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Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

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An Introduction To The Event Related Potential Technique

Measuring event-related potentials (ERPs) has been fundamental to our understanding of how language is encoded in the brain. One particular ERP response, the N400 response, has been especially influential as an index of lexical and semantic processing. However, there remains a lack of consensus on the interpretation of this component. Resolving this issue has important consequences for neural models of language comprehension. Here we show that evidence bearing on where the N400 response is generated provides key insights into what it reflects. A neuroanatomical model of semantic processing is used as a guide to interpret the pattern of activated regions in functional MRI, magnetoencephalography and intracranial recordings that are associated with contextual semantic manipulations that lead to N400 effects.

/pdf/a-cortical-network-for-semantics-de-constructing-the-n400-2mkch9vs5v.pdf

A cortical network for semantics: (de)constructing the N400.

In this review of 100 fMRI studies of speech comprehension and production, published in 2009, activation is reported for: prelexical speech perception in bilateral superior temporal gyri; meaningful speech in middle and inferior temporal cortex; semantic retrieval in the left angular gyrus and pars orbitalis; and sentence comprehension in bilateral superior temporal sulci. For incomprehensible sentences, activation increases in four inferior frontal regions, posterior planum temporale, and ventral supramarginal gyrus. These effects are associated with the use of prior knowledge of semantic associations, word sequences, and articulation that predict the content of the sentence. Speech production activates the same set of regions as speech comprehension but in addition, activation is reported for: word retrieval in left middle frontal cortex; articulatory planning in the left anterior insula; the initiation and execution of speech in left putamen, pre-SMA, SMA, and motor cortex; and for suppressing unintended responses in the anterior cingulate and bilateral head of caudate nuclei. Anatomical and functional connectivity studies are now required to identify the processing pathways that integrate these areas to support language.

/pdf/the-anatomy-of-language-a-review-of-100-fmri-studies-5axw9qxeya.pdf

The anatomy of language: a review of 100 fMRI studies published in 2009.

Unsupervised representation learning of spontaneous MEG data with nonlinear ICA

Learning induces structural changes in the brain. Especially repeated, long-term behaviors, such as extensive training of playing a musical instrument, are likely to produce characteristic features to brain structure. However, it is not clear to what extent such structural features can be extracted from magnetic resonance images of the brain. Here we show that it is possible to predict whether a person is a musician or a non-musician based on the thickness of the cerebral cortex measured at 148 brain regions encompassing the whole cortex. Using a supervised machine learning technique called support vector machines, we achieved significant ({kappa} = 0.321, p < 0.001) agreement between the actual and predicted participant groups of 30 musicians and 85 non-musicians. The areas contributing to the prediction were mostly in the frontal, parietal, and occipital lobes of the left hemisphere. Our results suggest that decoding an acquired skill from magnetic resonance images of brain structure is feasible to some extent. Further, the distribution of the areas that were informative in the classification, which mostly, but not entirely overlapped with earlier findings, implies that decoding-based analyses of structural properties of the brain can reveal novel aspects of musical aptitude.

/pdf/musical-skills-can-be-decoded-from-magnetic-resonance-images-euv4bsi1u2.pdf

Musical skills can be decoded from magnetic resonance images

The nature of auditory processing problems in children with developmental language disorder (DLD) is still poorly understood. Much research has been devoted to determining the extent to which DLD is associated with general auditory versus language-specific dysfunction. However, less emphasis has been given to the role of different task conditions in these dysfunctions. We explored whether children with DLD demonstrate atypical interhemispheric asymmetry during the auditory processing of speech and non-speech sounds and whether this interhemispheric balance is modulated by attention. Magnetoencephalography was used to record auditory evoked fields in 18 children (9 to 10 years old), 9 with DLD and 9 with language typical development, during active or passive listening to speech and non-speech sounds. A linear mixed model analysis revealed a bilateral effect of attention in both groups. Participants with DLD demonstrated atypical interhemispheric asymmetry, specifically in the later (185-600 ms) time window but only during the passive listening condition. During the active task, the DLD group did not differ from the typically developed children in terms of hemispheric balance of activation. Our results support the idea of an altered interhemispheric balance in passive auditory response properties in DLD. We further suggest that an active task condition, or top-down attention, can help to regain leftward lateralization, particularly in a later stage of activation. Our study highlights the highly dynamic and interhemispheric nature of auditory processing, which may contribute to the variability in reports of auditory language processing deficits in DLD. 

https://www.nature.com/articles/s41598-022-22820-x.pdf

Attentional modulation of interhemispheric (a)symmetry in children with developmental language disorder

Background In the last decades, electrophysiological imaging methodology has seen many advances and the computational power in the neuroscience laboratories has steadily increased. Still, the new methodologies are unavailable for many. There is a need for more versatile analysis approaches for neuroscience specialists without a programming background. New method Using a software which provides standard pipelines, provides good default values for parameters, has a good multi-subject support, and stores the used analysis steps with the parameters in one place for reporting, is efficient and fast. In addition to enabling analysis for people without background in programming, it enables analysis for people with background in programming but a limited background in neuroscience. When constructed with care, the GUI may guide the researcher to apply analysis steps in correct order with reasonable default parameters. Comparison to existing methods Two existing software, EEGLAB and Brainstorm, both provide an easy-to-use graphical user interface for end-to-end analysis for multiple subjects. The key difference to work presented here is the choice of language. The scientific community is moving en masse towards the python programming language, thus making it an ideal platform for extendable software. Another problem with Matlab is that it is not free - both from the perspective of open source and concrete monetary resources. Within the current trend towards increasing open science, covering data, analysis and reporting, the need for open source software is imperative. Results Meggie is an open source software for running MEG and EEG analysis with easy-to-use graphical user interface. It is written in Python 3, runs on Linux, macOS and Windows, and uses the MNE-python library under the hood to do heavy lifting. It is designed to allow end-to-end analysis of MEG and EEG datasets from multiple subjects with common sensor-level analysis steps such as preprocessing, epoching and averaging, spectral analysis and time-frequency analysis. Most of the analysis steps can be run for all the subjects in one go, and combining the results across subjects is made possible with grand averages. We have emphasized the extendibility of Meggie by implementing most of the Meggie itself as plugins, thus ensuring that new plugins have access to all necessary core features. Conclusion Meggie answers the demand for easy-to-use and extendable python-based graphical user interface that provides an end-to-end analysis environment for M/EEG data analysis. It is freely available at https://github.com/cibr-jyu/meggie under the BSD license. Installation instructions, documentation and tutorials are found on that website. Highlights - MNE-python-based graphical user interface for M/EEG analysis - Easy to use even without programming background - Has good support for multiple subjects - Written in python, and can be easily extended with plugins - Free and open source with BSD license

Meggie – easy-to-use graphical user interface for M/EEG analysis based on MNE-python

A Pilot Study on the Perioperative Change in Cognitive Function and Underlying Magnetoencephalography (MEG) Markers on Recently Symptomatic Patients Undergoing Carotid Endarterectomy

Tiina Parviainen

Papers

Unsupervised representation learning of spontaneous MEG data with nonlinear ICA

Musical skills can be decoded from magnetic resonance images

Attentional modulation of interhemispheric (a)symmetry in children with developmental language disorder

Meggie – easy-to-use graphical user interface for M/EEG analysis based on MNE-python

A Pilot Study on the Perioperative Change in Cognitive Function and Underlying Magnetoencephalography (MEG) Markers on Recently Symptomatic Patients Undergoing Carotid Endarterectomy