Accurate reconstruction of temporal correlation for neuronal sources using the enhanced dual-core MEG beamformer

Beamformer spatial filters are commonly used to explore the active neuronal sources underlying magnetoencephalography (MEG) recordings at low signal-to-noise ratio (SNR). Conventional beamformer techniques are successful in localizing uncorrelated neuronal sources under poor SNR conditions. However, the spatial and temporal features from conventional beamformer reconstructions suffer when sources are correlated, which is a common and important property of real neuronal networks. Dual-beamformer techniques, originally developed by Brookes et al. to deal with this limitation, successfully localize highly-correlated sources and determine their orientations and weightings, but their performance degrades at low correlations. They also lack the capability to produce individual time courses and therefore cannot quantify source correlation. In this paper, we present an enhanced formulation of our earlier dual-core beamformer (DCBF) approach that reconstructs individual source time courses and their correlations. Through computer simulations, we show that the enhanced DCBF (eDCBF) consistently and accurately models dual-source activity regardless of the correlation strength. Simulations also show that a multi-core extension of eDCBF effectively handles the presence of additional correlated sources. In a human auditory task, we further demonstrate that eDCBF accurately reconstructs left and right auditory temporal responses and their correlations. Spatial resolution and source localization strategies corresponding to different measures within the eDCBF framework are also discussed. In summary, eDCBF accurately reconstructs source spatio-temporal behavior, providing a means for characterizing complex neuronal networks and their communication.     

Localization of realistic cortical activity in MEG using current multipoles

We present a novel approach to MEG source estimation based on a regularized first-order multipole solution. The Gaussian regularizing prior is obtained by calculation of the sample mean and covariance matrix for the equivalent moments of realistic simulated cortical activity. We compare the regularized multipole localization framework to the classical dipole and general multipole source estimation methods by evaluating the ability of all three solutions to localize the centroids of physiologically plausible patches of activity simulated on the surface of a human cerebral cortex. The results, obtained with a realistic sensor configuration, a spherical head model, and given in terms of field and localization error, depict the performance of the dipolar and multipolar models as a function of variable source surface area (50-500 mm(2)), noise conditions (20, 10, and 5 dB SNR), source orientation (0-90 degrees ), and source depth (3-11 cm). We show that as the sources increase in size, they become less accurately modeled as current dipoles. The regularized multipole systematically outperforms the single dipole model, increasingly so as the spatial extent of the sources increases. In addition, our simulations demonstrate that as the orientation of the sources becomes more radial, dipole localization accuracy decreases substantially, while the performance of the regularized multipole model is far less sensitive to orientation and even succeeds in localizing quasi-radial source configurations. Furthermore, our results show that the multipole model is able to localize superficial sources with higher accuracy than the current dipole. These results indicate that the regularized multipole solution may be an attractive alternative to current-dipole-based source estimation methods in MEG.     

Multiresolution imaging of MEG cortical sources using an explicit piecewise model

Imaging neural generators from MEG magnetic fields is often considered as a compromise between computationally-reasonable methodology that usually yields poor spatial resolution on the one hand, and more sophisticated approaches on the other hand, potentially leading to intractable computational costs. We approach the problem of obtaining well-resolved source images with unexcessive computation load with a multiresolution image model selection (MiMS) technique. The building blocks of the MiMS source model are parcels of the cortical surface which can be designed at multiple spatial resolutions with the combination of anatomical and functional priors. Computation charge is reduced owing to 1) compact parametric models of the activation of extended brain parcels using current multipole expansions and 2) the optimization of the generalized cross-validation error on image models, which is closed-form for the broad class of linear estimators of neural currents. Model selection can be complemented by any conventional imaging approach of neural currents restricted to the optimal image support obtained from MiMS. The estimation of the location and spatial extent of brain activations is discussed and evaluated using extensive Monte-Carlo simulations. An experimental evaluation was conducted with MEG data from a somatotopic paradigm. Results show that MiMS is an efficient image model selection technique with robust performances at realistic noise levels.     

Localization of sensorimotor cortical rhythms induced by tactile stimulation using spatially filtered MEG

We applied the synthetic aperture magnetometry (SAM) spatial filtering method to localize sensorimotor mu (8-14 Hz) and beta (15-35 Hz) rhythms following tactile (brush) stimulation. Neuromagnetic activity was recorded from 10 adult subjects. Transient brush stimuli were applied separately to the right index finger, medial right toe and lower right lip. Differential images of mu and beta band source power were created for periods during (event-related desynchronization; ERD) or following (event-related synchronization; ERS) tactile stimulation, relative to prestimulus baseline activity. Mu ERD to finger brushing was localized to the contralateral somatosensory cortex and was organized somatotopically. Mu ERS, however, was not consistently observed for each subject. Beta ERD was consistently localized to sensory cortical areas and organized somatotopically in the post-central gyrus (SI), and beta ERS was observed to be organized motorotopically in the precentral gyrus (MI). Longer duration (2-3 s) stimulation of the index finger also produced beta ERS in the primary motor cortex, and its time course demonstrated that these oscillatory changes are an off-response to the termination of the presented sensory stimulus. Interestingly, lip and toe stimulation also produced post-stimulus increases in beta rhythms in the bilateral motor hand areas for all subjects, suggesting that common neural systems in the primary motor cortex are activated during tactile stimulation of different body regions.     

Change-driven cortical activation in multisensory environments: An MEG study

The quick detection of dynamic changes in multisensory environments is essential to survive dangerous events and orient attention to informative events. Previous studies have identified multimodal cortical areas activated by changes of visual, auditory, and tactile stimuli. In the present study, we used magnetoencephalography (MEG) to examine time-varying cortical processes responsive to unexpected unimodal changes during continuous multisensory stimulation. The results showed that there were change-driven cortical responses in multimodal areas, such as the temporo-parietal junction and middle and inferior frontal gyri, regardless of the sensory modalities where the change occurred. These multimodal activations accompanied unimodal activations, both of which in general had some peaks within 300 ms after the changes. Thus, neural processes responsive to unimodal changes in the multisensory environment are distributed at different timing in these cortical areas.     

Localization of correlated network activity at the cortical level with MEG

In both hemodynamic and neurophysiological imaging methods, analysis of functionally interconnected networks has typically focused on brain areas that show strong activation in specific tasks. Alternatively, connectivity measures may be used directly to localize network nodes, independent of their level of activation. This approach requires initial cortical reference areas which may be identified based on their high level of activation, their coherence with an external reference signal, or their strong connectivity with other brain areas. Irrespective of how the nodes have been localized the mathematical complexity of the analysis methods precludes verification of the accuracy and completeness of the network structure by direct comparison with the measured data. Therefore, it is critical to understand how the choices of parameters and procedures used in the analysis affect the network identification. Here, using simulated and measured magnetoencephalography (MEG) data, and Dynamic Imaging of Coherent Sources (DICS) for connectivity analysis, we quantify the veracity of network detection at the individual and group level as a function of relevant parameter choices. Using simulations, we demonstrate that coupling measures enable accurate identification of the network structure even without external reference signals, and illustrate the applicability of this approach to real data. We show that a valid estimate of interindividual variability is critical for reliable group-level analysis. Although this study focuses on application of DICS to MEG data, many issues considered here, especially those regarding individual vs. group-level analysis, are likely to be relevant for other neuroimaging methods and analysis approaches as well.     

Identifying protein-protein interactions in somatic hypermutation

Somatic hypermutation (SHM) in immunoglobulin genes is required for high affinity antibody-antigen binding. Cultured cell systems, mouse model systems, and human genetic deficiencies have been the key players in identifying likely SHM pathways, whereas "pure" biochemical approaches have been far less prominent, but change appears imminent. Here we comment on how, when, and why biochemistry is likely to emerge from the shadows and into the spotlight to elucidate how the somatic mutation of antibody variable (V) regions is generated.     

Investigations of dipole localization accuracy in MEG using the bootstrap

We describe the use of the nonparametric bootstrap to investigate the accuracy of current dipole localization from magnetoencephalography (MEG) studies of event-related neural activity. The bootstrap is well suited to the analysis of event-related MEG data since the experiments are repeated tens or even hundreds of times and averaged to achieve acceptable signal-to-noise ratios (SNRs). The set of repetitions or epochs can be viewed as a set of independent realizations of the brain's response to the experiment. Bootstrap resamples can be generated by sampling with replacement from these epochs and averaging. In this study, we applied the bootstrap resampling technique to MEG data from somatotopic experimental and simulated data. Four fingers of the right and left hand of a healthy subject were electrically stimulated, and about 400 trials per stimulation were recorded and averaged in order to measure the somatotopic mapping of the fingers in the S1 area of the brain. Based on single-trial recordings for each finger we performed 5000 bootstrap resamples. We reconstructed dipoles from these resampled averages using the Recursively Applied and Projected (RAP)-MUSIC source localization algorithm. We also performed a simulation for two dipolar sources with overlapping time courses embedded in realistic background brain activity generated using the prestimulus segments of the somatotopic data. To find correspondences between multiple sources in each bootstrap, sample dipoles with similar time series and forward fields were assumed to represent the same source. These dipoles were then clustered by a Gaussian Mixture Model (GMM) clustering algorithm using their combined normalized time series and topographies as feature vectors. The mean and standard deviation of the dipole position and the dipole time series in each cluster were computed to provide estimates of the accuracy of the reconstructed source locations and time series.     

Sadness enhances the experience of pain and affects pain-evoked cortical activities: an MEG study

Pain is a multidimensional phenomenon. Previous psychological studies have shown that a person's subjective pain threshold can change when certain emotions are recognized. We examined this association with magnetoencephalography. Magnetic field strength was recorded with a 306-channel neuromagnetometer while 19 healthy subjects (7 female, 12 male; age range = 20-30 years) experienced pain stimuli in different emotional contexts induced by the presentation of sad, happy, or neutral facial stimuli. Subjects also rated their subjective pain intensity. We hypothesized that pain stimuli were affected by sadness induced by facial recognition. We found: 1) the intensity of subjective pain ratings increased in the sad emotional context compared to the happy and the neutral contexts, and 2) event-related desynchronization of lower beta bands in the right hemisphere after pain stimuli was larger in the sad emotional condition than in the happy emotional condition. Previous studies have shown that event-related desynchronization in these bands could be consistently observed over the primary somatosensory cortex. These findings suggest that sadness can modulate neural responses to pain stimuli, and that brain processing of pain stimuli had already been affected, at the level of the primary somatosensory cortex, which is critical for sensory processing of pain. PERSPECTIVE: We found that subjective pain ratings and cortical beta rhythms after pain stimuli are influenced by the sad emotional context. These results may contribute to understanding the broader relationship between pain and negative emotion.     

Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space

The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a significant result. It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specifically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a finger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data.     

The cortical dynamics in building syntactic structures of sentences: An MEG study in a minimal-pair paradigm

The importance of abstract syntactic structures and their crucial role in analyzing sentences have long been emphasized in contemporary linguistics, whereas the linear order model, in which next-coming words in a sentence are claimed to be predictable based on lexico-semantic association or statistics alone, has also been proposed and widely assumed. We examined these possibilities with magnetoencephalography (MEG) and measured cortical responses to a verb with either object–verb (OV) or subject–verb (SV) sentence structures, which were tested in a minimal-pair paradigm to compare syntactic and semantic decision tasks. Significant responses to the normal OV sentences were found in the triangular part of the left inferior frontal gyrus (F3t) at 120–140 ms from the verb onset, which were selective for explicit syntactic processing. The earliest left F3t responses can thus be regarded as predictive effects for the syntactic information of the next-coming verb, which cannot be explained by associative memory or statistical factors. Moreover, subsequent responses in the left insula at 150–170 ms were selective for the processing of the OV sentence structure. On the other hand, responses in the left mediofrontal and inferior parietal regions at 240–280 ms were related to syntactic anomaly and verb transitivity, respectively. These results revealed the dynamics of the multiple cortical regions that work in concert to analyze hierarchical syntactic structures and task-related information, further elucidating the top-down syntactic processing that is crucial during on-line sentence processing.     

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.     

MEG imaging of sensorimotor areas using inter-trial coherence in vibrotactile steady-state responses

We utilized a novel analysis technique to identify brain areas that activate synchronously during the steady-state interval of responses to vibrotactile stimulation of the right index finger. The inter-trial coherence at the stimulation rate (23 Hz) was determined for whole-brain neural activity estimates based on a linearly-constrained minimum variance beamformer applied to the MEG data. Neural activity coherent with the stimulus occurred in the contralateral primary somatosensory cortex in all subjects, and matched well with equivalent dipole modeling of the same data. Subsets of subjects exhibited additional loci of strongly coherent activity in the contralateral primary motor cortex, posterior parietal cortex, and supplementary motor area, as well as in deeper brain structures above the brainstem. An activation delay of 7 ms from deep structures to cortical areas was estimated based on the mean phase at each coherent neural source within a single subject. This new approach - volumetric mapping of the statistical parameter of inter-trial coherence in steady-state oscillations - broadens the range of MEG beamformer applications specifically for identifying brain areas that are synchronized to repetitive stimuli.     

Identifying older people in need using action research

An ageing population has implications for community-based health promotion and disease prevention. There is concern about older people who do not fit into existing programmes and services yet need minimal support to maintain independence. A study was designed to develop approaches to gain access to this hard to reach population, assess needs and design and test interventions to integrate them into the community. The study, informed by theories of health promotion and social support, used action research methods. Participant observation documented in field notes, together with case notes and clinical assessments, provided a rich source of qualitative and quantitative data. This article discusses the needs assessment. Over a 3-year period, public health nurses linked with community groups in a predominantly francophone, urban community to identify the target group. Key characteristics of the target group included limitations with instrumental activities of daily living and low levels of social support combined with stressful life situations that challenged adaptation. Three patterns of inadequate support were identified.     

Age-related alterations in the modular organization of structural cortical network by using cortical thickness from MRI

Normal aging is accompanied by various cognitive functional declines. Recent studies have revealed disruptions in the coordination of large-scale functional brain networks such as the default mode network in advanced aging. However, organizational alterations of the structural brain network at the system level in aging are still poorly understood. Here, using cortical thickness, we investigated the modular organization of the cortical structural networks in 102 young and 97 normal aging adults. Brain networks for both cohorts displayed a modular organization overlapping with functional domains such as executive and auditory/language processing. However, compared with the modular organization of young adults, the aging group demonstrated a significantly reduced modularity that might be indicative of reduced functional segregation in the aging brain. More importantly, the aging brain network exhibited reduced intra-/inter-module connectivity in modules corresponding to the executive function and the default mode network of young adults, which might be associated with the decline of cognitive functions in aging. Finally, we observed age-associated alterations in the regional characterization in terms of their intra/inter-module connectivity. Our results indicate that aging is associated with an altered modular organization in the structural brain networks and provide new evidence for disrupted integrity in the large-scale brain networks that underlie cognition.     

Identifying cancer nursing research priorities using the Delphi technique

BACKGROUND: Nursing research is an integral component of improving the care of people with cancer. However, for research to be successfully integrated and applied to practice, ownership and identification must come from those in practice. The need for local and national strategies for cancer nursing research and the importance of establishing priorities for cancer nursing research have been repeatedly acknowledged. STUDY AIM: The aim of the study was to facilitate a strategic approach to cancer nursing research by identifying the research priorities of cancer nurses. RESEARCH METHOD: A three-round Delphi survey was administered to nurses (n = 112) attending a cancer nursing research conference in Northern Ireland. Participants were asked to identify five research questions that they considered a high priority for cancer nursing. A response rate of 54% (60 delegates) was obtained for round one and this generated 117 statements. These statements were content analysed. Two subsequent quantitative rounds followed this. RESULTS: The top priority areas identified were psychosocial issues, for example communication and information needs; professional issues relating to nurse burnout, stress and nurse-led care; and context of care issues including continuity of care. LIMITATIONS: A potential limitation of the study is the use of conference delegates. However, it is argued that these are the people we wanted to target as they could be considered as experts who already had an interest and clinical background in both cancer research and practice. CONCLUSION: These priorities have helped to provide both direction and focus for the development of a cancer nursing research strategy for Northern Ireland. It is recommended that future research questions should be focused around the highest ranked priorities.     

Age-related changes in causal interactions between cortical motor regions during hand grip

Brain activity during motor performance becomes more widespread and less lateralized with advancing age in response to ongoing degenerative processes. In this study, we were interested in the mechanism by which this change in the pattern of activity supports motor performance with advancing age. We used both transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) to assess age related changes in motor system connectivity during isometric hand grip. Paired pulse TMS was used to measure the change in interhemispheric inhibition (IHI) from contralateral M1 (cM1) to ipsilateral M1 (iM1) during right hand grip. Dynamic Causal Modelling (DCM) of fMRI data was used to investigate the effect of age on causal interactions throughout the cortical motor network during right hand grip. Bayesian model selection was used to identify the causal model that best explained the data for all subjects. Firstly, we confirmed that the TMS and DCM measures both demonstrated a less inhibitory/more facilitatory influence of cM1 on iM1 during hand grip with advancing age. These values correlated with one another providing face validity for our DCM measures of connectivity. We found increasing reciprocal facilitatory influences with advancing age (i) between all ipsilateral cortical motor areas and (ii) between cortical motor areas of both hemispheres and iM1. There were no differences in the performance of our task with ageing suggesting that the ipsilateral cortical motor areas, in particular iM1, play a central role in maintaining performance levels with ageing through increasingly facilitatory cortico-cortical influences.     

Source analysis of interictal spikes in polymicrogyria: loss of relevant cortical fissures requires simultaneous EEG to avoid MEG misinterpretation

PURPOSE: Multiple source analysis of interictal EEG and MEG spikes was used to identify irritative zones in polymicrogyria (PMG). Spike onset times and source localization were compared between both modalities. PMG is characterized by a marked loss of deep cortical fissures. Hence, differences between EEG and MEG were expected since MEG signals are predominantly generated from tangentially orientated neurons in fissures. PATIENTS: We studied 7 children and young adults (age 7.5 to 19 years) with localization-related epilepsy and unilateral polymicrogyria (PMG) as defined from anatomical MRI. METHODS: 122-channel whole-head MEG and 32-channel EEG were recorded simultaneously for 25 to 40 min. Using the BESA program, interictal spikes were identified visually and used as templates to search for similar spatio-temporal spike patterns throughout the recording. Detected similar spikes (r > 0.85) were averaged, high-pass filtered (5 Hz) to enhance spike onset, and subjected to multiple spatio-temporal source analysis. Source localization was visualized by superposition on T1-weighted MRI and compared to the lesion. RESULTS: Nine spike types were identified in seven patients (2 types in 2 patients). Eight out of nine EEG sources and seven MEG sources modeling spike onset were localized within the visible lesion. EEG spike onset preceded MEG significantly in two spike types by 19 and 25 ms. This was related to radial onset activity in EEG while MEG localized propagated activity. In one case, the earliest MEG spike activity was localized to the normal hemisphere while the preceding radial EEG onset activity was localized within the lesion. Distances between EEG and MEG onset sources varied markedly between 9 and 51 mm in the eight spike types with concordant lateralization. CONCLUSION: Interictal irritative zones were localized within the lesion in PMG comparable to other malformations, e.g., FCD. Discrepancies in MEG and EEG were related to the lack of deep fissures in PMG. In two cases, MEG was blind to the onset of radial interictal spike activity and localized propagated spike activity. In two other cases, MEG localized to the more peripheral parts of the irritative zone. Simultaneous EEG recordings with MEG and multiple source analysis are required to avoid problems of MEG interpretation.     

Nociceptive and non-nociceptive sub-regions in the human secondary somatosensory cortex: an MEG study using fMRI constraints

Previous evidence from functional magnetic resonance imaging (fMRI) has shown that a painful galvanic stimulation mainly activates a posterior sub-region in the secondary somatosensory cortex (SII), whereas a non-painful sensory stimulation mainly activates an anterior sub-region of SII [Ferretti, A., Babiloni, C., Del Gratta, C., Caulo, M., Tartaro, A., Bonomo, L., Rossini, P.M., Romani, G.L., 2003. Functional topography of the secondary somatosensory cortex for non-painful and painful stimuli: an fMRI study. Neuroimage 20 (3), 1625-1638.]. The present study, combining fMRI with magnetoencephalographic (MEG) findings, assessed the working hypothesis that the activity of such a posterior SII sub-region is characterized by an amplitude and temporal evolution in line with the bilateral functional organization of nociceptive systems. Somatosensory evoked magnetic fields (SEFs) recordings after alvanic median nerve stimulation were obtained from the same sample of subjects previously examined with fMRI [Ferretti, A., Babiloni, C., Del Gratta, C., Caulo, M., Tartaro, A., Bonomo, L., Rossini, P.M., Romani, G.L., 2003. Functional topography of the secondary somatosensory cortex for non-painful and painful stimuli: an fMRI study. Neuroimage 20 (3), 1625-1638.]. Constraints for dipole source localizations obtained from MEG recordings were applied according to fMRI activations, namely, at the posterior and the anterior SII sub-regions. It was shown that, after painful stimulation, the two posterior SII sub-regions of the contralateral and ipsilateral hemispheres were characterized by dipole sources with similar amplitudes and latencies. In contrast, the activity of anterior SII sub-regions showed statistically significant differences in amplitude and latency during both non-painful and painful stimulation conditions. In the contralateral hemisphere, the source activity was greater in amplitude and shorter in latency with respect to the ipsilateral. Finally, painful stimuli evoked a response from the posterior sub-regions peaking significantly earlier than from the anterior sub-regions. These results suggested that both ipsi and contra posterior SII sub-regions process painful stimuli in parallel, while the anterior SII sub-regions might play an integrative role in the processing of somatosensory stimuli.