Subthalamic Nucleus Activity Influences Sensory and Motor Cortex during Force Transduction.

The subthalamic nucleus (STN) is proposed to participate in pausing, or alternately, in dynamic scaling of behavioral responses, roles that have conflicting implications for understanding STN function in the context of deep brain stimulation (DBS) therapy. To examine the nature of event-related STN activity and subthalamic-cortical dynamics, we performed primary motor and somatosensory electrocorticography while subjects (n = 10) performed a grip force task during DBS implantation surgery. Phase-locking analyses demonstrated periods of STN-cortical coherence that bracketed force transduction, in both beta and gamma ranges. Event-related causality measures demonstrated that both STN beta and gamma activity predicted motor cortical beta and gamma activity not only during force generation but also prior to movement onset. These findings are consistent with the idea that the STN participates in motor planning, in addition to the modulation of ongoing movement. We also demonstrated bidirectional information flow between the STN and somatosensory cortex in both beta and gamma range frequencies, suggesting robust STN participation in somatosensory integration. In fact, interactions in beta activity between the STN and somatosensory cortex, and not between STN and motor cortex, predicted PD symptom severity. Thus, the STN contributes to multiple aspects of sensorimotor behavior dynamically across time.

Tracking cognitive processing stages with MEG: A spatio-temporal model of associative recognition in the brain.

In this study, we investigated the cognitive processing stages underlying associative recognition using MEG. Over the last four decades, a model of associative recognition has been developed in the ACT-R cognitive architecture. This model was first exclusively based on behavior, but was later evaluated and improved based on fMRI and EEG data. Unfortunately, the limited spatial resolution of EEG and the limited temporal resolution of fMRI have made it difficult to fully understand the spatiotemporal dynamics of associative recognition. We therefore conducted an associative recognition experiment with MEG, which combines excellent temporal resolution with reasonable spatial resolution. To analyze the data, we applied non-parametric cluster analyses and a multivariate classifier. This resulted in a detailed spatio-temporal model of associative recognition. After the visual encoding of the stimuli in occipital regions, three separable memory processes took place: a familiarity process (temporal cortex), a recollection process (temporal cortex and supramarginal gyrus), and a representational process (dorsolateral prefrontal cortex). A late decision process (superior parietal cortex) then acted upon the recollected information represented in the prefrontal cortex, culminating in a late response process (motor cortex). We conclude that existing theories of associative recognition, including the ACT-R model, should be adapted to include these processes.

Associative hallucinations result from stimulating left ventromedial temporal cortex.

Visual recognition requires connecting perceptual information with contextual information and existing knowledge. The ventromedial temporal cortex (VTC), including the medial fusiform, has been linked with object recognition, paired associate learning, contextual processing, and episodic memory, suggesting that this area may be critical in connecting visual processing, context, knowledge and experience. However, evidence for the link between associative processing, episodic memory, and visual recognition in VTC is currently lacking. Using electrocorticography (ECoG) in a single human patient, medial regions of the left VTC were found to be sensitive to the contextual associations of objects. Electrical brain stimulation (EBS) of this part of the left VTC of the patient, functionally defined as sensitive to associative processing, caused memory related, associative experiential visual phenomena. This provides evidence of a relationship between visual recognition, associative processing, and episodic memory. These results suggest a potential role for abnormalities of these processes as part of a mechanism that gives rise to some visual hallucinations.

Magnetoencephalography-based identification of functional connectivity network disruption following mild traumatic brain injury.

Mild traumatic brain injury (mTBI) leads to long-term cognitive sequelae in a significant portion of patients. Disruption of normal neural communication across functional brain networks may explain the deficits in memory and attention observed after mTBI. In this study, we used magnetoencephalography (MEG) to examine functional connectivity during a resting state in a group of mTBI subjects (n = 9) compared with age-matched control subjects (n = 15). We adopted a data-driven, exploratory analysis in source space using phase locking value across different frequency bands. We observed a significant reduction in functional connectivity in band-specific networks in mTBI compared with control subjects. These networks spanned multiple cortical regions involved in the default mode network (DMN). The DMN is thought to subserve memory and attention during periods when an individual is not engaged in a specific task, and its disruption may lead to cognitive deficits after mTBI. We further applied graph theoretical analysis on the functional connectivity matrices. Our data suggest reduced local efficiency in different brain regions in mTBI patients. In conclusion, MEG can be a potential tool to investigate and detect network alterations in patients with mTBI. The value of MEG to reveal potential neurophysiological biomarkers for mTBI patients warrants further exploration.

Movement-related dynamics of cortical oscillations in Parkinson's disease and essential tremor.

Recent electrocorticography data have demonstrated excessive coupling of beta-phase to gamma-amplitude in primary motor cortex and that deep brain stimulation facilitates motor improvement by decreasing baseline phase-amplitude coupling. However, both the dynamic modulation of phase-amplitude coupling during movement and the general cortical neurophysiology of other movement disorders, such as essential tremor, are relatively unexplored. To clarify the relationship of these interactions in cortical oscillatory activity to movement and disease state, we recorded local field potentials from hand sensorimotor cortex using subdural electrocorticography during a visually cued, incentivized handgrip task in subjects with Parkinson's disease (n = 11), with essential tremor (n = 9) and without a movement disorder (n = 6). We demonstrate that abnormal coupling of the phase of low frequency oscillations to the amplitude of gamma oscillations is not specific to Parkinson's disease, but also occurs in essential tremor, most prominently for the coupling of alpha to gamma oscillations. Movement kinematics were not significantly different between these groups, allowing us to show for the first time that robust alpha and beta desynchronization is a shared feature of sensorimotor cortical activity in Parkinson's disease and essential tremor, with the greatest high-beta desynchronization occurring in Parkinson's disease and the greatest alpha desynchronization occurring in essential tremor. We also show that the spatial extent of cortical phase-amplitude decoupling during movement is much greater in subjects with Parkinson's disease and essential tremor than in subjects without a movement disorder. These findings suggest that subjects with Parkinson's disease and essential tremor can produce movements that are kinematically similar to those of subjects without a movement disorder by reducing excess sensorimotor cortical phase-amplitude coupling that is characteristic of these diseases.

Frontal preparatory neural oscillations associated with cognitive control: A developmental study comparing young adults and adolescents.

Functional magnetic resonance imaging (fMRI) studies suggest that age-related changes in the frontal cortex may underlie developmental improvements in cognitive control. In the present study we used magnetoencephalography (MEG) to identify frontal oscillatory neurodynamics that support age-related improvements in cognitive control during adolescence. We characterized the differences in neural oscillations in adolescents and adults during the preparation to suppress a prepotent saccade (antisaccade trials-AS) compared to preparing to generate a more automatic saccade (prosaccade trials-PS). We found that for adults, AS were associated with increased beta-band (16-38Hz) power in the dorsal lateral prefrontal cortex (DLPFC), enhanced alpha- to low beta-band (10-18Hz) power in the frontal eye field (FEF) that predicted performance, and increased cross-frequency alpha-beta (10-26Hz) amplitude coupling between the DLPFC and the FEF. Developmental comparisons between adults and adolescents revealed similar engagement of DLPFC beta-band power but weaker FEF alpha-band power, and lower cross-frequency coupling between the DLPFC and the FEF in adolescents. These results suggest that lateral prefrontal neural activity associated with cognitive control is adult-like by adolescence; the development of cognitive control from adolescence to adulthood is instead associated with increases in frontal connectivity and strengthening of inhibition signaling for suppressing task-incompatible processes.

Network effects of deep brain stimulation.

The ability to differentially alter specific brain functions via deep brain stimulation (DBS) represents a monumental advance in clinical neuroscience, as well as within medicine as a whole. Despite the efficacy of DBS in the treatment of movement disorders, for which it is often the gold-standard therapy when medical management becomes inadequate, the mechanisms through which DBS in various brain targets produces therapeutic effects is still not well understood. This limited knowledge is a barrier to improving efficacy and reducing side effects in clinical brain stimulation. A field of study related to assessing the network effects of DBS is gradually emerging that promises to reveal aspects of the underlying pathophysiology of various brain disorders and their response to DBS that will be critical to advancing the field. This review summarizes the nascent literature related to network effects of DBS measured by cerebral blood flow and metabolic imaging, functional imaging, and electrophysiology (scalp and intracranial electroencephalography and magnetoencephalography) in order to establish a framework for future studies.

Cortical neurodynamics of inhibitory control.

The ability to inhibit prepotent responses is critical for successful goal-directed behaviors. To investigate the neural basis of inhibitory control, we conducted a magnetoencephalography study where human participants performed the antisaccade task. Results indicated that neural oscillations in the prefrontal cortex (PFC) showed significant task modulations in preparation to suppress saccades. Before successfully inhibiting a saccade, beta-band power (18-38 Hz) in the lateral PFC and alpha-band power (10-18 Hz) in the frontal eye field (FEF) increased. Trial-by-trial prestimulus FEF alpha-band power predicted successful saccadic inhibition. Further, inhibitory control enhanced cross-frequency amplitude coupling between PFC beta-band (18-38 Hz) activity and FEF alpha-band activity, and the coupling appeared to be initiated by the PFC. Our results suggest a generalized mechanism for top-down inhibitory control: prefrontal beta-band activity initiates alpha-band activity for functional inhibition of the effector and/or sensory system.

Whole brain functional connectivity using phase locking measures of resting state magnetoencephalography.

The analysis of spontaneous functional connectivity (sFC) reveals the statistical connections between regions of the brain consistent with underlying functional communication networks within the brain. In this work, we describe the implementation of a complete all-to-all network analysis of resting state neuronal activity from magnetoencephalography (MEG). Using graph theory to define networks at the dipole level, we established functionally defined regions by k-means clustering cortical surface locations using Eigenvector centrality (EVC) scores from the all-to-all adjacency model. Permutation testing was used to estimate regions with statistically significant connections compared to empty room data, which adjusts for spatial dependencies introduced by the MEG inverse problem. In order to test this model, we performed a series of numerical simulations investigating the effects of the MEG reconstruction on connectivity estimates. We subsequently applied the approach to subject data to investigate the effectiveness of our method in obtaining whole brain networks. Our findings indicated that our model provides statistically robust estimates of functional region networks. Application of our phase locking network methodology to real data produced networks with similar connectivity to previously published findings, specifically, we found connections between contralateral areas of the arcuate fasciculus that have been previously investigated. The use of data-driven methods for neuroscientific investigations provides a new tool for researchers in identifying and characterizing whole brain functional connectivity networks.

Bodies adapt orientation-independent face representations.

Faces and bodies share a great number of semantic attributes, such as gender, emotional expressiveness, and identity. Recent studies demonstrate that bodies can activate and modulate face perception. However, the nature of the face representation that is activated by bodies remains unknown. In particular, face and body representations have previously been shown to have a degree of orientation specificity. Here we use body-face adaptation aftereffects to test whether bodies activate face representations in an orientation-dependent manner. Specifically, we used a two-by-two design to examine the magnitude of the body-face aftereffect using upright and inverted body adaptors and upright and inverted face targets. All four conditions showed significant body-face adaptation. We found neither a main effect of body orientation nor an interaction between body and face orientation. There was a main effect of target face orientation, with inverted target faces showing larger aftereffects than upright target faces, consistent with traditional face-face adaptation. Taken together, these results suggest that bodies adapt and activate a relatively orientation-independent representation of faces.

The effects of priming on frontal-temporal communication.

Repeated exposure to a stimulus facilitates its processing. This is reflected in faster and more accurate identification, reduced perceptual identification thresholds, and more efficient classifications for repeated compared with novel items. Here, we test a hypothesis that this experience-based behavioral facilitation is a result of enhanced communication between distinct cortical regions, which reduces local processing demands. A magnetoencephalographic investigation revealed that repeated object classification led to decreased neural responses in the prefrontal cortex and temporal cortex. Critically, this decrease in absolute activity was accompanied by greater neural synchrony (a measure of functional connectivity) between these regions with repetition. Additionally, the onset of the enhanced interregional synchrony predicted the degree of behavioral facilitation. These findings suggest that object repetition results in enhanced interactions between brain regions, which facilitates performance and reduces processing demands on the regions involved.

Top-down predictions in the cognitive brain.

The human brain is not a passive organ simply waiting to be activated by external stimuli. Instead, we propose that the brain continuously employs memory of past experiences to interpret sensory information and predict the immediately relevant future. The basic elements of this proposal include analogical mapping, associative representations and the generation of predictions. This review concentrates on visual recognition as the model system for developing and testing ideas about the role and mechanisms of top-down predictions in the brain. We cover relevant behavioral, computational and neural aspects, explore links to emotion and action preparation, and consider clinical implications for schizophrenia and dyslexia. We then discuss the extension of the general principles of this proposal to other cognitive domains.

The influence of nonremembered affective associations on preference.

An important influence on our preference toward a specific object is its associations with affective information. Here, the authors concentrate on the role of memory on shaping such preferences. Specifically, the authors used a multistage behavioral paradigm that fostered associations between neutral shapes and affective images. Participants that explicitly remembered these affective associations preferred neutral shapes associated with positive images. Counterintuitively, participants who could not explicitly remember the associations preferred neutral shapes that were associated with negative images. Generally, the difference in preference between participants who could and could not remember the affective associations demonstrates a critical link between memory and preference formation. The authors propose that the preference for negatively associated items is a manifestation of a mechanism that produces an inherent incentive for rapidly assessing potentially threatening aspects in the environment.