MEG and EEG data analysis with MNE-Python

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne

MEG-BIDS, the brain imaging data structure extended to magnetoencephalography.

We present a significant extension of the Brain Imaging Data Structure (BIDS) to support the specific aspects of magnetoencephalography (MEG) data. MEG measures brain activity with millisecond temporal resolution and unique source imaging capabilities. So far, BIDS was a solution to organise magnetic resonance imaging (MRI) data. The nature and acquisition parameters of MRI and MEG data are strongly dissimilar. Although there is no standard data format for MEG, we propose MEG-BIDS as a principled solution to store, organise, process and share the multidimensional data volumes produced by the modality. The standard also includes well-defined metadata, to facilitate future data harmonisation and sharing efforts. This responds to unmet needs from the multimodal neuroimaging community and paves the way to further integration of other techniques in electrophysiology. MEG-BIDS builds on MRI-BIDS, extending BIDS to a multimodal data structure. We feature several data-analytics software that have adopted MEG-BIDS, and a diverse sample of open MEG-BIDS data resources available to everyone

The past, present, and future of the brain imaging data structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.Development of the BIDS Standard has been supported by the International Neuroinformatics Coordinating Facility, Laura and John Arnold Foundation, National Institutes of Health (R24MH114705, R24MH117179, R01MH126699, R24MH117295, P41EB019936, ZIAMH002977, R01MH109682, RF1MH126700, R01EB020740), National Science Foundation (OAC-1760950, BCS-1734853, CRCNS-1429999, CRCNS-1912266), Novo Nordisk Fonden (NNF20OC0063277), French National Research Agency (ANR-19-DATA-0023, ANR 19-DATA-0021), Digital Europe TEF-Health (101100700), EU H2020 Virtual Brain Cloud (826421), Human Brain Project (SGA2 785907, SGA3 945539), European Research Council (Consolidator 683049), German Research Foundation (SFB 1436/425899996), SFB 1315/327654276, SFB 936/178316478, SFB-TRR 295/424778381), SPP Computational Connectomics (RI 2073/6-1, RI 2073/10-2, RI 2073/9-1), European Innovation Council PHRASE Horizon (101058240), Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative, ERAPerMed Pattern-Cog, and the Virtual Research Environment at the Charité Berlin – a node of EBRAINS Health Data Cloud.N

The past, present, and future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS

Dissociating Morpheme Form and Meaning: Evidence for Morphological Decomposition of Compound Words during Reading

Compound words have two free morphemes whose individual meanings can have a transparent (e.g., roadside) or opaque (e.g., butterfly) relationship to the overall meaning. It is unclear when meaning is accessed during lexical processing of compounds (and other morphologically complex words), with conflicting results from priming in lexical-decision studies and from reading-time studies that examine how the characteristics of a compound affect its processing. The present studies examined eye-movement measures on target words in a sentence as a function of their relation in form and meaning to a prime word that occurred earlier in the sentence. In Experiment 1 the primes were transparent or opaque compounds and the targets were the first constituent of the compound (e.g., doll preceded by dollhouse vs. container; and brief preceded by briefcase vs. portfolio). First-pass measures showed that target-word recognition was facilitated by prior processing of the compound but that the amount of facilitation was not affected by semantic transparency, a pattern that suggests that there is a stage of processing where compounds are decomposed into their constituent morphemes regardless of their composite meaning. Experiment 2 used first constituents as primes and compounds as targets. First-pass measures showed priming on recognition of both transparent and opaque compounds. Priming facilitation persisted on later measures of lexical processing for transparent compounds but became inhibitory for opaque compounds. These results show that compounds are initially decomposed into their constituent words independently of meaning, but that later in processing activation of the meaning of a constituent word facilitates comprehension of semantically consistent compounds but competes with comprehension of semantically inconsistent compounds

Dissociating Morpheme Form and Meaning: Evidence for Morphological Decomposition of Compound Words during Reading

Compound words have two free morphemes whose individual meanings can have a transparent (e.g., roadside) or opaque (e.g., butterfly) relationship to the overall meaning. It is unclear when meaning is accessed during lexical processing of compounds (and other morphologically complex words), with conflicting results from priming in lexical-decision studies and from reading-time studies that examine how the characteristics of a compound affect its processing. The present studies examined eye-movement measures on target words in a sentence as a function of their relation in form and meaning to a prime word that occurred earlier in the sentence. In Experiment 1 the primes were transparent or opaque compounds and the targets were the first constituent of the compound (e.g., doll preceded by dollhouse vs. container; and brief preceded by briefcase vs. portfolio). First-pass measures showed that target-word recognition was facilitated by prior processing of the compound but that the amount of facilitation was not affected by semantic transparency, a pattern that suggests that there is a stage of processing where compounds are decomposed into their constituent morphemes regardless of their composite meaning. Experiment 2 used first constituents as primes and compounds as targets. First-pass measures showed priming on recognition of both transparent and opaque compounds. Priming facilitation persisted on later measures of lexical processing for transparent compounds but became inhibitory for opaque compounds. These results show that compounds are initially decomposed into their constituent words independently of meaning, but that later in processing activation of the meaning of a constituent word facilitates comprehension of semantically consistent compounds but competes with comprehension of semantically inconsistent compounds

Evidence for Morphological Recomposition in Compound Words using MEG

Psycholinguistic and electrophysiological studies of lexical processing show convergent evidence for morpheme-based lexical access for morphologically complex words that involves early decomposition into their constituent morphemes followed by some combinatorial operation. Considering that both semantically transparent (e.g., sailboat) and semantically opaque (e.g., bootleg) compounds undergo morphological decomposition during the earlier stages of lexical processing, subsequent combinatorial operations should account for the difference in the contribution of the constituent morphemes to the meaning of these different word types. In this study we use magnetoencephalography (MEG) to pinpoint the neural bases of this combinatorial stage in English compound word recognition. MEG data were acquired while participants performed a word naming task in which three word types, transparent compounds (e.g., roadside), opaque compounds (e.g., butterfly), and morphologically simple words (e.g., brothel) were contrasted in a partial-repetition priming paradigm where the word of interest was primed by one of its constituent morphemes. Analysis of onset latency revealed shorter latencies to name compound words than simplex words when primed, further supporting a stage of morphological decomposition in lexical access. An analysis of the associated MEG activity uncovered a region of interest implicated in morphological composition, the Left Anterior Temporal Lobe (LATL). Only transparent compounds showed increased activity in this area from 250 to 470 ms. Previous studies using sentences and phrases have highlighted the role of LATL in performing computations for basic combinatorial operations. Results are in tune with decomposition models for morpheme accessibility early in processing and suggest that semantics play a role in combining the meanings of morphemes when their composition is transparent to the overall word meaning

Eelbrain

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