Figure - available from: Scientific Reports
This content is subject to copyright. Terms and conditions apply.
Reconstructed theta band power based on hippocampal only (left panel) and whole-brain (right panel) correlated priors contrasted (paired T-test) against the uncorrelated source model. Left: Comparing uncorrelated to a model containing a correlated hippocampus, we noted a significant increase in theta-band (4–8 Hz) power over the anterior hippocampal areas. Right: Contrasting the whole-brain correlated model with the uncorrelated EBB solution. We observed bilateral anterior hippocampal clusters with significant increases in power in the parahippocampal, rhinal and temporal pole areas. Results have been tranformed into MNI space for visualisation purposes.
Source publication
Beamforming is one of the most commonly used source reconstruction methods for magneto- and electroencephalography (M/EEG). One underlying assumption, however, is that distant sources are uncorrelated and here we tested whether this is an appropriate model for the human hippocampal data. We revised the Empirical Bayesian Beamfomer (EBB) to accommod...
Similar publications
The question of how much sleep is best for the brain attracts scientific and public interest, and there is concern that insuficient sleep leads to poorer brain health. However, it is unknown how much sleep is sufficient and how much is too much. We analyzed 51,295 brain magnetic resonnance images from 47,039 participants, and calculated the self-re...
Citations
... LCMV beamformers are more likely to generate illusory effects in either the right or left hippocampus as the algorithm fails to separate correlated sources (O'Neill et al., 2021). Therefore, any . ...
... A subset of these same regions were involved in the theta/alpha changes found during 18 to 19s of overlap compared to non-overlap video-watching (Fig 2.B), including the Inferior parietal lobe, hippocampus, retrosplenial cortex, and precuneus. Although the effect appears to be isolated to the right hemisphere, we do not make inferences about the lateralisation because the beamformer algorithm can output illusory lateralised effects (O'Neill et al., 2021). Figure 2. Results from the cluster-based permutation tests in MEG sensor-space and estimated sources of the sensor-space effects. ...
Our ability to navigate in a new environment depends on learning new locations. Mental representations of locations are quickly accessible during navigation and allow us to know where we are regardless of our current viewpoint. Recent fMRI research has shown that these location-based representations emerge in the retrosplenial cortex and parahippocampal gyrus, regions theorised to be critically involved in spatial navigation. However, little is currently known about the oscillatory dynamics that support the formation of location-based representations. We used MEG to investigate region-specific oscillatory activity related to forming location-based representations. Participants viewed videos showing that two perceptually distinct scenes (180 degrees apart) belonged to the same location. This 'overlap' video allowed participants to bind the two distinct scenes together into a more coherent location-based representation. Participants also viewed control 'non-overlap' videos where two distinct scenes from two different locations were shown, where no location-based representation could be formed. In a post-video behavioural task, participants successfully matched the two viewpoints shown in the overlap videos, but not the non-overlap videos, indicating they successfully learned the locations in the overlap condition. Comparing oscillatory activity between the overlap and non-overlap videos, we found greater theta and alpha/beta power during the overlap relative to non-overlap videos, specifically at time-points when we expected scene integration to occur. These oscillations localised to regions in the medial parietal cortex (precuneus and retrosplenial cortex) and the medial temporal lobe, including the hippocampus. Therefore, we find that theta and alpha/beta oscillations in the hippocampus and medial parietal cortex are involved in the formation of location-based representations.
... However, these techniques encounter performance degradation when sources are highly correlated. Various dual-core/correlated source beamformers [11]- [13] and Bayesian-based scanning algorithms (Saketini [14], NSE-FALoc [15]) have been proposed to localize highly correlated sources. To function, these three classes of algorithms require an accurate estimation of noise and interference covariance from background or baseline brain activity. ...
Simultaneously estimating brain source activity and noise has long been a challenging task in electromagnetic brain imaging using magneto- and electroencephalography. The problem is challenging not only in terms of solving the NP-hard inverse problem of reconstructing unknown brain activity across thousands of voxels from a limited number of sensors, but also for the need to simultaneously estimate the noise and interference. We present a generative model with an augmented leadfield matrix to simultaneously estimate brain source activity and sensor noise statistics in electromagnetic brain imaging (EBI).
We then derive three Bayesian inference algorithms for this generative model (expectation-maximization (EBI-EM), convex bounding (EBI-Convex) and fixed-point (EBI-Mackay)) to simultaneously estimate the hyperparameters of the prior distribution for brain source activity and sensor noise. A comprehensive performance evaluation for these three algorithms is performed. Simulations consistently show that the performance of EBI-Convex and EBI-Mackay updates is superior to that of EBI-EM. In contrast to the EBI-EM algorithm, both EBI-Convex and EBI-Mackay updates are quite robust to initialization, and are computationally efficient with fast convergence in the presence of both Gaussian and real brain noise. We also demonstrate that EBI-Convex and EBI-Mackay update algorithms can reconstruct complex brain activity with only a few trials of sensor data, and for resting-state data, achieving significant improvement in source reconstruction and noise learning for electromagnetic brain imaging.
... where L i is the lead field of the i-th dipole and Q Y is the data covariance(Belardinelli et al., 2012;O'Neill et al., 2021). ...
The emergence of the optically pumped magnetometer (OPM)-based magnetoencephalography (MEG) has led to new developments in MEG technology. The source imaging results of different magnetic source imaging (MSI) methods show considerable differences, which makes it difficult for researchers to choose an appropriate method. This study assessed time-domain MSI methods implemented in the Brainstorm, FieldTrip, and SPM12 toolboxes using simulations. We proposed using a metric, variational free energy under the Bayesian framework, as an indicator to evaluate source imaging results because it does not require the ground truth of sources but uses the fitness of the measurement data. Our simulations demonstrated the effectiveness of the variational free energy in indicating the quality of the source reconstruction results. We then applied each MSI method to the real OPM-MEG experimental data. We aimed to highlight the characteristics of each method and provide references for researchers choosing an appropriate MSI method.
Our ability to navigate in a new environment depends on learning new locations. Mental representations of locations are quickly accessible during navigation and allow us to know where we are regardless of our current viewpoint. Recent functional magnetic resonance imaging (fMRI) research using pattern classification has shown that these location‐based representations emerge in the retrosplenial cortex and parahippocampal gyrus, regions theorized to be critically involved in spatial navigation. However, little is currently known about the oscillatory dynamics that support the formation of location‐based representations. We used magnetoencephalogram (MEG) recordings to investigate region‐specific oscillatory activity in a task where participants could form location‐based representations. Participants viewed videos showing that two perceptually distinct scenes (180° apart) belonged to the same location. This “overlap” video allowed participants to bind the two distinct scenes together into a more coherent location‐based representation. Participants also viewed control “non‐overlap” videos where two distinct scenes from two different locations were shown, where no location‐based representation could be formed. In a post‐video behavioral task, participants successfully matched the two viewpoints shown in the overlap videos, but not the non‐overlap videos, indicating they successfully learned the locations in the overlap condition. Comparing oscillatory activity between the overlap and non‐overlap videos, we found greater theta and alpha/beta power during the overlap relative to non‐overlap videos, specifically at time‐points when we expected scene integration to occur. These oscillations localized to regions in the medial parietal cortex (precuneus and retrosplenial cortex) and the medial temporal lobe, including the hippocampus. Therefore, we find that theta and alpha/beta oscillations in the hippocampus and medial parietal cortex are likely involved in the formation of location‐based representations.
One of the primary technical challenges facing magnetoencephalography (MEG) is that the magnitude of neuromagnetic fields is several orders of magnitude lower than interfering signals. Recently, a new type of sensor has been developed – the optically pumped magnetometer (OPM). These sensors can be placed directly on the scalp and move with the head during participant movement, making them wearable. This opens up a range of exciting experimental and clinical opportunities for OPM-based MEG experiments, including paediatric studies, and the incorporation of naturalistic movements into neuroimaging paradigms. However, OPMs face some unique challenges in terms of interference suppression, especially in situations involving mobile participants, and when OPMs are integrated with electrical equipment required for naturalistic paradigms, such as motion capture systems. Here we briefly review various hardware solutions for OPM interference suppression. We then outline several signal processing strategies aimed at increasing the signal from neuromagnetic sources. These include regression-based strategies, temporal filtering and spatial filtering approaches. The focus is on the practical application of these signal processing algorithms to OPM data. In a similar vein, we include two worked-through experiments using OPM data collected from a whole-head sensor array. These tutorial-style examples illustrate how the steps for suppressing external interference can be implemented, including the associated data and code so that researchers can try the pipelines for themselves. With the popularity of OPM-based MEG rising, there will be an increasing need to deal with interference suppression. We hope this practical paper provides a resource for OPM-based MEG researchers to build upon.
One of the primary technical challenges facing magnetoencephalography (MEG) is that the magnitude of neuromagnetic fields is several orders of magnitude lower than interfering signals. Recently, a new type of sensor has been developed - the optically pumped magnetometer (OPM). These sensors can be placed directly on the scalp and move with the head during participant movement, making them wearable. This opens up a range of exciting experimental and clinical opportunities for OPM-based MEG experiments, including paediatric studies, and the incorporation of naturalistic movements into neuroimaging paradigms. However, OPMs face some unique challenges in terms of interference suppression, especially in situations involving mobile participants, and when OPMs are integrated with electrical equipment required for naturalistic paradigms, such as motion capture systems. Here we briefly review various hardware solutions for OPM interference suppression. We then outline several signal processing strategies aimed at increasing the signal from neuromagnetic sources. These include regression-based strategies, temporal filtering and spatial filtering approaches. The focus is on the practical application of these signal processing algorithms to OPM data. In a similar vein, we include two worked-through experiments using OPM data collected from a whole-head sensor array. These tutorial-style examples illustrate how the steps for suppressing external interference can be implemented, including the associated data and code so that researchers can try the pipelines for themselves. With the popularity of OPM-based MEG rising, there will be an increasing need to deal with interference suppression. We hope this practical paper provides a resource for OPM-based MEG researchers to build upon.
To form an episodic memory, we must first process a vast amount of sensory information about the to-be-encoded event and then bind these sensory representations together to form a coherent memory trace. While these two cognitive capabilities are thought to have two distinct neural origins, with neocortical alpha/beta oscillations supporting information representation and hippocampal theta-gamma phase-amplitude coupling supporting mnemonic binding, evidence for a dissociation between these two neural markers is conspicuously absent. To address this, seventeen human participants completed an associative memory task that first involved processing information about three sequentially-presented stimuli, and then binding these stimuli together into a coherent memory trace, all the while undergoing MEG recordings. We found that decreases in neocortical alpha/beta power during sequence perception, but not mnemonic binding, correlated with enhanced memory performance. Hippocampal theta/gamma phase-amplitude coupling, however, showed the opposite pattern; increases during mnemonic binding (but not sequence perception) correlated with enhanced memory performance. These results demonstrate that memory-related decreases in neocortical alpha/beta power and memory-related increases in hippocampal theta/gamma phase-amplitude coupling arise at distinct stages of the memory formation process. We speculate that this temporal dissociation reflects a functional dissociation in which neocortical alpha/beta oscillations could support the processing of incoming information relevant to the memory, while hippocampal theta-gamma phase-amplitude coupling could support the binding of this information into a coherent memory trace.
There are rich structures in off-task neural activity which are hypothesised to reflect fundamental computations across a broad spectrum of cognitive functions. Here, we develop an analysis toolkit – Temporal Delayed Linear Modelling (TDLM) for analysing such activity. TDLM is a domain-general method for finding neural sequences that respect a pre-specified transition graph. It combines nonlinear classification and linear temporal modelling to test for statistical regularities in sequences of task-related reactivations. TDLM is developed on the non-invasive neuroimaging data and is designed to take care of confounds and maximize sequence detection ability. Notably, as a linear framework, TDLM can be easily extended, without loss of generality, to capture rodent replay in electrophysiology, including in continuous spaces, as well as addressing second-order inference questions, e.g., its temporal and spatial varying pattern. We hope TDLM will advance a deeper understanding of neural computation and promote a richer convergence between animal and human neuroscience.
