Timothy Bardouille

Semantic information alters neural activation during transverse patterning performance.

Memory tasks can be performed using multiple cognitive strategies, which are mediated by different brain systems. The transverse patterning (TP) task is dependent upon the integrity of the hippocampal system, however, we previously demonstrated successful TP following hippocampal damage using meaningful stimuli and relations (Moses, S.N., Ostreicher, M.L., Rosenbaum, R.S., Ryan, J.D., 2008. Successful transverse patterning in amnesia using semantic knowledge. Hippocampus 18, 121-124). Here, we used magnetoencephalgraphy (MEG) to directly observe the neural underpinnings of TP, and the changes that occur as stimuli and relations become more meaningful. In order to optimize our ability to detect signal from deep, non-dominant, brain sources we implemented the event-related synthetic aperture magnetometry minimum-variance beamformer algorithm (ER-SAM; Cheyne, D., Bakhtazad, L., Gaetz, W., 2006. Spatiotemporal mapping of cortical activity accompanying voluntary movements using an event-related beamforming approach. Human Brain Mapping 27, 213-229) coupled with the partial least squares (PLS) multivariate statistical approach (McIntosh, A.R., Bookstein, F.L., Haxby, J.V., Grady, C.L., 1996. Spatial pattern analysis of function brain images using partial least squares. NeuroImage 3, 143-157; McIntosh, A.R., Lobaugh, N.J., 2004. Partial least squares analysis of neuroimaging data: Applications and advances. NeuroImage 23, S250-S263). We found that increased meaningfulness elicited reduced bilateral hippocampal activation, along with increased activation of left prefrontal and temporal cortical structures, including inferior frontal (IFG), as well as anterior temporal and perirhinal cortices. These activation patterns may represent a shift towards reliance upon existing semantic knowledge. This shift likely permits successful TP performance with meaningful stimuli and relations following hippocampal damage.

Laterality of brain activity during motor imagery is modulated by the provision of source level neurofeedback.

Motor imagery (MI) may be effective as an adjunct to physical practice for motor skill acquisition. For example, MI is emerging as an effective treatment in stroke neurorehabilitation. As in physical practice, the repetitive activation of neural pathways during MI can drive short- and long-term brain changes that underlie functional recovery. However, the lack of feedback about MI performance may be a factor limiting its effectiveness. The provision of feedback about MI-related brain activity may overcome this limitation by providing the opportunity for individuals to monitor their own performance of this endogenous process. We completed a controlled study to isolate neurofeedback as the factor driving changes in MI-related brain activity across repeated sessions. Eighteen healthy participants took part in 3 sessions comprised of both actual and imagined performance of a button press task. During MI, participants in the neurofeedback group received source level feedback based on activity from the left and right sensorimotor cortex obtained using magnetoencephalography. Participants in the control group received no neurofeedback. MI-related brain activity increased in the sensorimotor cortex contralateral to the imagined movement across sessions in the neurofeedback group, but not in controls. Task performance improved across sessions but did not differ between groups. Our results indicate that the provision of neurofeedback during MI allows healthy individuals to modulate regional brain activity. This finding has the potential to improve the effectiveness of MI as a tool in neurorehabilitation.

Human Auditory Cortex Activity Shows Additive Effects of Spectral and Spatial Cues during Speech Segregation

In noisy social gatherings, listeners perceptually integrate sounds originating from one persons voice (e.g., fundamental frequency (f(0)) and harmonics) at a particular location and segregate these from concurrent sounds of other talkers. Though increasing the spectral or the spatial distance between talkers promotes speech segregation, synergetic effects of spatial and spectral distances are less well understood. We studied how spectral and/or spatial distances between 2 simultaneously presented steady-state vowels contribute to perception and activation in auditory cortex using magnetoencephalography. Participants were more accurate in identifying both vowels when they differed in f(0) and location than when they differed in a single cue only or when they shared the same f(0) and location. The combined effect of f(0) and location differences closely matched the sum of single effects. The improvement in concurrent vowel identification coincided with an object-related negativity that peaked at about 140 ms after vowel onset. The combined effect of f(0) and location closely matched the sum of the single effects even though vowels with different f(0), location, or both generated different time courses of neuromagnetic activity. We propose that during auditory scene analysis, acoustic differences among the various sources are combined linearly to increase the perceptual distance between the co-occurring sound objects.

Motor imagery-based brain activity parallels that of motor execution: Evidence from magnetic source imaging of cortical oscillations

Motor imagery (MI) is a form of practice in which an individual mentally performs a motor task. Previous research suggests that skill acquisition via MI is facilitated by repetitive activation of brain regions in the sensorimotor network similar to that of motor execution, however this evidence is conflicting. Further, many studies do not control for overt muscle activity and thus the activation patterns reported for MI may be driven in part by actual movement. The purpose of the current research is to further establish MI as a secondary modality of skill acquisition by providing electrophysiological evidence of an overlap between brain areas recruited for motor execution and imagery. Non-disabled participants (N=18; 24.7±3.8 years) performed both execution and imagery of a unilateral sequence button-press task. Magnetoencephalography (MEG) was utilized to capture neural activity, while electromyography used to rigorously monitor muscle activity. Event-related synchronization/desynchronization (ERS/ERD) analysis was conducted in the beta frequency band (15-30 Hz). Whole head dual-state beamformer analysis was applied to MEG data and 3D t-tests were conducted after Talairach normalization. Source-level analysis showed that MI has similar patterns of spatial activity as ME, including activation of contralateral primary motor and somatosensory cortices. However, this activation is significantly less intense during MI (p<0.05). As well, activation during ME was more lateralized (i.e., within the contralateral hemisphere). These results confirm that ME and MI have similar spatial activation patterns. Thus, the current research provides direct electrophysiological evidence to further establish MI as a secondary form of skill acquisition.

State-Related Changes in MEG Functional Connectivity Reveal the Task-Positive Sensorimotor Network

Functional connectivity measures applied to magnetoencephalography (MEG) data have the capacity to elucidate neuronal networks. However, the task-related modulation of these measures is essential to identifying the functional relevance of the identified network. In this study, we provide evidence for the efficacy of measuring “state-related” (i.e., task vs. rest) changes in MEG functional connectivity for revealing a sensorimotor network. We investigate changes in functional connectivity, measured as cortico-cortical coherence (CCC), between rest blocks and the performance of a visually directed motor task in a healthy cohort. Task-positive changes in CCC were interpreted in the context of any concomitant modulations in spectral power. Task-related increases in whole-head CCC relative to the resting state were identified between areas established as part of the sensorimotor network as well as frontal eye fields and prefrontal cortices, predominantly in the beta and gamma frequency bands. This study provides evidence for the use of MEG to identify task-specific functionally connected sensorimotor networks in a non-invasive, patient friendly manner.

Complexity analysis of source activity underlying the neuromagnetic somatosensory steady-state response

Using the notion of complexity and synchrony, this study presents a data-driven pipeline of nonlinear analysis of neuromagnetic sources reconstructed from human magnetoencephalographic (MEG) data collected in reaction to vibrostimulation of the right index finger. The dynamics of MEG source activity was reconstructed with synthetic aperture magnetometry (SAM) beam-forming technique. Considering brain as a complex system, we applied complexity-based tools to identify brain areas with dynamic patterns that remain regular across repeated stimulus presentations, and to characterize their synchronized behavior. Volumetric maps of brain activation were calculated using sample entropy as a measure of signal complexity. The complexity analysis identified activity in the primary somatosensory (SI) area contralateral to stimuli and bilaterally in the posterior parietal cortex (PPC) as regions with decreased complexity, consistently expressed in a group of subjects. Seeding an activated source with low complexity in the SI area, cross-sample entropy was used to generate synchrony maps. Cross-sample entropy analysis confirmed the synchronized dynamics of neuromagnetic activity between areas SI and PPC, robustly expressed across subjects. Our results extend the understanding of synchronization between co-activated brain regions, focusing on temporal coordination between events in terms of synchronized multidimensional signal patterns.

Neural generators underlying concurrent sound segregation.

Although an object-based account of auditory attention has become an increasingly popular model for understanding how temporally overlapping sounds are segregated, relatively little is known about the cortical circuit that supports such ability. In the present study, we applied a beamformer spatial filter to magnetoencephalography (MEG) data recorded during an auditory paradigm that used inharmonicity to promote the formation of multiple auditory objects. Using this unconstrained, data-driven approach, the evoked field component linked with the perception of multiple auditory objects (i.e., the object-related negativity; ORNm), was found to be associated with bilateral auditory cortex sources that were distinct from those coinciding with the P1m, N1m, and P2m responses elicited by sound onset. The right hemispheric ORNm source in particular was consistently positioned anterior to the other sources across two experiments. These findings are consistent with earlier proposals of multiple auditory object detection being associated with generators in the auditory cortex and further suggest that these neural populations are distinct from the long latency evoked responses reflecting the detection of sound onset.

Faster and improved 3-D head digitization in MEG using Kinect.

Accuracy in localizing the brain areas that generate neuromagnetic activity in magnetoencephalography (MEG) is dependent on properly co-registering MEG data to the participants structural magnetic resonance image (MRI). Effective MEG-MRI co-registration is, in turn, dependent on how accurately we can digitize anatomical landmarks on the surface of the head. In this study, we compared the performance of three devices—Polhemus electromagnetic system, NextEngine laser scanner and Microsoft Kinect for Windows—for source localization accuracy and MEG-MRI co-registration. A calibrated phantom was used for verifying the source localization accuracy. The Kinect improved source localization accuracy over the Polhemus and the laser scanner by 2.23 mm (137%) and 0.81 mm (50%), respectively. MEG-MRI co-registration accuracy was verified on data from five healthy human participants, who received the digitization process using all three devices. The Kinect device captured approximately 2000 times more surface points than the Polhemus in one third of the time (1 min compared to 3 min) and thrice as many points as the NextEngine laser scanner. Following automated surface matching, the calculated mean MEG-MRI co-registration error for the Kinect was improved by 2.85 mm with respect to the Polhemus device, and equivalent to the laser scanner. Importantly, the Kinect device automatically aligns 20–30 images per second in real-time, reducing the limitations on participant head movement during digitization that are implicit in the NextEngine laser scan (~1 min). We conclude that the Kinect scanner is an effective device for head digitization in MEG, providing the necessary accuracy in source localization and MEG-MRI co-registration, while reducing digitization time.

Asymmetric Weighting to Optimize Regional Sensitivity in Combined fMRI-MEG Maps.

Functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) are neuroimaging techniques that measure inherently different physiological processes, resulting in complementary estimates of brain activity in different regions. Combining the maps generated by each technique could thus provide a richer understanding of brain activation. However, present approaches to integration rely on a priori assumptions, such as expected patterns of brain activation in a task, or use fMRI to bias localization of MEG sources, diminishing fMRI-invisible sources. We aimed to optimize sensitivity to neural activity by developing a novel method of integrating data from the two imaging techniques. We present a data-driven method of integration that weights fMRI and MEG imaging data by estimates of data quality for each technique and region. This method was applied to a verbal object recognition task. As predicted, the two imaging techniques demonstrated sensitivity to activation in different regions. Activity was seen using fMRI, but not MEG, throughout the medial temporal lobes. Conversely, activation was seen using MEG, but not fMRI, in more lateral and anterior temporal lobe regions. Both imaging techniques were sensitive to activation in the inferior frontal gyrus. Importantly, integration maps retained activation from individual activation maps, and showed an increase in the extent of activation, owing to greater sensitivity of the integration map than either fMRI or MEG alone.

Head movement compensation in real-time magnetoencephalographic recordings.

Neurofeedback- and brain-computer interface (BCI)-based interventions can be implemented using real-time analysis of magnetoencephalographic (MEG) recordings. Head movement during MEG recordings, however, can lead to inaccurate estimates of brain activity, reducing the efficacy of the intervention. Most real-time applications in MEG have utilized analyses that do not correct for head movement. Effective means of correcting for head movement are needed to optimize the use of MEG in such applications. Here we provide preliminary validation of a novel analysis technique, real-time source estimation (rtSE), that measures head movement and generates corrected current source time course estimates in real-time. rtSE was applied while recording a calibrated phantom to determine phantom position localization accuracy and source amplitude estimation accuracy under stationary and moving conditions. Results were compared to off-line analysis methods to assess validity of the rtSE technique. The rtSE method allowed for accurate estimation of current source activity at the source-level in real-time, and accounted for movement of the source due to changes in phantom position. The rtSE technique requires modifications and specialized analysis of the following MEG work flow steps.• Data acquisition• Head position estimation• Source localization• Real-time source estimation This work explains the technical details and validates each of these steps.