamics of attentional reorienting, and to compute dynamic functional connectivity. Our results indicated strong increases in theta and decreases in alpha and beta activity across many nodes in the DAN and VAN. Interestingly, theta responses were generally stronger during trials that required attentional reorienting relative to those that did not, while alpha and beta oscillations were more dynamic, with many regions exhibiting significantly stronger responses during non-reorienting trials initially, and the opposite pattern during later processing. Finally, stronger functional connectivity was found following target presentation (575-700 ms) between bilateral superior parietal lobules during attentional reorienting. In sum, these data show that visual attention is served by multiple cortical regions within the DAN and VAN, and that attentional reorienting processes are often associated with spectrally-specific oscillations that have largely distinct spatiotemporal dynamics.

Oscillations during observations: Dynamic oscillatory networks serving visuospatial attention.

The dynamic allocation of neural resources to discrete features within a visual scene enables us to react quickly and accurately to salient environmental circumstances. A network of bilateral cortical regions is known to subserve such visuospatial attention functions; however the oscillatory and functional connectivity dynamics of information coding within this network are not fully understood. Particularly, the coding of information within prototypical attention-network hubs and the subsecond functional connections formed between these hubs have not been adequately characterized. Herein, we use the precise temporal resolution of magnetoencephalography (MEG) to define spectrally specific functional nodes and connections that underlie the deployment of attention in visual space. Twenty-three healthy young adults completed a visuospatial discrimination task designed to elicit multispectral activity in visual cortex during MEG, and the resulting data were preprocessed and reconstructed in the time-frequency domain. Oscillatory responses were projected to the cortical surface using a beamformer, and time series were extracted from peak voxels to examine their temporal evolution. Dynamic functional connectivity was then computed between nodes within each frequency band of interest. We find that visual attention network nodes are defined functionally by oscillatory frequency, that the allocation of attention to the visual space dynamically modulates functional connectivity between these regions on a millisecond timescale, and that these modulations significantly correlate with performance on a spatial discrimination task. We conclude that functional hubs underlying visuospatial attention are segregated not only anatomically but also by oscillatory frequency, and importantly that these oscillatory signatures promote dynamic communication between these hubs. Hum Brain Mapp 38:5128-5140, 2017. © 2017 Wiley Periodicals, Inc.

Children with cerebral palsy have altered oscillatory activity in the motor and visual cortices during a knee motor task.

The neuroimaging literature on cerebral palsy (CP) has predominantly focused on identifying structural aberrations within the white matter (e.g., fiber track integrity), with very few studies examining neural activity within the key networks that serve the production of motor actions. The current investigation used high-density magnetoencephalography to begin to fill this knowledge gap by quantifying the temporal dynamics of the alpha and beta cortical oscillations in children with CP (age = 15.5 ± 3 years; GMFCS levels II-III) and typically developing (TD) children (age = 14.1 ± 3 years) during a goal-directed isometric target-matching task using the knee joint. Advanced beamforming methods were used to image the cortical oscillations during the movement planning and execution stages. Compared with the TD children, our results showed that the children with CP had stronger alpha and beta event-related desynchronization (ERD) within the primary motor cortices, premotor area, inferior parietal lobule, and inferior frontal gyrus during the motor planning stage. Differences in beta ERD amplitude extended through the motor execution stage within the supplementary motor area and premotor cortices, and a stronger alpha ERD was detected in the anterior cingulate. Interestingly, our results also indicated that alpha and beta oscillations were weaker in the children with CP within the occipital cortices and visual MT area during movement execution. These altered alpha and beta oscillations were accompanied by slower reaction times and substantial target matching errors in the children with CP. We also identified that the strength of the alpha and beta ERDs during the motor planning and execution stages were correlated with the motor performance. Lastly, our regression analyses suggested that the beta ERD within visual areas during motor execution primarily predicted the amount of motor errors. Overall, these data suggest that uncharacteristic alpha and beta oscillations within visuomotor cortical networks play a prominent role in the atypical motor actions exhibited by children with CP.