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Abstract  The main objective of this paper is to present methods and results for the estimation of parameters of our proposed integrated magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) model. We use real auditory MEG and fMRI datasets from 7 normal subjects to estimate the parameters of the model. The MEG and fMRI data were acquired at different times, but the stimulus profile was the same for both techniques. We use independent component analysis (ICA) to extract activation-related signal from the MEG data. The stimulus-correlated ICA component is used to estimate MEG parameters of the model. The temporal and spatial information of the fMRI datasets are used to estimate fMRI parameters of the model. The estimated parameters have reasonable means and standard deviations for all subjects. Goodness of fit of the real data to our model shows the possibility of using the proposed model to simulate realistic datasets for evaluation of integrated MEG/fMRI analysis methods. [ABSTRACT FROM AUTHOR]

Introduction: Between 20 and 40% of patients with epilepsy are considered pharmacoresistant. Stereoelectroencephalography (sEEG) is frequently used as an invasive method for localizing seizures in patients with pharmacoresistant epilepsy who are surgical candidates; however, electrode nomenclature varies widely across institutions. This lack of standardization can have many downstream consequences, including difficulty with intercenter or intracenter interpretation, communication, and reliability., Methods: The authors propose a novel sEEG nomenclature that is both intuitive and comprehensive. Considerations include clear/precise entry and target anatomical locations, laterality, distinction of superficial and deep structures, functional mapping, and relative labeling of electrodes in close proximity if needed. Special consideration was also given to electrodes approximating radiographically distinct lesions. The accuracy of electrode identification and the use of correct entry-target labels were assessed by neurosurgeons and epileptologists, not directly involved in each case., Results: The authors' nomenclature was used in 41 consecutive sEEG cases (497 electrodes total) within their institution. After reconstruction was complete, the accuracy of electrode identification was 100%, and the correct use of entry-target labels was 98%. The last 30 sEEG cases had 100% correct use of entry-target labels., Conclusions: The proposed sEEG nomenclature demonstrated both high accuracy in electrode identification and consistent use of entry-target labeling. The authors submit this nomenclature as a model for standardization across epilepsy surgery centers. They intend to improve practicability, ease of use, and specificity of this nomenclature through collaboration with other surgical epilepsy centers., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2024 by the American Clinical Neurophysiology Society.)

Neural speech decoding aims at providing natural rate communication assistance to patients with locked-in state (e.g. due to amyotrophic lateral sclerosis, ALS) in contrast to the traditional brain-computer interface (BCI) spellers which are slow. Recent studies have shown that Magnetoencephalography (MEG) is a suitable neuroimaging modality to study neural speech decoding considering its excellent temporal resolution that can characterize the fast dynamics of speech. Gradiometers have been the preferred choice for sensor space analysis with MEG, due to their efficacy in noise suppression over magnetometers. However, recent development of optically pumped magnetometers (OPM) based wearable-MEG devices have shown great potential in future BCI applications, yet, no prior study has evaluated the performance of magnetometers in neural speech decoding. In this study, we decoded imagined and spoken speech from the MEG signals of seven healthy participants and compared the performance of magnetometers and gradiometers. Experimental results indicated that magnetometers also have the potential for neural speech decoding, although the performance was significantly lower than that obtained with gradiometers. Further, we implemented a wavelet based denoising strategy that improved the performance of both magnetometers and gradiometers significantly. These findings reconfirm that gradiometers are preferable in MEG based decoding analysis but also provide the possibility towards the use of magnetometers (or OPMs) for the development of the next-generation speech-BCIs.

Several neuroimaging studies have suggested that functional brain connectivity networks exhibit "small-world" characteristics, whereas recent studies based on structural data have proposed a "rich-club" organization of brain networks, whereby hubs of high connection density tend to connect among themselves compared to nodes of lower density. In this study, we adopted an "attack strategy" to compare the rich-club and small-world organizations and identify the model that describes best the topology of brain connectivity. We hypothesized that the highest reduction in global efficiency caused by a targeted attack on each model's hubs would reveal the organization that better describes the topology of the underlying brain networks. We applied this approach to magnetoencephalographic data obtained at rest from neurologically intact controls and mild traumatic brain injury patients. Functional connectivity networks were computed using phase-to-amplitude cross-frequency coupling between the δ and β frequency bands. Our results suggest that resting state MEG connectivity networks follow a rich-club organization.

Brain electrical activity exhibits scale-free dynamics that follow power law scaling. Previous works have shown that broadband spectral power exhibits state-dependent scaling with a log frequency exponent that systematically varies with neural state. However, the frequency ranges which best characterize biological state are not consistent across brain location or subject. An adaptive piecewise linear fitting solution was developed to extract features for classification of brain state. Performance was evaluated by comparison to an a posteriori based feature search method. This analysis, using the 1/ƒ characteristics of the human ECoG signal, demonstrates utility in advancing the ability to perform automated brain state discrimination.