Multiple constrained minimum variance beamformer (MCMV) performance in connectivity analyses

Multi-Modal Electrophysiological Source Imaging With Attention Neural Networks Based on Deep Fusion of EEG and MEG

The process of reconstructing underlying cortical and subcortical electrical activities from Electroencephalography (EEG) or Magnetoencephalography (MEG) recordings is called Electrophysiological Source Imaging (ESI). Given the complementarity between EEG and MEG in measuring radial and tangential cortical sources, combined EEG/MEG is considered beneficial in improving the reconstruction performance of ESI algorithms. Traditional algorithms mainly emphasize incorporating predesigned neurophysiological priors to solve the ESI problem. Deep learning frameworks aim to directly learn the mapping from scalp EEG/MEG measurements to the underlying brain source activities in a data-driven manner, demonstrating superior performance compared to traditional methods. However, most of the existing deep learning approaches for the ESI problem are performed on a single modality of EEG or MEG, meaning the complementarity of these two modalities has not been fully utilized. How to fuse the EEG and MEG in a more principled manner under the deep learning paradigm remains a challenging question. This study develops a Multi-Modal Deep Fusion (MMDF) framework using Attention Neural Networks (ANN) to fully leverage the complementary information between EEG and MEG for solving the ESI inverse problem, which is termed as MMDF-ANN. Specifically, our proposed brain source imaging approach consists of four phases, including feature extraction, weight generation, deep feature fusion, and source mapping. Our experimental results on both synthetic dataset and real dataset demonstrated that using a fusion of EEG and MEG can significantly improve the source localization accuracy compared to using a single-modality of EEG or MEG. Compared to the benchmark algorithms, MMDF-ANN demonstrated good stability when reconstructing sources with extended activation areas and situations of EEG/MEG measurements with a low signal-to-noise ratio.

Source localization using recursively applied and projected MUSIC with flexible extent estimation

Magneto- and electroencephalography (M/EEG) are widespread techniques to measure neural activity in-vivo at a high temporal resolution but low spatial resolution. Locating the neural sources underlying the M/EEG poses an inverse problem, which is ill-posed. We developed a new method based on Recursive Application of Multiple Signal Classification (MUSIC). Our proposed method is able to recover not only the locations but, in contrast to other inverse solutions, also the extent of active brain regions flexibly (FLEX-MUSIC). This is achieved by allowing it to search not only for single dipoles but also dipole clusters of increasing extent to find the best fit during each recursion. FLEX-MUSIC achieved the highest accuracy for both single dipole and extended sources compared to all other methods tested. Remarkably, FLEX-MUSIC was capable to accurately estimate the level of sparsity in the source space (r = 0.82), whereas all other approaches tested failed to do so (r ≤ 0.18). The average computation time of FLEX-MUSIC was considerably lower compared to a popular Bayesian approach and comparable to that of another recursive MUSIC approach and eLORETA. FLEX-MUSIC produces only few errors and was capable to reliably estimate the extent of sources. The accuracy and low computation time of FLEX-MUSIC renders it an improved technique to solve M/EEG inverse problems both in neuroscience research and potentially in pre-surgery diagnostic in epilepsy.

Neural processes responsible for the translation of sustained nociceptive inputs into subjective pain experience

Tracking and predicting the temporal structure of nociceptive inputs is crucial to promote survival, as proper and immediate reactions are necessary to avoid actual or potential bodily injury. Neural activities elicited by nociceptive stimuli with different temporal structures have been described, but the neural processes responsible for translating nociception into pain perception are not fully elucidated. To tap into this issue, we recorded electroencephalographic signals from 48 healthy participants receiving thermo-nociceptive stimuli with 3 different durations and 2 different intensities. We observed that pain perception and several brain responses are modulated by stimulus duration and intensity. Crucially, we identified 2 sustained brain responses that were related to the emergence of painful percepts: a low-frequency component (LFC, < 1 Hz) originated from the insula and anterior cingulate cortex, and an alpha-band event-related desynchronization (α-ERD, 8-13 Hz) generated from the sensorimotor cortex. These 2 sustained brain responses were highly coupled, with the α-oscillation amplitude that fluctuated with the LFC phase. Furthermore, the translation of stimulus duration into pain perception was serially mediated by α-ERD and LFC. The present study reveals how brain responses elicited by nociceptive stimulation reflect the complex processes occurring during the translation of nociceptive information into pain perception.

Functional Connectivity Hypersynchronization in Relatives of Alzheimer’s Disease Patients: An Early E/I Balance Dysfunction?

Alzheimer’s disease (AD) studies on animal models, and humans showed a tendency of the brain tissue to become hyperexcitable and hypersynchronized, causing neurodegeneration. However, we know little about either the onset of this phenomenon or its early effects on functional brain networks. We studied functional connectivity (FC) on 127 participants (92 middle-age relatives of AD patients and 35 age-matched nonrelatives) using magnetoencephalography. FC was estimated in the alpha band in areas known both for early amyloid accumulation and disrupted FC in MCI converters to AD. We found a frontoparietal network (anterior cingulate cortex, dorsal frontal, and precuneus) where relatives of AD patients showed hypersynchronization in high alpha (not modulated by APOE-ε4 genotype) in comparison to age-matched nonrelatives. These results represent the first evidence of neurophysiological events causing early network disruption in humans, opening a new perspective for intervention on the excitation/inhibition unbalance.

Atypical neuromagnetic resting activity associated with thalamic volume and cognitive outcome in very preterm children

Children born very preterm, even in the absence of overt brain injury or major impairment, are at increased risk of cognitive difficulties. This risk is associated with developmental disruptions of the thalamocortical system during critical periods while in the neonatal intensive care unit. The thalamus is an important structure that not only relays sensory information but acts as a hub for integration of cortical activity which regulates cortical power across a range of frequencies. In this study, we investigate the association between atypical power at rest in children born very preterm at school age using magnetoencephalography (MEG), neurocognitive function and structural alterations related to the thalamus using MRI. Our results indicate that children born extremely preterm have higher power at slow frequencies (delta and theta) and lower power at faster frequencies (alpha and beta), compared to controls born full-term. A similar pattern of spectral power was found to be associated with poorer neurocognitive outcomes, as well as with normalized T1 intensity and the volume of the thalamus. Overall, this study provides evidence regarding relations between structural alterations related to very preterm birth, atypical oscillatory power at rest and neurocognitive difficulties at school-age children born very preterm.

Mapping neural dynamics underlying saccade preparation and execution and their relation to reaction time and direction errors

Our ability to control and inhibit automatic behaviors is crucial for negotiating complex environments, all of which require rapid communication between sensory, motor, and cognitive networks. Here, we measured neuromagnetic brain activity to investigate the neural timing of cortical areas needed for inhibitory control, while 14 healthy young adults performed an interleaved prosaccade (look at a peripheral visual stimulus) and antisaccade (look away from stimulus) task. Analysis of how neural activity relates to saccade reaction time (SRT) and occurrence of direction errors (look at stimulus on antisaccade trials) provides insight into inhibitory control. Neuromagnetic source activity was used to extract stimulus‐aligned and saccade‐aligned activity to examine temporal differences between prosaccade and antisaccade trials in brain regions associated with saccade control. For stimulus‐aligned antisaccade trials, a longer SRT was associated with delayed onset of neural activity within the ipsilateral parietal eye field (PEF) and bilateral frontal eye field (FEF). Saccade‐aligned activity demonstrated peak activation 10ms before saccade‐onset within the contralateral PEF for prosaccade trials and within the bilateral FEF for antisaccade trials. In addition, failure to inhibit prosaccades on anti‐saccade trials was associated with increased activity prior to saccade onset within the FEF contralateral to the peripheral stimulus. This work on dynamic activity adds to our knowledge that direction errors were due, at least in part, to a failure to inhibit automatic prosaccades. These findings provide novel evidence in humans regarding the temporal dynamics within oculomotor areas needed for saccade programming and the role frontal brain regions have on top‐down inhibitory control.