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21 september 2020, Contains fulltext : 224765.pdf (Publisher’s version ) (Closed access), The Organization for Human Brain Mapping (OHBM) has been active in advocating for the instantiation of best practices in neuroimaging data acquisition, analysis, reporting and sharing of both data and analysis code to deal with issues in science related to reproducibility and replicability. Here we summarize recommendations for such practices in magnetoencephalographic (MEG) and electroencephalographic (EEG) research, recently developed by the OHBM neuroimaging community known by the abbreviated name of COBIDAS MEEG. We discuss the rationale for the guidelines and their general content, which encompass many topics under active discussion in the field. We highlight future opportunities and challenges to maximizing the sharing and exploitation of MEG and EEG data, and we also discuss how this 'living' set of guidelines will evolve to continually address new developments in neurophysiological assessment methods and multimodal integration of neurophysiological data with other data types.

There is an increasing interest to integrate electrophysiological and hemodynamic measures for characterizing spatial and temporal aspects of cortical processing. However, an informative combination of responses that have markedly different sensitivities to the underlying neural activity is not straightforward, especially in complex cognitive tasks. Here, we used parametric stimulus manipulation in magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) recordings on the same subjects, to study effects of noise on processing of spoken words and environmental sounds. The added noise influenced MEG response strengths in the bilateral supratemporal auditory cortex, at different times for the different stimulus types. Specifically for spoken words, the effect of noise on the electrophysiological response was remarkably nonlinear. Therefore, we used the single-subject MEG responses to construct parametrization for fMRI data analysis and obtained notably higher sensitivity than with conventional stimulus-based parametrization. fMRI results showed that partly different temporal areas were involved in noise-sensitive processing of words and environmental sounds. These results indicate that cortical processing of sounds in background noise is stimulus specific in both timing and location and provide a new functionally meaningful platform for combining information obtained with electrophysiological and hemodynamic measures of brain function.

Words forming a continuous story were presented to 9 subjects at frequencies ranging from 5 to 30 Hz, determined individually to render comprehension easy, effortful, or practically impossible. We identified a left-hemisphere neural network sensitive to reading performance directly from the time courses of activation in the brain, derived from magnetoencephalography data. Regardless of the stimulus rate, communication within the long-range neural network occurred at a frequency of 8-13 Hz. Our coherence-based detection of interconnected nodes reproduced several brain regions that have been previously reported as active in reading tasks, based on traditional contrast estimates. Intriguingly, the face motor cortex and the cerebellum, typically associated with speech production, and the orbitofrontal cortex, linked to visual recognition and working memory, additionally emerged as densely connected components of the network. The left inferior occipitotemporal cortex, involved in early letter-string or word-specific processing, and the cerebellum turned out to he the main forward driving nodes of the network. Synchronization within a subset of nodes formed by the left occipitotemporal, the left superior temporal, and orbitofrontal cortex was increased with the subjects' effort to comprehend the text. Our results link long-range neural synchronization and directionality with cognitive performance.

In speech perception, extraction of meaning from complex streams of sounds is surprisingly fast and efficient. By tracking the neural time course of syllable processing with magnetoencephalography we show that this continuous construction of meaning-based representations is aided by both top-down (context-based) expectations and bottom-up (acoustic-phonetic) cues in the speech signal. Syllables elicited a sustained response at 200-600 ms (N400m) which became most similar to that evoked by words when the expectation for meaningful speech was increased by presenting the syllables among words and sentences or using sentence-initial syllables. This word-like cortical processing of meaningless syllables emerged at the build-up of the N400m response, 200-300 ms after speech onset, during the transition from perceptual to lexical-semantic analysis. These findings show that the efficiency of meaning-based analysis of speech is subserved by a cortical system finely tuned to lexically relevant acoustic-phonetic and contextual cues.